On-Board, Real-Time Preprocessing System for Optical Remote-Sensing Imagery.

PubMed

Qi, Baogui; Shi, Hao; Zhuang, Yin; Chen, He; Chen, Liang

2018-04-25

With the development of remote-sensing technology, optical remote-sensing imagery processing has played an important role in many application fields, such as geological exploration and natural disaster prevention. However, relative radiation correction and geometric correction are key steps in preprocessing because raw image data without preprocessing will cause poor performance during application. Traditionally, remote-sensing data are downlinked to the ground station, preprocessed, and distributed to users. This process generates long delays, which is a major bottleneck in real-time applications for remote-sensing data. Therefore, on-board, real-time image preprocessing is greatly desired. In this paper, a real-time processing architecture for on-board imagery preprocessing is proposed. First, a hierarchical optimization and mapping method is proposed to realize the preprocessing algorithm in a hardware structure, which can effectively reduce the computation burden of on-board processing. Second, a co-processing system using a field-programmable gate array (FPGA) and a digital signal processor (DSP; altogether, FPGA-DSP) based on optimization is designed to realize real-time preprocessing. The experimental results demonstrate the potential application of our system to an on-board processor, for which resources and power consumption are limited.

Data preprocessing method for liquid chromatography-mass spectrometry based metabolomics.

PubMed

Wei, Xiaoli; Shi, Xue; Kim, Seongho; Zhang, Li; Patrick, Jeffrey S; Binkley, Joe; McClain, Craig; Zhang, Xiang

2012-09-18

A set of data preprocessing algorithms for peak detection and peak list alignment are reported for analysis of liquid chromatography-mass spectrometry (LC-MS)-based metabolomics data. For spectrum deconvolution, peak picking is achieved at the selected ion chromatogram (XIC) level. To estimate and remove the noise in XICs, each XIC is first segmented into several peak groups based on the continuity of scan number, and the noise level is estimated by all the XIC signals, except the regions potentially with presence of metabolite ion peaks. After removing noise, the peaks of molecular ions are detected using both the first and the second derivatives, followed by an efficient exponentially modified Gaussian-based peak deconvolution method for peak fitting. A two-stage alignment algorithm is also developed, where the retention times of all peaks are first transferred into the z-score domain and the peaks are aligned based on the measure of their mixture scores after retention time correction using a partial linear regression. Analysis of a set of spike-in LC-MS data from three groups of samples containing 16 metabolite standards mixed with metabolite extract from mouse livers demonstrates that the developed data preprocessing method performs better than two of the existing popular data analysis packages, MZmine2.6 and XCMS(2), for peak picking, peak list alignment, and quantification.

A Data Pre-processing Method for Liquid Chromatography Mass Spectrometry-based Metabolomics

PubMed Central

Wei, Xiaoli; Shi, Xue; Kim, Seongho; Zhang, Li; Patrick, Jeffrey S.; Binkley, Joe; McClain, Craig; Zhang, Xiang

2012-01-01

A set of data pre-processing algorithms for peak detection and peak list alignment are reported for analysis of LC-MS based metabolomics data. For spectrum deconvolution, peak picking is achieved at selected ion chromatogram (XIC) level. To estimate and remove the noise in XICs, each XIC is first segmented into several peak groups based on the continuity of scan number, and the noise level is estimated by all the XIC signals, except the regions potentially with presence of metabolite ion peaks. After removing noise, the peaks of molecular ions are detected using both the first and the second derivatives, followed by an efficient exponentially modified Gaussian-based peak deconvolution method for peak fitting. A two-stage alignment algorithm is also developed, where the retention times of all peaks are first transferred into z-score domain and the peaks are aligned based on the measure of their mixture scores after retention time correction using a partial linear regression. Analysis of a set of spike-in LC-MS data from three groups of samples containing 16 metabolite standards mixed with metabolite extract from mouse livers, demonstrates that the developed data pre-processing methods performs better than two of the existing popular data analysis packages, MZmine2.6 and XCMS2, for peak picking, peak list alignment and quantification. PMID:22931487

Rapid and non-destructive assessment of polyunsaturated fatty acids contents in Salmon using near-infrared hyperspectral imaging

NASA Astrophysics Data System (ADS)

Tao, Feifei; Mba, Ogan; Liu, Li; Ngadi, Michael

2017-04-01

Polyunsaturated fatty acids (PUFAs) are important nutrients present in Salmon. However, current methods for quantifying the fatty acids (FAs) contents in foods are generally based on gas chromatography (GC) technique, which is time-consuming, laborious and destructive to the tested samples. Therefore, the capability of near-infrared (NIR) hyperspectral imaging to predict the PUFAs contents of C20:2 n-6, C20:3 n-6, C20:5 n-3, C22:5 n-3 and C22:6 n-3 in Salmon fillets in a rapid and non-destructive way was investigated in this work. Mean reflectance spectra were first extracted from the region of interests (ROIs), and then the spectral pre-processing methods of 2nd derivative and Savitzky-Golay (SG) smoothing were performed on the original spectra. Based on the original and the pre-processed spectra, PLSR technique was employed to develop the quantitative models for predicting each PUFA content in Salmon fillets. The results showed that for all the studied PUFAs, the quantitative models developed using the pre-processed reflectance spectra by "2nd derivative + SG smoothing" could improve their modeling results. Good prediction results were achieved with RP and RMSEP of 0.91 and 0.75 mg/g dry weight, 0.86 and 1.44 mg/g dry weight, 0.82 and 3.01 mg/g dry weight for C20:3 n-6, C22:5 n-3 and C20:5 n-3, respectively after pre-processing by "2nd derivative + SG smoothing". The work demonstrated that NIR hyperspectral imaging could be a useful tool for rapid and non-destructive determination of the PUFA contents in fish fillets.

Adaptive and automatic red blood cell counting method based on microscopic hyperspectral imaging technology

NASA Astrophysics Data System (ADS)

Liu, Xi; Zhou, Mei; Qiu, Song; Sun, Li; Liu, Hongying; Li, Qingli; Wang, Yiting

2017-12-01

Red blood cell counting, as a routine examination, plays an important role in medical diagnoses. Although automated hematology analyzers are widely used, manual microscopic examination by a hematologist or pathologist is still unavoidable, which is time-consuming and error-prone. This paper proposes a full-automatic red blood cell counting method which is based on microscopic hyperspectral imaging of blood smears and combines spatial and spectral information to achieve high precision. The acquired hyperspectral image data of the blood smear in the visible and near-infrared spectral range are firstly preprocessed, and then a quadratic blind linear unmixing algorithm is used to get endmember abundance images. Based on mathematical morphological operation and an adaptive Otsu’s method, a binaryzation process is performed on the abundance images. Finally, the connected component labeling algorithm with magnification-based parameter setting is applied to automatically select the binary images of red blood cell cytoplasm. Experimental results show that the proposed method can perform well and has potential for clinical applications.

Conductivity map from scanning tunneling potentiometry.

PubMed

Zhang, Hao; Li, Xianqi; Chen, Yunmei; Durand, Corentin; Li, An-Ping; Zhang, X-G

2016-08-01

We present a novel method for extracting two-dimensional (2D) conductivity profiles from large electrochemical potential datasets acquired by scanning tunneling potentiometry of a 2D conductor. The method consists of a data preprocessing procedure to reduce/eliminate noise and a numerical conductivity reconstruction. The preprocessing procedure employs an inverse consistent image registration method to align the forward and backward scans of the same line for each image line followed by a total variation (TV) based image restoration method to obtain a (nearly) noise-free potential from the aligned scans. The preprocessed potential is then used for numerical conductivity reconstruction, based on a TV model solved by accelerated alternating direction method of multiplier. The method is demonstrated on a measurement of the grain boundary of a monolayer graphene, yielding a nearly 10:1 ratio for the grain boundary resistivity over bulk resistivity.

NASA End-to-End Data System /NEEDS/ information adaptive system - Performing image processing onboard the spacecraft

NASA Technical Reports Server (NTRS)

Kelly, W. L.; Howle, W. M.; Meredith, B. D.

1980-01-01

The Information Adaptive System (IAS) is an element of the NASA End-to-End Data System (NEEDS) Phase II and is focused toward onbaord image processing. Since the IAS is a data preprocessing system which is closely coupled to the sensor system, it serves as a first step in providing a 'Smart' imaging sensor. Some of the functions planned for the IAS include sensor response nonuniformity correction, geometric correction, data set selection, data formatting, packetization, and adaptive system control. The inclusion of these sensor data preprocessing functions onboard the spacecraft will significantly improve the extraction of information from the sensor data in a timely and cost effective manner and provide the opportunity to design sensor systems which can be reconfigured in near real time for optimum performance. The purpose of this paper is to present the preliminary design of the IAS and the plans for its development.

Pre-processing SAR image stream to facilitate compression for transport on bandwidth-limited-link

DOEpatents

Rush, Bobby G.; Riley, Robert

2015-09-29

Pre-processing is applied to a raw VideoSAR (or similar near-video rate) product to transform the image frame sequence into a product that resembles more closely the type of product for which conventional video codecs are designed, while sufficiently maintaining utility and visual quality of the product delivered by the codec.

EARLINET Single Calculus Chain - technical - Part 1: Pre-processing of raw lidar data

NASA Astrophysics Data System (ADS)

D'Amico, Giuseppe; Amodeo, Aldo; Mattis, Ina; Freudenthaler, Volker; Pappalardo, Gelsomina

2016-02-01

In this paper we describe an automatic tool for the pre-processing of aerosol lidar data called ELPP (EARLINET Lidar Pre-Processor). It is one of two calculus modules of the EARLINET Single Calculus Chain (SCC), the automatic tool for the analysis of EARLINET data. ELPP is an open source module that executes instrumental corrections and data handling of the raw lidar signals, making the lidar data ready to be processed by the optical retrieval algorithms. According to the specific lidar configuration, ELPP automatically performs dead-time correction, atmospheric and electronic background subtraction, gluing of lidar signals, and trigger-delay correction. Moreover, the signal-to-noise ratio of the pre-processed signals can be improved by means of configurable time integration of the raw signals and/or spatial smoothing. ELPP delivers the statistical uncertainties of the final products by means of error propagation or Monte Carlo simulations. During the development of ELPP, particular attention has been payed to make the tool flexible enough to handle all lidar configurations currently used within the EARLINET community. Moreover, it has been designed in a modular way to allow an easy extension to lidar configurations not yet implemented. The primary goal of ELPP is to enable the application of quality-assured procedures in the lidar data analysis starting from the raw lidar data. This provides the added value of full traceability of each delivered lidar product. Several tests have been performed to check the proper functioning of ELPP. The whole SCC has been tested with the same synthetic data sets, which were used for the EARLINET algorithm inter-comparison exercise. ELPP has been successfully employed for the automatic near-real-time pre-processing of the raw lidar data measured during several EARLINET inter-comparison campaigns as well as during intense field campaigns.

Study on for soluble solids contents measurement of grape juice beverage based on Vis/NIRS and chemomtrics

NASA Astrophysics Data System (ADS)

Wu, Di; He, Yong

2007-11-01

The aim of this study is to investigate the potential of the visible and near infrared spectroscopy (Vis/NIRS) technique for non-destructive measurement of soluble solids contents (SSC) in grape juice beverage. 380 samples were studied in this paper. Smoothing way of Savitzky-Golay and standard normal variate were applied for the pre-processing of spectral data. Least-squares support vector machines (LS-SVM) with RBF kernel function was applied to developing the SSC prediction model based on the Vis/NIRS absorbance data. The determination coefficient for prediction (Rp2) of the results predicted by LS-SVM model was 0. 962 and root mean square error (RMSEP) was 0. 434137. It is concluded that Vis/NIRS technique can quantify the SSC of grape juice beverage fast and non-destructively.. At the same time, LS-SVM model was compared with PLS and back propagation neural network (BP-NN) methods. The results showed that LS-SVM was superior to the conventional linear and non-linear methods in predicting SSC of grape juice beverage. In this study, the generation ability of LS-SVM, PLS and BP-NN models were also investigated. It is concluded that LS-SVM regression method is a promising technique for chemometrics in quantitative prediction.

Image pre-processing method for near-wall PIV measurements over moving curved interfaces

NASA Astrophysics Data System (ADS)

Jia, L. C.; Zhu, Y. D.; Jia, Y. X.; Yuan, H. J.; Lee, C. B.

2017-03-01

PIV measurements near a moving interface are always difficult. This paper presents a PIV image pre-processing method that returns high spatial resolution velocity profiles near the interface. Instead of re-shaping or re-orientating the interrogation windows, interface tracking and an image transformation are used to stretch the particle image strips near a curved interface into rectangles. Then the adaptive structured interrogation windows can be arranged at specified distances from the interface. Synthetic particles are also added into the solid region to minimize interfacial effects and to restrict particles on both sides of the interface. Since a high spatial resolution is only required in high velocity gradient region, adaptive meshing and stretching of the image strips in the normal direction is used to improve the cross-correlation signal-to-noise ratio (SN) by reducing the velocity difference and the particle image distortion within the interrogation window. A two dimensional Gaussian fit is used to compensate for the effects of stretching particle images. The working hypothesis is that fluid motion near the interface is ‘quasi-tangential flow’, which is reasonable in most fluid-structure interaction scenarios. The method was validated against the window deformation iterative multi-grid scheme (WIDIM) using synthetic image pairs with different velocity profiles. The method was tested for boundary layer measurements of a supersonic turbulent boundary layer on a flat plate, near a rotating blade and near a flexible flapping flag. This image pre-processing method provides higher spatial resolution than conventional WIDIM and good robustness for measuring velocity profiles near moving interfaces.

Leaf Chlorophyll Content Estimation of Winter Wheat Based on Visible and Near-Infrared Sensors.

PubMed

Zhang, Jianfeng; Han, Wenting; Huang, Lvwen; Zhang, Zhiyong; Ma, Yimian; Hu, Yamin

2016-03-25

The leaf chlorophyll content is one of the most important factors for the growth of winter wheat. Visual and near-infrared sensors are a quick and non-destructive testing technology for the estimation of crop leaf chlorophyll content. In this paper, a new approach is developed for leaf chlorophyll content estimation of winter wheat based on visible and near-infrared sensors. First, the sliding window smoothing (SWS) was integrated with the multiplicative scatter correction (MSC) or the standard normal variable transformation (SNV) to preprocess the reflectance spectra images of wheat leaves. Then, a model for the relationship between the leaf relative chlorophyll content and the reflectance spectra was developed using the partial least squares (PLS) and the back propagation neural network. A total of 300 samples from areas surrounding Yangling, China, were used for the experimental studies. The samples of visible and near-infrared spectroscopy at the wavelength of 450,900 nm were preprocessed using SWS, MSC and SNV. The experimental results indicate that the preprocessing using SWS and SNV and then modeling using PLS can achieve the most accurate estimation, with the correlation coefficient at 0.8492 and the root mean square error at 1.7216. Thus, the proposed approach can be widely used for winter wheat chlorophyll content analysis.

iHWG-μNIR: a miniaturised near-infrared gas sensor based on substrate-integrated hollow waveguides coupled to a micro-NIR-spectrophotometer.

PubMed

Rohwedder, J J R; Pasquini, C; Fortes, P R; Raimundo, I M; Wilk, A; Mizaikoff, B

2014-07-21

A miniaturised gas analyser is described and evaluated based on the use of a substrate-integrated hollow waveguide (iHWG) coupled to a microsized near-infrared spectrophotometer comprising a linear variable filter and an array of InGaAs detectors. This gas sensing system was applied to analyse surrogate samples of natural fuel gas containing methane, ethane, propane and butane, quantified by using multivariate regression models based on partial least square (PLS) algorithms and Savitzky-Golay 1(st) derivative data preprocessing. The external validation of the obtained models reveals root mean square errors of prediction of 0.37, 0.36, 0.67 and 0.37% (v/v), for methane, ethane, propane and butane, respectively. The developed sensing system provides particularly rapid response times upon composition changes of the gaseous sample (approximately 2 s) due the minute volume of the iHWG-based measurement cell. The sensing system developed in this study is fully portable with a hand-held sized analyser footprint, and thus ideally suited for field analysis. Last but not least, the obtained results corroborate the potential of NIR-iHWG analysers for monitoring the quality of natural gas and petrochemical gaseous products.

Comparison of preprocessing methods and storage times for touch DNA samples

PubMed Central

Dong, Hui; Wang, Jing; Zhang, Tao; Ge, Jian-ye; Dong, Ying-qiang; Sun, Qi-fan; Liu, Chao; Li, Cai-xia

2017-01-01

Aim To select appropriate preprocessing methods for different substrates by comparing the effects of four different preprocessing methods on touch DNA samples and to determine the effect of various storage times on the results of touch DNA sample analysis. Method Hand touch DNA samples were used to investigate the detection and inspection results of DNA on different substrates. Four preprocessing methods, including the direct cutting method, stubbing procedure, double swab technique, and vacuum cleaner method, were used in this study. DNA was extracted from mock samples with four different preprocessing methods. The best preprocess protocol determined from the study was further used to compare performance after various storage times. DNA extracted from all samples was quantified and amplified using standard procedures. Results The amounts of DNA and the number of alleles detected on the porous substrates were greater than those on the non-porous substrates. The performances of the four preprocessing methods varied with different substrates. The direct cutting method displayed advantages for porous substrates, and the vacuum cleaner method was advantageous for non-porous substrates. No significant degradation trend was observed as the storage times increased. Conclusion Different substrates require the use of different preprocessing method in order to obtain the highest DNA amount and allele number from touch DNA samples. This study provides a theoretical basis for explorations of touch DNA samples and may be used as a reference when dealing with touch DNA samples in case work. PMID:28252870

[State Recognition of Solid Fermentation Process Based on Near Infrared Spectroscopy with Adaboost and Spectral Regression Discriminant Analysis].

PubMed

Yu, Shuang; Liu, Guo-hai; Xia, Rong-sheng; Jiang, Hui

2016-01-01

In order to achieve the rapid monitoring of process state of solid state fermentation (SSF), this study attempted to qualitative identification of process state of SSF of feed protein by use of Fourier transform near infrared (FT-NIR) spectroscopy analysis technique. Even more specifically, the FT-NIR spectroscopy combined with Adaboost-SRDA-NN integrated learning algorithm as an ideal analysis tool was used to accurately and rapidly monitor chemical and physical changes in SSF of feed protein without the need for chemical analysis. Firstly, the raw spectra of all the 140 fermentation samples obtained were collected by use of Fourier transform near infrared spectrometer (Antaris II), and the raw spectra obtained were preprocessed by use of standard normal variate transformation (SNV) spectral preprocessing algorithm. Thereafter, the characteristic information of the preprocessed spectra was extracted by use of spectral regression discriminant analysis (SRDA). Finally, nearest neighbors (NN) algorithm as a basic classifier was selected and building state recognition model to identify different fermentation samples in the validation set. Experimental results showed as follows: the SRDA-NN model revealed its superior performance by compared with other two different NN models, which were developed by use of the feature information form principal component analysis (PCA) and linear discriminant analysis (LDA), and the correct recognition rate of SRDA-NN model achieved 94.28% in the validation set. In this work, in order to further improve the recognition accuracy of the final model, Adaboost-SRDA-NN ensemble learning algorithm was proposed by integrated the Adaboost and SRDA-NN methods, and the presented algorithm was used to construct the online monitoring model of process state of SSF of feed protein. Experimental results showed as follows: the prediction performance of SRDA-NN model has been further enhanced by use of Adaboost lifting algorithm, and the correct recognition rate of the Adaboost-SRDA-NN model achieved 100% in the validation set. The overall results demonstrate that SRDA algorithm can effectively achieve the spectral feature information extraction to the spectral dimension reduction in model calibration process of qualitative analysis of NIR spectroscopy. In addition, the Adaboost lifting algorithm can improve the classification accuracy of the final model. The results obtained in this work can provide research foundation for developing online monitoring instruments for the monitoring of SSF process.

Informed baseline subtraction of proteomic mass spectrometry data aided by a novel sliding window algorithm.

PubMed

Stanford, Tyman E; Bagley, Christopher J; Solomon, Patty J

2016-01-01

Proteomic matrix-assisted laser desorption/ionisation (MALDI) linear time-of-flight (TOF) mass spectrometry (MS) may be used to produce protein profiles from biological samples with the aim of discovering biomarkers for disease. However, the raw protein profiles suffer from several sources of bias or systematic variation which need to be removed via pre-processing before meaningful downstream analysis of the data can be undertaken. Baseline subtraction, an early pre-processing step that removes the non-peptide signal from the spectra, is complicated by the following: (i) each spectrum has, on average, wider peaks for peptides with higher mass-to-charge ratios ( m / z ), and (ii) the time-consuming and error-prone trial-and-error process for optimising the baseline subtraction input arguments. With reference to the aforementioned complications, we present an automated pipeline that includes (i) a novel 'continuous' line segment algorithm that efficiently operates over data with a transformed m / z -axis to remove the relationship between peptide mass and peak width, and (ii) an input-free algorithm to estimate peak widths on the transformed m / z scale. The automated baseline subtraction method was deployed on six publicly available proteomic MS datasets using six different m/z-axis transformations. Optimality of the automated baseline subtraction pipeline was assessed quantitatively using the mean absolute scaled error (MASE) when compared to a gold-standard baseline subtracted signal. Several of the transformations investigated were able to reduce, if not entirely remove, the peak width and peak location relationship resulting in near-optimal baseline subtraction using the automated pipeline. The proposed novel 'continuous' line segment algorithm is shown to far outperform naive sliding window algorithms with regard to the computational time required. The improvement in computational time was at least four-fold on real MALDI TOF-MS data and at least an order of magnitude on many simulated datasets. The advantages of the proposed pipeline include informed and data specific input arguments for baseline subtraction methods, the avoidance of time-intensive and subjective piecewise baseline subtraction, and the ability to automate baseline subtraction completely. Moreover, individual steps can be adopted as stand-alone routines.

zipHMMlib: a highly optimised HMM library exploiting repetitions in the input to speed up the forward algorithm.

PubMed

Sand, Andreas; Kristiansen, Martin; Pedersen, Christian N S; Mailund, Thomas

2013-11-22

Hidden Markov models are widely used for genome analysis as they combine ease of modelling with efficient analysis algorithms. Calculating the likelihood of a model using the forward algorithm has worst case time complexity linear in the length of the sequence and quadratic in the number of states in the model. For genome analysis, however, the length runs to millions or billions of observations, and when maximising the likelihood hundreds of evaluations are often needed. A time efficient forward algorithm is therefore a key ingredient in an efficient hidden Markov model library. We have built a software library for efficiently computing the likelihood of a hidden Markov model. The library exploits commonly occurring substrings in the input to reuse computations in the forward algorithm. In a pre-processing step our library identifies common substrings and builds a structure over the computations in the forward algorithm which can be reused. This analysis can be saved between uses of the library and is independent of concrete hidden Markov models so one preprocessing can be used to run a number of different models.Using this library, we achieve up to 78 times shorter wall-clock time for realistic whole-genome analyses with a real and reasonably complex hidden Markov model. In one particular case the analysis was performed in less than 8 minutes compared to 9.6 hours for the previously fastest library. We have implemented the preprocessing procedure and forward algorithm as a C++ library, zipHMM, with Python bindings for use in scripts. The library is available at http://birc.au.dk/software/ziphmm/.

Rapid Fuel Quality Surveillance Through Chemometric Modeling of Near-Infrared Spectra

DTIC Science & Technology

2009-01-01

measurements also have a first order advantage and are not time-dependent as is the case for chromatography. Thus, the data preprocessing requirements, while...due in part to the nature of hydrocarbon fuels, which imposes significant technical challenges that must be overcome, and in many cases , traditional...properties. The statistical significance of some other fuel properties is given in Table 2. Note also that in those cases where the property models

Real-time process monitoring in a semi-continuous fluid-bed dryer - microwave resonance technology versus near-infrared spectroscopy.

PubMed

Peters, Johanna; Teske, Andreas; Taute, Wolfgang; Döscher, Claas; Höft, Michael; Knöchel, Reinhard; Breitkreutz, Jörg

2018-02-15

The trend towards continuous manufacturing in the pharmaceutical industry is associated with an increasing demand for advanced control strategies. It is a mandatory requirement to obtain reliable real-time information on critical quality attributes (CQA) during every process step as the decision on diversion of material needs to be performed fast and automatically. Where possible, production equipment should provide redundant systems for in-process control (IPC) measurements to ensure continuous process monitoring even if one of the systems is not available. In this paper, two methods for real-time monitoring of granule moisture in a semi-continuous fluid-bed drying unit are compared. While near-infrared (NIR) spectroscopy has already proven to be a suitable process analytical technology (PAT) tool for moisture measurements in fluid-bed applications, microwave resonance technology (MRT) showed difficulties to monitor moistures above 8% until recently. The results indicate, that the newly developed MRT sensor operating at four resonances is capable to compete with NIR spectroscopy. While NIR spectra were preprocessed by mean centering and first derivative before application of partial least squares (PLS) regression to build predictive models (RMSEP = 0.20%), microwave moisture values of two resonances sufficed to build a statistically close multiple linear regression (MLR) model (RMSEP = 0.07%) for moisture prediction. Thereby, it could be verified that moisture monitoring by MRT sensor systems could be a valuable alternative to NIR spectroscopy or could be used as a redundant system providing great ease of application. Copyright © 2017 Elsevier B.V. All rights reserved.

Pre-processing ambient noise cross-correlations with equalizing the covariance matrix eigenspectrum

NASA Astrophysics Data System (ADS)

Seydoux, Léonard; de Rosny, Julien; Shapiro, Nikolai M.

2017-09-01

Passive imaging techniques from ambient seismic noise requires a nearly isotropic distribution of the noise sources in order to ensure reliable traveltime measurements between seismic stations. However, real ambient seismic noise often partially fulfils this condition. It is generated in preferential areas (in deep ocean or near continental shores), and some highly coherent pulse-like signals may be present in the data such as those generated by earthquakes. Several pre-processing techniques have been developed in order to attenuate the directional and deterministic behaviour of this real ambient noise. Most of them are applied to individual seismograms before cross-correlation computation. The most widely used techniques are the spectral whitening and temporal smoothing of the individual seismic traces. We here propose an additional pre-processing to be used together with the classical ones, which is based on the spatial analysis of the seismic wavefield. We compute the cross-spectra between all available stations pairs in spectral domain, leading to the data covariance matrix. We apply a one-bit normalization to the covariance matrix eigenspectrum before extracting the cross-correlations in the time domain. The efficiency of the method is shown with several numerical tests. We apply the method to the data collected by the USArray, when the M8.8 Maule earthquake occurred on 2010 February 27. The method shows a clear improvement compared with the classical equalization to attenuate the highly energetic and coherent waves incoming from the earthquake, and allows to perform reliable traveltime measurement even in the presence of the earthquake.

EARLINET Single Calculus Chain - technical - Part 1: Pre-processing of raw lidar data

NASA Astrophysics Data System (ADS)

D'Amico, G.; Amodeo, A.; Mattis, I.; Freudenthaler, V.; Pappalardo, G.

2015-10-01

In this paper we describe an automatic tool for the pre-processing of lidar data called ELPP (EARLINET Lidar Pre-Processor). It is one of two calculus modules of the EARLINET Single Calculus Chain (SCC), the automatic tool for the analysis of EARLINET data. The ELPP is an open source module that executes instrumental corrections and data handling of the raw lidar signals, making the lidar data ready to be processed by the optical retrieval algorithms. According to the specific lidar configuration, the ELPP automatically performs dead-time correction, atmospheric and electronic background subtraction, gluing of lidar signals, and trigger-delay correction. Moreover, the signal-to-noise ratio of the pre-processed signals can be improved by means of configurable time integration of the raw signals and/or spatial smoothing. The ELPP delivers the statistical uncertainties of the final products by means of error propagation or Monte Carlo simulations. During the development of the ELPP module, particular attention has been payed to make the tool flexible enough to handle all lidar configurations currently used within the EARLINET community. Moreover, it has been designed in a modular way to allow an easy extension to lidar configurations not yet implemented. The primary goal of the ELPP module is to enable the application of quality-assured procedures in the lidar data analysis starting from the raw lidar data. This provides the added value of full traceability of each delivered lidar product. Several tests have been performed to check the proper functioning of the ELPP module. The whole SCC has been tested with the same synthetic data sets, which were used for the EARLINET algorithm inter-comparison exercise. The ELPP module has been successfully employed for the automatic near-real-time pre-processing of the raw lidar data measured during several EARLINET inter-comparison campaigns as well as during intense field campaigns.

Tactile and bone-conduction auditory brain computer interface for vision and hearing impaired users.

PubMed

Rutkowski, Tomasz M; Mori, Hiromu

2015-04-15

The paper presents a report on the recently developed BCI alternative for users suffering from impaired vision (lack of focus or eye-movements) or from the so-called "ear-blocking-syndrome" (limited hearing). We report on our recent studies of the extents to which vibrotactile stimuli delivered to the head of a user can serve as a platform for a brain computer interface (BCI) paradigm. In the proposed tactile and bone-conduction auditory BCI novel multiple head positions are used to evoke combined somatosensory and auditory (via the bone conduction effect) P300 brain responses, in order to define a multimodal tactile and bone-conduction auditory brain computer interface (tbcaBCI). In order to further remove EEG interferences and to improve P300 response classification synchrosqueezing transform (SST) is applied. SST outperforms the classical time-frequency analysis methods of the non-linear and non-stationary signals such as EEG. The proposed method is also computationally more effective comparing to the empirical mode decomposition. The SST filtering allows for online EEG preprocessing application which is essential in the case of BCI. Experimental results with healthy BCI-naive users performing online tbcaBCI, validate the paradigm, while the feasibility of the concept is illuminated through information transfer rate case studies. We present a comparison of the proposed SST-based preprocessing method, combined with a logistic regression (LR) classifier, together with classical preprocessing and LDA-based classification BCI techniques. The proposed tbcaBCI paradigm together with data-driven preprocessing methods are a step forward in robust BCI applications research. Copyright © 2014 Elsevier B.V. All rights reserved.

Kernelization

NASA Astrophysics Data System (ADS)

Fomin, Fedor V.

Preprocessing (data reduction or kernelization) as a strategy of coping with hard problems is universally used in almost every implementation. The history of preprocessing, like applying reduction rules simplifying truth functions, can be traced back to the 1950's [6]. A natural question in this regard is how to measure the quality of preprocessing rules proposed for a specific problem. For a long time the mathematical analysis of polynomial time preprocessing algorithms was neglected. The basic reason for this anomaly was that if we start with an instance I of an NP-hard problem and can show that in polynomial time we can replace this with an equivalent instance I' with |I'| < |I| then that would imply P=NP in classical complexity.

Multivariate pattern analysis for MEG: A comparison of dissimilarity measures.

PubMed

Guggenmos, Matthias; Sterzer, Philipp; Cichy, Radoslaw Martin

2018-06-01

Multivariate pattern analysis (MVPA) methods such as decoding and representational similarity analysis (RSA) are growing rapidly in popularity for the analysis of magnetoencephalography (MEG) data. However, little is known about the relative performance and characteristics of the specific dissimilarity measures used to describe differences between evoked activation patterns. Here we used a multisession MEG data set to qualitatively characterize a range of dissimilarity measures and to quantitatively compare them with respect to decoding accuracy (for decoding) and between-session reliability of representational dissimilarity matrices (for RSA). We tested dissimilarity measures from a range of classifiers (Linear Discriminant Analysis - LDA, Support Vector Machine - SVM, Weighted Robust Distance - WeiRD, Gaussian Naïve Bayes - GNB) and distances (Euclidean distance, Pearson correlation). In addition, we evaluated three key processing choices: 1) preprocessing (noise normalisation, removal of the pattern mean), 2) weighting decoding accuracies by decision values, and 3) computing distances in three different partitioning schemes (non-cross-validated, cross-validated, within-class-corrected). Four main conclusions emerged from our results. First, appropriate multivariate noise normalization substantially improved decoding accuracies and the reliability of dissimilarity measures. Second, LDA, SVM and WeiRD yielded high peak decoding accuracies and nearly identical time courses. Third, while using decoding accuracies for RSA was markedly less reliable than continuous distances, this disadvantage was ameliorated by decision-value-weighting of decoding accuracies. Fourth, the cross-validated Euclidean distance provided unbiased distance estimates and highly replicable representational dissimilarity matrices. Overall, we strongly advise the use of multivariate noise normalisation as a general preprocessing step, recommend LDA, SVM and WeiRD as classifiers for decoding and highlight the cross-validated Euclidean distance as a reliable and unbiased default choice for RSA. Copyright © 2018 Elsevier Inc. All rights reserved.

The effects of pre-processing strategies in sentiment analysis of online movie reviews

NASA Astrophysics Data System (ADS)

Zin, Harnani Mat; Mustapha, Norwati; Murad, Masrah Azrifah Azmi; Sharef, Nurfadhlina Mohd

2017-10-01

With the ever increasing of internet applications and social networking sites, people nowadays can easily express their feelings towards any products and services. These online reviews act as an important source for further analysis and improved decision making. These reviews are mostly unstructured by nature and thus, need processing like sentiment analysis and classification to provide a meaningful information for future uses. In text analysis tasks, the appropriate selection of words/features will have a huge impact on the effectiveness of the classifier. Thus, this paper explores the effect of the pre-processing strategies in the sentiment analysis of online movie reviews. In this paper, supervised machine learning method was used to classify the reviews. The support vector machine (SVM) with linear and non-linear kernel has been considered as classifier for the classification of the reviews. The performance of the classifier is critically examined based on the results of precision, recall, f-measure, and accuracy. Two different features representations were used which are term frequency and term frequency-inverse document frequency. Results show that the pre-processing strategies give a significant impact on the classification process.

Frequency domain analysis of errors in cross-correlations of ambient seismic noise

NASA Astrophysics Data System (ADS)

Liu, Xin; Ben-Zion, Yehuda; Zigone, Dimitri

2016-12-01

We analyse random errors (variances) in cross-correlations of ambient seismic noise in the frequency domain, which differ from previous time domain methods. Extending previous theoretical results on ensemble averaged cross-spectrum, we estimate confidence interval of stacked cross-spectrum of finite amount of data at each frequency using non-overlapping windows with fixed length. The extended theory also connects amplitude and phase variances with the variance of each complex spectrum value. Analysis of synthetic stationary ambient noise is used to estimate the confidence interval of stacked cross-spectrum obtained with different length of noise data corresponding to different number of evenly spaced windows of the same duration. This method allows estimating Signal/Noise Ratio (SNR) of noise cross-correlation in the frequency domain, without specifying filter bandwidth or signal/noise windows that are needed for time domain SNR estimations. Based on synthetic ambient noise data, we also compare the probability distributions, causal part amplitude and SNR of stacked cross-spectrum function using one-bit normalization or pre-whitening with those obtained without these pre-processing steps. Natural continuous noise records contain both ambient noise and small earthquakes that are inseparable from the noise with the existing pre-processing steps. Using probability distributions of random cross-spectrum values based on the theoretical results provides an effective way to exclude such small earthquakes, and additional data segments (outliers) contaminated by signals of different statistics (e.g. rain, cultural noise), from continuous noise waveforms. This technique is applied to constrain values and uncertainties of amplitude and phase velocity of stacked noise cross-spectrum at different frequencies, using data from southern California at both regional scale (˜35 km) and dense linear array (˜20 m) across the plate-boundary faults. A block bootstrap resampling method is used to account for temporal correlation of noise cross-spectrum at low frequencies (0.05-0.2 Hz) near the ocean microseismic peaks.

Optimisation of near-infrared reflectance model in measuring protein and amylose content of rice flour.

PubMed

Xie, L H; Tang, S Q; Chen, N; Luo, J; Jiao, G A; Shao, G N; Wei, X J; Hu, P S

2014-01-01

Near-infrared reflectance spectroscopy (NIRS) has been used to predict the cooking quality parameters of rice, such as the protein (PC) and amylose content (AC). Using brown and milled flours from 519 rice samples representing a wide range of grain qualities, this study was to compare the calibration models generated by different mathematical, preprocessing treatments, and combinations of different regression algorithm. A modified partial least squares model (MPLS) with the mathematic treatment "2, 8, 8, 2" (2nd order derivative computed based on 8 data points, and 8 and 2 data points in the 1st and 2nd smoothing, respectively) and inverse multiplicative scattering correction preprocessing treatment was identified as the best model for simultaneously measurement of PC and AC in brown flours. MPLS/"2, 8, 8, 2"/detrend preprocessing was identified as the best model for milled flours. The results indicated that NIRS could be useful in estimation of PC and AC of breeding lines in early generations of the breeding programs, and for the purposes of quality control in the food industry. Copyright © 2013 Elsevier Ltd. All rights reserved.

Nondestructive evaluation of soluble solid content in strawberry by near infrared spectroscopy

NASA Astrophysics Data System (ADS)

Guo, Zhiming; Huang, Wenqian; Chen, Liping; Wang, Xiu; Peng, Yankun

This paper indicates the feasibility to use near infrared (NIR) spectroscopy combined with synergy interval partial least squares (siPLS) algorithms as a rapid nondestructive method to estimate the soluble solid content (SSC) in strawberry. Spectral preprocessing methods were optimized selected by cross-validation in the model calibration. Partial least squares (PLS) algorithm was conducted on the calibration of regression model. The performance of the final model was back-evaluated according to root mean square error of calibration (RMSEC) and correlation coefficient (R2 c) in calibration set, and tested by mean square error of prediction (RMSEP) and correlation coefficient (R2 p) in prediction set. The optimal siPLS model was obtained with after first derivation spectra preprocessing. The measurement results of best model were achieved as follow: RMSEC = 0.2259, R2 c = 0.9590 in the calibration set; and RMSEP = 0.2892, R2 p = 0.9390 in the prediction set. This work demonstrated that NIR spectroscopy and siPLS with efficient spectral preprocessing is a useful tool for nondestructively evaluation SSC in strawberry.

Measurement of single soybean seed attributes by near infrared technologies. A comparative study

USDA-ARS?s Scientific Manuscript database

Four near infrared spectrophotometers, and their associated spectral collection methods, were tested and compared for measuring three soybean single seed attributes: weight (g), protein (%), and oil (%). Using partial least squares (PLS) and 4 preprocessing methods, the attribute which was significa...

Wavelet Analysis Used for Spectral Background Removal in the Determination of Glucose from Near-Infrared Single-Beam Spectra

PubMed Central

Wan, Boyong; Small, Gary W.

2010-01-01

Wavelet analysis is developed as a preprocessing tool for use in removing background information from near-infrared (near-IR) single-beam spectra before the construction of multivariate calibration models. Three data sets collected with three different near-IR spectrometers are investigated that involve the determination of physiological levels of glucose (1-30 mM) in a simulated biological matrix containing alanine, ascorbate, lactate, triacetin, and urea in phosphate buffer. A factorial design is employed to optimize the specific wavelet function used and the level of decomposition applied, in addition to the spectral range and number of latent variables associated with a partial least-squares calibration model. The prediction performance of the computed models is studied with separate data acquired after the collection of the calibration spectra. This evaluation includes one data set collected over a period of more than six months. Preprocessing with wavelet analysis is also compared to the calculation of second-derivative spectra. Over the three data sets evaluated, wavelet analysis is observed to produce better-performing calibration models, with improvements in concentration predictions on the order of 30% being realized relative to models based on either second-derivative spectra or spectra preprocessed with simple additive and multiplicative scaling correction. This methodology allows the construction of stable calibrations directly with single-beam spectra, thereby eliminating the need for the collection of a separate background or reference spectrum. PMID:21035604

Wavelet analysis used for spectral background removal in the determination of glucose from near-infrared single-beam spectra.

PubMed

Wan, Boyong; Small, Gary W

2010-11-29

Wavelet analysis is developed as a preprocessing tool for use in removing background information from near-infrared (near-IR) single-beam spectra before the construction of multivariate calibration models. Three data sets collected with three different near-IR spectrometers are investigated that involve the determination of physiological levels of glucose (1-30 mM) in a simulated biological matrix containing alanine, ascorbate, lactate, triacetin, and urea in phosphate buffer. A factorial design is employed to optimize the specific wavelet function used and the level of decomposition applied, in addition to the spectral range and number of latent variables associated with a partial least-squares calibration model. The prediction performance of the computed models is studied with separate data acquired after the collection of the calibration spectra. This evaluation includes one data set collected over a period of more than 6 months. Preprocessing with wavelet analysis is also compared to the calculation of second-derivative spectra. Over the three data sets evaluated, wavelet analysis is observed to produce better-performing calibration models, with improvements in concentration predictions on the order of 30% being realized relative to models based on either second-derivative spectra or spectra preprocessed with simple additive and multiplicative scaling correction. This methodology allows the construction of stable calibrations directly with single-beam spectra, thereby eliminating the need for the collection of a separate background or reference spectrum. Copyright © 2010 Elsevier B.V. All rights reserved.

Identification of Medicinal Mugua Origin by Near Infrared Spectroscopy Combined with Partial Least-squares Discriminant Analysis.

PubMed

Han, Bangxing; Peng, Huasheng; Yan, Hui

2016-01-01

Mugua is a common Chinese herbal medicine. There are three main medicinal origin places in China, Xuancheng City Anhui Province, Qijiang District Chongqing City, Yichang City, Hubei Province, and suitable for food origin places Linyi City Shandong Province. To construct a qualitative analytical method to identify the origin of medicinal Mugua by near infrared spectroscopy (NIRS). Partial least squares discriminant analysis (PLSDA) model was established after the Mugua derived from five different origins were preprocessed by the original spectrum. Moreover, the hierarchical cluster analysis was performed. The result showed that PLSDA model was established. According to the relationship of the origins-related important score and wavenumber, and K-mean cluster analysis, the Muguas derived from different origins were effectively identified. NIRS technology can quickly and accurately identify the origin of Mugua, provide a new method and technology for the identification of Chinese medicinal materials. After preprocessed by D1+autoscale, more peaks were increased in the preprocessed Mugua in the near infrared spectrumFive latent variable scores could reflect the information related to the origin place of MuguaOrigins of Mugua were well-distinguished according to K. mean value clustering analysis. Abbreviations used: TCM: Traditional Chinese Medicine, NIRS: Near infrared spectroscopy, SG: Savitzky-Golay smoothness, D1: First derivative, D2: Second derivative, SNV: Standard normal variable transformation, MSC: Multiplicative scatter correction, PLSDA: Partial least squares discriminant analysis, LV: Latent variable, VIP scores: Important score.

How attention influences perceptual decision making: Single-trial EEG correlates of drift-diffusion model parameters

PubMed Central

Nunez, Michael D.; Vandekerckhove, Joachim; Srinivasan, Ramesh

2016-01-01

Perceptual decision making can be accounted for by drift-diffusion models, a class of decision-making models that assume a stochastic accumulation of evidence on each trial. Fitting response time and accuracy to a drift-diffusion model produces evidence accumulation rate and non-decision time parameter estimates that reflect cognitive processes. Our goal is to elucidate the effect of attention on visual decision making. In this study, we show that measures of attention obtained from simultaneous EEG recordings can explain per-trial evidence accumulation rates and perceptual preprocessing times during a visual decision making task. Models assuming linear relationships between diffusion model parameters and EEG measures as external inputs were fit in a single step in a hierarchical Bayesian framework. The EEG measures were features of the evoked potential (EP) to the onset of a masking noise and the onset of a task-relevant signal stimulus. Single-trial evoked EEG responses, P200s to the onsets of visual noise and N200s to the onsets of visual signal, explain single-trial evidence accumulation and preprocessing times. Within-trial evidence accumulation variance was not found to be influenced by attention to the signal or noise. Single-trial measures of attention lead to better out-of-sample predictions of accuracy and correct reaction time distributions for individual subjects. PMID:28435173

How attention influences perceptual decision making: Single-trial EEG correlates of drift-diffusion model parameters.

PubMed

Nunez, Michael D; Vandekerckhove, Joachim; Srinivasan, Ramesh

2017-02-01

Perceptual decision making can be accounted for by drift-diffusion models, a class of decision-making models that assume a stochastic accumulation of evidence on each trial. Fitting response time and accuracy to a drift-diffusion model produces evidence accumulation rate and non-decision time parameter estimates that reflect cognitive processes. Our goal is to elucidate the effect of attention on visual decision making. In this study, we show that measures of attention obtained from simultaneous EEG recordings can explain per-trial evidence accumulation rates and perceptual preprocessing times during a visual decision making task. Models assuming linear relationships between diffusion model parameters and EEG measures as external inputs were fit in a single step in a hierarchical Bayesian framework. The EEG measures were features of the evoked potential (EP) to the onset of a masking noise and the onset of a task-relevant signal stimulus. Single-trial evoked EEG responses, P200s to the onsets of visual noise and N200s to the onsets of visual signal, explain single-trial evidence accumulation and preprocessing times. Within-trial evidence accumulation variance was not found to be influenced by attention to the signal or noise. Single-trial measures of attention lead to better out-of-sample predictions of accuracy and correct reaction time distributions for individual subjects.

Acoustic Biometric System Based on Preprocessing Techniques and Linear Support Vector Machines

PubMed Central

del Val, Lara; Izquierdo-Fuente, Alberto; Villacorta, Juan J.; Raboso, Mariano

2015-01-01

Drawing on the results of an acoustic biometric system based on a MSE classifier, a new biometric system has been implemented. This new system preprocesses acoustic images, extracts several parameters and finally classifies them, based on Support Vector Machine (SVM). The preprocessing techniques used are spatial filtering, segmentation—based on a Gaussian Mixture Model (GMM) to separate the person from the background, masking—to reduce the dimensions of images—and binarization—to reduce the size of each image. An analysis of classification error and a study of the sensitivity of the error versus the computational burden of each implemented algorithm are presented. This allows the selection of the most relevant algorithms, according to the benefits required by the system. A significant improvement of the biometric system has been achieved by reducing the classification error, the computational burden and the storage requirements. PMID:26091392

Acoustic Biometric System Based on Preprocessing Techniques and Linear Support Vector Machines.

PubMed

del Val, Lara; Izquierdo-Fuente, Alberto; Villacorta, Juan J; Raboso, Mariano

2015-06-17

Drawing on the results of an acoustic biometric system based on a MSE classifier, a new biometric system has been implemented. This new system preprocesses acoustic images, extracts several parameters and finally classifies them, based on Support Vector Machine (SVM). The preprocessing techniques used are spatial filtering, segmentation-based on a Gaussian Mixture Model (GMM) to separate the person from the background, masking-to reduce the dimensions of images-and binarization-to reduce the size of each image. An analysis of classification error and a study of the sensitivity of the error versus the computational burden of each implemented algorithm are presented. This allows the selection of the most relevant algorithms, according to the benefits required by the system. A significant improvement of the biometric system has been achieved by reducing the classification error, the computational burden and the storage requirements.

Integrated Sensing Processor, Phase 2

DTIC Science & Technology

2005-12-01

performance analysis for several baseline classifiers including neural nets, linear classifiers, and kNN classifiers. Use of CCDR as a preprocessing step...below the level of the benchmark non-linear classifier for this problem ( kNN ). Furthermore, the CCDR preconditioned kNN achieved a 10% improvement over...the benchmark kNN without CCDR. Finally, we found an important connection between intrinsic dimension estimation via entropic graphs and the optimal

Quantitative analysis of glycated albumin in serum based on ATR-FTIR spectrum combined with SiPLS and SVM.

PubMed

Li, Yuanpeng; Li, Fucui; Yang, Xinhao; Guo, Liu; Huang, Furong; Chen, Zhenqiang; Chen, Xingdan; Zheng, Shifu

2018-08-05

A rapid quantitative analysis model for determining the glycated albumin (GA) content based on Attenuated total reflectance (ATR)-Fourier transform infrared spectroscopy (FTIR) combining with linear SiPLS and nonlinear SVM has been developed. Firstly, the real GA content in human serum was determined by GA enzymatic method, meanwhile, the ATR-FTIR spectra of serum samples from the population of health examination were obtained. The spectral data of the whole spectra mid-infrared region (4000-600 cm -1 ) and GA's characteristic region (1800-800 cm -1 ) were used as the research object of quantitative analysis. Secondly, several preprocessing steps including first derivative, second derivative, variable standardization and spectral normalization, were performed. Lastly, quantitative analysis regression models were established by using SiPLS and SVM respectively. The SiPLS modeling results are as follows: root mean square error of cross validation (RMSECV T ) = 0.523 g/L, calibration coefficient (R C ) = 0.937, Root Mean Square Error of Prediction (RMSEP T ) = 0.787 g/L, and prediction coefficient (R P ) = 0.938. The SVM modeling results are as follows: RMSECV T  = 0.0048 g/L, R C  = 0.998, RMSEP T  = 0.442 g/L, and R p  = 0.916. The results indicated that the model performance was improved significantly after preprocessing and optimization of characteristic regions. While modeling performance of nonlinear SVM was considerably better than that of linear SiPLS. Hence, the quantitative analysis model for GA in human serum based on ATR-FTIR combined with SiPLS and SVM is effective. And it does not need sample preprocessing while being characterized by simple operations and high time efficiency, providing a rapid and accurate method for GA content determination. Copyright © 2018 Elsevier B.V. All rights reserved.

Simultaneous data pre-processing and SVM classification model selection based on a parallel genetic algorithm applied to spectroscopic data of olive oils.

PubMed

Devos, Olivier; Downey, Gerard; Duponchel, Ludovic

2014-04-01

Classification is an important task in chemometrics. For several years now, support vector machines (SVMs) have proven to be powerful for infrared spectral data classification. However such methods require optimisation of parameters in order to control the risk of overfitting and the complexity of the boundary. Furthermore, it is established that the prediction ability of classification models can be improved using pre-processing in order to remove unwanted variance in the spectra. In this paper we propose a new methodology based on genetic algorithm (GA) for the simultaneous optimisation of SVM parameters and pre-processing (GENOPT-SVM). The method has been tested for the discrimination of the geographical origin of Italian olive oil (Ligurian and non-Ligurian) on the basis of near infrared (NIR) or mid infrared (FTIR) spectra. Different classification models (PLS-DA, SVM with mean centre data, GENOPT-SVM) have been tested and statistically compared using McNemar's statistical test. For the two datasets, SVM with optimised pre-processing give models with higher accuracy than the one obtained with PLS-DA on pre-processed data. In the case of the NIR dataset, most of this accuracy improvement (86.3% compared with 82.8% for PLS-DA) occurred using only a single pre-processing step. For the FTIR dataset, three optimised pre-processing steps are required to obtain SVM model with significant accuracy improvement (82.2%) compared to the one obtained with PLS-DA (78.6%). Furthermore, this study demonstrates that even SVM models have to be developed on the basis of well-corrected spectral data in order to obtain higher classification rates. Copyright © 2013 Elsevier Ltd. All rights reserved.

Macro-motion detection using ultra-wideband impulse radar.

PubMed

Xin Li; Dengyu Qiao; Ye Li

2014-01-01

Radar has the advantage of being able to detect hidden individuals, which can be used in homeland security, disaster rescue, and healthcare monitoring-related applications. Human macro-motion detection using ultra-wideband impulse radar is studied in this paper. First, a frequency domain analysis is carried out to show that the macro-motion yields a bandpass signal in slow-time. Second, the FTFW (fast-time frequency windowing), which has the advantage of avoiding the measuring range reduction, and the HLF (high-pass linear-phase filter), which can preserve the motion signal effectively, are proposed to preprocess the radar echo. Last, a threshold decision method, based on the energy detector structure, is presented.

Effect of shaping sensor data on pilot response

NASA Technical Reports Server (NTRS)

Bailey, Roger M.

1990-01-01

The pilot of a modern jet aircraft is subjected to varying workloads while being responsible for multiple, ongoing tasks. The ability to associate the pilot's responses with the task/situation, by modifying the way information is presented relative to the task, could provide a means of reducing workload. To examine the feasibility of this concept, a real time simulation study was undertaken to determine whether preprocessing of sensor data would affect pilot response. Results indicated that preprocessing could be an effective way to tailor the pilot's response to displayed data. The effects of three transformations or shaping functions were evaluated with respect to the pilot's ability to predict and detect out-of-tolerance conditions while monitoring an electronic engine display. Two nonlinear transformations, on being the inverse of the other, were compared to a linear transformation. Results indicate that a nonlinear transformation that increases the rate-or-change of output relative to input tends to advance the prediction response and improve the detection response, while a nonlinear transformation that decreases the rate-of-change of output relative to input tends to lengthen the prediction response and make detection more difficult.

Does preprocessing change nonlinear measures of heart rate variability?

PubMed

Gomes, Murilo E D; Guimarães, Homero N; Ribeiro, Antônio L P; Aguirre, Luis A

2002-11-01

This work investigated if methods used to produce a uniformly sampled heart rate variability (HRV) time series significantly change the deterministic signature underlying the dynamics of such signals and some nonlinear measures of HRV. Two methods of preprocessing were used: the convolution of inverse interval function values with a rectangular window and the cubic polynomial interpolation. The HRV time series were obtained from 33 Wistar rats submitted to autonomic blockade protocols and from 17 healthy adults. The analysis of determinism was carried out by the method of surrogate data sets and nonlinear autoregressive moving average modelling and prediction. The scaling exponents alpha, alpha(1) and alpha(2) derived from the detrended fluctuation analysis were calculated from raw HRV time series and respective preprocessed signals. It was shown that the technique of cubic interpolation of HRV time series did not significantly change any nonlinear characteristic studied in this work, while the method of convolution only affected the alpha(1) index. The results suggested that preprocessed time series may be used to study HRV in the field of nonlinear dynamics.

Automated pre-processing and multivariate vibrational spectra analysis software for rapid results in clinical settings

NASA Astrophysics Data System (ADS)

Bhattacharjee, T.; Kumar, P.; Fillipe, L.

2018-02-01

Vibrational spectroscopy, especially FTIR and Raman, has shown enormous potential in disease diagnosis, especially in cancers. Their potential for detecting varied pathological conditions are regularly reported. However, to prove their applicability in clinics, large multi-center multi-national studies need to be undertaken; and these will result in enormous amount of data. A parallel effort to develop analytical methods, including user-friendly software that can quickly pre-process data and subject them to required multivariate analysis is warranted in order to obtain results in real time. This study reports a MATLAB based script that can automatically import data, preprocess spectra— interpolation, derivatives, normalization, and then carry out Principal Component Analysis (PCA) followed by Linear Discriminant Analysis (LDA) of the first 10 PCs; all with a single click. The software has been verified on data obtained from cell lines, animal models, and in vivo patient datasets, and gives results comparable to Minitab 16 software. The software can be used to import variety of file extensions, asc, .txt., .xls, and many others. Options to ignore noisy data, plot all possible graphs with PCA factors 1 to 5, and save loading factors, confusion matrices and other parameters are also present. The software can provide results for a dataset of 300 spectra within 0.01 s. We believe that the software will be vital not only in clinical trials using vibrational spectroscopic data, but also to obtain rapid results when these tools get translated into clinics.

Multiple imputation of rainfall missing data in the Iberian Mediterranean context

NASA Astrophysics Data System (ADS)

Miró, Juan Javier; Caselles, Vicente; Estrela, María José

2017-11-01

Given the increasing need for complete rainfall data networks, in recent years have been proposed diverse methods for filling gaps in observed precipitation series, progressively more advanced that traditional approaches to overcome the problem. The present study has consisted in validate 10 methods (6 linear, 2 non-linear and 2 hybrid) that allow multiple imputation, i.e., fill at the same time missing data of multiple incomplete series in a dense network of neighboring stations. These were applied for daily and monthly rainfall in two sectors in the Júcar River Basin Authority (east Iberian Peninsula), which is characterized by a high spatial irregularity and difficulty of rainfall estimation. A classification of precipitation according to their genetic origin was applied as pre-processing, and a quantile-mapping adjusting as post-processing technique. The results showed in general a better performance for the non-linear and hybrid methods, highlighting that the non-linear PCA (NLPCA) method outperforms considerably the Self Organizing Maps (SOM) method within non-linear approaches. On linear methods, the Regularized Expectation Maximization method (RegEM) was the best, but far from NLPCA. Applying EOF filtering as post-processing of NLPCA (hybrid approach) yielded the best results.

Constrained optimization of image restoration filters

NASA Technical Reports Server (NTRS)

Riemer, T. E.; Mcgillem, C. D.

1973-01-01

A linear shift-invariant preprocessing technique is described which requires no specific knowledge of the image parameters and which is sufficiently general to allow the effective radius of the composite imaging system to be minimized while constraining other system parameters to remain within specified limits.

A travel time forecasting model based on change-point detection method

NASA Astrophysics Data System (ADS)

LI, Shupeng; GUANG, Xiaoping; QIAN, Yongsheng; ZENG, Junwei

2017-06-01

Travel time parameters obtained from road traffic sensors data play an important role in traffic management practice. A travel time forecasting model is proposed for urban road traffic sensors data based on the method of change-point detection in this paper. The first-order differential operation is used for preprocessing over the actual loop data; a change-point detection algorithm is designed to classify the sequence of large number of travel time data items into several patterns; then a travel time forecasting model is established based on autoregressive integrated moving average (ARIMA) model. By computer simulation, different control parameters are chosen for adaptive change point search for travel time series, which is divided into several sections of similar state.Then linear weight function is used to fit travel time sequence and to forecast travel time. The results show that the model has high accuracy in travel time forecasting.

Computational Testing for Automated Preprocessing 2: Practical Demonstration of a System for Scientific Data-Processing Workflow Management for High-Volume EEG

PubMed Central

Cowley, Benjamin U.; Korpela, Jussi

2018-01-01

Existing tools for the preprocessing of EEG data provide a large choice of methods to suitably prepare and analyse a given dataset. Yet it remains a challenge for the average user to integrate methods for batch processing of the increasingly large datasets of modern research, and compare methods to choose an optimal approach across the many possible parameter configurations. Additionally, many tools still require a high degree of manual decision making for, e.g., the classification of artifacts in channels, epochs or segments. This introduces extra subjectivity, is slow, and is not reproducible. Batching and well-designed automation can help to regularize EEG preprocessing, and thus reduce human effort, subjectivity, and consequent error. The Computational Testing for Automated Preprocessing (CTAP) toolbox facilitates: (i) batch processing that is easy for experts and novices alike; (ii) testing and comparison of preprocessing methods. Here we demonstrate the application of CTAP to high-resolution EEG data in three modes of use. First, a linear processing pipeline with mostly default parameters illustrates ease-of-use for naive users. Second, a branching pipeline illustrates CTAP's support for comparison of competing methods. Third, a pipeline with built-in parameter-sweeping illustrates CTAP's capability to support data-driven method parameterization. CTAP extends the existing functions and data structure from the well-known EEGLAB toolbox, based on Matlab, and produces extensive quality control outputs. CTAP is available under MIT open-source licence from https://github.com/bwrc/ctap. PMID:29692705

Computational Testing for Automated Preprocessing 2: Practical Demonstration of a System for Scientific Data-Processing Workflow Management for High-Volume EEG.

PubMed

Cowley, Benjamin U; Korpela, Jussi

2018-01-01

Existing tools for the preprocessing of EEG data provide a large choice of methods to suitably prepare and analyse a given dataset. Yet it remains a challenge for the average user to integrate methods for batch processing of the increasingly large datasets of modern research, and compare methods to choose an optimal approach across the many possible parameter configurations. Additionally, many tools still require a high degree of manual decision making for, e.g., the classification of artifacts in channels, epochs or segments. This introduces extra subjectivity, is slow, and is not reproducible. Batching and well-designed automation can help to regularize EEG preprocessing, and thus reduce human effort, subjectivity, and consequent error. The Computational Testing for Automated Preprocessing (CTAP) toolbox facilitates: (i) batch processing that is easy for experts and novices alike; (ii) testing and comparison of preprocessing methods. Here we demonstrate the application of CTAP to high-resolution EEG data in three modes of use. First, a linear processing pipeline with mostly default parameters illustrates ease-of-use for naive users. Second, a branching pipeline illustrates CTAP's support for comparison of competing methods. Third, a pipeline with built-in parameter-sweeping illustrates CTAP's capability to support data-driven method parameterization. CTAP extends the existing functions and data structure from the well-known EEGLAB toolbox, based on Matlab, and produces extensive quality control outputs. CTAP is available under MIT open-source licence from https://github.com/bwrc/ctap.

Automated Processing Workflow for Ambient Seismic Recordings

NASA Astrophysics Data System (ADS)

Girard, A. J.; Shragge, J.

2017-12-01

Structural imaging using body-wave energy present in ambient seismic data remains a challenging task, largely because these wave modes are commonly much weaker than surface wave energy. In a number of situations body-wave energy has been extracted successfully; however, (nearly) all successful body-wave extraction and imaging approaches have focused on cross-correlation processing. While this is useful for interferometric purposes, it can also lead to the inclusion of unwanted noise events that dominate the resulting stack, leaving body-wave energy overpowered by the coherent noise. Conversely, wave-equation imaging can be applied directly on non-correlated ambient data that has been preprocessed to mitigate unwanted energy (i.e., surface waves, burst-like and electromechanical noise) to enhance body-wave arrivals. Following this approach, though, requires a significant preprocessing effort on often Terabytes of ambient seismic data, which is expensive and requires automation to be a feasible approach. In this work we outline an automated processing workflow designed to optimize body wave energy from an ambient seismic data set acquired on a large-N array at a mine site near Lalor Lake, Manitoba, Canada. We show that processing ambient seismic data in the recording domain, rather than the cross-correlation domain, allows us to mitigate energy that is inappropriate for body-wave imaging. We first develop a method for window selection that automatically identifies and removes data contaminated by coherent high-energy bursts. We then apply time- and frequency-domain debursting techniques to mitigate the effects of remaining strong amplitude and/or monochromatic energy without severely degrading the overall waveforms. After each processing step we implement a QC check to investigate improvements in the convergence rates - and the emergence of reflection events - in the cross-correlation plus stack waveforms over hour-long windows. Overall, the QC analyses suggest that automated preprocessing of ambient seismic recordings in the recording domain successfully mitigates unwanted coherent noise events in both the time and frequency domain. Accordingly, we assert that this method is beneficial for direct wave-equation imaging with ambient seismic recordings.

Do High Dynamic Range threatments improve the results of Structure from Motion approaches in Geomorphology?

NASA Astrophysics Data System (ADS)

Gómez-Gutiérrez, Álvaro; Juan de Sanjosé-Blasco, José; Schnabel, Susanne; de Matías-Bejarano, Javier; Pulido-Fernández, Manuel; Berenguer-Sempere, Fernando

2015-04-01

In this work, the hypothesis of improving 3D models obtained with Structure from Motion (SfM) approaches using images pre-processed by High Dynamic Range (HDR) techniques is tested. Photographs of the Veleta Rock Glacier in Spain were captured with different exposure values (EV0, EV+1 and EV-1), two focal lengths (35 and 100 mm) and under different weather conditions for the years 2008, 2009, 2011, 2012 and 2014. HDR images were produced using the different EV steps within Fusion F.1 software. Point clouds were generated using commercial and free available SfM software: Agisoft Photoscan and 123D Catch. Models Obtained using pre-processed images and non-preprocessed images were compared in a 3D environment with a benchmark 3D model obtained by means of a Terrestrial Laser Scanner (TLS). A total of 40 point clouds were produced, georeferenced and compared. Results indicated that for Agisoft Photoscan software differences in the accuracy between models obtained with pre-processed and non-preprocessed images were not significant from a statistical viewpoint. However, in the case of the free available software 123D Catch, models obtained using images pre-processed by HDR techniques presented a higher point density and were more accurate. This tendency was observed along the 5 studied years and under different capture conditions. More work should be done in the near future to corroborate whether the results of similar software packages can be improved by HDR techniques (e.g. ARC3D, Bundler and PMVS2, CMP SfM, Photosynth and VisualSFM).

Authenticity assessment of banknotes using portable near infrared spectrometer and chemometrics.

PubMed

da Silva Oliveira, Vanessa; Honorato, Ricardo Saldanha; Honorato, Fernanda Araújo; Pereira, Claudete Fernandes

2018-05-01

Spectra recorded using a portable near infrared (NIR) spectrometer, Soft Independent Modeling of Class Analogy (SIMCA) and Linear Discriminant Analysis (LDA) associated to Successive Projections Algorithm (SPA) models were applied to identify counterfeit and authentic Brazilian Real (R$20, R$50 and R$100) banknotes, enabling a simple field analysis. NIR spectra (950-1650nm) were recorded from seven different areas of the banknotes (two with fluorescent ink, one over watermark, three with intaglio printing process and one over the serial numbers with typography printing). SIMCA and SPA-LDA models were built using 1st derivative preprocessed spectral data from one of the intaglio areas. For the SIMCA models, all authentic (300) banknotes were correctly classified and the counterfeits (227) were not classified. For the two classes SPA-LDA models (authentic and counterfeit currencies), all the test samples were correctly classified into their respective class. The number of selected variables by SPA varied from two to nineteen for R$20, R$50 and R$100 currencies. These results show that the use of the portable near-infrared with SIMCA or SPA-LDA models can be a completely effective, fast, and non-destructive way to identify authenticity of banknotes as well as permitting field analysis. Copyright © 2018 Elsevier B.V. All rights reserved.

Optimization of miRNA-seq data preprocessing.

PubMed

Tam, Shirley; Tsao, Ming-Sound; McPherson, John D

2015-11-01

The past two decades of microRNA (miRNA) research has solidified the role of these small non-coding RNAs as key regulators of many biological processes and promising biomarkers for disease. The concurrent development in high-throughput profiling technology has further advanced our understanding of the impact of their dysregulation on a global scale. Currently, next-generation sequencing is the platform of choice for the discovery and quantification of miRNAs. Despite this, there is no clear consensus on how the data should be preprocessed before conducting downstream analyses. Often overlooked, data preprocessing is an essential step in data analysis: the presence of unreliable features and noise can affect the conclusions drawn from downstream analyses. Using a spike-in dilution study, we evaluated the effects of several general-purpose aligners (BWA, Bowtie, Bowtie 2 and Novoalign), and normalization methods (counts-per-million, total count scaling, upper quartile scaling, Trimmed Mean of M, DESeq, linear regression, cyclic loess and quantile) with respect to the final miRNA count data distribution, variance, bias and accuracy of differential expression analysis. We make practical recommendations on the optimal preprocessing methods for the extraction and interpretation of miRNA count data from small RNA-sequencing experiments. © The Author 2015. Published by Oxford University Press.

Characterizing the continuously acquired cardiovascular time series during hemodialysis, using median hybrid filter preprocessing noise reduction.

PubMed

Wilson, Scott; Bowyer, Andrea; Harrap, Stephen B

2015-01-01

The clinical characterization of cardiovascular dynamics during hemodialysis (HD) has important pathophysiological implications in terms of diagnostic, cardiovascular risk assessment, and treatment efficacy perspectives. Currently the diagnosis of significant intradialytic systolic blood pressure (SBP) changes among HD patients is imprecise and opportunistic, reliant upon the presence of hypotensive symptoms in conjunction with coincident but isolated noninvasive brachial cuff blood pressure (NIBP) readings. Considering hemodynamic variables as a time series makes a continuous recording approach more desirable than intermittent measures; however, in the clinical environment, the data signal is susceptible to corruption due to both impulsive and Gaussian-type noise. Signal preprocessing is an attractive solution to this problem. Prospectively collected continuous noninvasive SBP data over the short-break intradialytic period in ten patients was preprocessed using a novel median hybrid filter (MHF) algorithm and compared with 50 time-coincident pairs of intradialytic NIBP measures from routine HD practice. The median hybrid preprocessing technique for continuously acquired cardiovascular data yielded a dynamic regression without significant noise and artifact, suitable for high-level profiling of time-dependent SBP behavior. Signal accuracy is highly comparable with standard NIBP measurement, with the added clinical benefit of dynamic real-time hemodynamic information.

Supervised Variational Relevance Learning, An Analytic Geometric Feature Selection with Applications to Omic Datasets.

PubMed

Boareto, Marcelo; Cesar, Jonatas; Leite, Vitor B P; Caticha, Nestor

2015-01-01

We introduce Supervised Variational Relevance Learning (Suvrel), a variational method to determine metric tensors to define distance based similarity in pattern classification, inspired in relevance learning. The variational method is applied to a cost function that penalizes large intraclass distances and favors small interclass distances. We find analytically the metric tensor that minimizes the cost function. Preprocessing the patterns by doing linear transformations using the metric tensor yields a dataset which can be more efficiently classified. We test our methods using publicly available datasets, for some standard classifiers. Among these datasets, two were tested by the MAQC-II project and, even without the use of further preprocessing, our results improve on their performance.

HydroClimATe: hydrologic and climatic analysis toolkit

USGS Publications Warehouse

Dickinson, Jesse; Hanson, Randall T.; Predmore, Steven K.

2014-01-01

The potential consequences of climate variability and climate change have been identified as major issues for the sustainability and availability of the worldwide water resources. Unlike global climate change, climate variability represents deviations from the long-term state of the climate over periods of a few years to several decades. Currently, rich hydrologic time-series data are available, but the combination of data preparation and statistical methods developed by the U.S. Geological Survey as part of the Groundwater Resources Program is relatively unavailable to hydrologists and engineers who could benefit from estimates of climate variability and its effects on periodic recharge and water-resource availability. This report documents HydroClimATe, a computer program for assessing the relations between variable climatic and hydrologic time-series data. HydroClimATe was developed for a Windows operating system. The software includes statistical tools for (1) time-series preprocessing, (2) spectral analysis, (3) spatial and temporal analysis, (4) correlation analysis, and (5) projections. The time-series preprocessing tools include spline fitting, standardization using a normal or gamma distribution, and transformation by a cumulative departure. The spectral analysis tools include discrete Fourier transform, maximum entropy method, and singular spectrum analysis. The spatial and temporal analysis tool is empirical orthogonal function analysis. The correlation analysis tools are linear regression and lag correlation. The projection tools include autoregressive time-series modeling and generation of many realizations. These tools are demonstrated in four examples that use stream-flow discharge data, groundwater-level records, gridded time series of precipitation data, and the Multivariate ENSO Index.

Real-time topic-aware influence maximization using preprocessing.

PubMed

Chen, Wei; Lin, Tian; Yang, Cheng

2016-01-01

Influence maximization is the task of finding a set of seed nodes in a social network such that the influence spread of these seed nodes based on certain influence diffusion model is maximized. Topic-aware influence diffusion models have been recently proposed to address the issue that influence between a pair of users are often topic-dependent and information, ideas, innovations etc. being propagated in networks are typically mixtures of topics. In this paper, we focus on the topic-aware influence maximization task. In particular, we study preprocessing methods to avoid redoing influence maximization for each mixture from scratch. We explore two preprocessing algorithms with theoretical justifications. Our empirical results on data obtained in a couple of existing studies demonstrate that one of our algorithms stands out as a strong candidate providing microsecond online response time and competitive influence spread, with reasonable preprocessing effort.

Detection of epileptiform activity in EEG signals based on time-frequency and non-linear analysis

PubMed Central

Gajic, Dragoljub; Djurovic, Zeljko; Gligorijevic, Jovan; Di Gennaro, Stefano; Savic-Gajic, Ivana

2015-01-01

We present a new technique for detection of epileptiform activity in EEG signals. After preprocessing of EEG signals we extract representative features in time, frequency and time-frequency domain as well as using non-linear analysis. The features are extracted in a few frequency sub-bands of clinical interest since these sub-bands showed much better discriminatory characteristics compared with the whole frequency band. Then we optimally reduce the dimension of feature space to two using scatter matrices. A decision about the presence of epileptiform activity in EEG signals is made by quadratic classifiers designed in the reduced two-dimensional feature space. The accuracy of the technique was tested on three sets of electroencephalographic (EEG) signals recorded at the University Hospital Bonn: surface EEG signals from healthy volunteers, intracranial EEG signals from the epilepsy patients during the seizure free interval from within the seizure focus and intracranial EEG signals of epileptic seizures also from within the seizure focus. An overall detection accuracy of 98.7% was achieved. PMID:25852534

EARLINET: potential operationality of a research network

NASA Astrophysics Data System (ADS)

Sicard, M.; D'Amico, G.; Comerón, A.; Mona, L.; Alados-Arboledas, L.; Amodeo, A.; Baars, H.; Baldasano, J. M.; Belegante, L.; Binietoglou, I.; Bravo-Aranda, J. A.; Fernández, A. J.; Fréville, P.; García-Vizcaíno, D.; Giunta, A.; Granados-Muñoz, M. J.; Guerrero-Rascado, J. L.; Hadjimitsis, D.; Haefele, A.; Hervo, M.; Iarlori, M.; Kokkalis, P.; Lange, D.; Mamouri, R. E.; Mattis, I.; Molero, F.; Montoux, N.; Muñoz, A.; Muñoz Porcar, C.; Navas-Guzmán, F.; Nicolae, D.; Nisantzi, A.; Papagiannopoulos, N.; Papayannis, A.; Pereira, S.; Preißler, J.; Pujadas, M.; Rizi, V.; Rocadenbosch, F.; Sellegri, K.; Simeonov, V.; Tsaknakis, G.; Wagner, F.; Pappalardo, G.

2015-11-01

In the framework of ACTRIS (Aerosols, Clouds, and Trace Gases Research Infrastructure Network) summer 2012 measurement campaign (8 June-17 July 2012), EARLINET organized and performed a controlled exercise of feasibility to demonstrate its potential to perform operational, coordinated measurements and deliver products in near-real time. Eleven lidar stations participated in the exercise which started on 9 July 2012 at 06:00 UT and ended 72 h later on 12 July at 06:00 UT. For the first time, the single calculus chain (SCC) - the common calculus chain developed within EARLINET for the automatic evaluation of lidar data from raw signals up to the final products - was used. All stations sent in real-time measurements of a 1 h duration to the SCC server in a predefined netcdf file format. The pre-processing of the data was performed in real time by the SCC, while the optical processing was performed in near-real time after the exercise ended. 98 and 79 % of the files sent to SCC were successfully pre-processed and processed, respectively. Those percentages are quite large taking into account that no cloud screening was performed on the lidar data. The paper draws present and future SCC users' attention to the most critical parameters of the SCC product configuration and their possible optimal value but also to the limitations inherent to the raw data. The continuous use of SCC direct and derived products in heterogeneous conditions is used to demonstrate two potential applications of EARLINET infrastructure: the monitoring of a Saharan dust intrusion event and the evaluation of two dust transport models. The efforts made to define the measurements protocol and to configure properly the SCC pave the way for applying this protocol for specific applications such as the monitoring of special events, atmospheric modeling, climate research and calibration/validation activities of spaceborne observations.

Data preprocessing methods of FT-NIR spectral data for the classification cooking oil

NASA Astrophysics Data System (ADS)

Ruah, Mas Ezatul Nadia Mohd; Rasaruddin, Nor Fazila; Fong, Sim Siong; Jaafar, Mohd Zuli

2014-12-01

This recent work describes the data pre-processing method of FT-NIR spectroscopy datasets of cooking oil and its quality parameters with chemometrics method. Pre-processing of near-infrared (NIR) spectral data has become an integral part of chemometrics modelling. Hence, this work is dedicated to investigate the utility and effectiveness of pre-processing algorithms namely row scaling, column scaling and single scaling process with Standard Normal Variate (SNV). The combinations of these scaling methods have impact on exploratory analysis and classification via Principle Component Analysis plot (PCA). The samples were divided into palm oil and non-palm cooking oil. The classification model was build using FT-NIR cooking oil spectra datasets in absorbance mode at the range of 4000cm-1-14000cm-1. Savitzky Golay derivative was applied before developing the classification model. Then, the data was separated into two sets which were training set and test set by using Duplex method. The number of each class was kept equal to 2/3 of the class that has the minimum number of sample. Then, the sample was employed t-statistic as variable selection method in order to select which variable is significant towards the classification models. The evaluation of data pre-processing were looking at value of modified silhouette width (mSW), PCA and also Percentage Correctly Classified (%CC). The results show that different data processing strategies resulting to substantial amount of model performances quality. The effects of several data pre-processing i.e. row scaling, column standardisation and single scaling process with Standard Normal Variate indicated by mSW and %CC. At two PCs model, all five classifier gave high %CC except Quadratic Distance Analysis.

Combining linear polarization spectroscopy and the Representative Layer Theory to measure the Beer-Lambert law absorbance of highly scattering materials.

PubMed

Gobrecht, Alexia; Bendoula, Ryad; Roger, Jean-Michel; Bellon-Maurel, Véronique

2015-01-01

Visible and Near Infrared (Vis-NIR) Spectroscopy is a powerful non destructive analytical method used to analyze major compounds in bulk materials and products and requiring no sample preparation. It is widely used in routine analysis and also in-line in industries, in-vivo with biomedical applications or in-field for agricultural and environmental applications. However, highly scattering samples subvert Beer-Lambert law's linear relationship between spectral absorbance and the concentrations. Instead of spectral pre-processing, which is commonly used by Vis-NIR spectroscopists to mitigate the scattering effect, we put forward an optical method, based on Polarized Light Spectroscopy to improve the absorbance signal measurement on highly scattering samples. This method selects part of the signal which is less impacted by scattering. The resulted signal is combined in the Absorption/Remission function defined in Dahm's Representative Layer Theory to compute an absorbance signal fulfilling Beer-Lambert's law, i.e. being linearly related to concentration of the chemicals composing the sample. The underpinning theories have been experimentally evaluated on scattering samples in liquid form and in powdered form. The method produced more accurate spectra and the Pearson's coefficient assessing the linearity between the absorbance spectra and the concentration of the added dye improved from 0.94 to 0.99 for liquid samples and 0.84-0.97 for powdered samples. Copyright © 2014 Elsevier B.V. All rights reserved.

Research on strategy marine noise map based on i4ocean platform: Constructing flow and key approach

NASA Astrophysics Data System (ADS)

Huang, Baoxiang; Chen, Ge; Han, Yong

2016-02-01

Noise level in a marine environment has raised extensive concern in the scientific community. The research is carried out on i4Ocean platform following the process of ocean noise model integrating, noise data extracting, processing, visualizing, and interpreting, ocean noise map constructing and publishing. For the convenience of numerical computation, based on the characteristics of ocean noise field, a hybrid model related to spatial locations is suggested in the propagation model. The normal mode method K/I model is used for far field and ray method CANARY model is used for near field. Visualizing marine ambient noise data is critical to understanding and predicting marine noise for relevant decision making. Marine noise map can be constructed on virtual ocean scene. The systematic marine noise visualization framework includes preprocessing, coordinate transformation interpolation, and rendering. The simulation of ocean noise depends on realistic surface. Then the dynamic water simulation gird was improved with GPU fusion to achieve seamless combination with the visualization result of ocean noise. At the same time, the profile and spherical visualization include space, and time dimensionality were also provided for the vertical field characteristics of ocean ambient noise. Finally, marine noise map can be published with grid pre-processing and multistage cache technology to better serve the public.

An approach to predict Sudden Cardiac Death (SCD) using time domain and bispectrum features from HRV signal.

PubMed

Houshyarifar, Vahid; Chehel Amirani, Mehdi

2016-08-12

In this paper we present a method to predict Sudden Cardiac Arrest (SCA) with higher order spectral (HOS) and linear (Time) features extracted from heart rate variability (HRV) signal. Predicting the occurrence of SCA is important in order to avoid the probability of Sudden Cardiac Death (SCD). This work is a challenge to predict five minutes before SCA onset. The method consists of four steps: pre-processing, feature extraction, feature reduction, and classification. In the first step, the QRS complexes are detected from the electrocardiogram (ECG) signal and then the HRV signal is extracted. In second step, bispectrum features of HRV signal and time-domain features are obtained. Six features are extracted from bispectrum and two features from time-domain. In the next step, these features are reduced to one feature by the linear discriminant analysis (LDA) technique. Finally, KNN and support vector machine-based classifiers are used to classify the HRV signals. We used two database named, MIT/BIH Sudden Cardiac Death (SCD) Database and Physiobank Normal Sinus Rhythm (NSR). In this work we achieved prediction of SCD occurrence for six minutes before the SCA with the accuracy over 91%.

Simultaneous determination of penicillin G salts by infrared spectroscopy: Evaluation of combining orthogonal signal correction with radial basis function-partial least squares regression

NASA Astrophysics Data System (ADS)

Talebpour, Zahra; Tavallaie, Roya; Ahmadi, Seyyed Hamid; Abdollahpour, Assem

2010-09-01

In this study, a new method for the simultaneous determination of penicillin G salts in pharmaceutical mixture via FT-IR spectroscopy combined with chemometrics was investigated. The mixture of penicillin G salts is a complex system due to similar analytical characteristics of components. Partial least squares (PLS) and radial basis function-partial least squares (RBF-PLS) were used to develop the linear and nonlinear relation between spectra and components, respectively. The orthogonal signal correction (OSC) preprocessing method was used to correct unexpected information, such as spectral overlapping and scattering effects. In order to compare the influence of OSC on PLS and RBF-PLS models, the optimal linear (PLS) and nonlinear (RBF-PLS) models based on conventional and OSC preprocessed spectra were established and compared. The obtained results demonstrated that OSC clearly enhanced the performance of both RBF-PLS and PLS calibration models. Also in the case of some nonlinear relation between spectra and component, OSC-RBF-PLS gave satisfactory results than OSC-PLS model which indicated that the OSC was helpful to remove extrinsic deviations from linearity without elimination of nonlinear information related to component. The chemometric models were tested on an external dataset and finally applied to the analysis commercialized injection product of penicillin G salts.

[Comparison of Three Spectroscopies for the Determination of Composition of LDPE/PP Blend with Partial Least-Squares].

PubMed

Chen, Ru-huang; Jin, Gang

2015-08-01

This paper presented an application of mid-infrared (MIR), near-infrared (NIR) and Raman spectroscopies for collecting the spectra of 31 kinds of low density polyethylene/polyprolene (LDPE/PP) samples with different proportions. The different pre-processing methods (multiplicative scatter correction, mean centering and Savitzky-Golay first derivative) and spectral region were explored to develop partial least-squares (PLS) model for LDPE, their influence on the accuracy of PLS model also being discussed. Three spectroscopies were compared about the accuracy of quantitative measurement. Consequently, the pre-processing methods and spectral region have a great impact on the accuracy of PLS model, especially the spectra with subtle difference, random noise and baseline variation. After being pre-processed and spectral region selected, the calibration model of MIR, NIR and Raman exhibited R2/RMSEC values of 0.9906/2.941, 0.9973/1.561 and 0.9972/1.598 respectively, which corrsponding to 0.8876/10.15, 0.8493/11.75 and 0.8757/10.67 before any treatment. The results also suggested MIR, NIR and Raman are three strong tools to predict the content of LDPE in LDPE/PP blend. However, NIR and Raman showed higher accuracy after being pre-processed and more suitability to fast quantitative characterization due to their high measuring speed.

Framework for Parallel Preprocessing of Microarray Data Using Hadoop

PubMed Central

2018-01-01

Nowadays, microarray technology has become one of the popular ways to study gene expression and diagnosis of disease. National Center for Biology Information (NCBI) hosts public databases containing large volumes of biological data required to be preprocessed, since they carry high levels of noise and bias. Robust Multiarray Average (RMA) is one of the standard and popular methods that is utilized to preprocess the data and remove the noises. Most of the preprocessing algorithms are time-consuming and not able to handle a large number of datasets with thousands of experiments. Parallel processing can be used to address the above-mentioned issues. Hadoop is a well-known and ideal distributed file system framework that provides a parallel environment to run the experiment. In this research, for the first time, the capability of Hadoop and statistical power of R have been leveraged to parallelize the available preprocessing algorithm called RMA to efficiently process microarray data. The experiment has been run on cluster containing 5 nodes, while each node has 16 cores and 16 GB memory. It compares efficiency and the performance of parallelized RMA using Hadoop with parallelized RMA using affyPara package as well as sequential RMA. The result shows the speed-up rate of the proposed approach outperforms the sequential approach and affyPara approach. PMID:29796018

Fast-SNP: a fast matrix pre-processing algorithm for efficient loopless flux optimization of metabolic models

PubMed Central

Saa, Pedro A.; Nielsen, Lars K.

2016-01-01

Motivation: Computation of steady-state flux solutions in large metabolic models is routinely performed using flux balance analysis based on a simple LP (Linear Programming) formulation. A minimal requirement for thermodynamic feasibility of the flux solution is the absence of internal loops, which are enforced using ‘loopless constraints’. The resulting loopless flux problem is a substantially harder MILP (Mixed Integer Linear Programming) problem, which is computationally expensive for large metabolic models. Results: We developed a pre-processing algorithm that significantly reduces the size of the original loopless problem into an easier and equivalent MILP problem. The pre-processing step employs a fast matrix sparsification algorithm—Fast- sparse null-space pursuit (SNP)—inspired by recent results on SNP. By finding a reduced feasible ‘loop-law’ matrix subject to known directionalities, Fast-SNP considerably improves the computational efficiency in several metabolic models running different loopless optimization problems. Furthermore, analysis of the topology encoded in the reduced loop matrix enabled identification of key directional constraints for the potential permanent elimination of infeasible loops in the underlying model. Overall, Fast-SNP is an effective and simple algorithm for efficient formulation of loop-law constraints, making loopless flux optimization feasible and numerically tractable at large scale. Availability and Implementation: Source code for MATLAB including examples is freely available for download at http://www.aibn.uq.edu.au/cssb-resources under Software. Optimization uses Gurobi, CPLEX or GLPK (the latter is included with the algorithm). Contact: lars.nielsen@uq.edu.au Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27559155

Enhancement of event related potentials by iterative restoration algorithms

NASA Astrophysics Data System (ADS)

Pomalaza-Raez, Carlos A.; McGillem, Clare D.

1986-12-01

An iterative procedure for the restoration of event related potentials (ERP) is proposed and implemented. The method makes use of assumed or measured statistical information about latency variations in the individual ERP components. The signal model used for the restoration algorithm consists of a time-varying linear distortion and a positivity/negativity constraint. Additional preprocessing in the form of low-pass filtering is needed in order to mitigate the effects of additive noise. Numerical results obtained with real data show clearly the presence of enhanced and regenerated components in the restored ERP's. The procedure is easy to implement which makes it convenient when compared to other proposed techniques for the restoration of ERP signals.

Locomotive track detection for underground

NASA Astrophysics Data System (ADS)

Ma, Zhonglei; Lang, Wenhui; Li, Xiaoming; Wei, Xing

2017-08-01

In order to improve the PC-based track detection system, this paper proposes a method to detect linear track for underground locomotive based on DSP + FPGA. Firstly, the analog signal outputted from the camera is sampled by A / D chip. Then the collected digital signal is preprocessed by FPGA. Secondly, the output signal of FPGA is transmitted to DSP via EMIF port. Subsequently, the adaptive threshold edge detection, polar angle and radius constrain based Hough transform are implemented by DSP. Lastly, the detected track information is transmitted to host computer through Ethernet interface. The experimental results show that the system can not only meet the requirements of real-time detection, but also has good robustness.

A semi-implicit level set method for multiphase flows and fluid-structure interaction problems

NASA Astrophysics Data System (ADS)

Cottet, Georges-Henri; Maitre, Emmanuel

2016-06-01

In this paper we present a novel semi-implicit time-discretization of the level set method introduced in [8] for fluid-structure interaction problems. The idea stems from a linear stability analysis derived on a simplified one-dimensional problem. The semi-implicit scheme relies on a simple filter operating as a pre-processing on the level set function. It applies to multiphase flows driven by surface tension as well as to fluid-structure interaction problems. The semi-implicit scheme avoids the stability constraints that explicit scheme need to satisfy and reduces significantly the computational cost. It is validated through comparisons with the original explicit scheme and refinement studies on two-dimensional benchmarks.

Optimal HRF and smoothing parameters for fMRI time series within an autoregressive modeling framework.

PubMed

Galka, Andreas; Siniatchkin, Michael; Stephani, Ulrich; Groening, Kristina; Wolff, Stephan; Bosch-Bayard, Jorge; Ozaki, Tohru

2010-12-01

The analysis of time series obtained by functional magnetic resonance imaging (fMRI) may be approached by fitting predictive parametric models, such as nearest-neighbor autoregressive models with exogeneous input (NNARX). As a part of the modeling procedure, it is possible to apply instantaneous linear transformations to the data. Spatial smoothing, a common preprocessing step, may be interpreted as such a transformation. The autoregressive parameters may be constrained, such that they provide a response behavior that corresponds to the canonical haemodynamic response function (HRF). We present an algorithm for estimating the parameters of the linear transformations and of the HRF within a rigorous maximum-likelihood framework. Using this approach, an optimal amount of both the spatial smoothing and the HRF can be estimated simultaneously for a given fMRI data set. An example from a motor-task experiment is discussed. It is found that, for this data set, weak, but non-zero, spatial smoothing is optimal. Furthermore, it is demonstrated that activated regions can be estimated within the maximum-likelihood framework.

Discrete pre-processing step effects in registration-based pipelines, a preliminary volumetric study on T1-weighted images.

PubMed

Muncy, Nathan M; Hedges-Muncy, Ariana M; Kirwan, C Brock

2017-01-01

Pre-processing MRI scans prior to performing volumetric analyses is common practice in MRI studies. As pre-processing steps adjust the voxel intensities, the space in which the scan exists, and the amount of data in the scan, it is possible that the steps have an effect on the volumetric output. To date, studies have compared between and not within pipelines, and so the impact of each step is unknown. This study aims to quantify the effects of pre-processing steps on volumetric measures in T1-weighted scans within a single pipeline. It was our hypothesis that pre-processing steps would significantly impact ROI volume estimations. One hundred fifteen participants from the OASIS dataset were used, where each participant contributed three scans. All scans were then pre-processed using a step-wise pipeline. Bilateral hippocampus, putamen, and middle temporal gyrus volume estimations were assessed following each successive step, and all data were processed by the same pipeline 5 times. Repeated-measures analyses tested for a main effects of pipeline step, scan-rescan (for MRI scanner consistency) and repeated pipeline runs (for algorithmic consistency). A main effect of pipeline step was detected, and interestingly an interaction between pipeline step and ROI exists. No effect for either scan-rescan or repeated pipeline run was detected. We then supply a correction for noise in the data resulting from pre-processing.

Control of a brain-computer interface using stereotactic depth electrodes in and adjacent to the hippocampus

NASA Astrophysics Data System (ADS)

Krusienski, D. J.; Shih, J. J.

2011-04-01

A brain-computer interface (BCI) is a device that enables severely disabled people to communicate and interact with their environments using their brain waves. Most research investigating BCI in humans has used scalp-recorded electroencephalography or intracranial electrocorticography. The use of brain signals obtained directly from stereotactic depth electrodes to control a BCI has not previously been explored. In this study, event-related potentials (ERPs) recorded from bilateral stereotactic depth electrodes implanted in and adjacent to the hippocampus were used to control a P300 Speller paradigm. The ERPs were preprocessed and used to train a linear classifier to subsequently predict the intended target letters. The classifier was able to predict the intended target character at or near 100% accuracy using fewer than 15 stimulation sequences in the two subjects tested. Our results demonstrate that ERPs from hippocampal and hippocampal adjacent depth electrodes can be used to reliably control the P300 Speller BCI paradigm.

Paroxysmal atrial fibrillation prediction based on HRV analysis and non-dominated sorting genetic algorithm III.

PubMed

Boon, K H; Khalil-Hani, M; Malarvili, M B

2018-01-01

This paper presents a method that able to predict the paroxysmal atrial fibrillation (PAF). The method uses shorter heart rate variability (HRV) signals when compared to existing methods, and achieves good prediction accuracy. PAF is a common cardiac arrhythmia that increases the health risk of a patient, and the development of an accurate predictor of the onset of PAF is clinical important because it increases the possibility to electrically stabilize and prevent the onset of atrial arrhythmias with different pacing techniques. We propose a multi-objective optimization algorithm based on the non-dominated sorting genetic algorithm III for optimizing the baseline PAF prediction system, that consists of the stages of pre-processing, HRV feature extraction, and support vector machine (SVM) model. The pre-processing stage comprises of heart rate correction, interpolation, and signal detrending. After that, time-domain, frequency-domain, non-linear HRV features are extracted from the pre-processed data in feature extraction stage. Then, these features are used as input to the SVM for predicting the PAF event. The proposed optimization algorithm is used to optimize the parameters and settings of various HRV feature extraction algorithms, select the best feature subsets, and tune the SVM parameters simultaneously for maximum prediction performance. The proposed method achieves an accuracy rate of 87.7%, which significantly outperforms most of the previous works. This accuracy rate is achieved even with the HRV signal length being reduced from the typical 30 min to just 5 min (a reduction of 83%). Furthermore, another significant result is the sensitivity rate, which is considered more important that other performance metrics in this paper, can be improved with the trade-off of lower specificity. Copyright © 2017 Elsevier B.V. All rights reserved.

Radar image processing for rock-type discrimination

NASA Technical Reports Server (NTRS)

Blom, R. G.; Daily, M.

1982-01-01

Image processing and enhancement techniques for improving the geologic utility of digital satellite radar images are reviewed. Preprocessing techniques such as mean and variance correction on a range or azimuth line by line basis to provide uniformly illuminated swaths, median value filtering for four-look imagery to eliminate speckle, and geometric rectification using a priori elevation data. Examples are presented of application of preprocessing methods to Seasat and Landsat data, and Seasat SAR imagery was coregistered with Landsat imagery to form composite scenes. A polynomial was developed to distort the radar picture to fit the Landsat image of a 90 x 90 km sq grid, using Landsat color ratios with Seasat intensities. Subsequent linear discrimination analysis was employed to discriminate rock types from known areas. Seasat additions to the Landsat data improved rock identification by 7%.

A graphical method to evaluate spectral preprocessing in multivariate regression calibrations: example with Savitzky-Golay filters and partial least squares regression.

PubMed

Delwiche, Stephen R; Reeves, James B

2010-01-01

In multivariate regression analysis of spectroscopy data, spectral preprocessing is often performed to reduce unwanted background information (offsets, sloped baselines) or accentuate absorption features in intrinsically overlapping bands. These procedures, also known as pretreatments, are commonly smoothing operations or derivatives. While such operations are often useful in reducing the number of latent variables of the actual decomposition and lowering residual error, they also run the risk of misleading the practitioner into accepting calibration equations that are poorly adapted to samples outside of the calibration. The current study developed a graphical method to examine this effect on partial least squares (PLS) regression calibrations of near-infrared (NIR) reflection spectra of ground wheat meal with two analytes, protein content and sodium dodecyl sulfate sedimentation (SDS) volume (an indicator of the quantity of the gluten proteins that contribute to strong doughs). These two properties were chosen because of their differing abilities to be modeled by NIR spectroscopy: excellent for protein content, fair for SDS sedimentation volume. To further demonstrate the potential pitfalls of preprocessing, an artificial component, a randomly generated value, was included in PLS regression trials. Savitzky-Golay (digital filter) smoothing, first-derivative, and second-derivative preprocess functions (5 to 25 centrally symmetric convolution points, derived from quadratic polynomials) were applied to PLS calibrations of 1 to 15 factors. The results demonstrated the danger of an over reliance on preprocessing when (1) the number of samples used in a multivariate calibration is low (<50), (2) the spectral response of the analyte is weak, and (3) the goodness of the calibration is based on the coefficient of determination (R(2)) rather than a term based on residual error. The graphical method has application to the evaluation of other preprocess functions and various types of spectroscopy data.

T-phase and tsunami pressure waveforms recorded by near-source IMS water-column hydrophone triplets during the 2015 Chile earthquake

NASA Astrophysics Data System (ADS)

Matsumoto, H.; Haralabus, G.; Zampolli, M.; Özel, N. M.

2016-12-01

Underwater acoustic signal waveforms recorded during the 2015 Chile earthquake (Mw 8.3) by the hydrophones of hydroacoustic station HA03, located at the Juan Fernandez Islands, are analyzed. HA03 is part of the Comprehensive Nuclear-Test-Ban Treaty International Monitoring System. The interest in the particular data set stems from the fact that HA03 is located only approximately 700 km SW from the epicenter of the earthquake. This makes it possible to study aspects of the signal associated with the tsunamigenic earthquake, which would be more difficult to detect had the hydrophones been located far from the source. The analysis shows that the direction of arrival of the T phase can be estimated by means of a three-step preprocessing technique which circumvents spatial aliasing caused by the hydrophone spacing, the latter being large compared to the wavelength. Following this preprocessing step, standard frequency-wave number analysis (F-K analysis) can accurately estimate back azimuth and slowness of T-phase signals. The data analysis also shows that the dispersive tsunami signals can be identified by the water-column hydrophones at the time when the tsunami surface gravity wave reaches the station.

Detection of epileptic seizure in EEG signals using linear least squares preprocessing.

PubMed

Roshan Zamir, Z

2016-09-01

An epileptic seizure is a transient event of abnormal excessive neuronal discharge in the brain. This unwanted event can be obstructed by detection of electrical changes in the brain that happen before the seizure takes place. The automatic detection of seizures is necessary since the visual screening of EEG recordings is a time consuming task and requires experts to improve the diagnosis. Much of the prior research in detection of seizures has been developed based on artificial neural network, genetic programming, and wavelet transforms. Although the highest achieved accuracy for classification is 100%, there are drawbacks, such as the existence of unbalanced datasets and the lack of investigations in performances consistency. To address these, four linear least squares-based preprocessing models are proposed to extract key features of an EEG signal in order to detect seizures. The first two models are newly developed. The original signal (EEG) is approximated by a sinusoidal curve. Its amplitude is formed by a polynomial function and compared with the predeveloped spline function. Different statistical measures, namely classification accuracy, true positive and negative rates, false positive and negative rates and precision, are utilised to assess the performance of the proposed models. These metrics are derived from confusion matrices obtained from classifiers. Different classifiers are used over the original dataset and the set of extracted features. The proposed models significantly reduce the dimension of the classification problem and the computational time while the classification accuracy is improved in most cases. The first and third models are promising feature extraction methods with the classification accuracy of 100%. Logistic, LazyIB1, LazyIB5, and J48 are the best classifiers. Their true positive and negative rates are 1 while false positive and negative rates are 0 and the corresponding precision values are 1. Numerical results suggest that these models are robust and efficient for detecting epileptic seizure. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Detection of starch adulteration in onion powder by FT-NIR and FT-IR spectroscopy

USDA-ARS?s Scientific Manuscript database

Adulteration of onion powder with cornstarch was identified by Fourier transform near-infrared (FT-NIR) and Fourier transform infrared (FT-IR) spectroscopy. The reflectance spectra of 180 pure and adulterated samples (1–35 wt% starch) were collected and preprocessed to generate calibration and predi...

Distributed Finite Element Analysis Using a Transputer Network

NASA Technical Reports Server (NTRS)

Watson, James; Favenesi, James; Danial, Albert; Tombrello, Joseph; Yang, Dabby; Reynolds, Brian; Turrentine, Ronald; Shephard, Mark; Baehmann, Peggy

1989-01-01

The principal objective of this research effort was to demonstrate the extraordinarily cost effective acceleration of finite element structural analysis problems using a transputer-based parallel processing network. This objective was accomplished in the form of a commercially viable parallel processing workstation. The workstation is a desktop size, low-maintenance computing unit capable of supercomputer performance yet costs two orders of magnitude less. To achieve the principal research objective, a transputer based structural analysis workstation termed XPFEM was implemented with linear static structural analysis capabilities resembling commercially available NASTRAN. Finite element model files, generated using the on-line preprocessing module or external preprocessing packages, are downloaded to a network of 32 transputers for accelerated solution. The system currently executes at about one third Cray X-MP24 speed but additional acceleration appears likely. For the NASA selected demonstration problem of a Space Shuttle main engine turbine blade model with about 1500 nodes and 4500 independent degrees of freedom, the Cray X-MP24 required 23.9 seconds to obtain a solution while the transputer network, operated from an IBM PC-AT compatible host computer, required 71.7 seconds. Consequently, the $80,000 transputer network demonstrated a cost-performance ratio about 60 times better than the $15,000,000 Cray X-MP24 system.

Face recognition system using multiple face model of hybrid Fourier feature under uncontrolled illumination variation.

PubMed

Hwang, Wonjun; Wang, Haitao; Kim, Hyunwoo; Kee, Seok-Cheol; Kim, Junmo

2011-04-01

The authors present a robust face recognition system for large-scale data sets taken under uncontrolled illumination variations. The proposed face recognition system consists of a novel illumination-insensitive preprocessing method, a hybrid Fourier-based facial feature extraction, and a score fusion scheme. First, in the preprocessing stage, a face image is transformed into an illumination-insensitive image, called an "integral normalized gradient image," by normalizing and integrating the smoothed gradients of a facial image. Then, for feature extraction of complementary classifiers, multiple face models based upon hybrid Fourier features are applied. The hybrid Fourier features are extracted from different Fourier domains in different frequency bandwidths, and then each feature is individually classified by linear discriminant analysis. In addition, multiple face models are generated by plural normalized face images that have different eye distances. Finally, to combine scores from multiple complementary classifiers, a log likelihood ratio-based score fusion scheme is applied. The proposed system using the face recognition grand challenge (FRGC) experimental protocols is evaluated; FRGC is a large available data set. Experimental results on the FRGC version 2.0 data sets have shown that the proposed method shows an average of 81.49% verification rate on 2-D face images under various environmental variations such as illumination changes, expression changes, and time elapses.

Rapid differentiation of Ghana cocoa beans by FT-NIR spectroscopy coupled with multivariate classification

NASA Astrophysics Data System (ADS)

Teye, Ernest; Huang, Xingyi; Dai, Huang; Chen, Quansheng

2013-10-01

Quick, accurate and reliable technique for discrimination of cocoa beans according to geographical origin is essential for quality control and traceability management. This current study presents the application of Near Infrared Spectroscopy technique and multivariate classification for the differentiation of Ghana cocoa beans. A total of 194 cocoa bean samples from seven cocoa growing regions were used. Principal component analysis (PCA) was used to extract relevant information from the spectral data and this gave visible cluster trends. The performance of four multivariate classification methods: Linear discriminant analysis (LDA), K-nearest neighbors (KNN), Back propagation artificial neural network (BPANN) and Support vector machine (SVM) were compared. The performances of the models were optimized by cross validation. The results revealed that; SVM model was superior to all the mathematical methods with a discrimination rate of 100% in both the training and prediction set after preprocessing with Mean centering (MC). BPANN had a discrimination rate of 99.23% for the training set and 96.88% for prediction set. While LDA model had 96.15% and 90.63% for the training and prediction sets respectively. KNN model had 75.01% for the training set and 72.31% for prediction set. The non-linear classification methods used were superior to the linear ones. Generally, the results revealed that NIR Spectroscopy coupled with SVM model could be used successfully to discriminate cocoa beans according to their geographical origins for effective quality assurance.

Development and integration of block operations for data invariant automation of digital preprocessing and analysis of biological and biomedical Raman spectra.

PubMed

Schulze, H Georg; Turner, Robin F B

2015-06-01

High-throughput information extraction from large numbers of Raman spectra is becoming an increasingly taxing problem due to the proliferation of new applications enabled using advances in instrumentation. Fortunately, in many of these applications, the entire process can be automated, yielding reproducibly good results with significant time and cost savings. Information extraction consists of two stages, preprocessing and analysis. We focus here on the preprocessing stage, which typically involves several steps, such as calibration, background subtraction, baseline flattening, artifact removal, smoothing, and so on, before the resulting spectra can be further analyzed. Because the results of some of these steps can affect the performance of subsequent ones, attention must be given to the sequencing of steps, the compatibility of these sequences, and the propensity of each step to generate spectral distortions. We outline here important considerations to effect full automation of Raman spectral preprocessing: what is considered full automation; putative general principles to effect full automation; the proper sequencing of processing and analysis steps; conflicts and circularities arising from sequencing; and the need for, and approaches to, preprocessing quality control. These considerations are discussed and illustrated with biological and biomedical examples reflecting both successful and faulty preprocessing.

Discrete pre-processing step effects in registration-based pipelines, a preliminary volumetric study on T1-weighted images

PubMed Central

2017-01-01

Pre-processing MRI scans prior to performing volumetric analyses is common practice in MRI studies. As pre-processing steps adjust the voxel intensities, the space in which the scan exists, and the amount of data in the scan, it is possible that the steps have an effect on the volumetric output. To date, studies have compared between and not within pipelines, and so the impact of each step is unknown. This study aims to quantify the effects of pre-processing steps on volumetric measures in T1-weighted scans within a single pipeline. It was our hypothesis that pre-processing steps would significantly impact ROI volume estimations. One hundred fifteen participants from the OASIS dataset were used, where each participant contributed three scans. All scans were then pre-processed using a step-wise pipeline. Bilateral hippocampus, putamen, and middle temporal gyrus volume estimations were assessed following each successive step, and all data were processed by the same pipeline 5 times. Repeated-measures analyses tested for a main effects of pipeline step, scan-rescan (for MRI scanner consistency) and repeated pipeline runs (for algorithmic consistency). A main effect of pipeline step was detected, and interestingly an interaction between pipeline step and ROI exists. No effect for either scan-rescan or repeated pipeline run was detected. We then supply a correction for noise in the data resulting from pre-processing. PMID:29023597

Assessing error sources for Landsat time series analysis for tropical test sites in Viet Nam and Ethiopia

NASA Astrophysics Data System (ADS)

Schultz, Michael; Verbesselt, Jan; Herold, Martin; Avitabile, Valerio

2013-10-01

Researchers who use remotely sensed data can spend half of their total effort analysing prior data. If this data preprocessing does not match the application, this time spent on data analysis can increase considerably and can lead to inaccuracies. Despite the existence of a number of methods for pre-processing Landsat time series, each method has shortcomings, particularly for mapping forest changes under varying illumination, data availability and atmospheric conditions. Based on the requirements of mapping forest changes as defined by the United Nations (UN) Reducing Emissions from Forest Degradation and Deforestation (REDD) program, the accurate reporting of the spatio-temporal properties of these changes is necessary. We compared the impact of three fundamentally different radiometric preprocessing techniques Moderate Resolution Atmospheric TRANsmission (MODTRAN), Second Simulation of a Satellite Signal in the Solar Spectrum (6S) and simple Dark Object Subtraction (DOS) on mapping forest changes using Landsat time series data. A modification of Breaks For Additive Season and Trend (BFAST) monitor was used to jointly map the spatial and temporal agreement of forest changes at test sites in Ethiopia and Viet Nam. The suitability of the pre-processing methods for the occurring forest change drivers was assessed using recently captured Ground Truth and high resolution data (1000 points). A method for creating robust generic forest maps used for the sampling design is presented. An assessment of error sources has been performed identifying haze as a major source for time series analysis commission error.

An Effective Measured Data Preprocessing Method in Electrical Impedance Tomography

PubMed Central

Yu, Chenglong; Yue, Shihong; Wang, Jianpei; Wang, Huaxiang

2014-01-01

As an advanced process detection technology, electrical impedance tomography (EIT) has widely been paid attention to and studied in the industrial fields. But the EIT techniques are greatly limited to the low spatial resolutions. This problem may result from the incorrect preprocessing of measuring data and lack of general criterion to evaluate different preprocessing processes. In this paper, an EIT data preprocessing method is proposed by all rooting measured data and evaluated by two constructed indexes based on all rooted EIT measured data. By finding the optimums of the two indexes, the proposed method can be applied to improve the EIT imaging spatial resolutions. In terms of a theoretical model, the optimal rooting times of the two indexes range in [0.23, 0.33] and in [0.22, 0.35], respectively. Moreover, these factors that affect the correctness of the proposed method are generally analyzed. The measuring data preprocessing is necessary and helpful for any imaging process. Thus, the proposed method can be generally and widely used in any imaging process. Experimental results validate the two proposed indexes. PMID:25165735

An Exact Algorithm to Compute the Double-Cut-and-Join Distance for Genomes with Duplicate Genes.

PubMed

Shao, Mingfu; Lin, Yu; Moret, Bernard M E

2015-05-01

Computing the edit distance between two genomes is a basic problem in the study of genome evolution. The double-cut-and-join (DCJ) model has formed the basis for most algorithmic research on rearrangements over the last few years. The edit distance under the DCJ model can be computed in linear time for genomes without duplicate genes, while the problem becomes NP-hard in the presence of duplicate genes. In this article, we propose an integer linear programming (ILP) formulation to compute the DCJ distance between two genomes with duplicate genes. We also provide an efficient preprocessing approach to simplify the ILP formulation while preserving optimality. Comparison on simulated genomes demonstrates that our method outperforms MSOAR in computing the edit distance, especially when the genomes contain long duplicated segments. We also apply our method to assign orthologous gene pairs among human, mouse, and rat genomes, where once again our method outperforms MSOAR.

Low-Rank Linear Dynamical Systems for Motor Imagery EEG.

PubMed

Zhang, Wenchang; Sun, Fuchun; Tan, Chuanqi; Liu, Shaobo

2016-01-01

The common spatial pattern (CSP) and other spatiospectral feature extraction methods have become the most effective and successful approaches to solve the problem of motor imagery electroencephalography (MI-EEG) pattern recognition from multichannel neural activity in recent years. However, these methods need a lot of preprocessing and postprocessing such as filtering, demean, and spatiospectral feature fusion, which influence the classification accuracy easily. In this paper, we utilize linear dynamical systems (LDSs) for EEG signals feature extraction and classification. LDSs model has lots of advantages such as simultaneous spatial and temporal feature matrix generation, free of preprocessing or postprocessing, and low cost. Furthermore, a low-rank matrix decomposition approach is introduced to get rid of noise and resting state component in order to improve the robustness of the system. Then, we propose a low-rank LDSs algorithm to decompose feature subspace of LDSs on finite Grassmannian and obtain a better performance. Extensive experiments are carried out on public dataset from "BCI Competition III Dataset IVa" and "BCI Competition IV Database 2a." The results show that our proposed three methods yield higher accuracies compared with prevailing approaches such as CSP and CSSP.

Detection of Glutamic Acid in Oilseed Rape Leaves Using Near Infrared Spectroscopy and the Least Squares-Support Vector Machine

PubMed Central

Bao, Yidan; Kong, Wenwen; Liu, Fei; Qiu, Zhengjun; He, Yong

2012-01-01

Amino acids are quite important indices to indicate the growth status of oilseed rape under herbicide stress. Near infrared (NIR) spectroscopy combined with chemometrics was applied for fast determination of glutamic acid in oilseed rape leaves. The optimal spectral preprocessing method was obtained after comparing Savitzky-Golay smoothing, standard normal variate, multiplicative scatter correction, first and second derivatives, detrending and direct orthogonal signal correction. Linear and nonlinear calibration methods were developed, including partial least squares (PLS) and least squares-support vector machine (LS-SVM). The most effective wavelengths (EWs) were determined by the successive projections algorithm (SPA), and these wavelengths were used as the inputs of PLS and LS-SVM model. The best prediction results were achieved by SPA-LS-SVM (Raw) model with correlation coefficient r = 0.9943 and root mean squares error of prediction (RMSEP) = 0.0569 for prediction set. These results indicated that NIR spectroscopy combined with SPA-LS-SVM was feasible for the fast and effective detection of glutamic acid in oilseed rape leaves. The selected EWs could be used to develop spectral sensors, and the important and basic amino acid data were helpful to study the function mechanism of herbicide. PMID:23203052

Medical image segmentation based on SLIC superpixels model

NASA Astrophysics Data System (ADS)

Chen, Xiang-ting; Zhang, Fan; Zhang, Ruo-ya

2017-01-01

Medical imaging has been widely used in clinical practice. It is an important basis for medical experts to diagnose the disease. However, medical images have many unstable factors such as complex imaging mechanism, the target displacement will cause constructed defect and the partial volume effect will lead to error and equipment wear, which increases the complexity of subsequent image processing greatly. The segmentation algorithm which based on SLIC (Simple Linear Iterative Clustering, SLIC) superpixels is used to eliminate the influence of constructed defect and noise by means of the feature similarity in the preprocessing stage. At the same time, excellent clustering effect can reduce the complexity of the algorithm extremely, which provides an effective basis for the rapid diagnosis of experts.

Genetic Algorithm for Optimization: Preprocessing with n Dimensional Bisection and Error Estimation

NASA Technical Reports Server (NTRS)

Sen, S. K.; Shaykhian, Gholam Ali

2006-01-01

A knowledge of the appropriate values of the parameters of a genetic algorithm (GA) such as the population size, the shrunk search space containing the solution, crossover and mutation probabilities is not available a priori for a general optimization problem. Recommended here is a polynomial-time preprocessing scheme that includes an n-dimensional bisection and that determines the foregoing parameters before deciding upon an appropriate GA for all problems of similar nature and type. Such a preprocessing is not only fast but also enables us to get the global optimal solution and its reasonably narrow error bounds with a high degree of confidence.

Mining Distance Based Outliers in Near Linear Time with Randomization and a Simple Pruning Rule

NASA Technical Reports Server (NTRS)

Bay, Stephen D.; Schwabacher, Mark

2003-01-01

Defining outliers by their distance to neighboring examples is a popular approach to finding unusual examples in a data set. Recently, much work has been conducted with the goal of finding fast algorithms for this task. We show that a simple nested loop algorithm that in the worst case is quadratic can give near linear time performance when the data is in random order and a simple pruning rule is used. We test our algorithm on real high-dimensional data sets with millions of examples and show that the near linear scaling holds over several orders of magnitude. Our average case analysis suggests that much of the efficiency is because the time to process non-outliers, which are the majority of examples, does not depend on the size of the data set.

Evaluation of factors in development of Vis/NIR spectroscopy models for discriminating PSE, DFD and normal broiler breast meat

USDA-ARS?s Scientific Manuscript database

1. To evaluate the performance of visible and near-infrared (Vis/NIR) spectroscopic models for discriminating true pale, soft and exudative (PSE), normal and dark, firm and dry (DFD) broiler breast meat in different conditions of preprocessing methods, spectral ranges, characteristic wavelength sele...

Triangle based TVD schemes for hyperbolic conservation laws

NASA Technical Reports Server (NTRS)

Durlofsky, Louis J.; Osher, Stanley; Engquist, Bjorn

1990-01-01

A triangle based total variation diminishing (TVD) scheme for the numerical approximation of hyperbolic conservation laws in two space dimensions is constructed. The novelty of the scheme lies in the nature of the preprocessing of the cell averaged data, which is accomplished via a nearest neighbor linear interpolation followed by a slope limiting procedures. Two such limiting procedures are suggested. The resulting method is considerably more simple than other triangle based non-oscillatory approximations which, like this scheme, approximate the flux up to second order accuracy. Numerical results for linear advection and Burgers' equation are presented.

Super-sensitive time-resolved fluoroimmunoassay for thyroid-stimulating hormone utilizing europium(III) nanoparticle labels achieved by protein corona stabilization, short binding time, and serum preprocessing.

PubMed

Näreoja, Tuomas; Rosenholm, Jessica M; Lamminmäki, Urpo; Hänninen, Pekka E

2017-05-01

Thyrotropin or thyroid-stimulating hormone (TSH) is used as a marker for thyroid function. More precise and more sensitive immunoassays are needed to facilitate continuous monitoring of thyroid dysfunctions and to assess the efficacy of the selected therapy and dosage of medication. Moreover, most thyroid diseases are autoimmune diseases making TSH assays very prone to immunoassay interferences due to autoantibodies in the sample matrix. We have developed a super-sensitive TSH immunoassay utilizing nanoparticle labels with a detection limit of 60 nU L -1 in preprocessed serum samples by reducing nonspecific binding. The developed preprocessing step by affinity purification removed interfering compounds and improved the recovery of spiked TSH from serum. The sensitivity enhancement was achieved by stabilization of the protein corona of the nanoparticle bioconjugates and a spot-coated configuration of the active solid-phase that reduced sedimentation of the nanoparticle bioconjugates and their contact time with antibody-coated solid phase, thus making use of the higher association rate of specific binding due to high avidity nanoparticle bioconjugates. Graphical Abstract We were able to decrease the lowest limit of detection and increase sensitivity of TSH immunoassay using Eu(III)-nanoparticles. The improvement was achieved by decreasing binding time of nanoparticle bioconjugates by small capture area and fast circular rotation. Also, we applied a step to stabilize protein corona of the nanoparticles and a serum-preprocessing step with a structurally related antibody.

Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications

NASA Astrophysics Data System (ADS)

Zhu, Zhe

2017-08-01

The free and open access to all archived Landsat images in 2008 has completely changed the way of using Landsat data. Many novel change detection algorithms based on Landsat time series have been developed We present a comprehensive review of four important aspects of change detection studies based on Landsat time series, including frequencies, preprocessing, algorithms, and applications. We observed the trend that the more recent the study, the higher the frequency of Landsat time series used. We reviewed a series of image preprocessing steps, including atmospheric correction, cloud and cloud shadow detection, and composite/fusion/metrics techniques. We divided all change detection algorithms into six categories, including thresholding, differencing, segmentation, trajectory classification, statistical boundary, and regression. Within each category, six major characteristics of different algorithms, such as frequency, change index, univariate/multivariate, online/offline, abrupt/gradual change, and sub-pixel/pixel/spatial were analyzed. Moreover, some of the widely-used change detection algorithms were also discussed. Finally, we reviewed different change detection applications by dividing these applications into two categories, change target and change agent detection.

Simulation of continuously logical base cells (CL BC) with advanced functions for analog-to-digital converters and image processors

NASA Astrophysics Data System (ADS)

Krasilenko, Vladimir G.; Lazarev, Alexander A.; Nikitovich, Diana V.

2017-10-01

The paper considers results of design and modeling of continuously logical base cells (CL BC) based on current mirrors (CM) with functions of preliminary analogue and subsequent analogue-digital processing for creating sensor multichannel analog-to-digital converters (SMC ADCs) and image processors (IP). For such with vector or matrix parallel inputs-outputs IP and SMC ADCs it is needed active basic photosensitive cells with an extended electronic circuit, which are considered in paper. Such basic cells and ADCs based on them have a number of advantages: high speed and reliability, simplicity, small power consumption, high integration level for linear and matrix structures. We show design of the CL BC and ADC of photocurrents and their various possible implementations and its simulations. We consider CL BC for methods of selection and rank preprocessing and linear array of ADCs with conversion to binary codes and Gray codes. In contrast to our previous works here we will dwell more on analogue preprocessing schemes for signals of neighboring cells. Let us show how the introduction of simple nodes based on current mirrors extends the range of functions performed by the image processor. Each channel of the structure consists of several digital-analog cells (DC) on 15-35 CMOS. The amount of DC does not exceed the number of digits of the formed code, and for an iteration type, only one cell of DC, complemented by the device of selection and holding (SHD), is required. One channel of ADC with iteration is based on one DC-(G) and SHD, and it has only 35 CMOS transistors. In such ADCs easily parallel code can be realized and also serial-parallel output code. The circuits and simulation results of their design with OrCAD are shown. The supply voltage of the DC is 1.8÷3.3V, the range of an input photocurrent is 0.1÷24μA, the transformation time is 20÷30nS at 6-8 bit binary or Gray codes. The general power consumption of the ADC with iteration is only 50÷100μW, if the maximum input current is 4μA. Such simple structure of linear array of ADCs with low power consumption and supply voltage 3.3V, and at the same time with good dynamic characteristics (frequency of digitization even for 1.5μm CMOS-technologies is 40÷50 MHz, and can be increased up to 10 times) and accuracy characteristics are show. The SMC ADCs based on CL BC and CM opens new prospects for realization of linear and matrix IP and photo-electronic structures with matrix operands, which are necessary for neural networks, digital optoelectronic processors, neural-fuzzy controllers.

An introduction to wavelet analysis in oceanography and meteorology - With application to the dispersion of Yanai waves

NASA Technical Reports Server (NTRS)

Meyers, Steven D.; Kelly, B. G.; O'Brien, J. J.

1993-01-01

Wavelet analysis is a relatively new technique that is an important addition to standard signal analysis methods. Unlike Fourier analysis that yields an average amplitude and phase for each harmonic in a dataset, the wavelet transform produces an instantaneous estimate or local value for the amplitude and phase of each harmonic. This allows detailed study of nonstationary spatial or time-dependent signal characteristics. The wavelet transform is discussed, examples are given, and some methods for preprocessing data for wavelet analysis are compared. By studying the dispersion of Yanai waves in a reduced gravity equatorial model, the usefulness of the transform is demonstrated. The group velocity is measured directly over a finite range of wavenumbers by examining the time evolution of the transform. The results agree well with linear theory at higher wavenumber but the measured group velocity is reduced at lower wavenumbers, possibly due to interaction with the basin boundaries.

Cortical Signal Analysis and Advances in Functional Near-Infrared Spectroscopy Signal: A Review.

PubMed

Kamran, Muhammad A; Mannan, Malik M Naeem; Jeong, Myung Yung

2016-01-01

Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging modality that measures the concentration changes of oxy-hemoglobin (HbO) and de-oxy hemoglobin (HbR) at the same time. It is an emerging cortical imaging modality with a good temporal resolution that is acceptable for brain-computer interface applications. Researchers have developed several methods in last two decades to extract the neuronal activation related waveform from the observed fNIRS time series. But still there is no standard method for analysis of fNIRS data. This article presents a brief review of existing methodologies to model and analyze the activation signal. The purpose of this review article is to give a general overview of variety of existing methodologies to extract useful information from measured fNIRS data including pre-processing steps, effects of differential path length factor (DPF), variations and attributes of hemodynamic response function (HRF), extraction of evoked response, removal of physiological noises, instrumentation, and environmental noises and resting/activation state functional connectivity. Finally, the challenges in the analysis of fNIRS signal are summarized.

Cortical Signal Analysis and Advances in Functional Near-Infrared Spectroscopy Signal: A Review

PubMed Central

Kamran, Muhammad A.; Mannan, Malik M. Naeem; Jeong, Myung Yung

2016-01-01

Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging modality that measures the concentration changes of oxy-hemoglobin (HbO) and de-oxy hemoglobin (HbR) at the same time. It is an emerging cortical imaging modality with a good temporal resolution that is acceptable for brain-computer interface applications. Researchers have developed several methods in last two decades to extract the neuronal activation related waveform from the observed fNIRS time series. But still there is no standard method for analysis of fNIRS data. This article presents a brief review of existing methodologies to model and analyze the activation signal. The purpose of this review article is to give a general overview of variety of existing methodologies to extract useful information from measured fNIRS data including pre-processing steps, effects of differential path length factor (DPF), variations and attributes of hemodynamic response function (HRF), extraction of evoked response, removal of physiological noises, instrumentation, and environmental noises and resting/activation state functional connectivity. Finally, the challenges in the analysis of fNIRS signal are summarized. PMID:27375458

[On-line monitoring of biomass in 1,3-propanediol fermentation by Fourier-transformed near-infrared spectra analysis].

PubMed

Wang, Lu; Liu, Tao; Chen, Yang; Sun, Yaqin; Xiu, Zhilong

2017-01-25

Biomass is an important parameter reflecting the fermentation dynamics. Real-time monitoring of biomass can be used to control and optimize a fermentation process. To overcome the deficiencies of measurement delay and manual errors from offline measurement, we designed an experimental platform for online monitoring the biomass during a 1,3-propanediol fermentation process, based on using the fourier-transformed near-infrared (FT-NIR) spectra analysis. By pre-processing the real-time sampled spectra and analyzing the sensitive spectra bands, a partial least-squares algorithm was proposed to establish a dynamic prediction model for the biomass change during a 1,3-propanediol fermentation process. The fermentation processes with substrate glycerol concentrations of 60 g/L and 40 g/L were used as the external validation experiments. The root mean square error of prediction (RMSEP) obtained by analyzing experimental data was 0.341 6 and 0.274 3, respectively. These results showed that the established model gave good prediction and could be effectively used for on-line monitoring the biomass during a 1,3-propanediol fermentation process.

Sentiment analysis of feature ranking methods for classification accuracy

NASA Astrophysics Data System (ADS)

Joseph, Shashank; Mugauri, Calvin; Sumathy, S.

2017-11-01

Text pre-processing and feature selection are important and critical steps in text mining. Text pre-processing of large volumes of datasets is a difficult task as unstructured raw data is converted into structured format. Traditional methods of processing and weighing took much time and were less accurate. To overcome this challenge, feature ranking techniques have been devised. A feature set from text preprocessing is fed as input for feature selection. Feature selection helps improve text classification accuracy. Of the three feature selection categories available, the filter category will be the focus. Five feature ranking methods namely: document frequency, standard deviation information gain, CHI-SQUARE, and weighted-log likelihood -ratio is analyzed.

Epileptic Seizures Prediction Using Machine Learning Methods

PubMed Central

Usman, Syed Muhammad

2017-01-01

Epileptic seizures occur due to disorder in brain functionality which can affect patient's health. Prediction of epileptic seizures before the beginning of the onset is quite useful for preventing the seizure by medication. Machine learning techniques and computational methods are used for predicting epileptic seizures from Electroencephalograms (EEG) signals. However, preprocessing of EEG signals for noise removal and features extraction are two major issues that have an adverse effect on both anticipation time and true positive prediction rate. Therefore, we propose a model that provides reliable methods of both preprocessing and feature extraction. Our model predicts epileptic seizures' sufficient time before the onset of seizure starts and provides a better true positive rate. We have applied empirical mode decomposition (EMD) for preprocessing and have extracted time and frequency domain features for training a prediction model. The proposed model detects the start of the preictal state, which is the state that starts few minutes before the onset of the seizure, with a higher true positive rate compared to traditional methods, 92.23%, and maximum anticipation time of 33 minutes and average prediction time of 23.6 minutes on scalp EEG CHB-MIT dataset of 22 subjects. PMID:29410700

Spectroscopic Diagnosis of Arsenic Contamination in Agricultural Soils

PubMed Central

Shi, Tiezhu; Liu, Huizeng; Chen, Yiyun; Fei, Teng; Wang, Junjie; Wu, Guofeng

2017-01-01

This study investigated the abilities of pre-processing, feature selection and machine-learning methods for the spectroscopic diagnosis of soil arsenic contamination. The spectral data were pre-processed by using Savitzky-Golay smoothing, first and second derivatives, multiplicative scatter correction, standard normal variate, and mean centering. Principle component analysis (PCA) and the RELIEF algorithm were used to extract spectral features. Machine-learning methods, including random forests (RF), artificial neural network (ANN), radial basis function- and linear function- based support vector machine (RBF- and LF-SVM) were employed for establishing diagnosis models. The model accuracies were evaluated and compared by using overall accuracies (OAs). The statistical significance of the difference between models was evaluated by using McNemar’s test (Z value). The results showed that the OAs varied with the different combinations of pre-processing, feature selection, and classification methods. Feature selection methods could improve the modeling efficiencies and diagnosis accuracies, and RELIEF often outperformed PCA. The optimal models established by RF (OA = 86%), ANN (OA = 89%), RBF- (OA = 89%) and LF-SVM (OA = 87%) had no statistical difference in diagnosis accuracies (Z < 1.96, p < 0.05). These results indicated that it was feasible to diagnose soil arsenic contamination using reflectance spectroscopy. The appropriate combination of multivariate methods was important to improve diagnosis accuracies. PMID:28471412

Run-time parallelization and scheduling of loops

NASA Technical Reports Server (NTRS)

Saltz, Joel H.; Mirchandaney, Ravi; Crowley, Kay

1991-01-01

Run-time methods are studied to automatically parallelize and schedule iterations of a do loop in certain cases where compile-time information is inadequate. The methods presented involve execution time preprocessing of the loop. At compile-time, these methods set up the framework for performing a loop dependency analysis. At run-time, wavefronts of concurrently executable loop iterations are identified. Using this wavefront information, loop iterations are reordered for increased parallelism. Symbolic transformation rules are used to produce: inspector procedures that perform execution time preprocessing, and executors or transformed versions of source code loop structures. These transformed loop structures carry out the calculations planned in the inspector procedures. Performance results are presented from experiments conducted on the Encore Multimax. These results illustrate that run-time reordering of loop indexes can have a significant impact on performance.

Short-term PV/T module temperature prediction based on PCA-RBF neural network

NASA Astrophysics Data System (ADS)

Li, Jiyong; Zhao, Zhendong; Li, Yisheng; Xiao, Jing; Tang, Yunfeng

2018-02-01

Aiming at the non-linearity and large inertia of temperature control in PV/T system, short-term temperature prediction of PV/T module is proposed, to make the PV/T system controller run forward according to the short-term forecasting situation to optimize control effect. Based on the analysis of the correlation between PV/T module temperature and meteorological factors, and the temperature of adjacent time series, the principal component analysis (PCA) method is used to pre-process the original input sample data. Combined with the RBF neural network theory, the simulation results show that the PCA method makes the prediction accuracy of the network model higher and the generalization performance stronger than that of the RBF neural network without the main component extraction.

Towards a global-scale ambient noise cross-correlation data base

NASA Astrophysics Data System (ADS)

Ermert, Laura; Fichtner, Andreas; Sleeman, Reinoud

2014-05-01

We aim to obtain a global-scale data base of ambient seismic noise correlations. This database - to be made publicly available at ORFEUS - will enable us to study the distribution of microseismic and hum sources, and to perform multi-scale full waveform inversion for crustal and mantle structure. Ambient noise tomography has developed into a standard technique. According to theory, cross-correlations equal inter-station Green's functions only if the wave field is equipartitioned or the sources are isotropically distributed. In an attempt to circumvent these assumptions, we aim to investigate possibilities to directly model noise cross-correlations and invert for their sources using adjoint techniques. A data base containing correlations of 'gently' preprocessed noise, excluding preprocessing steps which are explicitly taken to reduce the influence of a non-isotropic source distribution like spectral whitening, is a key ingredient in this undertaking. Raw data are acquired from IRIS/FDSN and ORFEUS. We preprocess and correlate the time series using a tool based on the Python package Obspy which is run in parallel on a cluster of the Swiss National Supercomputing Centre. Correlation is done in two ways: Besides the classical cross-correlation function, the phase cross-correlation is calculated, which is an amplitude-independent measure of waveform similarity and therefore insensitive to high-energy events. Besides linear stacks of these correlations, instantaneous phase stacks are calculated which can be applied as optional weight, enhancing coherent portions of the traces and facilitating the emergence of a meaningful signal. The _STS1 virtual network by IRIS contains about 250 globally distributed stations, several of which have been operating for more than 20 years. It is the first data collection we will use for correlations in the hum frequency range, as the STS-1 instrument response is flat in the largest part of the period range where hum is observed, up to a period of about 300 seconds. Thus they provide us with the best-suited measurements for hum.

Matrix preconditioning: a robust operation for optical linear algebra processors.

PubMed

Ghosh, A; Paparao, P

1987-07-15

Analog electrooptical processors are best suited for applications demanding high computational throughput with tolerance for inaccuracies. Matrix preconditioning is one such application. Matrix preconditioning is a preprocessing step for reducing the condition number of a matrix and is used extensively with gradient algorithms for increasing the rate of convergence and improving the accuracy of the solution. In this paper, we describe a simple parallel algorithm for matrix preconditioning, which can be implemented efficiently on a pipelined optical linear algebra processor. From the results of our numerical experiments we show that the efficacy of the preconditioning algorithm is affected very little by the errors of the optical system.

Time-Frequency Analyses of Tide-Gauge Sensor Data

PubMed Central

Erol, Serdar

2011-01-01

The real world phenomena being observed by sensors are generally non-stationary in nature. The classical linear techniques for analysis and modeling natural time-series observations are inefficient and should be replaced by non-linear techniques of whose theoretical aspects and performances are varied. In this manner adopting the most appropriate technique and strategy is essential in evaluating sensors’ data. In this study, two different time-series analysis approaches, namely least squares spectral analysis (LSSA) and wavelet analysis (continuous wavelet transform, cross wavelet transform and wavelet coherence algorithms as extensions of wavelet analysis), are applied to sea-level observations recorded by tide-gauge sensors, and the advantages and drawbacks of these methods are reviewed. The analyses were carried out using sea-level observations recorded at the Antalya-II and Erdek tide-gauge stations of the Turkish National Sea-Level Monitoring System. In the analyses, the useful information hidden in the noisy signals was detected, and the common features between the two sea-level time series were clarified. The tide-gauge records have data gaps in time because of issues such as instrumental shortcomings and power outages. Concerning the difficulties of the time-frequency analysis of data with voids, the sea-level observations were preprocessed, and the missing parts were predicted using the neural network method prior to the analysis. In conclusion the merits and limitations of the techniques in evaluating non-stationary observations by means of tide-gauge sensors records were documented and an analysis strategy for the sequential sensors observations was presented. PMID:22163829

Time-frequency analyses of tide-gauge sensor data.

PubMed

Erol, Serdar

2011-01-01

The real world phenomena being observed by sensors are generally non-stationary in nature. The classical linear techniques for analysis and modeling natural time-series observations are inefficient and should be replaced by non-linear techniques of whose theoretical aspects and performances are varied. In this manner adopting the most appropriate technique and strategy is essential in evaluating sensors' data. In this study, two different time-series analysis approaches, namely least squares spectral analysis (LSSA) and wavelet analysis (continuous wavelet transform, cross wavelet transform and wavelet coherence algorithms as extensions of wavelet analysis), are applied to sea-level observations recorded by tide-gauge sensors, and the advantages and drawbacks of these methods are reviewed. The analyses were carried out using sea-level observations recorded at the Antalya-II and Erdek tide-gauge stations of the Turkish National Sea-Level Monitoring System. In the analyses, the useful information hidden in the noisy signals was detected, and the common features between the two sea-level time series were clarified. The tide-gauge records have data gaps in time because of issues such as instrumental shortcomings and power outages. Concerning the difficulties of the time-frequency analysis of data with voids, the sea-level observations were preprocessed, and the missing parts were predicted using the neural network method prior to the analysis. In conclusion the merits and limitations of the techniques in evaluating non-stationary observations by means of tide-gauge sensors records were documented and an analysis strategy for the sequential sensors observations was presented.

Using near infrared spectroscopy to classify soybean oil according to expiration date.

PubMed

da Costa, Gean Bezerra; Fernandes, David Douglas Sousa; Gomes, Adriano A; de Almeida, Valber Elias; Veras, Germano

2016-04-01

A rapid and non-destructive methodology is proposed for the screening of edible vegetable oils according to conservation state expiration date employing near infrared (NIR) spectroscopy and chemometric tools. A total of fifty samples of soybean vegetable oil, of different brands andlots, were used in this study; these included thirty expired and twenty non-expired samples. The oil oxidation was measured by peroxide index. NIR spectra were employed in raw form and preprocessed by offset baseline correction and Savitzky-Golay derivative procedure, followed by PCA exploratory analysis, which showed that NIR spectra would be suitable for the classification task of soybean oil samples. The classification models were based in SPA-LDA (Linear Discriminant Analysis coupled with Successive Projection Algorithm) and PLS-DA (Discriminant Analysis by Partial Least Squares). The set of samples (50) was partitioned into two groups of training (35 samples: 15 non-expired and 20 expired) and test samples (15 samples 5 non-expired and 10 expired) using sample-selection approaches: (i) Kennard-Stone, (ii) Duplex, and (iii) Random, in order to evaluate the robustness of the models. The obtained results for the independent test set (in terms of correct classification rate) were 96% and 98% for SPA-LDA and PLS-DA, respectively, indicating that the NIR spectra can be used as an alternative to evaluate the degree of oxidation of soybean oil samples. Copyright © 2015 Elsevier Ltd. All rights reserved.

Prediction of valid acidity in intact apples with Fourier transform near infrared spectroscopy.

PubMed

Liu, Yan-De; Ying, Yi-Bin; Fu, Xia-Ping

2005-03-01

To develop nondestructive acidity prediction for intact Fuji apples, the potential of Fourier transform near infrared (FT-NIR) method with fiber optics in interactance mode was investigated. Interactance in the 800 nm to 2619 nm region was measured for intact apples, harvested from early to late maturity stages. Spectral data were analyzed by two multivariate calibration techniques including partial least squares (PLS) and principal component regression (PCR) methods. A total of 120 Fuji apples were tested and 80 of them were used to form a calibration data set. The influences of different data preprocessing and spectra treatments were also quantified. Calibration models based on smoothing spectra were slightly worse than that based on derivative spectra, and the best result was obtained when the segment length was 5 nm and the gap size was 10 points. Depending on data preprocessing and PLS method, the best prediction model yielded correlation coefficient of determination (r2) of 0.759, low root mean square error of prediction (RMSEP) of 0.0677, low root mean square error of calibration (RMSEC) of 0.0562. The results indicated the feasibility of FT-NIR spectral analysis for predicting apple valid acidity in a nondestructive way.

Prediction of valid acidity in intact apples with Fourier transform near infrared spectroscopy*

PubMed Central

Liu, Yan-de; Ying, Yi-bin; Fu, Xia-ping

2005-01-01

To develop nondestructive acidity prediction for intact Fuji apples, the potential of Fourier transform near infrared (FT-NIR) method with fiber optics in interactance mode was investigated. Interactance in the 800 nm to 2619 nm region was measured for intact apples, harvested from early to late maturity stages. Spectral data were analyzed by two multivariate calibration techniques including partial least squares (PLS) and principal component regression (PCR) methods. A total of 120 Fuji apples were tested and 80 of them were used to form a calibration data set. The influences of different data preprocessing and spectra treatments were also quantified. Calibration models based on smoothing spectra were slightly worse than that based on derivative spectra, and the best result was obtained when the segment length was 5 nm and the gap size was 10 points. Depending on data preprocessing and PLS method, the best prediction model yielded correlation coefficient of determination (r 2) of 0.759, low root mean square error of prediction (RMSEP) of 0.0677, low root mean square error of calibration (RMSEC) of 0.0562. The results indicated the feasibility of FT-NIR spectral analysis for predicting apple valid acidity in a nondestructive way. PMID:15682498

Investigating the enhancement of template-free activation detection of event-related fMRI data using wavelet shrinkage and figures of merit.

PubMed

Ngan, Shing-Chung; Hu, Xiaoping; Khong, Pek-Lan

2011-03-01

We propose a method for preprocessing event-related functional magnetic resonance imaging (fMRI) data that can lead to enhancement of template-free activation detection. The method is based on using a figure of merit to guide the wavelet shrinkage of a given fMRI data set. Several previous studies have demonstrated that in the root-mean-square error setting, wavelet shrinkage can improve the signal-to-noise ratio of fMRI time courses. However, preprocessing fMRI data in the root-mean-square error setting does not necessarily lead to enhancement of template-free activation detection. Motivated by this observation, in this paper, we move to the detection setting and investigate the possibility of using wavelet shrinkage to enhance template-free activation detection of fMRI data. The main ingredients of our method are (i) forward wavelet transform of the voxel time courses, (ii) shrinking the resulting wavelet coefficients as directed by an appropriate figure of merit, (iii) inverse wavelet transform of the shrunk data, and (iv) submitting these preprocessed time courses to a given activation detection algorithm. Two figures of merit are developed in the paper, and two other figures of merit adapted from the literature are described. Receiver-operating characteristic analyses with simulated fMRI data showed quantitative evidence that data preprocessing as guided by the figures of merit developed in the paper can yield improved detectability of the template-free measures. We also demonstrate the application of our methodology on an experimental fMRI data set. The proposed method is useful for enhancing template-free activation detection in event-related fMRI data. It is of significant interest to extend the present framework to produce comprehensive, adaptive and fully automated preprocessing of fMRI data optimally suited for subsequent data analysis steps. Copyright © 2010 Elsevier B.V. All rights reserved.

Predicting microRNA-disease associations using label propagation based on linear neighborhood similarity.

PubMed

Li, Guanghui; Luo, Jiawei; Xiao, Qiu; Liang, Cheng; Ding, Pingjian

2018-05-12

Interactions between microRNAs (miRNAs) and diseases can yield important information for uncovering novel prognostic markers. Since experimental determination of disease-miRNA associations is time-consuming and costly, attention has been given to designing efficient and robust computational techniques for identifying undiscovered interactions. In this study, we present a label propagation model with linear neighborhood similarity, called LPLNS, to predict unobserved miRNA-disease associations. Additionally, a preprocessing step is performed to derive new interaction likelihood profiles that will contribute to the prediction since new miRNAs and diseases lack known associations. Our results demonstrate that the LPLNS model based on the known disease-miRNA associations could achieve impressive performance with an AUC of 0.9034. Furthermore, we observed that the LPLNS model based on new interaction likelihood profiles could improve the performance to an AUC of 0.9127. This was better than other comparable methods. In addition, case studies also demonstrated our method's outstanding performance for inferring undiscovered interactions between miRNAs and diseases, especially for novel diseases. Copyright © 2018. Published by Elsevier Inc.

Reflectance Estimation from Urban Terrestrial Images: Validation of a Symbolic Ray-Tracing Method on Synthetic Data

NASA Astrophysics Data System (ADS)

Coubard, F.; Brédif, M.; Paparoditis, N.; Briottet, X.

2011-04-01

Terrestrial geolocalized images are nowadays widely used on the Internet, mainly in urban areas, through immersion services such as Google Street View. On the long run, we seek to enhance the visualization of these images; for that purpose, radiometric corrections must be performed to free them from illumination conditions at the time of acquisition. Given the simultaneously acquired 3D geometric model of the scene with LIDAR or vision techniques, we face an inverse problem where the illumination and the geometry of the scene are known and the reflectance of the scene is to be estimated. Our main contribution is the introduction of a symbolic ray-tracing rendering to generate parametric images, for quick evaluation and comparison with the acquired images. The proposed approach is then based on an iterative estimation of the reflectance parameters of the materials, using a single rendering pre-processing. We validate the method on synthetic data with linear BRDF models and discuss the limitations of the proposed approach with more general non-linear BRDF models.

Neural network versus classical time series forecasting models

NASA Astrophysics Data System (ADS)

Nor, Maria Elena; Safuan, Hamizah Mohd; Shab, Noorzehan Fazahiyah Md; Asrul, Mohd; Abdullah, Affendi; Mohamad, Nurul Asmaa Izzati; Lee, Muhammad Hisyam

2017-05-01

Artificial neural network (ANN) has advantage in time series forecasting as it has potential to solve complex forecasting problems. This is because ANN is data driven approach which able to be trained to map past values of a time series. In this study the forecast performance between neural network and classical time series forecasting method namely seasonal autoregressive integrated moving average models was being compared by utilizing gold price data. Moreover, the effect of different data preprocessing on the forecast performance of neural network being examined. The forecast accuracy was evaluated using mean absolute deviation, root mean square error and mean absolute percentage error. It was found that ANN produced the most accurate forecast when Box-Cox transformation was used as data preprocessing.

Trajectory data analyses for pedestrian space-time activity study.

PubMed

Qi, Feng; Du, Fei

2013-02-25

It is well recognized that human movement in the spatial and temporal dimensions has direct influence on disease transmission(1-3). An infectious disease typically spreads via contact between infected and susceptible individuals in their overlapped activity spaces. Therefore, daily mobility-activity information can be used as an indicator to measure exposures to risk factors of infection. However, a major difficulty and thus the reason for paucity of studies of infectious disease transmission at the micro scale arise from the lack of detailed individual mobility data. Previously in transportation and tourism research detailed space-time activity data often relied on the time-space diary technique, which requires subjects to actively record their activities in time and space. This is highly demanding for the participants and collaboration from the participants greatly affects the quality of data(4). Modern technologies such as GPS and mobile communications have made possible the automatic collection of trajectory data. The data collected, however, is not ideal for modeling human space-time activities, limited by the accuracies of existing devices. There is also no readily available tool for efficient processing of the data for human behavior study. We present here a suite of methods and an integrated ArcGIS desktop-based visual interface for the pre-processing and spatiotemporal analyses of trajectory data. We provide examples of how such processing may be used to model human space-time activities, especially with error-rich pedestrian trajectory data, that could be useful in public health studies such as infectious disease transmission modeling. The procedure presented includes pre-processing, trajectory segmentation, activity space characterization, density estimation and visualization, and a few other exploratory analysis methods. Pre-processing is the cleaning of noisy raw trajectory data. We introduce an interactive visual pre-processing interface as well as an automatic module. Trajectory segmentation(5) involves the identification of indoor and outdoor parts from pre-processed space-time tracks. Again, both interactive visual segmentation and automatic segmentation are supported. Segmented space-time tracks are then analyzed to derive characteristics of one's activity space such as activity radius etc. Density estimation and visualization are used to examine large amount of trajectory data to model hot spots and interactions. We demonstrate both density surface mapping(6) and density volume rendering(7). We also include a couple of other exploratory data analyses (EDA) and visualizations tools, such as Google Earth animation support and connection analysis. The suite of analytical as well as visual methods presented in this paper may be applied to any trajectory data for space-time activity studies.

Micro-Analyzer: automatic preprocessing of Affymetrix microarray data.

PubMed

Guzzi, Pietro Hiram; Cannataro, Mario

2013-08-01

A current trend in genomics is the investigation of the cell mechanism using different technologies, in order to explain the relationship among genes, molecular processes and diseases. For instance, the combined use of gene-expression arrays and genomic arrays has been demonstrated as an effective instrument in clinical practice. Consequently, in a single experiment different kind of microarrays may be used, resulting in the production of different types of binary data (images and textual raw data). The analysis of microarray data requires an initial preprocessing phase, that makes raw data suitable for use on existing analysis platforms, such as the TIGR M4 (TM4) Suite. An additional challenge to be faced by emerging data analysis platforms is the ability to treat in a combined way those different microarray formats coupled with clinical data. In fact, resulting integrated data may include both numerical and symbolic data (e.g. gene expression and SNPs regarding molecular data), as well as temporal data (e.g. the response to a drug, time to progression and survival rate), regarding clinical data. Raw data preprocessing is a crucial step in analysis but is often performed in a manual and error prone way using different software tools. Thus novel, platform independent, and possibly open source tools enabling the semi-automatic preprocessing and annotation of different microarray data are needed. The paper presents Micro-Analyzer (Microarray Analyzer), a cross-platform tool for the automatic normalization, summarization and annotation of Affymetrix gene expression and SNP binary data. It represents the evolution of the μ-CS tool, extending the preprocessing to SNP arrays that were not allowed in μ-CS. The Micro-Analyzer is provided as a Java standalone tool and enables users to read, preprocess and analyse binary microarray data (gene expression and SNPs) by invoking TM4 platform. It avoids: (i) the manual invocation of external tools (e.g. the Affymetrix Power Tools), (ii) the manual loading of preprocessing libraries, and (iii) the management of intermediate files, such as results and metadata. Micro-Analyzer users can directly manage Affymetrix binary data without worrying about locating and invoking the proper preprocessing tools and chip-specific libraries. Moreover, users of the Micro-Analyzer tool can load the preprocessed data directly into the well-known TM4 platform, extending in such a way also the TM4 capabilities. Consequently, Micro Analyzer offers the following advantages: (i) it reduces possible errors in the preprocessing and further analysis phases, e.g. due to the incorrect choice of parameters or due to the use of old libraries, (ii) it enables the combined and centralized pre-processing of different arrays, (iii) it may enhance the quality of further analysis by storing the workflow, i.e. information about the preprocessing steps, and (iv) finally Micro-Analzyer is freely available as a standalone application at the project web site http://sourceforge.net/projects/microanalyzer/. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Data preprocessing for a vehicle-based localization system used in road traffic applications

NASA Astrophysics Data System (ADS)

Patelczyk, Timo; Löffler, Andreas; Biebl, Erwin

2016-09-01

This paper presents a fixed-point implementation of the preprocessing using a field programmable gate array (FPGA), which is required for a multipath joint angle and delay estimation (JADE) used in road traffic applications. This paper lays the foundation for many model-based parameter estimation methods. Here, a simulation of a vehicle-based localization system application for protecting vulnerable road users, which were equipped with appropriate transponders, is considered. For such safety critical applications, the robustness and real-time capability of the localization is particularly important. Additionally, a motivation to use a fixed-point implementation for the data preprocessing is a limited computing power of the head unit of a vehicle. This study aims to process the raw data provided by the localization system used in this paper. The data preprocessing applied includes a wideband calibration of the physical localization system, separation of relevant information from the received sampled signal, and preparation of the incoming data via further processing. Further, a channel matrix estimation was implemented to complete the data preprocessing, which contains information on channel parameters, e.g., the positions of the objects to be located. In the presented case of a vehicle-based localization system application we assume an urban environment, in which multipath propagation occurs. Since most methods for localization are based on uncorrelated signals, this fact must be addressed. Hence, a decorrelation of incoming data stream in terms of a further localization is required. This decorrelation was accomplished by considering several snapshots in different time slots. As a final aspect of the use of fixed-point arithmetic, quantization errors are considered. In addition, the resources and runtime of the presented implementation are discussed; these factors are strongly linked to a practical implementation.

Novel near-infrared spectrum analysis tool: Synergy adaptive moving window model based on immune clone algorithm.

PubMed

Wang, Shenghao; Zhang, Yuyan; Cao, Fuyi; Pei, Zhenying; Gao, Xuewei; Zhang, Xu; Zhao, Yong

2018-02-13

This paper presents a novel spectrum analysis tool named synergy adaptive moving window modeling based on immune clone algorithm (SA-MWM-ICA) considering the tedious and inconvenient labor involved in the selection of pre-processing methods and spectral variables by prior experience. In this work, immune clone algorithm is first introduced into the spectrum analysis field as a new optimization strategy, covering the shortage of the relative traditional methods. Based on the working principle of the human immune system, the performance of the quantitative model is regarded as antigen, and a special vector corresponding to the above mentioned antigen is regarded as antibody. The antibody contains a pre-processing method optimization region which is created by 11 decimal digits, and a spectrum variable optimization region which is formed by some moving windows with changeable width and position. A set of original antibodies are created by modeling with this algorithm. After calculating the affinity of these antibodies, those with high affinity will be selected to clone. The regulation for cloning is that the higher the affinity, the more copies will be. In the next step, another import operation named hyper-mutation is applied to the antibodies after cloning. Moreover, the regulation for hyper-mutation is that the lower the affinity, the more possibility will be. Several antibodies with high affinity will be created on the basis of these steps. Groups of simulated dataset, gasoline near-infrared spectra dataset, and soil near-infrared spectra dataset are employed to verify and illustrate the performance of SA-MWM-ICA. Analysis results show that the performance of the quantitative models adopted by SA-MWM-ICA are better especially for structures with relatively complex spectra than traditional models such as partial least squares (PLS), moving window PLS (MWPLS), genetic algorithm PLS (GAPLS), and pretreatment method classification and adjustable parameter changeable size moving window PLS (CA-CSMWPLS). The selected pre-processing methods and spectrum variables are easily explained. The proposed method will converge in few generations and can be used not only for near-infrared spectroscopy analysis but also for other similar spectral analysis, such as infrared spectroscopy. Copyright © 2017 Elsevier B.V. All rights reserved.

Run-time parallelization and scheduling of loops

NASA Technical Reports Server (NTRS)

Saltz, Joel H.; Mirchandaney, Ravi; Crowley, Kay

1990-01-01

Run time methods are studied to automatically parallelize and schedule iterations of a do loop in certain cases, where compile-time information is inadequate. The methods presented involve execution time preprocessing of the loop. At compile-time, these methods set up the framework for performing a loop dependency analysis. At run time, wave fronts of concurrently executable loop iterations are identified. Using this wavefront information, loop iterations are reordered for increased parallelism. Symbolic transformation rules are used to produce: inspector procedures that perform execution time preprocessing and executors or transformed versions of source code loop structures. These transformed loop structures carry out the calculations planned in the inspector procedures. Performance results are presented from experiments conducted on the Encore Multimax. These results illustrate that run time reordering of loop indices can have a significant impact on performance. Furthermore, the overheads associated with this type of reordering are amortized when the loop is executed several times with the same dependency structure.

Dynamic Histogram Analysis To Determine Free Energies and Rates from Biased Simulations.

PubMed

Stelzl, Lukas S; Kells, Adam; Rosta, Edina; Hummer, Gerhard

2017-12-12

We present an algorithm to calculate free energies and rates from molecular simulations on biased potential energy surfaces. As input, it uses the accumulated times spent in each state or bin of a histogram and counts of transitions between them. Optimal unbiased equilibrium free energies for each of the states/bins are then obtained by maximizing the likelihood of a master equation (i.e., first-order kinetic rate model). The resulting free energies also determine the optimal rate coefficients for transitions between the states or bins on the biased potentials. Unbiased rates can be estimated, e.g., by imposing a linear free energy condition in the likelihood maximization. The resulting "dynamic histogram analysis method extended to detailed balance" (DHAMed) builds on the DHAM method. It is also closely related to the transition-based reweighting analysis method (TRAM) and the discrete TRAM (dTRAM). However, in the continuous-time formulation of DHAMed, the detailed balance constraints are more easily accounted for, resulting in compact expressions amenable to efficient numerical treatment. DHAMed produces accurate free energies in cases where the common weighted-histogram analysis method (WHAM) for umbrella sampling fails because of slow dynamics within the windows. Even in the limit of completely uncorrelated data, where WHAM is optimal in the maximum-likelihood sense, DHAMed results are nearly indistinguishable. We illustrate DHAMed with applications to ion channel conduction, RNA duplex formation, α-helix folding, and rate calculations from accelerated molecular dynamics. DHAMed can also be used to construct Markov state models from biased or replica-exchange molecular dynamics simulations. By using binless WHAM formulated as a numerical minimization problem, the bias factors for the individual states can be determined efficiently in a preprocessing step and, if needed, optimized globally afterward.

Arabic handwritten: pre-processing and segmentation

NASA Astrophysics Data System (ADS)

Maliki, Makki; Jassim, Sabah; Al-Jawad, Naseer; Sellahewa, Harin

2012-06-01

This paper is concerned with pre-processing and segmentation tasks that influence the performance of Optical Character Recognition (OCR) systems and handwritten/printed text recognition. In Arabic, these tasks are adversely effected by the fact that many words are made up of sub-words, with many sub-words there associated one or more diacritics that are not connected to the sub-word's body; there could be multiple instances of sub-words overlap. To overcome these problems we investigate and develop segmentation techniques that first segment a document into sub-words, link the diacritics with their sub-words, and removes possible overlapping between words and sub-words. We shall also investigate two approaches for pre-processing tasks to estimate sub-words baseline, and to determine parameters that yield appropriate slope correction, slant removal. We shall investigate the use of linear regression on sub-words pixels to determine their central x and y coordinates, as well as their high density part. We also develop a new incremental rotation procedure to be performed on sub-words that determines the best rotation angle needed to realign baselines. We shall demonstrate the benefits of these proposals by conducting extensive experiments on publicly available databases and in-house created databases. These algorithms help improve character segmentation accuracy by transforming handwritten Arabic text into a form that could benefit from analysis of printed text.

Personal exposure to dust and endotoxin in Robusta and Arabica coffee processing factories in Tanzania.

PubMed

Sakwari, Gloria; Mamuya, Simon H D; Bråtveit, Magne; Larsson, Lennart; Pehrson, Christina; Moen, Bente E

2013-03-01

Endotoxin exposure associated with organic dust exposure has been studied in several industries. Coffee cherries that are dried directly after harvest may differ in dust and endotoxin emissions to those that are peeled and washed before drying. The aim of this study was to measure personal total dust and endotoxin levels and to evaluate their determinants of exposure in coffee processing factories. Using Sidekick Casella pumps at a flow rate of 2l/min, total dust levels were measured in the workers' breathing zone throughout the shift. Endotoxin was analyzed using the kinetic chromogenic Limulus amebocyte lysate assay. Separate linear mixed-effects models were used to evaluate exposure determinants for dust and endotoxin. Total dust and endotoxin exposure were significantly higher in Robusta than in Arabica coffee factories (geometric mean 3.41 mg/m(3) and 10 800 EU/m(3) versus 2.10 mg/m(3) and 1400 EU/m(3), respectively). Dry pre-processed coffee and differences in work tasks explained 30% of the total variance for total dust and 71% of the variance for endotoxin exposure. High exposure in Robusta processing is associated with the dry pre-processing method used after harvest. Dust and endotoxin exposure is high, in particular when processing dry pre-processed coffee. Minimization of dust emissions and use of efficient dust exhaust systems are important to prevent the development of respiratory system impairment in workers.

An efficient coding algorithm for the compression of ECG signals using the wavelet transform.

PubMed

Rajoub, Bashar A

2002-04-01

A wavelet-based electrocardiogram (ECG) data compression algorithm is proposed in this paper. The ECG signal is first preprocessed, the discrete wavelet transform (DWT) is then applied to the preprocessed signal. Preprocessing guarantees that the magnitudes of the wavelet coefficients be less than one, and reduces the reconstruction errors near both ends of the compressed signal. The DWT coefficients are divided into three groups, each group is thresholded using a threshold based on a desired energy packing efficiency. A binary significance map is then generated by scanning the wavelet decomposition coefficients and outputting a binary one if the scanned coefficient is significant, and a binary zero if it is insignificant. Compression is achieved by 1) using a variable length code based on run length encoding to compress the significance map and 2) using direct binary representation for representing the significant coefficients. The ability of the coding algorithm to compress ECG signals is investigated, the results were obtained by compressing and decompressing the test signals. The proposed algorithm is compared with direct-based and wavelet-based compression algorithms and showed superior performance. A compression ratio of 24:1 was achieved for MIT-BIH record 117 with a percent root mean square difference as low as 1.08%.

On the influence of high-pass filtering on ICA-based artifact reduction in EEG-ERP.

PubMed

Winkler, Irene; Debener, Stefan; Müller, Klaus-Robert; Tangermann, Michael

2015-01-01

Standard artifact removal methods for electroencephalographic (EEG) signals are either based on Independent Component Analysis (ICA) or they regress out ocular activity measured at electrooculogram (EOG) channels. Successful ICA-based artifact reduction relies on suitable pre-processing. Here we systematically evaluate the effects of high-pass filtering at different frequencies. Offline analyses were based on event-related potential data from 21 participants performing a standard auditory oddball task and an automatic artifactual component classifier method (MARA). As a pre-processing step for ICA, high-pass filtering between 1-2 Hz consistently produced good results in terms of signal-to-noise ratio (SNR), single-trial classification accuracy and the percentage of `near-dipolar' ICA components. Relative to no artifact reduction, ICA-based artifact removal significantly improved SNR and classification accuracy. This was not the case for a regression-based approach to remove EOG artifacts.

Application of Nearly Linear Solvers to Electric Power System Computation

NASA Astrophysics Data System (ADS)

Grant, Lisa L.

To meet the future needs of the electric power system, improvements need to be made in the areas of power system algorithms, simulation, and modeling, specifically to achieve a time frame that is useful to industry. If power system time-domain simulations could run in real-time, then system operators would have situational awareness to implement online control and avoid cascading failures, significantly improving power system reliability. Several power system applications rely on the solution of a very large linear system. As the demands on power systems continue to grow, there is a greater computational complexity involved in solving these large linear systems within reasonable time. This project expands on the current work in fast linear solvers, developed for solving symmetric and diagonally dominant linear systems, in order to produce power system specific methods that can be solved in nearly-linear run times. The work explores a new theoretical method that is based on ideas in graph theory and combinatorics. The technique builds a chain of progressively smaller approximate systems with preconditioners based on the system's low stretch spanning tree. The method is compared to traditional linear solvers and shown to reduce the time and iterations required for an accurate solution, especially as the system size increases. A simulation validation is performed, comparing the solution capabilities of the chain method to LU factorization, which is the standard linear solver for power flow. The chain method was successfully demonstrated to produce accurate solutions for power flow simulation on a number of IEEE test cases, and a discussion on how to further improve the method's speed and accuracy is included.

Image preprocessing study on KPCA-based face recognition

NASA Astrophysics Data System (ADS)

Li, Xuan; Li, Dehua

2015-12-01

Face recognition as an important biometric identification method, with its friendly, natural, convenient advantages, has obtained more and more attention. This paper intends to research a face recognition system including face detection, feature extraction and face recognition, mainly through researching on related theory and the key technology of various preprocessing methods in face detection process, using KPCA method, focuses on the different recognition results in different preprocessing methods. In this paper, we choose YCbCr color space for skin segmentation and choose integral projection for face location. We use erosion and dilation of the opening and closing operation and illumination compensation method to preprocess face images, and then use the face recognition method based on kernel principal component analysis method for analysis and research, and the experiments were carried out using the typical face database. The algorithms experiment on MATLAB platform. Experimental results show that integration of the kernel method based on PCA algorithm under certain conditions make the extracted features represent the original image information better for using nonlinear feature extraction method, which can obtain higher recognition rate. In the image preprocessing stage, we found that images under various operations may appear different results, so as to obtain different recognition rate in recognition stage. At the same time, in the process of the kernel principal component analysis, the value of the power of the polynomial function can affect the recognition result.

Principal components technique analysis for vegetation and land use discrimination. [Brazilian cerrados

NASA Technical Reports Server (NTRS)

Parada, N. D. J. (Principal Investigator); Formaggio, A. R.; Dossantos, J. R.; Dias, L. A. V.

1984-01-01

Automatic pre-processing technique called Principal Components (PRINCO) in analyzing LANDSAT digitized data, for land use and vegetation cover, on the Brazilian cerrados was evaluated. The chosen pilot area, 223/67 of MSS/LANDSAT 3, was classified on a GE Image-100 System, through a maximum-likehood algorithm (MAXVER). The same procedure was applied to the PRINCO treated image. PRINCO consists of a linear transformation performed on the original bands, in order to eliminate the information redundancy of the LANDSAT channels. After PRINCO only two channels were used thus reducing computer effort. The original channels and the PRINCO channels grey levels for the five identified classes (grassland, "cerrado", burned areas, anthropic areas, and gallery forest) were obtained through the MAXVER algorithm. This algorithm also presented the average performance for both cases. In order to evaluate the results, the Jeffreys-Matusita distance (JM-distance) between classes was computed. The classification matrix, obtained through MAXVER, after a PRINCO pre-processing, showed approximately the same average performance in the classes separability.

A spectral water index based on visual bands

NASA Astrophysics Data System (ADS)

Basaeed, Essa; Bhaskar, Harish; Al-Mualla, Mohammed

2013-10-01

Land-water segmentation is an important preprocessing step in a number of remote sensing applications such as target detection, environmental monitoring, and map updating. A Normalized Optical Water Index (NOWI) is proposed to accurately discriminate between land and water regions in multi-spectral satellite imagery data from DubaiSat-1. NOWI exploits the spectral characteristics of water content (using visible bands) and uses a non-linear normalization procedure that renders strong emphasize on small changes in lower brightness values whilst guaranteeing that the segmentation process remains image-independent. The NOWI representation is validated through systematic experiments, evaluated using robust metrics, and compared against various supervised classification algorithms. Analysis has indicated that NOWI has the advantages that it: a) is a pixel-based method that requires no global knowledge of the scene under investigation, b) can be easily implemented in parallel processing, c) is image-independent and requires no training, d) works in different environmental conditions, e) provides high accuracy and efficiency, and f) works directly on the input image without any form of pre-processing.

Real-time acquisition and preprocessing system of transient electromagnetic data based on LabVIEW

NASA Astrophysics Data System (ADS)

Zhao, Huinan; Zhang, Shuang; Gu, Lingjia; Sun, Jian

2014-09-01

Transient electromagnetic method (TEM) is regarded as an everlasting issue for geological exploration. It is widely used in many research fields, such as mineral exploration, hydrogeology survey, engineering exploration and unexploded ordnance detection. The traditional measurement systems are often based on ARM DSP or FPGA, which have not real-time display, data preprocessing and data playback functions. In order to overcome the defects, a real-time data acquisition and preprocessing system based on LabVIEW virtual instrument development platform is proposed in the paper, moreover, a calibration model is established for TEM system based on a conductivity loop. The test results demonstrated that the system can complete real-time data acquisition and system calibration. For Transmit-Loop-Receive (TLR) response, the correlation coefficient between the measured results and the calculated results is 0.987. The measured results are basically consistent with the calculated results. Through the late inversion process for TLR, the signal of underground conductor was obtained. In the complex test environment, abnormal values usually exist in the measured data. In order to solve this problem, the judgment and revision algorithm of abnormal values is proposed in the paper. The test results proved that the proposed algorithm can effectively eliminate serious disturbance signals from the measured transient electromagnetic data.

EasyLCMS: an asynchronous web application for the automated quantification of LC-MS data

PubMed Central

2012-01-01

Background Downstream applications in metabolomics, as well as mathematical modelling, require data in a quantitative format, which may also necessitate the automated and simultaneous quantification of numerous metabolites. Although numerous applications have been previously developed for metabolomics data handling, automated calibration and calculation of the concentrations in terms of μmol have not been carried out. Moreover, most of the metabolomics applications are designed for GC-MS, and would not be suitable for LC-MS, since in LC, the deviation in the retention time is not linear, which is not taken into account in these applications. Moreover, only a few are web-based applications, which could improve stand-alone software in terms of compatibility, sharing capabilities and hardware requirements, even though a strong bandwidth is required. Furthermore, none of these incorporate asynchronous communication to allow real-time interaction with pre-processed results. Findings Here, we present EasyLCMS (http://www.easylcms.es/), a new application for automated quantification which was validated using more than 1000 concentration comparisons in real samples with manual operation. The results showed that only 1% of the quantifications presented a relative error higher than 15%. Using clustering analysis, the metabolites with the highest relative error distributions were identified and studied to solve recurrent mistakes. Conclusions EasyLCMS is a new web application designed to quantify numerous metabolites, simultaneously integrating LC distortions and asynchronous web technology to present a visual interface with dynamic interaction which allows checking and correction of LC-MS raw data pre-processing results. Moreover, quantified data obtained with EasyLCMS are fully compatible with numerous downstream applications, as well as for mathematical modelling in the systems biology field. PMID:22884039

Varietal discrimination of hop pellets by near and mid infrared spectroscopy.

PubMed

Machado, Julio C; Faria, Miguel A; Ferreira, Isabel M P L V O; Páscoa, Ricardo N M J; Lopes, João A

2018-04-01

Hop is one of the most important ingredients of beer production and several varieties are commercialized. Therefore, it is important to find an eco-real-time-friendly-low-cost technique to distinguish and discriminate hop varieties. This paper describes the development of a method based on vibrational spectroscopy techniques, namely near- and mid-infrared spectroscopy, for the discrimination of 33 commercial hop varieties. A total of 165 samples (five for each hop variety) were analysed by both techniques. Principal component analysis, hierarchical cluster analysis and partial least squares discrimination analysis were the chemometric tools used to discriminate positively the hop varieties. After optimizing the spectral regions and pre-processing methods a total of 94.2% and 96.6% correct hop varieties discrimination were obtained for near- and mid-infrared spectroscopy, respectively. The results obtained demonstrate the suitability of these vibrational spectroscopy techniques to discriminate different hop varieties and consequently their potential to be used as an authenticity tool. Compared with the reference procedures normally used for hops variety discrimination these techniques are quicker, cost-effective, non-destructive and eco-friendly. Copyright © 2017 Elsevier B.V. All rights reserved.

Multilevel summation with B-spline interpolation for pairwise interactions in molecular dynamics simulations

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

Hardy, David J., E-mail: dhardy@illinois.edu; Schulten, Klaus; Wolff, Matthew A.

2016-03-21

The multilevel summation method for calculating electrostatic interactions in molecular dynamics simulations constructs an approximation to a pairwise interaction kernel and its gradient, which can be evaluated at a cost that scales linearly with the number of atoms. The method smoothly splits the kernel into a sum of partial kernels of increasing range and decreasing variability with the longer-range parts interpolated from grids of increasing coarseness. Multilevel summation is especially appropriate in the context of dynamics and minimization, because it can produce continuous gradients. This article explores the use of B-splines to increase the accuracy of the multilevel summation methodmore » (for nonperiodic boundaries) without incurring additional computation other than a preprocessing step (whose cost also scales linearly). To obtain accurate results efficiently involves technical difficulties, which are overcome by a novel preprocessing algorithm. Numerical experiments demonstrate that the resulting method offers substantial improvements in accuracy and that its performance is competitive with an implementation of the fast multipole method in general and markedly better for Hamiltonian formulations of molecular dynamics. The improvement is great enough to establish multilevel summation as a serious contender for calculating pairwise interactions in molecular dynamics simulations. In particular, the method appears to be uniquely capable for molecular dynamics in two situations, nonperiodic boundary conditions and massively parallel computation, where the fast Fourier transform employed in the particle–mesh Ewald method falls short.« less

Multilevel summation with B-spline interpolation for pairwise interactions in molecular dynamics simulations.

PubMed

Hardy, David J; Wolff, Matthew A; Xia, Jianlin; Schulten, Klaus; Skeel, Robert D

2016-03-21

The multilevel summation method for calculating electrostatic interactions in molecular dynamics simulations constructs an approximation to a pairwise interaction kernel and its gradient, which can be evaluated at a cost that scales linearly with the number of atoms. The method smoothly splits the kernel into a sum of partial kernels of increasing range and decreasing variability with the longer-range parts interpolated from grids of increasing coarseness. Multilevel summation is especially appropriate in the context of dynamics and minimization, because it can produce continuous gradients. This article explores the use of B-splines to increase the accuracy of the multilevel summation method (for nonperiodic boundaries) without incurring additional computation other than a preprocessing step (whose cost also scales linearly). To obtain accurate results efficiently involves technical difficulties, which are overcome by a novel preprocessing algorithm. Numerical experiments demonstrate that the resulting method offers substantial improvements in accuracy and that its performance is competitive with an implementation of the fast multipole method in general and markedly better for Hamiltonian formulations of molecular dynamics. The improvement is great enough to establish multilevel summation as a serious contender for calculating pairwise interactions in molecular dynamics simulations. In particular, the method appears to be uniquely capable for molecular dynamics in two situations, nonperiodic boundary conditions and massively parallel computation, where the fast Fourier transform employed in the particle-mesh Ewald method falls short.

Multilevel summation with B-spline interpolation for pairwise interactions in molecular dynamics simulations

NASA Astrophysics Data System (ADS)

Hardy, David J.; Wolff, Matthew A.; Xia, Jianlin; Schulten, Klaus; Skeel, Robert D.

2016-03-01

The multilevel summation method for calculating electrostatic interactions in molecular dynamics simulations constructs an approximation to a pairwise interaction kernel and its gradient, which can be evaluated at a cost that scales linearly with the number of atoms. The method smoothly splits the kernel into a sum of partial kernels of increasing range and decreasing variability with the longer-range parts interpolated from grids of increasing coarseness. Multilevel summation is especially appropriate in the context of dynamics and minimization, because it can produce continuous gradients. This article explores the use of B-splines to increase the accuracy of the multilevel summation method (for nonperiodic boundaries) without incurring additional computation other than a preprocessing step (whose cost also scales linearly). To obtain accurate results efficiently involves technical difficulties, which are overcome by a novel preprocessing algorithm. Numerical experiments demonstrate that the resulting method offers substantial improvements in accuracy and that its performance is competitive with an implementation of the fast multipole method in general and markedly better for Hamiltonian formulations of molecular dynamics. The improvement is great enough to establish multilevel summation as a serious contender for calculating pairwise interactions in molecular dynamics simulations. In particular, the method appears to be uniquely capable for molecular dynamics in two situations, nonperiodic boundary conditions and massively parallel computation, where the fast Fourier transform employed in the particle-mesh Ewald method falls short.

Experimental phase synchronization detection in non-phase coherent chaotic systems by using the discrete complex wavelet approach

NASA Astrophysics Data System (ADS)

Ferreira, Maria Teodora; Follmann, Rosangela; Domingues, Margarete O.; Macau, Elbert E. N.; Kiss, István Z.

2017-08-01

Phase synchronization may emerge from mutually interacting non-linear oscillators, even under weak coupling, when phase differences are bounded, while amplitudes remain uncorrelated. However, the detection of this phenomenon can be a challenging problem to tackle. In this work, we apply the Discrete Complex Wavelet Approach (DCWA) for phase assignment, considering signals from coupled chaotic systems and experimental data. The DCWA is based on the Dual-Tree Complex Wavelet Transform (DT-CWT), which is a discrete transformation. Due to its multi-scale properties in the context of phase characterization, it is possible to obtain very good results from scalar time series, even with non-phase-coherent chaotic systems without state space reconstruction or pre-processing. The method correctly predicts the phase synchronization for a chemical experiment with three locally coupled, non-phase-coherent chaotic processes. The impact of different time-scales is demonstrated on the synchronization process that outlines the advantages of DCWA for analysis of experimental data.

Application of the Intuitionistic Fuzzy InterCriteria Analysis Method with Triples to a Neural Network Preprocessing Procedure

PubMed Central

Atanassova, Vassia; Sotirova, Evdokia; Doukovska, Lyubka; Bureva, Veselina; Mavrov, Deyan; Tomov, Jivko

2017-01-01

The approach of InterCriteria Analysis (ICA) was applied for the aim of reducing the set of variables on the input of a neural network, taking into account the fact that their large number increases the number of neurons in the network, thus making them unusable for hardware implementation. Here, for the first time, with the help of the ICA method, correlations between triples of the input parameters for training of the neural networks were obtained. In this case, we use the approach of ICA for data preprocessing, which may yield reduction of the total time for training the neural networks, hence, the time for the network's processing of data and images. PMID:28874908

Pre-processing Tasks in Indonesian Twitter Messages

NASA Astrophysics Data System (ADS)

Hidayatullah, A. F.; Ma'arif, M. R.

2017-01-01

Twitter text messages are very noisy. Moreover, tweet data are unstructured and complicated enough. The focus of this work is to investigate pre-processing technique for Twitter messages in Bahasa Indonesia. The main goal of this experiment is to clean the tweet data for further analysis. Thus, the objectives of this pre-processing task is simply removing all meaningless character and left valuable words. In this research, we divide our proposed pre-processing experiments into two parts. The first part is common pre-processing task. The second part is a specific pre-processing task for tweet data. From the experimental result we can conclude that by employing a specific pre-processing task related to tweet data characteristic we obtained more valuable result. The result obtained is better in terms of less meaningful word occurrence which is not significant in number comparing to the result obtained by just running common pre-processing tasks.

Distorted Character Recognition Via An Associative Neural Network

NASA Astrophysics Data System (ADS)

Messner, Richard A.; Szu, Harold H.

1987-03-01

The purpose of this paper is two-fold. First, it is intended to provide some preliminary results of a character recognition scheme which has foundations in on-going neural network architecture modeling, and secondly, to apply some of the neural network results in a real application area where thirty years of effort has had little effect on providing the machine an ability to recognize distorted objects within the same object class. It is the author's belief that the time is ripe to start applying in ernest the results of over twenty years of effort in neural modeling to some of the more difficult problems which seem so hard to solve by conventional means. The character recognition scheme proposed utilizes a preprocessing stage which performs a 2-dimensional Walsh transform of an input cartesian image field, then sequency filters this spectrum into three feature bands. Various features are then extracted and organized into three sets of feature vectors. These vector patterns that are stored and recalled associatively. Two possible associative neural memory models are proposed for further investigation. The first being an outer-product linear matrix associative memory with a threshold function controlling the strength of the output pattern (similar to Kohonen's crosscorrelation approach [1]). The second approach is based upon a modified version of Grossberg's neural architecture [2] which provides better self-organizing properties due to its adaptive nature. Preliminary results of the sequency filtering and feature extraction preprocessing stage and discussion about the use of the proposed neural architectures is included.

Determination of biodiesel content in biodiesel/diesel blends using NIR and visible spectroscopy with variable selection.

PubMed

Fernandes, David Douglas Sousa; Gomes, Adriano A; Costa, Gean Bezerra da; Silva, Gildo William B da; Véras, Germano

2011-12-15

This work is concerned of evaluate the use of visible and near-infrared (NIR) range, separately and combined, to determine the biodiesel content in biodiesel/diesel blends using Multiple Linear Regression (MLR) and variable selection by Successive Projections Algorithm (SPA). Full spectrum models employing Partial Least Squares (PLS) and variables selection by Stepwise (SW) regression coupled with Multiple Linear Regression (MLR) and PLS models also with variable selection by Jack-Knife (Jk) were compared the proposed methodology. Several preprocessing were evaluated, being chosen derivative Savitzky-Golay with second-order polynomial and 17-point window for NIR and visible-NIR range, with offset correction. A total of 100 blends with biodiesel content between 5 and 50% (v/v) prepared starting from ten sample of biodiesel. In the NIR and visible region the best model was the SPA-MLR using only two and eight wavelengths with RMSEP of 0.6439% (v/v) and 0.5741 respectively, while in the visible-NIR region the best model was the SW-MLR using five wavelengths and RMSEP of 0.9533% (v/v). Results indicate that both spectral ranges evaluated showed potential for developing a rapid and nondestructive method to quantify biodiesel in blends with mineral diesel. Finally, one can still mention that the improvement in terms of prediction error obtained with the procedure for variables selection was significant. Copyright © 2011 Elsevier B.V. All rights reserved.

Optimizing the models for rapid determination of chlorogenic acid, scopoletin and rutin in plant samples by near-infrared diffuse reflectance spectroscopy

NASA Astrophysics Data System (ADS)

Mao, Zhiyi; Shan, Ruifeng; Wang, Jiajun; Cai, Wensheng; Shao, Xueguang

2014-07-01

Polyphenols in plant samples have been extensively studied because phenolic compounds are ubiquitous in plants and can be used as antioxidants in promoting human health. A method for rapid determination of three phenolic compounds (chlorogenic acid, scopoletin and rutin) in plant samples using near-infrared diffuse reflectance spectroscopy (NIRDRS) is studied in this work. Partial least squares (PLS) regression was used for building the calibration models, and the effects of spectral preprocessing and variable selection on the models are investigated for optimization of the models. The results show that individual spectral preprocessing and variable selection has no or slight influence on the models, but the combination of the techniques can significantly improve the models. The combination of continuous wavelet transform (CWT) for removing the variant background, multiplicative scatter correction (MSC) for correcting the scattering effect and randomization test (RT) for selecting the informative variables was found to be the best way for building the optimal models. For validation of the models, the polyphenol contents in an independent sample set were predicted. The correlation coefficients between the predicted values and the contents determined by high performance liquid chromatography (HPLC) analysis are as high as 0.964, 0.948 and 0.934 for chlorogenic acid, scopoletin and rutin, respectively.

At-line monitoring of key parameters of nisin fermentation by near infrared spectroscopy, chemometric modeling and model improvement.

PubMed

Guo, Wei-Liang; Du, Yi-Ping; Zhou, Yong-Can; Yang, Shuang; Lu, Jia-Hui; Zhao, Hong-Yu; Wang, Yao; Teng, Li-Rong

2012-03-01

An analytical procedure has been developed for at-line (fast off-line) monitoring of 4 key parameters including nisin titer (NT), the concentration of reducing sugars, cell concentration and pH during a nisin fermentation process. This procedure is based on near infrared (NIR) spectroscopy and Partial Least Squares (PLS). Samples without any preprocessing were collected at intervals of 1 h during fifteen batch of fermentations. These fermentation processes were implemented in 3 different 5 l fermentors at various conditions. NIR spectra of the samples were collected in 10 min. And then, PLS was used for modeling the relationship between NIR spectra and the key parameters which were determined by reference methods. Monte Carlo Partial Least Squares (MCPLS) was applied to identify the outliers and select the most efficacious methods for preprocessing spectra, wavelengths and the suitable number of latent variables (n (LV)). Then, the optimum models for determining NT, concentration of reducing sugars, cell concentration and pH were established. The correlation coefficients of calibration set (R (c)) were 0.8255, 0.9000, 0.9883 and 0.9581, respectively. These results demonstrated that this method can be successfully applied to at-line monitor of NT, concentration of reducing sugars, cell concentration and pH during nisin fermentation processes.

Preprocessed Consortium for Neuropsychiatric Phenomics dataset.

PubMed

Gorgolewski, Krzysztof J; Durnez, Joke; Poldrack, Russell A

2017-01-01

Here we present preprocessed MRI data of 265 participants from the Consortium for Neuropsychiatric Phenomics (CNP) dataset. The preprocessed dataset includes minimally preprocessed data in the native, MNI and surface spaces accompanied with potential confound regressors, tissue probability masks, brain masks and transformations. In addition the preprocessed dataset includes unthresholded group level and single subject statistical maps from all tasks included in the original dataset. We hope that availability of this dataset will greatly accelerate research.

A dummy cell immersed boundary method for incompressible turbulence simulations over dirty geometries

NASA Astrophysics Data System (ADS)

Onishi, Keiji; Tsubokura, Makoto

2016-11-01

A methodology to eliminate the manual work required for correcting the surface imperfections of computer-aided-design (CAD) data, will be proposed. Such a technique is indispensable for CFD analysis of industrial applications involving complex geometries. The CAD geometry is degenerated into cell-oriented values based on Cartesian grid. This enables the parallel pre-processing as well as the ability to handle 'dirty' CAD data that has gaps, overlaps, or sharp edges without necessitating any fixes. An arbitrary boundary representation is used with a dummy-cell technique based on immersed boundary (IB) method. To model the IB, a forcing term is directly imposed at arbitrary ghost cells by linear interpolation of the momentum. The mass conservation is satisfied in the approximate domain that covers fluid region except the wall including cells. Attempts to Satisfy mass conservation in the wall containing cells leads to pressure oscillations near the IB. The consequence of this approximation will be discussed through fundamental study of an LES based channel flow simulation, and high Reynolds number flow around a sphere. And, an analysis comparing our results with wind tunnel experiments of flow around a full-vehicle geometry will also be presented.

Automated data selection method to improve robustness of diffuse optical tomography for breast cancer imaging

PubMed Central

Vavadi, Hamed; Zhu, Quing

2016-01-01

Imaging-guided near infrared diffuse optical tomography (DOT) has demonstrated a great potential as an adjunct modality for differentiation of malignant and benign breast lesions and for monitoring treatment response of breast cancers. However, diffused light measurements are sensitive to artifacts caused by outliers and errors in measurements due to probe-tissue coupling, patient and probe motions, and tissue heterogeneity. In general, pre-processing of the measurements is needed by experienced users to manually remove these outliers and therefore reduce imaging artifacts. An automated method of outlier removal, data selection, and filtering for diffuse optical tomography is introduced in this manuscript. This method consists of multiple steps to first combine several data sets collected from the same patient at contralateral normal breast and form a single robust reference data set using statistical tests and linear fitting of the measurements. The second step improves the perturbation measurements by filtering out outliers from the lesion site measurements using model based analysis. The results of 20 malignant and benign cases show similar performance between manual data processing and automated processing and improvement in tissue characterization of malignant to benign ratio by about 27%. PMID:27867711

Interpolation algorithm for asynchronous ADC-data

NASA Astrophysics Data System (ADS)

Bramburger, Stefan; Zinke, Benny; Killat, Dirk

2017-09-01

This paper presents a modified interpolation algorithm for signals with variable data rate from asynchronous ADCs. The Adaptive weights Conjugate gradient Toeplitz matrix (ACT) algorithm is extended to operate with a continuous data stream. An additional preprocessing of data with constant and linear sections and a weighted overlap of step-by-step into spectral domain transformed signals improve the reconstruction of the asycnhronous ADC signal. The interpolation method can be used if asynchronous ADC data is fed into synchronous digital signal processing.

Preprocessing of region of interest localization based on local surface curvature analysis for three-dimensional reconstruction with multiresolution

NASA Astrophysics Data System (ADS)

Li, Wanjing; Schütze, Rainer; Böhler, Martin; Boochs, Frank; Marzani, Franck S.; Voisin, Yvon

2009-06-01

We present an approach to integrate a preprocessing step of the region of interest (ROI) localization into 3-D scanners (laser or stereoscopic). The definite objective is to make the 3-D scanner intelligent enough to localize rapidly in the scene, during the preprocessing phase, the regions with high surface curvature, so that precise scanning will be done only in these regions instead of in the whole scene. In this way, the scanning time can be largely reduced, and the results contain only pertinent data. To test its feasibility and efficiency, we simulated the preprocessing process under an active stereoscopic system composed of two cameras and a video projector. The ROI localization is done in an iterative way. First, the video projector projects a regular point pattern in the scene, and then the pattern is modified iteratively according to the local surface curvature of each reconstructed 3-D point. Finally, the last pattern is used to determine the ROI. Our experiments showed that with this approach, the system is capable to localize all types of objects, including small objects with small depth.

Development of On-line Wildfire Emissions for the Operational Canadian Air Quality Forecast System

NASA Astrophysics Data System (ADS)

Pavlovic, R.; Menard, S.; Chen, J.; Anselmo, D.; Paul-Andre, B.; Gravel, S.; Moran, M. D.; Davignon, D.

2013-12-01

An emissions processing system has been developed to incorporate near-real-time emissions from wildfires and large prescribed burns into Environment Canada's real-time GEM-MACH air quality (AQ) forecast system. Since the GEM-MACH forecast domain covers Canada and most of the USA, including Alaska, fire location information is needed for both of these large countries. Near-real-time satellite data are obtained and processed separately for the two countries for organizational reasons. Fire location and fuel consumption data for Canada are provided by the Canadian Forest Service's Canadian Wild Fire Information System (CWFIS) while fire location and emissions data for the U.S. are provided by the SMARTFIRE (Satellite Mapping Automated Reanalysis Tool for Fire Incident Reconciliation) system via the on-line BlueSky Gateway. During AQ model runs, emissions from individual fire sources are injected into elevated model layers based on plume-rise calculations and then transport and chemistry calculations are performed. This 'on the fly' approach to the insertion of emissions provides greater flexibility since on-line meteorology is used and reduces computational overhead in emission pre-processing. An experimental wildfire version of GEM-MACH was run in real-time mode for the summers of 2012 and 2013. 48-hour forecasts were generated every 12 hours (at 00 and 12 UTC). Noticeable improvements in the AQ forecasts for PM2.5 were seen in numerous regions where fire activity was high. Case studies evaluating model performance for specific regions, computed objective scores, and subjective evaluations by AQ forecasters will be included in this presentation. Using the lessons learned from the last two summers, Environment Canada will continue to work towards the goal of incorporating near-real-time intermittent wildfire emissions within the operational air quality forecast system.

Poisson pre-processing of nonstationary photonic signals: Signals with equality between mean and variance.

PubMed

Poplová, Michaela; Sovka, Pavel; Cifra, Michal

2017-01-01

Photonic signals are broadly exploited in communication and sensing and they typically exhibit Poisson-like statistics. In a common scenario where the intensity of the photonic signals is low and one needs to remove a nonstationary trend of the signals for any further analysis, one faces an obstacle: due to the dependence between the mean and variance typical for a Poisson-like process, information about the trend remains in the variance even after the trend has been subtracted, possibly yielding artifactual results in further analyses. Commonly available detrending or normalizing methods cannot cope with this issue. To alleviate this issue we developed a suitable pre-processing method for the signals that originate from a Poisson-like process. In this paper, a Poisson pre-processing method for nonstationary time series with Poisson distribution is developed and tested on computer-generated model data and experimental data of chemiluminescence from human neutrophils and mung seeds. The presented method transforms a nonstationary Poisson signal into a stationary signal with a Poisson distribution while preserving the type of photocount distribution and phase-space structure of the signal. The importance of the suggested pre-processing method is shown in Fano factor and Hurst exponent analysis of both computer-generated model signals and experimental photonic signals. It is demonstrated that our pre-processing method is superior to standard detrending-based methods whenever further signal analysis is sensitive to variance of the signal.

Poisson pre-processing of nonstationary photonic signals: Signals with equality between mean and variance

PubMed Central

Poplová, Michaela; Sovka, Pavel

2017-01-01

Photonic signals are broadly exploited in communication and sensing and they typically exhibit Poisson-like statistics. In a common scenario where the intensity of the photonic signals is low and one needs to remove a nonstationary trend of the signals for any further analysis, one faces an obstacle: due to the dependence between the mean and variance typical for a Poisson-like process, information about the trend remains in the variance even after the trend has been subtracted, possibly yielding artifactual results in further analyses. Commonly available detrending or normalizing methods cannot cope with this issue. To alleviate this issue we developed a suitable pre-processing method for the signals that originate from a Poisson-like process. In this paper, a Poisson pre-processing method for nonstationary time series with Poisson distribution is developed and tested on computer-generated model data and experimental data of chemiluminescence from human neutrophils and mung seeds. The presented method transforms a nonstationary Poisson signal into a stationary signal with a Poisson distribution while preserving the type of photocount distribution and phase-space structure of the signal. The importance of the suggested pre-processing method is shown in Fano factor and Hurst exponent analysis of both computer-generated model signals and experimental photonic signals. It is demonstrated that our pre-processing method is superior to standard detrending-based methods whenever further signal analysis is sensitive to variance of the signal. PMID:29216207

The Neuro Bureau ADHD-200 Preprocessed repository.

PubMed

Bellec, Pierre; Chu, Carlton; Chouinard-Decorte, François; Benhajali, Yassine; Margulies, Daniel S; Craddock, R Cameron

2017-01-01

In 2011, the "ADHD-200 Global Competition" was held with the aim of identifying biomarkers of attention-deficit/hyperactivity disorder from resting-state functional magnetic resonance imaging (rs-fMRI) and structural MRI (s-MRI) data collected on 973 individuals. Statisticians and computer scientists were potentially the most qualified for the machine learning aspect of the competition, but generally lacked the specialized skills to implement the necessary steps of data preparation for rs-fMRI. Realizing this barrier to entry, the Neuro Bureau prospectively collaborated with all competitors by preprocessing the data and sharing these results at the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC) (http://www.nitrc.org/frs/?group_id=383). This "ADHD-200 Preprocessed" release included multiple analytical pipelines to cater to different philosophies of data analysis. The processed derivatives included denoised and registered 4D fMRI volumes, regional time series extracted from brain parcellations, maps of 10 intrinsic connectivity networks, fractional amplitude of low frequency fluctuation, and regional homogeneity, along with grey matter density maps. The data was used by several teams who competed in the ADHD-200 Global Competition, including the winning entry by a group of biostaticians. To the best of our knowledge, the ADHD-200 Preprocessed release was the first large public resource of preprocessed resting-state fMRI and structural MRI data, and remains to this day the only resource featuring a battery of alternative processing paths. Copyright © 2016 Elsevier Inc. All rights reserved.

Personal Exposure to Dust and Endotoxin in Robusta and Arabica Coffee Processing Factories in Tanzania

PubMed Central

Sakwari, Gloria

2013-01-01

Introduction: Endotoxin exposure associated with organic dust exposure has been studied in several industries. Coffee cherries that are dried directly after harvest may differ in dust and endotoxin emissions to those that are peeled and washed before drying. The aim of this study was to measure personal total dust and endotoxin levels and to evaluate their determinants of exposure in coffee processing factories. Methods: Using Sidekick Casella pumps at a flow rate of 2l/min, total dust levels were measured in the workers’ breathing zone throughout the shift. Endotoxin was analyzed using the kinetic chromogenic Limulus amebocyte lysate assay. Separate linear mixed-effects models were used to evaluate exposure determinants for dust and endotoxin. Results: Total dust and endotoxin exposure were significantly higher in Robusta than in Arabica coffee factories (geometric mean 3.41mg/m3 and 10 800 EU/m3 versus 2.10mg/m3 and 1400 EU/m3, respectively). Dry pre-processed coffee and differences in work tasks explained 30% of the total variance for total dust and 71% of the variance for endotoxin exposure. High exposure in Robusta processing is associated with the dry pre-processing method used after harvest. Conclusions: Dust and endotoxin exposure is high, in particular when processing dry pre-processed coffee. Minimization of dust emissions and use of efficient dust exhaust systems are important to prevent the development of respiratory system impairment in workers. PMID:23028014

Identification of moisture content in tobacco plant leaves using outlier sample eliminating algorithms and hyperspectral data.

PubMed

Sun, Jun; Zhou, Xin; Wu, Xiaohong; Zhang, Xiaodong; Li, Qinglin

2016-02-26

Fast identification of moisture content in tobacco plant leaves plays a key role in the tobacco cultivation industry and benefits the management of tobacco plant in the farm. In order to identify moisture content of tobacco plant leaves in a fast and nondestructive way, a method involving Mahalanobis distance coupled with Monte Carlo cross validation(MD-MCCV) was proposed to eliminate outlier sample in this study. The hyperspectral data of 200 tobacco plant leaf samples of 20 moisture gradients were obtained using FieldSpc(®) 3 spectrometer. Savitzky-Golay smoothing(SG), roughness penalty smoothing(RPS), kernel smoothing(KS) and median smoothing(MS) were used to preprocess the raw spectra. In addition, Mahalanobis distance(MD), Monte Carlo cross validation(MCCV) and Mahalanobis distance coupled to Monte Carlo cross validation(MD-MCCV) were applied to select the outlier sample of the raw spectrum and four smoothing preprocessing spectra. Successive projections algorithm (SPA) was used to extract the most influential wavelengths. Multiple Linear Regression (MLR) was applied to build the prediction models based on preprocessed spectra feature in characteristic wavelengths. The results showed that the preferably four prediction model were MD-MCCV-SG (Rp(2) = 0.8401 and RMSEP = 0.1355), MD-MCCV-RPS (Rp(2) = 0.8030 and RMSEP = 0.1274), MD-MCCV-KS (Rp(2) = 0.8117 and RMSEP = 0.1433), MD-MCCV-MS (Rp(2) = 0.9132 and RMSEP = 0.1162). MD-MCCV algorithm performed best among MD algorithm, MCCV algorithm and the method without sample pretreatment algorithm in the eliminating outlier sample from 20 different moisture gradients of tobacco plant leaves and MD-MCCV can be used to eliminate outlier sample in the spectral preprocessing. Copyright © 2016 Elsevier Inc. All rights reserved.

Experimental variability and data pre-processing as factors affecting the discrimination power of some chemometric approaches (PCA, CA and a new algorithm based on linear regression) applied to (+/-)ESI/MS and RPLC/UV data: Application on green tea extracts.

PubMed

Iorgulescu, E; Voicu, V A; Sârbu, C; Tache, F; Albu, F; Medvedovici, A

2016-08-01

The influence of the experimental variability (instrumental repeatability, instrumental intermediate precision and sample preparation variability) and data pre-processing (normalization, peak alignment, background subtraction) on the discrimination power of multivariate data analysis methods (Principal Component Analysis -PCA- and Cluster Analysis -CA-) as well as a new algorithm based on linear regression was studied. Data used in the study were obtained through positive or negative ion monitoring electrospray mass spectrometry (+/-ESI/MS) and reversed phase liquid chromatography/UV spectrometric detection (RPLC/UV) applied to green tea extracts. Extractions in ethanol and heated water infusion were used as sample preparation procedures. The multivariate methods were directly applied to mass spectra and chromatograms, involving strictly a holistic comparison of shapes, without assignment of any structural identity to compounds. An alternative data interpretation based on linear regression analysis mutually applied to data series is also discussed. Slopes, intercepts and correlation coefficients produced by the linear regression analysis applied on pairs of very large experimental data series successfully retain information resulting from high frequency instrumental acquisition rates, obviously better defining the profiles being compared. Consequently, each type of sample or comparison between samples produces in the Cartesian space an ellipsoidal volume defined by the normal variation intervals of the slope, intercept and correlation coefficient. Distances between volumes graphically illustrates (dis)similarities between compared data. The instrumental intermediate precision had the major effect on the discrimination power of the multivariate data analysis methods. Mass spectra produced through ionization from liquid state in atmospheric pressure conditions of bulk complex mixtures resulting from extracted materials of natural origins provided an excellent data basis for multivariate analysis methods, equivalent to data resulting from chromatographic separations. The alternative evaluation of very large data series based on linear regression analysis produced information equivalent to results obtained through application of PCA an CA. Copyright © 2016 Elsevier B.V. All rights reserved.

Reference-free spectroscopic determination of fat and protein in milk in the visible and near infrared region below 1000nm using spatially resolved diffuse reflectance fiber probe.

PubMed

Bogomolov, Andrey; Belikova, Valeria; Galyanin, Vladislav; Melenteva, Anastasiia; Meyer, Hans

2017-05-15

New technique of diffuse reflectance spectroscopic analysis of milk fat and total protein content in the visible (Vis) and adjacent near infrared (NIR) region (400-995nm) has been developed and tested. Sample analysis was performed through a probe having eight 200-µm fiber channels forming a linear array. One of the end fibers was used for the illumination and other seven - for the spectroscopic detection of diffusely reflected light. One of the detection channels was used as a reference to normalize the spectra and to convert them into absorbance-equivalent units. The method has been tested experimentally using a designed sample set prepared from industrial raw milk standards with widely varying fat and protein content. To increase the modelling robustness all milk samples were measured in three different homogenization degrees. Comprehensive data analysis has shown the advantage of combining both spectral and spatial resolution in the same measurement and revealed the most relevant channels and wavelength regions. The modelling accuracy was further improved using joint variable selection and preprocessing optimization method based on the genetic algorithm. The root mean-square errors of different validation methods were below 0.10% for fat and below 0.08% for total protein content. Based on the present experimental data, it was computationally shown that the full-spectrum analysis in this method can be replaced by a sensor measurement at several specific wavelengths, for instance, using light-emitting diodes (LEDs) for illumination. Two optimal sensor configurations have been suggested: with nine LEDs for the analysis of fat and seven - for protein content. Both simulated sensors exhibit nearly the same component determination accuracy as corresponding full-spectrum analysis. Copyright © 2017 Elsevier B.V. All rights reserved.

Network Adjustment of Orbit Errors in SAR Interferometry

NASA Astrophysics Data System (ADS)

Bahr, Hermann; Hanssen, Ramon

2010-03-01

Orbit errors can induce significant long wavelength error signals in synthetic aperture radar (SAR) interferograms and thus bias estimates of wide-scale deformation phenomena. The presented approach aims for correcting orbit errors in a preprocessing step to deformation analysis by modifying state vectors. Whereas absolute errors in the orbital trajectory are negligible, the influence of relative errors (baseline errors) is parametrised by their parallel and perpendicular component as a linear function of time. As the sensitivity of the interferometric phase is only significant with respect to the perpendicular base-line and the rate of change of the parallel baseline, the algorithm focuses on estimating updates to these two parameters. This is achieved by a least squares approach, where the unwrapped residual interferometric phase is observed and atmospheric contributions are considered to be stochastic with constant mean. To enhance reliability, baseline errors are adjusted in an overdetermined network of interferograms, yielding individual orbit corrections per acquisition.

EEG and MEG data analysis in SPM8.

PubMed

Litvak, Vladimir; Mattout, Jérémie; Kiebel, Stefan; Phillips, Christophe; Henson, Richard; Kilner, James; Barnes, Gareth; Oostenveld, Robert; Daunizeau, Jean; Flandin, Guillaume; Penny, Will; Friston, Karl

2011-01-01

SPM is a free and open source software written in MATLAB (The MathWorks, Inc.). In addition to standard M/EEG preprocessing, we presently offer three main analysis tools: (i) statistical analysis of scalp-maps, time-frequency images, and volumetric 3D source reconstruction images based on the general linear model, with correction for multiple comparisons using random field theory; (ii) Bayesian M/EEG source reconstruction, including support for group studies, simultaneous EEG and MEG, and fMRI priors; (iii) dynamic causal modelling (DCM), an approach combining neural modelling with data analysis for which there are several variants dealing with evoked responses, steady state responses (power spectra and cross-spectra), induced responses, and phase coupling. SPM8 is integrated with the FieldTrip toolbox , making it possible for users to combine a variety of standard analysis methods with new schemes implemented in SPM and build custom analysis tools using powerful graphical user interface (GUI) and batching tools.

Gaia DR2 documentation Chapter 3: Astrometry

NASA Astrophysics Data System (ADS)

Hobbs, D.; Lindegren, L.; Bastian, U.; Klioner, S.; Butkevich, A.; Stephenson, C.; Hernandez, J.; Lammers, U.; Bombrun, A.; Mignard, F.; Altmann, M.; Davidson, M.; de Bruijne, J. H. J.; Fernández-Hernández, J.; Siddiqui, H.; Utrilla Molina, E.

2018-04-01

This chapter of the Gaia DR2 documentation describes the models and processing steps used for the astrometric core solution, namely, the Astrometric Global Iterative Solution (AGIS). The inputs to this solution rely heavily on the basic observables (or astrometric elementaries) which have been pre-processed and discussed in Chapter 2, the results of which were published in Fabricius et al. (2016). The models consist of reference systems and time scales; assumed linear stellar motion and relativistic light deflection; in addition to fundamental constants and the transformation of coordinate systems. Higher level inputs such as: planetary and solar system ephemeris; Gaia tracking and orbit information; initial quasar catalogues and BAM data are all needed for the processing described here. The astrometric calibration models are outlined followed by the details processing steps which give AGIS its name. We also present a basic quality assessment and validation of the scientific results (for details, see Lindegren et al. 2018).

EEG and MEG Data Analysis in SPM8

PubMed Central

Litvak, Vladimir; Mattout, Jérémie; Kiebel, Stefan; Phillips, Christophe; Henson, Richard; Kilner, James; Barnes, Gareth; Oostenveld, Robert; Daunizeau, Jean; Flandin, Guillaume; Penny, Will; Friston, Karl

2011-01-01

SPM is a free and open source software written in MATLAB (The MathWorks, Inc.). In addition to standard M/EEG preprocessing, we presently offer three main analysis tools: (i) statistical analysis of scalp-maps, time-frequency images, and volumetric 3D source reconstruction images based on the general linear model, with correction for multiple comparisons using random field theory; (ii) Bayesian M/EEG source reconstruction, including support for group studies, simultaneous EEG and MEG, and fMRI priors; (iii) dynamic causal modelling (DCM), an approach combining neural modelling with data analysis for which there are several variants dealing with evoked responses, steady state responses (power spectra and cross-spectra), induced responses, and phase coupling. SPM8 is integrated with the FieldTrip toolbox , making it possible for users to combine a variety of standard analysis methods with new schemes implemented in SPM and build custom analysis tools using powerful graphical user interface (GUI) and batching tools. PMID:21437221

Automated segmentation of ventricles from serial brain MRI for the quantification of volumetric changes associated with communicating hydrocephalus in patients with brain tumor

NASA Astrophysics Data System (ADS)

Pura, John A.; Hamilton, Allison M.; Vargish, Geoffrey A.; Butman, John A.; Linguraru, Marius George

2011-03-01

Accurate ventricle volume estimates could improve the understanding and diagnosis of postoperative communicating hydrocephalus. For this category of patients, associated changes in ventricle volume can be difficult to identify, particularly over short time intervals. We present an automated segmentation algorithm that evaluates ventricle size from serial brain MRI examination. The technique combines serial T1- weighted images to increase SNR and segments the means image to generate a ventricle template. After pre-processing, the segmentation is initiated by a fuzzy c-means clustering algorithm to find the seeds used in a combination of fast marching methods and geodesic active contours. Finally, the ventricle template is propagated onto the serial data via non-linear registration. Serial volume estimates were obtained in an automated robust and accurate manner from difficult data.

Parallel processing of genomics data

NASA Astrophysics Data System (ADS)

Agapito, Giuseppe; Guzzi, Pietro Hiram; Cannataro, Mario

2016-10-01

The availability of high-throughput experimental platforms for the analysis of biological samples, such as mass spectrometry, microarrays and Next Generation Sequencing, have made possible to analyze a whole genome in a single experiment. Such platforms produce an enormous volume of data per single experiment, thus the analysis of this enormous flow of data poses several challenges in term of data storage, preprocessing, and analysis. To face those issues, efficient, possibly parallel, bioinformatics software needs to be used to preprocess and analyze data, for instance to highlight genetic variation associated with complex diseases. In this paper we present a parallel algorithm for the parallel preprocessing and statistical analysis of genomics data, able to face high dimension of data and resulting in good response time. The proposed system is able to find statistically significant biological markers able to discriminate classes of patients that respond to drugs in different ways. Experiments performed on real and synthetic genomic datasets show good speed-up and scalability.

The Role of GRAIL Orbit Determination in Preprocessing of Gravity Science Measurements

NASA Technical Reports Server (NTRS)

Kruizinga, Gerhard; Asmar, Sami; Fahnestock, Eugene; Harvey, Nate; Kahan, Daniel; Konopliv, Alex; Oudrhiri, Kamal; Paik, Meegyeong; Park, Ryan; Strekalov, Dmitry;

KONFIG and REKONFIG: Two interactive preprocessing to the Navy/NASA Engine Program (NNEP)

NASA Technical Reports Server (NTRS)

Fishbach, L. H.

1981-01-01

The NNEP is a computer program that is currently being used to simulate the thermodynamic cycle performance of almost all types of turbine engines by many government, industry, and university personnel. The NNEP uses arrays of input data to set up the engine simulation and component matching method as well as to describe the characteristics of the components. A preprocessing program (KONFIG) is described in which the user at a terminal on a time shared computer can interactively prepare the arrays of data required. It is intended to make it easier for the occasional or new user to operate NNEP. Another preprocessing program (REKONFIG) in which the user can modify the component specifications of a previously configured NNEP dataset is also described. It is intended to aid in preparing data for parametric studies and/or studies of similar engines such a mixed flow turbofans, turboshafts, etc.

Direct use of linear time-domain aerodynamics in aeroservoelastic analysis: Aerodynamic model

NASA Technical Reports Server (NTRS)

Woods, J. A.; Gilbert, Michael G.

1990-01-01

The work presented here is the first part of a continuing effort to expand existing capabilities in aeroelasticity by developing the methodology which is necessary to utilize unsteady time-domain aerodynamics directly in aeroservoelastic design and analysis. The ultimate objective is to define a fully integrated state-space model of an aeroelastic vehicle's aerodynamics, structure and controls which may be used to efficiently determine the vehicle's aeroservoelastic stability. Here, the current status of developing a state-space model for linear or near-linear time-domain indicial aerodynamic forces is presented.

Relationship between time-resolved and non-time-resolved Beer-Lambert law in turbid media.

PubMed

Nomura, Y; Hazeki, O; Tamura, M

1997-06-01

The time-resolved Beer-Lambert law proposed for oxygen monitoring using pulsed light was extended to the non-time-resolved case in a scattered medium such as living tissues with continuous illumination. The time-resolved Beer-Lambert law was valid for the phantom model and living tissues in the visible and near-infrared regions. The absolute concentration and oxygen saturation of haemoglobin in rat brain and thigh muscle could be determined. The temporal profile of rat brain was reproduced by Monte Carlo simulation. When the temporal profiles of rat brain under different oxygenation states were integrated with time, the absorbance difference was linearly related to changes in the absorption coefficient. When the simulated profiles were integrated, there was a linear relationship within the absorption coefficient which was predicted for fractional inspiratory oxygen concentration from 10 to 100% and, in the case beyond the range of the absorption coefficient, the deviation from linearity was slight. We concluded that an optical pathlength which is independent of changes in the absorption coefficient is a good approximation for near-infrared oxygen monitoring.

An automated tool for a daily harmful algal bloom monitoring using MODIS imagery downscaled to 250 meters spatial resolution

NASA Astrophysics Data System (ADS)

El Alem, A.

2016-12-01

Harmful algal bloom (HAB) causes negative impacts to other organisms by producing natural toxins, mechanical damage to other micro-organisms, or simply by degrading waters quality. Contaminated waters could expose several billions of population to serious intoxications problems. Traditionally, HAB monitoring is made with standard methods limited to a restricted network of sampling points. However, rapid evolution of HABs makes it difficult to monitor their variation in time and space, threating then public safety. Daily monitoring is then the best way to control and to mitigate their harmful effect upon population, particularly for sources feeding cities. Recently, an approach for estimating chlorophyll-a (Chl-a) concentration, as a proxy of HAB presence, in inland waters based MODIS imagery downscaled to 250 meters spatial resolution was developed. Statistical evaluation of the developed approach highlighted the accuracy of Chl-a estimate with a R2 = 0.98, a relative RMSE of 15%, a relative BIAS of -2%, and a relative NASH of 0.95. Temporal resolution of MODIS sensor allows then a daily monitoring of HAB spatial distribution for inland waters of more than 2.25 Km2 of surface. Groupe-Hemisphere, a company specialized in environmental and sustainable planning in Quebec, has shown a great interest to the developed approach. Given the complexity of the preprocessing (geometric and atmospheric corrections as well as downscaling spatial resolution) and processing (Chl-a estimate) of images, a standalone application under the MATLAB's GUI environment was developed. The application allows an automated process for all preprocessing and processing steps. Outputs produced by the application for end users, many of whom may be decision makers or policy makers in the public and private sectors, allows a near-real time monitoring of water quality for a more efficient management.

Source-Modeling Auditory Processes of EEG Data Using EEGLAB and Brainstorm.

PubMed

Stropahl, Maren; Bauer, Anna-Katharina R; Debener, Stefan; Bleichner, Martin G

2018-01-01

Electroencephalography (EEG) source localization approaches are often used to disentangle the spatial patterns mixed up in scalp EEG recordings. However, approaches differ substantially between experiments, may be strongly parameter-dependent, and results are not necessarily meaningful. In this paper we provide a pipeline for EEG source estimation, from raw EEG data pre-processing using EEGLAB functions up to source-level analysis as implemented in Brainstorm. The pipeline is tested using a data set of 10 individuals performing an auditory attention task. The analysis approach estimates sources of 64-channel EEG data without the prerequisite of individual anatomies or individually digitized sensor positions. First, we show advanced EEG pre-processing using EEGLAB, which includes artifact attenuation using independent component analysis (ICA). ICA is a linear decomposition technique that aims to reveal the underlying statistical sources of mixed signals and is further a powerful tool to attenuate stereotypical artifacts (e.g., eye movements or heartbeat). Data submitted to ICA are pre-processed to facilitate good-quality decompositions. Aiming toward an objective approach on component identification, the semi-automatic CORRMAP algorithm is applied for the identification of components representing prominent and stereotypic artifacts. Second, we present a step-wise approach to estimate active sources of auditory cortex event-related processing, on a single subject level. The presented approach assumes that no individual anatomy is available and therefore the default anatomy ICBM152, as implemented in Brainstorm, is used for all individuals. Individual noise modeling in this dataset is based on the pre-stimulus baseline period. For EEG source modeling we use the OpenMEEG algorithm as the underlying forward model based on the symmetric Boundary Element Method (BEM). We then apply the method of dynamical statistical parametric mapping (dSPM) to obtain physiologically plausible EEG source estimates. Finally, we show how to perform group level analysis in the time domain on anatomically defined regions of interest (auditory scout). The proposed pipeline needs to be tailored to the specific datasets and paradigms. However, the straightforward combination of EEGLAB and Brainstorm analysis tools may be of interest to others performing EEG source localization.

AFMPB: An adaptive fast multipole Poisson-Boltzmann solver for calculating electrostatics in biomolecular systems

NASA Astrophysics Data System (ADS)

Lu, Benzhuo; Cheng, Xiaolin; Huang, Jingfang; McCammon, J. Andrew

2010-06-01

A Fortran program package is introduced for rapid evaluation of the electrostatic potentials and forces in biomolecular systems modeled by the linearized Poisson-Boltzmann equation. The numerical solver utilizes a well-conditioned boundary integral equation (BIE) formulation, a node-patch discretization scheme, a Krylov subspace iterative solver package with reverse communication protocols, and an adaptive new version of fast multipole method in which the exponential expansions are used to diagonalize the multipole-to-local translations. The program and its full description, as well as several closely related libraries and utility tools are available at http://lsec.cc.ac.cn/~lubz/afmpb.html and a mirror site at http://mccammon.ucsd.edu/. This paper is a brief summary of the program: the algorithms, the implementation and the usage. Program summaryProgram title: AFMPB: Adaptive fast multipole Poisson-Boltzmann solver Catalogue identifier: AEGB_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEGB_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: GPL 2.0 No. of lines in distributed program, including test data, etc.: 453 649 No. of bytes in distributed program, including test data, etc.: 8 764 754 Distribution format: tar.gz Programming language: Fortran Computer: Any Operating system: Any RAM: Depends on the size of the discretized biomolecular system Classification: 3 External routines: Pre- and post-processing tools are required for generating the boundary elements and for visualization. Users can use MSMS ( http://www.scripps.edu/~sanner/html/msms_home.html) for pre-processing, and VMD ( http://www.ks.uiuc.edu/Research/vmd/) for visualization. Sub-programs included: An iterative Krylov subspace solvers package from SPARSKIT by Yousef Saad ( http://www-users.cs.umn.edu/~saad/software/SPARSKIT/sparskit.html), and the fast multipole methods subroutines from FMMSuite ( http://www.fastmultipole.org/). Nature of problem: Numerical solution of the linearized Poisson-Boltzmann equation that describes electrostatic interactions of molecular systems in ionic solutions. Solution method: A novel node-patch scheme is used to discretize the well-conditioned boundary integral equation formulation of the linearized Poisson-Boltzmann equation. Various Krylov subspace solvers can be subsequently applied to solve the resulting linear system, with a bounded number of iterations independent of the number of discretized unknowns. The matrix-vector multiplication at each iteration is accelerated by the adaptive new versions of fast multipole methods. The AFMPB solver requires other stand-alone pre-processing tools for boundary mesh generation, post-processing tools for data analysis and visualization, and can be conveniently coupled with different time stepping methods for dynamics simulation. Restrictions: Only three or six significant digits options are provided in this version. Unusual features: Most of the codes are in Fortran77 style. Memory allocation functions from Fortran90 and above are used in a few subroutines. Additional comments: The current version of the codes is designed and written for single core/processor desktop machines. Check http://lsec.cc.ac.cn/~lubz/afmpb.html and http://mccammon.ucsd.edu/ for updates and changes. Running time: The running time varies with the number of discretized elements ( N) in the system and their distributions. In most cases, it scales linearly as a function of N.

Retinex Preprocessing for Improved Multi-Spectral Image Classification

NASA Technical Reports Server (NTRS)

Thompson, B.; Rahman, Z.; Park, S.

2000-01-01

The goal of multi-image classification is to identify and label "similar regions" within a scene. The ability to correctly classify a remotely sensed multi-image of a scene is affected by the ability of the classification process to adequately compensate for the effects of atmospheric variations and sensor anomalies. Better classification may be obtained if the multi-image is preprocessed before classification, so as to reduce the adverse effects of image formation. In this paper, we discuss the overall impact on multi-spectral image classification when the retinex image enhancement algorithm is used to preprocess multi-spectral images. The retinex is a multi-purpose image enhancement algorithm that performs dynamic range compression, reduces the dependence on lighting conditions, and generally enhances apparent spatial resolution. The retinex has been successfully applied to the enhancement of many different types of grayscale and color images. We show in this paper that retinex preprocessing improves the spatial structure of multi-spectral images and thus provides better within-class variations than would otherwise be obtained without the preprocessing. For a series of multi-spectral images obtained with diffuse and direct lighting, we show that without retinex preprocessing the class spectral signatures vary substantially with the lighting conditions. Whereas multi-dimensional clustering without preprocessing produced one-class homogeneous regions, the classification on the preprocessed images produced multi-class non-homogeneous regions. This lack of homogeneity is explained by the interaction between different agronomic treatments applied to the regions: the preprocessed images are closer to ground truth. The principle advantage that the retinex offers is that for different lighting conditions classifications derived from the retinex preprocessed images look remarkably "similar", and thus more consistent, whereas classifications derived from the original images, without preprocessing, are much less similar.

Tapping the Vast Potential of the Data Deluge in Small-scale Food-Animal Production Businesses: Challenges to Near Real-time Data Analysis and Interpretation.

PubMed

Vial, Flavie; Tedder, Andrew

2017-01-01

Food-animal production businesses are part of a data-driven ecosystem shaped by stringent requirements for traceability along the value chain and the expanding capabilities of connected products. Within this sector, the generation of animal health intelligence, in particular, in terms of antimicrobial usage, is hindered by the lack of a centralized framework for data storage and usage. In this Perspective, we delimit the 11 processes required for evidence-based decisions and explore processes 3 (digital data acquisition) to 10 (communication to decision-makers) in more depth. We argue that small agribusinesses disproportionally face challenges related to economies of scale given the high price of equipment and services. There are two main areas of concern regarding the collection and usage of digital farm data. First, recording platforms must be developed with the needs and constraints of small businesses in mind and move away from local data storage, which hinders data accessibility and interoperability. Second, such data are unstructured and exhibit properties that can prove challenging to its near real-time preprocessing and analysis in a sector that is largely lagging behind others in terms of computing infrastructure and buying into digital technologies. To complete the digital transformation of this sector, investment in rural digital infrastructure is required alongside the development of new business models to empower small businesses to commit to near real-time data capture. This approach will deliver critical information to fill gaps in our understanding of emerging diseases and antimicrobial resistance in production animals, eventually leading to effective evidence-based policies.

Supervised filters for EEG signal in naturally occurring epilepsy forecasting.

PubMed

Muñoz-Almaraz, Francisco Javier; Zamora-Martínez, Francisco; Botella-Rocamora, Paloma; Pardo, Juan

2017-01-01

Nearly 1% of the global population has Epilepsy. Forecasting epileptic seizures with an acceptable confidence level, could improve the disease treatment and thus the lifestyle of the people who suffer it. To do that the electroencephalogram (EEG) signal is usually studied through spectral power band filtering, but this paper proposes an alternative novel method of preprocessing the EEG signal based on supervised filters. Such filters have been employed in a machine learning algorithm, such as the K-Nearest Neighbor (KNN), to improve the prediction of seizures. The proposed solution extends with this novel approach an algorithm that was submitted to win the third prize of an international Data Science challenge promoted by Kaggle contest platform and the American Epilepsy Society, the Epilepsy Foundation, National Institutes of Health (NIH) and Mayo Clinic. A formal description of these preprocessing methods is presented and a detailed analysis in terms of Receiver Operating Characteristics (ROC) curve and Area Under ROC curve is performed. The obtained results show statistical significant improvements when compared with the spectral power band filtering (PBF) typical baseline. A trend between performance and the dataset size is observed, suggesting that the supervised filters bring better information, compared to the conventional PBF filters, as the dataset grows in terms of monitored variables (sensors) and time length. The paper demonstrates a better accuracy in forecasting when new filters are employed and its main contribution is in the field of machine learning algorithms to develop more accurate predictive systems.

Supervised filters for EEG signal in naturally occurring epilepsy forecasting

PubMed Central

2017-01-01

Nearly 1% of the global population has Epilepsy. Forecasting epileptic seizures with an acceptable confidence level, could improve the disease treatment and thus the lifestyle of the people who suffer it. To do that the electroencephalogram (EEG) signal is usually studied through spectral power band filtering, but this paper proposes an alternative novel method of preprocessing the EEG signal based on supervised filters. Such filters have been employed in a machine learning algorithm, such as the K-Nearest Neighbor (KNN), to improve the prediction of seizures. The proposed solution extends with this novel approach an algorithm that was submitted to win the third prize of an international Data Science challenge promoted by Kaggle contest platform and the American Epilepsy Society, the Epilepsy Foundation, National Institutes of Health (NIH) and Mayo Clinic. A formal description of these preprocessing methods is presented and a detailed analysis in terms of Receiver Operating Characteristics (ROC) curve and Area Under ROC curve is performed. The obtained results show statistical significant improvements when compared with the spectral power band filtering (PBF) typical baseline. A trend between performance and the dataset size is observed, suggesting that the supervised filters bring better information, compared to the conventional PBF filters, as the dataset grows in terms of monitored variables (sensors) and time length. The paper demonstrates a better accuracy in forecasting when new filters are employed and its main contribution is in the field of machine learning algorithms to develop more accurate predictive systems. PMID:28632737

U.S. Geological Survey groundwater toolbox, a graphical and mapping interface for analysis of hydrologic data (version 1.0): user guide for estimation of base flow, runoff, and groundwater recharge from streamflow data

USGS Publications Warehouse

Barlow, Paul M.; Cunningham, William L.; Zhai, Tong; Gray, Mark

2015-01-01

This report is a user guide for the streamflow-hydrograph analysis methods provided with version 1.0 of the U.S. Geological Survey (USGS) Groundwater Toolbox computer program. These include six hydrograph-separation methods to determine the groundwater-discharge (base-flow) and surface-runoff components of streamflow—the Base-Flow Index (BFI; Standard and Modified), HYSEP (Fixed Interval, Sliding Interval, and Local Minimum), and PART methods—and the RORA recession-curve displacement method and associated RECESS program to estimate groundwater recharge from streamflow data. The Groundwater Toolbox is a customized interface built on the nonproprietary, open source MapWindow geographic information system software. The program provides graphing, mapping, and analysis capabilities in a Microsoft Windows computing environment. In addition to the four hydrograph-analysis methods, the Groundwater Toolbox allows for the retrieval of hydrologic time-series data (streamflow, groundwater levels, and precipitation) from the USGS National Water Information System, downloading of a suite of preprocessed geographic information system coverages and meteorological data from the National Oceanic and Atmospheric Administration National Climatic Data Center, and analysis of data with several preprocessing and postprocessing utilities. With its data retrieval and analysis tools, the Groundwater Toolbox provides methods to estimate many of the components of the water budget for a hydrologic basin, including precipitation; streamflow; base flow; runoff; groundwater recharge; and total, groundwater, and near-surface evapotranspiration.

Rapid determination of total protein and wet gluten in commercial wheat flour using siSVR-NIR.

PubMed

Chen, Jia; Zhu, Shipin; Zhao, Guohua

2017-04-15

The determination of total protein and wet gluten is of critical importance when screening commercial flour for desired processing suitability. To this end, a near-infrared spectroscopy (NIR) method with support vector regression was developed in the present study. The effects of spectral preprocessing and the synergy interval on model performance were investigated. The results showed that the models from raw spectra were not acceptable, but they were substantially improved by properly applying spectral preprocessing methods. Meanwhile, the synergy interval was validated with a good ability to improve the performance of models based on the whole spectrum. The coefficient of determination (R 2 ), the root mean square error of prediction (RMSEP) and the standard deviation ratio (SDR) of the best models for total protein (wet gluten) were 0.906 (0.850), 0.425 (1.024) and 3.065 (2.482), respectively. These two best models have similar and lower relative errors (approximately 8.8%), which indicates their feasibility. Copyright © 2016 Elsevier Ltd. All rights reserved.

Non-Gated Laser Induced Breakdown Spectroscopy Provides a Powerful Segmentation Tool on Concomitant Treatment of Characteristic and Continuum Emission

PubMed Central

Dasari, Ramachandra Rao; Barman, Ishan; Gundawar, Manoj Kumar

2014-01-01

We demonstrate the application of non-gated laser induced breakdown spectroscopy (LIBS) for characterization and classification of organic materials with similar chemical composition. While use of such a system introduces substantive continuum background in the spectral dataset, we show that appropriate treatment of the continuum and characteristic emission results in accurate discrimination of pharmaceutical formulations of similar stoichiometry. Specifically, our results suggest that near-perfect classification can be obtained by employing suitable multivariate analysis on the acquired spectra, without prior removal of the continuum background. Indeed, we conjecture that pre-processing in the form of background removal may introduce spurious features in the signal. Our findings in this report significantly advance the prior results in time-integrated LIBS application and suggest the possibility of a portable, non-gated LIBS system as a process analytical tool, given its simple instrumentation needs, real-time capability and lack of sample preparation requirements. PMID:25084522

Real-time imaging of human brain function by near-infrared spectroscopy using an adaptive general linear model

PubMed Central

Abdelnour, A. Farras; Huppert, Theodore

2009-01-01

Near-infrared spectroscopy is a non-invasive neuroimaging method which uses light to measure changes in cerebral blood oxygenation associated with brain activity. In this work, we demonstrate the ability to record and analyze images of brain activity in real-time using a 16-channel continuous wave optical NIRS system. We propose a novel real-time analysis framework using an adaptive Kalman filter and a state–space model based on a canonical general linear model of brain activity. We show that our adaptive model has the ability to estimate single-trial brain activity events as we apply this method to track and classify experimental data acquired during an alternating bilateral self-paced finger tapping task. PMID:19457389

The Influence of Preprocessing Steps on Graph Theory Measures Derived from Resting State fMRI

PubMed Central

Gargouri, Fatma; Kallel, Fathi; Delphine, Sebastien; Ben Hamida, Ahmed; Lehéricy, Stéphane; Valabregue, Romain

2018-01-01

Resting state functional MRI (rs-fMRI) is an imaging technique that allows the spontaneous activity of the brain to be measured. Measures of functional connectivity highly depend on the quality of the BOLD signal data processing. In this study, our aim was to study the influence of preprocessing steps and their order of application on small-world topology and their efficiency in resting state fMRI data analysis using graph theory. We applied the most standard preprocessing steps: slice-timing, realign, smoothing, filtering, and the tCompCor method. In particular, we were interested in how preprocessing can retain the small-world economic properties and how to maximize the local and global efficiency of a network while minimizing the cost. Tests that we conducted in 54 healthy subjects showed that the choice and ordering of preprocessing steps impacted the graph measures. We found that the csr (where we applied realignment, smoothing, and tCompCor as a final step) and the scr (where we applied realignment, tCompCor and smoothing as a final step) strategies had the highest mean values of global efficiency (eg). Furthermore, we found that the fscr strategy (where we applied realignment, tCompCor, smoothing, and filtering as a final step), had the highest mean local efficiency (el) values. These results confirm that the graph theory measures of functional connectivity depend on the ordering of the processing steps, with the best results being obtained using smoothing and tCompCor as the final steps for global efficiency with additional filtering for local efficiency. PMID:29497372

The Influence of Preprocessing Steps on Graph Theory Measures Derived from Resting State fMRI.

PubMed

Gargouri, Fatma; Kallel, Fathi; Delphine, Sebastien; Ben Hamida, Ahmed; Lehéricy, Stéphane; Valabregue, Romain

2018-01-01

Resting state functional MRI (rs-fMRI) is an imaging technique that allows the spontaneous activity of the brain to be measured. Measures of functional connectivity highly depend on the quality of the BOLD signal data processing. In this study, our aim was to study the influence of preprocessing steps and their order of application on small-world topology and their efficiency in resting state fMRI data analysis using graph theory. We applied the most standard preprocessing steps: slice-timing, realign, smoothing, filtering, and the tCompCor method. In particular, we were interested in how preprocessing can retain the small-world economic properties and how to maximize the local and global efficiency of a network while minimizing the cost. Tests that we conducted in 54 healthy subjects showed that the choice and ordering of preprocessing steps impacted the graph measures. We found that the csr (where we applied realignment, smoothing, and tCompCor as a final step) and the scr (where we applied realignment, tCompCor and smoothing as a final step) strategies had the highest mean values of global efficiency (eg) . Furthermore, we found that the fscr strategy (where we applied realignment, tCompCor, smoothing, and filtering as a final step), had the highest mean local efficiency (el) values. These results confirm that the graph theory measures of functional connectivity depend on the ordering of the processing steps, with the best results being obtained using smoothing and tCompCor as the final steps for global efficiency with additional filtering for local efficiency.

Optical Correlation of Images With Signal-Dependent Noise Using Constrained-Modulation Filter Devices

NASA Technical Reports Server (NTRS)

Downie, John D.

1995-01-01

Images with signal-dependent noise present challenges beyond those of images with additive white or colored signal-independent noise in terms of designing the optimal 4-f correlation filter that maximizes correlation-peak signal-to-noise ratio, or combinations of correlation-peak metrics. Determining the proper design becomes more difficult when the filter is to be implemented on a constrained-modulation spatial light modulator device. The design issues involved for updatable optical filters for images with signal-dependent film-grain noise and speckle noise are examined. It is shown that although design of the optimal linear filter in the Fourier domain is impossible for images with signal-dependent noise, proper nonlinear preprocessing of the images allows the application of previously developed design rules for optimal filters to be implemented on constrained-modulation devices. Thus the nonlinear preprocessing becomes necessary for correlation in optical systems with current spatial light modulator technology. These results are illustrated with computer simulations of images with signal-dependent noise correlated with binary-phase-only filters and ternary-phase-amplitude filters.

Statistical baseline assessment in cardiotocography.

PubMed

Agostinelli, Angela; Braccili, Eleonora; Marchegiani, Enrico; Rosati, Riccardo; Sbrollini, Agnese; Burattini, Luca; Morettini, Micaela; Di Nardo, Francesco; Fioretti, Sandro; Burattini, Laura

2017-07-01

Cardiotocography (CTG) is the most common non-invasive diagnostic technique to evaluate fetal well-being. It consists in the recording of fetal heart rate (FHR; bpm) and maternal uterine contractions. Among the main parameters characterizing FHR, baseline (BL) is fundamental to determine fetal hypoxia and distress. In computerized applications, BL is typically computed as mean FHR±ΔFHR, with ΔFHR=8 bpm or ΔFHR=10 bpm, both values being experimentally fixed. In this context, the present work aims: to propose a statistical procedure for ΔFHR assessment; to quantitatively determine ΔFHR value by applying such procedure to clinical data; and to compare the statistically-determined ΔFHR value against the experimentally-determined ΔFHR values. To these aims, the 552 recordings of the "CTU-UHB intrapartum CTG database" from Physionet were submitted to an automatic procedure, which consisted in a FHR preprocessing phase and a statistical BL assessment. During preprocessing, FHR time series were divided into 20-min sliding windows, in which missing data were removed by linear interpolation. Only windows with a correction rate lower than 10% were further processed for BL assessment, according to which ΔFHR was computed as FHR standard deviation. Total number of accepted windows was 1192 (38.5%) over 383 recordings (69.4%) with at least an accepted window. Statistically-determined ΔFHR value was 9.7 bpm. Such value was statistically different from 8 bpm (P<;10 -19 ) but not from 10 bpm (P=0.16). Thus, ΔFHR=10 bpm is preferable over 8 bpm because both experimentally and statistically validated.

Feature selection and recognition from nonspecific volatile profiles for discrimination of apple juices according to variety and geographical origin.

PubMed

Guo, Jing; Yue, Tianli; Yuan, Yahong

2012-10-01

Apple juice is a complex mixture of volatile and nonvolatile components. To develop discrimination models on the basis of the volatile composition for an efficient classification of apple juices according to apple variety and geographical origin, chromatography volatile profiles of 50 apple juice samples belonging to 6 varieties and from 5 counties of Shaanxi (China) were obtained by headspace solid-phase microextraction coupled with gas chromatography. The volatile profiles were processed as continuous and nonspecific signals through multivariate analysis techniques. Different preprocessing methods were applied to raw chromatographic data. The blind chemometric analysis of the preprocessed chromatographic profiles was carried out. Stepwise linear discriminant analysis (SLDA) revealed satisfactory discriminations of apple juices according to variety and geographical origin, provided respectively 100% and 89.8% success rate in terms of prediction ability. Finally, the discriminant volatile compounds selected by SLDA were identified by gas chromatography-mass spectrometry. The proposed strategy was able to verify the variety and geographical origin of apple juices involving only a reduced number of discriminate retention times selected by the stepwise procedure. This result encourages the similar procedures to be considered in quality control of apple juices. This work presented a method for an efficient discrimination of apple juices according to apple variety and geographical origin using HS-SPME-GC-MS together with chemometric tools. Discrimination models developed could help to achieve greater control over the quality of the juice and to detect possible adulteration of the product. © 2012 Institute of Food Technologists®

Near real-time estimation of ionosphere vertical total electron content from GNSS satellites using B-splines in a Kalman filter

NASA Astrophysics Data System (ADS)

Erdogan, Eren; Schmidt, Michael; Seitz, Florian; Durmaz, Murat

2017-02-01

Although the number of terrestrial global navigation satellite system (GNSS) receivers supported by the International GNSS Service (IGS) is rapidly growing, the worldwide rather inhomogeneously distributed observation sites do not allow the generation of high-resolution global ionosphere products. Conversely, with the regionally enormous increase in highly precise GNSS data, the demands on (near) real-time ionosphere products, necessary in many applications such as navigation, are growing very fast. Consequently, many analysis centers accepted the responsibility of generating such products. In this regard, the primary objective of our work is to develop a near real-time processing framework for the estimation of the vertical total electron content (VTEC) of the ionosphere using proper models that are capable of a global representation adapted to the real data distribution. The global VTEC representation developed in this work is based on a series expansion in terms of compactly supported B-spline functions, which allow for an appropriate handling of the heterogeneous data distribution, including data gaps. The corresponding series coefficients and additional parameters such as differential code biases of the GNSS satellites and receivers constitute the set of unknown parameters. The Kalman filter (KF), as a popular recursive estimator, allows processing of the data immediately after acquisition and paves the way of sequential (near) real-time estimation of the unknown parameters. To exploit the advantages of the chosen data representation and the estimation procedure, the B-spline model is incorporated into the KF under the consideration of necessary constraints. Based on a preprocessing strategy, the developed approach utilizes hourly batches of GPS and GLONASS observations provided by the IGS data centers with a latency of 1 h in its current realization. Two methods for validation of the results are performed, namely the self consistency analysis and a comparison with Jason-2 altimetry data. The highly promising validation results allow the conclusion that under the investigated conditions our derived near real-time product is of the same accuracy level as the so-called final post-processed products provided by the IGS with a latency of several days or even weeks.

Predicting soil properties for sustainable agriculture using vis-NIR spectroscopy: a case study in northern Greece

NASA Astrophysics Data System (ADS)

Tsakiridis, Nikolaos L.; Tziolas, Nikolaos; Dimitrakos, Agathoklis; Galanis, Georgios; Ntonou, Eleftheria; Tsirika, Anastasia; Terzopoulou, Evangelia; Kalopesa, Eleni; Zalidis, George C.

2017-09-01

Soil Spectral Libraries facilitate agricultural production taking into account the principles of a low-input sustainable agriculture and provide more valuable knowledge to environmental policy makers, enabling improved decision making and effective management of natural resources in the region. In this paper, a comparison in the predictive performance of two state of the art algorithms, one linear (Partial Least Squares Regression) and one non-linear (Cubist), employed in soil spectroscopy is conducted. The comparison was carried out in a regional Soil Spectral Library developed in the Eastern Macedonia and Thrace region of Northern Greece, comprised of roughly 450 Entisol soil samples from soil horizons A (0-30 cm) and B (30-60 cm). The soil spectra were acquired in the visible - Near Infrared Red region (vis- NIR, 350nm-2500nm) using a standard protocol in the laboratory. Three soil properties, which are essential for agriculture, were analyzed and taken into account for the comparison. These were the Organic Matter, the Clay content and the concentration of nitrate-N. Additionally, three different spectral pre-processing techniques were utilized, namely the continuum removal, the absorbance transformation, and the first derivative. Following the removal of outliers using the Mahalanobis distance in the first 5 principal components of the spectra (accounting for 99.8% of the variance), a five-fold cross-validation experiment was considered for all 12 datasets. Statistical comparisons were conducted on the results, which indicate that the Cubist algorithm outperforms PLSR, while the most informative transformation is the first derivative.

Development of the GEM-MACH-FireWork System: An Air Quality Model with On-line Wildfire Emissions within the Canadian Operational Air Quality Forecast System

NASA Astrophysics Data System (ADS)

Pavlovic, Radenko; Chen, Jack; Beaulieu, Paul-Andre; Anselmp, David; Gravel, Sylvie; Moran, Mike; Menard, Sylvain; Davignon, Didier

2014-05-01

A wildfire emissions processing system has been developed to incorporate near-real-time emissions from wildfires and large prescribed burns into Environment Canada's real-time GEM-MACH air quality (AQ) forecast system. Since the GEM-MACH forecast domain covers Canada and most of the U.S.A., including Alaska, fire location information is needed for both of these large countries. During AQ model runs, emissions from individual fire sources are injected into elevated model layers based on plume-rise calculations and then transport and chemistry calculations are performed. This "on the fly" approach to the insertion of the fire emissions provides flexibility and efficiency since on-line meteorology is used and computational overhead in emissions pre-processing is reduced. GEM-MACH-FireWork, an experimental wildfire version of GEM-MACH, was run in real-time mode for the summers of 2012 and 2013 in parallel with the normal operational version. 48-hour forecasts were generated every 12 hours (at 00 and 12 UTC). Noticeable improvements in the AQ forecasts for PM2.5 were seen in numerous regions where fire activity was high. Case studies evaluating model performance for specific regions and computed objective scores will be included in this presentation. Using the lessons learned from the last two summers, Environment Canada will continue to work towards the goal of incorporating near-real-time intermittent wildfire emissions into the operational air quality forecast system.

Reservoir computing with a slowly modulated mask signal for preprocessing using a mutually coupled optoelectronic system

NASA Astrophysics Data System (ADS)

Tezuka, Miwa; Kanno, Kazutaka; Bunsen, Masatoshi

2016-08-01

Reservoir computing is a machine-learning paradigm based on information processing in the human brain. We numerically demonstrate reservoir computing with a slowly modulated mask signal for preprocessing by using a mutually coupled optoelectronic system. The performance of our system is quantitatively evaluated by a chaotic time series prediction task. Our system can produce comparable performance with reservoir computing with a single feedback system and a fast modulated mask signal. We showed that it is possible to slow down the modulation speed of the mask signal by using the mutually coupled system in reservoir computing.

Multi-exponential analysis of magnitude MR images using a quantitative multispectral edge-preserving filter.

PubMed

Bonny, Jean Marie; Boespflug-Tanguly, Odile; Zanca, Michel; Renou, Jean Pierre

2003-03-01

A solution for discrete multi-exponential analysis of T(2) relaxation decay curves obtained in current multi-echo imaging protocol conditions is described. We propose a preprocessing step to improve the signal-to-noise ratio and thus lower the signal-to-noise ratio threshold from which a high percentage of true multi-exponential detection is detected. It consists of a multispectral nonlinear edge-preserving filter that takes into account the signal-dependent Rician distribution of noise affecting magnitude MR images. Discrete multi-exponential decomposition, which requires no a priori knowledge, is performed by a non-linear least-squares procedure initialized with estimates obtained from a total least-squares linear prediction algorithm. This approach was validated and optimized experimentally on simulated data sets of normal human brains.

Learning-Based Just-Noticeable-Quantization- Distortion Modeling for Perceptual Video Coding.

PubMed

Ki, Sehwan; Bae, Sung-Ho; Kim, Munchurl; Ko, Hyunsuk

2018-07-01

Conventional predictive video coding-based approaches are reaching the limit of their potential coding efficiency improvements, because of severely increasing computation complexity. As an alternative approach, perceptual video coding (PVC) has attempted to achieve high coding efficiency by eliminating perceptual redundancy, using just-noticeable-distortion (JND) directed PVC. The previous JNDs were modeled by adding white Gaussian noise or specific signal patterns into the original images, which were not appropriate in finding JND thresholds due to distortion with energy reduction. In this paper, we present a novel discrete cosine transform-based energy-reduced JND model, called ERJND, that is more suitable for JND-based PVC schemes. Then, the proposed ERJND model is extended to two learning-based just-noticeable-quantization-distortion (JNQD) models as preprocessing that can be applied for perceptual video coding. The two JNQD models can automatically adjust JND levels based on given quantization step sizes. One of the two JNQD models, called LR-JNQD, is based on linear regression and determines the model parameter for JNQD based on extracted handcraft features. The other JNQD model is based on a convolution neural network (CNN), called CNN-JNQD. To our best knowledge, our paper is the first approach to automatically adjust JND levels according to quantization step sizes for preprocessing the input to video encoders. In experiments, both the LR-JNQD and CNN-JNQD models were applied to high efficiency video coding (HEVC) and yielded maximum (average) bitrate reductions of 38.51% (10.38%) and 67.88% (24.91%), respectively, with little subjective video quality degradation, compared with the input without preprocessing applied.

Machine learning for the automatic localisation of foetal body parts in cine-MRI scans

NASA Astrophysics Data System (ADS)

Bowles, Christopher; Nowlan, Niamh C.; Hayat, Tayyib T. A.; Malamateniou, Christina; Rutherford, Mary; Hajnal, Joseph V.; Rueckert, Daniel; Kainz, Bernhard

2015-03-01

Being able to automate the location of individual foetal body parts has the potential to dramatically reduce the work required to analyse time resolved foetal Magnetic Resonance Imaging (cine-MRI) scans, for example, for use in the automatic evaluation of the foetal development. Currently, manual preprocessing of every scan is required to locate body parts before analysis can be performed, leading to a significant time overhead. With the volume of scans becoming available set to increase as cine-MRI scans become more prevalent in clinical practice, this stage of manual preprocessing is a bottleneck, limiting the data available for further analysis. Any tools which can automate this process will therefore save many hours of research time and increase the rate of new discoveries in what is a key area in understanding early human development. Here we present a series of techniques which can be applied to foetal cine-MRI scans in order to first locate and then differentiate between individual body parts. A novel approach to maternal movement suppression and segmentation using Fourier transforms is put forward as a preprocessing step, allowing for easy extraction of short movements of individual foetal body parts via the clustering of optical flow vector fields. These body part movements are compared to a labelled database and probabilistically classified before being spatially and temporally combined to give a final estimate for the location of each body part.

A hybrid algorithm for the segmentation of books in libraries

NASA Astrophysics Data System (ADS)

Hu, Zilong; Tang, Jinshan; Lei, Liang

2016-05-01

This paper proposes an algorithm for book segmentation based on bookshelves images. The algorithm can be separated into three parts. The first part is pre-processing, aiming at eliminating or decreasing the effect of image noise and illumination conditions. The second part is near-horizontal line detection based on Canny edge detector, and separating a bookshelves image into multiple sub-images so that each sub-image contains an individual shelf. The last part is book segmentation. In each shelf image, near-vertical line is detected, and obtained lines are used for book segmentation. The proposed algorithm was tested with the bookshelf images taken from OPIE library in MTU, and the experimental results demonstrate good performance.

[Application of near-infrared spectroscopy to agriculture and food analysis].

PubMed

Wang, Duo-jia; Zhou, Xiang-yang; Jin, Tong-ming; Hu, Xiang-na; Zhong, Jiao-e; Wu, Qi-tang

2004-04-01

Near-Infrared Spectroscopy (NIRS) is the most rapidly developing and the most noticeable spectrographic technique in the 90's (the last century). Its principle and characteristics were explained in this paper, and the development of NIRS instrumentation, the methodology of spectrum pre-processing, as well as the chemical metrology were also introduced. The anthors mainly summarized the applications to agriculture and food, especially in-line analysis methods, which have been used in production procedure by fiber optics. The authors analyzed the NIRS application status in China, and made the first proposal to establish information sharing mode between central database and end-user by using network technology and concentrating valuable resources.

A wavelet method for modeling and despiking motion artifacts from resting-state fMRI time series.

PubMed

Patel, Ameera X; Kundu, Prantik; Rubinov, Mikail; Jones, P Simon; Vértes, Petra E; Ersche, Karen D; Suckling, John; Bullmore, Edward T

2014-07-15

The impact of in-scanner head movement on functional magnetic resonance imaging (fMRI) signals has long been established as undesirable. These effects have been traditionally corrected by methods such as linear regression of head movement parameters. However, a number of recent independent studies have demonstrated that these techniques are insufficient to remove motion confounds, and that even small movements can spuriously bias estimates of functional connectivity. Here we propose a new data-driven, spatially-adaptive, wavelet-based method for identifying, modeling, and removing non-stationary events in fMRI time series, caused by head movement, without the need for data scrubbing. This method involves the addition of just one extra step, the Wavelet Despike, in standard pre-processing pipelines. With this method, we demonstrate robust removal of a range of different motion artifacts and motion-related biases including distance-dependent connectivity artifacts, at a group and single-subject level, using a range of previously published and new diagnostic measures. The Wavelet Despike is able to accommodate the substantial spatial and temporal heterogeneity of motion artifacts and can consequently remove a range of high and low frequency artifacts from fMRI time series, that may be linearly or non-linearly related to physical movements. Our methods are demonstrated by the analysis of three cohorts of resting-state fMRI data, including two high-motion datasets: a previously published dataset on children (N=22) and a new dataset on adults with stimulant drug dependence (N=40). We conclude that there is a real risk of motion-related bias in connectivity analysis of fMRI data, but that this risk is generally manageable, by effective time series denoising strategies designed to attenuate synchronized signal transients induced by abrupt head movements. The Wavelet Despiking software described in this article is freely available for download at www.brainwavelet.org. Copyright © 2014. Published by Elsevier Inc.

A wavelet method for modeling and despiking motion artifacts from resting-state fMRI time series

PubMed Central

Patel, Ameera X.; Kundu, Prantik; Rubinov, Mikail; Jones, P. Simon; Vértes, Petra E.; Ersche, Karen D.; Suckling, John; Bullmore, Edward T.

2014-01-01

The impact of in-scanner head movement on functional magnetic resonance imaging (fMRI) signals has long been established as undesirable. These effects have been traditionally corrected by methods such as linear regression of head movement parameters. However, a number of recent independent studies have demonstrated that these techniques are insufficient to remove motion confounds, and that even small movements can spuriously bias estimates of functional connectivity. Here we propose a new data-driven, spatially-adaptive, wavelet-based method for identifying, modeling, and removing non-stationary events in fMRI time series, caused by head movement, without the need for data scrubbing. This method involves the addition of just one extra step, the Wavelet Despike, in standard pre-processing pipelines. With this method, we demonstrate robust removal of a range of different motion artifacts and motion-related biases including distance-dependent connectivity artifacts, at a group and single-subject level, using a range of previously published and new diagnostic measures. The Wavelet Despike is able to accommodate the substantial spatial and temporal heterogeneity of motion artifacts and can consequently remove a range of high and low frequency artifacts from fMRI time series, that may be linearly or non-linearly related to physical movements. Our methods are demonstrated by the analysis of three cohorts of resting-state fMRI data, including two high-motion datasets: a previously published dataset on children (N = 22) and a new dataset on adults with stimulant drug dependence (N = 40). We conclude that there is a real risk of motion-related bias in connectivity analysis of fMRI data, but that this risk is generally manageable, by effective time series denoising strategies designed to attenuate synchronized signal transients induced by abrupt head movements. The Wavelet Despiking software described in this article is freely available for download at www.brainwavelet.org. PMID:24657353

Computer program for post-flight evaluation of the control surface response for an attitude controlled missile

NASA Technical Reports Server (NTRS)

Knauber, R. N.

1982-01-01

A FORTRAN IV coded computer program is presented for post-flight analysis of a missile's control surface response. It includes preprocessing of digitized telemetry data for time lags, biases, non-linear calibration changes and filtering. Measurements include autopilot attitude rate and displacement gyro output and four control surface deflections. Simple first order lags are assumed for the pitch, yaw and roll axes of control. Each actuator is also assumed to be represented by a first order lag. Mixing of pitch, yaw and roll commands to four control surfaces is assumed. A pseudo-inverse technique is used to obtain the pitch, yaw and roll components from the four measured deflections. This program has been used for over 10 years on the NASA/SCOUT launch vehicle for post-flight analysis and was helpful in detecting incipient actuator stall due to excessive hinge moments. The program is currently set up for a CDC CYBER 175 computer system. It requires 34K words of memory and contains 675 cards. A sample problem presented herein including the optional plotting requires eleven (11) seconds of central processor time.

Comparison of pre-processing methods for multiplex bead-based immunoassays.

PubMed

Rausch, Tanja K; Schillert, Arne; Ziegler, Andreas; Lüking, Angelika; Zucht, Hans-Dieter; Schulz-Knappe, Peter

2016-08-11

High throughput protein expression studies can be performed using bead-based protein immunoassays, such as the Luminex® xMAP® technology. Technical variability is inherent to these experiments and may lead to systematic bias and reduced power. To reduce technical variability, data pre-processing is performed. However, no recommendations exist for the pre-processing of Luminex® xMAP® data. We compared 37 different data pre-processing combinations of transformation and normalization methods in 42 samples on 384 analytes obtained from a multiplex immunoassay based on the Luminex® xMAP® technology. We evaluated the performance of each pre-processing approach with 6 different performance criteria. Three performance criteria were plots. All plots were evaluated by 15 independent and blinded readers. Four different combinations of transformation and normalization methods performed well as pre-processing procedure for this bead-based protein immunoassay. The following combinations of transformation and normalization were suitable for pre-processing Luminex® xMAP® data in this study: weighted Box-Cox followed by quantile or robust spline normalization (rsn), asinh transformation followed by loess normalization and Box-Cox followed by rsn.

Metabolomics Reveal Optimal Grain Preprocessing (Milling) toward Rice Koji Fermentation.

PubMed

Lee, Sunmin; Lee, Da Eun; Singh, Digar; Lee, Choong Hwan

2018-03-21

A time-correlated mass spectrometry (MS)-based metabolic profiling was performed for rice koji made using the substrates with varying degrees of milling (DOM). Overall, 67 primary and secondary metabolites were observed as significantly discriminant among different samples. Notably, a higher abundance of carbohydrate (sugars, sugar alcohols, organic acids, and phenolic acids) and lipid (fatty acids and lysophospholipids) derived metabolites with enhanced hydrolytic enzyme activities were observed for koji made with DOM of 5-7 substrates at 36 h. The antioxidant secondary metabolites (flavonoids and phenolic acid) were relatively higher in koji with DOM of 0 substrates, followed by DOM of 5 > DOM of 7 > DOM of 9 and 11 at 96 h. Hence, we conjecture that the rice substrate preprocessing between DOM of 5 and 7 was potentially optimal toward koji fermentation, with the end product being rich in distinctive organoleptic, nutritional, and functional metabolites. The study rationalizes the substrate preprocessing steps vital for commercial koji making.

A real time mobile-based face recognition with fisherface methods

NASA Astrophysics Data System (ADS)

Arisandi, D.; Syahputra, M. F.; Putri, I. L.; Purnamawati, S.; Rahmat, R. F.; Sari, P. P.

2018-03-01

Face Recognition is a field research in Computer Vision that study about learning face and determine the identity of the face from a picture sent to the system. By utilizing this face recognition technology, learning process about people’s identity between students in a university will become simpler. With this technology, student won’t need to browse student directory in university’s server site and look for the person with certain face trait. To obtain this goal, face recognition application use image processing methods consist of two phase, pre-processing phase and recognition phase. In pre-processing phase, system will process input image into the best image for recognition phase. Purpose of this pre-processing phase is to reduce noise and increase signal in image. Next, to recognize face phase, we use Fisherface Methods. This methods is chosen because of its advantage that would help system of its limited data. Therefore from experiment the accuracy of face recognition using fisherface is 90%.

The Effects of Pre-processing Strategies for Pediatric Cochlear Implant Recipients

PubMed Central

Rakszawski, Bernadette; Wright, Rose; Cadieux, Jamie H.; Davidson, Lisa S.; Brenner, Christine

2016-01-01

Background Cochlear implants (CIs) have been shown to improve children’s speech recognition over traditional amplification when severe to profound sensorineural hearing loss is present. Despite improvements, understanding speech at low-level intensities or in the presence of background noise remains difficult. In an effort to improve speech understanding in challenging environments, Cochlear Ltd. offers pre-processing strategies that apply various algorithms prior to mapping the signal to the internal array. Two of these strategies include Autosensitivity Control™ (ASC) and Adaptive Dynamic Range Optimization (ADRO®). Based on previous research, the manufacturer’s default pre-processing strategy for pediatrics’ everyday programs combines ASC+ADRO®. Purpose The purpose of this study is to compare pediatric speech perception performance across various pre-processing strategies while applying a specific programming protocol utilizing increased threshold (T) levels to ensure access to very low-level sounds. Research Design This was a prospective, cross-sectional, observational study. Participants completed speech perception tasks in four pre-processing conditions: no pre-processing, ADRO®, ASC, ASC+ADRO®. Study Sample Eleven pediatric Cochlear Ltd. cochlear implant users were recruited: six bilateral, one unilateral, and four bimodal. Intervention Four programs, with the participants’ everyday map, were loaded into the processor with different pre-processing strategies applied in each of the four positions: no pre-processing, ADRO®, ASC, and ASC+ADRO®. Data Collection and Analysis Participants repeated CNC words presented at 50 and 70 dB SPL in quiet and HINT sentences presented adaptively with competing R-Space noise at 60 and 70 dB SPL. Each measure was completed as participants listened with each of the four pre-processing strategies listed above. Test order and condition were randomized. A repeated-measures analysis of variance (ANOVA) was used to compare each pre-processing strategy across group data. Critical differences were utilized to determine significant score differences between each pre-processing strategy for individual participants. Results For CNC words presented at 50 dB SPL, the group data revealed significantly better scores using ASC+ADRO® compared to all other pre-processing conditions while ASC resulted in poorer scores compared to ADRO® and ASC+ADRO®. Group data for HINT sentences presented in 70 dB SPL of R-Space noise revealed significantly improved scores using ASC and ASC+ADRO® compared to no pre-processing, with ASC+ADRO® scores being better than ADRO® alone scores. Group data for CNC words presented at 70 dB SPL and adaptive HINT sentences presented in 60 dB SPL of R-Space noise showed no significant difference among conditions. Individual data showed that the pre-processing strategy yielding the best scores varied across measures and participants. Conclusions Group data reveals an advantage with ASC+ADRO® for speech perception presented at lower levels and in higher levels of background noise. Individual data revealed that the optimal pre-processing strategy varied among participants; indicating that a variety of pre-processing strategies should be explored for each CI user considering his or her performance in challenging listening environments. PMID:26905529

Color reproduction and processing algorithm based on real-time mapping for endoscopic images.

PubMed

Khan, Tareq H; Mohammed, Shahed K; Imtiaz, Mohammad S; Wahid, Khan A

2016-01-01

In this paper, we present a real-time preprocessing algorithm for image enhancement for endoscopic images. A novel dictionary based color mapping algorithm is used for reproducing the color information from a theme image. The theme image is selected from a nearby anatomical location. A database of color endoscopy image for different location is prepared for this purpose. The color map is dynamic as its contents change with the change of the theme image. This method is used on low contrast grayscale white light images and raw narrow band images to highlight the vascular and mucosa structures and to colorize the images. It can also be applied to enhance the tone of color images. The statistic visual representation and universal image quality measures show that the proposed method can highlight the mucosa structure compared to other methods. The color similarity has been verified using Delta E color difference, structure similarity index, mean structure similarity index and structure and hue similarity. The color enhancement was measured using color enhancement factor that shows considerable improvements. The proposed algorithm has low and linear time complexity, which results in higher execution speed than other related works.

TargetSearch--a Bioconductor package for the efficient preprocessing of GC-MS metabolite profiling data.

PubMed

Cuadros-Inostroza, Alvaro; Caldana, Camila; Redestig, Henning; Kusano, Miyako; Lisec, Jan; Peña-Cortés, Hugo; Willmitzer, Lothar; Hannah, Matthew A

2009-12-16

Metabolite profiling, the simultaneous quantification of multiple metabolites in an experiment, is becoming increasingly popular, particularly with the rise of systems-level biology. The workhorse in this field is gas-chromatography hyphenated with mass spectrometry (GC-MS). The high-throughput of this technology coupled with a demand for large experiments has led to data pre-processing, i.e. the quantification of metabolites across samples, becoming a major bottleneck. Existing software has several limitations, including restricted maximum sample size, systematic errors and low flexibility. However, the biggest limitation is that the resulting data usually require extensive hand-curation, which is subjective and can typically take several days to weeks. We introduce the TargetSearch package, an open source tool which is a flexible and accurate method for pre-processing even very large numbers of GC-MS samples within hours. We developed a novel strategy to iteratively correct and update retention time indices for searching and identifying metabolites. The package is written in the R programming language with computationally intensive functions written in C for speed and performance. The package includes a graphical user interface to allow easy use by those unfamiliar with R. TargetSearch allows fast and accurate data pre-processing for GC-MS experiments and overcomes the sample number limitations and manual curation requirements of existing software. We validate our method by carrying out an analysis against both a set of known chemical standard mixtures and of a biological experiment. In addition we demonstrate its capabilities and speed by comparing it with other GC-MS pre-processing tools. We believe this package will greatly ease current bottlenecks and facilitate the analysis of metabolic profiling data.

TargetSearch - a Bioconductor package for the efficient preprocessing of GC-MS metabolite profiling data

PubMed Central

2009-01-01

Background Metabolite profiling, the simultaneous quantification of multiple metabolites in an experiment, is becoming increasingly popular, particularly with the rise of systems-level biology. The workhorse in this field is gas-chromatography hyphenated with mass spectrometry (GC-MS). The high-throughput of this technology coupled with a demand for large experiments has led to data pre-processing, i.e. the quantification of metabolites across samples, becoming a major bottleneck. Existing software has several limitations, including restricted maximum sample size, systematic errors and low flexibility. However, the biggest limitation is that the resulting data usually require extensive hand-curation, which is subjective and can typically take several days to weeks. Results We introduce the TargetSearch package, an open source tool which is a flexible and accurate method for pre-processing even very large numbers of GC-MS samples within hours. We developed a novel strategy to iteratively correct and update retention time indices for searching and identifying metabolites. The package is written in the R programming language with computationally intensive functions written in C for speed and performance. The package includes a graphical user interface to allow easy use by those unfamiliar with R. Conclusions TargetSearch allows fast and accurate data pre-processing for GC-MS experiments and overcomes the sample number limitations and manual curation requirements of existing software. We validate our method by carrying out an analysis against both a set of known chemical standard mixtures and of a biological experiment. In addition we demonstrate its capabilities and speed by comparing it with other GC-MS pre-processing tools. We believe this package will greatly ease current bottlenecks and facilitate the analysis of metabolic profiling data. PMID:20015393

Low power sensor network for wireless condition monitoring

NASA Astrophysics Data System (ADS)

Richter, Ch.; Frankenstein, B.; Schubert, L.; Weihnacht, B.; Friedmann, H.; Ebert, C.

2009-03-01

For comprehensive fatigue tests and surveillance of large scale structures, a vibration monitoring system working in the Hz and sub Hz frequency range was realized and tested. The system is based on a wireless sensor network and focuses especially on the realization of a low power measurement, signal processing and communication. Regarding the development, we met the challenge of synchronizing the wireless connected sensor nodes with sufficient accuracy. The sensor nodes ware realized by compact, sensor near signal processing structures containing components for analog preprocessing of acoustic signals, their digitization, algorithms for data reduction and network communication. The core component is a digital micro controller which performs the basic algorithms necessary for the data acquisition synchronization and the filtering. As a first application, the system was installed in a rotor blade of a wind power turbine in order to monitor the Eigen modes over a longer period of time. Currently the sensor nodes are battery powered.

Development of NIRS method for quality control of drug combination artesunate–azithromycin for the treatment of severe malaria

PubMed Central

Boyer, Chantal; Gaudin, Karen; Kauss, Tina; Gaubert, Alexandra; Boudis, Abdelhakim; Verschelden, Justine; Franc, Mickaël; Roussille, Julie; Boucher, Jacques; Olliaro, Piero; White, Nicholas J.; Millet, Pascal; Dubost, Jean-Pierre

2012-01-01

Near infrared spectroscopy (NIRS) methods were developed for the determination of analytical content of an antimalarial-antibiotic (artesunate and azithromycin) co-formulation in hard gelatin capsule (HGC). The NIRS consists of pre-processing treatment of spectra (raw spectra and first-derivation of two spectral zones), a unique principal component analysis model to ensure the specificity and then two partial least-squares regression models for the determination content of each active pharmaceutical ingredient. The NIRS methods were developed and validated with no reference method, since the manufacturing process of HGC is basically mixed excipients with active pharmaceutical ingredients. The accuracy profiles showed β-expectation tolerance limits within the acceptance limits (±5%). The analytical control approach performed by reversed phase (HPLC) required two different methods involving two different preparation and chromatographic methods. NIRS offers advantages in terms of lower costs of equipment and procedures, time saving, environmentally friendly. PMID:22579599

Identification of pesticide varieties by detecting characteristics of Chlorella pyrenoidosa using Visible/Near infrared hyperspectral imaging and Raman microspectroscopy technology.

PubMed

Shao, Yongni; Li, Yuan; Jiang, Linjun; Pan, Jian; He, Yong; Dou, Xiaoming

2016-11-01

The main goal of this research is to examine the feasibility of applying Visible/Near-infrared hyperspectral imaging (Vis/NIR-HSI) and Raman microspectroscopy technology for non-destructive identification of pesticide varieties (glyphosate and butachlor). Both mentioned technologies were explored to investigate how internal elements or characteristics of Chlorella pyrenoidosa change when pesticides are applied, and in the meantime, to identify varieties of the pesticides during this procedure. Successive projections algorithm (SPA) was introduced to our study to identify seven most effective wavelengths. With those wavelengths suggested by SPA, a model of the linear discriminant analysis (LDA) was established to classify the pesticide varieties, and the correct classification rate of the SPA-LDA model reached as high as 100%. For the Raman technique, a few partial least squares discriminant analysis models were established with different preprocessing methods from which we also identified one processing approach that achieved the most optimal result. The sensitive wavelengths (SWs) which are related to algae's pigment were chosen, and a model of LDA was established with the correct identification reached a high level of 90.0%. The results showed that both Vis/NIR-HSI and Raman microspectroscopy techniques are capable to identify pesticide varieties in an indirect but effective way, and SPA is an effective wavelength extracting method. The SWs corresponding to microalgae pigments, which were influenced by pesticides, could also help to characterize different pesticide varieties and benefit the variety identification. Copyright © 2016 Elsevier Ltd. All rights reserved.

Pepper seed variety identification based on visible/near-infrared spectral technology

NASA Astrophysics Data System (ADS)

Li, Cuiling; Wang, Xiu; Meng, Zhijun; Fan, Pengfei; Cai, Jichen

2016-11-01

Pepper is a kind of important fruit vegetable, with the expansion of pepper hybrid planting area, detection of pepper seed purity is especially important. This research used visible/near infrared (VIS/NIR) spectral technology to detect the variety of single pepper seed, and chose hybrid pepper seeds "Zhuo Jiao NO.3", "Zhuo Jiao NO.4" and "Zhuo Jiao NO.5" as research sample. VIS/NIR spectral data of 80 "Zhuo Jiao NO.3", 80 "Zhuo Jiao NO.4" and 80 "Zhuo Jiao NO.5" pepper seeds were collected, and the original spectral data was pretreated with standard normal variable (SNV) transform, first derivative (FD), and Savitzky-Golay (SG) convolution smoothing methods. Principal component analysis (PCA) method was adopted to reduce the dimension of the spectral data and extract principal components, according to the distribution of the first principal component (PC1) along with the second principal component(PC2) in the twodimensional plane, similarly, the distribution of PC1 coupled with the third principal component(PC3), and the distribution of PC2 combined with PC3, distribution areas of three varieties of pepper seeds were divided in each twodimensional plane, and the discriminant accuracy of PCA was tested through observing the distribution area of samples' principal components in validation set. This study combined PCA and linear discriminant analysis (LDA) to identify single pepper seed varieties, results showed that with the FD preprocessing method, the discriminant accuracy of pepper seed varieties was 98% for validation set, it concludes that using VIS/NIR spectral technology is feasible for identification of single pepper seed varieties.

Feature extraction with deep neural networks by a generalized discriminant analysis.

PubMed

Stuhlsatz, André; Lippel, Jens; Zielke, Thomas

2012-04-01

We present an approach to feature extraction that is a generalization of the classical linear discriminant analysis (LDA) on the basis of deep neural networks (DNNs). As for LDA, discriminative features generated from independent Gaussian class conditionals are assumed. This modeling has the advantages that the intrinsic dimensionality of the feature space is bounded by the number of classes and that the optimal discriminant function is linear. Unfortunately, linear transformations are insufficient to extract optimal discriminative features from arbitrarily distributed raw measurements. The generalized discriminant analysis (GerDA) proposed in this paper uses nonlinear transformations that are learnt by DNNs in a semisupervised fashion. We show that the feature extraction based on our approach displays excellent performance on real-world recognition and detection tasks, such as handwritten digit recognition and face detection. In a series of experiments, we evaluate GerDA features with respect to dimensionality reduction, visualization, classification, and detection. Moreover, we show that GerDA DNNs can preprocess truly high-dimensional input data to low-dimensional representations that facilitate accurate predictions even if simple linear predictors or measures of similarity are used.

A synoptic study of Sudden Phase Anomalies (SPA's) effecting VLF navigation and timing

NASA Technical Reports Server (NTRS)

Swanson, E. R.; Kugel, C. P.

1973-01-01

Sudden phase anomalies (SPA's) observed on VLF recordings are related to sudden ionospheric disturbances due to solar flares. Results are presented for SPA statistics on 500 events observed in New York during the ten year period 1961 to 1970. Signals were at 10.2kHz and 13.6kHz emitted from the OMEGA transmitters in Hawaii and Trinidad. A relationship between SPA frequency and sun spot number was observed. For sun spot number near 85, about one SPA per day will be observed somewhere in the world. SPA activity nearly vanishes during periods of low sun spot number. During years of high solar activity, phase perturbations observed near noon are dominated by SPA effects beyond the 95th percentile. The SPA's can be represented by a rapid phase run-off which is approximately linear in time, peaking in about 6 minutes, and followed by a linear recovery. Typical duration is 49 minutes.

Design and implementation of a preprocessing system for a sodium lidar

NASA Technical Reports Server (NTRS)

Voelz, D. G.; Sechrist, C. F., Jr.

1983-01-01

A preprocessing system, designed and constructed for use with the University of Illinois sodium lidar system, was developed to increase the altitude resolution and range of the lidar system and also to decrease the processing burden of the main lidar computer. The preprocessing system hardware and the software required to implement the system are described. Some preliminary results of an airborne sodium lidar experiment conducted with the preprocessing system installed in the sodium lidar are presented.

A pressure-based semi-implicit space-time discontinuous Galerkin method on staggered unstructured meshes for the solution of the compressible Navier-Stokes equations at all Mach numbers

NASA Astrophysics Data System (ADS)

Tavelli, Maurizio; Dumbser, Michael

2017-07-01

We propose a new arbitrary high order accurate semi-implicit space-time discontinuous Galerkin (DG) method for the solution of the two and three dimensional compressible Euler and Navier-Stokes equations on staggered unstructured curved meshes. The method is pressure-based and semi-implicit and is able to deal with all Mach number flows. The new DG scheme extends the seminal ideas outlined in [1], where a second order semi-implicit finite volume method for the solution of the compressible Navier-Stokes equations with a general equation of state was introduced on staggered Cartesian grids. Regarding the high order extension we follow [2], where a staggered space-time DG scheme for the incompressible Navier-Stokes equations was presented. In our scheme, the discrete pressure is defined on the primal grid, while the discrete velocity field and the density are defined on a face-based staggered dual grid. Then, the mass conservation equation, as well as the nonlinear convective terms in the momentum equation and the transport of kinetic energy in the energy equation are discretized explicitly, while the pressure terms appearing in the momentum and energy equation are discretized implicitly. Formal substitution of the discrete momentum equation into the total energy conservation equation yields a linear system for only one unknown, namely the scalar pressure. Here the equation of state is assumed linear with respect to the pressure. The enthalpy and the kinetic energy are taken explicitly and are then updated using a simple Picard procedure. Thanks to the use of a staggered grid, the final pressure system is a very sparse block five-point system for three dimensional problems and it is a block four-point system in the two dimensional case. Furthermore, for high order in space and piecewise constant polynomials in time, the system is observed to be symmetric and positive definite. This allows to use fast linear solvers such as the conjugate gradient (CG) method. In addition, all the volume and surface integrals needed by the scheme depend only on the geometry and the polynomial degree of the basis and test functions and can therefore be precomputed and stored in a preprocessing stage. This leads to significant savings in terms of computational effort for the time evolution part. In this way also the extension to a fully curved isoparametric approach becomes natural and affects only the preprocessing step. The viscous terms and the heat flux are also discretized making use of the staggered grid by defining the viscous stress tensor and the heat flux vector on the dual grid, which corresponds to the use of a lifting operator, but on the dual grid. The time step of our new numerical method is limited by a CFL condition based only on the fluid velocity and not on the sound speed. This makes the method particularly interesting for low Mach number flows. Finally, a very simple combination of artificial viscosity and the a posteriori MOOD technique allows to deal with shock waves and thus permits also to simulate high Mach number flows. We show computational results for a large set of two and three-dimensional benchmark problems, including both low and high Mach number flows and using polynomial approximation degrees up to p = 4.

Demonstration of accuracy and clinical versatility of mutual information for automatic multimodality image fusion using affine and thin-plate spline warped geometric deformations.

PubMed

Meyer, C R; Boes, J L; Kim, B; Bland, P H; Zasadny, K R; Kison, P V; Koral, K; Frey, K A; Wahl, R L

1997-04-01

This paper applies and evaluates an automatic mutual information-based registration algorithm across a broad spectrum of multimodal volume data sets. The algorithm requires little or no pre-processing, minimal user input and easily implements either affine, i.e. linear or thin-plate spline (TPS) warped registrations. We have evaluated the algorithm in phantom studies as well as in selected cases where few other algorithms could perform as well, if at all, to demonstrate the value of this new method. Pairs of multimodal gray-scale volume data sets were registered by iteratively changing registration parameters to maximize mutual information. Quantitative registration errors were assessed in registrations of a thorax phantom using PET/CT and in the National Library of Medicine's Visible Male using MRI T2-/T1-weighted acquisitions. Registrations of diverse clinical data sets were demonstrated including rotate-translate mapping of PET/MRI brain scans with significant missing data, full affine mapping of thoracic PET/CT and rotate-translate mapping of abdominal SPECT/CT. A five-point thin-plate spline (TPS) warped registration of thoracic PET/CT is also demonstrated. The registration algorithm converged in times ranging between 3.5 and 31 min for affine clinical registrations and 57 min for TPS warping. Mean error vector lengths for rotate-translate registrations were measured to be subvoxel in phantoms. More importantly the rotate-translate algorithm performs well even with missing data. The demonstrated clinical fusions are qualitatively excellent at all levels. We conclude that such automatic, rapid, robust algorithms significantly increase the likelihood that multimodality registrations will be routinely used to aid clinical diagnoses and post-therapeutic assessment in the near future.

Tapping the Vast Potential of the Data Deluge in Small-scale Food-Animal Production Businesses: Challenges to Near Real-time Data Analysis and Interpretation

PubMed Central

Vial, Flavie; Tedder, Andrew

2017-01-01

Food-animal production businesses are part of a data-driven ecosystem shaped by stringent requirements for traceability along the value chain and the expanding capabilities of connected products. Within this sector, the generation of animal health intelligence, in particular, in terms of antimicrobial usage, is hindered by the lack of a centralized framework for data storage and usage. In this Perspective, we delimit the 11 processes required for evidence-based decisions and explore processes 3 (digital data acquisition) to 10 (communication to decision-makers) in more depth. We argue that small agribusinesses disproportionally face challenges related to economies of scale given the high price of equipment and services. There are two main areas of concern regarding the collection and usage of digital farm data. First, recording platforms must be developed with the needs and constraints of small businesses in mind and move away from local data storage, which hinders data accessibility and interoperability. Second, such data are unstructured and exhibit properties that can prove challenging to its near real-time preprocessing and analysis in a sector that is largely lagging behind others in terms of computing infrastructure and buying into digital technologies. To complete the digital transformation of this sector, investment in rural digital infrastructure is required alongside the development of new business models to empower small businesses to commit to near real-time data capture. This approach will deliver critical information to fill gaps in our understanding of emerging diseases and antimicrobial resistance in production animals, eventually leading to effective evidence-based policies. PMID:28932740

Navigation Operations with Prototype Components of an Automated Real-Time Spacecraft Navigation System

NASA Technical Reports Server (NTRS)

Cangahuala, L.; Drain, T. R.

1999-01-01

At present, ground navigation support for interplanetary spacecraft requires human intervention for data pre-processing, filtering, and post-processing activities; these actions must be repeated each time a new batch of data is collected by the ground data system.

Rapid non-destructive assessment of pork edible quality by using VIS/NIR spectroscopic technique

NASA Astrophysics Data System (ADS)

Zhang, Leilei; Peng, Yankun; Dhakal, Sagar; Song, Yulin; Zhao, Juan; Zhao, Songwei

2013-05-01

The objectives of this research were to develop a rapid non-destructive method to evaluate the edible quality of chilled pork. A total of 42 samples were packed in seal plastic bags and stored at 4°C for 1 to 21 days. Reflectance spectra were collected from visible/near-infrared spectroscopy system in the range of 400nm to 1100nm. Microbiological, physicochemical and organoleptic characteristics such as the total viable counts (TVC), total volatile basic-nitrogen (TVB-N), pH value and color parameters L* were determined to appraise pork edible quality. Savitzky-Golay (SG) based on five and eleven smoothing points, Multiple Scattering Correlation (MSC) and first derivative pre-processing methods were employed to eliminate the spectra noise. The support vector machines (SVM) and partial least square regression (PLSR) were applied to establish prediction models using the de-noised spectra. A linear correlation was developed between the VIS/NIR spectroscopy and parameters such as TVC, TVB-N, pH and color parameter L* indexes, which could gain prediction results with Rv of 0.931, 0.844, 0.805 and 0.852, respectively. The results demonstrated that VIS/NIR spectroscopy technique combined with SVM possesses a powerful assessment capability. It can provide a potential tool for detecting pork edible quality rapidly and non-destructively.

Reduced-Order Models Based on POD-Tpwl for Compositional Subsurface Flow Simulation

NASA Astrophysics Data System (ADS)

Durlofsky, L. J.; He, J.; Jin, L. Z.

2014-12-01

A reduced-order modeling procedure applicable for compositional subsurface flow simulation will be described and applied. The technique combines trajectory piecewise linearization (TPWL) and proper orthogonal decomposition (POD) to provide highly efficient surrogate models. The method is based on a molar formulation (which uses pressure and overall component mole fractions as the primary variables) and is applicable for two-phase, multicomponent systems. The POD-TPWL procedure expresses new solutions in terms of linearizations around solution states generated and saved during previously simulated 'training' runs. High-dimensional states are projected into a low-dimensional subspace using POD. Thus, at each time step, only a low-dimensional linear system needs to be solved. Results will be presented for heterogeneous three-dimensional simulation models involving CO2 injection. Both enhanced oil recovery and carbon storage applications (with horizontal CO2 injectors) will be considered. Reasonably close agreement between full-order reference solutions and compositional POD-TPWL simulations will be demonstrated for 'test' runs in which the well controls differ from those used for training. Construction of the POD-TPWL model requires preprocessing overhead computations equivalent to about 3-4 full-order runs. Runtime speedups using POD-TPWL are, however, very significant - typically O(100-1000). The use of POD-TPWL for well control optimization will also be illustrated. For this application, some amount of retraining during the course of the optimization is required, which leads to smaller, but still significant, speedup factors.

Noise-assisted data processing with empirical mode decomposition in biomedical signals.

PubMed

Karagiannis, Alexandros; Constantinou, Philip

2011-01-01

In this paper, a methodology is described in order to investigate the performance of empirical mode decomposition (EMD) in biomedical signals, and especially in the case of electrocardiogram (ECG). Synthetic ECG signals corrupted with white Gaussian noise are employed and time series of various lengths are processed with EMD in order to extract the intrinsic mode functions (IMFs). A statistical significance test is implemented for the identification of IMFs with high-level noise components and their exclusion from denoising procedures. Simulation campaign results reveal that a decrease of processing time is accomplished with the introduction of preprocessing stage, prior to the application of EMD in biomedical time series. Furthermore, the variation in the number of IMFs according to the type of the preprocessing stage is studied as a function of SNR and time-series length. The application of the methodology in MIT-BIH ECG records is also presented in order to verify the findings in real ECG signals.

Gender classification of running subjects using full-body kinematics

NASA Astrophysics Data System (ADS)

Williams, Christina M.; Flora, Jeffrey B.; Iftekharuddin, Khan M.

2016-05-01

This paper proposes novel automated gender classification of subjects while engaged in running activity. The machine learning techniques include preprocessing steps using principal component analysis followed by classification with linear discriminant analysis, and nonlinear support vector machines, and decision-stump with AdaBoost. The dataset consists of 49 subjects (25 males, 24 females, 2 trials each) all equipped with approximately 80 retroreflective markers. The trials are reflective of the subject's entire body moving unrestrained through a capture volume at a self-selected running speed, thus producing highly realistic data. The classification accuracy using leave-one-out cross validation for the 49 subjects is improved from 66.33% using linear discriminant analysis to 86.74% using the nonlinear support vector machine. Results are further improved to 87.76% by means of implementing a nonlinear decision stump with AdaBoost classifier. The experimental findings suggest that the linear classification approaches are inadequate in classifying gender for a large dataset with subjects running in a moderately uninhibited environment.

Saliency detection algorithm based on LSC-RC

NASA Astrophysics Data System (ADS)

Wu, Wei; Tian, Weiye; Wang, Ding; Luo, Xin; Wu, Yingfei; Zhang, Yu

2018-02-01

Image prominence is the most important region in an image, which can cause the visual attention and response of human beings. Preferentially allocating the computer resources for the image analysis and synthesis by the significant region is of great significance to improve the image area detecting. As a preprocessing of other disciplines in image processing field, the image prominence has widely applications in image retrieval and image segmentation. Among these applications, the super-pixel segmentation significance detection algorithm based on linear spectral clustering (LSC) has achieved good results. The significance detection algorithm proposed in this paper is better than the regional contrast ratio by replacing the method of regional formation in the latter with the linear spectral clustering image is super-pixel block. After combining with the latest depth learning method, the accuracy of the significant region detecting has a great promotion. At last, the superiority and feasibility of the super-pixel segmentation detection algorithm based on linear spectral clustering are proved by the comparative test.

An Automated, Adaptive Framework for Optimizing Preprocessing Pipelines in Task-Based Functional MRI

PubMed Central

Churchill, Nathan W.; Spring, Robyn; Afshin-Pour, Babak; Dong, Fan; Strother, Stephen C.

2015-01-01

BOLD fMRI is sensitive to blood-oxygenation changes correlated with brain function; however, it is limited by relatively weak signal and significant noise confounds. Many preprocessing algorithms have been developed to control noise and improve signal detection in fMRI. Although the chosen set of preprocessing and analysis steps (the “pipeline”) significantly affects signal detection, pipelines are rarely quantitatively validated in the neuroimaging literature, due to complex preprocessing interactions. This paper outlines and validates an adaptive resampling framework for evaluating and optimizing preprocessing choices by optimizing data-driven metrics of task prediction and spatial reproducibility. Compared to standard “fixed” preprocessing pipelines, this optimization approach significantly improves independent validation measures of within-subject test-retest, and between-subject activation overlap, and behavioural prediction accuracy. We demonstrate that preprocessing choices function as implicit model regularizers, and that improvements due to pipeline optimization generalize across a range of simple to complex experimental tasks and analysis models. Results are shown for brief scanning sessions (<3 minutes each), demonstrating that with pipeline optimization, it is possible to obtain reliable results and brain-behaviour correlations in relatively small datasets. PMID:26161667

DPPP: Default Pre-Processing Pipeline

NASA Astrophysics Data System (ADS)

van Diepen, Ger; Dijkema, Tammo Jan

2018-04-01

DPPP (Default Pre-Processing Pipeline, also referred to as NDPPP) reads and writes radio-interferometric data in the form of Measurement Sets, mainly those that are created by the LOFAR telescope. It goes through visibilities in time order and contains standard operations like averaging, phase-shifting and flagging bad stations. Between the steps in a pipeline, the data is not written to disk, making this tool suitable for operations where I/O dominates. More advanced procedures such as gain calibration are also included. Other computing steps can be provided by loading a shared library; currently supported external steps are the AOFlagger (ascl:1010.017) and a bridge that enables loading python steps.

Rapid Detection of Volatile Oil in Mentha haplocalyx by Near-Infrared Spectroscopy and Chemometrics.

PubMed

Yan, Hui; Guo, Cheng; Shao, Yang; Ouyang, Zhen

2017-01-01

Near-infrared spectroscopy combined with partial least squares regression (PLSR) and support vector machine (SVM) was applied for the rapid determination of chemical component of volatile oil content in Mentha haplocalyx . The effects of data pre-processing methods on the accuracy of the PLSR calibration models were investigated. The performance of the final model was evaluated according to the correlation coefficient ( R ) and root mean square error of prediction (RMSEP). For PLSR model, the best preprocessing method combination was first-order derivative, standard normal variate transformation (SNV), and mean centering, which had of 0.8805, of 0.8719, RMSEC of 0.091, and RMSEP of 0.097, respectively. The wave number variables linking to volatile oil are from 5500 to 4000 cm-1 by analyzing the loading weights and variable importance in projection (VIP) scores. For SVM model, six LVs (less than seven LVs in PLSR model) were adopted in model, and the result was better than PLSR model. The and were 0.9232 and 0.9202, respectively, with RMSEC and RMSEP of 0.084 and 0.082, respectively, which indicated that the predicted values were accurate and reliable. This work demonstrated that near infrared reflectance spectroscopy with chemometrics could be used to rapidly detect the main content volatile oil in M. haplocalyx . The quality of medicine directly links to clinical efficacy, thus, it is important to control the quality of Mentha haplocalyx . Near-infrared spectroscopy combined with partial least squares regression (PLSR) and support vector machine (SVM) was applied for the rapid determination of chemical component of volatile oil content in Mentha haplocalyx . For SVM model, 6 LVs (less than 7 LVs in PLSR model) were adopted in model, and the result was better than PLSR model. It demonstrated that near infrared reflectance spectroscopy with chemometrics could be used to rapidly detect the main content volatile oil in Mentha haplocalyx . Abbreviations used: 1 st der: First-order derivative; 2 nd der: Second-order derivative; LOO: Leave-one-out; LVs: Latent variables; MC: Mean centering, NIR: Near-infrared; NIRS: Near infrared spectroscopy; PCR: Principal component regression, PLSR: Partial least squares regression; RBF: Radial basis function; RMSEC: Root mean square error of cross validation, RMSEC: Root mean square error of calibration; RMSEP: Root mean square error of prediction; SNV: Standard normal variate transformation; SVM: Support vector machine; VIP: Variable Importance in projection.

Layered recognition networks that pre-process, classify, and describe

NASA Technical Reports Server (NTRS)

Uhr, L.

1971-01-01

A brief overview is presented of six types of pattern recognition programs that: (1) preprocess, then characterize; (2) preprocess and characterize together; (3) preprocess and characterize into a recognition cone; (4) describe as well as name; (5) compose interrelated descriptions; and (6) converse. A computer program (of types 3 through 6) is presented that transforms and characterizes the input scene through the successive layers of a recognition cone, and then engages in a stylized conversation to describe the scene.

[Application of near infrared reflectance spectroscopy to predict meat chemical compositions: a review].

PubMed

Tao, Lin-Li; Yang, Xiu-Juan; Deng, Jun-Ming; Zhang, Xi

2013-11-01

In contrast to conventional methods for the determination of meat chemical composition, near infrared reflectance spectroscopy enables rapid, simple, secure and simultaneous assessment of numerous meat properties. The present review focuses on the use of near infrared reflectance spectroscopy to predict meat chemical compositions. The potential of near infrared reflectance spectroscopy to predict crude protein, intramuscular fat, fatty acid, moisture, ash, myoglobin and collagen of beef, pork, chicken and lamb is reviewed. This paper discusses existing questions and reasons in the current research. According to the published results, although published results vary considerably, they suggest that near-infrared reflectance spectroscopy shows a great potential to replace the expensive and time-consuming chemical analysis of meat composition. In particular, under commercial conditions where simultaneous measurements of different chemical components are required, near infrared reflectance spectroscopy is expected to be the method of choice. The majority of studies selected feature-related wavelengths using principal components regression, developed the calibration model using partial least squares and modified partial least squares, and estimated the prediction accuracy by means of cross-validation using the same sample set previously used for the calibration. Meat fatty acid composition predicted by near-infrared spectroscopy and non-destructive prediction and visualization of chemical composition in meat using near-infrared hyperspectral imaging and multivariate regression are the hot studying field now. On the other hand, near infrared reflectance spectroscopy shows great difference for predicting different attributes of meat quality which are closely related to the selection of calibration sample set, preprocessing of near-infrared spectroscopy and modeling approach. Sample preparation also has an important effect on the reliability of NIR prediction; in particular, lack of homogeneity of the meat samples influenced the accuracy of estimation of chemical components. In general the predicting results of intramuscular fat, fatty acid and moisture are best, the predicting results of crude protein and myoglobin are better, while the predicting results of ash and collagen are less accurate.

LANDSAT data preprocessing

NASA Technical Reports Server (NTRS)

Austin, W. W.

1983-01-01

The effect on LANDSAT data of a Sun angle correction, an intersatellite LANDSAT-2 and LANDSAT-3 data range adjustment, and the atmospheric correction algorithm was evaluated. Fourteen 1978 crop year LACIE sites were used as the site data set. The preprocessing techniques were applied to multispectral scanner channel data and transformed data were plotted and used to analyze the effectiveness of the preprocessing techniques. Ratio transformations effectively reduce the need for preprocessing techniques to be applied directly to the data. Subtractive transformations are more sensitive to Sun angle and atmospheric corrections than ratios. Preprocessing techniques, other than those applied at the Goddard Space Flight Center, should only be applied as an option of the user. While performed on LANDSAT data the study results are also applicable to meteorological satellite data.

Bimolecular Recombination Kinetics of an Exciton-Trion Gas

DTIC Science & Technology

2015-07-01

3-D systems. Whereas a linear time-dependent system of first-order differential equations has only trivial steady- state solutions (all carrier...derivatives to zero, which reduces the system (Eq. 9) to the following set of 3 algebraic equations: ( ) ( ) ( ) ( ) 1 2 210 2 110...crossover around 20 ns. The exciton curve is nearly linear over a wide range from 10 ns to 50 ns. Fig. 2 Time dependence of carrier species for Λ = 4

Real-time loudness normalisation with combined cochlear implant and hearing aid stimulation

PubMed Central

Van Eeckhoutte, Maaike; Van Deun, Lieselot; Francart, Tom

2018-01-01

Background People who use a cochlear implant together with a contralateral hearing aid—so-called bimodal listeners—have poor localisation abilities and sounds are often not balanced in loudness across ears. In order to address the latter, a loudness balancing algorithm was created, which equalises the loudness growth functions for the two ears. The algorithm uses loudness models in order to continuously adjust the two signals to loudness targets. Previous tests demonstrated improved binaural balance, improved localisation, and better speech intelligibility in quiet for soft phonemes. In those studies, however, all stimuli were preprocessed so spontaneous head movements and individual head-related transfer functions were not taken into account. Furthermore, the hearing aid processing was linear. Study design In the present study, we simplified the acoustical loudness model and implemented the algorithm in a real-time system. We tested bimodal listeners on speech perception and on sound localisation, both in normal loudness growth configuration and in a configuration with a modified loudness growth function. We also used linear and compressive hearing aids. Results The comparison between the original acoustical loudness model and the new simplified model showed loudness differences below 3% for almost all tested speech-like stimuli and levels. We found no effect of balancing the loudness growth across ears for speech perception ability in quiet and in noise. We found some small improvements in localisation performance. Further investigation with a larger sample size is required. PMID:29617421

Efficient reconstruction method for ground layer adaptive optics with mixed natural and laser guide stars.

PubMed

Wagner, Roland; Helin, Tapio; Obereder, Andreas; Ramlau, Ronny

2016-02-20

The imaging quality of modern ground-based telescopes such as the planned European Extremely Large Telescope is affected by atmospheric turbulence. In consequence, they heavily depend on stable and high-performance adaptive optics (AO) systems. Using measurements of incoming light from guide stars, an AO system compensates for the effects of turbulence by adjusting so-called deformable mirror(s) (DMs) in real time. In this paper, we introduce a novel reconstruction method for ground layer adaptive optics. In the literature, a common approach to this problem is to use Bayesian inference in order to model the specific noise structure appearing due to spot elongation. This approach leads to large coupled systems with high computational effort. Recently, fast solvers of linear order, i.e., with computational complexity O(n), where n is the number of DM actuators, have emerged. However, the quality of such methods typically degrades in low flux conditions. Our key contribution is to achieve the high quality of the standard Bayesian approach while at the same time maintaining the linear order speed of the recent solvers. Our method is based on performing a separate preprocessing step before applying the cumulative reconstructor (CuReD). The efficiency and performance of the new reconstructor are demonstrated using the OCTOPUS, the official end-to-end simulation environment of the ESO for extremely large telescopes. For more specific simulations we also use the MOST toolbox.

Ensemble analyses improve signatures of tumour hypoxia and reveal inter-platform differences

PubMed Central

2014-01-01

Background The reproducibility of transcriptomic biomarkers across datasets remains poor, limiting clinical application. We and others have suggested that this is in-part caused by differential error-structure between datasets, and their incomplete removal by pre-processing algorithms. Methods To test this hypothesis, we systematically assessed the effects of pre-processing on biomarker classification using 24 different pre-processing methods and 15 distinct signatures of tumour hypoxia in 10 datasets (2,143 patients). Results We confirm strong pre-processing effects for all datasets and signatures, and find that these differ between microarray versions. Importantly, exploiting different pre-processing techniques in an ensemble technique improved classification for a majority of signatures. Conclusions Assessing biomarkers using an ensemble of pre-processing techniques shows clear value across multiple diseases, datasets and biomarkers. Importantly, ensemble classification improves biomarkers with initially good results but does not result in spuriously improved performance for poor biomarkers. While further research is required, this approach has the potential to become a standard for transcriptomic biomarkers. PMID:24902696

Comparative analysis of nonlinear dimensionality reduction techniques for breast MRI segmentation

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

Akhbardeh, Alireza; Jacobs, Michael A.; Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205

2012-04-15

Purpose: Visualization of anatomical structures using radiological imaging methods is an important tool in medicine to differentiate normal from pathological tissue and can generate large amounts of data for a radiologist to read. Integrating these large data sets is difficult and time-consuming. A new approach uses both supervised and unsupervised advanced machine learning techniques to visualize and segment radiological data. This study describes the application of a novel hybrid scheme, based on combining wavelet transform and nonlinear dimensionality reduction (NLDR) methods, to breast magnetic resonance imaging (MRI) data using three well-established NLDR techniques, namely, ISOMAP, local linear embedding (LLE), andmore » diffusion maps (DfM), to perform a comparative performance analysis. Methods: Twenty-five breast lesion subjects were scanned using a 3T scanner. MRI sequences used were T1-weighted, T2-weighted, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) imaging. The hybrid scheme consisted of two steps: preprocessing and postprocessing of the data. The preprocessing step was applied for B{sub 1} inhomogeneity correction, image registration, and wavelet-based image compression to match and denoise the data. In the postprocessing step, MRI parameters were considered data dimensions and the NLDR-based hybrid approach was applied to integrate the MRI parameters into a single image, termed the embedded image. This was achieved by mapping all pixel intensities from the higher dimension to a lower dimensional (embedded) space. For validation, the authors compared the hybrid NLDR with linear methods of principal component analysis (PCA) and multidimensional scaling (MDS) using synthetic data. For the clinical application, the authors used breast MRI data, comparison was performed using the postcontrast DCE MRI image and evaluating the congruence of the segmented lesions. Results: The NLDR-based hybrid approach was able to define and segment both synthetic and clinical data. In the synthetic data, the authors demonstrated the performance of the NLDR method compared with conventional linear DR methods. The NLDR approach enabled successful segmentation of the structures, whereas, in most cases, PCA and MDS failed. The NLDR approach was able to segment different breast tissue types with a high accuracy and the embedded image of the breast MRI data demonstrated fuzzy boundaries between the different types of breast tissue, i.e., fatty, glandular, and tissue with lesions (>86%). Conclusions: The proposed hybrid NLDR methods were able to segment clinical breast data with a high accuracy and construct an embedded image that visualized the contribution of different radiological parameters.« less

On estimating attenuation from the amplitude of the spectrally whitened ambient seismic field

NASA Astrophysics Data System (ADS)

Weemstra, Cornelis; Westra, Willem; Snieder, Roel; Boschi, Lapo

2014-06-01

Measuring attenuation on the basis of interferometric, receiver-receiver surface waves is a non-trivial task: the amplitude, more than the phase, of ensemble-averaged cross-correlations is strongly affected by non-uniformities in the ambient wavefield. In addition, ambient noise data are typically pre-processed in ways that affect the amplitude itself. Some authors have recently attempted to measure attenuation in receiver-receiver cross-correlations obtained after the usual pre-processing of seismic ambient-noise records, including, most notably, spectral whitening. Spectral whitening replaces the cross-spectrum with a unit amplitude spectrum. It is generally assumed that cross-terms have cancelled each other prior to spectral whitening. Cross-terms are peaks in the cross-correlation due to simultaneously acting noise sources, that is, spurious traveltime delays due to constructive interference of signal coming from different sources. Cancellation of these cross-terms is a requirement for the successful retrieval of interferometric receiver-receiver signal and results from ensemble averaging. In practice, ensemble averaging is replaced by integrating over sufficiently long time or averaging over several cross-correlation windows. Contrary to the general assumption, we show in this study that cross-terms are not required to cancel each other prior to spectral whitening, but may also cancel each other after the whitening procedure. Specifically, we derive an analytic approximation for the amplitude difference associated with the reversed order of cancellation and normalization. Our approximation shows that an amplitude decrease results from the reversed order. This decrease is predominantly non-linear at small receiver-receiver distances: at distances smaller than approximately two wavelengths, whitening prior to ensemble averaging causes a significantly stronger decay of the cross-spectrum.

Nighttime Foreground Pedestrian Detection Based on Three-Dimensional Voxel Surface Model.

PubMed

Li, Jing; Zhang, Fangbing; Wei, Lisong; Yang, Tao; Lu, Zhaoyang

2017-10-16

Pedestrian detection is among the most frequently-used preprocessing tasks in many surveillance application fields, from low-level people counting to high-level scene understanding. Even though many approaches perform well in the daytime with sufficient illumination, pedestrian detection at night is still a critical and challenging problem for video surveillance systems. To respond to this need, in this paper, we provide an affordable solution with a near-infrared stereo network camera, as well as a novel three-dimensional foreground pedestrian detection model. Specifically, instead of using an expensive thermal camera, we build a near-infrared stereo vision system with two calibrated network cameras and near-infrared lamps. The core of the system is a novel voxel surface model, which is able to estimate the dynamic changes of three-dimensional geometric information of the surveillance scene and to segment and locate foreground pedestrians in real time. A free update policy for unknown points is designed for model updating, and the extracted shadow of the pedestrian is adopted to remove foreground false alarms. To evaluate the performance of the proposed model, the system is deployed in several nighttime surveillance scenes. Experimental results demonstrate that our method is capable of nighttime pedestrian segmentation and detection in real time under heavy occlusion. In addition, the qualitative and quantitative comparison results show that our work outperforms classical background subtraction approaches and a recent RGB-D method, as well as achieving comparable performance with the state-of-the-art deep learning pedestrian detection method even with a much lower hardware cost.

Nighttime Foreground Pedestrian Detection Based on Three-Dimensional Voxel Surface Model

PubMed Central

Li, Jing; Zhang, Fangbing; Wei, Lisong; Lu, Zhaoyang

2017-01-01

Pedestrian detection is among the most frequently-used preprocessing tasks in many surveillance application fields, from low-level people counting to high-level scene understanding. Even though many approaches perform well in the daytime with sufficient illumination, pedestrian detection at night is still a critical and challenging problem for video surveillance systems. To respond to this need, in this paper, we provide an affordable solution with a near-infrared stereo network camera, as well as a novel three-dimensional foreground pedestrian detection model. Specifically, instead of using an expensive thermal camera, we build a near-infrared stereo vision system with two calibrated network cameras and near-infrared lamps. The core of the system is a novel voxel surface model, which is able to estimate the dynamic changes of three-dimensional geometric information of the surveillance scene and to segment and locate foreground pedestrians in real time. A free update policy for unknown points is designed for model updating, and the extracted shadow of the pedestrian is adopted to remove foreground false alarms. To evaluate the performance of the proposed model, the system is deployed in several nighttime surveillance scenes. Experimental results demonstrate that our method is capable of nighttime pedestrian segmentation and detection in real time under heavy occlusion. In addition, the qualitative and quantitative comparison results show that our work outperforms classical background subtraction approaches and a recent RGB-D method, as well as achieving comparable performance with the state-of-the-art deep learning pedestrian detection method even with a much lower hardware cost. PMID:29035295

A Relationship Between Visible and Near-IR Global Spectral Reflectance based on DSCOVR/EPIC

NASA Astrophysics Data System (ADS)

Wen, G.; Marshak, A.; Song, W.; Knyazikhin, Y.

2017-12-01

The launch of Deep Space Climate Observatory (DSCOVR) to the Earth's first Lagrange point (L1) allows us to see a new perspective of the Earth. The Earth Polychromatic Imaging Camera (EPIC) on the DSCOVR measures the back scattered radiation of the entire sunlit side of the Earth at 10 narrow band wavelengths ranging from ultraviolet to visible and near-infrared. We analyzed EPIC global averaged reflectance data. We found that the global averaged visible reflectance has a unique non-linear relationship with near infrared (NIR) reflectance. This non-linear relationship was not observed by any other satellite observations due to a limited spatial and temporal coverage of either low earth orbit (LEO) or geostationary satellite. The non-linear relationship is associated with the changing in the coverages of ocean, cloud, land, and vegetation as the Earth rotates. We used Terra and Aqua MODIS daily global radiance data to simulate EPIC observations. Since MODIS samples the Earth in a limited swath (2330km cross track) at a specific local time (10:30 am for Terra, 1:30 pm for Aqua) with approximately 15 orbits per day, the global average reflectance at a given time may be approximated by averaging the reflectance in the MODIS nearest-time swaths in the sunlit hemisphere. We found that MODIS simulated global visible and NIR spectral reflectance captured the major feature of the EPIC observed non-linear relationship with some errors. The difference between the two is mainly due to the sampling limitation of polar satellite. This suggests that that EPIC observations can be used to reconstruct MODIS global average reflectance time series for studying Earth system change in the past decade.

Artificial intelligence based models for stream-flow forecasting: 2000-2015

NASA Astrophysics Data System (ADS)

Yaseen, Zaher Mundher; El-shafie, Ahmed; Jaafar, Othman; Afan, Haitham Abdulmohsin; Sayl, Khamis Naba

2015-11-01

The use of Artificial Intelligence (AI) has increased since the middle of the 20th century as seen in its application in a wide range of engineering and science problems. The last two decades, for example, has seen a dramatic increase in the development and application of various types of AI approaches for stream-flow forecasting. Generally speaking, AI has exhibited significant progress in forecasting and modeling non-linear hydrological applications and in capturing the noise complexity in the dataset. This paper explores the state-of-the-art application of AI in stream-flow forecasting, focusing on defining the data-driven of AI, the advantages of complementary models, as well as the literature and their possible future application in modeling and forecasting stream-flow. The review also identifies the major challenges and opportunities for prospective research, including, a new scheme for modeling the inflow, a novel method for preprocessing time series frequency based on Fast Orthogonal Search (FOS) techniques, and Swarm Intelligence (SI) as an optimization approach.

Rapid prediction of total petroleum hydrocarbons concentration in contaminated soil using vis-NIR spectroscopy and regression techniques.

PubMed

Douglas, R K; Nawar, S; Alamar, M C; Mouazen, A M; Coulon, F

2018-03-01

Visible and near infrared spectrometry (vis-NIRS) coupled with data mining techniques can offer fast and cost-effective quantitative measurement of total petroleum hydrocarbons (TPH) in contaminated soils. Literature showed however significant differences in the performance on the vis-NIRS between linear and non-linear calibration methods. This study compared the performance of linear partial least squares regression (PLSR) with a nonlinear random forest (RF) regression for the calibration of vis-NIRS when analysing TPH in soils. 88 soil samples (3 uncontaminated and 85 contaminated) collected from three sites located in the Niger Delta were scanned using an analytical spectral device (ASD) spectrophotometer (350-2500nm) in diffuse reflectance mode. Sequential ultrasonic solvent extraction-gas chromatography (SUSE-GC) was used as reference quantification method for TPH which equal to the sum of aliphatic and aromatic fractions ranging between C 10 and C 35 . Prior to model development, spectra were subjected to pre-processing including noise cut, maximum normalization, first derivative and smoothing. Then 65 samples were selected as calibration set and the remaining 20 samples as validation set. Both vis-NIR spectrometry and gas chromatography profiles of the 85 soil samples were subjected to RF and PLSR with leave-one-out cross-validation (LOOCV) for the calibration models. Results showed that RF calibration model with a coefficient of determination (R 2 ) of 0.85, a root means square error of prediction (RMSEP) 68.43mgkg -1 , and a residual prediction deviation (RPD) of 2.61 outperformed PLSR (R 2 =0.63, RMSEP=107.54mgkg -1 and RDP=2.55) in cross-validation. These results indicate that RF modelling approach is accounting for the nonlinearity of the soil spectral responses hence, providing significantly higher prediction accuracy compared to the linear PLSR. It is recommended to adopt the vis-NIRS coupled with RF modelling approach as a portable and cost effective method for the rapid quantification of TPH in soils. Copyright © 2017 Elsevier B.V. All rights reserved.

The research of road and vehicle information extraction algorithm based on high resolution remote sensing image

NASA Astrophysics Data System (ADS)

Zhou, Tingting; Gu, Lingjia; Ren, Ruizhi; Cao, Qiong

2016-09-01

With the rapid development of remote sensing technology, the spatial resolution and temporal resolution of satellite imagery also have a huge increase. Meanwhile, High-spatial-resolution images are becoming increasingly popular for commercial applications. The remote sensing image technology has broad application prospects in intelligent traffic. Compared with traditional traffic information collection methods, vehicle information extraction using high-resolution remote sensing image has the advantages of high resolution and wide coverage. This has great guiding significance to urban planning, transportation management, travel route choice and so on. Firstly, this paper preprocessed the acquired high-resolution multi-spectral and panchromatic remote sensing images. After that, on the one hand, in order to get the optimal thresholding for image segmentation, histogram equalization and linear enhancement technologies were applied into the preprocessing results. On the other hand, considering distribution characteristics of road, the normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were used to suppress water and vegetation information of preprocessing results. Then, the above two processing result were combined. Finally, the geometric characteristics were used to completed road information extraction. The road vector extracted was used to limit the target vehicle area. Target vehicle extraction was divided into bright vehicles extraction and dark vehicles extraction. Eventually, the extraction results of the two kinds of vehicles were combined to get the final results. The experiment results demonstrated that the proposed algorithm has a high precision for the vehicle information extraction for different high resolution remote sensing images. Among these results, the average fault detection rate was about 5.36%, the average residual rate was about 13.60% and the average accuracy was approximately 91.26%.

Standardized processing of MALDI imaging raw data for enhancement of weak analyte signals in mouse models of gastric cancer and Alzheimer's disease.

PubMed

Schwartz, Matthias; Meyer, Björn; Wirnitzer, Bernhard; Hopf, Carsten

2015-03-01

Conventional mass spectrometry image preprocessing methods used for denoising, such as the Savitzky-Golay smoothing or discrete wavelet transformation, typically do not only remove noise but also weak signals. Recently, memory-efficient principal component analysis (PCA) in conjunction with random projections (RP) has been proposed for reversible compression and analysis of large mass spectrometry imaging datasets. It considers single-pixel spectra in their local context and consequently offers the prospect of using information from the spectra of adjacent pixels for denoising or signal enhancement. However, little systematic analysis of key RP-PCA parameters has been reported so far, and the utility and validity of this method for context-dependent enhancement of known medically or pharmacologically relevant weak analyte signals in linear-mode matrix-assisted laser desorption/ionization (MALDI) mass spectra has not been explored yet. Here, we investigate MALDI imaging datasets from mouse models of Alzheimer's disease and gastric cancer to systematically assess the importance of selecting the right number of random projections k and of principal components (PCs) L for reconstructing reproducibly denoised images after compression. We provide detailed quantitative data for comparison of RP-PCA-denoising with the Savitzky-Golay and wavelet-based denoising in these mouse models as a resource for the mass spectrometry imaging community. Most importantly, we demonstrate that RP-PCA preprocessing can enhance signals of low-intensity amyloid-β peptide isoforms such as Aβ1-26 even in sparsely distributed Alzheimer's β-amyloid plaques and that it enables enhanced imaging of multiply acetylated histone H4 isoforms in response to pharmacological histone deacetylase inhibition in vivo. We conclude that RP-PCA denoising may be a useful preprocessing step in biomarker discovery workflows.

Multistep-Ahead Air Passengers Traffic Prediction with Hybrid ARIMA-SVMs Models

PubMed Central

Ming, Wei; Xiong, Tao

2014-01-01

The hybrid ARIMA-SVMs prediction models have been established recently, which take advantage of the unique strength of ARIMA and SVMs models in linear and nonlinear modeling, respectively. Built upon this hybrid ARIMA-SVMs models alike, this study goes further to extend them into the case of multistep-ahead prediction for air passengers traffic with the two most commonly used multistep-ahead prediction strategies, that is, iterated strategy and direct strategy. Additionally, the effectiveness of data preprocessing approaches, such as deseasonalization and detrending, is investigated and proofed along with the two strategies. Real data sets including four selected airlines' monthly series were collected to justify the effectiveness of the proposed approach. Empirical results demonstrate that the direct strategy performs better than iterative one in long term prediction case while iterative one performs better in the case of short term prediction. Furthermore, both deseasonalization and detrending can significantly improve the prediction accuracy for both strategies, indicating the necessity of data preprocessing. As such, this study contributes as a full reference to the planners from air transportation industries on how to tackle multistep-ahead prediction tasks in the implementation of either prediction strategy. PMID:24723814

Super-Resolution for “Jilin-1” Satellite Video Imagery via a Convolutional Network

PubMed Central

Wang, Zhongyuan; Wang, Lei; Ren, Yexian

2018-01-01

Super-resolution for satellite video attaches much significance to earth observation accuracy, and the special imaging and transmission conditions on the video satellite pose great challenges to this task. The existing deep convolutional neural-network-based methods require pre-processing or post-processing to be adapted to a high-resolution size or pixel format, leading to reduced performance and extra complexity. To this end, this paper proposes a five-layer end-to-end network structure without any pre-processing and post-processing, but imposes a reshape or deconvolution layer at the end of the network to retain the distribution of ground objects within the image. Meanwhile, we formulate a joint loss function by combining the output and high-dimensional features of a non-linear mapping network to precisely learn the desirable mapping relationship between low-resolution images and their high-resolution counterparts. Also, we use satellite video data itself as a training set, which favors consistency between training and testing images and promotes the method’s practicality. Experimental results on “Jilin-1” satellite video imagery show that this method demonstrates a superior performance in terms of both visual effects and measure metrics over competing methods. PMID:29652838

Super-Resolution for "Jilin-1" Satellite Video Imagery via a Convolutional Network.

PubMed

Xiao, Aoran; Wang, Zhongyuan; Wang, Lei; Ren, Yexian

2018-04-13

Super-resolution for satellite video attaches much significance to earth observation accuracy, and the special imaging and transmission conditions on the video satellite pose great challenges to this task. The existing deep convolutional neural-network-based methods require pre-processing or post-processing to be adapted to a high-resolution size or pixel format, leading to reduced performance and extra complexity. To this end, this paper proposes a five-layer end-to-end network structure without any pre-processing and post-processing, but imposes a reshape or deconvolution layer at the end of the network to retain the distribution of ground objects within the image. Meanwhile, we formulate a joint loss function by combining the output and high-dimensional features of a non-linear mapping network to precisely learn the desirable mapping relationship between low-resolution images and their high-resolution counterparts. Also, we use satellite video data itself as a training set, which favors consistency between training and testing images and promotes the method's practicality. Experimental results on "Jilin-1" satellite video imagery show that this method demonstrates a superior performance in terms of both visual effects and measure metrics over competing methods.

Discrete Methods and their Applications

DTIC Science & Technology

1993-02-03

problem of finding all near-optimal solutions to a linear program. In paper [18], we give a brief and elementary proof of a result of Hoffman [1952) about...relies only on linear programming duality; second, we obtain geometric and algebraic representations of the bounds that are determined explicitly in...same. We have studied the problem of finding the minimum n such that a given unit interval graph is an n--graph. A linear time algorithm to compute

A General Algorithm for Reusing Krylov Subspace Information. I. Unsteady Navier-Stokes

NASA Technical Reports Server (NTRS)

Carpenter, Mark H.; Vuik, C.; Lucas, Peter; vanGijzen, Martin; Bijl, Hester

2010-01-01

A general algorithm is developed that reuses available information to accelerate the iterative convergence of linear systems with multiple right-hand sides A x = b (sup i), which are commonly encountered in steady or unsteady simulations of nonlinear equations. The algorithm is based on the classical GMRES algorithm with eigenvector enrichment but also includes a Galerkin projection preprocessing step and several novel Krylov subspace reuse strategies. The new approach is applied to a set of test problems, including an unsteady turbulent airfoil, and is shown in some cases to provide significant improvement in computational efficiency relative to baseline approaches.

Detection of Genetically Modified Sugarcane by Using Terahertz Spectroscopy and Chemometrics

NASA Astrophysics Data System (ADS)

Liu, J.; Xie, H.; Zha, B.; Ding, W.; Luo, J.; Hu, C.

2018-03-01

A methodology is proposed to identify genetically modified sugarcane from non-genetically modified sugarcane by using terahertz spectroscopy and chemometrics techniques, including linear discriminant analysis (LDA), support vector machine-discriminant analysis (SVM-DA), and partial least squares-discriminant analysis (PLS-DA). The classification rate of the above mentioned methods is compared, and different types of preprocessing are considered. According to the experimental results, the best option is PLS-DA, with an identification rate of 98%. The results indicated that THz spectroscopy and chemometrics techniques are a powerful tool to identify genetically modified and non-genetically modified sugarcane.

Pre-Processes for Urban Areas Detection in SAR Images

NASA Astrophysics Data System (ADS)

Altay Açar, S.; Bayır, Ş.

2017-11-01

In this study, pre-processes for urban areas detection in synthetic aperture radar (SAR) images are examined. These pre-processes are image smoothing, thresholding and white coloured regions determination. Image smoothing is carried out to remove noises then thresholding is applied to obtain binary image. Finally, candidate urban areas are detected by using white coloured regions determination. All pre-processes are applied by utilizing the developed software. Two different SAR images which are acquired by TerraSAR-X are used in experimental study. Obtained results are shown visually.

Differential Entropy Preserves Variational Information of Near-Infrared Spectroscopy Time Series Associated With Working Memory.

PubMed

Keshmiri, Soheil; Sumioka, Hidenubo; Yamazaki, Ryuji; Ishiguro, Hiroshi

2018-01-01

Neuroscience research shows a growing interest in the application of Near-Infrared Spectroscopy (NIRS) in analysis and decoding of the brain activity of human subjects. Given the correlation that is observed between the Blood Oxygen Dependent Level (BOLD) responses that are exhibited by the time series data of functional Magnetic Resonance Imaging (fMRI) and the hemoglobin oxy/deoxy-genation that is captured by NIRS, linear models play a central role in these applications. This, in turn, results in adaptation of the feature extraction strategies that are well-suited for discretization of data that exhibit a high degree of linearity, namely, slope and the mean as well as their combination, to summarize the informational contents of the NIRS time series. In this article, we demonstrate that these features are inefficient in capturing the variational information of NIRS data, limiting the reliability and the adequacy of the conclusion on their results. Alternatively, we propose the linear estimate of differential entropy of these time series as a natural representation of such information. We provide evidence for our claim through comparative analysis of the application of these features on NIRS data pertinent to several working memory tasks as well as naturalistic conversational stimuli.

Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R.

PubMed

McCarthy, Davis J; Campbell, Kieran R; Lun, Aaron T L; Wills, Quin F

2017-04-15

Single-cell RNA sequencing (scRNA-seq) is increasingly used to study gene expression at the level of individual cells. However, preparing raw sequence data for further analysis is not a straightforward process. Biases, artifacts and other sources of unwanted variation are present in the data, requiring substantial time and effort to be spent on pre-processing, quality control (QC) and normalization. We have developed the R/Bioconductor package scater to facilitate rigorous pre-processing, quality control, normalization and visualization of scRNA-seq data. The package provides a convenient, flexible workflow to process raw sequencing reads into a high-quality expression dataset ready for downstream analysis. scater provides a rich suite of plotting tools for single-cell data and a flexible data structure that is compatible with existing tools and can be used as infrastructure for future software development. The open-source code, along with installation instructions, vignettes and case studies, is available through Bioconductor at http://bioconductor.org/packages/scater . davis@ebi.ac.uk. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press.

A particle swarm optimized kernel-based clustering method for crop mapping from multi-temporal polarimetric L-band SAR observations

NASA Astrophysics Data System (ADS)

Tamiminia, Haifa; Homayouni, Saeid; McNairn, Heather; Safari, Abdoreza

2017-06-01

Polarimetric Synthetic Aperture Radar (PolSAR) data, thanks to their specific characteristics such as high resolution, weather and daylight independence, have become a valuable source of information for environment monitoring and management. The discrimination capability of observations acquired by these sensors can be used for land cover classification and mapping. The aim of this paper is to propose an optimized kernel-based C-means clustering algorithm for agriculture crop mapping from multi-temporal PolSAR data. Firstly, several polarimetric features are extracted from preprocessed data. These features are linear polarization intensities, and several statistical and physical based decompositions such as Cloude-Pottier, Freeman-Durden and Yamaguchi techniques. Then, the kernelized version of hard and fuzzy C-means clustering algorithms are applied to these polarimetric features in order to identify crop types. The kernel function, unlike the conventional partitioning clustering algorithms, simplifies the non-spherical and non-linearly patterns of data structure, to be clustered easily. In addition, in order to enhance the results, Particle Swarm Optimization (PSO) algorithm is used to tune the kernel parameters, cluster centers and to optimize features selection. The efficiency of this method was evaluated by using multi-temporal UAVSAR L-band images acquired over an agricultural area near Winnipeg, Manitoba, Canada, during June and July in 2012. The results demonstrate more accurate crop maps using the proposed method when compared to the classical approaches, (e.g. 12% improvement in general). In addition, when the optimization technique is used, greater improvement is observed in crop classification, e.g. 5% in overall. Furthermore, a strong relationship between Freeman-Durden volume scattering component, which is related to canopy structure, and phenological growth stages is observed.

Activity Recognition Invariant to Sensor Orientation with Wearable Motion Sensors.

PubMed

Yurtman, Aras; Barshan, Billur

2017-08-09

Most activity recognition studies that employ wearable sensors assume that the sensors are attached at pre-determined positions and orientations that do not change over time. Since this is not the case in practice, it is of interest to develop wearable systems that operate invariantly to sensor position and orientation. We focus on invariance to sensor orientation and develop two alternative transformations to remove the effect of absolute sensor orientation from the raw sensor data. We test the proposed methodology in activity recognition with four state-of-the-art classifiers using five publicly available datasets containing various types of human activities acquired by different sensor configurations. While the ordinary activity recognition system cannot handle incorrectly oriented sensors, the proposed transformations allow the sensors to be worn at any orientation at a given position on the body, and achieve nearly the same activity recognition performance as the ordinary system for which the sensor units are not rotatable. The proposed techniques can be applied to existing wearable systems without much effort, by simply transforming the time-domain sensor data at the pre-processing stage.

Altimeter waveform software design

NASA Technical Reports Server (NTRS)

Hayne, G. S.; Miller, L. S.; Brown, G. S.

1977-01-01

Techniques are described for preprocessing raw return waveform data from the GEOS-3 radar altimeter. Topics discussed include: (1) general altimeter data preprocessing to be done at the GEOS-3 Data Processing Center to correct altimeter waveform data for temperature calibrations, to convert between engineering and final data units and to convert telemetered parameter quantities to more appropriate final data distribution values: (2) time "tagging" of altimeter return waveform data quantities to compensate for various delays, misalignments and calculational intervals; (3) data processing procedures for use in estimating spacecraft attitude from altimeter waveform sampling gates; and (4) feasibility of use of a ground-based reflector or transponder to obtain in-flight calibration information on GEOS-3 altimeter performance.

Technical Manual for the Geospatial Stream Flow Model (GeoSFM)

USGS Publications Warehouse

Asante, Kwabena O.; Artan, Guleid A.; Pervez, Md Shahriar; Bandaragoda, Christina; Verdin, James P.

2008-01-01

The monitoring of wide-area hydrologic events requires the use of geospatial and time series data available in near-real time. These data sets must be manipulated into information products that speak to the location and magnitude of the event. Scientists at the U.S. Geological Survey Earth Resources Observation and Science (USGS EROS) Center have implemented a hydrologic modeling system which consists of an operational data processing system and the Geospatial Stream Flow Model (GeoSFM). The data processing system generates daily forcing evapotranspiration and precipitation data from various remotely sensed and ground-based data sources. To allow for rapid implementation in data scarce environments, widely available terrain, soil, and land cover data sets are used for model setup and initial parameter estimation. GeoSFM performs geospatial preprocessing and postprocessing tasks as well as hydrologic modeling tasks within an ArcView GIS environment. The integration of GIS routines and time series processing routines is achieved seamlessly through the use of dynamically linked libraries (DLLs) embedded within Avenue scripts. GeoSFM is run operationally to identify and map wide-area streamflow anomalies. Daily model results including daily streamflow and soil water maps are disseminated through Internet map servers, flood hazard bulletins and other media.

Doppler-based motion compensation algorithm for focusing the signature of a rotorcraft.

PubMed

Goldman, Geoffrey H

2013-02-01

A computationally efficient algorithm was developed and tested to compensate for the effects of motion on the acoustic signature of a rotorcraft. For target signatures with large spectral peaks that vary slowly in amplitude and have near constant frequency, the time-varying Doppler shift can be tracked and then removed from the data. The algorithm can be used to preprocess data for classification, tracking, and nulling algorithms. The algorithm was tested on rotorcraft data. The average instantaneous frequency of the first harmonic of a rotorcraft was tracked with a fixed-lag smoother. Then, state space estimates of the frequency were used to calculate a time warping that removed the effect of a time-varying Doppler shift from the data. The algorithm was evaluated by analyzing the increase in the amplitude of the harmonics in the spectrum of a rotorcraft. The results depended upon the frequency of the harmonics and the processing interval duration. Under good conditions, the results for the fundamental frequency of the target (~11 Hz) almost achieved an estimated upper bound. The results for higher frequency harmonics had larger increases in the amplitude of the peaks, but significantly lower than the estimated upper bounds.

Current State of the Art Historic Building Information Modelling

NASA Astrophysics Data System (ADS)

Dore, C.; Murphy, M.

2017-08-01

In an extensive review of existing literature a number of observations were made in relation to the current approaches for recording and modelling existing buildings and environments: Data collection and pre-processing techniques are becoming increasingly automated to allow for near real-time data capture and fast processing of this data for later modelling applications. Current BIM software is almost completely focused on new buildings and has very limited tools and pre-defined libraries for modelling existing and historic buildings. The development of reusable parametric library objects for existing and historic buildings supports modelling with high levels of detail while decreasing the modelling time. Mapping these parametric objects to survey data, however, is still a time-consuming task that requires further research. Promising developments have been made towards automatic object recognition and feature extraction from point clouds for as-built BIM. However, results are currently limited to simple and planar features. Further work is required for automatic accurate and reliable reconstruction of complex geometries from point cloud data. Procedural modelling can provide an automated solution for generating 3D geometries but lacks the detail and accuracy required for most as-built applications in AEC and heritage fields.

Near-monodisperse sodium polymethacrylates: characterization by linear salt gradient anion-exchange chromatography

PubMed

Freydank; Krasia; Tiddy; Patrickios

2000-05-01

A family of six near-monodisperse homopolymers of sodium methacrylate (Mn = 1100, 3200, 5500, 7200, 14100, and 21000) is characterized by linear salt gradient anion-exchange chromatography. Although the retention times depend on the initial and final salt concentrations of the gradient, they are almost independent of the molecular weight of poly(sodium methacrylate). This suggests that anion-exchange chromatography is incapable of resolving mixtures of a given polyelectrolyte to their components of various molecular weights, and it is therefore impossible to identify the polydispersity of such a sample using this method. The independence of the retention times from molecular weight is also predicted by a theory based on stoichiometric mass-action ion-exchange. Using this theory and our experimental retention times, the equilibrium anion-exchange constant and the corresponding Gibbs free energy of anion-exchange of the monomer repeat unit are calculated to be around 2.1 and -1.8 kJ/mol, respectively.

Prediction of soil organic carbon in a coal mining area by Vis-NIR spectroscopy.

PubMed

Sun, Wenjuan; Li, Xinju; Niu, Beibei

2018-01-01

Coal mining has led to increasingly serious land subsidence, and the reclamation of the subsided land has become a hot topic of concern for governments and scholars. Soil quality of reclaimed land is the key indicator to the evaluation of the reclamation effect; hence, rapid monitoring and evaluation of reclaimed land is of great significance. Visible-near infrared (Vis-NIR) spectroscopy has been shown to be a rapid, timely and efficient tool for the prediction of soil organic carbon (SOC). In this study, 104 soil samples were collected from the Baodian mining area of Shandong province. Vis-NIR reflectance spectra and soil organic carbon content were then measured under laboratory conditions. The spectral data were first denoised using the Savitzky-Golay (SG) convolution smoothing method or the multiple scattering correction (MSC) method, after which the spectral reflectance (R) was subjected to reciprocal, reciprocal logarithm and differential transformations to improve spectral sensitivity. Finally, regression models for estimating the SOC content by the spectral data were constructed using partial least squares regression (PLSR). The results showed that: (1) The SOC content in the mining area was generally low (at the below-average level) and exhibited great variability. (2) The spectral reflectance increased with the decrease of soil organic carbon content. In addition, the sensitivity of the spectrum to the change in SOC content, especially that in the near-infrared band of the original reflectance, decreased when the SOC content was low. (3) The modeling results performed best when the spectral reflectance was preprocessed by Savitzky-Golay (SG) smoothing coupled with multiple scattering correction (MSC) and first-order differential transformation (modeling R2 = 0.86, RMSE = 2.00 g/kg, verification R2 = 0.78, RMSE = 1.81 g/kg, and RPD = 2.69). In addition, the first-order differential of R combined with SG, MSC with R, SG together with MSC and R also produced better modeling results than other pretreatment combinations. Vis-NIR modeling with specific spectral preprocessing methods could predict SOC content effectively.

The ISMARA client

PubMed Central

Ioannidis, Vassilios; van Nimwegen, Erik; Stockinger, Heinz

2016-01-01

ISMARA ( ismara.unibas.ch) automatically infers the key regulators and regulatory interactions from high-throughput gene expression or chromatin state data. However, given the large sizes of current next generation sequencing (NGS) datasets, data uploading times are a major bottleneck. Additionally, for proprietary data, users may be uncomfortable with uploading entire raw datasets to an external server. Both these problems could be alleviated by providing a means by which users could pre-process their raw data locally, transferring only a small summary file to the ISMARA server. We developed a stand-alone client application that pre-processes large input files (RNA-seq or ChIP-seq data) on the user's computer for performing ISMARA analysis in a completely automated manner, including uploading of small processed summary files to the ISMARA server. This reduces file sizes by up to a factor of 1000, and upload times from many hours to mere seconds. The client application is available from ismara.unibas.ch/ISMARA/client. PMID:28232860

User data dissemination concepts for earth resources

NASA Technical Reports Server (NTRS)

Davies, R.; Scott, M.; Mitchell, C.; Torbett, A.

1976-01-01

Domestic data dissemination networks for earth-resources data in the 1985-1995 time frame were evaluated. The following topics were addressed: (1) earth-resources data sources and expected data volumes, (2) future user demand in terms of data volume and timeliness, (3) space-to-space and earth point-to-point transmission link requirements and implementation, (4) preprocessing requirements and implementation, (5) network costs, and (6) technological development to support this implementation. This study was parametric in that the data input (supply) was varied by a factor of about fifteen while the user request (demand) was varied by a factor of about nineteen. Correspondingly, the time from observation to delivery to the user was varied. This parametric evaluation was performed by a computer simulation that was based on network alternatives and resulted in preliminary transmission and preprocessing requirements. The earth-resource data sources considered were: shuttle sorties, synchronous satellites (e.g., SEOS), aircraft, and satellites in polar orbits.

Comparison of contamination of femoral heads and pre-processed bone chips during hip revision arthroplasty.

PubMed

Mathijssen, N M C; Sturm, P D; Pilot, P; Bloem, R M; Buma, P; Petit, P L; Schreurs, B W

2013-12-01

With bone impaction grafting, cancellous bone chips made from allograft femoral heads are impacted in a bone defect, which introduces an additional source of infection. The potential benefit of the use of pre-processed bone chips was investigated by comparing the bacterial contamination of bone chips prepared intraoperatively with the bacterial contamination of pre-processed bone chips at different stages in the surgical procedure. To investigate baseline contamination of the bone grafts, specimens were collected during 88 procedures before actual use or preparation of the bone chips: in 44 procedures intraoperatively prepared chips were used (Group A) and in the other 44 procedures pre-processed bone chips were used (Group B). In 64 of these procedures (32 using locally prepared bone chips and 32 using pre-processed bone chips) specimens were also collected later in the procedure to investigate contamination after use and preparation of the bone chips. In total, 8 procedures had one or more positive specimen(s) (12.5 %). Contamination rates were not significantly different between bone chips prepared at the operating theatre and pre-processed bone chips. In conclusion, there was no difference in bacterial contamination between bone chips prepared from whole femoral heads in the operating room and pre-processed bone chips, and therefore, both types of bone allografts are comparable with respect to risk of infection.

Groundwork for integration of hot water extraction as a potential pre-process in a biorefinery for downstream conversion and nano-fibrillation

NASA Astrophysics Data System (ADS)

Zhu, Rui

The economic competitiveness of biofuels production is highly dependent on feedstock cost, which constitutes 35-50 % of the total biofuels production cost. Economically viable feedstock pre-process has a significant influence on all the subsequent downstream processes in the biorefinery supply chain. In this work, hot water extraction (HWE) was exploited as a pre-process to initially fractionate cell wall structure of softwood Douglas fir, which is considerably more recalcitrant compared to hardwoods and agricultural feedstocks. A response surface model was developed and the highest hemicellulose extraction yield (HEY) was obtained when the temperature is 180 °C and the time is 79 min. HWE process partially removed hemicelluloses, reduced the moisture absorption and improved the thermal stability of wood. To investigate the effects of HWE pre-process on sulfite pretreatment to overcome recalcitrance of lignocellulose (SPORL), a series of SPORL with reduced combined severity factor (CSF) were conducted using HWE treated Douglas fir. Sugar analysis after enzymatic hydrolysis indicated that SPORL can be conducted at lower temperature (145 °C), shorter time (80 min), and lower acid volume (3 %), while still maintaining considerably high enzymatic digestibility ( 55-60%). Deriving valuable co-products would increase the overall revenue and improve the economics of the biofuels supply chain. The feasibility of extracting cellulose nanofibrils (CNFs) from HWE treated Douglas fir by ultrasonication and CNFs' reinforcing potentials in nylon 6 matrix were evaluated. Morphology analysis indicated that finer fibrils can be obtained by increasing ultrasonication time and/or amplitude. CNFs was found to have higher crystallinity and maintained the thermal stability compared to untreated fiber. A method of fabricating nylon 6/CNFs as-spun nanocomposite filaments using a combination of extrusion, compounding and capillary rheometer to minimize thermal degradation of CNFs was demonstrated. It was found that the nanocomposite filaments have slightly lower thermal stability and crystallinity compared to neat nylon 6 filaments. However, the incorporation of CNFs increased the tenacity and hydrophilicity of the nanocomposite filaments, indicating a potential for their use as precursor materials for textile yarns.

The Importance of Nonlinear Transformations Use in Medical Data Analysis.

PubMed

Shachar, Netta; Mitelpunkt, Alexis; Kozlovski, Tal; Galili, Tal; Frostig, Tzviel; Brill, Barak; Marcus-Kalish, Mira; Benjamini, Yoav

2018-05-11

The accumulation of data and its accessibility through easier-to-use platforms will allow data scientists and practitioners who are less sophisticated data analysts to get answers by using big data for many purposes in multiple ways. Data scientists working with medical data are aware of the importance of preprocessing, yet in many cases, the potential benefits of using nonlinear transformations is overlooked. Our aim is to present a semi-automated approach of symmetry-aiming transformations tailored for medical data analysis and its advantages. We describe 10 commonly encountered data types used in the medical field and the relevant transformations for each data type. Data from the Alzheimer's Disease Neuroimaging Initiative study, Parkinson's disease hospital cohort, and disease-simulating data were used to demonstrate the approach and its benefits. Symmetry-targeted monotone transformations were applied, and the advantages gained in variance, stability, linearity, and clustering are demonstrated. An open source application implementing the described methods was developed. Both linearity of relationships and increase of stability of variability improved after applying proper nonlinear transformation. Clustering simulated nonsymmetric data gave low agreement to the generating clusters (Rand value=0.681), while capturing the original structure after applying nonlinear transformation to symmetry (Rand value=0.986). This work presents the use of nonlinear transformations for medical data and the importance of their semi-automated choice. Using the described approach, the data analyst increases the ability to create simpler, more robust and translational models, thereby facilitating the interpretation and implementation of the analysis by medical practitioners. Applying nonlinear transformations as part of the preprocessing is essential to the quality and interpretability of results. ©Netta Shachar, Alexis Mitelpunkt, Tal Kozlovski, Tal Galili, Tzviel Frostig, Barak Brill, Mira Marcus-Kalish, Yoav Benjamini. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 11.05.2018.

Evaluation of a Stereo Music Preprocessing Scheme for Cochlear Implant Users.

PubMed

Buyens, Wim; van Dijk, Bas; Moonen, Marc; Wouters, Jan

2018-01-01

Although for most cochlear implant (CI) users good speech understanding is reached (at least in quiet environments), the perception and the appraisal of music are generally unsatisfactory. The improvement in music appraisal was evaluated in CI participants by using a stereo music preprocessing scheme implemented on a take-home device, in a comfortable listening environment. The preprocessing allowed adjusting the balance among vocals/bass/drums and other instruments, and was evaluated for different genres of music. The correlation between the preferred settings and the participants' speech and pitch detection performance was investigated. During the initial visit preceding the take-home test, the participants' speech-in-noise perception and pitch detection performance were measured, and a questionnaire about their music involvement was completed. The take-home device was provided, including the stereo music preprocessing scheme and seven playlists with six songs each. The participants were asked to adjust the balance by means of a turning wheel to make the music sound most enjoyable, and to repeat this three times for all songs. Twelve postlingually deafened CI users participated in the study. The data were collected by means of a take-home device, which preserved all the preferred settings for the different songs. Statistical analysis was done with a Friedman test (with post hoc Wilcoxon signed-rank test) to check the effect of "Genre." The correlations were investigated with Pearson's and Spearman's correlation coefficients. All participants preferred a balance significantly different from the original balance. Differences across participants were observed which could not be explained by perceptual abilities. An effect of "Genre" was found, showing significantly smaller preferred deviation from the original balance for Golden Oldies compared to the other genres. The stereo music preprocessing scheme showed an improvement in music appraisal with complex music and hence might be a good tool for music listening, training, or rehabilitation for CI users. American Academy of Audiology

Advanced linear and nonlinear compensations for 16QAM SC-400G unrepeatered transmission system

NASA Astrophysics Data System (ADS)

Zhang, Junwen; Yu, Jianjun; Chien, Hung-Chang

2018-02-01

Digital signal processing (DSP) with both linear equalization and nonlinear compensations are studied in this paper for the single-carrier 400G system based on 65-GBaud 16-quadrature amplitude modulation (QAM) signals. The 16-QAM signals are generated and pre-processed with pre-equalization (Pre-EQ) and Look-up-Table (LUT) based pre-distortion (Pre-DT) at the transmitter (Tx)-side. The implementation principle of training-based equalization and pre-distortion are presented here in this paper with experimental studies. At the receiver (Rx)-side, fiber-nonlinearity compensation based on digital backward propagation (DBP) are also utilized to further improve the transmission performances. With joint LUT-based Pre-DT and DBP-based post-compensation to mitigate the opto-electronic components and fiber nonlinearity impairments, we demonstrate the unrepeatered transmission of 1.6Tb/s based on 4-lane 400G single-carrier PDM-16QAM over 205-km SSMF without distributed amplifier.

Railway crossing risk area detection using linear regression and terrain drop compensation techniques.

PubMed

Chen, Wen-Yuan; Wang, Mei; Fu, Zhou-Xing

2014-06-16

Most railway accidents happen at railway crossings. Therefore, how to detect humans or objects present in the risk area of a railway crossing and thus prevent accidents are important tasks. In this paper, three strategies are used to detect the risk area of a railway crossing: (1) we use a terrain drop compensation (TDC) technique to solve the problem of the concavity of railway crossings; (2) we use a linear regression technique to predict the position and length of an object from image processing; (3) we have developed a novel strategy called calculating local maximum Y-coordinate object points (CLMYOP) to obtain the ground points of the object. In addition, image preprocessing is also applied to filter out the noise and successfully improve the object detection. From the experimental results, it is demonstrated that our scheme is an effective and corrective method for the detection of railway crossing risk areas.

Railway Crossing Risk Area Detection Using Linear Regression and Terrain Drop Compensation Techniques

PubMed Central

Chen, Wen-Yuan; Wang, Mei; Fu, Zhou-Xing

2014-01-01

Most railway accidents happen at railway crossings. Therefore, how to detect humans or objects present in the risk area of a railway crossing and thus prevent accidents are important tasks. In this paper, three strategies are used to detect the risk area of a railway crossing: (1) we use a terrain drop compensation (TDC) technique to solve the problem of the concavity of railway crossings; (2) we use a linear regression technique to predict the position and length of an object from image processing; (3) we have developed a novel strategy called calculating local maximum Y-coordinate object points (CLMYOP) to obtain the ground points of the object. In addition, image preprocessing is also applied to filter out the noise and successfully improve the object detection. From the experimental results, it is demonstrated that our scheme is an effective and corrective method for the detection of railway crossing risk areas. PMID:24936948

Warped linear mixed models for the genetic analysis of transformed phenotypes

PubMed Central

Fusi, Nicolo; Lippert, Christoph; Lawrence, Neil D.; Stegle, Oliver

2014-01-01

Linear mixed models (LMMs) are a powerful and established tool for studying genotype–phenotype relationships. A limitation of the LMM is that the model assumes Gaussian distributed residuals, a requirement that rarely holds in practice. Violations of this assumption can lead to false conclusions and loss in power. To mitigate this problem, it is common practice to pre-process the phenotypic values to make them as Gaussian as possible, for instance by applying logarithmic or other nonlinear transformations. Unfortunately, different phenotypes require different transformations, and choosing an appropriate transformation is challenging and subjective. Here we present an extension of the LMM that estimates an optimal transformation from the observed data. In simulations and applications to real data from human, mouse and yeast, we show that using transformations inferred by our model increases power in genome-wide association studies and increases the accuracy of heritability estimation and phenotype prediction. PMID:25234577

Warped linear mixed models for the genetic analysis of transformed phenotypes.

PubMed

Fusi, Nicolo; Lippert, Christoph; Lawrence, Neil D; Stegle, Oliver

2014-09-19

Linear mixed models (LMMs) are a powerful and established tool for studying genotype-phenotype relationships. A limitation of the LMM is that the model assumes Gaussian distributed residuals, a requirement that rarely holds in practice. Violations of this assumption can lead to false conclusions and loss in power. To mitigate this problem, it is common practice to pre-process the phenotypic values to make them as Gaussian as possible, for instance by applying logarithmic or other nonlinear transformations. Unfortunately, different phenotypes require different transformations, and choosing an appropriate transformation is challenging and subjective. Here we present an extension of the LMM that estimates an optimal transformation from the observed data. In simulations and applications to real data from human, mouse and yeast, we show that using transformations inferred by our model increases power in genome-wide association studies and increases the accuracy of heritability estimation and phenotype prediction.

A Data Analytical Framework for Improving Real-Time, Decision Support Systems in Healthcare

ERIC Educational Resources Information Center

Yahav, Inbal

2010-01-01

In this dissertation we develop a framework that combines data mining, statistics and operations research methods for improving real-time decision support systems in healthcare. Our approach consists of three main concepts: data gathering and preprocessing, modeling, and deployment. We introduce the notion of offline and semi-offline modeling to…

Impact of Autocorrelation on Functional Connectivity

PubMed Central

Arbabshirani, Mohammad R.; Damaraju, Eswar; Phlypo, Ronald; Plis, Sergey; Allen, Elena; Ma, Sai; Mathalon, Daniel; Preda, Adrian; Vaidya, Jatin G.; Adali, Tülay; Calhoun, Vince D.

2014-01-01

Although the impact of serial correlation (autocorrelation) in residuals of general linear models for fMRI time-series has been studied extensively, the effect of autocorrelation on functional connectivity studies has been largely neglected until recently. Some recent studies based on results from economics have questioned the conventional estimation of functional connectivity and argue that not correcting for autocorrelation in fMRI time-series results in “spurious” correlation coefficients. In this paper, first we assess the effect of autocorrelation on Pearson correlation coefficient through theoretical approximation and simulation. Then we present this effect on real fMRI data. To our knowledge this is the first work comprehensively investigating the effect of autocorrelation on functional connectivity estimates. Our results show that although FC values are altered, even following correction for autocorrelation, results of hypothesis testing on FC values remain very similar to those before correction. In real data we show this is true for main effects and also for group difference testing between healthy controls and schizophrenia patients. We further discuss model order selection in the context of autoregressive processes, effects of frequency filtering and propose a preprocessing pipeline for connectivity studies. PMID:25072392

Large-scale detection of repetitions

PubMed Central

Smyth, W. F.

2014-01-01

Combinatorics on words began more than a century ago with a demonstration that an infinitely long string with no repetitions could be constructed on an alphabet of only three letters. Computing all the repetitions (such as ⋯TTT⋯ or ⋯CGACGA⋯ ) in a given string x of length n is one of the oldest and most important problems of computational stringology, requiring time in the worst case. About a dozen years ago, it was discovered that repetitions can be computed as a by-product of the Θ(n)-time computation of all the maximal periodicities or runs in x. However, even though the computation is linear, it is also brute force: global data structures, such as the suffix array, the longest common prefix array and the Lempel–Ziv factorization, need to be computed in a preprocessing phase. Furthermore, all of this effort is required despite the fact that the expected number of runs in a string is generally a small fraction of the string length. In this paper, I explore the possibility that repetitions (perhaps also other regularities in strings) can be computed in a manner commensurate with the size of the output. PMID:24751872

An energy ratio feature extraction method for optical fiber vibration signal

NASA Astrophysics Data System (ADS)

Sheng, Zhiyong; Zhang, Xinyan; Wang, Yanping; Hou, Weiming; Yang, Dan

2018-03-01

The intrusion events in the optical fiber pre-warning system (OFPS) are divided into two types which are harmful intrusion event and harmless interference event. At present, the signal feature extraction methods of these two types of events are usually designed from the view of the time domain. However, the differences of time-domain characteristics for different harmful intrusion events are not obvious, which cannot reflect the diversity of them in detail. We find that the spectrum distribution of different intrusion signals has obvious differences. For this reason, the intrusion signal is transformed into the frequency domain. In this paper, an energy ratio feature extraction method of harmful intrusion event is drawn on. Firstly, the intrusion signals are pre-processed and the power spectral density (PSD) is calculated. Then, the energy ratio of different frequency bands is calculated, and the corresponding feature vector of each type of intrusion event is further formed. The linear discriminant analysis (LDA) classifier is used to identify the harmful intrusion events in the paper. Experimental results show that the algorithm improves the recognition rate of the intrusion signal, and further verifies the feasibility and validity of the algorithm.

Landsat Thematic Mapper Image Mosaic of Colorado

USGS Publications Warehouse

Cole, Christopher J.; Noble, Suzanne M.; Blauer, Steven L.; Friesen, Beverly A.; Bauer, Mark A.

2010-01-01

The U.S. Geological Survey (USGS) Rocky Mountain Geographic Science Center (RMGSC) produced a seamless, cloud-minimized remotely-sensed image spanning the State of Colorado. Multiple orthorectified Landsat 5 Thematic Mapper (TM) scenes collected during 2006-2008 were spectrally normalized via reflectance transformation and linear regression based upon pseudo-invariant features (PIFS) following the removal of clouds. Individual Landsat scenes were then mosaicked to form a six-band image composite spanning the visible to shortwave infrared spectrum. This image mosaic, presented here, will also be used to create a conifer health classification for Colorado in Scientific Investigations Map 3103. An archive of past and current Landsat imagery exists and is available to the scientific community (http://glovis.usgs.gov/), but significant pre-processing was required to produce a statewide mosaic from this information. Much of the data contained perennial cloud cover that complicated analysis and classification efforts. Existing Landsat mosaic products, typically three band image composites, did not include the full suite of multispectral information necessary to produce this assessment, and were derived using data collected in 2001 or earlier. A six-band image mosaic covering Colorado was produced. This mosaic includes blue (band 1), green (band 2), red (band 3), near infrared (band 4), and shortwave infrared information (bands 5 and 7). The image composite shown here displays three of the Landsat bands (7, 4, and 2), which are sensitive to the shortwave infrared, near infrared, and green ranges of the electromagnetic spectrum. Vegetation appears green in this image, while water looks black, and unforested areas appear pink. The lines that may be visible in the on-screen version of the PDF are an artifact of the export methods used to create this file. The file should be viewed at 150 percent zoom or greater for optimum viewing.

Artificial Neural Network Based Fault Diagnostics of Rolling Element Bearings Using Time-Domain Features

NASA Astrophysics Data System (ADS)

Samanta, B.; Al-Balushi, K. R.

2003-03-01

A procedure is presented for fault diagnosis of rolling element bearings through artificial neural network (ANN). The characteristic features of time-domain vibration signals of the rotating machinery with normal and defective bearings have been used as inputs to the ANN consisting of input, hidden and output layers. The features are obtained from direct processing of the signal segments using very simple preprocessing. The input layer consists of five nodes, one each for root mean square, variance, skewness, kurtosis and normalised sixth central moment of the time-domain vibration signals. The inputs are normalised in the range of 0.0 and 1.0 except for the skewness which is normalised between -1.0 and 1.0. The output layer consists of two binary nodes indicating the status of the machine—normal or defective bearings. Two hidden layers with different number of neurons have been used. The ANN is trained using backpropagation algorithm with a subset of the experimental data for known machine conditions. The ANN is tested using the remaining set of data. The effects of some preprocessing techniques like high-pass, band-pass filtration, envelope detection (demodulation) and wavelet transform of the vibration signals, prior to feature extraction, are also studied. The results show the effectiveness of the ANN in diagnosis of the machine condition. The proposed procedure requires only a few features extracted from the measured vibration data either directly or with simple preprocessing. The reduced number of inputs leads to faster training requiring far less iterations making the procedure suitable for on-line condition monitoring and diagnostics of machines.

Facilitating access to pre-processed research evidence in public health

PubMed Central

2010-01-01

Background Evidence-informed decision making is accepted in Canada and worldwide as necessary for the provision of effective health services. This process involves: 1) clearly articulating a practice-based issue; 2) searching for and accessing relevant evidence; 3) appraising methodological rigor and choosing the most synthesized evidence of the highest quality and relevance to the practice issue and setting that is available; and 4) extracting, interpreting, and translating knowledge, in light of the local context and resources, into practice, program and policy decisions. While the public health sector in Canada is working toward evidence-informed decision making, considerable barriers, including efficient access to synthesized resources, exist. Methods In this paper we map to a previously developed 6 level pyramid of pre-processed research evidence, relevant resources that include public health-related effectiveness evidence. The resources were identified through extensive searches of both the published and unpublished domains. Results Many resources with public health-related evidence were identified. While there were very few resources dedicated solely to public health evidence, many clinically focused resources include public health-related evidence, making tools such as the pyramid, that identify these resources, particularly helpful for public health decisions makers. A practical example illustrates the application of this model and highlights its potential to reduce the time and effort that would be required by public health decision makers to address their practice-based issues. Conclusions This paper describes an existing hierarchy of pre-processed evidence and its adaptation to the public health setting. A number of resources with public health-relevant content that are either freely accessible or requiring a subscription are identified. This will facilitate easier and faster access to pre-processed, public health-relevant evidence, with the intent of promoting evidence-informed decision making. Access to such resources addresses several barriers identified by public health decision makers to evidence-informed decision making, most importantly time, as well as lack of knowledge of resources that house public health-relevant evidence. PMID:20181270

Physiological self-regulation of regional brain activity using real-time functional magnetic resonance imaging (fMRI): methodology and exemplary data.

PubMed

Weiskopf, Nikolaus; Veit, Ralf; Erb, Michael; Mathiak, Klaus; Grodd, Wolfgang; Goebel, Rainer; Birbaumer, Niels

2003-07-01

A brain-computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI) is presented which allows human subjects to observe and control changes of their own blood oxygen level-dependent (BOLD) response. This BCI performs data preprocessing (including linear trend removal, 3D motion correction) and statistical analysis on-line. Local BOLD signals are continuously fed back to the subject in the magnetic resonance scanner with a delay of less than 2 s from image acquisition. The mean signal of a region of interest is plotted as a time-series superimposed on color-coded stripes which indicate the task, i.e., to increase or decrease the BOLD signal. We exemplify the presented BCI with one volunteer intending to control the signal of the rostral-ventral and dorsal part of the anterior cingulate cortex (ACC). The subject achieved significant changes of local BOLD responses as revealed by region of interest analysis and statistical parametric maps. The percent signal change increased across fMRI-feedback sessions suggesting a learning effect with training. This methodology of fMRI-feedback can assess voluntary control of circumscribed brain areas. As a further extension, behavioral effects of local self-regulation become accessible as a new field of research.

An Interactive Tool For Semi-automated Statistical Prediction Using Earth Observations and Models

NASA Astrophysics Data System (ADS)

Zaitchik, B. F.; Berhane, F.; Tadesse, T.

2015-12-01

We developed a semi-automated statistical prediction tool applicable to concurrent analysis or seasonal prediction of any time series variable in any geographic location. The tool was developed using Shiny, JavaScript, HTML and CSS. A user can extract a predictand by drawing a polygon over a region of interest on the provided user interface (global map). The user can select the Climatic Research Unit (CRU) precipitation or Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) as predictand. They can also upload their own predictand time series. Predictors can be extracted from sea surface temperature, sea level pressure, winds at different pressure levels, air temperature at various pressure levels, and geopotential height at different pressure levels. By default, reanalysis fields are applied as predictors, but the user can also upload their own predictors, including a wide range of compatible satellite-derived datasets. The package generates correlations of the variables selected with the predictand. The user also has the option to generate composites of the variables based on the predictand. Next, the user can extract predictors by drawing polygons over the regions that show strong correlations (composites). Then, the user can select some or all of the statistical prediction models provided. Provided models include Linear Regression models (GLM, SGLM), Tree-based models (bagging, random forest, boosting), Artificial Neural Network, and other non-linear models such as Generalized Additive Model (GAM) and Multivariate Adaptive Regression Splines (MARS). Finally, the user can download the analysis steps they used, such as the region they selected, the time period they specified, the predictand and predictors they chose and preprocessing options they used, and the model results in PDF or HTML format. Key words: Semi-automated prediction, Shiny, R, GLM, ANN, RF, GAM, MARS

A developed nearly analytic discrete method for forward modeling in the frequency domain

NASA Astrophysics Data System (ADS)

Liu, Shaolin; Lang, Chao; Yang, Hui; Wang, Wenshuai

2018-02-01

High-efficiency forward modeling methods play a fundamental role in full waveform inversion (FWI). In this paper, the developed nearly analytic discrete (DNAD) method is proposed to accelerate frequency-domain forward modeling processes. We first derive the discretization of frequency-domain wave equations via numerical schemes based on the nearly analytic discrete (NAD) method to obtain a linear system. The coefficients of numerical stencils are optimized to make the linear system easier to solve and to minimize computing time. Wavefield simulation and numerical dispersion analysis are performed to compare the numerical behavior of DNAD method with that of the conventional NAD method. The results demonstrate the superiority of our proposed method. Finally, the DNAD method is implemented in frequency-domain FWI, and high-resolution inverse results are obtained.

Speech perception for adult cochlear implant recipients in a realistic background noise: effectiveness of preprocessing strategies and external options for improving speech recognition in noise.

PubMed

Gifford, René H; Revit, Lawrence J

2010-01-01

Although cochlear implant patients are achieving increasingly higher levels of performance, speech perception in noise continues to be problematic. The newest generations of implant speech processors are equipped with preprocessing and/or external accessories that are purported to improve listening in noise. Most speech perception measures in the clinical setting, however, do not provide a close approximation to real-world listening environments. To assess speech perception for adult cochlear implant recipients in the presence of a realistic restaurant simulation generated by an eight-loudspeaker (R-SPACE) array in order to determine whether commercially available preprocessing strategies and/or external accessories yield improved sentence recognition in noise. Single-subject, repeated-measures design with two groups of participants: Advanced Bionics and Cochlear Corporation recipients. Thirty-four subjects, ranging in age from 18 to 90 yr (mean 54.5 yr), participated in this prospective study. Fourteen subjects were Advanced Bionics recipients, and 20 subjects were Cochlear Corporation recipients. Speech reception thresholds (SRTs) in semidiffuse restaurant noise originating from an eight-loudspeaker array were assessed with the subjects' preferred listening programs as well as with the addition of either Beam preprocessing (Cochlear Corporation) or the T-Mic accessory option (Advanced Bionics). In Experiment 1, adaptive SRTs with the Hearing in Noise Test sentences were obtained for all 34 subjects. For Cochlear Corporation recipients, SRTs were obtained with their preferred everyday listening program as well as with the addition of Focus preprocessing. For Advanced Bionics recipients, SRTs were obtained with the integrated behind-the-ear (BTE) mic as well as with the T-Mic. Statistical analysis using a repeated-measures analysis of variance (ANOVA) evaluated the effects of the preprocessing strategy or external accessory in reducing the SRT in noise. In addition, a standard t-test was run to evaluate effectiveness across manufacturer for improving the SRT in noise. In Experiment 2, 16 of the 20 Cochlear Corporation subjects were reassessed obtaining an SRT in noise using the manufacturer-suggested "Everyday," "Noise," and "Focus" preprocessing strategies. A repeated-measures ANOVA was employed to assess the effects of preprocessing. The primary findings were (i) both Noise and Focus preprocessing strategies (Cochlear Corporation) significantly improved the SRT in noise as compared to Everyday preprocessing, (ii) the T-Mic accessory option (Advanced Bionics) significantly improved the SRT as compared to the BTE mic, and (iii) Focus preprocessing and the T-Mic resulted in similar degrees of improvement that were not found to be significantly different from one another. Options available in current cochlear implant sound processors are able to significantly improve speech understanding in a realistic, semidiffuse noise with both Cochlear Corporation and Advanced Bionics systems. For Cochlear Corporation recipients, Focus preprocessing yields the best speech-recognition performance in a complex listening environment; however, it is recommended that Noise preprocessing be used as the new default for everyday listening environments to avoid the need for switching programs throughout the day. For Advanced Bionics recipients, the T-Mic offers significantly improved performance in noise and is recommended for everyday use in all listening environments. American Academy of Audiology.

VSP Monitoring of CO2 Injection at the Aneth Oil Field in Utah

NASA Astrophysics Data System (ADS)

Huang, L.; Rutledge, J.; Zhou, R.; Denli, H.; Cheng, A.; Zhao, M.; Peron, J.

2008-12-01

Remotely tracking the movement of injected CO2 within a geological formation is critically important for ensuring safe and long-term geologic carbon sequestration. To study the capability of vertical seismic profiling (VSP) for remote monitoring of CO2 injection, a geophone string with 60 levels and 96 channels was cemented into a monitoring well at the Aneth oil field in Utah operated by Resolute Natural Resources and Navajo National Oil and Gas Company. The oil field is located in the Paradox Basin of southeastern Utah, and was selected by the Southwest Regional Partnership on Carbon Sequestration, supported by the U.S. Department of Energy, to demonstrate combined enhanced oil recovery (EOR) and CO2 sequestration. The geophones are placed at depths from 805 m to 1704 m, and the oil reservoir is located approximately from 1731 m to 1786 m in depth. A baseline VSP dataset with one zero-offset and seven offset source locations was acquired in October, 2007 before CO2 injection. The offsets/source locations are approximately 1 km away from the monitoring well with buried geophone string. A time-lapse VSP dataset with the same source locations was collected in July, 2008 after five months of CO2/water injection into a horizontal well adjacent to the monitoring well. The total amount of CO2 injected during the time interval between the two VSP surveys was 181,000 MCF (million cubic feet), or 10,500 tons. The time-lapse VSP data are pre-processed to balance the phase and amplitude of seismic events above the oil reservoir. We conduct wave-equation migration imaging and interferometry analysis using the pre-processed time-lapse VSP data. The results demonstrate that time-lapse VSP surveys with high-resolution migration imaging and scattering analysis can provide reliable information about CO2 migration. Both the repeatability of VSP surveys and sophisticated time-lapse data pre-processing are essential to make VSP as an effective tool for monitoring CO2 injection.

A survey of visual preprocessing and shape representation techniques

NASA Technical Reports Server (NTRS)

Olshausen, Bruno A.

1988-01-01

Many recent theories and methods proposed for visual preprocessing and shape representation are summarized. The survey brings together research from the fields of biology, psychology, computer science, electrical engineering, and most recently, neural networks. It was motivated by the need to preprocess images for a sparse distributed memory (SDM), but the techniques presented may also prove useful for applying other associative memories to visual pattern recognition. The material of this survey is divided into three sections: an overview of biological visual processing; methods of preprocessing (extracting parts of shape, texture, motion, and depth); and shape representation and recognition (form invariance, primitives and structural descriptions, and theories of attention).

Influence of spatial and temporal scales in identifying temperature extremes

NASA Astrophysics Data System (ADS)

van Eck, Christel M.; Friedlingstein, Pierre; Mulder, Vera L.; Regnier, Pierre A. G.

2016-04-01

Extreme heat events are becoming more frequent. Notable are severe heatwaves such as the European heatwave of 2003, the Russian heat wave of 2010 and the Australian heatwave of 2013. Surface temperature is attaining new maxima not only during the summer but also during the winter. The year of 2015 is reported to be a temperature record breaking year for both summer and winter. These extreme temperatures are taking their human and environmental toll, emphasizing the need for an accurate method to define a heat extreme in order to fully understand the spatial and temporal spread of an extreme and its impact. This research aims to explore how the use of different spatial and temporal scales influences the identification of a heat extreme. For this purpose, two near-surface temperature datasets of different temporal scale and spatial scale are being used. First, the daily ERA-Interim dataset of 0.25 degree and a time span of 32 years (1979-2010). Second, the daily Princeton Meteorological Forcing Dataset of 0.5 degree and a time span of 63 years (1948-2010). A temperature is considered extreme anomalous when it is surpassing the 90th, 95th, or the 99th percentile threshold based on the aforementioned pre-processed datasets. The analysis is conducted on a global scale, dividing the world in IPCC's so-called SREX regions developed for the analysis of extreme climate events. Pre-processing is done by detrending and/or subtracting the monthly climatology based on 32 years of data for both datasets and on 63 years of data for only the Princeton Meteorological Forcing Dataset. This results in 6 datasets of temperature anomalies from which the location in time and space of the anomalous warm days are identified. Comparison of the differences between these 6 datasets in terms of absolute threshold temperatures for extremes and the temporal and spatial spread of the extreme anomalous warm days show a dependence of the results on the datasets and methodology used. This stresses the need for a careful selection of data and methodology when identifying heat extremes.

Status of pre-processing of waste electrical and electronic equipment in Germany and its influence on the recovery of gold.

PubMed

Chancerel, Perrine; Bolland, Til; Rotter, Vera Susanne

2011-03-01

Waste electrical and electronic equipment (WEEE) contains gold in low but from an environmental and economic point of view relevant concentration. After collection, WEEE is pre-processed in order to generate appropriate material fractions that are sent to the subsequent end-processing stages (recovery, reuse or disposal). The goal of this research is to quantify the overall recovery rates of pre-processing technologies used in Germany for the reference year 2007. To achieve this goal, facilities operating in Germany were listed and classified according to the technology they apply. Information on their processing capacity was gathered by evaluating statistical databases. Based on a literature review of experimental results for gold recovery rates of different pre-processing technologies, the German overall recovery rate of gold at the pre-processing level was quantified depending on the characteristics of the treated WEEE. The results reveal that - depending on the equipment groups - pre-processing recovery rates of gold of 29 to 61% are achieved in Germany. Some practical recommendations to reduce the losses during pre-processing could be formulated. Defining mass-based recovery targets in the legislation does not set incentives to recover trace elements. Instead, the priorities for recycling could be defined based on other parameters like the environmental impacts of the materials. The implementation of measures to reduce the gold losses would also improve the recovery of several other non-ferrous metals like tin, nickel, and palladium.

A comparison of performance of automatic cloud coverage assessment algorithm for Formosat-2 image using clustering-based and spatial thresholding methods

NASA Astrophysics Data System (ADS)

Hsu, Kuo-Hsien

2012-11-01

Formosat-2 image is a kind of high-spatial-resolution (2 meters GSD) remote sensing satellite data, which includes one panchromatic band and four multispectral bands (Blue, Green, Red, near-infrared). An essential sector in the daily processing of received Formosat-2 image is to estimate the cloud statistic of image using Automatic Cloud Coverage Assessment (ACCA) algorithm. The information of cloud statistic of image is subsequently recorded as an important metadata for image product catalog. In this paper, we propose an ACCA method with two consecutive stages: preprocessing and post-processing analysis. For pre-processing analysis, the un-supervised K-means classification, Sobel's method, thresholding method, non-cloudy pixels reexamination, and cross-band filter method are implemented in sequence for cloud statistic determination. For post-processing analysis, Box-Counting fractal method is implemented. In other words, the cloud statistic is firstly determined via pre-processing analysis, the correctness of cloud statistic of image of different spectral band is eventually cross-examined qualitatively and quantitatively via post-processing analysis. The selection of an appropriate thresholding method is very critical to the result of ACCA method. Therefore, in this work, We firstly conduct a series of experiments of the clustering-based and spatial thresholding methods that include Otsu's, Local Entropy(LE), Joint Entropy(JE), Global Entropy(GE), and Global Relative Entropy(GRE) method, for performance comparison. The result shows that Otsu's and GE methods both perform better than others for Formosat-2 image. Additionally, our proposed ACCA method by selecting Otsu's method as the threshoding method has successfully extracted the cloudy pixels of Formosat-2 image for accurate cloud statistic estimation.

Complexity-reduced implementations of complete and null-space-based linear discriminant analysis.

PubMed

Lu, Gui-Fu; Zheng, Wenming

2013-10-01

Dimensionality reduction has become an important data preprocessing step in a lot of applications. Linear discriminant analysis (LDA) is one of the most well-known dimensionality reduction methods. However, the classical LDA cannot be used directly in the small sample size (SSS) problem where the within-class scatter matrix is singular. In the past, many generalized LDA methods has been reported to address the SSS problem. Among these methods, complete linear discriminant analysis (CLDA) and null-space-based LDA (NLDA) provide good performances. The existing implementations of CLDA are computationally expensive. In this paper, we propose a new and fast implementation of CLDA. Our proposed implementation of CLDA, which is the most efficient one, is equivalent to the existing implementations of CLDA in theory. Since CLDA is an extension of null-space-based LDA (NLDA), our implementation of CLDA also provides a fast implementation of NLDA. Experiments on some real-world data sets demonstrate the effectiveness of our proposed new CLDA and NLDA algorithms. Copyright © 2013 Elsevier Ltd. All rights reserved.

Authentication of organic feed by near-infrared spectroscopy combined with chemometrics: a feasibility study.

PubMed

Tres, A; van der Veer, G; Perez-Marin, M D; van Ruth, S M; Garrido-Varo, A

2012-08-22

Organic products tend to retail at a higher price than their conventional counterparts, which makes them susceptible to fraud. In this study we evaluate the application of near-infrared spectroscopy (NIRS) as a rapid, cost-effective method to verify the organic identity of feed for laying hens. For this purpose a total of 36 organic and 60 conventional feed samples from The Netherlands were measured by NIRS. A binary classification model (organic vs conventional feed) was developed using partial least squares discriminant analysis. Models were developed using five different data preprocessing techniques, which were externally validated by a stratified random resampling strategy using 1000 realizations. Spectral regions related to the protein and fat content were among the most important ones for the classification model. The models based on data preprocessed using direct orthogonal signal correction (DOSC), standard normal variate (SNV), and first and second derivatives provided the most successful results in terms of median sensitivity (0.91 in external validation) and median specificity (1.00 for external validation of SNV models and 0.94 for DOSC and first and second derivative models). A previously developed model, which was based on fatty acid fingerprinting of the same set of feed samples, provided a higher sensitivity (1.00). This shows that the NIRS-based approach provides a rapid and low-cost screening tool, whereas the fatty acid fingerprinting model can be used for further confirmation of the organic identity of feed samples for laying hens. These methods provide additional assurance to the administrative controls currently conducted in the organic feed sector.

Nonlinear time dependence of dark current in charge-coupled devices

NASA Astrophysics Data System (ADS)

Dunlap, Justin C.; Bodegom, Erik; Widenhorn, Ralf

2011-03-01

It is generally assumed that charge-coupled device (CCD) imagers produce a linear response of dark current versus exposure time except near saturation. We found a large number of pixels with nonlinear dark current response to exposure time to be present in two scientific CCD imagers. These pixels are found to exhibit distinguishable behavior with other analogous pixels and therefore can be characterized in groupings. Data from two Kodak CCD sensors are presented for exposure times from a few seconds up to two hours. Linear behavior is traditionally taken for granted when carrying out dark current correction and as a result, pixels with nonlinear behavior will be corrected inaccurately.

Differential Entropy Preserves Variational Information of Near-Infrared Spectroscopy Time Series Associated With Working Memory

PubMed Central

Keshmiri, Soheil; Sumioka, Hidenubo; Yamazaki, Ryuji; Ishiguro, Hiroshi

2018-01-01

Neuroscience research shows a growing interest in the application of Near-Infrared Spectroscopy (NIRS) in analysis and decoding of the brain activity of human subjects. Given the correlation that is observed between the Blood Oxygen Dependent Level (BOLD) responses that are exhibited by the time series data of functional Magnetic Resonance Imaging (fMRI) and the hemoglobin oxy/deoxy-genation that is captured by NIRS, linear models play a central role in these applications. This, in turn, results in adaptation of the feature extraction strategies that are well-suited for discretization of data that exhibit a high degree of linearity, namely, slope and the mean as well as their combination, to summarize the informational contents of the NIRS time series. In this article, we demonstrate that these features are inefficient in capturing the variational information of NIRS data, limiting the reliability and the adequacy of the conclusion on their results. Alternatively, we propose the linear estimate of differential entropy of these time series as a natural representation of such information. We provide evidence for our claim through comparative analysis of the application of these features on NIRS data pertinent to several working memory tasks as well as naturalistic conversational stimuli. PMID:29922144

Computing Fourier integral operators with caustics

NASA Astrophysics Data System (ADS)

Caday, Peter

2016-12-01

Fourier integral operators (FIOs) have widespread applications in imaging, inverse problems, and PDEs. An implementation of a generic algorithm for computing FIOs associated with canonical graphs is presented, based on a recent paper of de Hoop et al. Given the canonical transformation and principal symbol of the operator, a preprocessing step reduces application of an FIO approximately to multiplications, pushforwards and forward and inverse discrete Fourier transforms, which can be computed in O({N}n+(n-1)/2{log}N) time for an n-dimensional FIO. The same preprocessed data also allows computation of the inverse and transpose of the FIO, with identical runtime. Examples demonstrate the algorithm’s output, and easily extendible MATLAB/C++ source code is available from the author.

On the vertical resolution for near-nadir looking spaceborne rain radar

NASA Astrophysics Data System (ADS)

Kozu, Toshiaki

A definition of radar resolution for an arbitrary direction is proposed and used to calculate the vertical resolution for a near-nadir looking spaceborne rain radar. Based on the calculation result, a scanning strategy is proposed which efficiently distributes the measurement time to each angle bin and thus increases the number of independent samples compared with a simple linear scanning.

A generic multi-flex-body dynamics, controls simulation tool for space station

NASA Technical Reports Server (NTRS)

London, Ken W.; Lee, John F.; Singh, Ramen P.; Schubele, Buddy

1991-01-01

An order (n) multiflex body Space Station simulation tool is introduced. The flex multibody modeling is generic enough to model all phases of Space Station from build up through to Assembly Complete configuration and beyond. Multibody subsystems such as the Mobile Servicing System (MSS) undergoing a prescribed translation and rotation are also allowed. The software includes aerodynamic, gravity gradient, and magnetic field models. User defined controllers can be discrete or continuous. Extensive preprocessing of 'body by body' NASTRAN flex data is built in. A significant aspect, too, is the integrated controls design capability which includes model reduction and analytic linearization.

Natural Resources Inventory and Land Evaluation in Switzerland

NASA Technical Reports Server (NTRS)

Haefner, H. (Principal Investigator)

1975-01-01

The author has identified the following significant results. A system was developed to operationally map and measure the areal extent of various land use categories for updating existing and producing new and actual thematic maps showing the latest state of rural and urban landscapes and its changes. The processing system includes: (1) preprocessing steps for radiometric and geometric corrections; (2) classification of the data by a multivariate procedure, using a stepwise linear discriminant analysis based on carefully selected training cells; and (3) output in form of color maps by printing black and white theme overlays of a selected scale with photomation system and its coloring and combination into a color composite.

LAPR: An experimental aircraft pushbroom scanner

NASA Technical Reports Server (NTRS)

Wharton, S. W.; Irons, J. I.; Heugel, F.

1980-01-01

A three band Linear Array Pushbroom Radiometer (LAPR) was built and flown on an experimental basis by NASA at the Goddard Space Flight Center. The functional characteristics of the instrument and the methods used to preprocess the data, including radiometric correction, are described. The radiometric sensitivity of the instrument was tested and compared to that of the Thematic Mapper and the Multispectral Scanner. The radiometric correction procedure was evaluated quantitatively, using laboratory testing, and qualitatively, via visual examination of the LAPR test flight imagery. Although effective radiometric correction could not yet be demonstrated via laboratory testing, radiometric distortion did not preclude the visual interpretation or parallel piped classification of the test imagery.

Improving performances of suboptimal greedy iterative biclustering heuristics via localization.

PubMed

Erten, Cesim; Sözdinler, Melih

2010-10-15

Biclustering gene expression data is the problem of extracting submatrices of genes and conditions exhibiting significant correlation across both the rows and the columns of a data matrix of expression values. Even the simplest versions of the problem are computationally hard. Most of the proposed solutions therefore employ greedy iterative heuristics that locally optimize a suitably assigned scoring function. We provide a fast and simple pre-processing algorithm called localization that reorders the rows and columns of the input data matrix in such a way as to group correlated entries in small local neighborhoods within the matrix. The proposed localization algorithm takes its roots from effective use of graph-theoretical methods applied to problems exhibiting a similar structure to that of biclustering. In order to evaluate the effectivenesss of the localization pre-processing algorithm, we focus on three representative greedy iterative heuristic methods. We show how the localization pre-processing can be incorporated into each representative algorithm to improve biclustering performance. Furthermore, we propose a simple biclustering algorithm, Random Extraction After Localization (REAL) that randomly extracts submatrices from the localization pre-processed data matrix, eliminates those with low similarity scores, and provides the rest as correlated structures representing biclusters. We compare the proposed localization pre-processing with another pre-processing alternative, non-negative matrix factorization. We show that our fast and simple localization procedure provides similar or even better results than the computationally heavy matrix factorization pre-processing with regards to H-value tests. We next demonstrate that the performances of the three representative greedy iterative heuristic methods improve with localization pre-processing when biological correlations in the form of functional enrichment and PPI verification constitute the main performance criteria. The fact that the random extraction method based on localization REAL performs better than the representative greedy heuristic methods under same criteria also confirms the effectiveness of the suggested pre-processing method. Supplementary material including code implementations in LEDA C++ library, experimental data, and the results are available at http://code.google.com/p/biclustering/ cesim@khas.edu.tr; melihsozdinler@boun.edu.tr Supplementary data are available at Bioinformatics online.

New technique for real-time distortion-invariant multiobject recognition and classification

NASA Astrophysics Data System (ADS)

Hong, Rutong; Li, Xiaoshun; Hong, En; Wang, Zuyi; Wei, Hongan

2001-04-01

A real-time hybrid distortion-invariant OPR system was established to make 3D multiobject distortion-invariant automatic pattern recognition. Wavelet transform technique was used to make digital preprocessing of the input scene, to depress the noisy background and enhance the recognized object. A three-layer backpropagation artificial neural network was used in correlation signal post-processing to perform multiobject distortion-invariant recognition and classification. The C-80 and NOA real-time processing ability and the multithread programming technology were used to perform high speed parallel multitask processing and speed up the post processing rate to ROIs. The reference filter library was constructed for the distortion version of 3D object model images based on the distortion parameter tolerance measuring as rotation, azimuth and scale. The real-time optical correlation recognition testing of this OPR system demonstrates that using the preprocessing, post- processing, the nonlinear algorithm os optimum filtering, RFL construction technique and the multithread programming technology, a high possibility of recognition and recognition rate ere obtained for the real-time multiobject distortion-invariant OPR system. The recognition reliability and rate was improved greatly. These techniques are very useful to automatic target recognition.

Control of a visual keyboard using an electrocorticographic brain-computer interface.

PubMed

Krusienski, Dean J; Shih, Jerry J

2011-05-01

Brain-computer interfaces (BCIs) are devices that enable severely disabled people to communicate and interact with their environments using their brain waves. Most studies investigating BCI in humans have used scalp EEG as the source of electrical signals and focused on motor control of prostheses or computer cursors on a screen. The authors hypothesize that the use of brain signals obtained directly from the cortical surface will more effectively control a communication/spelling task compared to scalp EEG. A total of 6 patients with medically intractable epilepsy were tested for the ability to control a visual keyboard using electrocorticographic (ECOG) signals. ECOG data collected during a P300 visual task paradigm were preprocessed and used to train a linear classifier to subsequently predict the intended target letters. The classifier was able to predict the intended target character at or near 100% accuracy using fewer than 15 stimulation sequences in 5 of the 6 people tested. ECOG data from electrodes outside the language cortex contributed to the classifier and enabled participants to write words on a visual keyboard. This is a novel finding because previous invasive BCI research in humans used signals exclusively from the motor cortex to control a computer cursor or prosthetic device. These results demonstrate that ECOG signals from electrodes both overlying and outside the language cortex can reliably control a visual keyboard to generate language output without voice or limb movements.

Low-damage direct patterning of silicon oxide mask by mechanical processing

PubMed Central

2014-01-01

To realize the nanofabrication of silicon surfaces using atomic force microscopy (AFM), we investigated the etching of mechanically processed oxide masks using potassium hydroxide (KOH) solution. The dependence of the KOH solution etching rate on the load and scanning density of the mechanical pre-processing was evaluated. Particular load ranges were found to increase the etching rate, and the silicon etching rate also increased with removal of the natural oxide layer by diamond tip sliding. In contrast, the local oxide pattern formed (due to mechanochemical reaction of the silicon) by tip sliding at higher load was found to have higher etching resistance than that of unprocessed areas. The profile changes caused by the etching of the mechanically pre-processed areas with the KOH solution were also investigated. First, protuberances were processed by diamond tip sliding at lower and higher stresses than that of the shearing strength. Mechanical processing at low load and scanning density to remove the natural oxide layer was then performed. The KOH solution selectively etched the low load and scanning density processed area first and then etched the unprocessed silicon area. In contrast, the protuberances pre-processed at higher load were hardly etched. The etching resistance of plastic deformed layers was decreased, and their etching rate was increased because of surface damage induced by the pre-processing. These results show that etching depth can be controlled by controlling the etching time through natural oxide layer removal and mechanochemical oxide layer formation. These oxide layer removal and formation processes can be exploited to realize low-damage mask patterns. PMID:24948891

Deblurring adaptive optics retinal images using deep convolutional neural networks.

PubMed

Fei, Xiao; Zhao, Junlei; Zhao, Haoxin; Yun, Dai; Zhang, Yudong

2017-12-01

The adaptive optics (AO) can be used to compensate for ocular aberrations to achieve near diffraction limited high-resolution retinal images. However, many factors such as the limited aberration measurement and correction accuracy with AO, intraocular scatter, imaging noise and so on will degrade the quality of retinal images. Image post processing is an indispensable and economical method to make up for the limitation of AO retinal imaging procedure. In this paper, we proposed a deep learning method to restore the degraded retinal images for the first time. The method directly learned an end-to-end mapping between the blurred and restored retinal images. The mapping was represented as a deep convolutional neural network that was trained to output high-quality images directly from blurry inputs without any preprocessing. This network was validated on synthetically generated retinal images as well as real AO retinal images. The assessment of the restored retinal images demonstrated that the image quality had been significantly improved.

Deblurring adaptive optics retinal images using deep convolutional neural networks

PubMed Central

Fei, Xiao; Zhao, Junlei; Zhao, Haoxin; Yun, Dai; Zhang, Yudong

2017-01-01

The adaptive optics (AO) can be used to compensate for ocular aberrations to achieve near diffraction limited high-resolution retinal images. However, many factors such as the limited aberration measurement and correction accuracy with AO, intraocular scatter, imaging noise and so on will degrade the quality of retinal images. Image post processing is an indispensable and economical method to make up for the limitation of AO retinal imaging procedure. In this paper, we proposed a deep learning method to restore the degraded retinal images for the first time. The method directly learned an end-to-end mapping between the blurred and restored retinal images. The mapping was represented as a deep convolutional neural network that was trained to output high-quality images directly from blurry inputs without any preprocessing. This network was validated on synthetically generated retinal images as well as real AO retinal images. The assessment of the restored retinal images demonstrated that the image quality had been significantly improved. PMID:29296496

Study to assess the importance of errors introduced by applying NOAA 6 and NOAA 7 AVHRR data as an estimator of vegetative vigor: Feasibility study of data normalization

NASA Technical Reports Server (NTRS)

Duggin, M. J. (Principal Investigator); Piwinski, D.

1982-01-01

The use of NOAA AVHRR data to map and monitor vegetation types and conditions in near real-time can be enhanced by using a portion of each GAC image that is larger than the central 25% now considered. Enlargement of the cloud free image data set can permit development of a series of algorithms for correcting imagery for ground reflectance and for atmospheric scattering anisotropy within certain accuracy limits. Empirical correction algorithms used to normalize digital radiance or VIN data must contain factors for growth stage and for instrument spectral response. While it is not possible to correct for random fluctuations in target radiance, it is possible to estimate the necessary radiance difference between targets in order to provide target discrimination and quantification within predetermined limits of accuracy. A major difficulty lies in the lack of documentation of preprocessing algorithms used on AVHRR digital data.

A Visual Analytics Paradigm Enabling Trillion-Edge Graph Exploration

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

Wong, Pak C.; Haglin, David J.; Gillen, David S.

We present a visual analytics paradigm and a system prototype for exploring web-scale graphs. A web-scale graph is described as a graph with ~one trillion edges and ~50 billion vertices. While there is an aggressive R&D effort in processing and exploring web-scale graphs among internet vendors such as Facebook and Google, visualizing a graph of that scale still remains an underexplored R&D area. The paper describes a nontraditional peek-and-filter strategy that facilitates the exploration of a graph database of unprecedented size for visualization and analytics. We demonstrate that our system prototype can 1) preprocess a graph with ~25 billion edgesmore » in less than two hours and 2) support database query and visualization on the processed graph database afterward. Based on our computational performance results, we argue that we most likely will achieve the one trillion edge mark (a computational performance improvement of 40 times) for graph visual analytics in the near future.« less

Fast data preprocessing with Graphics Processing Units for inverse problem solving in light-scattering measurements

NASA Astrophysics Data System (ADS)

Derkachov, G.; Jakubczyk, T.; Jakubczyk, D.; Archer, J.; Woźniak, M.

2017-07-01

Utilising Compute Unified Device Architecture (CUDA) platform for Graphics Processing Units (GPUs) enables significant reduction of computation time at a moderate cost, by means of parallel computing. In the paper [Jakubczyk et al., Opto-Electron. Rev., 2016] we reported using GPU for Mie scattering inverse problem solving (up to 800-fold speed-up). Here we report the development of two subroutines utilising GPU at data preprocessing stages for the inversion procedure: (i) A subroutine, based on ray tracing, for finding spherical aberration correction function. (ii) A subroutine performing the conversion of an image to a 1D distribution of light intensity versus azimuth angle (i.e. scattering diagram), fed from a movie-reading CPU subroutine running in parallel. All subroutines are incorporated in PikeReader application, which we make available on GitHub repository. PikeReader returns a sequence of intensity distributions versus a common azimuth angle vector, corresponding to the recorded movie. We obtained an overall ∼ 400 -fold speed-up of calculations at data preprocessing stages using CUDA codes running on GPU in comparison to single thread MATLAB-only code running on CPU.

A Systematic Evaluation of Blood Serum and Plasma Pre-Analytics for Metabolomics Cohort Studies

PubMed Central

Jobard, Elodie; Trédan, Olivier; Postoly, Déborah; André, Fabrice; Martin, Anne-Laure; Elena-Herrmann, Bénédicte; Boyault, Sandrine

2016-01-01

The recent thriving development of biobanks and associated high-throughput phenotyping studies requires the elaboration of large-scale approaches for monitoring biological sample quality and compliance with standard protocols. We present a metabolomic investigation of human blood samples that delineates pitfalls and guidelines for the collection, storage and handling procedures for serum and plasma. A series of eight pre-processing technical parameters is systematically investigated along variable ranges commonly encountered across clinical studies. While metabolic fingerprints, as assessed by nuclear magnetic resonance, are not significantly affected by altered centrifugation parameters or delays between sample pre-processing (blood centrifugation) and storage, our metabolomic investigation highlights that both the delay and storage temperature between blood draw and centrifugation are the primary parameters impacting serum and plasma metabolic profiles. Storing the blood drawn at 4 °C is shown to be a reliable routine to confine variability associated with idle time prior to sample pre-processing. Based on their fine sensitivity to pre-analytical parameters and protocol variations, metabolic fingerprints could be exploited as valuable ways to determine compliance with standard procedures and quality assessment of blood samples within large multi-omic clinical and translational cohort studies. PMID:27929400

Automatic luminous reflections detector using global threshold with increased luminosity contrast in images

NASA Astrophysics Data System (ADS)

Silva, Ricardo Petri; Naozuka, Gustavo Taiji; Mastelini, Saulo Martiello; Felinto, Alan Salvany

2018-01-01

The incidence of luminous reflections (LR) in captured images can interfere with the color of the affected regions. These regions tend to oversaturate, becoming whitish and, consequently, losing the original color information of the scene. Decision processes that employ images acquired from digital cameras can be impaired by the LR incidence. Such applications include real-time video surgeries, facial, and ocular recognition. This work proposes an algorithm called contrast enhancement of potential LR regions, which is a preprocessing to increase the contrast of potential LR regions, in order to improve the performance of automatic LR detectors. In addition, three automatic detectors were compared with and without the employment of our preprocessing method. The first one is a technique already consolidated in the literature called the Chang-Tseng threshold. We propose two automatic detectors called adapted histogram peak and global threshold. We employed four performance metrics to evaluate the detectors, namely, accuracy, precision, exactitude, and root mean square error. The exactitude metric is developed by this work. Thus, a manually defined reference model was created. The global threshold detector combined with our preprocessing method presented the best results, with an average exactitude rate of 82.47%.

Rapid detection of talcum powder in tea using FT-IR spectroscopy coupled with chemometrics

PubMed Central

Li, Xiaoli; Zhang, Yuying; He, Yong

2016-01-01

This paper investigated the feasibility of Fourier transform infrared transmission (FT-IR) spectroscopy to detect talcum powder illegally added in tea based on chemometric methods. Firstly, 210 samples of tea powder with 13 dose levels of talcum powder were prepared for FT-IR spectra acquirement. In order to highlight the slight variations in FT-IR spectra, smoothing, normalize and standard normal variate (SNV) were employed to preprocess the raw spectra. Among them, SNV preprocessing had the best performance with high correlation of prediction (RP = 0.948) and low root mean square error of prediction (RMSEP = 0.108) of partial least squares (PLS) model. Then 18 characteristic wavenumbers were selected based on a hybrid of backward interval partial least squares (biPLS) regression, competitive adaptive reweighted sampling (CARS) algorithm and successive projections algorithm (SPA). These characteristic wavenumbers only accounted for 0.64% of the full wavenumbers. Following that, 18 characteristic wavenumbers were used to build linear and nonlinear determination models by PLS regression and extreme learning machine (ELM), respectively. The optimal model with RP = 0.963 and RMSEP = 0.137 was achieved by ELM algorithm. These results demonstrated that FT-IR spectroscopy with chemometrics could be used successfully to detect talcum powder in tea. PMID:27468701

Speech Perception for Adult Cochlear Implant Recipients in a Realistic Background Noise: Effectiveness of Preprocessing Strategies and External Options for Improving Speech Recognition in Noise

PubMed Central

Gifford, René H.; Revit, Lawrence J.

2014-01-01

Background Although cochlear implant patients are achieving increasingly higher levels of performance, speech perception in noise continues to be problematic. The newest generations of implant speech processors are equipped with preprocessing and/or external accessories that are purported to improve listening in noise. Most speech perception measures in the clinical setting, however, do not provide a close approximation to real-world listening environments. Purpose To assess speech perception for adult cochlear implant recipients in the presence of a realistic restaurant simulation generated by an eight-loudspeaker (R-SPACE™) array in order to determine whether commercially available preprocessing strategies and/or external accessories yield improved sentence recognition in noise. Research Design Single-subject, repeated-measures design with two groups of participants: Advanced Bionics and Cochlear Corporation recipients. Study Sample Thirty-four subjects, ranging in age from 18 to 90 yr (mean 54.5 yr), participated in this prospective study. Fourteen subjects were Advanced Bionics recipients, and 20 subjects were Cochlear Corporation recipients. Intervention Speech reception thresholds (SRTs) in semidiffuse restaurant noise originating from an eight-loudspeaker array were assessed with the subjects’ preferred listening programs as well as with the addition of either Beam™ preprocessing (Cochlear Corporation) or the T-Mic® accessory option (Advanced Bionics). Data Collection and Analysis In Experiment 1, adaptive SRTs with the Hearing in Noise Test sentences were obtained for all 34 subjects. For Cochlear Corporation recipients, SRTs were obtained with their preferred everyday listening program as well as with the addition of Focus preprocessing. For Advanced Bionics recipients, SRTs were obtained with the integrated behind-the-ear (BTE) mic as well as with the T-Mic. Statistical analysis using a repeated-measures analysis of variance (ANOVA) evaluated the effects of the preprocessing strategy or external accessory in reducing the SRT in noise. In addition, a standard t-test was run to evaluate effectiveness across manufacturer for improving the SRT in noise. In Experiment 2, 16 of the 20 Cochlear Corporation subjects were reassessed obtaining an SRT in noise using the manufacturer-suggested “Everyday,” “Noise,” and “Focus” preprocessing strategies. A repeated-measures ANOVA was employed to assess the effects of preprocessing. Results The primary findings were (i) both Noise and Focus preprocessing strategies (Cochlear Corporation) significantly improved the SRT in noise as compared to Everyday preprocessing, (ii) the T-Mic accessory option (Advanced Bionics) significantly improved the SRT as compared to the BTE mic, and (iii) Focus preprocessing and the T-Mic resulted in similar degrees of improvement that were not found to be significantly different from one another. Conclusion Options available in current cochlear implant sound processors are able to significantly improve speech understanding in a realistic, semidiffuse noise with both Cochlear Corporation and Advanced Bionics systems. For Cochlear Corporation recipients, Focus preprocessing yields the best speech-recognition performance in a complex listening environment; however, it is recommended that Noise preprocessing be used as the new default for everyday listening environments to avoid the need for switching programs throughout the day. For Advanced Bionics recipients, the T-Mic offers significantly improved performance in noise and is recommended for everyday use in all listening environments. PMID:20807480

DVD-COOP: Innovative Conjunction Prediction Using Voronoi-filter based on the Dynamic Voronoi Diagram of 3D Spheres

NASA Astrophysics Data System (ADS)

Cha, J.; Ryu, J.; Lee, M.; Song, C.; Cho, Y.; Schumacher, P.; Mah, M.; Kim, D.

Conjunction prediction is one of the critical operations in space situational awareness (SSA). For geospace objects, common algorithms for conjunction prediction are usually based on all-pairwise check, spatial hash, or kd-tree. Computational load is usually reduced through some filters. However, there exists a good chance of missing potential collisions between space objects. We present a novel algorithm which both guarantees no missing conjunction and is efficient to answer to a variety of spatial queries including pairwise conjunction prediction. The algorithm takes only O(k log N) time for N objects in the worst case to answer conjunctions where k is a constant which is linear to prediction time length. The proposed algorithm, named DVD-COOP (Dynamic Voronoi Diagram-based Conjunctive Orbital Object Predictor), is based on the dynamic Voronoi diagram of moving spherical balls in 3D space. The algorithm has a preprocessing which consists of two steps: The construction of an initial Voronoi diagram (taking O(N) time on average) and the construction of a priority queue for the events of topology changes in the Voronoi diagram (taking O(N log N) time in the worst case). The scalability of the proposed algorithm is also discussed. We hope that the proposed Voronoi-approach will change the computational paradigm in spatial reasoning among space objects.

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.

Output Tracking for Systems with Non-Hyperbolic and Near Non-Hyperbolic Internal Dynamics: Helicopter Hover Control

NASA Technical Reports Server (NTRS)

Devasia, Santosh

1996-01-01

A technique to achieve output tracking for nonminimum phase linear systems with non-hyperbolic and near non-hyperbolic internal dynamics is presented. This approach integrates stable inversion techniques, that achieve exact-tracking, with approximation techniques, that modify the internal dynamics to achieve desirable performance. Such modification of the internal dynamics is used (1) to remove non-hyperbolicity which an obstruction to applying stable inversion techniques and (2) to reduce large pre-actuation time needed to apply stable inversion for near non-hyperbolic cases. The method is applied to an example helicopter hover control problem with near non-hyperbolic internal dynamic for illustrating the trade-off between exact tracking and reduction of pre-actuation time.

Preprocessing of emotional visual information in the human piriform cortex.

PubMed

Schulze, Patrick; Bestgen, Anne-Kathrin; Lech, Robert K; Kuchinke, Lars; Suchan, Boris

2017-08-23

This study examines the processing of visual information by the olfactory system in humans. Recent data point to the processing of visual stimuli by the piriform cortex, a region mainly known as part of the primary olfactory cortex. Moreover, the piriform cortex generates predictive templates of olfactory stimuli to facilitate olfactory processing. This study fills the gap relating to the question whether this region is also capable of preprocessing emotional visual information. To gain insight into the preprocessing and transfer of emotional visual information into olfactory processing, we recorded hemodynamic responses during affective priming using functional magnetic resonance imaging (fMRI). Odors of different valence (pleasant, neutral and unpleasant) were primed by images of emotional facial expressions (happy, neutral and disgust). Our findings are the first to demonstrate that the piriform cortex preprocesses emotional visual information prior to any olfactory stimulation and that the emotional connotation of this preprocessing is subsequently transferred and integrated into an extended olfactory network for olfactory processing.

Preprocessing of A-scan GPR data based on energy features

NASA Astrophysics Data System (ADS)

Dogan, Mesut; Turhan-Sayan, Gonul

2016-05-01

There is an increasing demand for noninvasive real-time detection and classification of buried objects in various civil and military applications. The problem of detection and annihilation of landmines is particularly important due to strong safety concerns. The requirement for a fast real-time decision process is as important as the requirements for high detection rates and low false alarm rates. In this paper, we introduce and demonstrate a computationally simple, timeefficient, energy-based preprocessing approach that can be used in ground penetrating radar (GPR) applications to eliminate reflections from the air-ground boundary and to locate the buried objects, simultaneously, at one easy step. The instantaneous power signals, the total energy values and the cumulative energy curves are extracted from the A-scan GPR data. The cumulative energy curves, in particular, are shown to be useful to detect the presence and location of buried objects in a fast and simple way while preserving the spectral content of the original A-scan data for further steps of physics-based target classification. The proposed method is demonstrated using the GPR data collected at the facilities of IPA Defense, Ankara at outdoor test lanes. Cylindrically shaped plastic containers were buried in fine-medium sand to simulate buried landmines. These plastic containers were half-filled by ammonium nitrate including metal pins. Results of this pilot study are demonstrated to be highly promising to motivate further research for the use of energy-based preprocessing features in landmine detection problem.

Optimizing Preprocessing and Analysis Pipelines for Single-Subject FMRI. I. Standard Temporal Motion and Physiological Noise Correction Methods

PubMed Central

Churchill, Nathan W.; Oder, Anita; Abdi, Hervé; Tam, Fred; Lee, Wayne; Thomas, Christopher; Ween, Jon E.; Graham, Simon J.; Strother, Stephen C.

2016-01-01

Subject-specific artifacts caused by head motion and physiological noise are major confounds in BOLD fMRI analyses. However, there is little consensus on the optimal choice of data preprocessing steps to minimize these effects. To evaluate the effects of various preprocessing strategies, we present a framework which comprises a combination of (1) nonparametric testing including reproducibility and prediction metrics of the data-driven NPAIRS framework (Strother et al. [2002]: NeuroImage 15:747–771), and (2) intersubject comparison of SPM effects, using DISTATIS (a three-way version of metric multidimensional scaling (Abdi et al. [2009]: NeuroImage 45:89–95). It is shown that the quality of brain activation maps may be significantly limited by sub-optimal choices of data preprocessing steps (or “pipeline”) in a clinical task-design, an fMRI adaptation of the widely used Trail-Making Test. The relative importance of motion correction, physiological noise correction, motion parameter regression, and temporal detrending were examined for fMRI data acquired in young, healthy adults. Analysis performance and the quality of activation maps were evaluated based on Penalized Discriminant Analysis (PDA). The relative importance of different preprocessing steps was assessed by (1) a nonparametric Friedman rank test for fixed sets of preprocessing steps, applied to all subjects; and (2) evaluating pipelines chosen specifically for each subject. Results demonstrate that preprocessing choices have significant, but subject-dependant effects, and that individually-optimized pipelines may significantly improve the reproducibility of fMRI results over fixed pipelines. This was demonstrated by the detection of a significant interaction with motion parameter regression and physiological noise correction, even though the range of subject head motion was small across the group (≪ 1 voxel). Optimizing pipelines on an individual-subject basis also revealed brain activation patterns either weak or absent under fixed pipelines, which has implications for the overall interpretation of fMRI data, and the relative importance of preprocessing methods. PMID:21455942

Environmental impact to multimedia systems on the example of fingerprint aging behavior at crime scenes

NASA Astrophysics Data System (ADS)

Merkel, Ronny; Breuhan, Andy; Hildebrandt, Mario; Vielhauer, Claus; Bräutigam, Anja

2012-06-01

In the field of crime scene forensics, current methods of evidence collection, such as the acquisition of shoe-marks, tireimpressions, palm-prints or fingerprints are in most cases still performed in an analogue way. For example, fingerprints are captured by powdering and sticky tape lifting, ninhydrine bathing or cyanoacrylate fuming and subsequent photographing. Images of the evidence are then further processed by forensic experts. With the upcoming use of new multimedia systems for the digital capturing and processing of crime scene traces in forensics, higher resolutions can be achieved, leading to a much better quality of forensic images. Furthermore, the fast and mostly automated preprocessing of such data using digital signal processing techniques is an emerging field. Also, by the optical and non-destructive lifting of forensic evidence, traces are not destroyed and therefore can be re-captured, e.g. by creating time series of a trace, to extract its aging behavior and maybe determine the time the trace was left. However, such new methods and tools face different challenges, which need to be addressed before a practical application in the field. Based on the example of fingerprint age determination, which is an unresolved research challenge to forensic experts since decades, we evaluate the influences of different environmental conditions as well as different types of sweating and their implications to the capturing sensory, preprocessing methods and feature extraction. We use a Chromatic White Light (CWL) sensor to exemplary represent such a new optical and contactless measurement device and investigate the influence of 16 different environmental conditions, 8 different sweat types and 11 different preprocessing methods on the aging behavior of 48 fingerprint time series (2592 fingerprint scans in total). We show the challenges that arise for such new multimedia systems capturing and processing forensic evidence

Aeroacoustics Computation for Nearly Fully Expanded Supersonic Jets Using the CE/SE Method

NASA Technical Reports Server (NTRS)

Loh, Ching Y.; Hultgren, Lennart S.; Wang, Xiao Y.; Chang, Sin-Chung; Jorgenson, Philip C. E.

2000-01-01

In this paper, the space-time conservation element solution element (CE/SE) method is tested in the classical axisymmetric jet instability problem, rendering good agreement with the linear theory. The CE/SE method is then applied to numerical simulations of several nearly fully expanded axisymmetric jet flows and their noise fields and qualitative agreement with available experimental and theoretical results is demonstrated.

A consensus least squares support vector regression (LS-SVR) for analysis of near-infrared spectra of plant samples.

PubMed

Li, Yankun; Shao, Xueguang; Cai, Wensheng

2007-04-15

Consensus modeling of combining the results of multiple independent models to produce a single prediction avoids the instability of single model. Based on the principle of consensus modeling, a consensus least squares support vector regression (LS-SVR) method for calibrating the near-infrared (NIR) spectra was proposed. In the proposed approach, NIR spectra of plant samples were firstly preprocessed using discrete wavelet transform (DWT) for filtering the spectral background and noise, then, consensus LS-SVR technique was used for building the calibration model. With an optimization of the parameters involved in the modeling, a satisfied model was achieved for predicting the content of reducing sugar in plant samples. The predicted results show that consensus LS-SVR model is more robust and reliable than the conventional partial least squares (PLS) and LS-SVR methods.

Nondestructive quantification of the soluble-solids content and the available acidity of apples by Fourier-transform near-infrared spectroscopy

NASA Astrophysics Data System (ADS)

Ying, Yibin; Liu, Yande; Tao, Yang

2005-09-01

This research evaluated the feasibility of using Fourier-transform near-infrared (FT-NIR) spectroscopy to quantify the soluble-solids content (SSC) and the available acidity (VA) in intact apples. Partial least-squares calibration models, obtained from several preprocessing techniques (smoothing, derivative, etc.) in several wave-number ranges were compared. The best models were obtained with the high coefficient determination (r) 0.940 for the SSC and a moderate r of 0.801 for the VA, root-mean-square errors of prediction of 0.272% and 0.053%, and root-mean-square errors of calibration of 0.261% and 0.046%, respectively. The results indicate that the FT-NIR spectroscopy yields good predictions of the SSC and also showed the feasibility of using it to predict the VA of apples.

The crack detection algorithm of pavement image based on edge information

NASA Astrophysics Data System (ADS)

Yang, Chunde; Geng, Mingyue

2018-05-01

As the images of pavement cracks are affected by a large amount of complicated noises, such as uneven illumination and water stains, the detected cracks are discontinuous and the main body information at the edge of the cracks is easily lost. In order to solve the problem, a crack detection algorithm in pavement image based on edge information is proposed. Firstly, the image is pre-processed by the nonlinear gray-scale transform function and reconstruction filter to enhance the linear characteristic of the crack. At the same time, an adaptive thresholding method is designed to coarsely extract the cracks edge according to the gray-scale gradient feature and obtain the crack gradient information map. Secondly, the candidate edge points are obtained according to the gradient information, and the edge is detected based on the single pixel percolation processing, which is improved by using the local difference between pixels in the fixed region. Finally, complete crack is obtained by filling the crack edge. Experimental results show that the proposed method can accurately detect pavement cracks and preserve edge information.

An improved interface to process GPR data by means of microwave tomography

NASA Astrophysics Data System (ADS)

Catapano, Ilaria; Affinito, Antonio; Soldovieri, Francesco

2015-04-01

Ground Penetrating Radar (GPR) systems are well assessed non-invasive diagnostic tools, which are worth being considered in civil engineering surveys since they allow to gather information on constructive materials and techniques of manmade structures as well as on the aging and risk factors affecting their healthiness. However, the practical use of GPR depends strictly on the availability of data processing tools, on one hand, capable of providing reliable and easily interpretable images of the probed scenarios and, on the other side, easy to be used by not expert users. In this frame, 2D and full 3D microwave tomographic approaches based on the Born approximation have been developed and proved to be effective in several practical conditions [1, 2]. Generally speaking, a GPR data processing chain exploiting microwave tomography is made by two main steps: the pre-processing and the data inversion. The pre-processing groups standard procedures like start time correction, muting and background removal, which are performed in time domain to remove the direct antennas coupling, to reduce noise and to improve the targets footprint. The data inversion faces the imaging as the solution of a linear inverse scattering problem in the frequency domain. Hence, a linear integral equation relating the scattered field (i.e. the data) to the unknown electric contrast function is solved by using the truncated Singular Value Decomposition (SVD) as a regularized inversion scheme. Pre-processing and the data inversion are linked by a Discrete Fourier Transform (DFT), which allows to pass from the time domain to the frequency domain. In this respect, a frequency analysis of the GPR signals (traces) is also performed to identify the actual frequency range of the data. Unfortunately, the adoption of microwave tomography is strongly subjected to the involvement of expert people capable of managing properly the processing chain. To overcome this drawback, a couple of years ago, an end-user friendly software interface was developed to make possible a simple management of 2D microwave tomographic approaches [3]. Aim of this communication, is to present a novel interface, which is a significantly improved version with respect to the previous one. In particular, the new interface allows both 2D and full 3D imaging by taking as input GPR data gathered by means of different measurement configurations, i.e. by using down looking systems, with the antenna located close to the air-medium interface or at non negligible (in terms of the probing wavelength) distance from it, as well as by means of airborne and forward looking systems. In this frame, the users can select the data format among those of the most common commercial GPR systems or process data gathered by means of GPR prototypes, provided that they are saved in ASCII format. Moreover, the users can perform all the steps, which are needed to obtain tomographic images, and select the Born approximation based approach most suitable to the adopted measurement configuration. Raw-radargrams, intermediate and final results can be displayed for users convenience. REFERENCES [1] I. Catapano, R. Di Napoli, F. Soldovieri, M. Bavusi, A. Loperte, J. Dumoulin, "Structural monitoring via microwave tomography-enhanced GPR: the Montagnole test site", J. Geophys. Eng. 9, S100-S107, 2012. [2] I. Catapano, A. Affinito, G. Gennarelli, F.di Maio, A. Loperte, F. Soldovieri, "Full three-dimensional imaging via ground penetrating radar: assessment in controlled conditions and on field for archaeological prospecting", Appl. Phys. A, 2013, DOI 10.1007/s00339-013-8053-0. [3] I. Catapano, A. Affinito, F. Soldovieri, A user friendly interface for microwave tomography enhanced GPR surveys", EGU General Assembly 2013, vol. 15.

Variable threshold method for ECG R-peak detection.

PubMed

Kew, Hsein-Ping; Jeong, Do-Un

2011-10-01

In this paper, a wearable belt-type ECG electrode worn around the chest by measuring the real-time ECG is produced in order to minimize the inconvenient in wearing. ECG signal is detected using a potential instrument system. The measured ECG signal is transmits via an ultra low power consumption wireless data communications unit to personal computer using Zigbee-compatible wireless sensor node. ECG signals carry a lot of clinical information for a cardiologist especially the R-peak detection in ECG. R-peak detection generally uses the threshold value which is fixed. There will be errors in peak detection when the baseline changes due to motion artifacts and signal size changes. Preprocessing process which includes differentiation process and Hilbert transform is used as signal preprocessing algorithm. Thereafter, variable threshold method is used to detect the R-peak which is more accurate and efficient than fixed threshold value method. R-peak detection using MIT-BIH databases and Long Term Real-Time ECG is performed in this research in order to evaluate the performance analysis.

User data dissemination concepts for earth resources: Executive summary

NASA Technical Reports Server (NTRS)

Davies, R.; Scott, M.; Mitchell, C.; Torbett, A.

1976-01-01

The impact of the future capabilities of earth-resources data sensors (both satellite and airborne) and their requirements on the data dissemination network were investigated and optimum ways of configuring this network were determined. The scope of this study was limited to the continental U.S.A. (including Alaska) and to the 1985-1995 time period. Some of the conclusions and recommendations reached were: (1) Data from satellites in sun-synchronous polar orbits (700-920 km) will generate most of the earth-resources data in the specified time period. (2) Data from aircraft and shuttle sorties cannot be readily integrated in a data-dissemination network unless already preprocessed in a digitized form to a standard geometric coordinate system. (3) Data transmission between readout stations and central preprocessing facilities, and between processing facilities and user facilities are most economically performed by domestic communication satellites. (4) The effect of the following factors should be studied: cloud cover, expanded coverage, pricing strategies, multidiscipline missions.

PCA-based artifact removal algorithm for stroke detection using UWB radar imaging.

PubMed

Ricci, Elisa; di Domenico, Simone; Cianca, Ernestina; Rossi, Tommaso; Diomedi, Marina

2017-06-01

Stroke patients should be dispatched at the highest level of care available in the shortest time. In this context, a transportable system in specialized ambulances, able to evaluate the presence of an acute brain lesion in a short time interval (i.e., few minutes), could shorten delay of treatment. UWB radar imaging is an emerging diagnostic branch that has great potential for the implementation of a transportable and low-cost device. Transportability, low cost and short response time pose challenges to the signal processing algorithms of the backscattered signals as they should guarantee good performance with a reasonably low number of antennas and low computational complexity, tightly related to the response time of the device. The paper shows that a PCA-based preprocessing algorithm can: (1) achieve good performance already with a computationally simple beamforming algorithm; (2) outperform state-of-the-art preprocessing algorithms; (3) enable a further improvement in the performance (and/or decrease in the number of antennas) by using a multistatic approach with just a modest increase in computational complexity. This is an important result toward the implementation of such a diagnostic device that could play an important role in emergency scenario.

A General Method for Solving Systems of Non-Linear Equations

NASA Technical Reports Server (NTRS)

Nachtsheim, Philip R.; Deiss, Ron (Technical Monitor)

1995-01-01

The method of steepest descent is modified so that accelerated convergence is achieved near a root. It is assumed that the function of interest can be approximated near a root by a quadratic form. An eigenvector of the quadratic form is found by evaluating the function and its gradient at an arbitrary point and another suitably selected point. The terminal point of the eigenvector is chosen to lie on the line segment joining the two points. The terminal point found lies on an axis of the quadratic form. The selection of a suitable step size at this point leads directly to the root in the direction of steepest descent in a single step. Newton's root finding method not infrequently diverges if the starting point is far from the root. However, the current method in these regions merely reverts to the method of steepest descent with an adaptive step size. The current method's performance should match that of the Levenberg-Marquardt root finding method since they both share the ability to converge from a starting point far from the root and both exhibit quadratic convergence near a root. The Levenberg-Marquardt method requires storage for coefficients of linear equations. The current method which does not require the solution of linear equations requires more time for additional function and gradient evaluations. The classic trade off of time for space separates the two methods.

Simulation of white light generation and near light bullets using a novel numerical technique

NASA Astrophysics Data System (ADS)

Zia, Haider

2018-01-01

An accurate and efficient simulation has been devised, employing a new numerical technique to simulate the derivative generalised non-linear Schrödinger equation in all three spatial dimensions and time. The simulation models all pertinent effects such as self-steepening and plasma for the non-linear propagation of ultrafast optical radiation in bulk material. Simulation results are compared to published experimental spectral data of an example ytterbium aluminum garnet system at 3.1 μm radiation and fits to within a factor of 5. The simulation shows that there is a stability point near the end of the 2 mm crystal where a quasi-light bullet (spatial temporal soliton) is present. Within this region, the pulse is collimated at a reduced diameter (factor of ∼2) and there exists a near temporal soliton at the spatial center. The temporal intensity within this stable region is compressed by a factor of ∼4 compared to the input. This study shows that the simulation highlights new physical phenomena based on the interplay of various linear, non-linear and plasma effects that go beyond the experiment and is thus integral to achieving accurate designs of white light generation systems for optical applications. An adaptive error reduction algorithm tailor made for this simulation will also be presented in appendix.

Study of Huizhou architecture component point cloud in surface reconstruction

NASA Astrophysics Data System (ADS)

Zhang, Runmei; Wang, Guangyin; Ma, Jixiang; Wu, Yulu; Zhang, Guangbin

2017-06-01

Surface reconfiguration softwares have many problems such as complicated operation on point cloud data, too many interaction definitions, and too stringent requirements for inputing data. Thus, it has not been widely popularized so far. This paper selects the unique Huizhou Architecture chuandou wooden beam framework as the research object, and presents a complete set of implementation in data acquisition from point, point cloud preprocessing and finally implemented surface reconstruction. Firstly, preprocessing the acquired point cloud data, including segmentation and filtering. Secondly, the surface’s normals are deduced directly from the point cloud dataset. Finally, the surface reconstruction is studied by using Greedy Projection Triangulation Algorithm. Comparing the reconstructed model with the three-dimensional surface reconstruction softwares, the results show that the proposed scheme is more smooth, time efficient and portable.

Preprocessing Inconsistent Linear System for a Meaningful Least Squares Solution

NASA Technical Reports Server (NTRS)

Sen, Syamal K.; Shaykhian, Gholam Ali

2011-01-01

Mathematical models of many physical/statistical problems are systems of linear equations. Due to measurement and possible human errors/mistakes in modeling/data, as well as due to certain assumptions to reduce complexity, inconsistency (contradiction) is injected into the model, viz. the linear system. While any inconsistent system irrespective of the degree of inconsistency has always a least-squares solution, one needs to check whether an equation is too much inconsistent or, equivalently too much contradictory. Such an equation will affect/distort the least-squares solution to such an extent that renders it unacceptable/unfit to be used in a real-world application. We propose an algorithm which (i) prunes numerically redundant linear equations from the system as these do not add any new information to the model, (ii) detects contradictory linear equations along with their degree of contradiction (inconsistency index), (iii) removes those equations presumed to be too contradictory, and then (iv) obtain the minimum norm least-squares solution of the acceptably inconsistent reduced linear system. The algorithm presented in Matlab reduces the computational and storage complexities and also improves the accuracy of the solution. It also provides the necessary warning about the existence of too much contradiction in the model. In addition, we suggest a thorough relook into the mathematical modeling to determine the reason why unacceptable contradiction has occurred thus prompting us to make necessary corrections/modifications to the models - both mathematical and, if necessary, physical.

Preprocessing in Matlab Inconsistent Linear System for a Meaningful Least Squares Solution

NASA Technical Reports Server (NTRS)

Sen, Symal K.; Shaykhian, Gholam Ali

2011-01-01

Mathematical models of many physical/statistical problems are systems of linear equations Due to measurement and possible human errors/mistakes in modeling/data, as well as due to certain assumptions to reduce complexity, inconsistency (contradiction) is injected into the model, viz. the linear system. While any inconsistent system irrespective of the degree of inconsistency has always a least-squares solution, one needs to check whether an equation is too much inconsistent or, equivalently too much contradictory. Such an equation will affect/distort the least-squares solution to such an extent that renders it unacceptable/unfit to be used in a real-world application. We propose an algorithm which (i) prunes numerically redundant linear equations from the system as these do not add any new information to the model, (ii) detects contradictory linear equations along with their degree of contradiction (inconsistency index), (iii) removes those equations presumed to be too contradictory, and then (iv) obtain the . minimum norm least-squares solution of the acceptably inconsistent reduced linear system. The algorithm presented in Matlab reduces the computational and storage complexities and also improves the accuracy of the solution. It also provides the necessary warning about the existence of too much contradiction in the model. In addition, we suggest a thorough relook into the mathematical modeling to determine the reason why unacceptable contradiction has occurred thus prompting us to make necessary corrections/modifications to the models - both mathematical and, if necessary, physical.

Effects of preprocessing 1 Common and 2A Common red oak lumber on gang-rip-first rough-mill dimension part yields

Treesearch

Charles J. Gatchell; R. Edward Thomas; Elizabeth S. Walker

1999-01-01

Using the ROMI-RIP simulator we examined the implications of preprocessing for gang-rip-first rough mills. Rip-first rough mills can improve yield and throughput by preprocessing 1 Common and 2A Common hardwood lumber. This can be achieved by using a chop saw to separate poorer quality board segments from better ones and remove waste areas with little or no yield. This...

Data pre-processing in record linkage to find the same companies from different databases

NASA Astrophysics Data System (ADS)

Gunawan, D.; Lubis, M. S.; Arisandi, D.; Azzahry, B.

2018-03-01

As public agencies, the Badan Pelayanan Perizinan Terpadu (BPPT) and the Badan Lingkungan Hidup (BLH) of Medan city manage process to obtain a business license from the public. However, each agency might have a different corporate data because of a separate data input process, even though the data may refer to the same company’s data. Therefore, it is required to identify and correlate data that refer to the same company which lie in different data sources. This research focuses on data pre-processing such as data cleaning, text pre-processing, indexing and record comparison. In addition, this research implements data matching using support vector machine algorithm. The result of this algorithm will be used to record linkage of data that can be used to identify and connect the company’s data based on the degree of similarity of each data. Previous data will be standardized in accordance with the format and structure appropriate to the stage of preprocessing data. After analyzing data pre-processing, we found that both database structures are not designed to support data integration. We decide that the data matching can be done with blocking criteria such as company name and the name of the owner (or applicant). In addition to data pre-processing, the result of data classification with a high level of similarity as many as 90 pairs of records.

Classifier dependent feature preprocessing methods

NASA Astrophysics Data System (ADS)

Rodriguez, Benjamin M., II; Peterson, Gilbert L.

2008-04-01

In mobile applications, computational complexity is an issue that limits sophisticated algorithms from being implemented on these devices. This paper provides an initial solution to applying pattern recognition systems on mobile devices by combining existing preprocessing algorithms for recognition. In pattern recognition systems, it is essential to properly apply feature preprocessing tools prior to training classification models in an attempt to reduce computational complexity and improve the overall classification accuracy. The feature preprocessing tools extended for the mobile environment are feature ranking, feature extraction, data preparation and outlier removal. Most desktop systems today are capable of processing a majority of the available classification algorithms without concern of processing while the same is not true on mobile platforms. As an application of pattern recognition for mobile devices, the recognition system targets the problem of steganalysis, determining if an image contains hidden information. The measure of performance shows that feature preprocessing increases the overall steganalysis classification accuracy by an average of 22%. The methods in this paper are tested on a workstation and a Nokia 6620 (Symbian operating system) camera phone with similar results.

A comparison of PCA/ICA for data preprocessing in remote sensing imagery classification

NASA Astrophysics Data System (ADS)

He, Hui; Yu, Xianchuan

2005-10-01

In this paper a performance comparison of a variety of data preprocessing algorithms in remote sensing image classification is presented. These selected algorithms are principal component analysis (PCA) and three different independent component analyses, ICA (Fast-ICA (Aapo Hyvarinen, 1999), Kernel-ICA (KCCA and KGV (Bach & Jordan, 2002), EFFICA (Aiyou Chen & Peter Bickel, 2003). These algorithms were applied to a remote sensing imagery (1600×1197), obtained from Shunyi, Beijing. For classification, a MLC method is used for the raw and preprocessed data. The results show that classification with the preprocessed data have more confident results than that with raw data and among the preprocessing algorithms, ICA algorithms improve on PCA and EFFICA performs better than the others. The convergence of these ICA algorithms (for data points more than a million) are also studied, the result shows EFFICA converges much faster than the others. Furthermore, because EFFICA is a one-step maximum likelihood estimate (MLE) which reaches asymptotic Fisher efficiency (EFFICA), it computers quite small so that its demand of memory come down greatly, which settled the "out of memory" problem occurred in the other algorithms.

Preprocessing with Photoshop Software on Microscopic Images of A549 Cells in Epithelial-Mesenchymal Transition.

PubMed

Ren, Zhou-Xin; Yu, Hai-Bin; Shen, Jun-Ling; Li, Ya; Li, Jian-Sheng

2015-06-01

To establish a preprocessing method for cell morphometry in microscopic images of A549 cells in epithelial-mesenchymal transition (EMT). Adobe Photoshop CS2 (Adobe Systems, Inc.) was used for preprocessing the images. First, all images were processed for size uniformity and high distinguishability between the cell and background area. Then, a blank image with the same size and grids was established and cross points of the grids were added into a distinct color. The blank image was merged into a processed image. In the merged images, the cells with 1 or more cross points were chosen, and then the cell areas were enclosed and were replaced in a distinct color. Except for chosen cellular areas, all areas were changed into a unique hue. Three observers quantified roundness of cells in images with the image preprocess (IPP) or without the method (Controls), respectively. Furthermore, 1 observer measured the roundness 3 times with the 2 methods, respectively. The results between IPPs and Controls were compared for repeatability and reproducibility. As compared with the Control method, among 3 observers, use of the IPP method resulted in a higher number and a higher percentage of same-chosen cells in an image. The relative average deviation values of roundness, either for 3 observers or 1 observer, were significantly higher in Controls than in IPPs (p < 0.01 or 0.001). The values of intraclass correlation coefficient, both in Single Type or Average, were higher in IPPs than in Controls both for 3 observers and 1 observer. Processed with Adobe Photoshop, a chosen cell from an image was more objective, regular, and accurate, creating an increase of reproducibility and repeatability on morphometry of A549 cells in epithelial to mesenchymal transition.

Software for pre-processing Illumina next-generation sequencing short read sequences

PubMed Central

2014-01-01

Background When compared to Sanger sequencing technology, next-generation sequencing (NGS) technologies are hindered by shorter sequence read length, higher base-call error rate, non-uniform coverage, and platform-specific sequencing artifacts. These characteristics lower the quality of their downstream analyses, e.g. de novo and reference-based assembly, by introducing sequencing artifacts and errors that may contribute to incorrect interpretation of data. Although many tools have been developed for quality control and pre-processing of NGS data, none of them provide flexible and comprehensive trimming options in conjunction with parallel processing to expedite pre-processing of large NGS datasets. Methods We developed ngsShoRT (next-generation sequencing Short Reads Trimmer), a flexible and comprehensive open-source software package written in Perl that provides a set of algorithms commonly used for pre-processing NGS short read sequences. We compared the features and performance of ngsShoRT with existing tools: CutAdapt, NGS QC Toolkit and Trimmomatic. We also compared the effects of using pre-processed short read sequences generated by different algorithms on de novo and reference-based assembly for three different genomes: Caenorhabditis elegans, Saccharomyces cerevisiae S288c, and Escherichia coli O157 H7. Results Several combinations of ngsShoRT algorithms were tested on publicly available Illumina GA II, HiSeq 2000, and MiSeq eukaryotic and bacteria genomic short read sequences with the focus on removing sequencing artifacts and low-quality reads and/or bases. Our results show that across three organisms and three sequencing platforms, trimming improved the mean quality scores of trimmed sequences. Using trimmed sequences for de novo and reference-based assembly improved assembly quality as well as assembler performance. In general, ngsShoRT outperformed comparable trimming tools in terms of trimming speed and improvement of de novo and reference-based assembly as measured by assembly contiguity and correctness. Conclusions Trimming of short read sequences can improve the quality of de novo and reference-based assembly and assembler performance. The parallel processing capability of ngsShoRT reduces trimming time and improves the memory efficiency when dealing with large datasets. We recommend combining sequencing artifacts removal, and quality score based read filtering and base trimming as the most consistent method for improving sequence quality and downstream assemblies. ngsShoRT source code, user guide and tutorial are available at http://research.bioinformatics.udel.edu/genomics/ngsShoRT/. ngsShoRT can be incorporated as a pre-processing step in genome and transcriptome assembly projects. PMID:24955109

A tetrahedron beam computed tomography benchtop system with a multiple pixel field emission x-ray tube.

PubMed

Xu, Xiaochao; Kim, Joshua; Laganis, Philip; Schulze, Derek; Liang, Yongguang; Zhang, Tiezhi

2011-10-01

To demonstrate the feasibility of Tetrahedron Beam Computed Tomography (TBCT) using a carbon nanotube (CNT) multiple pixel field emission x-ray (MPFEX) tube. A multiple pixel x-ray source facilitates the creation of novel x-ray imaging modalities. In a previous publication, the authors proposed a Tetrahedron Beam Computed Tomography (TBCT) imaging system which comprises a linear source array and a linear detector array that are orthogonal to each other. TBCT is expected to reduce scatter compared with Cone Beam Computed Tomography (CBCT) and to have better detector performance. Therefore, it may produce improved image quality for image guided radiotherapy. In this study, a TBCT benchtop system has been developed with an MPFEX tube. The tube has 75 CNT cold cathodes, which generate 75 x-ray focal spots on an elongated anode, and has 4 mm pixel spacing. An in-house-developed, 5-row CT detector array using silicon photodiodes and CdWO(4) scintillators was employed in the system. Hardware and software were developed for tube control and detector data acquisition. The raw data were preprocessed for beam hardening and detector response linearity and were reconstructed with an FDK-based image reconstruction algorithm. The focal spots were measured at about 1 × 2 mm(2) using a star phantom. Each cathode generates around 3 mA cathode current with 2190 V gate voltage. The benchtop system is able to perform TBCT scans with a prolonged scanning time. Images of a commercial CT phantom were successfully acquired. A prototype system was developed, and preliminary phantom images were successfully acquired. MPFEX is a promising x-ray source for TBCT. Further improvement of tube output is needed in order for it to be used in clinical TBCT systems.

Optimum parameters of image preprocessing method for Shack-Hartmann wavefront sensor in different SNR condition

NASA Astrophysics Data System (ADS)

Wei, Ping; Li, Xinyang; Luo, Xi; Li, Jianfeng

2018-02-01

The centroid method is commonly adopted to locate the spot in the sub-apertures in the Shack-Hartmann wavefront sensor (SH-WFS), in which preprocessing image is required before calculating the spot location due to that the centroid method is extremely sensitive to noises. In this paper, the SH-WFS image was simulated according to the characteristics of the noises, background and intensity distribution. The Optimal parameters of SH-WFS image preprocessing method were put forward, in different signal-to-noise ratio (SNR) conditions, where the wavefront reconstruction error was considered as the evaluation index. Two methods of image preprocessing, thresholding method and windowing combing with thresholding method, were compared by studying the applicable range of SNR and analyzing the stability of the two methods, respectively.

Real-time visualization of cross-sectional data in three dimensions

NASA Technical Reports Server (NTRS)

Mayes, Terrence J.; Foley, Theodore T.; Hamilton, Joseph A.; Duncavage, Tom C.

2005-01-01

This paper describes a technique for viewing and interacting with 2-D medical data in three dimensions. The approach requires little pre-processing, runs on personal computers, and has a wide range of application. Implementation details are discussed, examples are presented, and results are summarized.

Quantitative estimation of soil salinity by means of different modeling methods and visible-near infrared (VIS–NIR) spectroscopy, Ebinur Lake Wetland, Northwest China

PubMed Central

Wang, Jingzhe; Abulimiti, Aerzuna; Cai, Lianghong

2018-01-01

Soil salinization is one of the most common forms of land degradation. The detection and assessment of soil salinity is critical for the prevention of environmental deterioration especially in arid and semi-arid areas. This study introduced the fractional derivative in the pretreatment of visible and near infrared (VIS–NIR) spectroscopy. The soil samples (n = 400) collected from the Ebinur Lake Wetland, Xinjiang Uyghur Autonomous Region (XUAR), China, were used as the dataset. After measuring the spectral reflectance and salinity in the laboratory, the raw spectral reflectance was preprocessed by means of the absorbance and the fractional derivative order in the range of 0.0–2.0 order with an interval of 0.1. Two different modeling methods, namely, partial least squares regression (PLSR) and random forest (RF) with preprocessed reflectance were used for quantifying soil salinity. The results showed that more spectral characteristics were refined for the spectrum reflectance treated via fractional derivative. The validation accuracies showed that RF models performed better than those of PLSR. The most effective model was established based on RF with the 1.5 order derivative of absorbance with the optimal values of R2 (0.93), RMSE (4.57 dS m−1), and RPD (2.78 ≥ 2.50). The developed RF model was stable and accurate in the application of spectral reflectance for determining the soil salinity of the Ebinur Lake wetland. The pretreatment of fractional derivative could be useful for monitoring multiple soil parameters with higher accuracy, which could effectively help to analyze the soil salinity. PMID:29736341

Quantitative estimation of soil salinity by means of different modeling methods and visible-near infrared (VIS-NIR) spectroscopy, Ebinur Lake Wetland, Northwest China.

PubMed

Wang, Jingzhe; Ding, Jianli; Abulimiti, Aerzuna; Cai, Lianghong

2018-01-01

Soil salinization is one of the most common forms of land degradation. The detection and assessment of soil salinity is critical for the prevention of environmental deterioration especially in arid and semi-arid areas. This study introduced the fractional derivative in the pretreatment of visible and near infrared (VIS-NIR) spectroscopy. The soil samples ( n  = 400) collected from the Ebinur Lake Wetland, Xinjiang Uyghur Autonomous Region (XUAR), China, were used as the dataset. After measuring the spectral reflectance and salinity in the laboratory, the raw spectral reflectance was preprocessed by means of the absorbance and the fractional derivative order in the range of 0.0-2.0 order with an interval of 0.1. Two different modeling methods, namely, partial least squares regression (PLSR) and random forest (RF) with preprocessed reflectance were used for quantifying soil salinity. The results showed that more spectral characteristics were refined for the spectrum reflectance treated via fractional derivative. The validation accuracies showed that RF models performed better than those of PLSR. The most effective model was established based on RF with the 1.5 order derivative of absorbance with the optimal values of R 2 (0.93), RMSE (4.57 dS m -1 ), and RPD (2.78 ≥ 2.50). The developed RF model was stable and accurate in the application of spectral reflectance for determining the soil salinity of the Ebinur Lake wetland. The pretreatment of fractional derivative could be useful for monitoring multiple soil parameters with higher accuracy, which could effectively help to analyze the soil salinity.

Non-destructive determination of Malondialdehyde (MDA) distribution in oilseed rape leaves by laboratory scale NIR hyperspectral imaging

NASA Astrophysics Data System (ADS)

Kong, Wenwen; Liu, Fei; Zhang, Chu; Zhang, Jianfeng; Feng, Hailin

2016-10-01

The feasibility of hyperspectral imaging with 400-1000 nm was investigated to detect malondialdehyde (MDA) content in oilseed rape leaves under herbicide stress. After comparing the performance of different preprocessing methods, linear and nonlinear calibration models, the optimal prediction performance was achieved by extreme learning machine (ELM) model with only 23 wavelengths selected by competitive adaptive reweighted sampling (CARS), and the result was RP = 0.929 and RMSEP = 2.951. Furthermore, MDA distribution map was successfully achieved by partial least squares (PLS) model with CARS. This study indicated that hyperspectral imaging technology provided a fast and nondestructive solution for MDA content detection in plant leaves.

Machine learning modeling of plant phenology based on coupling satellite and gridded meteorological dataset

NASA Astrophysics Data System (ADS)

Czernecki, Bartosz; Nowosad, Jakub; Jabłońska, Katarzyna

2018-04-01

Changes in the timing of plant phenological phases are important proxies in contemporary climate research. However, most of the commonly used traditional phenological observations do not give any coherent spatial information. While consistent spatial data can be obtained from airborne sensors and preprocessed gridded meteorological data, not many studies robustly benefit from these data sources. Therefore, the main aim of this study is to create and evaluate different statistical models for reconstructing, predicting, and improving quality of phenological phases monitoring with the use of satellite and meteorological products. A quality-controlled dataset of the 13 BBCH plant phenophases in Poland was collected for the period 2007-2014. For each phenophase, statistical models were built using the most commonly applied regression-based machine learning techniques, such as multiple linear regression, lasso, principal component regression, generalized boosted models, and random forest. The quality of the models was estimated using a k-fold cross-validation. The obtained results showed varying potential for coupling meteorological derived indices with remote sensing products in terms of phenological modeling; however, application of both data sources improves models' accuracy from 0.6 to 4.6 day in terms of obtained RMSE. It is shown that a robust prediction of early phenological phases is mostly related to meteorological indices, whereas for autumn phenophases, there is a stronger information signal provided by satellite-derived vegetation metrics. Choosing a specific set of predictors and applying a robust preprocessing procedures is more important for final results than the selection of a particular statistical model. The average RMSE for the best models of all phenophases is 6.3, while the individual RMSE vary seasonally from 3.5 to 10 days. Models give reliable proxy for ground observations with RMSE below 5 days for early spring and late spring phenophases. For other phenophases, RMSE are higher and rise up to 9-10 days in the case of the earliest spring phenophases.

An EEMD-ICA Approach to Enhancing Artifact Rejection for Noisy Multivariate Neural Data.

PubMed

Zeng, Ke; Chen, Dan; Ouyang, Gaoxiang; Wang, Lizhe; Liu, Xianzeng; Li, Xiaoli

2016-06-01

As neural data are generally noisy, artifact rejection is crucial for data preprocessing. It has long been a grand research challenge for an approach which is able: 1) to remove the artifacts and 2) to avoid loss or disruption of the structural information at the same time, thus the risk of introducing bias to data interpretation may be minimized. In this study, an approach (namely EEMD-ICA) was proposed to first decompose multivariate neural data that are possibly noisy into intrinsic mode functions (IMFs) using ensemble empirical mode decomposition (EEMD). Independent component analysis (ICA) was then applied to the IMFs to separate the artifactual components. The approach was tested against the classical ICA and the automatic wavelet ICA (AWICA) methods, which were dominant methods for artifact rejection. In order to evaluate the effectiveness of the proposed approach in handling neural data possibly with intensive noises, experiments on artifact removal were performed using semi-simulated data mixed with a variety of noises. Experimental results indicate that the proposed approach continuously outperforms the counterparts in terms of both normalized mean square error (NMSE) and Structure SIMilarity (SSIM). The superiority becomes even greater with the decrease of SNR in all cases, e.g., SSIM of the EEMD-ICA can almost double that of AWICA and triple that of ICA. To further examine the potentials of the approach in sophisticated applications, the approach together with the counterparts were used to preprocess a real-life epileptic EEG with absence seizure. Experiments were carried out with the focus on characterizing the dynamics of the data after artifact rejection, i.e., distinguishing seizure-free, pre-seizure and seizure states. Using multi-scale permutation entropy to extract feature and linear discriminant analysis for classification, the EEMD-ICA performed the best for classifying the states (87.4%, about 4.1% and 8.7% higher than that of AWICA and ICA respectively), which was closest to the results of the manually selected dataset (89.7%).

chipPCR: an R package to pre-process raw data of amplification curves.

PubMed

Rödiger, Stefan; Burdukiewicz, Michał; Schierack, Peter

2015-09-01

Both the quantitative real-time polymerase chain reaction (qPCR) and quantitative isothermal amplification (qIA) are standard methods for nucleic acid quantification. Numerous real-time read-out technologies have been developed. Despite the continuous interest in amplification-based techniques, there are only few tools for pre-processing of amplification data. However, a transparent tool for precise control of raw data is indispensable in several scenarios, for example, during the development of new instruments. chipPCR is an R: package for the pre-processing and quality analysis of raw data of amplification curves. The package takes advantage of R: 's S4 object model and offers an extensible environment. chipPCR contains tools for raw data exploration: normalization, baselining, imputation of missing values, a powerful wrapper for amplification curve smoothing and a function to detect the start and end of an amplification curve. The capabilities of the software are enhanced by the implementation of algorithms unavailable in R: , such as a 5-point stencil for derivative interpolation. Simulation tools, statistical tests, plots for data quality management, amplification efficiency/quantification cycle calculation, and datasets from qPCR and qIA experiments are part of the package. Core functionalities are integrated in GUIs (web-based and standalone shiny applications), thus streamlining analysis and report generation. http://cran.r-project.org/web/packages/chipPCR. Source code: https://github.com/michbur/chipPCR. stefan.roediger@b-tu.de Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Selective monitoring

NASA Astrophysics Data System (ADS)

Homem-de-Mello, Luiz S.

1992-04-01

While in NASA's earlier space missions such as Voyager the number of sensors was in the hundreds, future platforms such as the Space Station Freedom will have tens of thousands sensors. For these planned missions it will be impossible to use the comprehensive monitoring strategy that was used in the past in which human operators monitored all sensors all the time. A selective monitoring strategy must be substituted for the current comprehensive strategy. This selective monitoring strategy uses computer tools to preprocess the incoming data and direct the operators' attention to the most critical parts of the physical system at any given time. There are several techniques that can be used to preprocess the incoming information. This paper presents an approach to using diagnostic reasoning techniques to preprocess the sensor data and detect which parts of the physical system require more attention because components have failed or are most likely to have failed. Given the sensor readings and a model of the physical system, a number of assertions are generated and expressed as Boolean equations. The resulting system of Boolean equations is solved symbolically. Using a priori probabilities of component failure and Bayes' rule, revised probabilities of failure can be computed. These will indicate what components have failed or are the most likely to have failed. This approach is suitable for systems that are well understood and for which the correctness of the assertions can be guaranteed. Also, the system must be such that assertions can be made from instantaneous measurements. And the system must be such that changes are slow enough to allow the computation.

A Segmentation Method for Lung Parenchyma Image Sequences Based on Superpixels and a Self-Generating Neural Forest

PubMed Central

Liao, Xiaolei; Zhao, Juanjuan; Jiao, Cheng; Lei, Lei; Qiang, Yan; Cui, Qiang

2016-01-01

Background Lung parenchyma segmentation is often performed as an important pre-processing step in the computer-aided diagnosis of lung nodules based on CT image sequences. However, existing lung parenchyma image segmentation methods cannot fully segment all lung parenchyma images and have a slow processing speed, particularly for images in the top and bottom of the lung and the images that contain lung nodules. Method Our proposed method first uses the position of the lung parenchyma image features to obtain lung parenchyma ROI image sequences. A gradient and sequential linear iterative clustering algorithm (GSLIC) for sequence image segmentation is then proposed to segment the ROI image sequences and obtain superpixel samples. The SGNF, which is optimized by a genetic algorithm (GA), is then utilized for superpixel clustering. Finally, the grey and geometric features of the superpixel samples are used to identify and segment all of the lung parenchyma image sequences. Results Our proposed method achieves higher segmentation precision and greater accuracy in less time. It has an average processing time of 42.21 seconds for each dataset and an average volume pixel overlap ratio of 92.22 ± 4.02% for four types of lung parenchyma image sequences. PMID:27532214

Contrast enhancement of mail piece images

NASA Astrophysics Data System (ADS)

Shin, Yong-Chul; Sridhar, Ramalingam; Demjanenko, Victor; Palumbo, Paul W.; Hull, Jonathan J.

1992-08-01

A New approach to contrast enhancement of mail piece images is presented. The contrast enhancement is used as a preprocessing step in the real-time address block location (RT-ABL) system. The RT-ABL system processes a stream of mail piece images and locates destination address blocks. Most of the mail pieces (classified into letters) show high contrast between background and foreground. As an extreme case, however, the seasonal greeting cards usually use colored envelopes which results in reduced contrast osured by an error rate by using a linear distributed associative memory (DAM). The DAM is trained to recognize the spectra of three classes of images: with high, medium, and low OCR error rates. The DAM is not forced to make a classification every time. It is allowed to reject as unknown a spectrum presented that does not closely resemble any that has been stored in the DAM. The DAM was fairly accurate with noisy images but conservative (i.e., rejected several text images as unknowns) when there was little ground and foreground degradations without affecting the nondegraded images. This approach provides local enhancement which adapts to local features. In order to simplify the computation of A and (sigma) , dynamic programming technique is used. Implementation details, performance, and the results on test images are presented in this paper.

Detection and Length Estimation of Linear Scratch on Solid Surfaces Using an Angle Constrained Ant Colony Technique

NASA Astrophysics Data System (ADS)

Pal, Siddharth; Basak, Aniruddha; Das, Swagatam

In many manufacturing areas the detection of surface defects is one of the most important processes in quality control. Currently in order to detect small scratches on solid surfaces most of the industries working on material manufacturing rely on visual inspection primarily. In this article we propose a hybrid computational intelligence technique to automatically detect a linear scratch from a solid surface and estimate its length (in pixel unit) simultaneously. The approach is based on a swarm intelligence algorithm called Ant Colony Optimization (ACO) and image preprocessing with Wiener and Sobel filters as well as the Canny edge detector. The ACO algorithm is mostly used to compensate for the broken parts of the scratch. Our experimental results confirm that the proposed technique can be used for detecting scratches from noisy and degraded images, even when it is very difficult for conventional image processing to distinguish the scratch area from its background.

Linear model for fast background subtraction in oligonucleotide microarrays.

PubMed

Kroll, K Myriam; Barkema, Gerard T; Carlon, Enrico

2009-11-16

One important preprocessing step in the analysis of microarray data is background subtraction. In high-density oligonucleotide arrays this is recognized as a crucial step for the global performance of the data analysis from raw intensities to expression values. We propose here an algorithm for background estimation based on a model in which the cost function is quadratic in a set of fitting parameters such that minimization can be performed through linear algebra. The model incorporates two effects: 1) Correlated intensities between neighboring features in the chip and 2) sequence-dependent affinities for non-specific hybridization fitted by an extended nearest-neighbor model. The algorithm has been tested on 360 GeneChips from publicly available data of recent expression experiments. The algorithm is fast and accurate. Strong correlations between the fitted values for different experiments as well as between the free-energy parameters and their counterparts in aqueous solution indicate that the model captures a significant part of the underlying physical chemistry.

Diagnostics of Dielectric Materials with Several Relaxation Times

NASA Astrophysics Data System (ADS)

Karpov, A. G.; Klemeshev, V. A.

2018-04-01

A set of means for detection and preprocessing of dielectrometric information has been suggested for studying the polarization/depolarization of dielectrics. Special attention has been paid to the processing of dielectrometric data for inhomogeneous materials using dielectric diagrams. Rapid analysis has been carried out the results of which can be used as initial approximations in more accurate (more complicated and time-consuming) iterative algorithms for model fitting.

Design of the first optical system for real-time tomographic holography (RTTH)

NASA Astrophysics Data System (ADS)

Galeotti, John M.; Siegel, Mel; Rallison, Richard D.; Stetten, George

2008-08-01

The design of the first Real-Time-Tomographic-Holography (RTTH) optical system for augmented-reality applications is presented. RTTH places a viewpoint-independent real-time (RT) virtual image (VI) of an object into its actual location, enabling natural hand-eye coordination to guide invasive procedures, without requiring tracking or a head-mounted device. The VI is viewed through a narrow-band Holographic Optical Element (HOE) with built-in power that generates the largest possible near-field, in-situ VI from a small display chip without noticeable parallax error or obscuring direct view of the physical world. Rigidly fixed upon a medical-ultrasound probe, RTTH could show the scan in its actual location inside the patient, because the VI would move with the probe. We designed the image source along with the system-optics, allowing us to ignore both planer geometric distortions and field curvature, respectively compensated by using RT pre-processing software and attaching a custom-surfaced fiber-optic-faceplate (FOFP) to our image source. Focus in our fast, non-axial system was achieved by placing correcting lenses near the FOFP and custom-optically-fabricating our volume-phase HOE using a recording beam that was specially shaped by extra lenses. By simultaneously simulating and optimizing the system's playback performance across variations in both the total playback and HOE-recording optical systems, we derived and built a design that projects a 104x112 mm planar VI 1 m from the HOE using a laser-illuminated 19x16 mm LCD+FOFP image-source. The VI appeared fixed in space and well focused. Viewpoint-induced location errors were <3 mm, and unexpected first-order astigmatism produced 3 cm (3% of 1 m) ambiguity in depth, typically unnoticed by human observers.

Four points function fitted and first derivative procedure for determining the end points in potentiometric titration curves: statistical analysis and method comparison.

PubMed

Kholeif, S A

2001-06-01

A new method that belongs to the differential category for determining the end points from potentiometric titration curves is presented. It uses a preprocess to find first derivative values by fitting four data points in and around the region of inflection to a non-linear function, and then locate the end point, usually as a maximum or minimum, using an inverse parabolic interpolation procedure that has an analytical solution. The behavior and accuracy of the sigmoid and cumulative non-linear functions used are investigated against three factors. A statistical evaluation of the new method using linear least-squares method validation and multifactor data analysis are covered. The new method is generally applied to symmetrical and unsymmetrical potentiometric titration curves, and the end point is calculated using numerical procedures only. It outperforms the "parent" regular differential method in almost all factors levels and gives accurate results comparable to the true or estimated true end points. Calculated end points from selected experimental titration curves compatible with the equivalence point category of methods, such as Gran or Fortuin, are also compared with the new method.

[Study on the early detection of Sclerotinia of Brassica napus based on combinational-stimulated bands].

PubMed

Liu, Fei; Feng, Lei; Lou, Bing-gan; Sun, Guang-ming; Wang, Lian-ping; He, Yong

2010-07-01

The combinational-stimulated bands were used to develop linear and nonlinear calibrations for the early detection of sclerotinia of oilseed rape (Brassica napus L.). Eighty healthy and 100 Sclerotinia leaf samples were scanned, and different preprocessing methods combined with successive projections algorithm (SPA) were applied to develop partial least squares (PLS) discriminant models, multiple linear regression (MLR) and least squares-support vector machine (LS-SVM) models. The results indicated that the optimal full-spectrum PLS model was achieved by direct orthogonal signal correction (DOSC), then De-trending and Raw spectra with correct recognition ratio of 100%, 95.7% and 95.7%, respectively. When using combinational-stimulated bands, the optimal linear models were SPA-MLR (DOSC) and SPA-PLS (DOSC) with correct recognition ratio of 100%. All SPA-LSSVM models using DOSC, De-trending and Raw spectra achieved perfect results with recognition of 100%. The overall results demonstrated that it was feasible to use combinational-stimulated bands for the early detection of Sclerotinia of oilseed rape, and DOSC-SPA was a powerful way for informative wavelength selection. This method supplied a new approach to the early detection and portable monitoring instrument of sclerotinia.

Nonlinear to Linear Elastic Code Coupling in 2-D Axisymmetric Media.

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

Preston, Leiph

Explosions within the earth nonlinearly deform the local media, but at typical seismological observation distances, the seismic waves can be considered linear. Although nonlinear algorithms can simulate explosions in the very near field well, these codes are computationally expensive and inaccurate at propagating these signals to great distances. A linearized wave propagation code, coupled to a nonlinear code, provides an efficient mechanism to both accurately simulate the explosion itself and to propagate these signals to distant receivers. To this end we have coupled Sandia's nonlinear simulation algorithm CTH to a linearized elastic wave propagation code for 2-D axisymmetric media (axiElasti)more » by passing information from the nonlinear to the linear code via time-varying boundary conditions. In this report, we first develop the 2-D axisymmetric elastic wave equations in cylindrical coordinates. Next we show how we design the time-varying boundary conditions passing information from CTH to axiElasti, and finally we demonstrate the coupling code via a simple study of the elastic radius.« less

Convolutional neural networks for vibrational spectroscopic data analysis.

PubMed

Acquarelli, Jacopo; van Laarhoven, Twan; Gerretzen, Jan; Tran, Thanh N; Buydens, Lutgarde M C; Marchiori, Elena

2017-02-15

In this work we show that convolutional neural networks (CNNs) can be efficiently used to classify vibrational spectroscopic data and identify important spectral regions. CNNs are the current state-of-the-art in image classification and speech recognition and can learn interpretable representations of the data. These characteristics make CNNs a good candidate for reducing the need for preprocessing and for highlighting important spectral regions, both of which are crucial steps in the analysis of vibrational spectroscopic data. Chemometric analysis of vibrational spectroscopic data often relies on preprocessing methods involving baseline correction, scatter correction and noise removal, which are applied to the spectra prior to model building. Preprocessing is a critical step because even in simple problems using 'reasonable' preprocessing methods may decrease the performance of the final model. We develop a new CNN based method and provide an accompanying publicly available software. It is based on a simple CNN architecture with a single convolutional layer (a so-called shallow CNN). Our method outperforms standard classification algorithms used in chemometrics (e.g. PLS) in terms of accuracy when applied to non-preprocessed test data (86% average accuracy compared to the 62% achieved by PLS), and it achieves better performance even on preprocessed test data (96% average accuracy compared to the 89% achieved by PLS). For interpretability purposes, our method includes a procedure for finding important spectral regions, thereby facilitating qualitative interpretation of results. Copyright © 2016 Elsevier B.V. All rights reserved.

Analysis of protein structures and interactions in complex food by near-infrared spectroscopy. 2. Hydrated gluten.

PubMed

Bruun, Susanne Wrang; Søndergaard, Ib; Jacobsen, Susanne

2007-09-05

Hydrated gluten, treated with various salts, was analyzed by near-infrared (NIR) spectroscopy to assess the ability of this method to reveal protein structure and interaction changes in perturbed food systems. The spectra were pretreated with second-derivative transformation and extended multiplicative signal correction for improving the band resolution and removing physical and quantitative spectral variations. Principal component analysis of the preprocessed spectra showed spectral effects that depended on salt type and concentration. Although both gluten texture and the NIR spectra were little influenced by treatment with salt solutions of low concentrations (0.1-0.2 M), they were significantly and diversely affected by treatment with 1.0 M salt solutions. Compared to hydration in water, hydration in 1.0 M sulfate salts caused spectral effects similar to a drying-out effect, which could be explained by salting-out.

ATS-6 - Preliminary results from the 13/18-GHz COMSAT Propagation Experiment

NASA Technical Reports Server (NTRS)

Hyde, G.

1975-01-01

The 13/18-GHz COMSAT Propagation Experiment (CPE) is reviewed, the data acquisition and processing are discussed, and samples of preliminary results are presented. The need for measurements of both hydrometeor-induced attenuation statistics and diversity effectiveness is brought out. The facilitation of the experiment - CPE dual frequency and diversity site location, the CPE ground transmit terminals, the CPE transponder on Applications Technology Satellite-6 (ATS-6), and the CPE receive and data acquisition system - is briefly examined. The on-line preprocessing of the received signal is reviewed, followed by a discussion of the off-line processing of this database to remove signal fluctuations not due to hydrometeors. Finally, samples of the results of first-level analysis of the resultant data for the 18-GHz diversity site near Boston, Mass., and for the dual frequency 13/18-GHz site near Detroit, Mich., are presented and discussed.

Nondestructive quantification of the soluble-solids content and the available acidity of apples by Fourier-transform near-infrared spectroscopy

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

Ying Yibin; Liu Yande; Tao Yang

2005-09-01

This research evaluated the feasibility of using Fourier-transform near-infrared (FT-NIR) spectroscopy to quantify the soluble-solids content (SSC) and the available acidity (VA) in intact apples. Partial least-squares calibration models, obtained from several preprocessing techniques (smoothing, derivative, etc.) in several wave-number ranges were compared. The best models were obtained with the high coefficient determination (r{sup 2}) 0.940 for the SSC and a moderate r{sup 2} of 0.801 for the VA, root-mean-square errors of prediction of 0.272% and 0.053%, and root-mean-square errors of calibration of 0.261% and 0.046%, respectively. The results indicate that the FT-NIR spectroscopy yields good predictions of the SSCmore » and also showed the feasibility of using it to predict the VA of apples.« less

Noninvasive Recognition and Biomarkers of Early Allergic Asthma in Cats Using Multivariate Statistical Analysis of NMR Spectra of Exhaled Breath Condensate

PubMed Central

Fulcher, Yan G.; Fotso, Martial; Chang, Chee-Hoon; Rindt, Hans; Reinero, Carol R.

2016-01-01

Asthma is prevalent in children and cats, and needs means of noninvasive diagnosis. We sought to distinguish noninvasively the differences in 53 cats before and soon after induction of allergic asthma, using NMR spectra of exhaled breath condensate (EBC). Statistical pattern recognition was improved considerably by preprocessing the spectra with probabilistic quotient normalization and glog transformation. Classification of the 106 preprocessed spectra by principal component analysis and partial least squares with discriminant analysis (PLS-DA) appears to be impaired by variances unrelated to eosinophilic asthma. By filtering out confounding variances, orthogonal signal correction (OSC) PLS-DA greatly improved the separation of the healthy and early asthmatic states, attaining 94% specificity and 94% sensitivity in predictions. OSC enhancement of multi-level PLS-DA boosted the specificity of the prediction to 100%. OSC-PLS-DA of the normalized spectra suggest the most promising biomarkers of allergic asthma in cats to include increased acetone, metabolite(s) with overlapped NMR peaks near 5.8 ppm, and a hydroxyphenyl-containing metabolite, as well as decreased phthalate. Acetone is elevated in the EBC of 74% of the cats with early asthma. The noninvasive detection of early experimental asthma, biomarkers in EBC, and metabolic perturbation invite further investigation of the diagnostic potential in humans. PMID:27764146

3D quantitative photoacoustic image reconstruction using Monte Carlo method and linearization

NASA Astrophysics Data System (ADS)

Okawa, Shinpei; Hirasawa, Takeshi; Tsujita, Kazuhiro; Kushibiki, Toshihiro; Ishihara, Miya

2018-02-01

To quantify the functional and structural information of peripheral blood vessels for diagnoses of diseases which affects peripheral blood vessels such as diabetes and peripheral vascular disease, a 3D quantitative photoacoustic tomography (QPAT) reconstructing the optical properties such as the absorption coefficient reflecting microvascular structures and hemoglobin concentration and oxygenation saturation is studied. QPAT image reconstruction algorithms based on radiative transfer equation (RTE) and photon diffusion equation (PDE) have been proposed. However, it is not easy to use RTE in the clinical practice because of the huge computational load and long calculation time. On the other hand, it is always considered problematic to use PDE, because it does not approximate RTE well near the illuminating position. In this study, we developed the 3D QPAT image reconstruction using Monte Carlo (MC) method which approximates RTE better than PDE to reconstruct the optical properties in the region near the illuminating surface. To reduce the calculation time, we applied linearization. The QPAT image reconstruction algorithm with MC method and linearization was examined in numerical simulations and phantom experiment by use of a scanning system with a single probe consisting of P(VDF-TrFE) piezo electric film and optical fiber.

Electroencephalography Based Fusion Two-Dimensional (2D)-Convolution Neural Networks (CNN) Model for Emotion Recognition System.

PubMed

Kwon, Yea-Hoon; Shin, Sae-Byuk; Kim, Shin-Dug

2018-04-30

The purpose of this study is to improve human emotional classification accuracy using a convolution neural networks (CNN) model and to suggest an overall method to classify emotion based on multimodal data. We improved classification performance by combining electroencephalogram (EEG) and galvanic skin response (GSR) signals. GSR signals are preprocessed using by the zero-crossing rate. Sufficient EEG feature extraction can be obtained through CNN. Therefore, we propose a suitable CNN model for feature extraction by tuning hyper parameters in convolution filters. The EEG signal is preprocessed prior to convolution by a wavelet transform while considering time and frequency simultaneously. We use a database for emotion analysis using the physiological signals open dataset to verify the proposed process, achieving 73.4% accuracy, showing significant performance improvement over the current best practice models.

Classification of product inspection items using nonlinear features

NASA Astrophysics Data System (ADS)

Talukder, Ashit; Casasent, David P.; Lee, H.-W.

1998-03-01

Automated processing and classification of real-time x-ray images of randomly oriented touching pistachio nuts is discussed. The ultimate objective is the development of a system for automated non-invasive detection of defective product items on a conveyor belt. This approach involves two main steps: preprocessing and classification. Preprocessing locates individual items and segments ones that touch using a modified watershed algorithm. The second stage involves extraction of features that allow discrimination between damaged and clean items (pistachio nuts). This feature extraction and classification stage is the new aspect of this paper. We use a new nonlinear feature extraction scheme called the maximum representation and discriminating feature (MRDF) extraction method to compute nonlinear features that are used as inputs to a classifier. The MRDF is shown to provide better classification and a better ROC (receiver operating characteristic) curve than other methods.

Real-time system for imaging and object detection with a multistatic GPR array

DOEpatents

Paglieroni, David W; Beer, N Reginald; Bond, Steven W; Top, Philip L; Chambers, David H; Mast, Jeffrey E; Donetti, John G; Mason, Blake C; Jones, Steven M

2014-10-07

A method and system for detecting the presence of subsurface objects within a medium is provided. In some embodiments, the imaging and detection system operates in a multistatic mode to collect radar return signals generated by an array of transceiver antenna pairs that is positioned across the surface and that travels down the surface. The imaging and detection system pre-processes the return signal to suppress certain undesirable effects. The imaging and detection system then generates synthetic aperture radar images from real aperture radar images generated from the pre-processed return signal. The imaging and detection system then post-processes the synthetic aperture radar images to improve detection of subsurface objects. The imaging and detection system identifies peaks in the energy levels of the post-processed image frame, which indicates the presence of a subsurface object.

A quantitative comparison of numerical methods for the compressible Euler equations: fifth-order WENO and piecewise-linear Godunov

NASA Astrophysics Data System (ADS)

Greenough, J. A.; Rider, W. J.

2004-05-01

A numerical study is undertaken comparing a fifth-order version of the weighted essentially non-oscillatory numerical (WENO5) method to a modern piecewise-linear, second-order, version of Godunov's (PLMDE) method for the compressible Euler equations. A series of one-dimensional test problems are examined beginning with classical linear problems and ending with complex shock interactions. The problems considered are: (1) linear advection of a Gaussian pulse in density, (2) Sod's shock tube problem, (3) the "peak" shock tube problem, (4) a version of the Shu and Osher shock entropy wave interaction and (5) the Woodward and Colella interacting shock wave problem. For each problem and method, run times, density error norms and convergence rates are reported for each method as produced from a common code test-bed. The linear problem exhibits the advertised convergence rate for both methods as well as the expected large disparity in overall error levels; WENO5 has the smaller errors and an enormous advantage in overall efficiency (in accuracy per unit CPU time). For the nonlinear problems with discontinuities, however, we generally see both first-order self-convergence of error as compared to an exact solution, or when an analytic solution is not available, a converged solution generated on an extremely fine grid. The overall comparison of error levels shows some variation from problem to problem. For Sod's shock tube, PLMDE has nearly half the error, while on the peak problem the errors are nearly the same. For the interacting blast wave problem the two methods again produce a similar level of error with a slight edge for the PLMDE. On the other hand, for the Shu-Osher problem, the errors are similar on the coarser grids, but favors WENO by a factor of nearly 1.5 on the finer grids used. In all cases holding mesh resolution constant though, PLMDE is less costly in terms of CPU time by approximately a factor of 6. If the CPU cost is taken as fixed, that is run times are equal for both numerical methods, then PLMDE uniformly produces lower errors than WENO for the fixed computation cost on the test problems considered here.

Observation of Rayleigh-Taylor-instability evolution in a plasma with magnetic and viscous effects

DOE PAGES

Adams, Colin S.; Moser, Auna L.; Hsu, Scott C.

2015-11-06

We present time-resolved observations of Rayleigh-Taylor-instability (RTI) evolution at the interface between an unmagnetized plasma jet colliding with a stagnated, magnetized plasma. The observed instability growth time (~10μs) is consistent with the estimated linear RTI growth rate calculated using experimentally inferred values of density (~10 14cm–3) and deceleration (~10 9 m/s 2). The observed mode wavelength (≳1 cm) nearly doubles within a linear growth time. Furthermore, theoretical estimates of magnetic and viscous stabilization and idealized magnetohydrodynamic simulations including a physical viscosity model both suggest that the observed instability evolution is subject to magnetic and/or viscous effects.

Power-law scaling of extreme dynamics near higher-order exceptional points

NASA Astrophysics Data System (ADS)

Zhong, Q.; Christodoulides, D. N.; Khajavikhan, M.; Makris, K. G.; El-Ganainy, R.

2018-02-01

We investigate the extreme dynamics of non-Hermitian systems near higher-order exceptional points in photonic networks constructed using the bosonic algebra method. We show that strong power oscillations for certain initial conditions can occur as a result of the peculiar eigenspace geometry and its dimensionality collapse near these singularities. By using complementary numerical and analytical approaches, we show that, in the parity-time (PT ) phase near exceptional points, the logarithm of the maximum optical power amplification scales linearly with the order of the exceptional point. We focus in our discussion on photonic systems, but we note that our results apply to other physical systems as well.

Zseq: An Approach for Preprocessing Next-Generation Sequencing Data.

PubMed

Alkhateeb, Abedalrhman; Rueda, Luis

2017-08-01

Next-generation sequencing technology generates a huge number of reads (short sequences), which contain a vast amount of genomic data. The sequencing process, however, comes with artifacts. Preprocessing of sequences is mandatory for further downstream analysis. We present Zseq, a linear method that identifies the most informative genomic sequences and reduces the number of biased sequences, sequence duplications, and ambiguous nucleotides. Zseq finds the complexity of the sequences by counting the number of unique k-mers in each sequence as its corresponding score and also takes into the account other factors such as ambiguous nucleotides or high GC-content percentage in k-mers. Based on a z-score threshold, Zseq sweeps through the sequences again and filters those with a z-score less than the user-defined threshold. Zseq algorithm is able to provide a better mapping rate; it reduces the number of ambiguous bases significantly in comparison with other methods. Evaluation of the filtered reads has been conducted by aligning the reads and assembling the transcripts using the reference genome as well as de novo assembly. The assembled transcripts show a better discriminative ability to separate cancer and normal samples in comparison with another state-of-the-art method. Moreover, de novo assembled transcripts from the reads filtered by Zseq have longer genomic sequences than other tested methods. Estimating the threshold of the cutoff point is introduced using labeling rules with optimistic results.

Research on registration algorithm for check seal verification

NASA Astrophysics Data System (ADS)

Wang, Shuang; Liu, Tiegen

2008-03-01

Nowadays seals play an important role in China. With the development of social economy, the traditional method of manual check seal identification can't meet the need s of banking transactions badly. This paper focus on pre-processing and registration algorithm for check seal verification using theory of image processing and pattern recognition. First of all, analyze the complex characteristics of check seals. To eliminate the difference of producing conditions and the disturbance caused by background and writing in check image, many methods are used in the pre-processing of check seal verification, such as color components transformation, linearity transform to gray-scale image, medium value filter, Otsu, close calculations and labeling algorithm of mathematical morphology. After the processes above, the good binary seal image can be obtained. On the basis of traditional registration algorithm, a double-level registration method including rough and precise registration method is proposed. The deflection angle of precise registration method can be precise to 0.1°. This paper introduces the concepts of difference inside and difference outside and use the percent of difference inside and difference outside to judge whether the seal is real or fake. The experimental results of a mass of check seals are satisfied. It shows that the methods and algorithmic presented have good robustness to noise sealing conditions and satisfactory tolerance of difference within class.

Test of the diffusing-diffusivity mechanism using near-wall colloidal dynamics

NASA Astrophysics Data System (ADS)

Matse, Mpumelelo; Chubynsky, Mykyta V.; Bechhoefer, John

2017-10-01

The mechanism of diffusing diffusivity predicts that, in environments where the diffusivity changes gradually, the displacement distribution becomes non-Gaussian, even though the mean-square displacement grows linearly with time. Here, we report single-particle tracking measurements of the diffusion of colloidal spheres near a planar substrate. Because the local effective diffusivity is known, we have been able to carry out a direct test of this mechanism for diffusion in inhomogeneous media.

The Minimal Preprocessing Pipelines for the Human Connectome Project

PubMed Central

Glasser, Matthew F.; Sotiropoulos, Stamatios N; Wilson, J Anthony; Coalson, Timothy S; Fischl, Bruce; Andersson, Jesper L; Xu, Junqian; Jbabdi, Saad; Webster, Matthew; Polimeni, Jonathan R; Van Essen, David C; Jenkinson, Mark

2013-01-01

The Human Connectome Project (HCP) faces the challenging task of bringing multiple magnetic resonance imaging (MRI) modalities together in a common automated preprocessing framework across a large cohort of subjects. The MRI data acquired by the HCP differ in many ways from data acquired on conventional 3 Tesla scanners and often require newly developed preprocessing methods. We describe the minimal preprocessing pipelines for structural, functional, and diffusion MRI that were developed by the HCP to accomplish many low level tasks, including spatial artifact/distortion removal, surface generation, cross-modal registration, and alignment to standard space. These pipelines are specially designed to capitalize on the high quality data offered by the HCP. The final standard space makes use of a recently introduced CIFTI file format and the associated grayordinates spatial coordinate system. This allows for combined cortical surface and subcortical volume analyses while reducing the storage and processing requirements for high spatial and temporal resolution data. Here, we provide the minimum image acquisition requirements for the HCP minimal preprocessing pipelines and additional advice for investigators interested in replicating the HCP’s acquisition protocols or using these pipelines. Finally, we discuss some potential future improvements for the pipelines. PMID:23668970

An Improved Framework for Confound Regression and Filtering for Control of Motion Artifact in the Preprocessing of Resting-State Functional Connectivity Data

PubMed Central

Satterthwaite, Theodore D.; Elliott, Mark A.; Gerraty, Raphael T.; Ruparel, Kosha; Loughead, James; Calkins, Monica E.; Eickhoff, Simon B.; Hakonarson, Hakon; Gur, Ruben C.; Gur, Raquel E.; Wolf, Daniel H.

2013-01-01

Several recent reports in large, independent samples have demonstrated the influence of motion artifact on resting-state functional connectivity MRI (rsfc-MRI). Standard rsfc-MRI preprocessing typically includes regression of confounding signals and band-pass filtering. However, substantial heterogeneity exists in how these techniques are implemented across studies, and no prior study has examined the effect of differing approaches for the control of motion-induced artifacts. To better understand how in-scanner head motion affects rsfc-MRI data, we describe the spatial, temporal, and spectral characteristics of motion artifacts in a sample of 348 adolescents. Analyses utilize a novel approach for describing head motion on a voxelwise basis. Next, we systematically evaluate the efficacy of a range of confound regression and filtering techniques for the control of motion-induced artifacts. Results reveal that the effectiveness of preprocessing procedures on the control of motion is heterogeneous, and that improved preprocessing provides a substantial benefit beyond typical procedures. These results demonstrate that the effect of motion on rsfc-MRI can be substantially attenuated through improved preprocessing procedures, but not completely removed. PMID:22926292

Multi-objective optimization for an automated and simultaneous phase and baseline correction of NMR spectral data

NASA Astrophysics Data System (ADS)

Sawall, Mathias; von Harbou, Erik; Moog, Annekathrin; Behrens, Richard; Schröder, Henning; Simoneau, Joël; Steimers, Ellen; Neymeyr, Klaus

2018-04-01

Spectral data preprocessing is an integral and sometimes inevitable part of chemometric analyses. For Nuclear Magnetic Resonance (NMR) spectra a possible first preprocessing step is a phase correction which is applied to the Fourier transformed free induction decay (FID) signal. This preprocessing step can be followed by a separate baseline correction step. Especially if series of high-resolution spectra are considered, then automated and computationally fast preprocessing routines are desirable. A new method is suggested that applies the phase and the baseline corrections simultaneously in an automated form without manual input, which distinguishes this work from other approaches. The underlying multi-objective optimization or Pareto optimization provides improved results compared to consecutively applied correction steps. The optimization process uses an objective function which applies strong penalty constraints and weaker regularization conditions. The new method includes an approach for the detection of zero baseline regions. The baseline correction uses a modified Whittaker smoother. The functionality of the new method is demonstrated for experimental NMR spectra. The results are verified against gravimetric data. The method is compared to alternative preprocessing tools. Additionally, the simultaneous correction method is compared to a consecutive application of the two correction steps.

H-II launch vehicle telemetry system realizing intelligent control of pre-processed data from remote terminal

NASA Astrophysics Data System (ADS)

Tanioka, Noritaka; Yoshida, Yasunori; Obi, Shinzo; Chiba, Ryoichi; Nakai, Kazumoto

The development of a PCM telemetry system for the Japanese H-II launch vehicle is discussed. PCM data streams acquire and process data from remote terminals which can be located at any place near the data source. The data are synchronized by a clock and are individually controlled by a central PCM data processing unit. The system allows the launch vehicle to acquire data from many different areas of the rocket, with a total of 879 channels. The data are multiplexed and processed into one PCM data stream and are down-linked on a phase-modulated RF carrier.

Unstructured Cartesian/prismatic grid generation for complex geometries

NASA Technical Reports Server (NTRS)

Karman, Steve L., Jr.

1995-01-01

The generation of a hybrid grid system for discretizing complex three dimensional (3D) geometries is described. The primary grid system is an unstructured Cartesian grid automatically generated using recursive cell subdivision. This grid system is sufficient for computing Euler solutions about extremely complex 3D geometries. A secondary grid system, using triangular-prismatic elements, may be added for resolving the boundary layer region of viscous flows near surfaces of solid bodies. This paper describes the grid generation processes used to generate each grid type. Several example grids are shown, demonstrating the ability of the method to discretize complex geometries, with very little pre-processing required by the user.

Diffractive-optical correlators: chances to make optical image preprocessing as intelligent as human vision

NASA Astrophysics Data System (ADS)

Lauinger, Norbert

2004-10-01

The human eye is a good model for the engineering of optical correlators. Three prominent intelligent functionalities in human vision could in the near future become realized by a new diffractive-optical hardware design of optical imaging sensors: (1) Illuminant-adaptive RGB-based color Vision, (2) Monocular 3D Vision based on RGB data processing, (3) Patchwise fourier-optical Object-Classification and Identification. The hardware design of the human eye has specific diffractive-optical elements (DOE's) in aperture and in image space and seems to execute the three jobs at -- or not far behind -- the loci of the images of objects.

Control design and performance analysis of a 6 MW wind turbine-generator

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

Murdoch, A.; Barton, R.S.; Javid, S.H.

1983-05-01

This paper discusses an approach to the modeling and performance for the preliminary design phase of a large (6.2 MW) horizontal axis wind turbine generator (WTG). Two control philosophies are presented, both of which are based on linearized models of the WT mechanical and electrical systems. The control designs are compared by showing the performance through detailed non-linear time simulation. The disturbances considered are wind gusts, and electrical faults near the WT terminals.

Control design and performance analysis of a 6 MW wind turbine-generator

NASA Technical Reports Server (NTRS)

Murdoch, A.; Winkelman, J. R.; Javid, S. H.; Barton, R. S.

1983-01-01

This paper discusses an approach to the modeling and performance for the preliminary design phase of a large (6.2 MW) horizontal axis wind turbine generator (WTG). Two control philosophies are presented, both of which are based on linearized models of the WT mechanical and electrical systems. The control designs are compared by showing the performance through detailed non-linear time simulation. The disturbances considered are wind gusts, and electrical faults near the WT terminals.

Linear parameter varying identification of ankle joint intrinsic stiffness during imposed walking movements.

PubMed

Sobhani Tehrani, Ehsan; Jalaleddini, Kian; Kearney, Robert E

2013-01-01

This paper describes a novel model structure and identification method for the time-varying, intrinsic stiffness of human ankle joint during imposed walking (IW) movements. The model structure is based on the superposition of a large signal, linear, time-invariant (LTI) model and a small signal linear-parameter varying (LPV) model. The methodology is based on a two-step algorithm; the LTI model is first estimated using data from an unperturbed IW trial. Then, the LPV model is identified using data from a perturbed IW trial with the output predictions of the LTI model removed from the measured torque. Experimental results demonstrate that the method accurately tracks the continuous-time variation of normal ankle intrinsic stiffness when the joint position changes during the IW movement. Intrinsic stiffness gain decreases from full plantarflexion to near the mid-point of plantarflexion and then increases substantially as the ankle is dosriflexed.

Preprocessing of 2-Dimensional Gel Electrophoresis Images Applied to Proteomic Analysis: A Review.

PubMed

Goez, Manuel Mauricio; Torres-Madroñero, Maria Constanza; Röthlisberger, Sarah; Delgado-Trejos, Edilson

2018-02-01

Various methods and specialized software programs are available for processing two-dimensional gel electrophoresis (2-DGE) images. However, due to the anomalies present in these images, a reliable, automated, and highly reproducible system for 2-DGE image analysis has still not been achieved. The most common anomalies found in 2-DGE images include vertical and horizontal streaking, fuzzy spots, and background noise, which greatly complicate computational analysis. In this paper, we review the preprocessing techniques applied to 2-DGE images for noise reduction, intensity normalization, and background correction. We also present a quantitative comparison of non-linear filtering techniques applied to synthetic gel images, through analyzing the performance of the filters under specific conditions. Synthetic proteins were modeled into a two-dimensional Gaussian distribution with adjustable parameters for changing the size, intensity, and degradation. Three types of noise were added to the images: Gaussian, Rayleigh, and exponential, with signal-to-noise ratios (SNRs) ranging 8-20 decibels (dB). We compared the performance of wavelet, contourlet, total variation (TV), and wavelet-total variation (WTTV) techniques using parameters SNR and spot efficiency. In terms of spot efficiency, contourlet and TV were more sensitive to noise than wavelet and WTTV. Wavelet worked the best for images with SNR ranging 10-20 dB, whereas WTTV performed better with high noise levels. Wavelet also presented the best performance with any level of Gaussian noise and low levels (20-14 dB) of Rayleigh and exponential noise in terms of SNR. Finally, the performance of the non-linear filtering techniques was evaluated using a real 2-DGE image with previously identified proteins marked. Wavelet achieved the best detection rate for the real image. Copyright © 2018 Beijing Institute of Genomics, Chinese Academy of Sciences and Genetics Society of China. Production and hosting by Elsevier B.V. All rights reserved.

Towards a Near Real-Time Satellite-Based Flux Monitoring System for the MENA Region

NASA Astrophysics Data System (ADS)

Ershadi, A.; Houborg, R.; McCabe, M. F.; Anderson, M. C.; Hain, C.

2013-12-01

Satellite remote sensing has the potential to offer spatially and temporally distributed information on land surface characteristics, which may be used as inputs and constraints for estimating land surface fluxes of carbon, water and energy. Enhanced satellite-based monitoring systems for aiding local water resource assessments and agricultural management activities are particularly needed for the Middle East and North Africa (MENA) region. The MENA region is an area characterized by limited fresh water resources, an often inefficient use of these, and relatively poor in-situ monitoring as a result of sparse meteorological observations. To address these issues, an integrated modeling approach for near real-time monitoring of land surface states and fluxes at fine spatio-temporal scales over the MENA region is presented. This approach is based on synergistic application of multiple sensors and wavebands in the visible to shortwave infrared and thermal infrared (TIR) domain. The multi-scale flux mapping and monitoring system uses the Atmosphere-Land Exchange Inverse (ALEXI) model and associated flux disaggregation scheme (DisALEXI), and the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) in conjunction with model reanalysis data and multi-sensor remotely sensed data from polar orbiting (e.g. Landsat and MODerate resolution Imaging Spectroradiometer (MODIS)) and geostationary (MSG; Meteosat Second Generation) satellite platforms to facilitate time-continuous (i.e. daily) estimates of field-scale water, energy and carbon fluxes. Within this modeling system, TIR satellite data provide information about the sub-surface moisture status and plant stress, obviating the need for precipitation input and a detailed soil surface characterization (i.e. for prognostic modeling of soil transport processes). The STARFM fusion methodology blends aspects of high frequency (spatially coarse) and spatially fine resolution sensors and is applied directly to flux output fields to facilitate daily mapping of fluxes at sub-field scales. A complete processing infrastructure to automatically ingest and pre-process all required input data and to execute the integrated modeling system for near real-time agricultural monitoring purposes over targeted MENA sites is being developed, and initial results from this concerted effort will be discussed.

ACTS (Advanced Communications Technology Satellite) Propagation Experiment: Preprocessing Software User's Manual

NASA Technical Reports Server (NTRS)

Crane, Robert K.; Wang, Xuhe; Westenhaver, David

1996-01-01

The preprocessing software manual describes the Actspp program originally developed to observe and diagnose Advanced Communications Technology Satellite (ACTS) propagation terminal/receiver problems. However, it has been quite useful for automating the preprocessing functions needed to convert the terminal output to useful attenuation estimates. Prior to having data acceptable for archival functions, the individual receiver system must be calibrated and the power level shifts caused by ranging tone modulation must be received. Actspp provides three output files: the daylog, the diurnal coefficient file, and the file that contains calibration information.

Regional trace gas monitoring simplified - A linear retrieval scheme for carbon monoxide from hyperspectral soundings

NASA Astrophysics Data System (ADS)

Smith, N.; Huang, A.; Weisz, E.; Annegarn, H. J.

2011-12-01

The Fast Linear Inversion Trace gas System (FLITS) is designed to retrieve tropospheric total column trace gas densities [molec.cm-2] from space-borne hyperspectral infrared soundings. The objective to develop a new retrieval scheme was motivated by the need for near real-time air quality monitoring at high spatial resolution. We present a case study of FLITS carbon monoxide (CO) retrievals from daytime (descending orbit) Infrared Atmospheric Sounding Interferometer (IASI) measurements that have a 0.5 cm-1 spectral resolution and 12 km footprint at nadir. The standard Level 2 IASI CO retrieval product (COL2) is available in near real-time but is spatially averaged over 2 x 2 pixels, or 50 x 50 km, and thus more suitable for global analysis. The study region is Southern Africa (south of the equator) for the period 28-31 August 2008. An atmospheric background estimate is obtained from a chemical transport model, emissivity from regional measurements and surface temperature (ST) from space-borne retrievals. The CO background error is set to 10%. FLITS retrieves CO by assuming a simple linear relationship between the IASI measurements and background estimate of the atmosphere and surface parameters. This differs from the COL2 algorithm that treats CO retrieval as a moderately non-linear problem. When compared to COL2, the FLITS retrievals display similar trends in distribution and transport of CO over time with the advantage of an improved spatial resolution (single-pixel). The value of the averaging kernel (A) is consistently above 0.5 and indicates that FLITS retrievals have a stable dependence on the measurement. This stability is achieved through careful channel selection in the strongest CO absorption lines (2050-2225 cm-1) and joint retrieval with skin temperature (IASI sensitivity to CO is highly correlated with ST), thus no spatial averaging is necessary. We conclude that the simplicity and stability of FLITS make it useful first as a research tool, i.e. the algorithm is easy to understand and computationally simple enough to run on most desktop computers, and second, as an operational tool that can calculate near real-time CO retrievals at instrument resolution for regional monitoring.

Evaluation of the robustness of the preprocessing technique improving reversible compressibility of CT images: Tested on various CT examinations

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

Jeon, Chang Ho; Kim, Bohyoung; Gu, Bon Seung

2013-10-15

Purpose: To modify the preprocessing technique, which was previously proposed, improving compressibility of computed tomography (CT) images to cover the diversity of three dimensional configurations of different body parts and to evaluate the robustness of the technique in terms of segmentation correctness and increase in reversible compression ratio (CR) for various CT examinations.Methods: This study had institutional review board approval with waiver of informed patient consent. A preprocessing technique was previously proposed to improve the compressibility of CT images by replacing pixel values outside the body region with a constant value resulting in maximizing data redundancy. Since the technique wasmore » developed aiming at only chest CT images, the authors modified the segmentation method to cover the diversity of three dimensional configurations of different body parts. The modified version was evaluated as follows. In randomly selected 368 CT examinations (352 787 images), each image was preprocessed by using the modified preprocessing technique. Radiologists visually confirmed whether the segmented region covers the body region or not. The images with and without the preprocessing were reversibly compressed using Joint Photographic Experts Group (JPEG), JPEG2000 two-dimensional (2D), and JPEG2000 three-dimensional (3D) compressions. The percentage increase in CR per examination (CR{sub I}) was measured.Results: The rate of correct segmentation was 100.0% (95% CI: 99.9%, 100.0%) for all the examinations. The median of CR{sub I} were 26.1% (95% CI: 24.9%, 27.1%), 40.2% (38.5%, 41.1%), and 34.5% (32.7%, 36.2%) in JPEG, JPEG2000 2D, and JPEG2000 3D, respectively.Conclusions: In various CT examinations, the modified preprocessing technique can increase in the CR by 25% or more without concerning about degradation of diagnostic information.« less

Fast reconstruction of optical properties for complex segmentations in near infrared imaging

NASA Astrophysics Data System (ADS)

Jiang, Jingjing; Wolf, Martin; Sánchez Majos, Salvador

2017-04-01

The intrinsic ill-posed nature of the inverse problem in near infrared imaging makes the reconstruction of fine details of objects deeply embedded in turbid media challenging even for the large amounts of data provided by time-resolved cameras. In addition, most reconstruction algorithms for this type of measurements are only suitable for highly symmetric geometries and rely on a linear approximation to the diffusion equation since a numerical solution of the fully non-linear problem is computationally too expensive. In this paper, we will show that a problem of practical interest can be successfully addressed making efficient use of the totality of the information supplied by time-resolved cameras. We set aside the goal of achieving high spatial resolution for deep structures and focus on the reconstruction of complex arrangements of large regions. We show numerical results based on a combined approach of wavelength-normalized data and prior geometrical information, defining a fully parallelizable problem in arbitrary geometries for time-resolved measurements. Fast reconstructions are obtained using a diffusion approximation and Monte-Carlo simulations, parallelized in a multicore computer and a GPU respectively.

Robust Flood Monitoring Using Sentinel-1 SAR Time Series

NASA Astrophysics Data System (ADS)

DeVries, B.; Huang, C.; Armston, J.; Huang, W.

2017-12-01

The 2017 hurricane season in North and Central America has resulted in unprecedented levels of flooding that have affected millions of people and continue to impact communities across the region. The extent of casualties and damage to property incurred by these floods underscores the need for reliable systems to track flood location, timing and duration to aid response and recovery efforts. While a diverse range of data sources provide vital information on flood status in near real-time, only spaceborne Synthetic Aperture Radar (SAR) sensors can ensure wall-to-wall coverage over large areas, mostly independently of weather conditions or site accessibility. The European Space Agency's Sentinel-1 constellation represents the only SAR mission currently providing open access and systematic global coverage, allowing for a consistent stream of observations over flood-prone regions. Importantly, both the data and pre-processing software are freely available, enabling the development of improved methods, tools and data products to monitor floods in near real-time. We tracked flood onset and progression in Southeastern Texas, Southern Florida, and Puerto Rico using a novel approach based on temporal backscatter anomalies derived from times series of Sentinel-1 observations and historic baselines defined for each of the three sites. This approach was shown to provide a more objective measure of flood occurrence than the simple backscatter thresholds often employed in operational flood monitoring systems. Additionally, the use of temporal anomaly measures allowed us to partially overcome biases introduced by varying sensor view angles and image acquisition modes, allowing increased temporal resolution in areas where additional targeted observations are available. Our results demonstrate the distinct advantages offered by data from operational SAR missions such as Sentinel-1 and NASA's planned NISAR mission, and call attention to the continuing need for SAR Earth Observation missions that provide systematic repeat observations to facilitate continuous monitoring of flood-affected regions.

Simulating Nonequilibrium Radiation via Orthogonal Polynomial Refinement

DTIC Science & Technology

2015-01-07

measured by the preprocessing time, computer memory space, and average query time. In many search procedures for the number of points np of a data set, a...analytic expression for the radiative flux density is possible by the commonly accepted local thermal equilibrium ( LTE ) approximation. A semi...Vol. 227, pp. 9463-9476, 2008. 10. Galvez, M., Ray-Tracing model for radiation transport in three-dimensional LTE system, App. Physics, Vol. 38

Comparative performance evaluation of transform coding in image pre-processing

NASA Astrophysics Data System (ADS)

Menon, Vignesh V.; NB, Harikrishnan; Narayanan, Gayathri; CK, Niveditha

2017-07-01

We are in the midst of a communication transmute which drives the development as largely as dissemination of pioneering communication systems with ever-increasing fidelity and resolution. Distinguishable researches have been appreciative in image processing techniques crazed by a growing thirst for faster and easier encoding, storage and transmission of visual information. In this paper, the researchers intend to throw light on many techniques which could be worn at the transmitter-end in order to ease the transmission and reconstruction of the images. The researchers investigate the performance of different image transform coding schemes used in pre-processing, their comparison, and effectiveness, the necessary and sufficient conditions, properties and complexity in implementation. Whimsical by prior advancements in image processing techniques, the researchers compare various contemporary image pre-processing frameworks- Compressed Sensing, Singular Value Decomposition, Integer Wavelet Transform on performance. The paper exposes the potential of Integer Wavelet transform to be an efficient pre-processing scheme.

Effect of microaerobic fermentation in preprocessing fibrous lignocellulosic materials.

PubMed

Alattar, Manar Arica; Green, Terrence R; Henry, Jordan; Gulca, Vitalie; Tizazu, Mikias; Bergstrom, Robby; Popa, Radu

2012-06-01

Amending soil with organic matter is common in agricultural and logging practices. Such amendments have benefits to soil fertility and crop yields. These benefits may be increased if material is preprocessed before introduction into soil. We analyzed the efficiency of microaerobic fermentation (MF), also referred to as Bokashi, in preprocessing fibrous lignocellulosic (FLC) organic materials using varying produce amendments and leachate treatments. Adding produce amendments increased leachate production and fermentation rates and decreased the biological oxygen demand of the leachate. Continuously draining leachate without returning it to the fermentors led to acidification and decreased concentrations of polysaccharides (PS) in leachates. PS fragmentation and the production of soluble metabolites and gases stabilized in fermentors in about 2-4 weeks. About 2 % of the carbon content was lost as CO(2). PS degradation rates, upon introduction of processed materials into soil, were similar to unfermented FLC. Our results indicate that MF is insufficient for adequate preprocessing of FLC material.

Analyzing large scale genomic data on the cloud with Sparkhit

PubMed Central

Huang, Liren; Krüger, Jan

2018-01-01

Abstract Motivation The increasing amount of next-generation sequencing data poses a fundamental challenge on large scale genomic analytics. Existing tools use different distributed computational platforms to scale-out bioinformatics workloads. However, the scalability of these tools is not efficient. Moreover, they have heavy run time overheads when pre-processing large amounts of data. To address these limitations, we have developed Sparkhit: a distributed bioinformatics framework built on top of the Apache Spark platform. Results Sparkhit integrates a variety of analytical methods. It is implemented in the Spark extended MapReduce model. It runs 92–157 times faster than MetaSpark on metagenomic fragment recruitment and 18–32 times faster than Crossbow on data pre-processing. We analyzed 100 terabytes of data across four genomic projects in the cloud in 21 h, which includes the run times of cluster deployment and data downloading. Furthermore, our application on the entire Human Microbiome Project shotgun sequencing data was completed in 2 h, presenting an approach to easily associate large amounts of public datasets with reference data. Availability and implementation Sparkhit is freely available at: https://rhinempi.github.io/sparkhit/. Contact asczyrba@cebitec.uni-bielefeld.de Supplementary information Supplementary data are available at Bioinformatics online. PMID:29253074

Classifying Human Voices by Using Hybrid SFX Time-Series Preprocessing and Ensemble Feature Selection

PubMed Central

Wong, Raymond

2013-01-01

Voice biometrics is one kind of physiological characteristics whose voice is different for each individual person. Due to this uniqueness, voice classification has found useful applications in classifying speakers' gender, mother tongue or ethnicity (accent), emotion states, identity verification, verbal command control, and so forth. In this paper, we adopt a new preprocessing method named Statistical Feature Extraction (SFX) for extracting important features in training a classification model, based on piecewise transformation treating an audio waveform as a time-series. Using SFX we can faithfully remodel statistical characteristics of the time-series; together with spectral analysis, a substantial amount of features are extracted in combination. An ensemble is utilized in selecting only the influential features to be used in classification model induction. We focus on the comparison of effects of various popular data mining algorithms on multiple datasets. Our experiment consists of classification tests over four typical categories of human voice data, namely, Female and Male, Emotional Speech, Speaker Identification, and Language Recognition. The experiments yield encouraging results supporting the fact that heuristically choosing significant features from both time and frequency domains indeed produces better performance in voice classification than traditional signal processing techniques alone, like wavelets and LPC-to-CC. PMID:24288684

Gaussian pre-filtering for uncertainty minimization in digital image correlation using numerically-designed speckle patterns

NASA Astrophysics Data System (ADS)

Mazzoleni, Paolo; Matta, Fabio; Zappa, Emanuele; Sutton, Michael A.; Cigada, Alfredo

2015-03-01

This paper discusses the effect of pre-processing image blurring on the uncertainty of two-dimensional digital image correlation (DIC) measurements for the specific case of numerically-designed speckle patterns having particles with well-defined and consistent shape, size and spacing. Such patterns are more suitable for large measurement surfaces on large-scale specimens than traditional spray-painted random patterns without well-defined particles. The methodology consists of numerical simulations where Gaussian digital filters with varying standard deviation are applied to a reference speckle pattern. To simplify the pattern application process for large areas and increase contrast to reduce measurement uncertainty, the speckle shape, mean size and on-center spacing were selected to be representative of numerically-designed patterns that can be applied on large surfaces through different techniques (e.g., spray-painting through stencils). Such 'designer patterns' are characterized by well-defined regions of non-zero frequency content and non-zero peaks, and are fundamentally different from typical spray-painted patterns whose frequency content exhibits near-zero peaks. The effect of blurring filters is examined for constant, linear, quadratic and cubic displacement fields. Maximum strains between ±250 and ±20,000 με are simulated, thus covering a relevant range for structural materials subjected to service and ultimate stresses. The robustness of the simulation procedure is verified experimentally using a physical speckle pattern subjected to constant displacements. The stability of the relation between standard deviation of the Gaussian filter and measurement uncertainty is assessed for linear displacement fields at varying image noise levels, subset size, and frequency content of the speckle pattern. It is shown that bias error as well as measurement uncertainty are minimized through Gaussian pre-filtering. This finding does not apply to typical spray-painted patterns without well-defined particles, for which image blurring is only beneficial in reducing bias errors.

Optically induced metal-to-dielectric transition in Epsilon-Near-Zero metamaterials

PubMed Central

Kaipurath, R. M.; Pietrzyk, M.; Caspani, L.; Roger, T.; Clerici, M.; Rizza, C.; Ciattoni, A.; Di Falco, A.; Faccio, D.

2016-01-01

Epsilon-Near-Zero materials exhibit a transition in the real part of the dielectric permittivity from positive to negative value as a function of wavelength. Here we study metal-dielectric layered metamaterials in the homogenised regime (each layer has strongly subwavelength thickness) with zero real part of the permittivity in the near-infrared region. By optically pumping the metamaterial we experimentally show that close to the Epsilon-Near-Zero (ENZ) wavelength the permittivity exhibits a marked transition from metallic (negative permittivity) to dielectric (positive permittivity) as a function of the optical power. Remarkably, this transition is linear as a function of pump power and occurs on time scales of the order of the 100 fs pump pulse that need not be tuned to a specific wavelength. The linearity of the permittivity increase allows us to express the response of the metamaterial in terms of a standard third order optical nonlinearity: this shows a clear inversion of the roles of the real and imaginary parts in crossing the ENZ wavelength, further supporting an optically induced change in the physical behaviour of the metamaterial. PMID:27292270

Yield and Production Properties of Wood chips and Particles Torrefied in a Crucible Furnace Retort

Treesearch

Thomas L. Eberhardt; Chi-Leung So; Karen G. Reed

2016-01-01

Biomass preprocessing by torrefaction improves feedstock consistency and thereby improves the efficiency of biofuels operations, including pyrolysis, gasification, and combustion. A crucible furnace retort was fabricated of sufficient size to handle a commercially available wood chip feedstock. Varying the torrefaction times and temperatures provided an array of...

Fast Automatic Segmentation of White Matter Streamlines Based on a Multi-Subject Bundle Atlas.

PubMed

Labra, Nicole; Guevara, Pamela; Duclap, Delphine; Houenou, Josselin; Poupon, Cyril; Mangin, Jean-François; Figueroa, Miguel

2017-01-01

This paper presents an algorithm for fast segmentation of white matter bundles from massive dMRI tractography datasets using a multisubject atlas. We use a distance metric to compare streamlines in a subject dataset to labeled centroids in the atlas, and label them using a per-bundle configurable threshold. In order to reduce segmentation time, the algorithm first preprocesses the data using a simplified distance metric to rapidly discard candidate streamlines in multiple stages, while guaranteeing that no false negatives are produced. The smaller set of remaining streamlines is then segmented using the original metric, thus eliminating any false positives from the preprocessing stage. As a result, a single-thread implementation of the algorithm can segment a dataset of almost 9 million streamlines in less than 6 minutes. Moreover, parallel versions of our algorithm for multicore processors and graphics processing units further reduce the segmentation time to less than 22 seconds and to 5 seconds, respectively. This performance enables the use of the algorithm in truly interactive applications for visualization, analysis, and segmentation of large white matter tractography datasets.

Large-Scale Point-Cloud Visualization through Localized Textured Surface Reconstruction.

PubMed

Arikan, Murat; Preiner, Reinhold; Scheiblauer, Claus; Jeschke, Stefan; Wimmer, Michael

2014-09-01

In this paper, we introduce a novel scene representation for the visualization of large-scale point clouds accompanied by a set of high-resolution photographs. Many real-world applications deal with very densely sampled point-cloud data, which are augmented with photographs that often reveal lighting variations and inaccuracies in registration. Consequently, the high-quality representation of the captured data, i.e., both point clouds and photographs together, is a challenging and time-consuming task. We propose a two-phase approach, in which the first (preprocessing) phase generates multiple overlapping surface patches and handles the problem of seamless texture generation locally for each patch. The second phase stitches these patches at render-time to produce a high-quality visualization of the data. As a result of the proposed localization of the global texturing problem, our algorithm is more than an order of magnitude faster than equivalent mesh-based texturing techniques. Furthermore, since our preprocessing phase requires only a minor fraction of the whole data set at once, we provide maximum flexibility when dealing with growing data sets.

Theoretical foundations of apparent-damping phenomena and nearly irreversible energy exchange in linear conservative systems.

PubMed

Carcaterra, A; Akay, A

2007-04-01

This paper discusses a class of unexpected irreversible phenomena that can develop in linear conservative systems and provides a theoretical foundation that explains the underlying principles. Recent studies have shown that energy can be introduced to a linear system with near irreversibility, or energy within a system can migrate to a subsystem nearly irreversibly, even in the absence of dissipation, provided that the system has a particular natural frequency distribution. The present work introduces a general theory that provides a mathematical foundation and a physical explanation for the near irreversibility phenomena observed and reported in previous publications. Inspired by the properties of probability distribution functions, the general formulation developed here is based on particular properties of harmonic series, which form the common basis of linear dynamic system models. The results demonstrate the existence of a special class of linear nondissipative dynamic systems that exhibit nearly irreversible energy exchange and possess a decaying impulse response. In addition to uncovering a new class of dynamic system properties, the results have far-reaching implications in engineering applications where classical vibration damping or absorption techniques may not be effective. Furthermore, the results also support the notion of nearly irreversible energy transfer in conservative linear systems, which until now has been a concept associated exclusively with nonlinear systems.

Phantom solution in a non-linear Israel-Stewart theory

NASA Astrophysics Data System (ADS)

Cruz, Miguel; Cruz, Norman; Lepe, Samuel

2017-06-01

In this paper we present a phantom solution with a big rip singularity in a non-linear regime of the Israel-Stewart formalism. In this framework it is possible to extend this causal formalism in order to describe accelerated expansion, where assumption of near equilibrium is no longer valid. We assume a flat universe filled with a single viscous fluid ruled by a barotropic EoS, p = ωρ, which can represent a late time accelerated phase of the cosmic evolution. The solution allows to cross the phantom divide without evoking an exotic matter fluid and the effective EoS parameter is always lesser than -1 and constant in time.

Crack displacement sensing and measurement in concrete using circular grating moire fringes and pattern matching

NASA Astrophysics Data System (ADS)

Chan, H. M.; Yen, K. S.; Ratnam, M. M.

2008-09-01

The moire method has been extensively studied in the past and applied in various engineering applications. Several techniques are available for generating the moire fringes in these applications, which include moire interferometry, projection moire, shadow moire, moire deflectometry etc. Most of these methods use the superposition of linear gratings to generate the moire patterns. The use of non-linear gratings, such as circular, radial and elongated gratings has received less attention from the research community. The potential of non-linear gratings in engineering measurement has been realized in a limited number of applications, such as rotation measurement, measurement of linear displacement, measurement of expansion coefficients of materials and measurement of strain distribution. In this work, circular gratings of different pitch were applied to the sensing and measurement of crack displacement in concrete structures. Gratings of pitch 0.50 mm and 0.55 mm were generated using computer software and attached to two overlapping acrylic plates that were bonded to either side of the crack. The resulting moire patterns were captured using a standard digital camera and compared with a set of reference patterns generated using a precision positioning stage. Using several image pre-processing stages, such as filtering and morphological operations, and pattern matching the magnitude displacements along two orthogonal axes can be detected with a resolution of 0.05 mm.

Statistical aspects of solar flares

NASA Technical Reports Server (NTRS)

Wilson, Robert M.

1987-01-01

A survey of the statistical properties of 850 H alpha solar flares during 1975 is presented. Comparison of the results found here with those reported elsewhere for different epochs is accomplished. Distributions of rise time, decay time, and duration are given, as are the mean, mode, median, and 90th percentile values. Proportions by selected groupings are also determined. For flares in general, mean values for rise time, decay time, and duration are 5.2 + or - 0.4 min, and 18.1 + or 1.1 min, respectively. Subflares, accounting for nearly 90 percent of the flares, had mean values lower than those found for flares of H alpha importance greater than 1, and the differences are statistically significant. Likewise, flares of bright and normal relative brightness have mean values of decay time and duration that are significantly longer than those computed for faint flares, and mass-motion related flares are significantly longer than non-mass-motion related flares. Seventy-three percent of the mass-motion related flares are categorized as being a two-ribbon flare and/or being accompanied by a high-speed dark filament. Slow rise time flares (rise time greater than 5 min) have a mean value for duration that is significantly longer than that computed for fast rise time flares, and long-lived duration flares (duration greater than 18 min) have a mean value for rise time that is significantly longer than that computed for short-lived duration flares, suggesting a positive linear relationship between rise time and duration for flares. Monthly occurrence rates for flares in general and by group are found to be linearly related in a positive sense to monthly sunspot number. Statistical testing reveals the association between sunspot number and numbers of flares to be significant at the 95 percent level of confidence, and the t statistic for slope is significant at greater than 99 percent level of confidence. Dependent upon the specific fit, between 58 percent and 94 percent of the variation can be accounted for with the linear fits. A statistically significant Northern Hemisphere flare excess (P less than 1 percent) was found, as was a Western Hemisphere excess (P approx 3 percent). Subflares were more prolific within 45 deg of central meridian (P less than 1 percent), while flares of H alpha importance or = 1 were more prolific near the limbs greater than 45 deg from central meridian; P approx 2 percent). Two-ribbon flares were more frequent within 45 deg of central meridian (P less than 1 percent). Slow rise time flares occurred more frequently in the western hemisphere (P approx 2 percent), as did short-lived duration flares (P approx 9 percent), but fast rise time flares were not preferentially distributed (in terms of east-west or limb-disk). Long-lived duration flares occurred more often within 45 deg 0 central meridian (P approx 7 percent). Mean durations for subflares and flares of H alpha importance or + 1, found within 45 deg of central meridian, are 14 percent and 70 percent, respectively, longer than those found for flares closer to the limb. As compared to flares occurring near cycle maximum, the flares of 1975 (near solar minimum) have mean values of rise time, decay time, and duration that are significantly shorter. A flare near solar maximum, on average, is about 1.6 times longer than one occurring near solar minimum.

IceTrendr: a linear time-series approach to monitoring glacier environments using Landsat

NASA Astrophysics Data System (ADS)

Nelson, P.; Kennedy, R. E.; Nolin, A. W.; Hughes, J. M.; Braaten, J.

2017-12-01

Arctic glaciers in Alaska and Canada have experienced some of the greatest ice mass loss of any region in recent decades. A challenge to understanding these changing ecosystems, however, is developing globally-consistent, multi-decadal monitoring of glacier ice. We present a toolset and approach that captures, labels, and maps glacier change for use in climate science, hydrology, and Earth science education using Landsat Time Series (LTS). The core step is "temporal segmentation," wherein a yearly LTS is cleaned using pre-processing steps, converted to a snow/ice index, and then simplified into the salient shape of the change trajectory ("temporal signature") using linear segmentation. Such signatures can be characterized as simple `stable' or `transition of glacier ice to rock' to more complex multi-year changes like `transition of glacier ice to debris-covered glacier ice to open water to bare rock to vegetation'. This pilot study demonstrates the potential for interactively mapping, visualizing, and labeling glacier changes. What is truly innovative is that IceTrendr not only maps the changes but also uses expert knowledge to label the changes and such labels can be applied to other glaciers exhibiting statistically similar temporal signatures. Our key findings are that the IceTrendr concept and software can provide important functionality for glaciologists and educators interested in studying glacier changes during the Landsat TM timeframe (1984-present). Issues of concern with using dense Landsat time-series approaches for glacier monitoring include many missing images during the period 1984-1995 and that automated cloud mask are challenged and require the user to manually identify cloud-free images. IceTrendr is much more than just a simple "then and now" approach to glacier mapping. This process is a means of integrating the power of computing, remote sensing, and expert knowledge to "tell the story" of glacier changes.

Data Pre-Processing Method to Remove Interference of Gas Bubbles and Cell Clusters During Anaerobic and Aerobic Yeast Fermentations in a Stirred Tank Bioreactor

NASA Astrophysics Data System (ADS)

Princz, S.; Wenzel, U.; Miller, R.; Hessling, M.

2014-11-01

One aerobic and four anaerobic batch fermentations of the yeast Saccharomyces cerevisiae were conducted in a stirred bioreactor and monitored inline by NIR spectroscopy and a transflectance dip probe. From the acquired NIR spectra, chemometric partial least squares regression (PLSR) models for predicting biomass, glucose and ethanol were constructed. The spectra were directly measured in the fermentation broth and successfully inspected for adulteration using our novel data pre-processing method. These adulterations manifested as strong fluctuations in the shape and offset of the absorption spectra. They resulted from cells, cell clusters, or gas bubbles intercepting the optical path of the dip probe. In the proposed data pre-processing method, adulterated signals are removed by passing the time-scanned non-averaged spectra through two filter algorithms with a 5% quantile cutoff. The filtered spectra containing meaningful data are then averaged. A second step checks whether the whole time scan is analyzable. If true, the average is calculated and used to prepare the PLSR models. This new method distinctly improved the prediction results. To dissociate possible correlations between analyte concentrations, such as glucose and ethanol, the feeding analytes were alternately supplied at different concentrations (spiking) at the end of the four anaerobic fermentations. This procedure yielded low-error (anaerobic) PLSR models for predicting analyte concentrations of 0.31 g/l for biomass, 3.41 g/l for glucose, and 2.17 g/l for ethanol. The maximum concentrations were 14 g/l biomass, 167 g/l glucose, and 80 g/l ethanol. Data from the aerobic fermentation, carried out under high agitation and high aeration, were incorporated to realize combined PLSR models, which have not been previously reported to our knowledge.

Achieving Accurate Automatic Sleep Staging on Manually Pre-processed EEG Data Through Synchronization Feature Extraction and Graph Metrics.

PubMed

Chriskos, Panteleimon; Frantzidis, Christos A; Gkivogkli, Polyxeni T; Bamidis, Panagiotis D; Kourtidou-Papadeli, Chrysoula

2018-01-01

Sleep staging, the process of assigning labels to epochs of sleep, depending on the stage of sleep they belong, is an arduous, time consuming and error prone process as the initial recordings are quite often polluted by noise from different sources. To properly analyze such data and extract clinical knowledge, noise components must be removed or alleviated. In this paper a pre-processing and subsequent sleep staging pipeline for the sleep analysis of electroencephalographic signals is described. Two novel methods of functional connectivity estimation (Synchronization Likelihood/SL and Relative Wavelet Entropy/RWE) are comparatively investigated for automatic sleep staging through manually pre-processed electroencephalographic recordings. A multi-step process that renders signals suitable for further analysis is initially described. Then, two methods that rely on extracting synchronization features from electroencephalographic recordings to achieve computerized sleep staging are proposed, based on bivariate features which provide a functional overview of the brain network, contrary to most proposed methods that rely on extracting univariate time and frequency features. Annotation of sleep epochs is achieved through the presented feature extraction methods by training classifiers, which are in turn able to accurately classify new epochs. Analysis of data from sleep experiments on a randomized, controlled bed-rest study, which was organized by the European Space Agency and was conducted in the "ENVIHAB" facility of the Institute of Aerospace Medicine at the German Aerospace Center (DLR) in Cologne, Germany attains high accuracy rates, over 90% based on ground truth that resulted from manual sleep staging by two experienced sleep experts. Therefore, it can be concluded that the above feature extraction methods are suitable for semi-automatic sleep staging.

Achieving Accurate Automatic Sleep Staging on Manually Pre-processed EEG Data Through Synchronization Feature Extraction and Graph Metrics

PubMed Central

Chriskos, Panteleimon; Frantzidis, Christos A.; Gkivogkli, Polyxeni T.; Bamidis, Panagiotis D.; Kourtidou-Papadeli, Chrysoula

2018-01-01

Sleep staging, the process of assigning labels to epochs of sleep, depending on the stage of sleep they belong, is an arduous, time consuming and error prone process as the initial recordings are quite often polluted by noise from different sources. To properly analyze such data and extract clinical knowledge, noise components must be removed or alleviated. In this paper a pre-processing and subsequent sleep staging pipeline for the sleep analysis of electroencephalographic signals is described. Two novel methods of functional connectivity estimation (Synchronization Likelihood/SL and Relative Wavelet Entropy/RWE) are comparatively investigated for automatic sleep staging through manually pre-processed electroencephalographic recordings. A multi-step process that renders signals suitable for further analysis is initially described. Then, two methods that rely on extracting synchronization features from electroencephalographic recordings to achieve computerized sleep staging are proposed, based on bivariate features which provide a functional overview of the brain network, contrary to most proposed methods that rely on extracting univariate time and frequency features. Annotation of sleep epochs is achieved through the presented feature extraction methods by training classifiers, which are in turn able to accurately classify new epochs. Analysis of data from sleep experiments on a randomized, controlled bed-rest study, which was organized by the European Space Agency and was conducted in the “ENVIHAB” facility of the Institute of Aerospace Medicine at the German Aerospace Center (DLR) in Cologne, Germany attains high accuracy rates, over 90% based on ground truth that resulted from manual sleep staging by two experienced sleep experts. Therefore, it can be concluded that the above feature extraction methods are suitable for semi-automatic sleep staging. PMID:29628883

Living near the port area is associated with physical inactivity and sedentary behavior.

PubMed

Sperandio, Evandro Fornias; Arantes, Rodolfo Leite; Chao, Tsai Ping; Romiti, Marcello; Gagliardi, Antônio Ricardo de Toledo; Dourado, Victor Zuniga

2017-01-01

The impact of the port of Santos, Brazil, on the population's health is unknown. We aimed to evaluate the association between living near the port area and physical inactivity and sedentary behavior. Cross-sectional study developed at a university laboratory and a diagnostic clinic. 553 healthy adults were selected and their level of physical activity in daily life was assessed using accelerometers. Multiple linear and logistic regressions were performed using physical inactivity and sedentary behavior as the outcomes and living near the port area as the main risk factor, with adjustments for the main confounders. Among all the participants, 15% were resident near the port area. They took 699 steps/day and presented, weekly, 2.4% more sedentary physical activity, 2.0% less time in standing position and 0.9% more time lying down than residents of other regions. Additionally, living near the port area increased the risk of physical inactivity by 2.50 times and the risk of higher amounts of sedentary behavior (≥ 10 hours/day) by 1.32 times. Living near the port of Santos is associated with physical inactivity and higher sedentary behavior among adults, regardless of confounders. The reasons for this association should be investigated in longitudinal studies.

A Method for Generating Reduced-Order Linear Models of Multidimensional Supersonic Inlets

NASA Technical Reports Server (NTRS)

Chicatelli, Amy; Hartley, Tom T.

1998-01-01

Simulation of high speed propulsion systems may be divided into two categories, nonlinear and linear. The nonlinear simulations are usually based on multidimensional computational fluid dynamics (CFD) methodologies and tend to provide high resolution results that show the fine detail of the flow. Consequently, these simulations are large, numerically intensive, and run much slower than real-time. ne linear simulations are usually based on large lumping techniques that are linearized about a steady-state operating condition. These simplistic models often run at or near real-time but do not always capture the detailed dynamics of the plant. Under a grant sponsored by the NASA Lewis Research Center, Cleveland, Ohio, a new method has been developed that can be used to generate improved linear models for control design from multidimensional steady-state CFD results. This CFD-based linear modeling technique provides a small perturbation model that can be used for control applications and real-time simulations. It is important to note the utility of the modeling procedure; all that is needed to obtain a linear model of the propulsion system is the geometry and steady-state operating conditions from a multidimensional CFD simulation or experiment. This research represents a beginning step in establishing a bridge between the controls discipline and the CFD discipline so that the control engineer is able to effectively use multidimensional CFD results in control system design and analysis.

fMRI paradigm designing and post-processing tools

PubMed Central

James, Jija S; Rajesh, PG; Chandran, Anuvitha VS; Kesavadas, Chandrasekharan

2014-01-01

In this article, we first review some aspects of functional magnetic resonance imaging (fMRI) paradigm designing for major cognitive functions by using stimulus delivery systems like Cogent, E-Prime, Presentation, etc., along with their technical aspects. We also review the stimulus presentation possibilities (block, event-related) for visual or auditory paradigms and their advantage in both clinical and research setting. The second part mainly focus on various fMRI data post-processing tools such as Statistical Parametric Mapping (SPM) and Brain Voyager, and discuss the particulars of various preprocessing steps involved (realignment, co-registration, normalization, smoothing) in these software and also the statistical analysis principles of General Linear Modeling for final interpretation of a functional activation result. PMID:24851001

Source localization in an ocean waveguide using supervised machine learning.

PubMed

Niu, Haiqiang; Reeves, Emma; Gerstoft, Peter

2017-09-01

Source localization in ocean acoustics is posed as a machine learning problem in which data-driven methods learn source ranges directly from observed acoustic data. The pressure received by a vertical linear array is preprocessed by constructing a normalized sample covariance matrix and used as the input for three machine learning methods: feed-forward neural networks (FNN), support vector machines (SVM), and random forests (RF). The range estimation problem is solved both as a classification problem and as a regression problem by these three machine learning algorithms. The results of range estimation for the Noise09 experiment are compared for FNN, SVM, RF, and conventional matched-field processing and demonstrate the potential of machine learning for underwater source localization.

Crop identification technology assessment for remote sensing (CITARS). Volume 10: Interpretation of results

NASA Technical Reports Server (NTRS)

Bizzell, R. M.; Feiveson, A. H.; Hall, F. G.; Bauer, M. E.; Davis, B. J.; Malila, W. A.; Rice, D. P.

1975-01-01

The CITARS was an experiment designed to quantitatively evaluate crop identification performance for corn and soybeans in various environments using a well-defined set of automatic data processing (ADP) techniques. Each technique was applied to data acquired to recognize and estimate proportions of corn and soybeans. The CITARS documentation summarizes, interprets, and discusses the crop identification performances obtained using (1) different ADP procedures; (2) a linear versus a quadratic classifier; (3) prior probability information derived from historic data; (4) local versus nonlocal recognition training statistics and the associated use of preprocessing; (5) multitemporal data; (6) classification bias and mixed pixels in proportion estimation; and (7) data with differnt site characteristics, including crop, soil, atmospheric effects, and stages of crop maturity.

Meteor tracking via local pattern clustering in spatio-temporal domain

NASA Astrophysics Data System (ADS)

Kukal, Jaromír.; Klimt, Martin; Švihlík, Jan; Fliegel, Karel

2016-09-01

Reliable meteor detection is one of the crucial disciplines in astronomy. A variety of imaging systems is used for meteor path reconstruction. The traditional approach is based on analysis of 2D image sequences obtained from a double station video observation system. Precise localization of meteor path is difficult due to atmospheric turbulence and other factors causing spatio-temporal fluctuations of the image background. The proposed technique performs non-linear preprocessing of image intensity using Box-Cox transform as recommended in our previous work. Both symmetric and asymmetric spatio-temporal differences are designed to be robust in the statistical sense. Resulting local patterns are processed by data whitening technique and obtained vectors are classified via cluster analysis and Self-Organized Map (SOM).

Non-destructive determination of Malondialdehyde (MDA) distribution in oilseed rape leaves by laboratory scale NIR hyperspectral imaging

PubMed Central

Kong, Wenwen; Liu, Fei; Zhang, Chu; Zhang, Jianfeng; Feng, Hailin

2016-01-01

The feasibility of hyperspectral imaging with 400–1000 nm was investigated to detect malondialdehyde (MDA) content in oilseed rape leaves under herbicide stress. After comparing the performance of different preprocessing methods, linear and nonlinear calibration models, the optimal prediction performance was achieved by extreme learning machine (ELM) model with only 23 wavelengths selected by competitive adaptive reweighted sampling (CARS), and the result was RP = 0.929 and RMSEP = 2.951. Furthermore, MDA distribution map was successfully achieved by partial least squares (PLS) model with CARS. This study indicated that hyperspectral imaging technology provided a fast and nondestructive solution for MDA content detection in plant leaves. PMID:27739491

An improved feature extraction algorithm based on KAZE for multi-spectral image

NASA Astrophysics Data System (ADS)

Yang, Jianping; Li, Jun

2018-02-01

Multi-spectral image contains abundant spectral information, which is widely used in all fields like resource exploration, meteorological observation and modern military. Image preprocessing, such as image feature extraction and matching, is indispensable while dealing with multi-spectral remote sensing image. Although the feature matching algorithm based on linear scale such as SIFT and SURF performs strong on robustness, the local accuracy cannot be guaranteed. Therefore, this paper proposes an improved KAZE algorithm, which is based on nonlinear scale, to raise the number of feature and to enhance the matching rate by using the adjusted-cosine vector. The experiment result shows that the number of feature and the matching rate of the improved KAZE are remarkably than the original KAZE algorithm.

An investigation of emotion dynamics in major depressive disorder patients and healthy persons using sparse longitudinal networks.

PubMed

de Vos, Stijn; Wardenaar, Klaas J; Bos, Elisabeth H; Wit, Ernst C; Bouwmans, Mara E J; de Jonge, Peter

2017-01-01

Differences in within-person emotion dynamics may be an important source of heterogeneity in depression. To investigate these dynamics, researchers have previously combined multilevel regression analyses with network representations. However, sparse network methods, specifically developed for longitudinal network analyses, have not been applied. Therefore, this study used this approach to investigate population-level and individual-level emotion dynamics in healthy and depressed persons and compared this method with the multilevel approach. Time-series data were collected in pair-matched healthy persons and major depressive disorder (MDD) patients (n = 54). Seven positive affect (PA) and seven negative affect (NA) items were administered electronically at 90 times (30 days; thrice per day). The population-level (healthy vs. MDD) and individual-level time series were analyzed using a sparse longitudinal network model based on vector autoregression. The population-level model was also estimated with a multilevel approach. Effects of different preprocessing steps were evaluated as well. The characteristics of the longitudinal networks were investigated to gain insight into the emotion dynamics. In the population-level networks, longitudinal network connectivity was strongest in the healthy group, with nodes showing more and stronger longitudinal associations with each other. Individually estimated networks varied strongly across individuals. Individual variations in network connectivity were unrelated to baseline characteristics (depression status, neuroticism, severity). A multilevel approach applied to the same data showed higher connectivity in the MDD group, which seemed partly related to the preprocessing approach. The sparse network approach can be useful for the estimation of networks with multiple nodes, where overparameterization is an issue, and for individual-level networks. However, its current inability to model random effects makes it less useful as a population-level approach in case of large heterogeneity. Different preprocessing strategies appeared to strongly influence the results, complicating inferences about network density.

Image preprocessing for improving computational efficiency in implementation of restoration and superresolution algorithms.

PubMed

Sundareshan, Malur K; Bhattacharjee, Supratik; Inampudi, Radhika; Pang, Ho-Yuen

2002-12-10

Computational complexity is a major impediment to the real-time implementation of image restoration and superresolution algorithms in many applications. Although powerful restoration algorithms have been developed within the past few years utilizing sophisticated mathematical machinery (based on statistical optimization and convex set theory), these algorithms are typically iterative in nature and require a sufficient number of iterations to be executed to achieve the desired resolution improvement that may be needed to meaningfully perform postprocessing image exploitation tasks in practice. Additionally, recent technological breakthroughs have facilitated novel sensor designs (focal plane arrays, for instance) that make it possible to capture megapixel imagery data at video frame rates. A major challenge in the processing of these large-format images is to complete the execution of the image processing steps within the frame capture times and to keep up with the output rate of the sensor so that all data captured by the sensor can be efficiently utilized. Consequently, development of novel methods that facilitate real-time implementation of image restoration and superresolution algorithms is of significant practical interest and is the primary focus of this study. The key to designing computationally efficient processing schemes lies in strategically introducing appropriate preprocessing steps together with the superresolution iterations to tailor optimized overall processing sequences for imagery data of specific formats. For substantiating this assertion, three distinct methods for tailoring a preprocessing filter and integrating it with the superresolution processing steps are outlined. These methods consist of a region-of-interest extraction scheme, a background-detail separation procedure, and a scene-derived information extraction step for implementing a set-theoretic restoration of the image that is less demanding in computation compared with the superresolution iterations. A quantitative evaluation of the performance of these algorithms for restoring and superresolving various imagery data captured by diffraction-limited sensing operations are also presented.

Low-cost digital image processing at the University of Oklahoma

NASA Technical Reports Server (NTRS)

Harrington, J. A., Jr.

1981-01-01

Computer assisted instruction in remote sensing at the University of Oklahoma involves two separate approaches and is dependent upon initial preprocessing of a LANDSAT computer compatible tape using software developed for an IBM 370/158 computer. In-house generated preprocessing algorithms permits students or researchers to select a subset of a LANDSAT scene for subsequent analysis using either general purpose statistical packages or color graphic image processing software developed for Apple II microcomputers. Procedures for preprocessing the data and image analysis using either of the two approaches for low-cost LANDSAT data processing are described.

Identification of coffee bean varieties using hyperspectral imaging: influence of preprocessing methods and pixel-wise spectra analysis.

PubMed

Zhang, Chu; Liu, Fei; He, Yong

2018-02-01

Hyperspectral imaging was used to identify and to visualize the coffee bean varieties. Spectral preprocessing of pixel-wise spectra was conducted by different methods, including moving average smoothing (MA), wavelet transform (WT) and empirical mode decomposition (EMD). Meanwhile, spatial preprocessing of the gray-scale image at each wavelength was conducted by median filter (MF). Support vector machine (SVM) models using full sample average spectra and pixel-wise spectra, and the selected optimal wavelengths by second derivative spectra all achieved classification accuracy over 80%. Primarily, the SVM models using pixel-wise spectra were used to predict the sample average spectra, and these models obtained over 80% of the classification accuracy. Secondly, the SVM models using sample average spectra were used to predict pixel-wise spectra, but achieved with lower than 50% of classification accuracy. The results indicated that WT and EMD were suitable for pixel-wise spectra preprocessing. The use of pixel-wise spectra could extend the calibration set, and resulted in the good prediction results for pixel-wise spectra and sample average spectra. The overall results indicated the effectiveness of using spectral preprocessing and the adoption of pixel-wise spectra. The results provided an alternative way of data processing for applications of hyperspectral imaging in food industry.

Multiwavelet grading of prostate pathological images

NASA Astrophysics Data System (ADS)

Soltanian-Zadeh, Hamid; Jafari-Khouzani, Kourosh

2002-05-01

We have developed image analysis methods to automatically grade pathological images of prostate. The proposed method generates Gleason grades to images, where each image is assigned a grade between 1 and 5. This is done using features extracted from multiwavelet transformations. We extract energy and entropy features from submatrices obtained in the decomposition. Next, we apply a k-NN classifier to grade the image. To find optimal multiwavelet basis, preprocessing, and classifier, we use features extracted by different multiwavelets with either critically sampled preprocessing or repeated row preprocessing and different k-NN classifiers and compare their performances, evaluated by total misclassification rate (TMR). To evaluate sensitivity to noise, we add white Gaussian noise to images and compare the results (TMR's). We applied proposed methods to 100 images. We evaluated the first and second levels of decomposition using Geronimo, Hardin, and Massopust (GHM), Chui and Lian (CL), and Shen (SA4) multiwavelets. We also evaluated k-NN classifier for k=1,2,3,4,5. Experimental results illustrate that first level of decomposition is quite noisy. They also show that critically sampled preprocessing outperforms repeated row preprocessing and has less sensitivity to noise. Finally, comparison studies indicate that SA4 multiwavelet and k-NN classifier (k=1) generates optimal results (with smallest TMR of 3%).

Multisubject Learning for Common Spatial Patterns in Motor-Imagery BCI

PubMed Central

Devlaminck, Dieter; Wyns, Bart; Grosse-Wentrup, Moritz; Otte, Georges; Santens, Patrick

2011-01-01

Motor-imagery-based brain-computer interfaces (BCIs) commonly use the common spatial pattern filter (CSP) as preprocessing step before feature extraction and classification. The CSP method is a supervised algorithm and therefore needs subject-specific training data for calibration, which is very time consuming to collect. In order to reduce the amount of calibration data that is needed for a new subject, one can apply multitask (from now on called multisubject) machine learning techniques to the preprocessing phase. Here, the goal of multisubject learning is to learn a spatial filter for a new subject based on its own data and that of other subjects. This paper outlines the details of the multitask CSP algorithm and shows results on two data sets. In certain subjects a clear improvement can be seen, especially when the number of training trials is relatively low. PMID:22007194

MZmine 2 Data-Preprocessing To Enhance Molecular Networking Reliability.

PubMed

Olivon, Florent; Grelier, Gwendal; Roussi, Fanny; Litaudon, Marc; Touboul, David

2017-08-01

Molecular networking is becoming more and more popular into the metabolomic community to organize tandem mass spectrometry (MS 2 ) data. Even though this approach allows the treatment and comparison of large data sets, several drawbacks related to the MS-Cluster tool routinely used on the Global Natural Product Social Molecular Networking platform (GNPS) limit its potential. MS-Cluster cannot distinguish between chromatography well-resolved isomers as retention times are not taken into account. Annotation with predicted chemical formulas is also not implemented and semiquantification is only based on the number of MS 2 scans. We propose to introduce a data-preprocessing workflow including the preliminary data treatment by MZmine 2 followed by a homemade Python script freely available to the community that clears the major previously mentioned GNPS drawbacks. The efficiency of this workflow is exemplified with the analysis of six fractions of increasing polarities obtained from a sequential supercritical CO 2 extraction of Stillingia lineata leaves.

Piecewise Polynomial Aggregation as Preprocessing for Data Numerical Modeling

NASA Astrophysics Data System (ADS)

Dobronets, B. S.; Popova, O. A.

2018-05-01

Data aggregation issues for numerical modeling are reviewed in the present study. The authors discuss data aggregation procedures as preprocessing for subsequent numerical modeling. To calculate the data aggregation, the authors propose using numerical probabilistic analysis (NPA). An important feature of this study is how the authors represent the aggregated data. The study shows that the offered approach to data aggregation can be interpreted as the frequency distribution of a variable. To study its properties, the density function is used. For this purpose, the authors propose using the piecewise polynomial models. A suitable example of such approach is the spline. The authors show that their approach to data aggregation allows reducing the level of data uncertainty and significantly increasing the efficiency of numerical calculations. To demonstrate the degree of the correspondence of the proposed methods to reality, the authors developed a theoretical framework and considered numerical examples devoted to time series aggregation.

Generating a Long-Term Land Data Record from the AVHRR and MODIS Instruments

NASA Technical Reports Server (NTRS)

Pedelty, Jeffrey; Devadiga, Sadashiva; Masuoka, Edward; Brown, Molly; Pinzon, Jorge; Tucker, Compton; Vermote, Eric; Prince, Stephen; Nagol, Jyotheshwar; Justice, Christopher;

New method for calculating a mathematical expression for streamflow recession

USGS Publications Warehouse

Rutledge, Albert T.

1991-01-01

An empirical method has been devised to calculate the master recession curve, which is a mathematical expression for streamflow recession during times of negligible direct runoff. The method is based on the assumption that the storage-delay factor, which is the time per log cycle of streamflow recession, varies linearly with the logarithm of streamflow. The resulting master recession curve can be nonlinear. The method can be executed by a computer program that reads a data file of daily mean streamflow, then allows the user to select several near-linear segments of streamflow recession. The storage-delay factor for each segment is one of the coefficients of the equation that results from linear least-squares regression. Using results for each recession segment, a mathematical expression of the storage-delay factor as a function of the log of streamflow is determined by linear least-squares regression. The master recession curve, which is a second-order polynomial expression for time as a function of log of streamflow, is then derived using the coefficients of this function.

Berry ripening, pre-processing and thermal treatments affect the phenolic composition and antioxidant capacity of grape (Vitis vinifera L.) juice.

PubMed

Genova, Giuseppe; Tosetti, Roberta; Tonutti, Pietro

2016-01-30

Grape juice is an important dietary source of health-promoting antioxidant molecules. Different factors may affect juice composition and nutraceutical properties. The effects of some of these factors (harvest time, pre-processing ethylene treatment of grapes and juice thermal pasteurization) were here evaluated, considering in particular the phenolic composition and antioxidant capacity. Grapes (Vitis vinifera L., red-skinned variety Sangiovese) were collected twice in relation to the technological harvest (TH) and 12 days before TH (early harvest, EH) and treated with gaseous ethylene (1000 ppm) or air for 48 h. Fresh and pasteurized (78 °C for 30 min) juices were produced using a water bath. Three-way analysis of variance showed that the harvest date had the strongest impact on total polyphenols, hydroxycinnamates, flavonols, and especially on total flavonoids. Pre-processing ethylene treatment significantly increased the proanthocyanidin, anthocyanin and flavan-3-ol content in the juices. Pasteurization induced a significant increase in anthocyanin concentration. Antioxidant capacity was enhanced by ethylene treatment and pasteurization in juices from both TH and EH grapes. These results suggest that an appropriate management of grape harvesting date, postharvest and processing may lead to an improvement in nutraceutical quality of juices. Further research is needed to study the effect of the investigated factors on juice organoleptic properties. © 2015 Society of Chemical Industry.

Digital image processing and analysis for activated sludge wastewater treatment.

PubMed

Khan, Muhammad Burhan; Lee, Xue Yong; Nisar, Humaira; Ng, Choon Aun; Yeap, Kim Ho; Malik, Aamir Saeed

2015-01-01

Activated sludge system is generally used in wastewater treatment plants for processing domestic influent. Conventionally the activated sludge wastewater treatment is monitored by measuring physico-chemical parameters like total suspended solids (TSSol), sludge volume index (SVI) and chemical oxygen demand (COD) etc. For the measurement, tests are conducted in the laboratory, which take many hours to give the final measurement. Digital image processing and analysis offers a better alternative not only to monitor and characterize the current state of activated sludge but also to predict the future state. The characterization by image processing and analysis is done by correlating the time evolution of parameters extracted by image analysis of floc and filaments with the physico-chemical parameters. This chapter briefly reviews the activated sludge wastewater treatment; and, procedures of image acquisition, preprocessing, segmentation and analysis in the specific context of activated sludge wastewater treatment. In the latter part additional procedures like z-stacking, image stitching are introduced for wastewater image preprocessing, which are not previously used in the context of activated sludge. Different preprocessing and segmentation techniques are proposed, along with the survey of imaging procedures reported in the literature. Finally the image analysis based morphological parameters and correlation of the parameters with regard to monitoring and prediction of activated sludge are discussed. Hence it is observed that image analysis can play a very useful role in the monitoring of activated sludge wastewater treatment plants.

Pre-Processing and Cross-Correlation Techniques for Time-Distance Helioseismology

NASA Astrophysics Data System (ADS)

Wang, N.; de Ridder, S.; Zhao, J.

2014-12-01

In chaotic wave fields excited by a random distribution of noise sources a cross-correlation of the recordings made at two stations yield the interstation wave-field response. After early successes in helioseismology, laboratory studies and earth-seismology, this technique found broad application in global and regional seismology. This development came with an increasing understanding of pre-processing and cross-correlation workflows to yield an optimal signal-to-noise ratio (SNR). Helioseismologist rely heavily on stacking to increase the SNR. Until now, they have not studied different spectral-whitening and cross-correlation workflows and relies heavily on stacking to increase the SNR. The recordings vary considerably between sunspots and regular portions of the sun. Within the sunspot the periodic effects of the observation satellite orbit are difficult to remove. We remove a running alpha-mean from the data and apply a soft clip to deal with data glitches. The recordings contain energy of both flow and waves. A frequency domain filter selects the wave energy. Then the data is input to several pre-processing and cross-correlation techniques, common to earth seismology. We anticipate that spectral whitening will flatten the energy spectrum of the cross-correlations. We also expect that the cross-correlations converge faster to their expected value when the data is processed over overlapping windows. The result of this study are expected to aid in decreasing the stacking while maintaining good SNR.

Carbide-derived carbon (CDC) linear actuator properties in combination with conducting polymers

NASA Astrophysics Data System (ADS)

Kiefer, Rudolf; Aydemir, Nihan; Torop, Janno; Kilmartin, Paul A.; Tamm, Tarmo; Kaasik, Friedrich; Kesküla, Arko; Travas-Sejdic, Jadranka; Aabloo, Alvo

2014-03-01

Carbide-derived Carbon (CDC) material is applied for super capacitors due to their nanoporous structure and their high charging/discharging capability. In this work we report for the first time CDC linear actuators and CDC combined with polypyrrole (CDC-PPy) in ECMD (Electrochemomechanical deformation) under isotonic (constant force) and isometric (constant length) measurements in aqueous electrolyte. CDC-PPy actuators showing nearly double strain under cyclic voltammetric and square wave potential measurements in comparison to CDC linear actuators. The new material is investigated by SEM (scanning electron microscopy) and EDX (energy dispersive X-ray analysis) to reveal how the conducting polymer layer and the CDC layer interfere together.

Characterization of intermittency in renewal processes: Application to earthquakes

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

Akimoto, Takuma; Hasumi, Tomohiro; Aizawa, Yoji

2010-03-15

We construct a one-dimensional piecewise linear intermittent map from the interevent time distribution for a given renewal process. Then, we characterize intermittency by the asymptotic behavior near the indifferent fixed point in the piecewise linear intermittent map. Thus, we provide a framework to understand a unified characterization of intermittency and also present the Lyapunov exponent for renewal processes. This method is applied to the occurrence of earthquakes using the Japan Meteorological Agency and the National Earthquake Information Center catalog. By analyzing the return map of interevent times, we find that interevent times are not independent and identically distributed random variablesmore » but that the conditional probability distribution functions in the tail obey the Weibull distribution.« less

Near-road air pollutant concentrations of CO and PM 2.5: A comparison of MOBILE6.2/CALINE4 and generalized additive models

NASA Astrophysics Data System (ADS)

Zhang, Kai; Batterman, Stuart

2010-05-01

The contribution of vehicular traffic to air pollutant concentrations is often difficult to establish. This paper utilizes both time-series and simulation models to estimate vehicle contributions to pollutant levels near roadways. The time-series model used generalized additive models (GAMs) and fitted pollutant observations to traffic counts and meteorological variables. A one year period (2004) was analyzed on a seasonal basis using hourly measurements of carbon monoxide (CO) and particulate matter less than 2.5 μm in diameter (PM 2.5) monitored near a major highway in Detroit, Michigan, along with hourly traffic counts and local meteorological data. Traffic counts showed statistically significant and approximately linear relationships with CO concentrations in fall, and piecewise linear relationships in spring, summer and winter. The same period was simulated using emission and dispersion models (Motor Vehicle Emissions Factor Model/MOBILE6.2; California Line Source Dispersion Model/CALINE4). CO emissions derived from the GAM were similar, on average, to those estimated by MOBILE6.2. The same analyses for PM 2.5 showed that GAM emission estimates were much higher (by 4-5 times) than the dispersion model results, and that the traffic-PM 2.5 relationship varied seasonally. This analysis suggests that the simulation model performed reasonably well for CO, but it significantly underestimated PM 2.5 concentrations, a likely result of underestimating PM 2.5 emission factors. Comparisons between statistical and simulation models can help identify model deficiencies and improve estimates of vehicle emissions and near-road air quality.

Registration of fast cine cardiac MR slices to 3D preprocedural images: toward real-time registration for MRI-guided procedures

NASA Astrophysics Data System (ADS)

Smolikova, Renata; Wachowiak, Mark P.; Drangova, Maria

2004-05-01

Interventional cardiac magnetic resonance (MR) procedures are the subject of an increasing number of research studies. Typically, during the procedure only two-dimensional images of oblique slices can be presented to the interventionalist in real time. There is a clear benefit to being able to register the real-time 2D slices to a previously acquired 3D computed tomography (CT) or MR image of the heart. Results from a study of the accuracy of registration of 2D cardiac images of an anesthetized pig to a 3D volume obtained in diastole are presented. Fast cine MR images representing twenty phases of the cardiac cycle were obtained of a 2D slice in a known oblique orientation. The 2D images were initially mis-oriented at distances ranging from 2 to 20 mm, and rotations of +/-10 degrees about all three axes. Images from all 20 cardiac phases were registered to examine the effect of timing between the 2D image and the 3D pre-procedural image. Linear registration using mutual information computed with 64 histogram bins yielded the highest accuracy. For the diastolic phases, mean translation and rotation errors ranged between 0.91 and 1.32 mm and between 1.73 and 2.10 degrees. Scans acquired at other phases also had high accuracy. These results are promising for the use of real time MR in image-guided cardiac interventions, and demonstrate the feasibility of registering 2D oblique MR slices to previously acquired single-phase volumes without preprocessing.

Critical bounds on noise and SNR for robust estimation of real-time brain activity from functional near infra-red spectroscopy.

PubMed

Aqil, Muhammad; Jeong, Myung Yung

2018-04-24

The robust characterization of real-time brain activity carries potential for many applications. However, the contamination of measured signals by various instrumental, environmental, and physiological sources of noise introduces a substantial amount of signal variance and, consequently, challenges real-time estimation of contributions from underlying neuronal sources. Functional near infra-red spectroscopy (fNIRS) is an emerging imaging modality whose real-time potential is yet to be fully explored. The objectives of the current study are to (i) validate a time-dependent linear model of hemodynamic responses in fNIRS, and (ii) test the robustness of this approach against measurement noise (instrumental and physiological) and mis-specification of the hemodynamic response basis functions (amplitude, latency, and duration). We propose a linear hemodynamic model with time-varying parameters, which are estimated (adapted and tracked) using a dynamic recursive least square algorithm. Owing to the linear nature of the activation model, the problem of achieving robust convergence to an accurate estimation of the model parameters is recast as a problem of parameter error stability around the origin. We show that robust convergence of the proposed method is guaranteed in the presence of an acceptable degree of model misspecification and we derive an upper bound on noise under which reliable parameters can still be inferred. We also derived a lower bound on signal-to-noise-ratio over which the reliable parameters can still be inferred from a channel/voxel. Whilst here applied to fNIRS, the proposed methodology is applicable to other hemodynamic-based imaging technologies such as functional magnetic resonance imaging. Copyright © 2018 Elsevier Inc. All rights reserved.

A fast iterative scheme for the linearized Boltzmann equation

NASA Astrophysics Data System (ADS)

Wu, Lei; Zhang, Jun; Liu, Haihu; Zhang, Yonghao; Reese, Jason M.

2017-06-01

Iterative schemes to find steady-state solutions to the Boltzmann equation are efficient for highly rarefied gas flows, but can be very slow to converge in the near-continuum flow regime. In this paper, a synthetic iterative scheme is developed to speed up the solution of the linearized Boltzmann equation by penalizing the collision operator L into the form L = (L + Nδh) - Nδh, where δ is the gas rarefaction parameter, h is the velocity distribution function, and N is a tuning parameter controlling the convergence rate. The velocity distribution function is first solved by the conventional iterative scheme, then it is corrected such that the macroscopic flow velocity is governed by a diffusion-type equation that is asymptotic-preserving into the Navier-Stokes limit. The efficiency of this new scheme is assessed by calculating the eigenvalue of the iteration, as well as solving for Poiseuille and thermal transpiration flows. We find that the fastest convergence of our synthetic scheme for the linearized Boltzmann equation is achieved when Nδ is close to the average collision frequency. The synthetic iterative scheme is significantly faster than the conventional iterative scheme in both the transition and the near-continuum gas flow regimes. Moreover, due to its asymptotic-preserving properties, the synthetic iterative scheme does not need high spatial resolution in the near-continuum flow regime, which makes it even faster than the conventional iterative scheme. Using this synthetic scheme, with the fast spectral approximation of the linearized Boltzmann collision operator, Poiseuille and thermal transpiration flows between two parallel plates, through channels of circular/rectangular cross sections and various porous media are calculated over the whole range of gas rarefaction. Finally, the flow of a Ne-Ar gas mixture is solved based on the linearized Boltzmann equation with the Lennard-Jones intermolecular potential for the first time, and the difference between these results and those using the hard-sphere potential is discussed.

Impact of solids on composite materials

NASA Technical Reports Server (NTRS)

Bronson, Arturo; Maldonado, Jerry; Chern, Tzong; Martinez, Francisco; Mccord-Medrano, Johnnie; Roschke, Paul N.

1987-01-01

The failure modes of composite materials as a result of low velocity impact were investigated by simulating the impact with a finite element analysis. An important facet of the project is the modeling of the impact of a solid onto cylindrical shells composed of composite materials. The model under development will simulate the delamination sustained when a composite material encounters impact from another rigid body. The computer equipment was installed, the computer network tested, and a finite element method model was developed to compare results with known experimental data. The model simulated the impact of a steel rod onto a rotating shaft. Pre-processing programs (GMESH and TANVEL) were developed to generate node and element data for the input into the three dimensional, dynamic finite element analysis code (DYNA3D). The finite element mesh was configured with a fine mesh near the impact zone and a coarser mesh for the impacting rod and the regions surrounding the impacting zone. For the computer simulation, five impacting loads were used to determine the time history of the stresses, the scribed surface areas, and the amount of ridging. The processing time of the computer codes amounted from 1 to 4 days. The calculated surface area were within 6-12 percent, relative error when compated to the actual scratch area.

Real-time high-level video understanding using data warehouse

NASA Astrophysics Data System (ADS)

Lienard, Bruno; Desurmont, Xavier; Barrie, Bertrand; Delaigle, Jean-Francois

2006-02-01

High-level Video content analysis such as video-surveillance is often limited by computational aspects of automatic image understanding, i.e. it requires huge computing resources for reasoning processes like categorization and huge amount of data to represent knowledge of objects, scenarios and other models. This article explains how to design and develop a "near real-time adaptive image datamart", used, as a decisional support system for vision algorithms, and then as a mass storage system. Using RDF specification as storing format of vision algorithms meta-data, we can optimise the data warehouse concepts for video analysis, add some processes able to adapt the current model and pre-process data to speed-up queries. In this way, when new data is sent from a sensor to the data warehouse for long term storage, using remote procedure call embedded in object-oriented interfaces to simplified queries, they are processed and in memory data-model is updated. After some processing, possible interpretations of this data can be returned back to the sensor. To demonstrate this new approach, we will present typical scenarios applied to this architecture such as people tracking and events detection in a multi-camera network. Finally we will show how this system becomes a high-semantic data container for external data-mining.

Asymptotic stability estimates near an equilibrium point

NASA Astrophysics Data System (ADS)

Dumas, H. Scott; Meyer, Kenneth R.; Palacián, Jesús F.; Yanguas, Patricia

2017-07-01

We use the error bounds for adiabatic invariants found in the work of Chartier, Murua and Sanz-Serna [3] to bound the solutions of a Hamiltonian system near an equilibrium over exponentially long times. Our estimates depend only on the linearized system and not on the higher order terms as in KAM theory, nor do we require any steepness or convexity conditions as in Nekhoroshev theory. We require that the equilibrium point where our estimate applies satisfy a type of formal stability called Lie stability.

Generic Kalman Filter Software

NASA Technical Reports Server (NTRS)

Lisano, Michael E., II; Crues, Edwin Z.

2005-01-01

The Generic Kalman Filter (GKF) software provides a standard basis for the development of application-specific Kalman-filter programs. Historically, Kalman filters have been implemented by customized programs that must be written, coded, and debugged anew for each unique application, then tested and tuned with simulated or actual measurement data. Total development times for typical Kalman-filter application programs have ranged from months to weeks. The GKF software can simplify the development process and reduce the development time by eliminating the need to re-create the fundamental implementation of the Kalman filter for each new application. The GKF software is written in the ANSI C programming language. It contains a generic Kalman-filter-development directory that, in turn, contains a code for a generic Kalman filter function; more specifically, it contains a generically designed and generically coded implementation of linear, linearized, and extended Kalman filtering algorithms, including algorithms for state- and covariance-update and -propagation functions. The mathematical theory that underlies the algorithms is well known and has been reported extensively in the open technical literature. Also contained in the directory are a header file that defines generic Kalman-filter data structures and prototype functions and template versions of application-specific subfunction and calling navigation/estimation routine code and headers. Once the user has provided a calling routine and the required application-specific subfunctions, the application-specific Kalman-filter software can be compiled and executed immediately. During execution, the generic Kalman-filter function is called from a higher-level navigation or estimation routine that preprocesses measurement data and post-processes output data. The generic Kalman-filter function uses the aforementioned data structures and five implementation- specific subfunctions, which have been developed by the user on the basis of the aforementioned templates. The GKF software can be used to develop many different types of unfactorized Kalman filters. A developer can choose to implement either a linearized or an extended Kalman filter algorithm, without having to modify the GKF software. Control dynamics can be taken into account or neglected in the filter-dynamics model. Filter programs developed by use of the GKF software can be made to propagate equations of motion for linear or nonlinear dynamical systems that are deterministic or stochastic. In addition, filter programs can be made to operate in user-selectable "covariance analysis" and "propagation-only" modes that are useful in design and development stages.

Compact Circuit Preprocesses Accelerometer Output

NASA Technical Reports Server (NTRS)

Bozeman, Richard J., Jr.

1993-01-01

Compact electronic circuit transfers dc power to, and preprocesses ac output of, accelerometer and associated preamplifier. Incorporated into accelerometer case during initial fabrication or retrofit onto commercial accelerometer. Made of commercial integrated circuits and other conventional components; made smaller by use of micrologic and surface-mount technology.

The Foreign-Language Teacher and Cognitive Psychology or Where Do We Go from Here?

ERIC Educational Resources Information Center

Rivers, Wilga M.

Research into the psychology of perception can uncover important discoveries for more efficient learning. There must be increased understanding of the processing of input and the pre-processing of output for improved language instruction. Educators must at the present time be extremely wary of basing what they do in the foreign-language classroom…

A new approach to telemetry data processing. Ph.D. Thesis - Maryland Univ.

NASA Technical Reports Server (NTRS)

Broglio, C. J.

1973-01-01

An approach for a preprocessing system for telemetry data processing was developed. The philosophy of the approach is the development of a preprocessing system to interface with the main processor and relieve it of the burden of stripping information from a telemetry data stream. To accomplish this task, a telemetry preprocessing language was developed. Also, a hardware device for implementing the operation of this language was designed using a cellular logic module concept. In the development of the hardware device and the cellular logic module, a distributed form of control was implemented. This is accomplished by a technique of one-to-one intermodule communications and a set of privileged communication operations. By transferring this control state from module to module, the control function is dispersed through the system. A compiler for translating the preprocessing language statements into an operations table for the hardware device was also developed. Finally, to complete the system design and verify it, a simulator for the collular logic module was written using the APL/360 system.

Near-optimal alternative generation using modified hit-and-run sampling for non-linear, non-convex problems

NASA Astrophysics Data System (ADS)

Rosenberg, D. E.; Alafifi, A.

2016-12-01

Water resources systems analysis often focuses on finding optimal solutions. Yet an optimal solution is optimal only for the modelled issues and managers often seek near-optimal alternatives that address un-modelled objectives, preferences, limits, uncertainties, and other issues. Early on, Modelling to Generate Alternatives (MGA) formalized near-optimal as the region comprising the original problem constraints plus a new constraint that allowed performance within a specified tolerance of the optimal objective function value. MGA identified a few maximally-different alternatives from the near-optimal region. Subsequent work applied Markov Chain Monte Carlo (MCMC) sampling to generate a larger number of alternatives that span the near-optimal region of linear problems or select portions for non-linear problems. We extend the MCMC Hit-And-Run method to generate alternatives that span the full extent of the near-optimal region for non-linear, non-convex problems. First, start at a feasible hit point within the near-optimal region, then run a random distance in a random direction to a new hit point. Next, repeat until generating the desired number of alternatives. The key step at each iterate is to run a random distance along the line in the specified direction to a new hit point. If linear equity constraints exist, we construct an orthogonal basis and use a null space transformation to confine hits and runs to a lower-dimensional space. Linear inequity constraints define the convex bounds on the line that runs through the current hit point in the specified direction. We then use slice sampling to identify a new hit point along the line within bounds defined by the non-linear inequity constraints. This technique is computationally efficient compared to prior near-optimal alternative generation techniques such MGA, MCMC Metropolis-Hastings, evolutionary, or firefly algorithms because search at each iteration is confined to the hit line, the algorithm can move in one step to any point in the near-optimal region, and each iterate generates a new, feasible alternative. We use the method to generate alternatives that span the near-optimal regions of simple and more complicated water management problems and may be preferred to optimal solutions. We also discuss extensions to handle non-linear equity constraints.

Climate change impacts on projections of excess mortality at 2030 using spatially varying ozone-temperature

EPA Science Inventory

We project the change in ozone-related mortality burden attributable to changes in climate between a historical (1995-2005) and near-future (2025-2035) time period while incorporating a non-linear and synergistic effect of ozone and temperature on mortality. We simulate air quali...

Solving Large Problems with a Small Working Memory

ERIC Educational Resources Information Center

Pizlo, Zygmunt; Stefanov, Emil

2013-01-01

We describe an important elaboration of our multiscale/multiresolution model for solving the Traveling Salesman Problem (TSP). Our previous model emulated the non-uniform distribution of receptors on the human retina and the shifts of visual attention. This model produced near-optimal solutions of TSP in linear time by performing hierarchical…

Demonstration of angular anisotropy in the output of Thematic Mapper

NASA Technical Reports Server (NTRS)

Duggin, M. J. (Principal Investigator); Lindsay, J.; Piwinski, D. J.; Schoch, L. B.

1984-01-01

There is a dependence of TM output (proportional to scene radiance in a manner which will be discussed) upon season, upon cover type and upon view angle. The existence of a significant systematic variation across uniform scenes in p-type (radiometrically and geometrically pre-processed) data is demonstrated. Present pre-processing does remove the effects and the problem must be addressed because the effects are large. While this is in no way attributable to any shortcomings in the thematic mapper, it is an effect which is sufficiently important to warrant more study, with a view to developing suitable pre-processing correction algorithms.

Optimized method for atmospheric signal reduction in irregular sampled InSAR time series assisted by external atmospheric information

NASA Astrophysics Data System (ADS)

Gong, W.; Meyer, F. J.

2013-12-01

It is well known that spatio-temporal the tropospheric phase signatures complicate the interpretation and detection of smaller magnitude deformation signals or unstudied motion fields. Several advanced time-series InSAR techniques were developed in the last decade that make assumptions about the stochastic properties of the signal components in interferometric phases to reduce atmospheric delay effects on surface deformation estimates. However, their need for large datasets to successfully separate the different phase contributions limits their performance if data is scarce and irregularly sampled. Limited SAR data coverage is true for many areas affected by geophysical deformation. This is either due to their low priority in mission programming, unfavorable ground coverage condition, or turbulent seasonal weather effects. In this paper, we present new adaptive atmospheric phase filtering algorithms that are specifically designed to reconstruct surface deformation signals from atmosphere-affected and irregularly sampled InSAR time series. The filters take advantage of auxiliary atmospheric delay information that is extracted from various sources, e.g. atmospheric weather models. They are embedded into a model-free Persistent Scatterer Interferometry (PSI) approach that was selected to accommodate non-linear deformation patterns that are often observed near volcanoes and earthquake zones. Two types of adaptive phase filters were developed that operate in the time dimension and separate atmosphere from deformation based on their different temporal correlation properties. Both filter types use the fact that atmospheric models can reliably predict the spatial statistics and signal power of atmospheric phase delay fields in order to automatically optimize the filter's shape parameters. In essence, both filter types will attempt to maximize the linear correlation between a-priori and the extracted atmospheric phase information. Topography-related phase components, orbit errors and the master atmospheric delays are first removed in a pre-processing step before the atmospheric filters are applied. The first adaptive filter type is using a filter kernel of Gaussian shape and is adaptively adjusting the width (defined in days) of this filter until the correlation of extracted and modeled atmospheric signal power is maximized. If atmospheric properties vary along the time series, this approach will lead to filter setting that are adapted to best reproduce atmospheric conditions at a certain observation epoch. Despite the superior performance of this first filter design, its Gaussian shape imposes non-physical relative weights onto acquisitions that ignore the known atmospheric noise in the data. Hence, in our second approach we are using atmospheric a-priori information to adaptively define the full shape of the atmospheric filter. For this process, we use a so-called normalized convolution (NC) approach that is often used in image reconstruction. Several NC designs will be presented in this paper and studied for relative performance. A cross-validation of all developed algorithms was done using both synthetic and real data. This validation showed designed filters are outperforming conventional filter methods that particularly useful for regions with limited data coverage or lack of a deformation field prior.

Airborne Hyperspectral Imaging of Seagrass and Coral Reef

NASA Astrophysics Data System (ADS)

Merrill, J.; Pan, Z.; Mewes, T.; Herwitz, S.

2013-12-01

This talk presents the process of project preparation, airborne data collection, data pre-processing and comparative analysis of a series of airborne hyperspectral projects focused on the mapping of seagrass and coral reef communities in the Florida Keys. As part of a series of large collaborative projects funded by the NASA ROSES program and the Florida Fish and Wildlife Conservation Commission and administered by the NASA UAV Collaborative, a series of airborne hyperspectral datasets were collected over six sites in the Florida Keys in May 2012, October 2012 and May 2013 by Galileo Group, Inc. using a manned Cessna 172 and NASA's SIERRA Unmanned Aerial Vehicle. Precise solar and tidal data were used to calculate airborne collection parameters and develop flight plans designed to optimize data quality. Two independent Visible and Near-Infrared (VNIR) hyperspectral imaging systems covering 400-100nm were used to collect imagery over six Areas of Interest (AOIs). Multiple collections were performed over all sites across strict solar windows in the mornings and afternoons. Independently developed pre-processing algorithms were employed to radiometrically correct, synchronize and georectify individual flight lines which were then combined into color balanced mosaics for each Area of Interest. The use of two different hyperspectral sensor as well as environmental variations between each collection allow for the comparative analysis of data quality as well as the iterative refinement of flight planning and collection parameters.

Study on rapid valid acidity evaluation of apple by fiber optic diffuse reflectance technique

NASA Astrophysics Data System (ADS)

Liu, Yande; Ying, Yibin; Fu, Xiaping; Jiang, Xuesong

2004-03-01

Some issues related to nondestructive evaluation of valid acidity in intact apples by means of Fourier transform near infrared (FTNIR) (800-2631nm) method were addressed. A relationship was established between the diffuse reflectance spectra recorded with a bifurcated optic fiber and the valid acidity. The data were analyzed by multivariate calibration analysis such as partial least squares (PLS) analysis and principal component regression (PCR) technique. A total of 120 Fuji apples were tested and 80 of them were used to form a calibration data set. The influence of data preprocessing and different spectra treatments were also investigated. Models based on smoothing spectra were slightly worse than models based on derivative spectra and the best result was obtained when the segment length was 5 and the gap size was 10. Depending on data preprocessing and multivariate calibration technique, the best prediction model had a correlation efficient (0.871), a low RMSEP (0.0677), a low RMSEC (0.056) and a small difference between RMSEP and RMSEC by PLS analysis. The results point out the feasibility of FTNIR spectral analysis to predict the fruit valid acidity non-destructively. The ratio of data standard deviation to the root mean square error of prediction (SDR) is better to be less than 3 in calibration models, however, the results cannot meet the demand of actual application. Therefore, further study is required for better calibration and prediction.

Advanced techniques and technology for efficient data storage, access, and transfer

NASA Technical Reports Server (NTRS)

Rice, Robert F.; Miller, Warner

1991-01-01

Advanced techniques for efficiently representing most forms of data are being implemented in practical hardware and software form through the joint efforts of three NASA centers. These techniques adapt to local statistical variations to continually provide near optimum code efficiency when representing data without error. Demonstrated in several earlier space applications, these techniques are the basis of initial NASA data compression standards specifications. Since the techniques clearly apply to most NASA science data, NASA invested in the development of both hardware and software implementations for general use. This investment includes high-speed single-chip very large scale integration (VLSI) coding and decoding modules as well as machine-transferrable software routines. The hardware chips were tested in the laboratory at data rates as high as 700 Mbits/s. A coding module's definition includes a predictive preprocessing stage and a powerful adaptive coding stage. The function of the preprocessor is to optimally process incoming data into a standard form data source that the second stage can handle.The built-in preprocessor of the VLSI coder chips is ideal for high-speed sampled data applications such as imaging and high-quality audio, but additionally, the second stage adaptive coder can be used separately with any source that can be externally preprocessed into the 'standard form'. This generic functionality assures that the applicability of these techniques and their recent high-speed implementations should be equally broad outside of NASA.

The Python Spectral Analysis Tool (PySAT) for Powerful, Flexible, and Easy Preprocessing and Machine Learning with Point Spectral Data

NASA Astrophysics Data System (ADS)

Anderson, R. B.; Finch, N.; Clegg, S. M.; Graff, T.; Morris, R. V.; Laura, J.

2018-04-01

The PySAT point spectra tool provides a flexible graphical interface, enabling scientists to apply a wide variety of preprocessing and machine learning methods to point spectral data, with an emphasis on multivariate regression.

2D DOST based local phase pattern for face recognition

NASA Astrophysics Data System (ADS)

Moniruzzaman, Md.; Alam, Mohammad S.

2017-05-01

A new two dimensional (2-D) Discrete Orthogonal Stcokwell Transform (DOST) based Local Phase Pattern (LPP) technique has been proposed for efficient face recognition. The proposed technique uses 2-D DOST as preliminary preprocessing and local phase pattern to form robust feature signature which can effectively accommodate various 3D facial distortions and illumination variations. The S-transform, is an extension of the ideas of the continuous wavelet transform (CWT), is also known for its local spectral phase properties in time-frequency representation (TFR). It provides a frequency dependent resolution of the time-frequency space and absolutely referenced local phase information while maintaining a direct relationship with the Fourier spectrum which is unique in TFR. After utilizing 2-D Stransform as the preprocessing and build local phase pattern from extracted phase information yield fast and efficient technique for face recognition. The proposed technique shows better correlation discrimination compared to alternate pattern recognition techniques such as wavelet or Gabor based face recognition. The performance of the proposed method has been tested using the Yale and extended Yale facial database under different environments such as illumination variation and 3D changes in facial expressions. Test results show that the proposed technique yields better performance compared to alternate time-frequency representation (TFR) based face recognition techniques.

Simplified energy-balance model for pragmatic multi-dimensional device simulation

NASA Astrophysics Data System (ADS)

Chang, Duckhyun; Fossum, Jerry G.

1997-11-01

To pragmatically account for non-local carrier heating and hot-carrier effects such as velocity overshoot and impact ionization in multi-dimensional numerical device simulation, a new simplified energy-balance (SEB) model is developed and implemented in FLOODS[16] as a pragmatic option. In the SEB model, the energy-relaxation length is estimated from a pre-process drift-diffusion simulation using the carrier-velocity distribution predicted throughout the device domain, and is used without change in a subsequent simpler hydrodynamic (SHD) simulation. The new SEB model was verified by comparison of two-dimensional SHD and full HD DC simulations of a submicron MOSFET. The SHD simulations yield detailed distributions of carrier temperature, carrier velocity, and impact-ionization rate, which agree well with the full HD simulation results obtained with FLOODS. The most noteworthy feature of the new SEB/SHD model is its computational efficiency, which results from reduced Newton iteration counts caused by the enhanced linearity. Relative to full HD, SHD simulation times can be shorter by as much as an order of magnitude since larger voltage steps for DC sweeps and larger time steps for transient simulations can be used. The improved computational efficiency can enable pragmatic three-dimensional SHD device simulation as well, for which the SEB implementation would be straightforward as it is in FLOODS or any robust HD simulator.

Quantum Search in Hilbert Space

NASA Technical Reports Server (NTRS)

Zak, Michail

2003-01-01

A proposed quantum-computing algorithm would perform a search for an item of information in a database stored in a Hilbert-space memory structure. The algorithm is intended to make it possible to search relatively quickly through a large database under conditions in which available computing resources would otherwise be considered inadequate to perform such a task. The algorithm would apply, more specifically, to a relational database in which information would be stored in a set of N complex orthonormal vectors, each of N dimensions (where N can be exponentially large). Each vector would constitute one row of a unitary matrix, from which one would derive the Hamiltonian operator (and hence the evolutionary operator) of a quantum system. In other words, all the stored information would be mapped onto a unitary operator acting on a quantum state that would represent the item of information to be retrieved. Then one could exploit quantum parallelism: one could pose all search queries simultaneously by performing a quantum measurement on the system. In so doing, one would effectively solve the search problem in one computational step. One could exploit the direct- and inner-product decomposability of the unitary matrix to make the dimensionality of the memory space exponentially large by use of only linear resources. However, inasmuch as the necessary preprocessing (the mapping of the stored information into a Hilbert space) could be exponentially expensive, the proposed algorithm would likely be most beneficial in applications in which the resources available for preprocessing were much greater than those available for searching.

Spatial Normalization of Reverse Phase Protein Array Data

PubMed Central

Kaushik, Poorvi; Molinelli, Evan J.; Miller, Martin L.; Wang, Weiqing; Korkut, Anil; Liu, Wenbin; Ju, Zhenlin; Lu, Yiling; Mills, Gordon; Sander, Chris

2014-01-01

Reverse phase protein arrays (RPPA) are an efficient, high-throughput, cost-effective method for the quantification of specific proteins in complex biological samples. The quality of RPPA data may be affected by various sources of error. One of these, spatial variation, is caused by uneven exposure of different parts of an RPPA slide to the reagents used in protein detection. We present a method for the determination and correction of systematic spatial variation in RPPA slides using positive control spots printed on each slide. The method uses a simple bi-linear interpolation technique to obtain a surface representing the spatial variation occurring across the dimensions of a slide. This surface is used to calculate correction factors that can normalize the relative protein concentrations of the samples on each slide. The adoption of the method results in increased agreement between technical and biological replicates of various tumor and cell-line derived samples. Further, in data from a study of the melanoma cell-line SKMEL-133, several slides that had previously been rejected because they had a coefficient of variation (CV) greater than 15%, are rescued by reduction of CV below this threshold in each case. The method is implemented in the R statistical programing language. It is compatible with MicroVigene and SuperCurve, packages commonly used in RPPA data analysis. The method is made available, along with suggestions for implementation, at http://bitbucket.org/rppa_preprocess/rppa_preprocess/src. PMID:25501559

SeqTrim: a high-throughput pipeline for pre-processing any type of sequence read

PubMed Central

2010-01-01

Background High-throughput automated sequencing has enabled an exponential growth rate of sequencing data. This requires increasing sequence quality and reliability in order to avoid database contamination with artefactual sequences. The arrival of pyrosequencing enhances this problem and necessitates customisable pre-processing algorithms. Results SeqTrim has been implemented both as a Web and as a standalone command line application. Already-published and newly-designed algorithms have been included to identify sequence inserts, to remove low quality, vector, adaptor, low complexity and contaminant sequences, and to detect chimeric reads. The availability of several input and output formats allows its inclusion in sequence processing workflows. Due to its specific algorithms, SeqTrim outperforms other pre-processors implemented as Web services or standalone applications. It performs equally well with sequences from EST libraries, SSH libraries, genomic DNA libraries and pyrosequencing reads and does not lead to over-trimming. Conclusions SeqTrim is an efficient pipeline designed for pre-processing of any type of sequence read, including next-generation sequencing. It is easily configurable and provides a friendly interface that allows users to know what happened with sequences at every pre-processing stage, and to verify pre-processing of an individual sequence if desired. The recommended pipeline reveals more information about each sequence than previously described pre-processors and can discard more sequencing or experimental artefacts. PMID:20089148

Automated volumetric lung segmentation of thoracic CT images using fully convolutional neural network

NASA Astrophysics Data System (ADS)

Negahdar, Mohammadreza; Beymer, David; Syeda-Mahmood, Tanveer

2018-02-01

Deep Learning models such as Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in 2D medical image analysis. In clinical practice; however, most analyzed and acquired medical data are formed of 3D volumes. In this paper, we present a fast and efficient 3D lung segmentation method based on V-net: a purely volumetric fully CNN. Our model is trained on chest CT images through volume to volume learning, which palliates overfitting problem on limited number of annotated training data. Adopting a pre-processing step and training an objective function based on Dice coefficient addresses the imbalance between the number of lung voxels against that of background. We have leveraged Vnet model by using batch normalization for training which enables us to use higher learning rate and accelerates the training of the model. To address the inadequacy of training data and obtain better robustness, we augment the data applying random linear and non-linear transformations. Experimental results on two challenging medical image data show that our proposed method achieved competitive result with a much faster speed.

Combustion monitoring of a water tube boiler using a discriminant radial basis network.

PubMed

Sujatha, K; Pappa, N

2011-01-01

This research work includes a combination of Fisher's linear discriminant (FLD) analysis and a radial basis network (RBN) for monitoring the combustion conditions for a coal fired boiler so as to allow control of the air/fuel ratio. For this, two-dimensional flame images are required, which were captured with a CCD camera; the features of the images-average intensity, area, brightness and orientation etc of the flame-are extracted after preprocessing the images. The FLD is applied to reduce the n-dimensional feature size to a two-dimensional feature size for faster learning of the RBN. Also, three classes of images corresponding to different burning conditions of the flames have been extracted from continuous video processing. In this, the corresponding temperatures, and the carbon monoxide (CO) emissions and those of other flue gases have been obtained through measurement. Further, the training and testing of Fisher's linear discriminant radial basis network (FLDRBN), with the data collected, have been carried out and the performance of the algorithms is presented. Copyright © 2010 ISA. Published by Elsevier Ltd. All rights reserved.

Highly parallel demagnetization field calculation using the fast multipole method on tetrahedral meshes with continuous sources

NASA Astrophysics Data System (ADS)

Palmesi, P.; Exl, L.; Bruckner, F.; Abert, C.; Suess, D.

2017-11-01

The long-range magnetic field is the most time-consuming part in micromagnetic simulations. Computational improvements can relieve problems related to this bottleneck. This work presents an efficient implementation of the Fast Multipole Method [FMM] for the magnetic scalar potential as used in micromagnetics. The novelty lies in extending FMM to linearly magnetized tetrahedral sources making it interesting also for other areas of computational physics. We treat the near field directly and in use (exact) numerical integration on the multipole expansion in the far field. This approach tackles important issues like the vectorial and continuous nature of the magnetic field. By using FMM the calculations scale linearly in time and memory.

Optimization of neural network architecture for classification of radar jamming FM signals

NASA Astrophysics Data System (ADS)

Soto, Alberto; Mendoza, Ariadna; Flores, Benjamin C.

2017-05-01

The purpose of this study is to investigate several artificial Neural Network (NN) architectures in order to design a cognitive radar system capable of optimally distinguishing linear Frequency-Modulated (FM) signals from bandlimited Additive White Gaussian Noise (AWGN). The goal is to create a theoretical framework to determine an optimal NN architecture to achieve a Probability of Detection (PD) of 95% or higher and a Probability of False Alarm (PFA) of 1.5% or lower at 5 dB Signal to Noise Ratio (SNR). Literature research reveals that the frequency-domain power spectral densities characterize a signal more efficiently than its time-domain counterparts. Therefore, the input data is preprocessed by calculating the magnitude square of the Discrete Fourier Transform of the digitally sampled bandlimited AWGN and linear FM signals to populate a matrix containing N number of samples and M number of spectra. This matrix is used as input for the NN, and the spectra are divided as follows: 70% for training, 15% for validation, and 15% for testing. The study begins by experimentally deducing the optimal number of hidden neurons (1-40 neurons), then the optimal number of hidden layers (1-5 layers), and lastly, the most efficient learning algorithm. The training algorithms examined are: Resilient Backpropagation, Scaled Conjugate Gradient, Conjugate Gradient with Powell/Beale Restarts, Polak-Ribiére Conjugate Gradient, and Variable Learning Rate Backpropagation. We determine that an architecture with ten hidden neurons (or higher), one hidden layer, and a Scaled Conjugate Gradient for training algorithm encapsulates an optimal architecture for our application.

Instability of supersonic cold streams feeding galaxies - I. Linear Kelvin-Helmholtz instability with body modes

NASA Astrophysics Data System (ADS)

Mandelker, Nir; Padnos, Dan; Dekel, Avishai; Birnboim, Yuval; Burkert, Andreas; Krumholz, Mark R.; Steinberg, Elad

2016-12-01

Massive galaxies at high redshift are predicted to be fed from the cosmic web by narrow, dense streams of cold gas that penetrate through the hot medium encompassed by a stable shock near the virial radius of the dark-matter halo. Our long-term goal is to explore the heating and dissipation rate of the streams and their fragmentation and possible breakup, in order to understand how galaxies are fed, and how this affects their star formation rate and morphology. We present here the first step, where we analyse the linear Kelvin-Helmholtz instability (KHI) of a cold, dense slab or cylinder in 3D flowing supersonically through a hot, dilute medium. The current analysis is limited to the adiabatic case with no gravity. By analytically solving the linear dispersion relation, we find a transition from a dominance of the familiar rapidly growing surface modes in the subsonic regime to more slowly growing body modes in the supersonic regime. The system is parametrized by three parameters: the density contrast between stream and medium, the Mach number of stream velocity with respect to the medium and the stream width with respect to the halo virial radius. A realistic choice for these parameters places the streams near the mode transition, with the KHI exponential-growth time in the range 0.01-10 virial crossing times for a perturbation wavelength comparable to the stream width. We confirm our analytic predictions with idealized hydrodynamical simulations. Our linear estimates thus indicate that KHI may be effective in the evolution of streams before they reach the galaxy. More definite conclusions await the extension of the analysis to the non-linear regime and the inclusion of cooling, thermal conduction, the halo potential well, self-gravity and magnetic fields.

Digital filtering and model updating methods for improving the robustness of near-infrared multivariate calibrations.

PubMed

Kramer, Kirsten E; Small, Gary W

2009-02-01

Fourier transform near-infrared (NIR) transmission spectra are used for quantitative analysis of glucose for 17 sets of prediction data sampled as much as six months outside the timeframe of the corresponding calibration data. Aqueous samples containing physiological levels of glucose in a matrix of bovine serum albumin and triacetin are used to simulate clinical samples such as blood plasma. Background spectra of a single analyte-free matrix sample acquired during the instrumental warm-up period on the prediction day are used for calibration updating and for determining the optimal frequency response of a preprocessing infinite impulse response time-domain digital filter. By tuning the filter and the calibration model to the specific instrumental response associated with the prediction day, the calibration model is given enhanced ability to operate over time. This methodology is demonstrated in conjunction with partial least squares calibration models built with a spectral range of 4700-4300 cm(-1). By using a subset of the background spectra to evaluate the prediction performance of the updated model, projections can be made regarding the success of subsequent glucose predictions. If a threshold standard error of prediction (SEP) of 1.5 mM is used to establish successful model performance with the glucose samples, the corresponding threshold for the SEP of the background spectra is found to be 1.3 mM. For calibration updating in conjunction with digital filtering, SEP values of all 17 prediction sets collected over 3-178 days displaced from the calibration data are below 1.5 mM. In addition, the diagnostic based on the background spectra correctly assesses the prediction performance in 16 of the 17 cases.

Pupils' over-reliance on linearity: a scholastic effect?

PubMed

Van Dooren, Wim; De Bock, Dirk; Janssens, Dirk; Verschaffel, Lieven

2007-06-01

From upper elementary education on, children develop a tendency to over-use linearity. Particularly, it is found that many pupils assume that if a figure enlarges k times, the area enlarges k times too. However, most research was conducted with traditional, school-like word problems. This study examines whether pupils also over-use linearity if non-linear problems are embedded in meaningful, authentic performance tasks instead of traditional, school-like word problems, and whether this experience influences later behaviour. Ninety-three sixth graders from two primary schools in Flanders, Belgium. Pupils received a pre-test with traditional word problems. Those who made a linear error on the non-linear area problem were subjected to individual interviews. They received one new non-linear problem, in the S-condition (again a traditional, scholastic word problem), D-condition (the same word problem with a drawing) or P-condition (a meaningful performance-based task). Shortly afterwards, pupils received a post-test, containing again a non-linear word problem. Most pupils from the S-condition displayed linear reasoning during the interview. Offering drawings (D-condition) had a positive effect, but presenting the problem as a performance task (P-condition) was more beneficial. Linear reasoning was nearly absent in the P-condition. Remarkably, at the post-test, most pupils from all three groups again applied linear strategies. Pupils' over-reliance on linearity seems partly elicited by the school-like word problem format of test items. Pupils perform much better if non-linear problems are offered as performance tasks. However, a single experience does not change performances on a comparable word problem test afterwards.

CYCLOPS-3 System Research.

ERIC Educational Resources Information Center

Marill, Thomas; And Others

The aim of the CYCLOPS Project research is the development of techniques for allowing computers to perform visual scene analysis, pre-processing of visual imagery, and perceptual learning. Work on scene analysis and learning has previously been described. The present report deals with research on pre-processing and with further work on scene…

A graphical method to evaluate spectral preprocessing in multivariate regression calibrations: example with Savitzky-Golay filters and partial least squares regression

USDA-ARS?s Scientific Manuscript database

In multivariate regression analysis of spectroscopy data, spectral preprocessing is often performed to reduce unwanted background information (offsets, sloped baselines) or accentuate absorption features in intrinsically overlapping bands. These procedures, also known as pretreatments, are commonly ...

Fast quantifying collision strength index of ethylene-vinyl acetate copolymer coverings on the fields based on near infrared hyperspectral imaging techniques

PubMed Central

Chen, Y. M.; Lin, P.; He, Y.; He, J. Q.; Zhang, J.; Li, X. L.

2016-01-01

A novel strategy based on the near infrared hyperspectral imaging techniques and chemometrics were explored for fast quantifying the collision strength index of ethylene-vinyl acetate copolymer (EVAC) coverings on the fields. The reflectance spectral data of EVAC coverings was obtained by using the near infrared hyperspectral meter. The collision analysis equipment was employed to measure the collision intensity of EVAC materials. The preprocessing algorithms were firstly performed before the calibration. The algorithms of random frog and successive projection (SP) were applied to extracting the fingerprint wavebands. A correlation model between the significant spectral curves which reflected the cross-linking attributions of the inner organic molecules and the degree of collision strength was set up by taking advantage of the support vector machine regression (SVMR) approach. The SP-SVMR model attained the residual predictive deviation of 3.074, the square of percentage of correlation coefficient of 93.48% and 93.05% and the root mean square error of 1.963 and 2.091 for the calibration and validation sets, respectively, which exhibited the best forecast performance. The results indicated that the approaches of integrating the near infrared hyperspectral imaging techniques with the chemometrics could be utilized to rapidly determine the degree of collision strength of EVAC. PMID:26875544

Probabilistic measurement of non-physical constructs during early childhood: Epistemological implications for advancing psychosocial science

NASA Astrophysics Data System (ADS)

Bezruczko, N.; Fatani, S. S.

2010-07-01

Social researchers commonly compute ordinal raw scores and ratings to quantify human aptitudes, attitudes, and abilities but without a clear understanding of their limitations for scientific knowledge. In this research, common ordinal measures were compared to higher order linear (equal interval) scale measures to clarify implications for objectivity, precision, ontological coherence, and meaningfulness. Raw score gains, residualized raw gains, and linear gains calculated with a Rasch model were compared between Time 1 and Time 2 for observations from two early childhood learning assessments. Comparisons show major inconsistencies between ratings and linear gains. When gain distribution was dense, relatively compact, and initial status near item mid-range, linear measures and ratings were indistinguishable. When Time 1 status was distributed more broadly and magnitude of change variable, ratings were unrelated to linear gain, which emphasizes problematic implications of ordinal measures. Surprisingly, residualized gain scores did not significantly improve ordinal measurement of change. In general, raw scores and ratings may be meaningful in specific samples to establish order and high/low rank, but raw score differences suffer from non-uniform units. Even meaningfulness of sample comparisons, as well as derived proportions and percentages, are seriously affected by rank order distortions and should be avoided.

Linear decomposition approach for a class of nonconvex programming problems.

PubMed

Shen, Peiping; Wang, Chunfeng

2017-01-01

This paper presents a linear decomposition approach for a class of nonconvex programming problems by dividing the input space into polynomially many grids. It shows that under certain assumptions the original problem can be transformed and decomposed into a polynomial number of equivalent linear programming subproblems. Based on solving a series of liner programming subproblems corresponding to those grid points we can obtain the near-optimal solution of the original problem. Compared to existing results in the literature, the proposed algorithm does not require the assumptions of quasi-concavity and differentiability of the objective function, and it differs significantly giving an interesting approach to solving the problem with a reduced running time.

Linear and Nonlinear Analysis of Magnetic Bearing Bandwidth Due to Eddy Current Limitations

NASA Technical Reports Server (NTRS)

Kenny, Andrew; Palazzolo, Alan

2000-01-01

Finite element analysis was used to study the bandwidth of alloy hyperco50a and silicon iron laminated rotors and stators in magnetic bearings. A three dimensional model was made of a heteropolar bearing in which all the flux circulated in the plane of the rotor and stator laminate. A three dimensional model of a plate similar to the region of a pole near the gap was also studied with a very fine mesh. Nonlinear time transient solutions for the net flux carried by the plate were compared to steady state time harmonic solutions. Both linear and quasi-nonlinear steady state time harmonic solutions were calculated and compared. The finite element solutions for power loss and flux bandwidth were compared to those determined from classical analytical solutions to Maxwell's equations.

A numerical method for solving systems of linear ordinary differential equations with rapidly oscillating solutions

NASA Technical Reports Server (NTRS)

Bernstein, Ira B.; Brookshaw, Leigh; Fox, Peter A.

1992-01-01

The present numerical method for accurate and efficient solution of systems of linear equations proceeds by numerically developing a set of basis solutions characterized by slowly varying dependent variables. The solutions thus obtained are shown to have a computational overhead largely independent of the small size of the scale length which characterizes the solutions; in many cases, the technique obviates series solutions near singular points, and its known sources of error can be easily controlled without a substantial increase in computational time.

Generation of an optical frequency comb with a Gaussian spectrum using a linear time-to-space mapping system.

PubMed

Hisatake, Shintaro; Tada, Keiji; Nagatsuma, Tadao

2010-03-01

We demonstrate the generation of an optical frequency comb (OFC) with a Gaussian spectrum using a continuous-wave (CW) laser, based on spatial convolution of a slit and a periodically moving optical beam spot in a linear time-to-space mapping system. A CW optical beam is linearly mapped to a spatial signal using two sinusoidal electro-optic (EO) deflections and an OFC is extracted by inserting a narrow spatial slit in the Fourier-transform plane of a second EO deflector (EOD). The spectral shape of the OFC corresponds to the spatial beam profile in the near-field region of the second EOD, which can be manipulated by a spatial filter without spectral dispersers. In a proof-of-concept experiment, a 16.25-GHz-spaced, 240-GHz-wide Gaussian-envelope OFC (corresponding to 1.8 ps Gaussian pulse generation) was demonstrated.

Time-resolved fluorescence polarization spectroscopy of visible and near infrared dyes in picosecond dynamics

NASA Astrophysics Data System (ADS)

Pu, Yang; Alfano, Robert R.

2015-03-01

Near-infrared (NIR) dyes absorb and emit light within the range from 700 to 900 nm have several benefits in biological studies for one- and/or two-photon excitation for deeper penetration of tissues. These molecules undergo vibrational and rotational motion in the relaxation of the excited electronic states, Due to the less than ideal anisotropy behavior of NIR dyes stemming from the fluorophores elongated structures and short fluorescence lifetime in picosecond range, no significant efforts have been made to recognize the theory of these dyes in time-resolved polarization dynamics. In this study, the depolarization of the fluorescence due to emission from rotational deactivation in solution will be measured with the excitation of a linearly polarized femtosecond laser pulse and a streak camera. The theory, experiment and application of the ultrafast fluorescence polarization dynamics and anisotropy are illustrated with examples of two of the most important medical based dyes. One is NIR dye, namely Indocyanine Green (ICG) and is compared with Fluorescein which is in visible range with much longer lifetime. A set of first-order linear differential equations was developed to model fluorescence polarization dynamics of NIR dye in picosecond range. Using this model, the important parameters of ultrafast polarization spectroscopy were identified: risetime, initial time, fluorescence lifetime, and rotation times.

Non-linear motions in reprocessed GPS station position time series

NASA Astrophysics Data System (ADS)

Rudenko, Sergei; Gendt, Gerd

2010-05-01

Global Positioning System (GPS) data of about 400 globally distributed stations obtained at time span from 1998 till 2007 were reprocessed using GFZ Potsdam EPOS (Earth Parameter and Orbit System) software within International GNSS Service (IGS) Tide Gauge Benchmark Monitoring (TIGA) Pilot Project and IGS Data Reprocessing Campaign with the purpose to determine weekly precise coordinates of GPS stations located at or near tide gauges. Vertical motions of these stations are used to correct the vertical motions of tide gauges for local motions and to tie tide gauge measurements to the geocentric reference frame. Other estimated parameters include daily values of the Earth rotation parameters and their rates, as well as satellite antenna offsets. The solution GT1 derived is based on using absolute phase center variation model, ITRF2005 as a priori reference frame, and other new models. The solution contributed also to ITRF2008. The time series of station positions are analyzed to identify non-linear motions caused by different effects. The paper presents the time series of GPS station coordinates and investigates apparent non-linear motions and their influence on GPS station height rates.

Rapid estimation of nutritional elements on citrus leaves by near infrared reflectance spectroscopy.

PubMed

Galvez-Sola, Luis; García-Sánchez, Francisco; Pérez-Pérez, Juan G; Gimeno, Vicente; Navarro, Josefa M; Moral, Raul; Martínez-Nicolás, Juan J; Nieves, Manuel

2015-01-01

Sufficient nutrient application is one of the most important factors in producing quality citrus fruits. One of the main guides in planning citrus fertilizer programs is by directly monitoring the plant nutrient content. However, this requires analysis of a large number of leaf samples using expensive and time-consuming chemical techniques. Over the last 5 years, it has been demonstrated that it is possible to quantitatively estimate certain nutritional elements in citrus leaves by using the spectral reflectance values, obtained by using near infrared reflectance spectroscopy (NIRS). This technique is rapid, non-destructive, cost-effective and environmentally friendly. Therefore, the estimation of macro and micronutrients in citrus leaves by this method would be beneficial in identifying the mineral status of the trees. However, to be used effectively NIRS must be evaluated against the standard techniques across different cultivars. In this study, NIRS spectral analysis, and subsequent nutrient estimations for N, K, Ca, Mg, B, Fe, Cu, Mn, and Zn concentration, were performed using 217 leaf samples from different citrus trees species. Partial least square regression and different pre-processing signal treatments were used to generate the best estimation against the current best practice techniques. It was verified a high proficiency in the estimation of N (Rv = 0.99) and Ca (Rv = 0.98) as well as achieving acceptable estimation for K, Mg, Fe, and Zn. However, no successful calibrations were obtained for the estimation of B, Cu, and Mn.

Rapid estimation of nutritional elements on citrus leaves by near infrared reflectance spectroscopy

PubMed Central

Galvez-Sola, Luis; García-Sánchez, Francisco; Pérez-Pérez, Juan G.; Gimeno, Vicente; Navarro, Josefa M.; Moral, Raul; Martínez-Nicolás, Juan J.; Nieves, Manuel

2015-01-01

Sufficient nutrient application is one of the most important factors in producing quality citrus fruits. One of the main guides in planning citrus fertilizer programs is by directly monitoring the plant nutrient content. However, this requires analysis of a large number of leaf samples using expensive and time-consuming chemical techniques. Over the last 5 years, it has been demonstrated that it is possible to quantitatively estimate certain nutritional elements in citrus leaves by using the spectral reflectance values, obtained by using near infrared reflectance spectroscopy (NIRS). This technique is rapid, non-destructive, cost-effective and environmentally friendly. Therefore, the estimation of macro and micronutrients in citrus leaves by this method would be beneficial in identifying the mineral status of the trees. However, to be used effectively NIRS must be evaluated against the standard techniques across different cultivars. In this study, NIRS spectral analysis, and subsequent nutrient estimations for N, K, Ca, Mg, B, Fe, Cu, Mn, and Zn concentration, were performed using 217 leaf samples from different citrus trees species. Partial least square regression and different pre-processing signal treatments were used to generate the best estimation against the current best practice techniques. It was verified a high proficiency in the estimation of N (Rv = 0.99) and Ca (Rv = 0.98) as well as achieving acceptable estimation for K, Mg, Fe, and Zn. However, no successful calibrations were obtained for the estimation of B, Cu, and Mn. PMID:26257767

Data preprocessing for determining outer/inner parallelization in the nested loop problem using OpenMP

NASA Astrophysics Data System (ADS)

Handhika, T.; Bustamam, A.; Ernastuti, Kerami, D.

2017-07-01

Multi-thread programming using OpenMP on the shared-memory architecture with hyperthreading technology allows the resource to be accessed by multiple processors simultaneously. Each processor can execute more than one thread for a certain period of time. However, its speedup depends on the ability of the processor to execute threads in limited quantities, especially the sequential algorithm which contains a nested loop. The number of the outer loop iterations is greater than the maximum number of threads that can be executed by a processor. The thread distribution technique that had been found previously only be applied by the high-level programmer. This paper generates a parallelization procedure for low-level programmer in dealing with 2-level nested loop problems with the maximum number of threads that can be executed by a processor is smaller than the number of the outer loop iterations. Data preprocessing which is related to the number of the outer loop and the inner loop iterations, the computational time required to execute each iteration and the maximum number of threads that can be executed by a processor are used as a strategy to determine which parallel region that will produce optimal speedup.

Browsing Through Closed Books: Evaluation of Preprocessing Methods for Page Extraction of a 3-D CT Book Volume

NASA Astrophysics Data System (ADS)

Stromer, D.; Christlein, V.; Schön, T.; Holub, W.; Maier, A.

2017-09-01

It is often the case that a document can not be opened, page-turned or touched anymore due to damages caused by aging processes, moisture or fire. To counter this, special imaging systems can be used. One of our earlier work revealed that a common 3-D X-ray micro-CT scanner is well suited for imaging and reconstructing historical documents written with iron gall ink - an ink consisting of metallic particles. We acquired a volume of a self-made book without opening or page-turning with a single 3-D scan. However, when investigating the reconstructed volume, we faced the problem of a proper automatic extraction of single pages within the volume in an acceptable time without losing information of the writings. Within this work, we evaluate different appropriate pre-processing methods with respect to computation time and accuracy which are decisive for a proper extraction of book pages from the reconstructed X-ray volume and the subsequent ink identification. The different methods were tested for an extreme case with low resolution, noisy input data and wavy pages. Finally, we present results of the page extraction after applying the evaluated methods.

Sunspot Pattern Classification using PCA and Neural Networks (Poster)

NASA Technical Reports Server (NTRS)

Rajkumar, T.; Thompson, D. E.; Slater, G. L.

2005-01-01

The sunspot classification scheme presented in this paper is considered as a 2-D classification problem on archived datasets, and is not a real-time system. As a first step, it mirrors the Zuerich/McIntosh historical classification system and reproduces classification of sunspot patterns based on preprocessing and neural net training datasets. Ultimately, the project intends to move from more rudimentary schemes, to develop spatial-temporal-spectral classes derived by correlating spatial and temporal variations in various wavelengths to the brightness fluctuation spectrum of the sun in those wavelengths. Once the approach is generalized, then the focus will naturally move from a 2-D to an n-D classification, where "n" includes time and frequency. Here, the 2-D perspective refers both to the actual SOH0 Michelson Doppler Imager (MDI) images that are processed, but also refers to the fact that a 2-D matrix is created from each image during preprocessing. The 2-D matrix is the result of running Principal Component Analysis (PCA) over the selected dataset images, and the resulting matrices and their eigenvalues are the objects that are stored in a database, classified, and compared. These matrices are indexed according to the standard McIntosh classification scheme.

EEMD-Based Steady-State Indexes and Their Applications to Condition Monitoring and Fault Diagnosis of Railway Axle Bearings

PubMed Central

Fan, Wei; Tsui, Kwok-Leung; Lin, Jianhui

2018-01-01

Railway axle bearings are one of the most important components used in vehicles and their failures probably result in unexpected accidents and economic losses. To realize a condition monitoring and fault diagnosis scheme of railway axle bearings, three dimensionless steadiness indexes in a time domain, a frequency domain, and a shape domain are respectively proposed to measure the steady states of bearing vibration signals. Firstly, vibration data collected from some designed experiments are pre-processed by using ensemble empirical mode decomposition (EEMD). Then, the coefficient of variation is introduced to construct two steady-state indexes from pre-processed vibration data in a time domain and a frequency domain, respectively. A shape function is used to construct a steady-state index in a shape domain. At last, to distinguish normal and abnormal bearing health states, some guideline thresholds are proposed. Further, to identify axle bearings with outer race defects, a pin roller defect, a cage defect, and coupling defects, the boundaries of all steadiness indexes are experimentally established. Experimental results showed that the proposed condition monitoring and fault diagnosis scheme is effective in identifying different bearing health conditions. PMID:29495446

Non-destructive and fast identification of cotton-polyester blend fabrics by the portable near-infrared spectrometer.

PubMed

Li, Wen-xia; Li, Feng; Zhao, Guo-liang; Tang, Shi-jun; Liu, Xiao-ying

2014-12-01

A series of 376 cotton-polyester (PET) blend fabrics were studied by a portable near-infrared (NIR) spectrometer. A NIR semi-quantitative-qualitative calibration model was established by Partial Least Squares (PLS) method combined with qualitative identification coefficient. In this process, PLS method in a quantitative analysis was used as a correction method, and the qualitative identification coefficient was set by the content of cotton and polyester in blend fabrics. Cotton-polyester blend fabrics were identified qualitatively by the model and their relative contents were obtained quantitatively, the model can be used for semi-quantitative identification analysis. In the course of establishing the model, the noise and baseline drift of the spectra were eliminated by Savitzky-Golay(S-G) derivative. The influence of waveband selection and different pre-processing method was also studied in the qualitative calibration model. The major absorption bands of 100% cotton samples were in the 1400~1600 nm region, and the one for 100% polyester were around 1600~1800 nm, the absorption intensity was enhancing with the content increasing of cotton or polyester. Therefore, the cotton-polyester's major absorption region was selected as the base waveband, the optimal waveband (1100~2500 nm) was found by expanding the waveband in two directions (the correlation coefficient was 0.6, and wave-point number was 934). The validation samples were predicted by the calibration model, the results showed that the model evaluation parameters was optimum in the 1100~2500 nm region, and the combination of S-G derivative, multiplicative scatter correction (MSC) and mean centering was used as the pre-processing method. RC (relational coefficient of calibration) value was 0.978, RP (relational coefficient of prediction) value was 0.940, SEC (standard error of calibration) value was 1.264, SEP (standard error of prediction) value was 1.590, and the sample's recognition accuracy was up to 93.4%. It showed that the cotton-polyester blend fabrics could be predicted by the semi-quantitative-qualitative calibration model.

Safe and sensible preprocessing and baseline correction of pupil-size data.

PubMed

Mathôt, Sebastiaan; Fabius, Jasper; Van Heusden, Elle; Van der Stigchel, Stefan

2018-02-01

Measurement of pupil size (pupillometry) has recently gained renewed interest from psychologists, but there is little agreement on how pupil-size data is best analyzed. Here we focus on one aspect of pupillometric analyses: baseline correction, i.e., analyzing changes in pupil size relative to a baseline period. Baseline correction is useful in experiments that investigate the effect of some experimental manipulation on pupil size. In such experiments, baseline correction improves statistical power by taking into account random fluctuations in pupil size over time. However, we show that baseline correction can also distort data if unrealistically small pupil sizes are recorded during the baseline period, which can easily occur due to eye blinks, data loss, or other distortions. Divisive baseline correction (corrected pupil size = pupil size/baseline) is affected more strongly by such distortions than subtractive baseline correction (corrected pupil size = pupil size - baseline). We discuss the role of baseline correction as a part of preprocessing of pupillometric data, and make five recommendations: (1) before baseline correction, perform data preprocessing to mark missing and invalid data, but assume that some distortions will remain in the data; (2) use subtractive baseline correction; (3) visually compare your corrected and uncorrected data; (4) be wary of pupil-size effects that emerge faster than the latency of the pupillary response allows (within ±220 ms after the manipulation that induces the effect); and (5) remove trials on which baseline pupil size is unrealistically small (indicative of blinks and other distortions).

Multi-profile Bayesian alignment model for LC-MS data analysis with integration of internal standards

PubMed Central

Tsai, Tsung-Heng; Tadesse, Mahlet G.; Di Poto, Cristina; Pannell, Lewis K.; Mechref, Yehia; Wang, Yue; Ressom, Habtom W.

2013-01-01

Motivation: Liquid chromatography-mass spectrometry (LC-MS) has been widely used for profiling expression levels of biomolecules in various ‘-omic’ studies including proteomics, metabolomics and glycomics. Appropriate LC-MS data preprocessing steps are needed to detect true differences between biological groups. Retention time (RT) alignment, which is required to ensure that ion intensity measurements among multiple LC-MS runs are comparable, is one of the most important yet challenging preprocessing steps. Current alignment approaches estimate RT variability using either single chromatograms or detected peaks, but do not simultaneously take into account the complementary information embedded in the entire LC-MS data. Results: We propose a Bayesian alignment model for LC-MS data analysis. The alignment model provides estimates of the RT variability along with uncertainty measures. The model enables integration of multiple sources of information including internal standards and clustered chromatograms in a mathematically rigorous framework. We apply the model to LC-MS metabolomic, proteomic and glycomic data. The performance of the model is evaluated based on ground-truth data, by measuring correlation of variation, RT difference across runs and peak-matching performance. We demonstrate that Bayesian alignment model improves significantly the RT alignment performance through appropriate integration of relevant information. Availability and implementation: MATLAB code, raw and preprocessed LC-MS data are available at http://omics.georgetown.edu/alignLCMS.html Contact: hwr@georgetown.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24013927

Research on pre-processing of QR Code

NASA Astrophysics Data System (ADS)

Sun, Haixing; Xia, Haojie; Dong, Ning

2013-10-01

QR code encodes many kinds of information because of its advantages: large storage capacity, high reliability, full arrange of utter-high-speed reading, small printing size and high-efficient representation of Chinese characters, etc. In order to obtain the clearer binarization image from complex background, and improve the recognition rate of QR code, this paper researches on pre-processing methods of QR code (Quick Response Code), and shows algorithms and results of image pre-processing for QR code recognition. Improve the conventional method by changing the Souvola's adaptive text recognition method. Additionally, introduce the QR code Extraction which adapts to different image size, flexible image correction approach, and improve the efficiency and accuracy of QR code image processing.

Comparison of classification algorithms for various methods of preprocessing radar images of the MSTAR base

NASA Astrophysics Data System (ADS)

Borodinov, A. A.; Myasnikov, V. V.

2018-04-01

The present work is devoted to comparing the accuracy of the known qualification algorithms in the task of recognizing local objects on radar images for various image preprocessing methods. Preprocessing involves speckle noise filtering and normalization of the object orientation in the image by the method of image moments and by a method based on the Hough transform. In comparison, the following classification algorithms are used: Decision tree; Support vector machine, AdaBoost, Random forest. The principal component analysis is used to reduce the dimension. The research is carried out on the objects from the base of radar images MSTAR. The paper presents the results of the conducted studies.

Human Performance on Hard Non-Euclidean Graph Problems: Vertex Cover

ERIC Educational Resources Information Center

Carruthers, Sarah; Masson, Michael E. J.; Stege, Ulrike

2012-01-01

Recent studies on a computationally hard visual optimization problem, the Traveling Salesperson Problem (TSP), indicate that humans are capable of finding close to optimal solutions in near-linear time. The current study is a preliminary step in investigating human performance on another hard problem, the Minimum Vertex Cover Problem, in which…

Periodic Methods for Controlling a Satellite in Formation

DTIC Science & Technology

2002-03-01

5 5. Clohessy - Wiltshire Reference Frame................................................................... 10 6...techniques to study relative position errors within a satellite cluster [19, 24]. The dynamics were based on Clohessy - Wiltshire equations with near...dynamics model by solving the time periodic, linearized system using Floquet Theory. More accurate than the Clohessy - Wiltshire solutions used in previous

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

Gaitsgory, Vladimir, E-mail: vladimir.gaitsgory@mq.edu.au; Rossomakhine, Sergey, E-mail: serguei.rossomakhine@flinders.edu.au

The paper aims at the development of an apparatus for analysis and construction of near optimal solutions of singularly perturbed (SP) optimal controls problems (that is, problems of optimal control of SP systems) considered on the infinite time horizon. We mostly focus on problems with time discounting criteria but a possibility of the extension of results to periodic optimization problems is discussed as well. Our consideration is based on earlier results on averaging of SP control systems and on linear programming formulations of optimal control problems. The idea that we exploit is to first asymptotically approximate a given problem ofmore » optimal control of the SP system by a certain averaged optimal control problem, then reformulate this averaged problem as an infinite-dimensional linear programming (LP) problem, and then approximate the latter by semi-infinite LP problems. We show that the optimal solution of these semi-infinite LP problems and their duals (that can be found with the help of a modification of an available LP software) allow one to construct near optimal controls of the SP system. We demonstrate the construction with two numerical examples.« less

Proposal to evaluate the use of ERTS-1 imagery in mapping and managing soil and range resources in the Sand Hills region of Nebraska

NASA Technical Reports Server (NTRS)

Drew, J. V. (Principal Investigator)

1974-01-01

The author has identified the following significant results. Increase in radiance values is directly related to decrease in vegetative biomass, though not in a linear manner. Should the relationship hold true over an entire growing season, this would allow an extremely rapid evaluation of range condition. Computer access by remote terminal would allow production of this type of range condition evaluation in near real time, which is essential if grazing practice decisions are to be made based on satellite imagery acquisition. Negating the manipulation of photographic products appears to be the logical way to provide satellite imagery data to the user in near real time. There appears to be a direct linear relationship between radiance values of bands 4 and 5 and increase in total inorganic ions (6 ions) of lakes in the Sand hills region. Consistent ion concentration of lakes during the year could allow their radiance values to serve as a means of equating radiance values from image to image.

On the analytic lunar and solar perturbations of a near earth satellite

NASA Technical Reports Server (NTRS)

Estes, R. H.

1972-01-01

The disturbing function of the moon (sun) is expanded as a sum of products of two harmonic functions, one depending on the position of the satellite and the other on the position of the moon (sun). The harmonic functions depending on the position of the perturbing body are developed into trigonometric series with the ecliptic elements l, l', F, D, and Gamma of the lunar theory which are nearly linear with respect to time. Perturbation of elements are in the form of trigonometric series with the ecliptic lunar elements and the equatorial elements omega and Omega of the satellite so that analytic integration is simple and the results accurate over a long period of time.

Parafoveal Preprocessing in Reading Revisited: Evidence from a Novel Preview Manipulation

ERIC Educational Resources Information Center

Gagl, Benjamin; Hawelka, Stefan; Richlan, Fabio; Schuster, Sarah; Hutzler, Florian

2014-01-01

The study investigated parafoveal preprocessing by the means of the classical invisible boundary paradigm and a novel manipulation of the parafoveal previews (i.e., visual degradation). Eye movements were investigated on 5-letter target words with constraining (i.e., highly informative) initial letters or similarly constraining final letters.…

Microarray Data Processing Techniques for Genome-Scale Network Inference from Large Public Repositories.

PubMed

Chockalingam, Sriram; Aluru, Maneesha; Aluru, Srinivas

2016-09-19

Pre-processing of microarray data is a well-studied problem. Furthermore, all popular platforms come with their own recommended best practices for differential analysis of genes. However, for genome-scale network inference using microarray data collected from large public repositories, these methods filter out a considerable number of genes. This is primarily due to the effects of aggregating a diverse array of experiments with different technical and biological scenarios. Here we introduce a pre-processing pipeline suitable for inferring genome-scale gene networks from large microarray datasets. We show that partitioning of the available microarray datasets according to biological relevance into tissue- and process-specific categories significantly extends the limits of downstream network construction. We demonstrate the effectiveness of our pre-processing pipeline by inferring genome-scale networks for the model plant Arabidopsis thaliana using two different construction methods and a collection of 11,760 Affymetrix ATH1 microarray chips. Our pre-processing pipeline and the datasets used in this paper are made available at http://alurulab.cc.gatech.edu/microarray-pp.

Pre-processing and post-processing in group-cluster mergers

NASA Astrophysics Data System (ADS)

Vijayaraghavan, R.; Ricker, P. M.

2013-11-01

Galaxies in clusters are more likely to be of early type and to have lower star formation rates than galaxies in the field. Recent observations and simulations suggest that cluster galaxies may be `pre-processed' by group or filament environments and that galaxies that fall into a cluster as part of a larger group can stay coherent within the cluster for up to one orbital period (`post-processing'). We investigate these ideas by means of a cosmological N-body simulation and idealized N-body plus hydrodynamics simulations of a group-cluster merger. We find that group environments can contribute significantly to galaxy pre-processing by means of enhanced galaxy-galaxy merger rates, removal of galaxies' hot halo gas by ram pressure stripping and tidal truncation of their galaxies. Tidal distortion of the group during infall does not contribute to pre-processing. Post-processing is also shown to be effective: galaxy-galaxy collisions are enhanced during a group's pericentric passage within a cluster, the merger shock enhances the ram pressure on group and cluster galaxies and an increase in local density during the merger leads to greater galactic tidal truncation.