GEOS-2 C-band radar system project. Spectral analysis as related to C-band radar data analysis

NASA Technical Reports Server (NTRS)

1972-01-01

Work performed on spectral analysis of data from the C-band radars tracking GEOS-2 and on the development of a data compaction method for the GEOS-2 C-band radar data is described. The purposes of the spectral analysis study were to determine the optimum data recording and sampling rates for C-band radar data and to determine the optimum method of filtering and smoothing the data. The optimum data recording and sampling rate is defined as the rate which includes an optimum compromise between serial correlation and the effects of frequency folding. The goal in development of a data compaction method was to reduce to a minimum the amount of data stored, while maintaining all of the statistical information content of the non-compacted data. A digital computer program for computing estimates of the power spectral density function of sampled data was used to perform the spectral analysis study.

Power Spectral Density Error Analysis of Spectral Subtraction Type of Speech Enhancement Methods

NASA Astrophysics Data System (ADS)

Händel, Peter

2006-12-01

A theoretical framework for analysis of speech enhancement algorithms is introduced for performance assessment of spectral subtraction type of methods. The quality of the enhanced speech is related to physical quantities of the speech and noise (such as stationarity time and spectral flatness), as well as to design variables of the noise suppressor. The derived theoretical results are compared with the outcome of subjective listening tests as well as successful design strategies, performed by independent research groups.

Development of seismic fragility curves for low-rise masonry infilled reinforced concrete buildings by a coefficient-based method

NASA Astrophysics Data System (ADS)

Su, Ray Kai Leung; Lee, Chien-Liang

2013-06-01

This study presents a seismic fragility analysis and ultimate spectral displacement assessment of regular low-rise masonry infilled (MI) reinforced concrete (RC) buildings using a coefficient-based method. The coefficient-based method does not require a complicated finite element analysis; instead, it is a simplified procedure for assessing the spectral acceleration and displacement of buildings subjected to earthquakes. A regression analysis was first performed to obtain the best-fitting equations for the inter-story drift ratio (IDR) and period shift factor of low-rise MI RC buildings in response to the peak ground acceleration of earthquakes using published results obtained from shaking table tests. Both spectral acceleration- and spectral displacement-based fragility curves under various damage states (in terms of IDR) were then constructed using the coefficient-based method. Finally, the spectral displacements of low-rise MI RC buildings at the ultimate (or nearcollapse) state obtained from this paper and the literature were compared. The simulation results indicate that the fragility curves obtained from this study and other previous work correspond well. Furthermore, most of the spectral displacements of low-rise MI RC buildings at the ultimate state from the literature fall within the bounded spectral displacements predicted by the coefficient-based method.

Augmented classical least squares multivariate spectral analysis

DOEpatents

Haaland, David M.; Melgaard, David K.

2004-02-03

A method of multivariate spectral analysis, termed augmented classical least squares (ACLS), provides an improved CLS calibration model when unmodeled sources of spectral variation are contained in a calibration sample set. The ACLS methods use information derived from component or spectral residuals during the CLS calibration to provide an improved calibration-augmented CLS model. The ACLS methods are based on CLS so that they retain the qualitative benefits of CLS, yet they have the flexibility of PLS and other hybrid techniques in that they can define a prediction model even with unmodeled sources of spectral variation that are not explicitly included in the calibration model. The unmodeled sources of spectral variation may be unknown constituents, constituents with unknown concentrations, nonlinear responses, non-uniform and correlated errors, or other sources of spectral variation that are present in the calibration sample spectra. Also, since the various ACLS methods are based on CLS, they can incorporate the new prediction-augmented CLS (PACLS) method of updating the prediction model for new sources of spectral variation contained in the prediction sample set without having to return to the calibration process. The ACLS methods can also be applied to alternating least squares models. The ACLS methods can be applied to all types of multivariate data.

Augmented Classical Least Squares Multivariate Spectral Analysis

DOEpatents

Haaland, David M.; Melgaard, David K.

2005-07-26

A method of multivariate spectral analysis, termed augmented classical least squares (ACLS), provides an improved CLS calibration model when unmodeled sources of spectral variation are contained in a calibration sample set. The ACLS methods use information derived from component or spectral residuals during the CLS calibration to provide an improved calibration-augmented CLS model. The ACLS methods are based on CLS so that they retain the qualitative benefits of CLS, yet they have the flexibility of PLS and other hybrid techniques in that they can define a prediction model even with unmodeled sources of spectral variation that are not explicitly included in the calibration model. The unmodeled sources of spectral variation may be unknown constituents, constituents with unknown concentrations, nonlinear responses, non-uniform and correlated errors, or other sources of spectral variation that are present in the calibration sample spectra. Also, since the various ACLS methods are based on CLS, they can incorporate the new prediction-augmented CLS (PACLS) method of updating the prediction model for new sources of spectral variation contained in the prediction sample set without having to return to the calibration process. The ACLS methods can also be applied to alternating least squares models. The ACLS methods can be applied to all types of multivariate data.

Augmented Classical Least Squares Multivariate Spectral Analysis

DOEpatents

Haaland, David M.; Melgaard, David K.

2005-01-11

A method of multivariate spectral analysis, termed augmented classical least squares (ACLS), provides an improved CLS calibration model when unmodeled sources of spectral variation are contained in a calibration sample set. The ACLS methods use information derived from component or spectral residuals during the CLS calibration to provide an improved calibration-augmented CLS model. The ACLS methods are based on CLS so that they retain the qualitative benefits of CLS, yet they have the flexibility of PLS and other hybrid techniques in that they can define a prediction model even with unmodeled sources of spectral variation that are not explicitly included in the calibration model. The unmodeled sources of spectral variation may be unknown constituents, constituents with unknown concentrations, nonlinear responses, non-uniform and correlated errors, or other sources of spectral variation that are present in the calibration sample spectra. Also, since the various ACLS methods are based on CLS, they can incorporate the new prediction-augmented CLS (PACLS) method of updating the prediction model for new sources of spectral variation contained in the prediction sample set without having to return to the calibration process. The ACLS methods can also be applied to alternating least squares models. The ACLS methods can be applied to all types of multivariate data.

Spectral signature verification using statistical analysis and text mining

NASA Astrophysics Data System (ADS)

DeCoster, Mallory E.; Firpi, Alexe H.; Jacobs, Samantha K.; Cone, Shelli R.; Tzeng, Nigel H.; Rodriguez, Benjamin M.

2016-05-01

In the spectral science community, numerous spectral signatures are stored in databases representative of many sample materials collected from a variety of spectrometers and spectroscopists. Due to the variety and variability of the spectra that comprise many spectral databases, it is necessary to establish a metric for validating the quality of spectral signatures. This has been an area of great discussion and debate in the spectral science community. This paper discusses a method that independently validates two different aspects of a spectral signature to arrive at a final qualitative assessment; the textual meta-data and numerical spectral data. Results associated with the spectral data stored in the Signature Database1 (SigDB) are proposed. The numerical data comprising a sample material's spectrum is validated based on statistical properties derived from an ideal population set. The quality of the test spectrum is ranked based on a spectral angle mapper (SAM) comparison to the mean spectrum derived from the population set. Additionally, the contextual data of a test spectrum is qualitatively analyzed using lexical analysis text mining. This technique analyzes to understand the syntax of the meta-data to provide local learning patterns and trends within the spectral data, indicative of the test spectrum's quality. Text mining applications have successfully been implemented for security2 (text encryption/decryption), biomedical3 , and marketing4 applications. The text mining lexical analysis algorithm is trained on the meta-data patterns of a subset of high and low quality spectra, in order to have a model to apply to the entire SigDB data set. The statistical and textual methods combine to assess the quality of a test spectrum existing in a database without the need of an expert user. This method has been compared to other validation methods accepted by the spectral science community, and has provided promising results when a baseline spectral signature is present for comparison. The spectral validation method proposed is described from a practical application and analytical perspective.

EXPLORING FUNCTIONAL CONNECTIVITY IN FMRI VIA CLUSTERING.

PubMed

Venkataraman, Archana; Van Dijk, Koene R A; Buckner, Randy L; Golland, Polina

2009-04-01

In this paper we investigate the use of data driven clustering methods for functional connectivity analysis in fMRI. In particular, we consider the K-Means and Spectral Clustering algorithms as alternatives to the commonly used Seed-Based Analysis. To enable clustering of the entire brain volume, we use the Nyström Method to approximate the necessary spectral decompositions. We apply K-Means, Spectral Clustering and Seed-Based Analysis to resting-state fMRI data collected from 45 healthy young adults. Without placing any a priori constraints, both clustering methods yield partitions that are associated with brain systems previously identified via Seed-Based Analysis. Our empirical results suggest that clustering provides a valuable tool for functional connectivity analysis.

Potential of FTIR spectroscopy for analysis of tears for diagnosis purposes.

PubMed

Travo, Adrian; Paya, Clément; Déléris, Gérard; Colin, Joseph; Mortemousque, Bruno; Forfar, Isabelle

2014-04-01

It has been widely reported that the tear film, which is crucially important as a protective barrier of the eye, undergoes biochemical changes as a result of a wide range of ocular pathology. This tends to suggest the possibility of early detection of ocular diseases on the basis of biochemical analysis of tears. However, studies of tears by conventional methods of biomolecular and biochemical analysis are often limited by methodological difficulties. Moreover, such analysis could not be applied in the clinic, where structural and morphological analyses by, mainly, slit-lamp biomicroscopy remains the recommended method. In this study, we assessed, for the first time, the potential of FTIR spectroscopy combined with advanced chemometric processing of spectral data for analysis of raw tears for diagnosis purposes. We first optimized sampling and spectral acquisition (tears collection method, tear sample volume, and preservation of the samples) for accurate spectral measurement. On the basis of the results, we focused our study on the possibility of discriminating tears from normal individuals from those of patients with different ocular pathologies, and showed that the most discriminating spectral range is that corresponding to variations of CH2 and CH3 of lipid aliphatic chains. We also report more subtle discrimination of tears from patients with keratoconus and those from patients with non-specific inflammatory ocular diseases, on the basis of variations in spectral ranges attributed notably to lipid and carbohydrate vibrations. Finally, we also succeeded in distinguishing tears from patients with early-stage and late-stage keratoconus on the basis of spectral features attributed to protein structure. Therefore, this study strongly suggests that FTIR spectral analysis of tears could be developed as a valuable and cost-saving tool for biochemical-based detection of ocular diseases, potentially before the appearance of the first morphological signs of diseases. Combined with supervised modelling methods and with use of a spectral data base acquired for representative patients, such a spectral approach could be a useful addition to current methods of clinical analysis for improvement of patient care.

An evaluation of random analysis methods for the determination of panel damping

NASA Technical Reports Server (NTRS)

Bhat, W. V.; Wilby, J. F.

1972-01-01

An analysis is made of steady-state and non-steady-state methods for the measurement of panel damping. Particular emphasis is placed on the use of random process techniques in conjunction with digital data reduction methods. The steady-state methods considered use the response power spectral density, response autocorrelation, excitation-response crosspower spectral density, or single-sided Fourier transform (SSFT) of the response autocorrelation function. Non-steady-state methods are associated mainly with the use of rapid frequency sweep excitation. Problems associated with the practical application of each method are evaluated with specific reference to the case of a panel exposed to a turbulent airflow, and two methods, the power spectral density and the single-sided Fourier transform methods, are selected as being the most suitable. These two methods are demonstrated experimentally, and it is shown that the power spectral density method is satisfactory under most conditions, provided that appropriate corrections are applied to account for filter bandwidth and background noise errors. Thus, the response power spectral density method is recommended for the measurement of the damping of panels exposed to a moving airflow.

What drives high flow events in the Swiss Alps? Recent developments in wavelet spectral analysis and their application to hydrology

NASA Astrophysics Data System (ADS)

Schaefli, B.; Maraun, D.; Holschneider, M.

2007-12-01

Extreme hydrological events are often triggered by exceptional co-variations of the relevant hydrometeorological processes and in particular by exceptional co-oscillations at various temporal scales. Wavelet and cross wavelet spectral analysis offers promising time-scale resolved analysis methods to detect and analyze such exceptional co-oscillations. This paper presents the state-of-the-art methods of wavelet spectral analysis, discusses related subtleties, potential pitfalls and recently developed solutions to overcome them and shows how wavelet spectral analysis, if combined to a rigorous significance test, can lead to reliable new insights into hydrometeorological processes for real-world applications. The presented methods are applied to detect potentially flood triggering situations in a high Alpine catchment for which a recent re-estimation of design floods encountered significant problems simulating the observed high flows. For this case study, wavelet spectral analysis of precipitation, temperature and discharge offers a powerful tool to help detecting potentially flood producing meteorological situations and to distinguish between different types of floods with respect to the prevailing critical hydrometeorological conditions. This opens very new perspectives for the analysis of model performances focusing on the occurrence and non-occurrence of different types of high flow events. Based on the obtained results, the paper summarizes important recommendations for future applications of wavelet spectral analysis in hydrology.

Nearest clusters based partial least squares discriminant analysis for the classification of spectral data.

PubMed

Song, Weiran; Wang, Hui; Maguire, Paul; Nibouche, Omar

2018-06-07

Partial Least Squares Discriminant Analysis (PLS-DA) is one of the most effective multivariate analysis methods for spectral data analysis, which extracts latent variables and uses them to predict responses. In particular, it is an effective method for handling high-dimensional and collinear spectral data. However, PLS-DA does not explicitly address data multimodality, i.e., within-class multimodal distribution of data. In this paper, we present a novel method termed nearest clusters based PLS-DA (NCPLS-DA) for addressing the multimodality and nonlinearity issues explicitly and improving the performance of PLS-DA on spectral data classification. The new method applies hierarchical clustering to divide samples into clusters and calculates the corresponding centre of every cluster. For a given query point, only clusters whose centres are nearest to such a query point are used for PLS-DA. Such a method can provide a simple and effective tool for separating multimodal and nonlinear classes into clusters which are locally linear and unimodal. Experimental results on 17 datasets, including 12 UCI and 5 spectral datasets, show that NCPLS-DA can outperform 4 baseline methods, namely, PLS-DA, kernel PLS-DA, local PLS-DA and k-NN, achieving the highest classification accuracy most of the time. Copyright © 2018 Elsevier B.V. All rights reserved.

Ratiometric spectral imaging for fast tumor detection and chemotherapy monitoring in vivo

PubMed Central

Hwang, Jae Youn; Gross, Zeev; Gray, Harry B.; Medina-Kauwe, Lali K.; Farkas, Daniel L.

2011-01-01

We report a novel in vivo spectral imaging approach to cancer detection and chemotherapy assessment. We describe and characterize a ratiometric spectral imaging and analysis method and evaluate its performance for tumor detection and delineation by quantitatively monitoring the specific accumulation of targeted gallium corrole (HerGa) into HER2-positive (HER2 +) breast tumors. HerGa temporal accumulation in nude mice bearing HER2 + breast tumors was monitored comparatively by a. this new ratiometric imaging and analysis method; b. established (reflectance and fluorescence) spectral imaging; c. more commonly used fluorescence intensity imaging. We also tested the feasibility of HerGa imaging in vivo using the ratiometric spectral imaging method for tumor detection and delineation. Our results show that the new method not only provides better quantitative information than typical spectral imaging, but also better specificity than standard fluorescence intensity imaging, thus allowing enhanced in vivo outlining of tumors and dynamic, quantitative monitoring of targeted chemotherapy agent accumulation into them. PMID:21721808

Investigating cardiorespiratory interaction by cross-spectral analysis of event series

NASA Astrophysics Data System (ADS)

Schäfer, Carsten; Rosenblum, Michael G.; Pikovsky, Arkady S.; Kurths, Jürgen

2000-02-01

The human cardiovascular and respiratory systems interact with each other and show effects of modulation and synchronization. Here we present a cross-spectral technique that specifically considers the event-like character of the heartbeat and avoids typical restrictions of other spectral methods. Using models as well as experimental data, we demonstrate how modulation and synchronization can be distinguished. Finally, we compare the method to traditional techniques and to the analysis of instantaneous phases.

Models and methods to characterize site amplification from a pair of records

USGS Publications Warehouse

Safak, E.

1997-01-01

The paper presents a tutorial review of the models and methods that are used to characterize site amplification from the pairs of rock- and soil-site records, and introduces some new techniques with better theoretical foundations. The models and methods discussed include spectral and cross-spectral ratios, spectral ratios for downhole records, response spectral ratios, constant amplification factors, parametric models, physical models, and time-varying filters. An extensive analytical and numerical error analysis of spectral and cross-spectral ratios shows that probabilistically cross-spectral ratios give more reliable estimates of site amplification. Spectral ratios should not be used to determine site amplification from downhole-surface recording pairs because of the feedback in the downhole sensor. Response spectral ratios are appropriate for low frequencies, but overestimate the amplification at high frequencies. The best method to be used depends on how much precision is required in the estimates.

Low Streamflow Forcasting using Minimum Relative Entropy

NASA Astrophysics Data System (ADS)

Cui, H.; Singh, V. P.

2013-12-01

Minimum relative entropy spectral analysis is derived in this study, and applied to forecast streamflow time series. Proposed method extends the autocorrelation in the manner that the relative entropy of underlying process is minimized so that time series data can be forecasted. Different prior estimation, such as uniform, exponential and Gaussian assumption, is taken to estimate the spectral density depending on the autocorrelation structure. Seasonal and nonseasonal low streamflow series obtained from Colorado River (Texas) under draught condition is successfully forecasted using proposed method. Minimum relative entropy determines spectral of low streamflow series with higher resolution than conventional method. Forecasted streamflow is compared to the prediction using Burg's maximum entropy spectral analysis (MESA) and Configurational entropy. The advantage and disadvantage of each method in forecasting low streamflow is discussed.

Fusion and quality analysis for remote sensing images using contourlet transform

NASA Astrophysics Data System (ADS)

Choi, Yoonsuk; Sharifahmadian, Ershad; Latifi, Shahram

2013-05-01

Recent developments in remote sensing technologies have provided various images with high spatial and spectral resolutions. However, multispectral images have low spatial resolution and panchromatic images have low spectral resolution. Therefore, image fusion techniques are necessary to improve the spatial resolution of spectral images by injecting spatial details of high-resolution panchromatic images. The objective of image fusion is to provide useful information by improving the spatial resolution and the spectral information of the original images. The fusion results can be utilized in various applications, such as military, medical imaging, and remote sensing. This paper addresses two issues in image fusion: i) image fusion method and ii) quality analysis of fusion results. First, a new contourlet-based image fusion method is presented, which is an improvement over the wavelet-based fusion. This fusion method is then applied to a case study to demonstrate its fusion performance. Fusion framework and scheme used in the study are discussed in detail. Second, quality analysis for the fusion results is discussed. We employed various quality metrics in order to analyze the fusion results both spatially and spectrally. Our results indicate that the proposed contourlet-based fusion method performs better than the conventional wavelet-based fusion methods.

A New View of Earthquake Ground Motion Data: The Hilbert Spectral Analysis

NASA Technical Reports Server (NTRS)

Huang, Norden; Busalacchi, Antonio J. (Technical Monitor)

2000-01-01

A brief description of the newly developed Empirical Mode Decomposition (ENID) and Hilbert Spectral Analysis (HSA) method will be given. The decomposition is adaptive and can be applied to both nonlinear and nonstationary data. Example of the method applied to a sample earthquake record will be given. The results indicate those low frequency components, totally missed by the Fourier analysis, are clearly identified by the new method. Comparisons with Wavelet and window Fourier analysis show the new method offers much better temporal and frequency resolutions.

Spectral compression algorithms for the analysis of very large multivariate images

DOEpatents

Keenan, Michael R.

2007-10-16

A method for spectrally compressing data sets enables the efficient analysis of very large multivariate images. The spectral compression algorithm uses a factored representation of the data that can be obtained from Principal Components Analysis or other factorization technique. Furthermore, a block algorithm can be used for performing common operations more efficiently. An image analysis can be performed on the factored representation of the data, using only the most significant factors. The spectral compression algorithm can be combined with a spatial compression algorithm to provide further computational efficiencies.

Signal-to-noise contribution of principal component loads in reconstructed near-infrared Raman tissue spectra.

PubMed

Grimbergen, M C M; van Swol, C F P; Kendall, C; Verdaasdonk, R M; Stone, N; Bosch, J L H R

2010-01-01

The overall quality of Raman spectra in the near-infrared region, where biological samples are often studied, has benefited from various improvements to optical instrumentation over the past decade. However, obtaining ample spectral quality for analysis is still challenging due to device requirements and short integration times required for (in vivo) clinical applications of Raman spectroscopy. Multivariate analytical methods, such as principal component analysis (PCA) and linear discriminant analysis (LDA), are routinely applied to Raman spectral datasets to develop classification models. Data compression is necessary prior to discriminant analysis to prevent or decrease the degree of over-fitting. The logical threshold for the selection of principal components (PCs) to be used in discriminant analysis is likely to be at a point before the PCs begin to introduce equivalent signal and noise and, hence, include no additional value. Assessment of the signal-to-noise ratio (SNR) at a certain peak or over a specific spectral region will depend on the sample measured. Therefore, the mean SNR over the whole spectral region (SNR(msr)) is determined in the original spectrum as well as for spectra reconstructed from an increasing number of principal components. This paper introduces a method of assessing the influence of signal and noise from individual PC loads and indicates a method of selection of PCs for LDA. To evaluate this method, two data sets with different SNRs were used. The sets were obtained with the same Raman system and the same measurement parameters on bladder tissue collected during white light cystoscopy (set A) and fluorescence-guided cystoscopy (set B). This method shows that the mean SNR over the spectral range in the original Raman spectra of these two data sets is related to the signal and noise contribution of principal component loads. The difference in mean SNR over the spectral range can also be appreciated since fewer principal components can reliably be used in the low SNR data set (set B) compared to the high SNR data set (set A). Despite the fact that no definitive threshold could be found, this method may help to determine the cutoff for the number of principal components used in discriminant analysis. Future analysis of a selection of spectral databases using this technique will allow optimum thresholds to be selected for different applications and spectral data quality levels.

MEM spectral analysis for predicting influenza epidemics in Japan.

PubMed

Sumi, Ayako; Kamo, Ken-ichi

2012-03-01

The prediction of influenza epidemics has long been the focus of attention in epidemiology and mathematical biology. In this study, we tested whether time series analysis was useful for predicting the incidence of influenza in Japan. The method of time series analysis we used consists of spectral analysis based on the maximum entropy method (MEM) in the frequency domain and the nonlinear least squares method in the time domain. Using this time series analysis, we analyzed the incidence data of influenza in Japan from January 1948 to December 1998; these data are unique in that they covered the periods of pandemics in Japan in 1957, 1968, and 1977. On the basis of the MEM spectral analysis, we identified the periodic modes explaining the underlying variations of the incidence data. The optimum least squares fitting (LSF) curve calculated with the periodic modes reproduced the underlying variation of the incidence data. An extension of the LSF curve could be used to predict the incidence of influenza quantitatively. Our study suggested that MEM spectral analysis would allow us to model temporal variations of influenza epidemics with multiple periodic modes much more effectively than by using the method of conventional time series analysis, which has been used previously to investigate the behavior of temporal variations in influenza data.

Spectral methods for time dependent problems

NASA Technical Reports Server (NTRS)

Tadmor, Eitan

1990-01-01

Spectral approximations are reviewed for time dependent problems. Some basic ingredients from the spectral Fourier and Chebyshev approximations theory are discussed. A brief survey was made of hyperbolic and parabolic time dependent problems which are dealt with by both the energy method and the related Fourier analysis. The ideas presented above are combined in the study of accuracy stability and convergence of the spectral Fourier approximation to time dependent problems.

Classical least squares multivariate spectral analysis

DOEpatents

Haaland, David M.

2002-01-01

An improved classical least squares multivariate spectral analysis method that adds spectral shapes describing non-calibrated components and system effects (other than baseline corrections) present in the analyzed mixture to the prediction phase of the method. These improvements decrease or eliminate many of the restrictions to the CLS-type methods and greatly extend their capabilities, accuracy, and precision. One new application of PACLS includes the ability to accurately predict unknown sample concentrations when new unmodeled spectral components are present in the unknown samples. Other applications of PACLS include the incorporation of spectrometer drift into the quantitative multivariate model and the maintenance of a calibration on a drifting spectrometer. Finally, the ability of PACLS to transfer a multivariate model between spectrometers is demonstrated.

Prediction Analysis for Measles Epidemics

NASA Astrophysics Data System (ADS)

Sumi, Ayako; Ohtomo, Norio; Tanaka, Yukio; Sawamura, Sadashi; Olsen, Lars Folke; Kobayashi, Nobumichi

2003-12-01

A newly devised procedure of prediction analysis, which is a linearized version of the nonlinear least squares method combined with the maximum entropy spectral analysis method, was proposed. This method was applied to time series data of measles case notification in several communities in the UK, USA and Denmark. The dominant spectral lines observed in each power spectral density (PSD) can be safely assigned as fundamental periods. The optimum least squares fitting (LSF) curve calculated using these fundamental periods can essentially reproduce the underlying variation of the measles data. An extension of the LSF curve can be used to predict measles case notification quantitatively. Some discussions including a predictability of chaotic time series are presented.

Spectral Analysis of Rich Network Topology in Social Networks

ERIC Educational Resources Information Center

Wu, Leting

2013-01-01

Social networks have received much attention these days. Researchers have developed different methods to study the structure and characteristics of the network topology. Our focus is on spectral analysis of the adjacency matrix of the underlying network. Recent work showed good properties in the adjacency spectral space but there are few…

An Improved Variational Method for Hyperspectral Image Pansharpening with the Constraint of Spectral Difference Minimization

NASA Astrophysics Data System (ADS)

Huang, Z.; Chen, Q.; Shen, Y.; Chen, Q.; Liu, X.

2017-09-01

Variational pansharpening can enhance the spatial resolution of a hyperspectral (HS) image using a high-resolution panchromatic (PAN) image. However, this technology may lead to spectral distortion that obviously affect the accuracy of data analysis. In this article, we propose an improved variational method for HS image pansharpening with the constraint of spectral difference minimization. We extend the energy function of the classic variational pansharpening method by adding a new spectral fidelity term. This fidelity term is designed following the definition of spectral angle mapper, which means that for every pixel, the spectral difference value of any two bands in the HS image is in equal proportion to that of the two corresponding bands in the pansharpened image. Gradient descent method is adopted to find the optimal solution of the modified energy function, and the pansharpened image can be reconstructed. Experimental results demonstrate that the constraint of spectral difference minimization is able to preserve the original spectral information well in HS images, and reduce the spectral distortion effectively. Compared to original variational method, our method performs better in both visual and quantitative evaluation, and achieves a good trade-off between spatial and spectral information.

Site Characterization in the Urban Area of Tijuana, B. C., Mexico by Means of: H/V Spectral Ratios, Spectral Analysis of Surface Waves, and Random Decrement Method

NASA Astrophysics Data System (ADS)

Tapia-Herrera, R.; Huerta-Lopez, C. I.; Martinez-Cruzado, J. A.

2009-05-01

Results of site characterization for an experimental site in the metropolitan area of Tijuana, B. C., Mexico are presented as part of the on-going research in which time series of earthquakes, ambient noise, and induced vibrations were processed with three different methods: H/V spectral ratios, Spectral Analysis of Surface Waves (SASW), and the Random Decrement Method, (RDM). Forward modeling using the wave propagation stiffness matrix method (Roësset and Kausel, 1981) was used to compute the theoretical SH/P, SV/P spectral ratios, and the experimental H/V spectral ratios were computed following the conventional concepts of Fourier analysis. The modeling/comparison between the theoretical and experimental H/V spectral ratios was carried out. For the SASW method the theoretical dispersion curves were also computed and compared with the experimental one, and finally the theoretical free vibration decay curve was compared with the experimental one obtained with the RDM. All three methods were tested with ambient noise, induced vibrations, and earthquake signals. Both experimental spectral ratios obtained with ambient noise as well as earthquake signals agree quite well with the theoretical spectral ratios, particularly at the fundamental vibration frequency of the recording site. Differences between the fundamental vibration frequencies are evident for sites located at alluvial fill (~0.6 Hz) and at sites located at conglomerate/sandstones fill (0.75 Hz). Shear wave velocities for the soft soil layers of the 4-layer discrete soil model ranges as low as 100 m/s and up to 280 m/s. The results with the SASW provided information that allows to identify low velocity layers, not seen before with the traditional seismic methods. The damping estimations obtained with the RDM are within the expected values, and the dominant frequency of the system also obtained with the RDM correlates within the range of plus-minus 20 % with the one obtained by means of the H/V spectral ratio.

Determining cantilever stiffness from thermal noise.

PubMed

Lübbe, Jannis; Temmen, Matthias; Rahe, Philipp; Kühnle, Angelika; Reichling, Michael

2013-01-01

We critically discuss the extraction of intrinsic cantilever properties, namely eigenfrequency f n , quality factor Q n and specifically the stiffness k n of the nth cantilever oscillation mode from thermal noise by an analysis of the power spectral density of displacement fluctuations of the cantilever in contact with a thermal bath. The practical applicability of this approach is demonstrated for several cantilevers with eigenfrequencies ranging from 50 kHz to 2 MHz. As such an analysis requires a sophisticated spectral analysis, we introduce a new method to determine k n from a spectral analysis of the demodulated oscillation signal of the excited cantilever that can be performed in the frequency range of 10 Hz to 1 kHz regardless of the eigenfrequency of the cantilever. We demonstrate that the latter method is in particular useful for noncontact atomic force microscopy (NC-AFM) where the required simple instrumentation for spectral analysis is available in most experimental systems.

Systems and methods for detection of blowout precursors in combustors

DOEpatents

Lieuwen, Tim C.; Nair, Suraj

2006-08-15

The present invention comprises systems and methods for detecting flame blowout precursors in combustors. The blowout precursor detection system comprises a combustor, a pressure measuring device, and blowout precursor detection unit. A combustion controller may also be used to control combustor parameters. The methods of the present invention comprise receiving pressure data measured by an acoustic pressure measuring device, performing one or a combination of spectral analysis, statistical analysis, and wavelet analysis on received pressure data, and determining the existence of a blowout precursor based on such analyses. The spectral analysis, statistical analysis, and wavelet analysis further comprise their respective sub-methods to determine the existence of blowout precursors.

Shear-wave velocity and site-amplification factors for 50 Australian sites determined by the spectral analysis of surface waves method

USGS Publications Warehouse

Kayen, Robert E.; Carkin, Bradley A.; Allen, Trevor; Collins, Clive; McPherson, Andrew; Minasian, Diane L.

2015-01-01

One-dimensional shear-wave velocity (VS ) profiles are presented at 50 strong motion sites in New South Wales and Victoria, Australia. The VS profiles are estimated with the spectral analysis of surface waves (SASW) method. The SASW method is a noninvasive method that indirectly estimates the VS at depth from variations in the Rayleigh wave phase velocity at the surface.

Error analysis for spectral approximation of the Korteweg-De Vries equation

NASA Technical Reports Server (NTRS)

Maday, Y.; recent years.

1987-01-01

The conservation and convergence properties of spectral Fourier methods for the numerical approximation of the Korteweg-de Vries equation are analyzed. It is proved that the (aliased) collocation pseudospectral method enjoys the same convergence properties as the spectral Galerkin method, which is less effective from the computational point of view. This result provides a precise mathematical answer to a question raised by several authors in recent years.

Wavelength selection for portable noninvasive blood component measurement system based on spectral difference coefficient and dynamic spectrum

NASA Astrophysics Data System (ADS)

Feng, Ximeng; Li, Gang; Yu, Haixia; Wang, Shaohui; Yi, Xiaoqing; Lin, Ling

2018-03-01

Noninvasive blood component analysis by spectroscopy has been a hotspot in biomedical engineering in recent years. Dynamic spectrum provides an excellent idea for noninvasive blood component measurement, but studies have been limited to the application of broadband light sources and high-resolution spectroscopy instruments. In order to remove redundant information, a more effective wavelength selection method has been presented in this paper. In contrast to many common wavelength selection methods, this method is based on sensing mechanism which has a clear mechanism and can effectively avoid the noise from acquisition system. The spectral difference coefficient was theoretically proved to have a guiding significance for wavelength selection. After theoretical analysis, the multi-band spectral difference coefficient-wavelength selection method combining with the dynamic spectrum was proposed. An experimental analysis based on clinical trial data from 200 volunteers has been conducted to illustrate the effectiveness of this method. The extreme learning machine was used to develop the calibration models between the dynamic spectrum data and hemoglobin concentration. The experiment result shows that the prediction precision of hemoglobin concentration using multi-band spectral difference coefficient-wavelength selection method is higher compared with other methods.

An augmented classical least squares method for quantitative Raman spectral analysis against component information loss.

PubMed

Zhou, Yan; Cao, Hui

2013-01-01

We propose an augmented classical least squares (ACLS) calibration method for quantitative Raman spectral analysis against component information loss. The Raman spectral signals with low analyte concentration correlations were selected and used as the substitutes for unknown quantitative component information during the CLS calibration procedure. The number of selected signals was determined by using the leave-one-out root-mean-square error of cross-validation (RMSECV) curve. An ACLS model was built based on the augmented concentration matrix and the reference spectral signal matrix. The proposed method was compared with partial least squares (PLS) and principal component regression (PCR) using one example: a data set recorded from an experiment of analyte concentration determination using Raman spectroscopy. A 2-fold cross-validation with Venetian blinds strategy was exploited to evaluate the predictive power of the proposed method. The one-way variance analysis (ANOVA) was used to access the predictive power difference between the proposed method and existing methods. Results indicated that the proposed method is effective at increasing the robust predictive power of traditional CLS model against component information loss and its predictive power is comparable to that of PLS or PCR.

Statistical analysis and machine learning algorithms for optical biopsy

NASA Astrophysics Data System (ADS)

Wu, Binlin; Liu, Cheng-hui; Boydston-White, Susie; Beckman, Hugh; Sriramoju, Vidyasagar; Sordillo, Laura; Zhang, Chunyuan; Zhang, Lin; Shi, Lingyan; Smith, Jason; Bailin, Jacob; Alfano, Robert R.

2018-02-01

Analyzing spectral or imaging data collected with various optical biopsy methods is often times difficult due to the complexity of the biological basis. Robust methods that can utilize the spectral or imaging data and detect the characteristic spectral or spatial signatures for different types of tissue is challenging but highly desired. In this study, we used various machine learning algorithms to analyze a spectral dataset acquired from human skin normal and cancerous tissue samples using resonance Raman spectroscopy with 532nm excitation. The algorithms including principal component analysis, nonnegative matrix factorization, and autoencoder artificial neural network are used to reduce dimension of the dataset and detect features. A support vector machine with a linear kernel is used to classify the normal tissue and cancerous tissue samples. The efficacies of the methods are compared.

Analysis of the spectral vanishing viscosity method for periodic conservation laws

NASA Technical Reports Server (NTRS)

Maday, Yvon; Tadmor, Eitan

1988-01-01

The convergence of the spectral vanishing method for both the spectral and pseudospectral discretizations of the inviscid Burgers' equation is analyzed. It is proven that this kind of vanishing viscosity is responsible for a spectral decay of those Fourier coefficients located toward the end of the computed spectrum; consequently, the discretization error is shown to be spectrally small independent of whether the underlying solution is smooth or not. This in turn implies that the numerical solution remains uniformly bounded and convergence follows by compensated compactness arguments.

Fast algorithm for spectral mixture analysis of imaging spectrometer data

NASA Astrophysics Data System (ADS)

Schouten, Theo E.; Klein Gebbinck, Maurice S.; Liu, Z. K.; Chen, Shaowei

1996-12-01

Imaging spectrometers acquire images in many narrow spectral bands but have limited spatial resolution. Spectral mixture analysis (SMA) is used to determine the fractions of the ground cover categories (the end-members) present in each pixel. In this paper a new iterative SMA method is presented and tested using a 30 band MAIS image. The time needed for each iteration is independent of the number of bands, thus the method can be used for spectrometers with a large number of bands. Further a new method, based on K-means clustering, for obtaining endmembers from image data is described and compared with existing methods. Using the developed methods the available MAIS image was analyzed using 2 to 6 endmembers.

Three-stage Fabry-Perot liquid crystal tunable filter with extended spectral range.

PubMed

Zheng, Zhenrong; Yang, Guowei; Li, Haifeng; Liu, Xu

2011-01-31

A method to extend spectral range of tunable optical filter is proposed in this paper. Two same tunable Fabry-Perot filters and an additional tunable filter with different free spectral range are cascaded to extend spectral range and reduce sidelobes. Over 400 nm of free spectral range and 4 nm of full width at half maximum of the filter were achieved. Design procedure and simulation are described in detail. An experimental 3-stage tunable Fabry-Perot filter with visible and infrared spectra is demonstrated. The experimental results and the theoretical analysis are presented in detail to verify this method. The results revealed that a compact and extended tunable spectral range of Fabry-Perot filter can be easily attainable by this method.

Objective determination of image end-members in spectral mixture analysis of AVIRIS data

NASA Technical Reports Server (NTRS)

Tompkins, Stefanie; Mustard, John F.; Pieters, Carle M.; Forsyth, Donald W.

1993-01-01

Spectral mixture analysis has been shown to be a powerful, multifaceted tool for analysis of multi- and hyper-spectral data. Applications of AVIRIS data have ranged from mapping soils and bedrock to ecosystem studies. During the first phase of the approach, a set of end-members are selected from an image cube (image end-members) that best account for its spectral variance within a constrained, linear least squares mixing model. These image end-members are usually selected using a priori knowledge and successive trial and error solutions to refine the total number and physical location of the end-members. However, in many situations a more objective method of determining these essential components is desired. We approach the problem of image end-member determination objectively by using the inherent variance of the data. Unlike purely statistical methods such as factor analysis, this approach derives solutions that conform to a physically realistic model.

Advanced spectral methods for climatic time series

USGS Publications Warehouse

Ghil, M.; Allen, M.R.; Dettinger, M.D.; Ide, K.; Kondrashov, D.; Mann, M.E.; Robertson, A.W.; Saunders, A.; Tian, Y.; Varadi, F.; Yiou, P.

2002-01-01

The analysis of univariate or multivariate time series provides crucial information to describe, understand, and predict climatic variability. The discovery and implementation of a number of novel methods for extracting useful information from time series has recently revitalized this classical field of study. Considerable progress has also been made in interpreting the information so obtained in terms of dynamical systems theory. In this review we describe the connections between time series analysis and nonlinear dynamics, discuss signal- to-noise enhancement, and present some of the novel methods for spectral analysis. The various steps, as well as the advantages and disadvantages of these methods, are illustrated by their application to an important climatic time series, the Southern Oscillation Index. This index captures major features of interannual climate variability and is used extensively in its prediction. Regional and global sea surface temperature data sets are used to illustrate multivariate spectral methods. Open questions and further prospects conclude the review.

Methods for spectral image analysis by exploiting spatial simplicity

DOEpatents

Keenan, Michael R.

2010-05-25

Several full-spectrum imaging techniques have been introduced in recent years that promise to provide rapid and comprehensive chemical characterization of complex samples. One of the remaining obstacles to adopting these techniques for routine use is the difficulty of reducing the vast quantities of raw spectral data to meaningful chemical information. Multivariate factor analysis techniques, such as Principal Component Analysis and Alternating Least Squares-based Multivariate Curve Resolution, have proven effective for extracting the essential chemical information from high dimensional spectral image data sets into a limited number of components that describe the spectral characteristics and spatial distributions of the chemical species comprising the sample. There are many cases, however, in which those constraints are not effective and where alternative approaches may provide new analytical insights. For many cases of practical importance, imaged samples are "simple" in the sense that they consist of relatively discrete chemical phases. That is, at any given location, only one or a few of the chemical species comprising the entire sample have non-zero concentrations. The methods of spectral image analysis of the present invention exploit this simplicity in the spatial domain to make the resulting factor models more realistic. Therefore, more physically accurate and interpretable spectral and abundance components can be extracted from spectral images that have spatially simple structure.

Methods for spectral image analysis by exploiting spatial simplicity

DOEpatents

Keenan, Michael R.

2010-11-23

Several full-spectrum imaging techniques have been introduced in recent years that promise to provide rapid and comprehensive chemical characterization of complex samples. One of the remaining obstacles to adopting these techniques for routine use is the difficulty of reducing the vast quantities of raw spectral data to meaningful chemical information. Multivariate factor analysis techniques, such as Principal Component Analysis and Alternating Least Squares-based Multivariate Curve Resolution, have proven effective for extracting the essential chemical information from high dimensional spectral image data sets into a limited number of components that describe the spectral characteristics and spatial distributions of the chemical species comprising the sample. There are many cases, however, in which those constraints are not effective and where alternative approaches may provide new analytical insights. For many cases of practical importance, imaged samples are "simple" in the sense that they consist of relatively discrete chemical phases. That is, at any given location, only one or a few of the chemical species comprising the entire sample have non-zero concentrations. The methods of spectral image analysis of the present invention exploit this simplicity in the spatial domain to make the resulting factor models more realistic. Therefore, more physically accurate and interpretable spectral and abundance components can be extracted from spectral images that have spatially simple structure.

Spectral Analysis of B Stars: An Application of Bayesian Statistics

NASA Astrophysics Data System (ADS)

Mugnes, J.-M.; Robert, C.

2012-12-01

To better understand the processes involved in stellar physics, it is necessary to obtain accurate stellar parameters (effective temperature, surface gravity, abundances…). Spectral analysis is a powerful tool for investigating stars, but it is also vital to reduce uncertainties at a decent computational cost. Here we present a spectral analysis method based on a combination of Bayesian statistics and grids of synthetic spectra obtained with TLUSTY. This method simultaneously constrains the stellar parameters by using all the lines accessible in observed spectra and thus greatly reduces uncertainties and improves the overall spectrum fitting. Preliminary results are shown using spectra from the Observatoire du Mont-Mégantic.

Multicomponent quantitative spectroscopic analysis without reference substances based on ICA modelling.

PubMed

Monakhova, Yulia B; Mushtakova, Svetlana P

2017-05-01

A fast and reliable spectroscopic method for multicomponent quantitative analysis of targeted compounds with overlapping signals in complex mixtures has been established. The innovative analytical approach is based on the preliminary chemometric extraction of qualitative and quantitative information from UV-vis and IR spectral profiles of a calibration system using independent component analysis (ICA). Using this quantitative model and ICA resolution results of spectral profiling of "unknown" model mixtures, the absolute analyte concentrations in multicomponent mixtures and authentic samples were then calculated without reference solutions. Good recoveries generally between 95% and 105% were obtained. The method can be applied to any spectroscopic data that obey the Beer-Lambert-Bouguer law. The proposed method was tested on analysis of vitamins and caffeine in energy drinks and aromatic hydrocarbons in motor fuel with 10% error. The results demonstrated that the proposed method is a promising tool for rapid simultaneous multicomponent analysis in the case of spectral overlap and the absence/inaccessibility of reference materials.

Linearized spectrum correlation analysis for line emission measurements

NASA Astrophysics Data System (ADS)

Nishizawa, T.; Nornberg, M. D.; Den Hartog, D. J.; Sarff, J. S.

2017-08-01

A new spectral analysis method, Linearized Spectrum Correlation Analysis (LSCA), for charge exchange and passive ion Doppler spectroscopy is introduced to provide a means of measuring fast spectral line shape changes associated with ion-scale micro-instabilities. This analysis method is designed to resolve the fluctuations in the emission line shape from a stationary ion-scale wave. The method linearizes the fluctuations around a time-averaged line shape (e.g., Gaussian) and subdivides the spectral output channels into two sets to reduce contributions from uncorrelated fluctuations without averaging over the fast time dynamics. In principle, small fluctuations in the parameters used for a line shape model can be measured by evaluating the cross spectrum between different channel groupings to isolate a particular fluctuating quantity. High-frequency ion velocity measurements (100-200 kHz) were made by using this method. We also conducted simulations to compare LSCA with a moment analysis technique under a low photon count condition. Both experimental and synthetic measurements demonstrate the effectiveness of LSCA.

A Review of Spectral Methods for Variable Amplitude Fatigue Prediction and New Results

NASA Technical Reports Server (NTRS)

Larsen, Curtis E.; Irvine, Tom

2013-01-01

A comprehensive review of the available methods for estimating fatigue damage from variable amplitude loading is presented. The dependence of fatigue damage accumulation on power spectral density (psd) is investigated for random processes relevant to real structures such as in offshore or aerospace applications. Beginning with the Rayleigh (or narrow band) approximation, attempts at improved approximations or corrections to the Rayleigh approximation are examined by comparison to rainflow analysis of time histories simulated from psd functions representative of simple theoretical and real world applications. Spectral methods investigated include corrections by Wirsching and Light, Ortiz and Chen, the Dirlik formula, and the Single-Moment method, among other more recent proposed methods. Good agreement is obtained between the spectral methods and the time-domain rainflow identification for most cases, with some limitations. Guidelines are given for using the several spectral methods to increase confidence in the damage estimate.

An Excel‐based implementation of the spectral method of action potential alternans analysis

PubMed Central

Pearman, Charles M.

2014-01-01

Abstract Action potential (AP) alternans has been well established as a mechanism of arrhythmogenesis and sudden cardiac death. Proper interpretation of AP alternans requires a robust method of alternans quantification. Traditional methods of alternans analysis neglect higher order periodicities that may have greater pro‐arrhythmic potential than classical 2:1 alternans. The spectral method of alternans analysis, already widely used in the related study of microvolt T‐wave alternans, has also been used to study AP alternans. Software to meet the specific needs of AP alternans analysis is not currently available in the public domain. An AP analysis tool is implemented here, written in Visual Basic for Applications and using Microsoft Excel as a shell. This performs a sophisticated analysis of alternans behavior allowing reliable distinction of alternans from random fluctuations, quantification of alternans magnitude, and identification of which phases of the AP are most affected. In addition, the spectral method has been adapted to allow detection and quantification of higher order regular oscillations. Analysis of action potential morphology is also performed. A simple user interface enables easy import, analysis, and export of collated results. PMID:25501439

A hybrid spatial-spectral denoising method for infrared hyperspectral images using 2DPCA

NASA Astrophysics Data System (ADS)

Huang, Jun; Ma, Yong; Mei, Xiaoguang; Fan, Fan

2016-11-01

The traditional noise reduction methods for 3-D infrared hyperspectral images typically operate independently in either the spatial or spectral domain, and such methods overlook the relationship between the two domains. To address this issue, we propose a hybrid spatial-spectral method in this paper to link both domains. First, principal component analysis and bivariate wavelet shrinkage are performed in the 2-D spatial domain. Second, 2-D principal component analysis transformation is conducted in the 1-D spectral domain to separate the basic components from detail ones. The energy distribution of noise is unaffected by orthogonal transformation; therefore, the signal-to-noise ratio of each component is used as a criterion to determine whether a component should be protected from over-denoising or denoised with certain 1-D denoising methods. This study implements the 1-D wavelet shrinking threshold method based on Stein's unbiased risk estimator, and the quantitative results on publicly available datasets demonstrate that our method can improve denoising performance more effectively than other state-of-the-art methods can.

A Steady-State Kalman Predictor-Based Filtering Strategy for Non-Overlapping Sub-Band Spectral Estimation

PubMed Central

Li, Zenghui; Xu, Bin; Yang, Jian; Song, Jianshe

2015-01-01

This paper focuses on suppressing spectral overlap for sub-band spectral estimation, with which we can greatly decrease the computational complexity of existing spectral estimation algorithms, such as nonlinear least squares spectral analysis and non-quadratic regularized sparse representation. Firstly, our study shows that the nominal ability of the high-order analysis filter to suppress spectral overlap is greatly weakened when filtering a finite-length sequence, because many meaningless zeros are used as samples in convolution operations. Next, an extrapolation-based filtering strategy is proposed to produce a series of estimates as the substitutions of the zeros and to recover the suppression ability. Meanwhile, a steady-state Kalman predictor is applied to perform a linearly-optimal extrapolation. Finally, several typical methods for spectral analysis are applied to demonstrate the effectiveness of the proposed strategy. PMID:25609038

Comparison of three different methods to merge multiresolution and multispectral data: Landsat TM and SPOT panchromatic

USGS Publications Warehouse

Chavez, P.S.; Sides, S.C.; Anderson, J.A.

1991-01-01

The merging of multisensor image data is becoming a widely used procedure because of the complementary nature of various data sets. Ideally, the method used to merge data sets with high-spatial and high-spectral resolution should not distort the spectral characteristics of the high-spectral resolution data. This paper compares the results of three different methods used to merge the information contents of the Landsat Thematic Mapper (TM) and Satellite Pour l'Observation de la Terre (SPOT) panchromatic data. The comparison is based on spectral characteristics and is made using statistical, visual, and graphical analyses of the results. The three methods used to merge the information contents of the Landsat TM and SPOT panchromatic data were the Hue-Intensity-Saturation (HIS), Principal Component Analysis (PCA), and High-Pass Filter (HPF) procedures. The HIS method distorted the spectral characteristics of the data the most. The HPF method distorted the spectral characteristics the least; the distortions were minimal and difficult to detect. -Authors

[Analysis of software for identifying spectral line of laser-induced breakdown spectroscopy based on LabVIEW].

PubMed

Hu, Zhi-yu; Zhang, Lei; Ma, Wei-guang; Yan, Xiao-juan; Li, Zhi-xin; Zhang, Yong-zhi; Wang, Le; Dong, Lei; Yin, Wang-bao; Jia, Suo-tang

2012-03-01

Self-designed identifying software for LIBS spectral line was introduced. Being integrated with LabVIEW, the soft ware can smooth spectral lines and pick peaks. The second difference and threshold methods were employed. Characteristic spectrum of several elements matches the NIST database, and realizes automatic spectral line identification and qualitative analysis of the basic composition of sample. This software can analyze spectrum handily and rapidly. It will be a useful tool for LIBS.

Hyperspectral Image Denoising Using a Nonlocal Spectral Spatial Principal Component Analysis

NASA Astrophysics Data System (ADS)

Li, D.; Xu, L.; Peng, J.; Ma, J.

2018-04-01

Hyperspectral images (HSIs) denoising is a critical research area in image processing duo to its importance in improving the quality of HSIs, which has a negative impact on object detection and classification and so on. In this paper, we develop a noise reduction method based on principal component analysis (PCA) for hyperspectral imagery, which is dependent on the assumption that the noise can be removed by selecting the leading principal components. The main contribution of paper is to introduce the spectral spatial structure and nonlocal similarity of the HSIs into the PCA denoising model. PCA with spectral spatial structure can exploit spectral correlation and spatial correlation of HSI by using 3D blocks instead of 2D patches. Nonlocal similarity means the similarity between the referenced pixel and other pixels in nonlocal area, where Mahalanobis distance algorithm is used to estimate the spatial spectral similarity by calculating the distance in 3D blocks. The proposed method is tested on both simulated and real hyperspectral images, the results demonstrate that the proposed method is superior to several other popular methods in HSI denoising.

The Application of Deterministic Spectral Domain Method to the Analysis of Planar Circuit Discontinuities on Open Substrates

DTIC Science & Technology

1990-08-01

the spectral domain is extended to include the effects of two-dimensional, two-component current flow in planar transmission line discontinuities 6n...PROFESSOR: Tatsuo Itoh A deterministic formulation of the method of moments carried out in the spectral domain is extended to include the effects of...two-dimensional, two- component current flow in planar transmission line discontinuities on open substrates. The method includes the effects of space

A practical approach to spectral calibration of short wavelength infrared hyper-spectral imaging systems

NASA Astrophysics Data System (ADS)

Bürmen, Miran; Pernuš, Franjo; Likar, Boštjan

2010-02-01

Near-infrared spectroscopy is a promising, rapidly developing, reliable and noninvasive technique, used extensively in the biomedicine and in pharmaceutical industry. With the introduction of acousto-optic tunable filters (AOTF) and highly sensitive InGaAs focal plane sensor arrays, real-time high resolution hyper-spectral imaging has become feasible for a number of new biomedical in vivo applications. However, due to the specificity of the AOTF technology and lack of spectral calibration standardization, maintaining long-term stability and compatibility of the acquired hyper-spectral images across different systems is still a challenging problem. Efficiently solving both is essential as the majority of methods for analysis of hyper-spectral images relay on a priori knowledge extracted from large spectral databases, serving as the basis for reliable qualitative or quantitative analysis of various biological samples. In this study, we propose and evaluate fast and reliable spectral calibration of hyper-spectral imaging systems in the short wavelength infrared spectral region. The proposed spectral calibration method is based on light sources or materials, exhibiting distinct spectral features, which enable robust non-rigid registration of the acquired spectra. The calibration accounts for all of the components of a typical hyper-spectral imaging system such as AOTF, light source, lens and optical fibers. The obtained results indicated that practical, fast and reliable spectral calibration of hyper-spectral imaging systems is possible, thereby assuring long-term stability and inter-system compatibility of the acquired hyper-spectral images.

Least Squares Moving-Window Spectral Analysis.

PubMed

Lee, Young Jong

2017-08-01

Least squares regression is proposed as a moving-windows method for analysis of a series of spectra acquired as a function of external perturbation. The least squares moving-window (LSMW) method can be considered an extended form of the Savitzky-Golay differentiation for nonuniform perturbation spacing. LSMW is characterized in terms of moving-window size, perturbation spacing type, and intensity noise. Simulation results from LSMW are compared with results from other numerical differentiation methods, such as single-interval differentiation, autocorrelation moving-window, and perturbation correlation moving-window methods. It is demonstrated that this simple LSMW method can be useful for quantitative analysis of nonuniformly spaced spectral data with high frequency noise.

Discrimination among Panax species using spectral fingerprinting

USDA-ARS?s Scientific Manuscript database

Spectral fingerprints of samples of three Panax species (P. quinquefolius L., P. ginseng, and P. notoginseng) were acquired using UV, NIR, and MS spectrometry. With principal components analysis (PCA), all three methods allowed visual discrimination between all three species. All three methods wer...

Hyperspectral image classification by a variable interval spectral average and spectral curve matching combined algorithm

NASA Astrophysics Data System (ADS)

Senthil Kumar, A.; Keerthi, V.; Manjunath, A. S.; Werff, Harald van der; Meer, Freek van der

2010-08-01

Classification of hyperspectral images has been receiving considerable attention with many new applications reported from commercial and military sectors. Hyperspectral images are composed of a large number of spectral channels, and have the potential to deliver a great deal of information about a remotely sensed scene. However, in addition to high dimensionality, hyperspectral image classification is compounded with a coarse ground pixel size of the sensor for want of adequate sensor signal to noise ratio within a fine spectral passband. This makes multiple ground features jointly occupying a single pixel. Spectral mixture analysis typically begins with pixel classification with spectral matching techniques, followed by the use of spectral unmixing algorithms for estimating endmembers abundance values in the pixel. The spectral matching techniques are analogous to supervised pattern recognition approaches, and try to estimate some similarity between spectral signatures of the pixel and reference target. In this paper, we propose a spectral matching approach by combining two schemes—variable interval spectral average (VISA) method and spectral curve matching (SCM) method. The VISA method helps to detect transient spectral features at different scales of spectral windows, while the SCM method finds a match between these features of the pixel and one of library spectra by least square fitting. Here we also compare the performance of the combined algorithm with other spectral matching techniques using a simulated and the AVIRIS hyperspectral data sets. Our results indicate that the proposed combination technique exhibits a stronger performance over the other methods in the classification of both the pure and mixed class pixels simultaneously.

Method for predicting dry mechanical properties from wet wood and standing trees

DOEpatents

Meglen, Robert R.; Kelley, Stephen S.

2003-08-12

A method for determining the dry mechanical strength for a green wood comprising: illuminating a surface of the wood to be determined with light between 350-2,500 nm, the wood having a green moisture content; analyzing the surface using a spectrometric method, the method generating a first spectral data, and using a multivariate analysis to predict the dry mechanical strength of green wood when dry by comparing the first spectral data with a calibration model, the calibration model comprising a second spectrometric method of spectral data obtained from a reference wood having a green moisture content, the second spectral data correlated with a known mechanical strength analytical result obtained from a reference wood when dried and having a dry moisture content.

Semiclassical analysis of spectral singularities and their applications in optics

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

Mostafazadeh, Ali

2011-08-15

Motivated by possible applications of spectral singularities in optics, we develop a semiclassical method of computing spectral singularities. We use this method to examine the spectral singularities of a planar slab gain medium whose gain coefficient varies due to the exponential decay of the intensity of the pumping beam inside the medium. For both singly and doublypumped samples, we obtain universal upper bounds on the decay constant beyond which no lasing occurs. Furthermore, we show that the dependence of the wavelength of the spectral singularities on the value of the decay constant is extremely mild. This is an indication ofmore » the stability of optical spectral singularities.« less

Vibrations Detection in Industrial Pumps Based on Spectral Analysis to Increase Their Efficiency

NASA Astrophysics Data System (ADS)

Rachid, Belhadef; Hafaifa, Ahmed; Boumehraz, Mohamed

2016-03-01

Spectral analysis is the key tool for the study of vibration signals in rotating machinery. In this work, the vibration analysis applied for conditional preventive maintenance of such machines is proposed, as part of resolved problems related to vibration detection on the organs of these machines. The vibration signal of a centrifugal pump was treated to mount the benefits of the approach proposed. The obtained results present the signal estimation of a pump vibration using Fourier transform technique compared by the spectral analysis methods based on Prony approach.

UV spectroscopy including ISM line absorption: of the exciting star of Abell 35

NASA Astrophysics Data System (ADS)

Ziegler, M.; Rauch, T.; Werner, K.; Kruk, J. W.

Reliable spectral analysis that is based on high-resolution UV observations requires an adequate, simultaneous modeling of the interstellar line absorption and reddening. In the case of the central star of the planetary nebula Abell 35, BD-22 3467, we demonstrate our current standard spectral-analysis method that is based on the Tübingen NLTE Model-Atmosphere Package (TMAP). We present an on- going spectral analysis of FUSE and HST/STIS observations of BD-22 3467.

Demodulation circuit for AC motor current spectral analysis

DOEpatents

Hendrix, Donald E.; Smith, Stephen F.

1990-12-18

A motor current analysis method for the remote, noninvasive inspection of electric motor-operated systems. Synchronous amplitude demodulation and phase demodulation circuits are used singly and in combination along with a frequency analyzer to produce improved spectral analysis of load-induced frequencies present in the electric current flowing in a motor-driven system.

A two dimensional power spectral estimate for some nonstationary processes. M.S. Thesis

NASA Technical Reports Server (NTRS)

Smith, Gregory L.

1989-01-01

A two dimensional estimate for the power spectral density of a nonstationary process is being developed. The estimate will be applied to helicopter noise data which is clearly nonstationary. The acoustic pressure from the isolated main rotor and isolated tail rotor is known to be periodically correlated (PC) and the combined noise from the main and tail rotors is assumed to be correlation autoregressive (CAR). The results of this nonstationary analysis will be compared with the current method of assuming that the data is stationary and analyzing it as such. Another method of analysis is to introduce a random phase shift into the data as shown by Papoulis to produce a time history which can then be accurately modeled as stationary. This method will also be investigated for the helicopter data. A method used to determine the period of a PC process when the period is not know is discussed. The period of a PC process must be known in order to produce an accurate spectral representation for the process. The spectral estimate is developed. The bias and variability of the estimate are also discussed. Finally, the current method for analyzing nonstationary data is compared to that of using a two dimensional spectral representation. In addition, the method of phase shifting the data is examined.

Sleep Neurophysiological Dynamics Through the Lens of Multitaper Spectral Analysis

PubMed Central

Prerau, Michael J.; Brown, Ritchie E.; Bianchi, Matt T.; Ellenbogen, Jeffrey M.; Purdon, Patrick L.

2016-01-01

During sleep, cortical and subcortical structures within the brain engage in highly structured oscillatory dynamics that can be observed in the electroencephalogram (EEG). The ability to accurately describe changes in sleep state from these oscillations has thus been a major goal of sleep medicine. While numerous studies over the past 50 years have shown sleep to be a continuous, multifocal, dynamic process, long-standing clinical practice categorizes sleep EEG into discrete stages through visual inspection of 30-s epochs. By representing sleep as a coarsely discretized progression of stages, vital neurophysiological information on the dynamic interplay between sleep and arousal is lost. However, by using principled time-frequency spectral analysis methods, the rich dynamics of the sleep EEG are immediately visible—elegantly depicted and quantified at time scales ranging from a full night down to individual microevents. In this paper, we review the neurophysiology of sleep through this lens of dynamic spectral analysis. We begin by reviewing spectral estimation techniques traditionally used in sleep EEG analysis and introduce multitaper spectral analysis, a method that makes EEG spectral estimates clearer and more accurate than traditional approaches. Through the lens of the multitaper spectrogram, we review the oscillations and mechanisms underlying the traditional sleep stages. In doing so, we will demonstrate how multitaper spectral analysis makes the oscillatory structure of traditional sleep states instantaneously visible, closely paralleling the traditional hypnogram, but with a richness of information that suggests novel insights into the neural mechanisms of sleep, as well as novel clinical and research applications. PMID:27927806

An Excel-based implementation of the spectral method of action potential alternans analysis.

PubMed

Pearman, Charles M

2014-12-01

Action potential (AP) alternans has been well established as a mechanism of arrhythmogenesis and sudden cardiac death. Proper interpretation of AP alternans requires a robust method of alternans quantification. Traditional methods of alternans analysis neglect higher order periodicities that may have greater pro-arrhythmic potential than classical 2:1 alternans. The spectral method of alternans analysis, already widely used in the related study of microvolt T-wave alternans, has also been used to study AP alternans. Software to meet the specific needs of AP alternans analysis is not currently available in the public domain. An AP analysis tool is implemented here, written in Visual Basic for Applications and using Microsoft Excel as a shell. This performs a sophisticated analysis of alternans behavior allowing reliable distinction of alternans from random fluctuations, quantification of alternans magnitude, and identification of which phases of the AP are most affected. In addition, the spectral method has been adapted to allow detection and quantification of higher order regular oscillations. Analysis of action potential morphology is also performed. A simple user interface enables easy import, analysis, and export of collated results. © 2014 The Author. Physiological Reports published by Wiley Periodicals, Inc. on behalf of the American Physiological Society and The Physiological Society.

[Study on Application of NIR Spectral Information Screening in Identification of Maca Origin].

PubMed

Wang, Yuan-zhong; Zhao, Yan-li; Zhang, Ji; Jin, Hang

2016-02-01

Medicinal and edible plant Maca is rich in various nutrients and owns great medicinal value. Based on near infrared diffuse reflectance spectra, 139 Maca samples collected from Peru and Yunnan were used to identify their geographical origins. Multiplication signal correction (MSC) coupled with second derivative (SD) and Norris derivative filter (ND) was employed in spectral pretreatment. Spectrum range (7,500-4,061 cm⁻¹) was chosen by spectrum standard deviation. Combined with principal component analysis-mahalanobis distance (PCA-MD), the appropriate number of principal components was selected as 5. Based on the spectrum range and the number of principal components selected, two abnormal samples were eliminated by modular group iterative singular sample diagnosis method. Then, four methods were used to filter spectral variable information, competitive adaptive reweighted sampling (CARS), monte carlo-uninformative variable elimination (MC-UVE), genetic algorithm (GA) and subwindow permutation analysis (SPA). The spectral variable information filtered was evaluated by model population analysis (MPA). The results showed that RMSECV(SPA) > RMSECV(CARS) > RMSECV(MC-UVE) > RMSECV(GA), were 2. 14, 2. 05, 2. 02, and 1. 98, and the spectral variables were 250, 240, 250 and 70, respectively. According to the spectral variable filtered, partial least squares discriminant analysis (PLS-DA) was used to build the model, with random selection of 97 samples as training set, and the other 40 samples as validation set. The results showed that, R²: GA > MC-UVE > CARS > SPA, RMSEC and RMSEP: GA < MC-UVE < CARS

On modelling three-dimensional piezoelectric smart structures with boundary spectral element method

NASA Astrophysics Data System (ADS)

Zou, Fangxin; Aliabadi, M. H.

2017-05-01

The computational efficiency of the boundary element method in elastodynamic analysis can be significantly improved by employing high-order spectral elements for boundary discretisation. In this work, for the first time, the so-called boundary spectral element method is utilised to formulate the piezoelectric smart structures that are widely used in structural health monitoring (SHM) applications. The resultant boundary spectral element formulation has been validated by the finite element method (FEM) and physical experiments. The new formulation has demonstrated a lower demand on computational resources and a higher numerical stability than commercial FEM packages. Comparing to the conventional boundary element formulation, a significant reduction in computational expenses has been achieved. In summary, the boundary spectral element formulation presented in this paper provides a highly efficient and stable mathematical tool for the development of SHM applications.

Infrared spectral imaging as a novel approach for histopathological recognition in colon cancer diagnosis

NASA Astrophysics Data System (ADS)

Nallala, Jayakrupakar; Gobinet, Cyril; Diebold, Marie-Danièle; Untereiner, Valérie; Bouché, Olivier; Manfait, Michel; Sockalingum, Ganesh Dhruvananda; Piot, Olivier

2012-11-01

Innovative diagnostic methods are the need of the hour that could complement conventional histopathology for cancer diagnosis. In this perspective, we propose a new concept based on spectral histopathology, using IR spectral micro-imaging, directly applied to paraffinized colon tissue array stabilized in an agarose matrix without any chemical pre-treatment. In order to correct spectral interferences from paraffin and agarose, a mathematical procedure is implemented. The corrected spectral images are then processed by a multivariate clustering method to automatically recover, on the basis of their intrinsic molecular composition, the main histological classes of the normal and the tumoral colon tissue. The spectral signatures from different histological classes of the colonic tissues are analyzed using statistical methods (Kruskal-Wallis test and principal component analysis) to identify the most discriminant IR features. These features allow characterizing some of the biomolecular alterations associated with malignancy. Thus, via a single analysis, in a label-free and nondestructive manner, main changes associated with nucleotide, carbohydrates, and collagen features can be identified simultaneously between the compared normal and the cancerous tissues. The present study demonstrates the potential of IR spectral imaging as a complementary modern tool, to conventional histopathology, for an objective cancer diagnosis directly from paraffin-embedded tissue arrays.

Sensitive sub-Doppler nonlinear spectroscopy for hyperfine-structure analysis using simple atomizers

NASA Astrophysics Data System (ADS)

Mickadeit, Fritz K.; Kemp, Helen; Schafer, Julia; Tong, William M.

1998-05-01

Laser wave-mixing spectroscopy is presented as a sub-Doppler method that offers not only high spectral resolution, but also excellent detection sensitivity. It offers spectral resolution suitable for hyperfine structure analysis and isotope ratio measurements. In a non-planar backward- scattering four-wave mixing optical configuration, two of the three input beams counter propagate and the Doppler broadening is minimized, and hence, spectral resolution is enhanced. Since the signal is a coherent beam, optical collection is efficient and signal detection is convenient. This simple multi-photon nonlinear laser method offers un usually sensitive detection limits that are suitable for trace-concentration isotope analysis using a few different types of simple analytical atomizers. Reliable measurement of hyperfine structures allows effective determination of isotope ratios for chemical analysis.

[Evaluation of a simplified index (spectral entropy) about sleep state of electrocardiogram recorded by a simplified polygraph, MemCalc-Makin2].

PubMed

Ohisa, Noriko; Ogawa, Hiromasa; Murayama, Nobuki; Yoshida, Katsumi

2010-02-01

Polysomnography (PSG) is the gold standard for the diagnosis of sleep apnea hypopnea syndrome (SAHS), but it takes time to analyze the PSG and PSG cannot be performed repeatedly because of efforts and costs. Therefore, simplified sleep respiratory disorder indices in which are reflected the PSG results are needed. The Memcalc method, which is a combination of the maximum entropy method for spectral analysis and the non-linear least squares method for fitting analysis (Makin2, Suwa Trust, Tokyo, Japan) has recently been developed. Spectral entropy which is derived by the Memcalc method might be useful to expressing the trend of time-series behavior. Spectral entropy of ECG which is calculated with the Memcalc method was evaluated by comparing to the PSG results. Obstructive SAS patients (n = 79) and control volanteer (n = 7) ECG was recorded using MemCalc-Makin2 (GMS) with PSG recording using Alice IV (Respironics) from 20:00 to 6:00. Spectral entropy of ECG, which was calculated every 2 seconds using the Memcalc method, was compared to sleep stages which were analyzed manually from PSG recordings. Spectral entropy value (-0.473 vs. -0.418, p < 0.05) were significantly increased in the OSAHS compared to the control. For the entropy cutoff level of -0.423, sensitivity and specificity for OSAHS were 86.1% and 71.4%, respectively, resulting in a receiver operating characteristic with an area under the curve of 0.837. The absolute value of entropy had inverse correlation with stage 3. Spectral entropy, which was calculated with Memcalc method, might be a possible index evaluating the quality of sleep.

Evaluating the utility of mid-infrared spectral subspaces for predicting soil properties.

PubMed

Sila, Andrew M; Shepherd, Keith D; Pokhariyal, Ganesh P

2016-04-15

We propose four methods for finding local subspaces in large spectral libraries. The proposed four methods include (a) cosine angle spectral matching; (b) hit quality index spectral matching; (c) self-organizing maps and (d) archetypal analysis methods. Then evaluate prediction accuracies for global and subspaces calibration models. These methods were tested on a mid-infrared spectral library containing 1907 soil samples collected from 19 different countries under the Africa Soil Information Service project. Calibration models for pH, Mehlich-3 Ca, Mehlich-3 Al, total carbon and clay soil properties were developed for the whole library and for the subspace. Root mean square error of prediction was used to evaluate predictive performance of subspace and global models. The root mean square error of prediction was computed using a one-third-holdout validation set. Effect of pretreating spectra with different methods was tested for 1st and 2nd derivative Savitzky-Golay algorithm, multiplicative scatter correction, standard normal variate and standard normal variate followed by detrending methods. In summary, the results show that global models outperformed the subspace models. We, therefore, conclude that global models are more accurate than the local models except in few cases. For instance, sand and clay root mean square error values from local models from archetypal analysis method were 50% poorer than the global models except for subspace models obtained using multiplicative scatter corrected spectra with which were 12% better. However, the subspace approach provides novel methods for discovering data pattern that may exist in large spectral libraries.

SVD analysis of Aura TES spectral residuals

NASA Technical Reports Server (NTRS)

Beer, Reinhard; Kulawik, Susan S.; Rodgers, Clive D.; Bowman, Kevin W.

2005-01-01

Singular Value Decomposition (SVD) analysis is both a powerful diagnostic tool and an effective method of noise filtering. We present the results of an SVD analysis of an ensemble of spectral residuals acquired in September 2004 from a 16-orbit Aura Tropospheric Emission Spectrometer (TES) Global Survey and compare them to alternative methods such as zonal averages. In particular, the technique highlights issues such as the orbital variation of instrument response and incompletely modeled effects of surface emissivity and atmospheric composition.

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.

ACCOUNTING FOR CALIBRATION UNCERTAINTIES IN X-RAY ANALYSIS: EFFECTIVE AREAS IN SPECTRAL FITTING

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

Lee, Hyunsook; Kashyap, Vinay L.; Drake, Jeremy J.

2011-04-20

While considerable advance has been made to account for statistical uncertainties in astronomical analyses, systematic instrumental uncertainties have been generally ignored. This can be crucial to a proper interpretation of analysis results because instrumental calibration uncertainty is a form of systematic uncertainty. Ignoring it can underestimate error bars and introduce bias into the fitted values of model parameters. Accounting for such uncertainties currently requires extensive case-specific simulations if using existing analysis packages. Here, we present general statistical methods that incorporate calibration uncertainties into spectral analysis of high-energy data. We first present a method based on multiple imputation that can bemore » applied with any fitting method, but is necessarily approximate. We then describe a more exact Bayesian approach that works in conjunction with a Markov chain Monte Carlo based fitting. We explore methods for improving computational efficiency, and in particular detail a method of summarizing calibration uncertainties with a principal component analysis of samples of plausible calibration files. This method is implemented using recently codified Chandra effective area uncertainties for low-resolution spectral analysis and is verified using both simulated and actual Chandra data. Our procedure for incorporating effective area uncertainty is easily generalized to other types of calibration uncertainties.« less

Identifying areas of high economic-potential copper mineralization using ASTER data in the Urumieh-Dokhtar Volcanic Belt, Iran

NASA Astrophysics Data System (ADS)

Pour, Amin Beiranvand; Hashim, Mazlan

2012-02-01

This study investigates the application of spectral image processing methods to ASTER data for mapping hydrothermal alteration zones associated with porphyry copper mineralization and related host rock. The study area is located in the southeastern segment of the Urumieh-Dokhtar Volcanic Belt of Iran. This area has been selected because it is a potential zone for exploration of new porphyry copper deposits. Spectral transform approaches, namely principal component analysis, band ratio and minimum noise fraction were used for mapping hydrothermally altered rocks and lithological units at regional scale. Spectral mapping methods, including spectral angle mapper, linear spectral unmixing, matched filtering and mixture tuned matched filtering were applied to differentiate hydrothermal alteration zones associated with porphyry copper mineralization such as phyllic, argillic and propylitic mineral assemblages.Spectral transform methods enhanced hydrothermally altered rocks associated with the known porphyry copper deposits and new identified prospects using shortwave infrared (SWIR) bands of ASTER. These methods showed the discrimination of quartz rich igneous rocks from the magmatic background and the boundary between igneous and sedimentary rocks using the thermal infrared (TIR) bands of ASTER at regional scale. Spectral mapping methods distinguished the sericitically- and argillically-altered rocks (the phyllic and argillic alteration zones) that surrounded by discontinuous to extensive zones of propylitized rocks (the propylitic alteration zone) using SWIR bands of ASTER at both regional and district scales. Linear spectral unmixing method can be best suited for distinguishing specific high economic-potential hydrothermal alteration zone (the phyllic zone) and mineral assemblages using SWIR bands of ASTER. Results have proven to be effective, and in accordance with the results of field surveying, spectral reflectance measurements and X-ray diffraction (XRD) analysis. In conclusion, the image processing methods used can provide cost-effective information to discover possible locations of porphyry copper and epithermal gold mineralization prior to detailed and costly ground investigations. The extraction of spectral information from ASTER data can produce comprehensive and accurate information for copper and gold resource investigations around the world, including those yet to be discovered.

Spectral analysis and multigrid preconditioners for two-dimensional space-fractional diffusion equations

NASA Astrophysics Data System (ADS)

Moghaderi, Hamid; Dehghan, Mehdi; Donatelli, Marco; Mazza, Mariarosa

2017-12-01

Fractional diffusion equations (FDEs) are a mathematical tool used for describing some special diffusion phenomena arising in many different applications like porous media and computational finance. In this paper, we focus on a two-dimensional space-FDE problem discretized by means of a second order finite difference scheme obtained as combination of the Crank-Nicolson scheme and the so-called weighted and shifted Grünwald formula. By fully exploiting the Toeplitz-like structure of the resulting linear system, we provide a detailed spectral analysis of the coefficient matrix at each time step, both in the case of constant and variable diffusion coefficients. Such a spectral analysis has a very crucial role, since it can be used for designing fast and robust iterative solvers. In particular, we employ the obtained spectral information to define a Galerkin multigrid method based on the classical linear interpolation as grid transfer operator and damped-Jacobi as smoother, and to prove the linear convergence rate of the corresponding two-grid method. The theoretical analysis suggests that the proposed grid transfer operator is strong enough for working also with the V-cycle method and the geometric multigrid. On this basis, we introduce two computationally favourable variants of the proposed multigrid method and we use them as preconditioners for Krylov methods. Several numerical results confirm that the resulting preconditioning strategies still keep a linear convergence rate.

Systems and methods for analyzing building operations sensor data

DOEpatents

Mezic, Igor; Eisenhower, Bryan A.

2015-05-26

Systems and methods are disclosed for analyzing building sensor information and decomposing the information therein to a more manageable and more useful form. Certain embodiments integrate energy-based and spectral-based analysis methods with parameter sampling and uncertainty/sensitivity analysis to achieve a more comprehensive perspective of building behavior. The results of this analysis may be presented to a user via a plurality of visualizations and/or used to automatically adjust certain building operations. In certain embodiments, advanced spectral techniques, including Koopman-based operations, are employed to discern features from the collected building sensor data.

Method of predicting mechanical properties of decayed wood

DOEpatents

Kelley, Stephen S.

2003-07-15

A method for determining the mechanical properties of decayed wood that has been exposed to wood decay microorganisms, comprising: a) illuminating a surface of decayed wood that has been exposed to wood decay microorganisms with wavelengths from visible and near infrared (VIS-NIR) spectra; b) analyzing the surface of the decayed wood using a spectrometric method, the method generating a first spectral data of wavelengths in VIS-NIR spectra region; and c) using a multivariate analysis to predict mechanical properties of decayed wood by comparing the first spectral data with a calibration model, the calibration model comprising a second spectrometric method of spectral data of wavelengths in VIS-NIR spectra obtained from a reference decay wood, the second spectral data being correlated with a known mechanical property analytical result obtained from the reference decayed wood.

Analysis of Spectral Features of Seawaterbiooptical Components Fluorescence from the Excitation-emission Matrix

NASA Astrophysics Data System (ADS)

Salyuk, P. A.; Nagorny, I. G.

The paper presents the method for processing of excitation-emission matrix of sea water and the allocation of the spectral characteristics of different types of colored dissolved organic matter (CDOM) and phytoplankton cells in seawater. The method consists of identification of regularly observed fluorescence peaks of CDOM in marine waters of different type and definition of the spectral ranges, where the predominant influence of these peaks are observed.

[A spatial adaptive algorithm for endmember extraction on multispectral remote sensing image].

PubMed

Zhu, Chang-Ming; Luo, Jian-Cheng; Shen, Zhan-Feng; Li, Jun-Li; Hu, Xiao-Dong

2011-10-01

Due to the problem that the convex cone analysis (CCA) method can only extract limited endmember in multispectral imagery, this paper proposed a new endmember extraction method by spatial adaptive spectral feature analysis in multispectral remote sensing image based on spatial clustering and imagery slice. Firstly, in order to remove spatial and spectral redundancies, the principal component analysis (PCA) algorithm was used for lowering the dimensions of the multispectral data. Secondly, iterative self-organizing data analysis technology algorithm (ISODATA) was used for image cluster through the similarity of the pixel spectral. And then, through clustering post process and litter clusters combination, we divided the whole image data into several blocks (tiles). Lastly, according to the complexity of image blocks' landscape and the feature of the scatter diagrams analysis, the authors can determine the number of endmembers. Then using hourglass algorithm extracts endmembers. Through the endmember extraction experiment on TM multispectral imagery, the experiment result showed that the method can extract endmember spectra form multispectral imagery effectively. What's more, the method resolved the problem of the amount of endmember limitation and improved accuracy of the endmember extraction. The method has provided a new way for multispectral image endmember extraction.

Decoding magnetoencephalographic rhythmic activity using spectrospatial information.

PubMed

Kauppi, Jukka-Pekka; Parkkonen, Lauri; Hari, Riitta; Hyvärinen, Aapo

2013-12-01

We propose a new data-driven decoding method called Spectral Linear Discriminant Analysis (Spectral LDA) for the analysis of magnetoencephalography (MEG). The method allows investigation of changes in rhythmic neural activity as a result of different stimuli and tasks. The introduced classification model only assumes that each "brain state" can be characterized as a combination of neural sources, each of which shows rhythmic activity at one or several frequency bands. Furthermore, the model allows the oscillation frequencies to be different for each such state. We present decoding results from 9 subjects in a four-category classification problem defined by an experiment involving randomly alternating epochs of auditory, visual and tactile stimuli interspersed with rest periods. The performance of Spectral LDA was very competitive compared with four alternative classifiers based on different assumptions concerning the organization of rhythmic brain activity. In addition, the spectral and spatial patterns extracted automatically on the basis of trained classifiers showed that Spectral LDA offers a novel and interesting way of analyzing spectrospatial oscillatory neural activity across the brain. All the presented classification methods and visualization tools are freely available as a Matlab toolbox. © 2013.

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

NASA Astrophysics Data System (ADS)

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

2014-11-01

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

Novel hyperspectral prediction method and apparatus

NASA Astrophysics Data System (ADS)

Kemeny, Gabor J.; Crothers, Natalie A.; Groth, Gard A.; Speck, Kathy A.; Marbach, Ralf

2009-05-01

Both the power and the challenge of hyperspectral technologies is the very large amount of data produced by spectral cameras. While off-line methodologies allow the collection of gigabytes of data, extended data analysis sessions are required to convert the data into useful information. In contrast, real-time monitoring, such as on-line process control, requires that compression of spectral data and analysis occur at a sustained full camera data rate. Efficient, high-speed practical methods for calibration and prediction are therefore sought to optimize the value of hyperspectral imaging. A novel method of matched filtering known as science based multivariate calibration (SBC) was developed for hyperspectral calibration. Classical (MLR) and inverse (PLS, PCR) methods are combined by spectroscopically measuring the spectral "signal" and by statistically estimating the spectral "noise." The accuracy of the inverse model is thus combined with the easy interpretability of the classical model. The SBC method is optimized for hyperspectral data in the Hyper-CalTM software used for the present work. The prediction algorithms can then be downloaded into a dedicated FPGA based High-Speed Prediction EngineTM module. Spectral pretreatments and calibration coefficients are stored on interchangeable SD memory cards, and predicted compositions are produced on a USB interface at real-time camera output rates. Applications include minerals, pharmaceuticals, food processing and remote sensing.

Retrieval of spheroid particle size distribution from spectral extinction data in the independent mode using PCA approach

NASA Astrophysics Data System (ADS)

Tang, Hong; Lin, Jian-Zhong

2013-01-01

An improved anomalous diffraction approximation (ADA) method is presented for calculating the extinction efficiency of spheroids firstly. In this approach, the extinction efficiency of spheroid particles can be calculated with good accuracy and high efficiency in a wider size range by combining the Latimer method and the ADA theory, and this method can present a more general expression for calculating the extinction efficiency of spheroid particles with various complex refractive indices and aspect ratios. Meanwhile, the visible spectral extinction with varied spheroid particle size distributions and complex refractive indices is surveyed. Furthermore, a selection principle about the spectral extinction data is developed based on PCA (principle component analysis) of first derivative spectral extinction. By calculating the contribution rate of first derivative spectral extinction, the spectral extinction with more significant features can be selected as the input data, and those with less features is removed from the inversion data. In addition, we propose an improved Tikhonov iteration method to retrieve the spheroid particle size distributions in the independent mode. Simulation experiments indicate that the spheroid particle size distributions obtained with the proposed method coincide fairly well with the given distributions, and this inversion method provides a simple, reliable and efficient method to retrieve the spheroid particle size distributions from the spectral extinction data.

Distinguishing low frequency oscillations within the 1/f spectral behaviour of electromagnetic brain signals.

PubMed

Demanuele, Charmaine; James, Christopher J; Sonuga-Barke, Edmund Js

2007-12-10

It has been acknowledged that the frequency spectrum of measured electromagnetic (EM) brain signals shows a decrease in power with increasing frequency. This spectral behaviour may lead to difficulty in distinguishing event-related peaks from ongoing brain activity in the electro- and magnetoencephalographic (EEG and MEG) signal spectra. This can become an issue especially in the analysis of low frequency oscillations (LFOs) - below 0.5 Hz - which are currently being observed in signal recordings linked with specific pathologies such as epileptic seizures or attention deficit hyperactivity disorder (ADHD), in sleep studies, etc. In this work we propose a simple method that can be used to compensate for this 1/f trend hence achieving spectral normalisation. This method involves filtering the raw measured EM signal through a differentiator prior to further data analysis. Applying the proposed method to various exemplary datasets including very low frequency EEG recordings, epileptic seizure recordings, MEG data and Evoked Response data showed that this compensating procedure provides a flat spectral base onto which event related peaks can be clearly observed. Findings suggest that the proposed filter is a useful tool for the analysis of physiological data especially in revealing very low frequency peaks which may otherwise be obscured by the 1/f spectral activity inherent in EEG/MEG recordings.

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

NASA Astrophysics Data System (ADS)

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

2015-11-01

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

Analysis of airborne MAIS imaging spectrometric data for mineral exploration

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

Wang Jinnian; Zheng Lanfen; Tong Qingxi

1996-11-01

The high spectral resolution imaging spectrometric system made quantitative analysis and mapping of surface composition possible. The key issue will be the quantitative approach for analysis of surface parameters for imaging spectrometer data. This paper describes the methods and the stages of quantitative analysis. (1) Extracting surface reflectance from imaging spectrometer image. Lab. and inflight field measurements are conducted for calibration of imaging spectrometer data, and the atmospheric correction has also been used to obtain ground reflectance by using empirical line method and radiation transfer modeling. (2) Determining quantitative relationship between absorption band parameters from the imaging spectrometer data andmore » chemical composition of minerals. (3) Spectral comparison between the spectra of spectral library and the spectra derived from the imagery. The wavelet analysis-based spectrum-matching techniques for quantitative analysis of imaging spectrometer data has beer, developed. Airborne MAIS imaging spectrometer data were used for analysis and the analysis results have been applied to the mineral and petroleum exploration in Tarim Basin area china. 8 refs., 8 figs.« less

[Analysis of H2S/PH3/NH3/AsH3/Cl2 by Full-Spectral Flame Photometric Detector].

PubMed

Ding, Zhi-jun; Wang, Pu-hong; Li, Zhi-jun; Du, Bin; Guo, Lei; Yu, Jian-hua

2015-07-01

Flame photometric analysis technology has been proven to be a rapid and sensitive method for sulfur and phosphorus detection. It has been widely used in environmental inspections, pesticide detection, industrial and agricultural production. By improving the design of the traditional flame photometric detector, using grating and CCD sensor array as a photoelectric conversion device, the types of compounds that can be detected were expanded. Instead of a single point of characteristic spectral lines, full spectral information has been used for qualitative and quantitative analysis of H2S, PH3, NH3, AsH3 and Cl2. Combined with chemometric method, flame photometric analysis technology is expected to become an alternative fast, real-time on-site detection technology to simultaneously detect multiple toxic and harmful gases.

Determination of awareness in patients with severe brain injury using EEG power spectral analysis

PubMed Central

Goldfine, Andrew M.; Victor, Jonathan D.; Conte, Mary M.; Bardin, Jonathan C.; Schiff, Nicholas D.

2011-01-01

Objective To determine whether EEG spectral analysis could be used to demonstrate awareness in patients with severe brain injury. Methods We recorded EEG from healthy controls and three patients with severe brain injury, ranging from minimally conscious state (MCS) to locked-in-state (LIS), while they were asked to imagine motor and spatial navigation tasks. We assessed EEG spectral differences from 4 to 24 Hz with univariate comparisons (individual frequencies) and multivariate comparisons (patterns across the frequency range). Results In controls, EEG spectral power differed at multiple frequency bands and channels during performance of both tasks compared to a resting baseline. As patterns of signal change were inconsistent between controls, we defined a positive response in patient subjects as consistent spectral changes across task performances. One patient in MCS and one in LIS showed evidence of motor imagery task performance, though with patterns of spectral change different from the controls. Conclusion EEG power spectral analysis demonstrates evidence for performance of mental imagery tasks in healthy controls and patients with severe brain injury. Significance EEG power spectral analysis can be used as a flexible bedside tool to demonstrate awareness in brain-injured patients who are otherwise unable to communicate. PMID:21514214

[Local Regression Algorithm Based on Net Analyte Signal and Its Application in Near Infrared Spectral Analysis].

PubMed

Zhang, Hong-guang; Lu, Jian-gang

2016-02-01

Abstract To overcome the problems of significant difference among samples and nonlinearity between the property and spectra of samples in spectral quantitative analysis, a local regression algorithm is proposed in this paper. In this algorithm, net signal analysis method(NAS) was firstly used to obtain the net analyte signal of the calibration samples and unknown samples, then the Euclidean distance between net analyte signal of the sample and net analyte signal of calibration samples was calculated and utilized as similarity index. According to the defined similarity index, the local calibration sets were individually selected for each unknown sample. Finally, a local PLS regression model was built on each local calibration sets for each unknown sample. The proposed method was applied to a set of near infrared spectra of meat samples. The results demonstrate that the prediction precision and model complexity of the proposed method are superior to global PLS regression method and conventional local regression algorithm based on spectral Euclidean distance.

Calibration-free quantitative elemental analysis of meteor plasma using reference laser-induced breakdown spectroscopy of meteorite samples

NASA Astrophysics Data System (ADS)

Ferus, Martin; Koukal, Jakub; Lenža, Libor; Srba, Jiří; Kubelík, Petr; Laitl, Vojtěch; Zanozina, Ekaterina M.; Váňa, Pavel; Kaiserová, Tereza; Knížek, Antonín; Rimmer, Paul; Chatzitheodoridis, Elias; Civiš, Svatopluk

2018-03-01

Aims: We aim to analyse real-time Perseid and Leonid meteor spectra using a novel calibration-free (CF) method, which is usually applied in the laboratory for laser-induced breakdown spectroscopic (LIBS) chemical analysis. Methods: Reference laser ablation spectra of specimens of chondritic meteorites were measured in situ simultaneously with a high-resolution laboratory echelle spectrograph and a spectral camera for meteor observation. Laboratory data were subsequently evaluated via the CF method and compared with real meteor emission spectra. Additionally, spectral features related to airglow plasma were compared with the spectra of laser-induced breakdown and electric discharge in the air. Results: We show that this method can be applied in the evaluation of meteor spectral data observed in real time. Specifically, CF analysis can be used to determine the chemical composition of meteor plasma, which, in the case of the Perseid and Leonid meteors analysed in this study, corresponds to that of the C-group of chondrites.

Spectral analysis method and sample generation for real time visualization of speech

NASA Astrophysics Data System (ADS)

Hobohm, Klaus

A method for translating speech signals into optical models, characterized by high sound discrimination and learnability and designed to provide to deaf persons a feedback towards control of their way of speaking, is presented. Important properties of speech production and perception processes and organs involved in these mechanisms are recalled in order to define requirements for speech visualization. It is established that the spectral representation of time, frequency and amplitude resolution of hearing must be fair and continuous variations of acoustic parameters of speech signal must be depicted by a continuous variation of images. A color table was developed for dynamic illustration and sonograms were generated with five spectral analysis methods such as Fourier transformations and linear prediction coding. For evaluating sonogram quality, test persons had to recognize consonant/vocal/consonant words and an optimized analysis method was achieved with a fast Fourier transformation and a postprocessor. A hardware concept of a real time speech visualization system, based on multiprocessor technology in a personal computer, is presented.

Exploratory Item Classification Via Spectral Graph Clustering

PubMed Central

Chen, Yunxiao; Li, Xiaoou; Liu, Jingchen; Xu, Gongjun; Ying, Zhiliang

2017-01-01

Large-scale assessments are supported by a large item pool. An important task in test development is to assign items into scales that measure different characteristics of individuals, and a popular approach is cluster analysis of items. Classical methods in cluster analysis, such as the hierarchical clustering, K-means method, and latent-class analysis, often induce a high computational overhead and have difficulty handling missing data, especially in the presence of high-dimensional responses. In this article, the authors propose a spectral clustering algorithm for exploratory item cluster analysis. The method is computationally efficient, effective for data with missing or incomplete responses, easy to implement, and often outperforms traditional clustering algorithms in the context of high dimensionality. The spectral clustering algorithm is based on graph theory, a branch of mathematics that studies the properties of graphs. The algorithm first constructs a graph of items, characterizing the similarity structure among items. It then extracts item clusters based on the graphical structure, grouping similar items together. The proposed method is evaluated through simulations and an application to the revised Eysenck Personality Questionnaire. PMID:29033476

Multispectral analysis tools can increase utility of RGB color images in histology

NASA Astrophysics Data System (ADS)

Fereidouni, Farzad; Griffin, Croix; Todd, Austin; Levenson, Richard

2018-04-01

Multispectral imaging (MSI) is increasingly finding application in the study and characterization of biological specimens. However, the methods typically used come with challenges on both the acquisition and the analysis front. MSI can be slow and photon-inefficient, leading to long imaging times and possible phototoxicity and photobleaching. The resulting datasets can be large and complex, prompting the development of a number of mathematical approaches for segmentation and signal unmixing. We show that under certain circumstances, just three spectral channels provided by standard color cameras, coupled with multispectral analysis tools, including a more recent spectral phasor approach, can efficiently provide useful insights. These findings are supported with a mathematical model relating spectral bandwidth and spectral channel number to achievable spectral accuracy. The utility of 3-band RGB and MSI analysis tools are demonstrated on images acquired using brightfield and fluorescence techniques, as well as a novel microscopy approach employing UV-surface excitation. Supervised linear unmixing, automated non-negative matrix factorization and phasor analysis tools all provide useful results, with phasors generating particularly helpful spectral display plots for sample exploration.

Dispersion analysis of the Pn -Pn-1DG mixed finite element pair for atmospheric modelling

NASA Astrophysics Data System (ADS)

Melvin, Thomas

2018-02-01

Mixed finite element methods provide a generalisation of staggered grid finite difference methods with a framework to extend the method to high orders. The ability to generate a high order method is appealing for applications on the kind of quasi-uniform grids that are popular for atmospheric modelling, so that the method retains an acceptable level of accuracy even around special points in the grid. The dispersion properties of such schemes are important to study as they provide insight into the numerical adjustment to imbalance that is an important component in atmospheric modelling. This paper extends the recent analysis of the P2 - P1DG pair, that is a quadratic continuous and linear discontinuous finite element pair, to higher polynomial orders and also spectral element type pairs. In common with the previously studied element pair, and also with other schemes such as the spectral element and discontinuous Galerkin methods, increasing the polynomial order is found to provide a more accurate dispersion relation for the well resolved part of the spectrum but at the cost of a number of unphysical spectral gaps. The effects of these spectral gaps are investigated and shown to have a varying impact depending upon the width of the gap. Finally, the tensor product nature of the finite element spaces is exploited to extend the dispersion analysis into two-dimensions.

Spectral analysis of the structure of ultradispersed diamonds

NASA Astrophysics Data System (ADS)

Uglov, V. V.; Shimanski, V. I.; Rusalsky, D. P.; Samtsov, M. P.

2008-07-01

The structure of ultradispersed diamonds (UDD) is studied by spectral methods. The presence of diamond crystal phase in the UDD is found based on x-ray analysis and Raman spectra. The Raman spectra also show sp2-and sp3-hybridized carbon. Analysis of IR absorption spectra suggests that the composition of functional groups present in the particles changes during the treatment.

An Investigation of the Overlap Between the Statistical Discrete Gust and the Power Spectral Density Analysis Methods

NASA Technical Reports Server (NTRS)

Perry, Boyd, III; Pototzky, Anthony S.; Woods, Jessica A.

1989-01-01

The results of a NASA investigation of a claimed Overlap between two gust response analysis methods: the Statistical Discrete Gust (SDG) Method and the Power Spectral Density (PSD) Method are presented. The claim is that the ratio of an SDG response to the corresponding PSD response is 10.4. Analytical results presented for several different airplanes at several different flight conditions indicate that such an Overlap does appear to exist. However, the claim was not met precisely: a scatter of up to about 10 percent about the 10.4 factor can be expected.

Spectral element multigrid. Part 2: Theoretical justification

NASA Technical Reports Server (NTRS)

Maday, Yvon; Munoz, Rafael

1988-01-01

A multigrid algorithm is analyzed which is used for solving iteratively the algebraic system resulting from tha approximation of a second order problem by spectral or spectral element methods. The analysis, performed here in the one dimensional case, justifies the good smoothing properties of the Jacobi preconditioner that was presented in Part 1 of this paper.

A 1064 nm dispersive Raman spectral imaging system for food safety and quality evaluation

USDA-ARS?s Scientific Manuscript database

Raman spectral imaging is an effective method to analyze and evaluate chemical composition and structure of a sample, and has many applications for food safety and quality research. This study developed a 1064 nm Raman spectral imaging system for surface and subsurface analysis of food samples. A 10...

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

I. W. Ginsberg

Multiresolutional decompositions known as spectral fingerprints are often used to extract spectral features from multispectral/hyperspectral data. In this study, the authors investigate the use of wavelet-based algorithms for generating spectral fingerprints. The wavelet-based algorithms are compared to the currently used method, traditional convolution with first-derivative Gaussian filters. The comparison analyses consists of two parts: (a) the computational expense of the new method is compared with the computational costs of the current method and (b) the outputs of the wavelet-based methods are compared with those of the current method to determine any practical differences in the resulting spectral fingerprints. The resultsmore » show that the wavelet-based algorithms can greatly reduce the computational expense of generating spectral fingerprints, while practically no differences exist in the resulting fingerprints. The analysis is conducted on a database of hyperspectral signatures, namely, Hyperspectral Digital Image Collection Experiment (HYDICE) signatures. The reduction in computational expense is by a factor of about 30, and the average Euclidean distance between resulting fingerprints is on the order of 0.02.« less

Multispectral image fusion for target detection

NASA Astrophysics Data System (ADS)

Leviner, Marom; Maltz, Masha

2009-09-01

Various different methods to perform multi-spectral image fusion have been suggested, mostly on the pixel level. However, the jury is still out on the benefits of a fused image compared to its source images. We present here a new multi-spectral image fusion method, multi-spectral segmentation fusion (MSSF), which uses a feature level processing paradigm. To test our method, we compared human observer performance in an experiment using MSSF against two established methods: Averaging and Principle Components Analysis (PCA), and against its two source bands, visible and infrared. The task that we studied was: target detection in the cluttered environment. MSSF proved superior to the other fusion methods. Based on these findings, current speculation about the circumstances in which multi-spectral image fusion in general and specific fusion methods in particular would be superior to using the original image sources can be further addressed.

A new multi-spectral feature level image fusion method for human interpretation

NASA Astrophysics Data System (ADS)

Leviner, Marom; Maltz, Masha

2009-03-01

Various different methods to perform multi-spectral image fusion have been suggested, mostly on the pixel level. However, the jury is still out on the benefits of a fused image compared to its source images. We present here a new multi-spectral image fusion method, multi-spectral segmentation fusion (MSSF), which uses a feature level processing paradigm. To test our method, we compared human observer performance in a three-task experiment using MSSF against two established methods: averaging and principle components analysis (PCA), and against its two source bands, visible and infrared. The three tasks that we studied were: (1) simple target detection, (2) spatial orientation, and (3) camouflaged target detection. MSSF proved superior to the other fusion methods in all three tests; MSSF also outperformed the source images in the spatial orientation and camouflaged target detection tasks. Based on these findings, current speculation about the circumstances in which multi-spectral image fusion in general and specific fusion methods in particular would be superior to using the original image sources can be further addressed.

Numerical solution of the unsteady diffusion-convection-reaction equation based on improved spectral Galerkin method

NASA Astrophysics Data System (ADS)

Zhong, Jiaqi; Zeng, Cheng; Yuan, Yupeng; Zhang, Yuzhe; Zhang, Ye

2018-04-01

The aim of this paper is to present an explicit numerical algorithm based on improved spectral Galerkin method for solving the unsteady diffusion-convection-reaction equation. The principal characteristics of this approach give the explicit eigenvalues and eigenvectors based on the time-space separation method and boundary condition analysis. With the help of Fourier series and Galerkin truncation, we can obtain the finite-dimensional ordinary differential equations which facilitate the system analysis and controller design. By comparing with the finite element method, the numerical solutions are demonstrated via two examples. It is shown that the proposed method is effective.

A Comparison of Raman Spectral Features of Frozen and Deparaffinized Tissues in Neuroblastoma and Ganglioneuroma

NASA Astrophysics Data System (ADS)

Devpura, Suneetha; Thakur, Jagdish S.; Poulik, Janet M.; Rabah, Raja; Naik, Vaman M.; Naik, Ratna

2012-02-01

We have investigated the cellular regions in neuroblastoma and ganglioneuroma using Raman spectroscopy and compared their spectral characteristics with those of normal adrenal gland. Thin sections from both frozen and deparaffinized tissues, obtained from the same tissue specimen, were studied in conjunction with the pathological examination of the tissues. We found a significant difference in the spectral features of frozen sections of normal adrenal gland, neuroblastoma, and ganglioneuroma when compared to deparaffinized tissues. The quantitative analysis of the Raman data using chemometric methods of principal component analysis and discriminant function analysis obtained from the frozen tissues show a sensitivity and specificity of 100% each. The biochemical identification based on the spectral differences shows that the normal adrenal gland tissues have higher levels of carotenoids, lipids, and cholesterol compared to the neuroblastoma and ganglioneuroma frozen tissues. However, deparaffinized tissues show complete removal of these biochemicals in adrenal tissues. This study demonstrates that Raman spectroscopy combined with chemometric methods can successfully distinguish neuroblastoma and ganglioneuroma at cellular level.

(abstract) Cross with Your Spectra? Cross-Correlate Instead!

NASA Technical Reports Server (NTRS)

Beer, Reinhard

1994-01-01

The use of cross-correlation for certain types of spectral analysis is discussed. Under certain circumstances, the use of cross-correlation between a real spectrum and either a model or another spectrum can provide a very powerful tool for spectral analysis. The method (and its limitations) will be described with concrete examples using ATMOS data.

[Rapid determination of major and trace elements in the salt lake clay minerals by X-ray fluorescence spectrometry].

PubMed

Wang, Xiao-Huan; Meng, Qing-Fen; Dong, Ya-Ping; Chen, Mei-Da; Li, Wu

2010-03-01

A rapid multi-element analysis method for clay mineral samples was described. This method utilized a polarized wave-length dispersive X-ray fluorescence spectrometer--Axios PW4400, which had a maximum tube power of 4 000 watts. The method was developed for the determination of As, Mn, Co, Cu, Cr, Dy, Ga, Mo, P, Pb, Rb, S, Sr, Ni, ,Cs, Ta, Th, Ti, U, V, Y, Zn, Zr, MgO, K2O, Na2O, CaO, Fe2O3, Al2O3, SiO2 and so on. Thirty elements in clay mineral species were measured by X-ray fluorescence spectrometry with pressed powder pellets. Spectral interferences, in particular the indirect interferences of each element, were studied. A method to distinguish the interference between each other periodic elements in element periodic table was put forward. The measuring conditions and existence were mainly investigated, and the selected background position as well as corrected spectral overlap for the trace elements were also discussed. It was found that the indirect spectral overlap line was the same important as direct spectral overlap line. Due to inducing the effect of indirect spectral overlap, some elements jlike Bi, Sn, W which do not need analysis were also added to the elements channel. The relative standard deviation (RSD) was in the range of 0.01% to 5.45% except three elements Mo, Cs and Ta. The detection limits, precisions and accuracies for most elements using this method can meet the requirements of sample analysis in clay mineral species.

Spectroscopy by joint spectral and time domain optical coherence tomography

NASA Astrophysics Data System (ADS)

Szkulmowski, Maciej; Tamborski, Szymon; Wojtkowski, Maciej

2015-03-01

We present the methodology for spectroscopic examination of absorbing media being the combination of Spectral Optical Coherence Tomography and Fourier Transform Spectroscopy. The method bases on the joint Spectral and Time OCT computational scheme and simplifies data analysis procedure as compared to the mostly used windowing-based Spectroscopic OCT methods. The proposed experimental setup is self-calibrating in terms of wavelength-pixel assignment. The performance of the method in measuring absorption spectrum was checked with the use of the reflecting phantom filled with the absorbing agent (indocyanine green). The results show quantitative accordance with the controlled exact results provided by the reference method.

Investigation of computational and spectral analysis methods for aeroacoustic wave propagation

NASA Technical Reports Server (NTRS)

Vanel, Florence O.

1995-01-01

Most computational fluid dynamics (CFD) schemes are not adequately accurate for solving aeroacoustics problems, which have wave amplitudes several orders of magnitude smaller yet with frequencies larger than the flow field variations generating the sound. Hence, a computational aeroacoustics (CAA) algorithm should have minimal dispersion and dissipation features. A dispersion relation preserving (DRP) scheme is, therefore, applied to solve the linearized Euler equations in order to simulate the propagation of three types of waves, namely: acoustic, vorticity, and entropy waves. The scheme is derived using an optimization procedure to ensure that the numerical derivatives preserve the wave number and angular frequency of the partial differential equations being discretized. Consequently, simulated waves propagate with the correct wave speeds and exhibit their appropriate properties. A set of radiation and outflow boundary conditions, compatible with the DRP scheme and derived from the asymptotic solutions of the governing equations, are also implemented. Numerical simulations are performed to test the effectiveness of the DRP scheme and its boundary conditions. The computed solutions are shown to agree favorably with the exact solutions. The major restriction appears to be that the dispersion relations can be preserved only for waves with wave lengths longer than four or five spacings. The boundary conditions are found to be transparent to the outgoing disturbances. However, when the disturbance source is placed closer to a boundary, small acoustic reflections start appearing. CAA generates enormous amounts of temporal data which needs to be reduced to understand the physical problem being simulated. Spectral analysis is one approach that helps us in extracting information which often can not be easily interpreted in the time domain. Thus, three different methods for the spectral analysis of numerically generated aeroacoustic data are studied. First, the capabilities of two traditional methods for spectral analysis, namely, the Blackman-Tukey method and periodogram method, are compared in estimating the spectra of a simple-periodic process. The periodogram is then applied to analyze transitory-deterministic processes. Finally, these two methods are compared with a more recent method, referred as the Weighted-Overlapped-Segment-Averaging (WOSA) method, in estimating the spectra of a chaotic (random-like) process. From the demonstrative case for the spectral analyses of data generated by simple-periodic process, the periodogram method is found to give a better estimate of the steep-sloped spectra than the Blackman-Tukey method. Also, for this problem, the Hanning window is found to perform better with the periodogram method than with the Blackman-Tukey method. Finally, for the spectral analysis of data generated by the chaotic process, the periodogram method does not perform well, whereas, the WOSA and Blackman-Tukey methods give equivalently good results.

Novel Multidimensional Cross-Correlation Data Comparison Techniques for Spectroscopic Discernment in a Volumetrically Sensitive, Moderating Type Neutron Spectrometer

NASA Astrophysics Data System (ADS)

Hoshor, Cory; Young, Stephan; Rogers, Brent; Currie, James; Oakes, Thomas; Scott, Paul; Miller, William; Caruso, Anthony

2014-03-01

A novel application of the Pearson Cross-Correlation to neutron spectral discernment in a moderating type neutron spectrometer is introduced. This cross-correlation analysis will be applied to spectral response data collected through both MCNP simulation and empirical measurement by the volumetrically sensitive spectrometer for comparison in 1, 2, and 3 spatial dimensions. The spectroscopic analysis methods discussed will be demonstrated to discern various common spectral and monoenergetic neutron sources.

Wetlands delineation by spectral signature analysis and legal implications

NASA Technical Reports Server (NTRS)

Anderon, R. R.; Carter, V.

1972-01-01

High altitude analysis of wetland resources and the use of such information in an operational mode to address specific problems of wetland preservation at a state level are discussed. Work efforts were directed toward: (1) developing techniques for using large scale color IR photography in state wetlands mapping program, (2) developing methods for obtaining wetlands ecology information from high altitude photography, (3) developing means by which spectral data can be more accurately analyzed visually, and (4) developing spectral data for automatic mapping of wetlands.

Recent Applications of Higher-Order Spectral Analysis to Nonlinear Aeroelastic Phenomena

NASA Technical Reports Server (NTRS)

Silva, Walter A.; Hajj, Muhammad R.; Dunn, Shane; Strganac, Thomas W.; Powers, Edward J.; Stearman, Ronald

2005-01-01

Recent applications of higher-order spectral (HOS) methods to nonlinear aeroelastic phenomena are presented. Applications include the analysis of data from a simulated nonlinear pitch and plunge apparatus and from F-18 flight flutter tests. A MATLAB model of the Texas A&MUniversity s Nonlinear Aeroelastic Testbed Apparatus (NATA) is used to generate aeroelastic transients at various conditions including limit cycle oscillations (LCO). The Gaussian or non-Gaussian nature of the transients is investigated, related to HOS methods, and used to identify levels of increasing nonlinear aeroelastic response. Royal Australian Air Force (RAAF) F/A-18 flight flutter test data is presented and analyzed. The data includes high-quality measurements of forced responses and LCO phenomena. Standard power spectral density (PSD) techniques and HOS methods are applied to the data and presented. The goal of this research is to develop methods that can identify the onset of nonlinear aeroelastic phenomena, such as LCO, during flutter testing.

Harmonic component detection: Optimized Spectral Kurtosis for operational modal analysis

NASA Astrophysics Data System (ADS)

Dion, J.-L.; Tawfiq, I.; Chevallier, G.

2012-01-01

This work is a contribution in the field of Operational Modal Analysis to identify the modal parameters of mechanical structures using only measured responses. The study deals with structural responses coupled with harmonic components amplitude and frequency modulated in a short range, a common combination for mechanical systems with engines and other rotating machines in operation. These harmonic components generate misleading data interpreted erroneously by the classical methods used in OMA. The present work attempts to differentiate maxima in spectra stemming from harmonic components and structural modes. The detection method proposed is based on the so-called Optimized Spectral Kurtosis and compared with others definitions of Spectral Kurtosis described in the literature. After a parametric study of the method, a critical study is performed on numerical simulations and then on an experimental structure in operation in order to assess the method's performance.

Application of principal component regression and partial least squares regression in ultraviolet spectrum water quality detection

NASA Astrophysics Data System (ADS)

Li, Jiangtong; Luo, Yongdao; Dai, Honglin

2018-01-01

Water is the source of life and the essential foundation of all life. With the development of industrialization, the phenomenon of water pollution is becoming more and more frequent, which directly affects the survival and development of human. Water quality detection is one of the necessary measures to protect water resources. Ultraviolet (UV) spectral analysis is an important research method in the field of water quality detection, which partial least squares regression (PLSR) analysis method is becoming predominant technology, however, in some special cases, PLSR's analysis produce considerable errors. In order to solve this problem, the traditional principal component regression (PCR) analysis method was improved by using the principle of PLSR in this paper. The experimental results show that for some special experimental data set, improved PCR analysis method performance is better than PLSR. The PCR and PLSR is the focus of this paper. Firstly, the principal component analysis (PCA) is performed by MATLAB to reduce the dimensionality of the spectral data; on the basis of a large number of experiments, the optimized principal component is extracted by using the principle of PLSR, which carries most of the original data information. Secondly, the linear regression analysis of the principal component is carried out with statistic package for social science (SPSS), which the coefficients and relations of principal components can be obtained. Finally, calculating a same water spectral data set by PLSR and improved PCR, analyzing and comparing two results, improved PCR and PLSR is similar for most data, but improved PCR is better than PLSR for data near the detection limit. Both PLSR and improved PCR can be used in Ultraviolet spectral analysis of water, but for data near the detection limit, improved PCR's result better than PLSR.

Model-free methods to study membrane environmental probes: a comparison of the spectral phasor and generalized polarization approaches

PubMed Central

Malacrida, Leonel; Gratton, Enrico; Jameson, David M

2016-01-01

In this note, we present a discussion of the advantages and scope of model-free analysis methods applied to the popular solvatochromic probe LAURDAN, which is widely used as an environmental probe to study dynamics and structure in membranes. In particular, we compare and contrast the generalized polarization approach with the spectral phasor approach. To illustrate our points we utilize several model membrane systems containing pure lipid phases and, in some cases, cholesterol or surfactants. We demonstrate that the spectral phasor method offers definitive advantages in the case of complex systems. PMID:27182438

Multiband tissue classification for ultrasonic transmission tomography using spectral profile detection

NASA Astrophysics Data System (ADS)

Jeong, Jeong-Won; Kim, Tae-Seong; Shin, Dae-Chul; Do, Synho; Marmarelis, Vasilis Z.

2004-04-01

Recently it was shown that soft tissue can be differentiated with spectral unmixing and detection methods that utilize multi-band information obtained from a High-Resolution Ultrasonic Transmission Tomography (HUTT) system. In this study, we focus on tissue differentiation using the spectral target detection method based on Constrained Energy Minimization (CEM). We have developed a new tissue differentiation method called "CEM filter bank". Statistical inference on the output of each CEM filter of a filter bank is used to make a decision based on the maximum statistical significance rather than the magnitude of each CEM filter output. We validate this method through 3-D inter/intra-phantom soft tissue classification where target profiles obtained from an arbitrary single slice are used for differentiation in multiple tomographic slices. Also spectral coherence between target and object profiles of an identical tissue at different slices and phantoms is evaluated by conventional cross-correlation analysis. The performance of the proposed classifier is assessed using Receiver Operating Characteristic (ROC) analysis. Finally we apply our method to classify tiny structures inside a beef kidney such as Styrofoam balls (~1mm), chicken tissue (~5mm), and vessel-duct structures.

Non-Invasive Survey of Old Paintings Using Vnir Hyperspectral Sensor

NASA Astrophysics Data System (ADS)

Matouskova, E.; Pavelka, K.; Svadlenkova, Z.

2013-07-01

Hyperspectral imaging is relatively new method developed primarily for army applications with respect to detection of possible chemical weapon existence and as an efficient assistant for a geological survey. The method is based on recording spectral profile for many hundreds of narrow spectral band. The technique gives full spectral curve of explored pixel which is an unparalleled signature of pixels material. Spectral signatures can then be compared with pre-defined spectral libraries or they can be created with respect to application. A new project named "New Modern Methods of Non-invasive Survey of Historical Site Objects" started at CTU in Prague with the New Year. The project is designed for 4 years and is funded by the Ministry of Culture in the Czech Republic. It is focused on material and chemical composition, damage diagnostics, condition description of paintings, images, construction components and whole structure object analysis in cultural heritage domain. This paper shows first results of the project on painting documentation field as well as used instrument. Hyperspec VNIR by Headwall Photonics was used for this analysis. It operates in the spectral range between 400 and 1000 nm. Comparison with infrared photography is discussed. The goal of this contribution is a non-destructive deep exploration of specific paintings. Two original 17th century paintings by Flemish authors Thomas van Apshoven ("On the Road") and David Teniers the Younger ("The Interior of a Mill") were chosen for the first analysis with a kind permission of academic painter Mr. M. Martan. Both paintings oil painted on wooden panel. This combination was chosen because of the possibility of underdrawing visualization which is supposed to be the most uncomplicated painting combination for this type of analysis.

Discriminant Analysis of Time Series in the Presence of Within-Group Spectral Variability.

PubMed

Krafty, Robert T

2016-07-01

Many studies record replicated time series epochs from different groups with the goal of using frequency domain properties to discriminate between the groups. In many applications, there exists variation in cyclical patterns from time series in the same group. Although a number of frequency domain methods for the discriminant analysis of time series have been explored, there is a dearth of models and methods that account for within-group spectral variability. This article proposes a model for groups of time series in which transfer functions are modeled as stochastic variables that can account for both between-group and within-group differences in spectra that are identified from individual replicates. An ensuing discriminant analysis of stochastic cepstra under this model is developed to obtain parsimonious measures of relative power that optimally separate groups in the presence of within-group spectral variability. The approach possess favorable properties in classifying new observations and can be consistently estimated through a simple discriminant analysis of a finite number of estimated cepstral coefficients. Benefits in accounting for within-group spectral variability are empirically illustrated in a simulation study and through an analysis of gait variability.

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

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

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

[Rapid assessment of critical quality attributes of Chinese materia medica (II): strategy of NIR assignment].

PubMed

Pei, Yan-Ling; Wu, Zhi-Sheng; Shi, Xin-Yuan; Zhou, Lu-Wei; Qiao, Yan-Jiang

2014-09-01

The present paper firstly reviewed the research progress and main methods of NIR spectral assignment coupled with our research results. Principal component analysis was focused on characteristic signal extraction to reflect spectral differences. Partial least squares method was concerned with variable selection to discover characteristic absorption band. Two-dimensional correlation spectroscopy was mainly adopted for spectral assignment. Autocorrelation peaks were obtained from spectral changes, which were disturbed by external factors, such as concentration, temperature and pressure. Density functional theory was used to calculate energy from substance structure to establish the relationship between molecular energy and spectra change. Based on the above reviewed method, taking a NIR spectral assignment of chlorogenic acid as example, a reliable spectral assignment for critical quality attributes of Chinese materia medica (CMM) was established using deuterium technology and spectral variable selection. The result demonstrated the assignment consistency according to spectral features of different concentrations of chlorogenic acid and variable selection region of online NIR model in extract process. Although spectral assignment was initial using an active pharmaceutical ingredient, it is meaningful to look forward to the futurity of the complex components in CMM. Therefore, it provided methodology for NIR spectral assignment of critical quality attributes in CMM.

From ultraviolet to Prussian blue: a spectral response for the cyanotype process and a safe educational activity to explain UV exposure for all ages.

PubMed

Turner, J; Parisi, A V; Downs, N; Lynch, M

2014-12-01

Engaging students and the public in understanding UV radiation and its effects is achievable using the real time experiment that incorporates blueprint paper, an "educational toy" that is a safe and easy demonstration of the cyanotype chemical process. The cyanotype process works through the presence of UV radiation. The blueprint paper was investigated to be used as not only engagement in discussion for public outreach about UV radiation, but also as a practical way to introduce the exploration of measurement of UV radiation exposure and as a consequence, digital image analysis. Tests of print methods and experiments, dose response, spectral response and dark response were investigated. Two methods of image analysis for dose response calculation are provided using easy to access software and two methods of pixel count analysis were used to determine spectral response characteristics. Variation in manufacture of the blueprint paper product indicates some variance between measurements. Most importantly, as a result of this investigation, a preliminary spectral response range for the radiation required to produce the cyanotype reaction is presented here, which has until now been unknown.

Quantitative method to assess caries via fluorescence imaging from the perspective of autofluorescence spectral analysis

NASA Astrophysics Data System (ADS)

Chen, Q. G.; Zhu, H. H.; Xu, Y.; Lin, B.; Chen, H.

2015-08-01

A quantitative method to discriminate caries lesions for a fluorescence imaging system is proposed in this paper. The autofluorescence spectral investigation of 39 teeth samples classified by the International Caries Detection and Assessment System levels was performed at 405 nm excitation. The major differences in the different caries lesions focused on the relative spectral intensity range of 565-750 nm. The spectral parameter, defined as the ratio of wavebands at 565-750 nm to the whole spectral range, was calculated. The image component ratio R/(G + B) of color components was statistically computed by considering the spectral parameters (e.g. autofluorescence, optical filter, and spectral sensitivity) in our fluorescence color imaging system. Results showed that the spectral parameter and image component ratio presented a linear relation. Therefore, the image component ratio was graded as <0.66, 0.66-1.06, 1.06-1.62, and >1.62 to quantitatively classify sound, early decay, established decay, and severe decay tissues, respectively. Finally, the fluorescence images of caries were experimentally obtained, and the corresponding image component ratio distribution was compared with the classification result. A method to determine the numerical grades of caries using a fluorescence imaging system was proposed. This method can be applied to similar imaging systems.

Effect of genetic algorithm as a variable selection method on different chemometric models applied for the analysis of binary mixture of amoxicillin and flucloxacillin: A comparative study

NASA Astrophysics Data System (ADS)

Attia, Khalid A. M.; Nassar, Mohammed W. I.; El-Zeiny, Mohamed B.; Serag, Ahmed

2016-03-01

Different chemometric models were applied for the quantitative analysis of amoxicillin (AMX), and flucloxacillin (FLX) in their binary mixtures, namely, partial least squares (PLS), spectral residual augmented classical least squares (SRACLS), concentration residual augmented classical least squares (CRACLS) and artificial neural networks (ANNs). All methods were applied with and without variable selection procedure (genetic algorithm GA). The methods were used for the quantitative analysis of the drugs in laboratory prepared mixtures and real market sample via handling the UV spectral data. Robust and simpler models were obtained by applying GA. The proposed methods were found to be rapid, simple and required no preliminary separation steps.

Application of the Spectral Element Method to Acoustic Radiation

NASA Technical Reports Server (NTRS)

Doyle, James F.; Rizzi, Stephen A. (Technical Monitor)

2000-01-01

This report summarizes research to develop a capability for analysis of interior noise in enclosed structures when acoustically excited by an external random source. Of particular interest was the application to the study of noise and vibration transmission in thin-walled structures as typified by aircraft fuselages. Three related topics are focused upon. The first concerns the development of a curved frame spectral element, the second shows how the spectral element method for wave propagation in folded plate structures is extended to problems involving curved segmented plates. These are of significance because by combining these curved spectral elements with previously presented flat spectral elements, the dynamic response of geometrically complex structures can be determined. The third topic shows how spectral elements, which incorporate the effect of fluid loading on the structure, are developed for analyzing acoustic radiation from dynamically loaded extended plates.

Application of spectral methods for high-frequency financial data to quantifying states of market participants

NASA Astrophysics Data System (ADS)

Sato, Aki-Hiro

2008-06-01

Empirical analysis of the foreign exchange market is conducted based on methods to quantify similarities among multi-dimensional time series with spectral distances introduced in [A.-H. Sato, Physica A 382 (2007) 258-270]. As a result it is found that the similarities among currency pairs fluctuate with the rotation of the earth, and that the similarities among best quotation rates are associated with those among quotation frequencies. Furthermore, it is shown that the Jensen-Shannon spectral divergence is proportional to a mean of the Kullback-Leibler spectral distance both empirically and numerically. It is confirmed that these spectral distances are connected with distributions for behavioural parameters of the market participants from numerical simulation. This concludes that spectral distances of representative quantities of financial markets are related into diversification of behavioural parameters of the market participants.

Multi-Centrality Graph Spectral Decompositions and Their Application to Cyber Intrusion Detection

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

Chen, Pin-Yu; Choudhury, Sutanay; Hero, Alfred

Many modern datasets can be represented as graphs and hence spectral decompositions such as graph principal component analysis (PCA) can be useful. Distinct from previous graph decomposition approaches based on subspace projection of a single topological feature, e.g., the centered graph adjacency matrix (graph Laplacian), we propose spectral decomposition approaches to graph PCA and graph dictionary learning that integrate multiple features, including graph walk statistics, centrality measures and graph distances to reference nodes. In this paper we propose a new PCA method for single graph analysis, called multi-centrality graph PCA (MC-GPCA), and a new dictionary learning method for ensembles ofmore » graphs, called multi-centrality graph dictionary learning (MC-GDL), both based on spectral decomposition of multi-centrality matrices. As an application to cyber intrusion detection, MC-GPCA can be an effective indicator of anomalous connectivity pattern and MC-GDL can provide discriminative basis for attack classification.« less

Using spectral imaging for the analysis of abnormalities for colorectal cancer: When is it helpful?

PubMed

Awan, Ruqayya; Al-Maadeed, Somaya; Al-Saady, Rafif

2018-01-01

The spectral imaging technique has been shown to provide more discriminative information than the RGB images and has been proposed for a range of problems. There are many studies demonstrating its potential for the analysis of histopathology images for abnormality detection but there have been discrepancies among previous studies as well. Many multispectral based methods have been proposed for histopathology images but the significance of the use of whole multispectral cube versus a subset of bands or a single band is still arguable. We performed comprehensive analysis using individual bands and different subsets of bands to determine the effectiveness of spectral information for determining the anomaly in colorectal images. Our multispectral colorectal dataset consists of four classes, each represented by infra-red spectrum bands in addition to the visual spectrum bands. We performed our analysis of spectral imaging by stratifying the abnormalities using both spatial and spectral information. For our experiments, we used a combination of texture descriptors with an ensemble classification approach that performed best on our dataset. We applied our method to another dataset and got comparable results with those obtained using the state-of-the-art method and convolutional neural network based method. Moreover, we explored the relationship of the number of bands with the problem complexity and found that higher number of bands is required for a complex task to achieve improved performance. Our results demonstrate a synergy between infra-red and visual spectrum by improving the classification accuracy (by 6%) on incorporating the infra-red representation. We also highlight the importance of how the dataset should be divided into training and testing set for evaluating the histopathology image-based approaches, which has not been considered in previous studies on multispectral histopathology images.

Using spectral imaging for the analysis of abnormalities for colorectal cancer: When is it helpful?

PubMed Central

Al-Maadeed, Somaya; Al-Saady, Rafif

2018-01-01

The spectral imaging technique has been shown to provide more discriminative information than the RGB images and has been proposed for a range of problems. There are many studies demonstrating its potential for the analysis of histopathology images for abnormality detection but there have been discrepancies among previous studies as well. Many multispectral based methods have been proposed for histopathology images but the significance of the use of whole multispectral cube versus a subset of bands or a single band is still arguable. We performed comprehensive analysis using individual bands and different subsets of bands to determine the effectiveness of spectral information for determining the anomaly in colorectal images. Our multispectral colorectal dataset consists of four classes, each represented by infra-red spectrum bands in addition to the visual spectrum bands. We performed our analysis of spectral imaging by stratifying the abnormalities using both spatial and spectral information. For our experiments, we used a combination of texture descriptors with an ensemble classification approach that performed best on our dataset. We applied our method to another dataset and got comparable results with those obtained using the state-of-the-art method and convolutional neural network based method. Moreover, we explored the relationship of the number of bands with the problem complexity and found that higher number of bands is required for a complex task to achieve improved performance. Our results demonstrate a synergy between infra-red and visual spectrum by improving the classification accuracy (by 6%) on incorporating the infra-red representation. We also highlight the importance of how the dataset should be divided into training and testing set for evaluating the histopathology image-based approaches, which has not been considered in previous studies on multispectral histopathology images. PMID:29874262

Water quality parameter measurement using spectral signatures

NASA Technical Reports Server (NTRS)

White, P. E.

1973-01-01

Regression analysis is applied to the problem of measuring water quality parameters from remote sensing spectral signature data. The equations necessary to perform regression analysis are presented and methods of testing the strength and reliability of a regression are described. An efficient algorithm for selecting an optimal subset of the independent variables available for a regression is also presented.

An investigation of the 'Overlap' between the Statistical-Discrete-Gust and the Power-Spectral-Density analysis methods

NASA Technical Reports Server (NTRS)

Perry, Boyd, III; Pototzky, Anthony S.; Woods, Jessica A.

1989-01-01

This paper presents the results of a NASA investigation of a claimed 'Overlap' between two gust response analysis methods: the Statistical Discrete Gust (SDG) method and the Power Spectral Density (PSD) method. The claim is that the ratio of an SDG response to the corresponding PSD response is 10.4. Analytical results presented in this paper for several different airplanes at several different flight conditions indicate that such an 'Overlap' does appear to exist. However, the claim was not met precisely: a scatter of up to about 10 percent about the 10.4 factor can be expected.

Spectral reconstruction analysis for enhancing signal-to-noise in time-resolved spectroscopies

NASA Astrophysics Data System (ADS)

Wilhelm, Michael J.; Smith, Jonathan M.; Dai, Hai-Lung

2015-09-01

We demonstrate a new spectral analysis for the enhancement of the signal-to-noise ratio (SNR) in time-resolved spectroscopies. Unlike the simple linear average which produces a single representative spectrum with enhanced SNR, this Spectral Reconstruction analysis (SRa) improves the SNR (by a factor of ca. 0 . 6 √{ n } ) for all n experimentally recorded time-resolved spectra. SRa operates by eliminating noise in the temporal domain, thereby attenuating noise in the spectral domain, as follows: Temporal profiles at each measured frequency are fit to a generic mathematical function that best represents the temporal evolution; spectra at each time are then reconstructed with data points from the fitted profiles. The SRa method is validated with simulated control spectral data sets. Finally, we apply SRa to two distinct experimentally measured sets of time-resolved IR emission spectra: (1) UV photolysis of carbonyl cyanide and (2) UV photolysis of vinyl cyanide.

Comparative study of human blood Raman spectra and biochemical analysis of patients with cancer

NASA Astrophysics Data System (ADS)

Shamina, Lyudmila A.; Bratchenko, Ivan A.; Artemyev, Dmitry N.; Myakinin, Oleg O.; Moryatov, Alexander A.; Orlov, Andrey E.; Kozlov, Sergey V.; Zakharov, Valery P.

2018-04-01

In this study we measured spectral features of blood by Raman spectroscopy. Correlation of the obtained spectral data and biochemical studies results is investigated. Analysis of specific spectra allows for identification of informative spectral bands proportional to components whose content is associated with body fluids homeostasis changes at various pathological conditions. Regression analysis of the obtained spectral data allows for discriminating the lung cancer from other tumors with a posteriori probability of 88.3%. The potentiality of applying surface-enhanced Raman spectroscopy with utilized experimental setup for further studies of the body fluids component composition was estimated. The greatest signal amplification was achieved for the gold substrate with a surface roughness of 1 μm. In general, the developed approach of body fluids analysis provides the basis of a useful and minimally invasive method of pathologies screening.

Analysis of multi-layered films. [determining dye densities by applying a regression analysis to the spectral response of the composite transparency

NASA Technical Reports Server (NTRS)

Scarpace, F. L.; Voss, A. W.

1973-01-01

Dye densities of multi-layered films are determined by applying a regression analysis to the spectral response of the composite transparency. The amount of dye in each layer is determined by fitting the sum of the individual dye layer densities to the measured dye densities. From this, dye content constants are calculated. Methods of calculating equivalent exposures are discussed. Equivalent exposures are a constant amount of energy over a limited band-width that will give the same dye content constants as the real incident energy. Methods of using these equivalent exposures for analysis of photographic data are presented.

Multi range spectral feature fitting for hyperspectral imagery in extracting oilseed rape planting area

NASA Astrophysics Data System (ADS)

Pan, Zhuokun; Huang, Jingfeng; Wang, Fumin

2013-12-01

Spectral feature fitting (SFF) is a commonly used strategy for hyperspectral imagery analysis to discriminate ground targets. Compared to other image analysis techniques, SFF does not secure higher accuracy in extracting image information in all circumstances. Multi range spectral feature fitting (MRSFF) from ENVI software allows user to focus on those interesting spectral features to yield better performance. Thus spectral wavelength ranges and their corresponding weights must be determined. The purpose of this article is to demonstrate the performance of MRSFF in oilseed rape planting area extraction. A practical method for defining the weighted values, the variance coefficient weight method, was proposed to set up criterion. Oilseed rape field canopy spectra from the whole growth stage were collected prior to investigating its phenological varieties; oilseed rape endmember spectra were extracted from the Hyperion image as identifying samples to be used in analyzing the oilseed rape field. Wavelength range divisions were determined by the difference between field-measured spectra and image spectra, and image spectral variance coefficient weights for each wavelength range were calculated corresponding to field-measured spectra from the closest date. By using MRSFF, wavelength ranges were classified to characterize the target's spectral features without compromising spectral profile's entirety. The analysis was substantially successful in extracting oilseed rape planting areas (RMSE ≤ 0.06), and the RMSE histogram indicated a superior result compared to a conventional SFF. Accuracy assessment was based on the mapping result compared with spectral angle mapping (SAM) and the normalized difference vegetation index (NDVI). The MRSFF yielded a robust, convincible result and, therefore, may further the use of hyperspectral imagery in precision agriculture.

Use of spectral analysis with iterative filter for voxelwise determination of regional rates of cerebral protein synthesis with L-[1-11C]leucine PET.

PubMed

Veronese, Mattia; Schmidt, Kathleen C; Smith, Carolyn Beebe; Bertoldo, Alessandra

2012-06-01

A spectral analysis approach was used to estimate kinetic parameters of the L-[1-(11)C]leucine positron emission tomography (PET) method and regional rates of cerebral protein synthesis (rCPS) on a voxel-by-voxel basis. Spectral analysis applies to both heterogeneous and homogeneous tissues; it does not require prior assumptions concerning number of tissue compartments. Parameters estimated with spectral analysis can be strongly affected by noise, but numerical filters improve estimation performance. Spectral analysis with iterative filter (SAIF) was originally developed to improve estimation of leucine kinetic parameters and rCPS in region-of-interest (ROI) data analyses. In the present study, we optimized SAIF for application at the voxel level. In measured L-[1-(11)C]leucine PET data, voxel-level SAIF parameter estimates averaged over all voxels within a ROI (mean voxel-SAIF) generally agreed well with corresponding estimates derived by applying the originally developed SAIF to ROI time-activity curves (ROI-SAIF). Region-of-interest-SAIF and mean voxel-SAIF estimates of rCPS were highly correlated. Simulations showed that mean voxel-SAIF rCPS estimates were less biased and less variable than ROI-SAIF estimates in the whole brain and cortex; biases were similar in white matter. We conclude that estimation of rCPS with SAIF is improved when the method is applied at voxel level than in ROI analysis.

[Estimation of Hunan forest carbon density based on spectral mixture analysis of MODIS data].

PubMed

Yan, En-ping; Lin, Hui; Wang, Guang-xing; Chen, Zhen-xiong

2015-11-01

With the fast development of remote sensing technology, combining forest inventory sample plot data and remotely sensed images has become a widely used method to map forest carbon density. However, the existence of mixed pixels often impedes the improvement of forest carbon density mapping, especially when low spatial resolution images such as MODIS are used. In this study, MODIS images and national forest inventory sample plot data were used to conduct the study of estimation for forest carbon density. Linear spectral mixture analysis with and without constraint, and nonlinear spectral mixture analysis were compared to derive the fractions of different land use and land cover (LULC) types. Then sequential Gaussian co-simulation algorithm with and without the fraction images from spectral mixture analyses were employed to estimate forest carbon density of Hunan Province. Results showed that 1) Linear spectral mixture analysis with constraint, leading to a mean RMSE of 0.002, more accurately estimated the fractions of LULC types than linear spectral and nonlinear spectral mixture analyses; 2) Integrating spectral mixture analysis model and sequential Gaussian co-simulation algorithm increased the estimation accuracy of forest carbon density to 81.5% from 74.1%, and decreased the RMSE to 5.18 from 7.26; and 3) The mean value of forest carbon density for the province was 30.06 t · hm(-2), ranging from 0.00 to 67.35 t · hm(-2). This implied that the spectral mixture analysis provided a great potential to increase the estimation accuracy of forest carbon density on regional and global level.

Different techniques of multispectral data analysis for vegetation fraction retrieval

NASA Astrophysics Data System (ADS)

Kancheva, Rumiana; Georgiev, Georgi

2012-07-01

Vegetation monitoring is one of the most important applications of remote sensing technologies. In respect to farmlands, the assessment of crop condition constitutes the basis of growth, development, and yield processes monitoring. Plant condition is defined by a set of biometric variables, such as density, height, biomass amount, leaf area index, and etc. The canopy cover fraction is closely related to these variables, and is state-indicative of the growth process. At the same time it is a defining factor of the soil-vegetation system spectral signatures. That is why spectral mixtures decomposition is a primary objective in remotely sensed data processing and interpretation, specifically in agricultural applications. The actual usefulness of the applied methods depends on their prediction reliability. The goal of this paper is to present and compare different techniques for quantitative endmember extraction from soil-crop patterns reflectance. These techniques include: linear spectral unmixing, two-dimensional spectra analysis, spectral ratio analysis (vegetation indices), spectral derivative analysis (red edge position), colorimetric analysis (tristimulus values sum, chromaticity coordinates and dominant wavelength). The objective is to reveal their potential, accuracy and robustness for plant fraction estimation from multispectral data. Regression relationships have been established between crop canopy cover and various spectral estimators.

Ventilatory thresholds determined from HRV: comparison of 2 methods in obese adolescents.

PubMed

Quinart, S; Mourot, L; Nègre, V; Simon-Rigaud, M-L; Nicolet-Guénat, M; Bertrand, A-M; Meneveau, N; Mougin, F

2014-03-01

The development of personalised training programmes is crucial in the management of obesity. We evaluated the ability of 2 heart rate variability analyses to determine ventilatory thresholds (VT) in obese adolescents. 20 adolescents (mean age 14.3±1.6 years and body mass index z-score 4.2±0.1) performed an incremental test to exhaustion before and after a 9-month multidisciplinary management programme. The first (VT1) and second (VT2) ventilatory thresholds were identified by the reference method (gas exchanges). We recorded RR intervals to estimate VT1 and VT2 from heart rate variability using time-domain analysis and time-varying spectral-domain analysis. The coefficient correlations between thresholds were higher with spectral-domain analysis compared to time-domain analysis: Heart rate at VT1: r=0.91 vs. =0.66 and VT2: r=0.91 vs. =0.66; power at VT1: r=0.91 vs. =0.74 and VT2: r=0.93 vs. =0.78; spectral-domain vs. time-domain analysis respectively). No systematic bias in heart rate at VT1 and VT2 with standard deviations <6 bpm were found, confirming that spectral-domain analysis could replace the reference method for the detection of ventilatory thresholds. Furthermore, this technique is sensitive to rehabilitation and re-training, which underlines its utility in clinical practice. This inexpensive and non-invasive tool is promising for prescribing physical activity programs in obese adolescents. © Georg Thieme Verlag KG Stuttgart · New York.

Topochemical Analysis of Cell Wall Components by TOF-SIMS.

PubMed

Aoki, Dan; Fukushima, Kazuhiko

2017-01-01

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) is a recently developing analytical tool and a type of imaging mass spectrometry. TOF-SIMS provides mass spectral information with a lateral resolution on the order of submicrons, with widespread applicability. Sometimes, it is described as a surface analysis method without the requirement for sample pretreatment; however, several points need to be taken into account for the complete utilization of the capabilities of TOF-SIMS. In this chapter, we introduce methods for TOF-SIMS sample treatments, as well as basic knowledge of wood samples TOF-SIMS spectral and image data analysis.

[Variable selection methods combined with local linear embedding theory used for optimization of near infrared spectral quantitative models].

PubMed

Hao, Yong; Sun, Xu-Dong; Yang, Qiang

2012-12-01

Variables selection strategy combined with local linear embedding (LLE) was introduced for the analysis of complex samples by using near infrared spectroscopy (NIRS). Three methods include Monte Carlo uninformation variable elimination (MCUVE), successive projections algorithm (SPA) and MCUVE connected with SPA were used for eliminating redundancy spectral variables. Partial least squares regression (PLSR) and LLE-PLSR were used for modeling complex samples. The results shown that MCUVE can both extract effective informative variables and improve the precision of models. Compared with PLSR models, LLE-PLSR models can achieve more accurate analysis results. MCUVE combined with LLE-PLSR is an effective modeling method for NIRS quantitative analysis.

On Holo-Hilbert Spectral Analysis: A Full Informational Spectral Representation for Nonlinear and Non-Stationary Data

NASA Technical Reports Server (NTRS)

Huang, Norden E.; Hu, Kun; Yang, Albert C. C.; Chang, Hsing-Chih; Jia, Deng; Liang, Wei-Kuang; Yeh, Jia Rong; Kao, Chu-Lan; Juan, Chi-Huang; Peng, Chung Kang;

On Holo-Hilbert spectral analysis: a full informational spectral representation for nonlinear and non-stationary data

PubMed Central

Huang, Norden E.; Hu, Kun; Yang, Albert C. C.; Chang, Hsing-Chih; Jia, Deng; Liang, Wei-Kuang; Yeh, Jia Rong; Kao, Chu-Lan; Juan, Chi-Hung; Peng, Chung Kang; Meijer, Johanna H.; Wang, Yung-Hung; Long, Steven R.; Wu, Zhauhua

2016-01-01

The Holo-Hilbert spectral analysis (HHSA) method is introduced to cure the deficiencies of traditional spectral analysis and to give a full informational representation of nonlinear and non-stationary data. It uses a nested empirical mode decomposition and Hilbert–Huang transform (HHT) approach to identify intrinsic amplitude and frequency modulations often present in nonlinear systems. Comparisons are first made with traditional spectrum analysis, which usually achieved its results through convolutional integral transforms based on additive expansions of an a priori determined basis, mostly under linear and stationary assumptions. Thus, for non-stationary processes, the best one could do historically was to use the time–frequency representations, in which the amplitude (or energy density) variation is still represented in terms of time. For nonlinear processes, the data can have both amplitude and frequency modulations (intra-mode and inter-mode) generated by two different mechanisms: linear additive or nonlinear multiplicative processes. As all existing spectral analysis methods are based on additive expansions, either a priori or adaptive, none of them could possibly represent the multiplicative processes. While the earlier adaptive HHT spectral analysis approach could accommodate the intra-wave nonlinearity quite remarkably, it remained that any inter-wave nonlinear multiplicative mechanisms that include cross-scale coupling and phase-lock modulations were left untreated. To resolve the multiplicative processes issue, additional dimensions in the spectrum result are needed to account for the variations in both the amplitude and frequency modulations simultaneously. HHSA accommodates all the processes: additive and multiplicative, intra-mode and inter-mode, stationary and non-stationary, linear and nonlinear interactions. The Holo prefix in HHSA denotes a multiple dimensional representation with both additive and multiplicative capabilities. PMID:26953180

Hyperspectral imaging coupled with chemometric analysis for non-invasive differentiation of black pens

NASA Astrophysics Data System (ADS)

Chlebda, Damian K.; Majda, Alicja; Łojewski, Tomasz; Łojewska, Joanna

2016-11-01

Differentiation of the written text can be performed with a non-invasive and non-contact tool that connects conventional imaging methods with spectroscopy. Hyperspectral imaging (HSI) is a relatively new and rapid analytical technique that can be applied in forensic science disciplines. It allows an image of the sample to be acquired, with full spectral information within every pixel. For this paper, HSI and three statistical methods (hierarchical cluster analysis, principal component analysis, and spectral angle mapper) were used to distinguish between traces of modern black gel pen inks. Non-invasiveness and high efficiency are among the unquestionable advantages of ink differentiation using HSI. It is also less time-consuming than traditional methods such as chromatography. In this study, a set of 45 modern gel pen ink marks deposited on a paper sheet were registered. The spectral characteristics embodied in every pixel were extracted from an image and analysed using statistical methods, externally and directly on the hypercube. As a result, different black gel inks deposited on paper can be distinguished and classified into several groups, in a non-invasive manner.

Automatic classification of spectral units in the Aristarchus plateau

NASA Astrophysics Data System (ADS)

Erard, S.; Le Mouelic, S.; Langevin, Y.

1999-09-01

A reduction scheme has been recently proposed for the NIR images of Clementine (Le Mouelic et al, JGR 1999). This reduction has been used to build an integrated UVvis-NIR image cube of the Aristarchus region, from which compositional and maturity variations can be studied (Pinet et al, LPSC 1999). We will present an analysis of this image cube, providing a classification in spectral types and spectral units. The image cube is processed with Gmode analysis using three different data sets: Normalized spectra provide a classification based mainly on spectral slope variations (ie. maturity and volcanic glasses). This analysis discriminates between craters plus ejecta, mare basalts, and DMD. Olivine-rich areas and Aristarchus central peak are also recognized. Continuum-removed spectra provide a classification more related to compositional variations, which correctly identifies olivine and pyroxenes-rich areas (in Aristarchus, Krieger, Schiaparelli\\ldots). A third analysis uses spectral parameters related to maturity and Fe composition (reflectance, 1 mu m band depth, and spectral slope) rather than intensities. It provides the most spatially consistent picture, but fails in detecting Vallis Schroeteri and DMDs. A supplementary unit, younger and rich in pyroxene, is found on Aristarchus south rim. In conclusion, Gmode analysis can discriminate between different spectral types already identified with more classic methods (PCA, linear mixing\\ldots). No previous assumption is made on the data structure, such as endmembers number and nature, or linear relationship between input variables. The variability of the spectral types is intrinsically accounted for, so that the level of analysis is always restricted to meaningful limits. A complete classification should integrate several analyses based on different sets of parameters. Gmode is therefore a powerful light toll to perform first look analysis of spectral imaging data. This research has been partly founded by the French Programme National de Planetologie.

Classification of Hyperspectral Data Based on Guided Filtering and Random Forest

NASA Astrophysics Data System (ADS)

Ma, H.; Feng, W.; Cao, X.; Wang, L.

2017-09-01

Hyperspectral images usually consist of more than one hundred spectral bands, which have potentials to provide rich spatial and spectral information. However, the application of hyperspectral data is still challengeable due to "the curse of dimensionality". In this context, many techniques, which aim to make full use of both the spatial and spectral information, are investigated. In order to preserve the geometrical information, meanwhile, with less spectral bands, we propose a novel method, which combines principal components analysis (PCA), guided image filtering and the random forest classifier (RF). In detail, PCA is firstly employed to reduce the dimension of spectral bands. Secondly, the guided image filtering technique is introduced to smooth land object, meanwhile preserving the edge of objects. Finally, the features are fed into RF classifier. To illustrate the effectiveness of the method, we carry out experiments over the popular Indian Pines data set, which is collected by Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor. By comparing the proposed method with the method of only using PCA or guided image filter, we find that effect of the proposed method is better.

Advances in spectroscopic methods for quantifying soil carbon

USGS Publications Warehouse

Reeves, James B.; McCarty, Gregory W.; Calderon, Francisco; Hively, W. Dean

2012-01-01

The current gold standard for soil carbon (C) determination is elemental C analysis using dry combustion. However, this method requires expensive consumables, is limited by the number of samples that can be processed (~100/d), and is restricted to the determination of total carbon. With increased interest in soil C sequestration, faster methods of analysis are needed, and there is growing interest in methods based on diffuse reflectance spectroscopy in the visible, near-infrared or mid-infrared spectral ranges. These spectral methods can decrease analytical requirements and speed sample processing, be applied to large landscape areas using remote sensing imagery, and be used to predict multiple analytes simultaneously. However, the methods require localized calibrations to establish the relationship between spectral data and reference analytical data, and also have additional, specific problems. For example, remote sensing is capable of scanning entire watersheds for soil carbon content but is limited to the surface layer of tilled soils and may require difficult and extensive field sampling to obtain proper localized calibration reference values. The objective of this chapter is to discuss the present state of spectroscopic methods for determination of soil carbon.

A study of the application of power-spectral methods of generalized harmonic analysis to gust loads on airplanes

NASA Technical Reports Server (NTRS)

Press, Harry; Mazelsky, Bernard

1954-01-01

The applicability of some results from the theory of generalized harmonic analysis (or power-spectral analysis) to the analysis of gust loads on airplanes in continuous rough air is examined. The general relations for linear systems between power spectrums of a random input disturbance and an output response are used to relate the spectrum of airplane load in rough air to the spectrum of atmospheric gust velocity. The power spectrum of loads is shown to provide a measure of the load intensity in terms of the standard deviation (root mean square) of the load distribution for an airplane in flight through continuous rough air. For the case of a load output having a normal distribution, which appears from experimental evidence to apply to homogeneous rough air, the standard deviation is shown to describe the probability distribution of loads or the proportion of total time that the load has given values. Thus, for airplane in flight through homogeneous rough air, the probability distribution of loads may be determined from a power-spectral analysis. In order to illustrate the application of power-spectral analysis to gust-load analysis and to obtain an insight into the relations between loads and airplane gust-response characteristics, two selected series of calculations are presented. The results indicate that both methods of analysis yield results that are consistent to a first approximation.

Spectral statistics of the uni-modular ensemble

NASA Astrophysics Data System (ADS)

Joyner, Christopher H.; Smilansky, Uzy; Weidenmüller, Hans A.

2017-09-01

We investigate the spectral statistics of Hermitian matrices in which the elements are chosen uniformly from U(1) , called the uni-modular ensemble (UME), in the limit of large matrix size. Using three complimentary methods; a supersymmetric integration method, a combinatorial graph-theoretical analysis and a Brownian motion approach, we are able to derive expressions for 1 / N corrections to the mean spectral moments and also analyse the fluctuations about this mean. By addressing the same ensemble from three different point of view, we can critically compare their relative advantages and derive some new results.

Research progress in Asia on methods of processing laser-induced breakdown spectroscopy data

NASA Astrophysics Data System (ADS)

Guo, Yang-Min; Guo, Lian-Bo; Li, Jia-Ming; Liu, Hong-Di; Zhu, Zhi-Hao; Li, Xiang-You; Lu, Yong-Feng; Zeng, Xiao-Yan

2016-10-01

Laser-induced breakdown spectroscopy (LIBS) has attracted much attention in terms of both scientific research and industrial application. An important branch of LIBS research in Asia, the development of data processing methods for LIBS, is reviewed. First, the basic principle of LIBS and the characteristics of spectral data are briefly introduced. Next, two aspects of research on and problems with data processing methods are described: i) the basic principles of data preprocessing methods are elaborated in detail on the basis of the characteristics of spectral data; ii) the performance of data analysis methods in qualitative and quantitative analysis of LIBS is described. Finally, a direction for future development of data processing methods for LIBS is also proposed.

Research on hyperspectral dynamic scene and image sequence simulation

NASA Astrophysics Data System (ADS)

Sun, Dandan; Gao, Jiaobo; Sun, Kefeng; Hu, Yu; Li, Yu; Xie, Junhu; Zhang, Lei

2016-10-01

This paper presents a simulation method of hyper-spectral dynamic scene and image sequence for hyper-spectral equipment evaluation and target detection algorithm. Because of high spectral resolution, strong band continuity, anti-interference and other advantages, in recent years, hyper-spectral imaging technology has been rapidly developed and is widely used in many areas such as optoelectronic target detection, military defense and remote sensing systems. Digital imaging simulation, as a crucial part of hardware in loop simulation, can be applied to testing and evaluation hyper-spectral imaging equipment with lower development cost and shorter development period. Meanwhile, visual simulation can produce a lot of original image data under various conditions for hyper-spectral image feature extraction and classification algorithm. Based on radiation physic model and material characteristic parameters this paper proposes a generation method of digital scene. By building multiple sensor models under different bands and different bandwidths, hyper-spectral scenes in visible, MWIR, LWIR band, with spectral resolution 0.01μm, 0.05μm and 0.1μm have been simulated in this paper. The final dynamic scenes have high real-time and realistic, with frequency up to 100 HZ. By means of saving all the scene gray data in the same viewpoint image sequence is obtained. The analysis results show whether in the infrared band or the visible band, the grayscale variations of simulated hyper-spectral images are consistent with the theoretical analysis results.

Spectral Discrete Probability Density Function of Measured Wind Turbine Noise in the Far Field

PubMed Central

Ashtiani, Payam; Denison, Adelaide

2015-01-01

Of interest is the spectral character of wind turbine noise at typical residential set-back distances. In this paper, a spectral statistical analysis has been applied to immission measurements conducted at three locations. This method provides discrete probability density functions for the Turbine ONLY component of the measured noise. This analysis is completed for one-third octave sound levels, at integer wind speeds, and is compared to existing metrics for measuring acoustic comfort as well as previous discussions on low-frequency noise sources. PMID:25905097

Laser-induced plasma characterization through self-absorption quantification

NASA Astrophysics Data System (ADS)

Hou, JiaJia; Zhang, Lei; Zhao, Yang; Yan, Xingyu; Ma, Weiguang; Dong, Lei; Yin, Wangbao; Xiao, Liantuan; Jia, Suotang

2018-07-01

A self-absorption quantification method is proposed to quantify the self-absorption degree of spectral lines, in which plasma characteristics including electron temperature, elemental concentration ratio, and absolute species number density can be deduced directly. Since there is no spectral intensity involved in the calculation, the analysis results are independent of the self-absorption effects and the additional spectral efficiency calibration is not required. In order to evaluate the practicality, the limitation for application and the precision of this method are also discussed. Experimental results of aluminum-lithium alloy prove that the proposed method is qualified to realize semi-quantitative measurements and fast plasma characteristics diagnostics.

Considerations on the quantitative analysis of apparent amorphicity of milled lactose by Raman spectroscopy.

PubMed

Pazesh, Samaneh; Lazorova, Lucia; Berggren, Jonas; Alderborn, Göran; Gråsjö, Johan

2016-09-10

The main purpose of the study was to evaluate various pre-processing and quantification approaches of Raman spectrum to quantify low level of amorphous content in milled lactose powder. To improve the quantification analysis, several spectral pre-processing methods were used to adjust background effects. The effects of spectral noise on the variation of determined amorphous content were also investigated theoretically by propagation of error analysis and were compared to the experimentally obtained values. Additionally, the applicability of calibration method with crystalline or amorphous domains in the estimation of amorphous content in milled lactose powder was discussed. Two straight baseline pre-processing methods gave the best and almost equal performance. By the succeeding quantification methods, PCA performed best, although the classical least square analysis (CLS) gave comparable results, while peak parameter analysis displayed to be inferior. The standard deviations of experimental determined percentage amorphous content were 0.94% and 0.25% for pure crystalline and pure amorphous samples respectively, which was very close to the standard deviation values from propagated spectral noise. The reasonable conformity between the milled samples spectra and synthesized spectra indicated representativeness of physical mixtures with crystalline or amorphous domains in the estimation of apparent amorphous content in milled lactose. Copyright © 2016 The Author(s). Published by Elsevier B.V. All rights reserved.

Information content in spectral dependencies of optical unit volume parameters under action of He-Ne laser on blood

NASA Astrophysics Data System (ADS)

Khairullina, Alphiya Y.; Oleinik, Tatiana V.

1995-01-01

Our previous works concerned with the development of methods for studying blood and action of low-intensity laser radiation on blood and erythrocyte suspensions had shown the light- scattering methods gave a large body of information on a medium studied due to the methodological relationship between irradiation processes and techniques for investigations. Detail analysis of spectral diffuse reflectivities and transmissivities of optically thick blood layers, spectral absorptivities calculated on this basis over 600 - 900 nm, by using different approximations, for a pathological state owing to hypoxia testifies to the optical significance of not only hemoglobin derivatives but also products of hemoglobin decomposition. Laser action on blood is specific and related to an initial state of blood absorption due to different composition of chromoproteids. This work gives the interpretation of spectral observations. Analysis of spectral dependencies of the exinction coefficient e, mean cosine m of phase function, and parameter Q equals (epsilon) (1-(mu) )H/(lambda) (H - hematocrit) testifies to decreasing the relative index of refraction of erythrocytes and to morphological changes during laser action under pathology owing to hypoxia. The possibility to obtain physical and chemical information on the state of blood under laser action in vivo is shown to be based on the method proposed by us for calculating multilayered structures modeling human organs and on the technical implementation of this method.

Arbitrary-order Hilbert Spectral Analysis and Intermittency in Solar Wind Density Fluctuations

NASA Astrophysics Data System (ADS)

Carbone, Francesco; Sorriso-Valvo, Luca; Alberti, Tommaso; Lepreti, Fabio; Chen, Christopher H. K.; Němeček, Zdenek; Šafránková, Jana

2018-05-01

The properties of inertial- and kinetic-range solar wind turbulence have been investigated with the arbitrary-order Hilbert spectral analysis method, applied to high-resolution density measurements. Due to the small sample size and to the presence of strong nonstationary behavior and large-scale structures, the classical analysis in terms of structure functions may prove to be unsuccessful in detecting the power-law behavior in the inertial range, and may underestimate the scaling exponents. However, the Hilbert spectral method provides an optimal estimation of the scaling exponents, which have been found to be close to those for velocity fluctuations in fully developed hydrodynamic turbulence. At smaller scales, below the proton gyroscale, the system loses its intermittent multiscaling properties and converges to a monofractal process. The resulting scaling exponents, obtained at small scales, are in good agreement with those of classical fractional Brownian motion, indicating a long-term memory in the process, and the absence of correlations around the spectral-break scale. These results provide important constraints on models of kinetic-range turbulence in the solar wind.

Corrigendum to "Enhancing endmember selection in multiple endmember spectral mixture analysis (MESMA) for urban impervious surface area mapping using spectral angle and spectral distance parameters" [Int. J. Appl. Earth Observ. Geoinf. 33 (2014) 290-301

NASA Astrophysics Data System (ADS)

Fan, Fenglei; Deng, Yingbin

2015-04-01

Since publication, the authors have been advised of one prior methodological article not cited in our original paper. The missed reference from the paper is "Andreou, C., Karathanassi, V., 2012. A novel multiple endmember spectral mixture analysis using spectral angle distance. Geoscience and Remote Sensing Symposium (IGARSS) 2012 IEEE International, 4110-4113." This reference "Andreou, C., Karathanassi, V., 2012 in proceeding of IGARSS" should be cited in the Part 3 and Table 1, whereby in the fist sentence of Part 3 and the last sentence of caption of Table 1. The following should be added (in Part 3: According to the traditional MESMA and referring to MESMA-SAD method which is reported by Andreou and Karathanassi; in caption of Table 1: Based on the MESMA-SAD method of Andreou and Karathanassi). We apologize to the authors and workers concerned for this oversight and are pleased to acknowledge their contributions.

Fusion of spectral models for dynamic modeling of sEMG and skeletal muscle force.

PubMed

Potluri, Chandrasekhar; Anugolu, Madhavi; Chiu, Steve; Urfer, Alex; Schoen, Marco P; Naidu, D Subbaram

2012-01-01

In this paper, we present a method of combining spectral models using a Kullback Information Criterion (KIC) data fusion algorithm. Surface Electromyographic (sEMG) signals and their corresponding skeletal muscle force signals are acquired from three sensors and pre-processed using a Half-Gaussian filter and a Chebyshev Type- II filter, respectively. Spectral models - Spectral Analysis (SPA), Empirical Transfer Function Estimate (ETFE), Spectral Analysis with Frequency Dependent Resolution (SPFRD) - are extracted from sEMG signals as input and skeletal muscle force as output signal. These signals are then employed in a System Identification (SI) routine to establish the dynamic models relating the input and output. After the individual models are extracted, the models are fused by a probability based KIC fusion algorithm. The results show that the SPFRD spectral models perform better than SPA and ETFE models in modeling the frequency content of the sEMG/skeletal muscle force data.

Tacholess order-tracking approach for wind turbine gearbox fault detection

NASA Astrophysics Data System (ADS)

Wang, Yi; Xie, Yong; Xu, Guanghua; Zhang, Sicong; Hou, Chenggang

2017-09-01

Monitoring of wind turbines under variable-speed operating conditions has become an important issue in recent years. The gearbox of a wind turbine is the most important transmission unit; it generally exhibits complex vibration signatures due to random variations in operating conditions. Spectral analysis is one of the main approaches in vibration signal processing. However, spectral analysis is based on a stationary assumption and thus inapplicable to the fault diagnosis of wind turbines under variable-speed operating conditions. This constraint limits the application of spectral analysis to wind turbine diagnosis in industrial applications. Although order-tracking methods have been proposed for wind turbine fault detection in recent years, current methods are only applicable to cases in which the instantaneous shaft phase is available. For wind turbines with limited structural spaces, collecting phase signals with tachometers or encoders is difficult. In this study, a tacholess order-tracking method for wind turbines is proposed to overcome the limitations of traditional techniques. The proposed method extracts the instantaneous phase from the vibration signal, resamples the signal at equiangular increments, and calculates the order spectrum for wind turbine fault identification. The effectiveness of the proposed method is experimentally validated with the vibration signals of wind turbines.

Bayesian statistics as a new tool for spectral analysis - I. Application for the determination of basic parameters of massive stars

NASA Astrophysics Data System (ADS)

Mugnes, J.-M.; Robert, C.

2015-11-01

Spectral analysis is a powerful tool to investigate stellar properties and it has been widely used for decades now. However, the methods considered to perform this kind of analysis are mostly based on iteration among a few diagnostic lines to determine the stellar parameters. While these methods are often simple and fast, they can lead to errors and large uncertainties due to the required assumptions. Here, we present a method based on Bayesian statistics to find simultaneously the best combination of effective temperature, surface gravity, projected rotational velocity, and microturbulence velocity, using all the available spectral lines. Different tests are discussed to demonstrate the strength of our method, which we apply to 54 mid-resolution spectra of field and cluster B stars obtained at the Observatoire du Mont-Mégantic. We compare our results with those found in the literature. Differences are seen which are well explained by the different methods used. We conclude that the B-star microturbulence velocities are often underestimated. We also confirm the trend that B stars in clusters are on average faster rotators than field B stars.

Proper orthogonal decomposition-based spectral higher-order stochastic estimation

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

Baars, Woutijn J., E-mail: wbaars@unimelb.edu.au; Tinney, Charles E.

A unique routine, capable of identifying both linear and higher-order coherence in multiple-input/output systems, is presented. The technique combines two well-established methods: Proper Orthogonal Decomposition (POD) and Higher-Order Spectra Analysis. The latter of these is based on known methods for characterizing nonlinear systems by way of Volterra series. In that, both linear and higher-order kernels are formed to quantify the spectral (nonlinear) transfer of energy between the system's input and output. This reduces essentially to spectral Linear Stochastic Estimation when only first-order terms are considered, and is therefore presented in the context of stochastic estimation as spectral Higher-Order Stochastic Estimationmore » (HOSE). The trade-off to seeking higher-order transfer kernels is that the increased complexity restricts the analysis to single-input/output systems. Low-dimensional (POD-based) analysis techniques are inserted to alleviate this void as POD coefficients represent the dynamics of the spatial structures (modes) of a multi-degree-of-freedom system. The mathematical framework behind this POD-based HOSE method is first described. The method is then tested in the context of jet aeroacoustics by modeling acoustically efficient large-scale instabilities as combinations of wave packets. The growth, saturation, and decay of these spatially convecting wave packets are shown to couple both linearly and nonlinearly in the near-field to produce waveforms that propagate acoustically to the far-field for different frequency combinations.« less

Wavelet packets for multi- and hyper-spectral imagery

NASA Astrophysics Data System (ADS)

Benedetto, J. J.; Czaja, W.; Ehler, M.; Flake, C.; Hirn, M.

2010-01-01

State of the art dimension reduction and classification schemes in multi- and hyper-spectral imaging rely primarily on the information contained in the spectral component. To better capture the joint spatial and spectral data distribution we combine the Wavelet Packet Transform with the linear dimension reduction method of Principal Component Analysis. Each spectral band is decomposed by means of the Wavelet Packet Transform and we consider a joint entropy across all the spectral bands as a tool to exploit the spatial information. Dimension reduction is then applied to the Wavelet Packets coefficients. We present examples of this technique for hyper-spectral satellite imaging. We also investigate the role of various shrinkage techniques to model non-linearity in our approach.

Recent advances in the modeling of plasmas with the Particle-In-Cell methods

NASA Astrophysics Data System (ADS)

Vay, Jean-Luc; Lehe, Remi; Vincenti, Henri; Godfrey, Brendan; Lee, Patrick; Haber, Irv

2015-11-01

The Particle-In-Cell (PIC) approach is the method of choice for self-consistent simulations of plasmas from first principles. The fundamentals of the PIC method were established decades ago but improvements or variations are continuously being proposed. We report on several recent advances in PIC related algorithms, including: (a) detailed analysis of the numerical Cherenkov instability and its remediation, (b) analytic pseudo-spectral electromagnetic solvers in Cartesian and cylindrical (with azimuthal modes decomposition) geometries, (c) arbitrary-order finite-difference and generalized pseudo-spectral Maxwell solvers, (d) novel analysis of Maxwell's solvers' stencil variation and truncation, in application to domain decomposition strategies and implementation of Perfectly Matched Layers in high-order and pseudo-spectral solvers. Work supported by US-DOE Contracts DE-AC02-05CH11231 and the US-DOE SciDAC program ComPASS. Used resources of NERSC, supported by US-DOE Contract DE-AC02-05CH11231.

A Comparison of Analytical and Data Preprocessing Methods for Spectral Fingerprinting

PubMed Central

LUTHRIA, DEVANAND L.; MUKHOPADHYAY, SUDARSAN; LIN, LONG-ZE; HARNLY, JAMES M.

2013-01-01

Spectral fingerprinting, as a method of discriminating between plant cultivars and growing treatments for a common set of broccoli samples, was compared for six analytical instruments. Spectra were acquired for finely powdered solid samples using Fourier transform infrared (FT-IR) and Fourier transform near-infrared (NIR) spectrometry. Spectra were also acquired for unfractionated aqueous methanol extracts of the powders using molecular absorption in the ultraviolet (UV) and visible (VIS) regions and mass spectrometry with negative (MS−) and positive (MS+) ionization. The spectra were analyzed using nested one-way analysis of variance (ANOVA) and principal component analysis (PCA) to statistically evaluate the quality of discrimination. All six methods showed statistically significant differences between the cultivars and treatments. The significance of the statistical tests was improved by the judicious selection of spectral regions (IR and NIR), masses (MS+ and MS−), and derivatives (IR, NIR, UV, and VIS). PMID:21352644

Direct measurement of group delay with joint time-frequency analysis of a white-light spectral interferogram.

PubMed

Deng, Yuqiang; Yang, Weijian; Zhou, Chun; Wang, Xi; Tao, Jun; Kong, Weipeng; Zhang, Zhigang

2008-12-01

We propose and demonstrate an analysis method to directly extract the group delay rather than the phase from the white-light spectral interferogram. By the joint time-frequency analysis technique, group delay is directly read from the ridge of wavelet transform, and group-delay dispersion is easily obtained by additional differentiation. The technique shows reasonable potential for the characterization of ultra-broadband chirped mirrors.

Computational mass spectrometry for small molecules

PubMed Central

2013-01-01

The identification of small molecules from mass spectrometry (MS) data remains a major challenge in the interpretation of MS data. This review covers the computational aspects of identifying small molecules, from the identification of a compound searching a reference spectral library, to the structural elucidation of unknowns. In detail, we describe the basic principles and pitfalls of searching mass spectral reference libraries. Determining the molecular formula of the compound can serve as a basis for subsequent structural elucidation; consequently, we cover different methods for molecular formula identification, focussing on isotope pattern analysis. We then discuss automated methods to deal with mass spectra of compounds that are not present in spectral libraries, and provide an insight into de novo analysis of fragmentation spectra using fragmentation trees. In addition, this review shortly covers the reconstruction of metabolic networks using MS data. Finally, we list available software for different steps of the analysis pipeline. PMID:23453222

New Variance-Reducing Methods for the PSD Analysis of Large Optical Surfaces

NASA Technical Reports Server (NTRS)

Sidick, Erkin

2010-01-01

Edge data of a measured surface map of a circular optic result in large variance or "spectral leakage" behavior in the corresponding Power Spectral Density (PSD) data. In this paper we present two new, alternative methods for reducing such variance in the PSD data by replacing the zeros outside the circular area of a surface map by non-zero values either obtained from a PSD fit (method 1) or taken from the inside of the circular area (method 2).

Method and system for calibrating acquired spectra for use in spectral analysis

DOEpatents

Reber, Edward L.; Rohde, Kenneth W.; Blackwood, Larry G.

2010-09-14

A method for calibrating acquired spectra for use in spectral analysis includes performing Gaussian peak fitting to spectra acquired by a plurality of NaI detectors to define peak regions. A Na and annihilation doublet may be located among the peak regions. A predetermined energy level may be applied to one of the peaks in the doublet and a location of a hydrogen peak may be predicted based on the location of at least one of the peaks of the doublet. Control systems for calibrating spectra are also disclosed.

Integrating seasonal optical and thermal infrared spectra to characterize urban impervious surfaces with extreme spectral complexity: a Shanghai case study

NASA Astrophysics Data System (ADS)

Wang, Wei; Yao, Xinfeng; Ji, Minhe

2016-01-01

Despite recent rapid advancement in remote sensing technology, accurate mapping of the urban landscape in China still faces a great challenge due to unusually high spectral complexity in many big cities. Much of this complication comes from severe spectral confusion of impervious surfaces with polluted water bodies and bright bare soils. This paper proposes a two-step land cover decomposition method, which combines optical and thermal spectra from different seasons to cope with the issue of urban spectral complexity. First, a linear spectral mixture analysis was employed to generate fraction images for three preliminary endmembers (high albedo, low albedo, and vegetation). Seasonal change analysis on land surface temperature induced from thermal infrared spectra and coarse component fractions obtained from the first step was then used to reduce the confusion between impervious surfaces and nonimpervious materials. This method was tested with two-date Landsat multispectral data in Shanghai, one of China's megacities. The results showed that the method was capable of consistently estimating impervious surfaces in highly complex urban environments with an accuracy of R2 greater than 0.70 and both root mean square error and mean average error less than 0.20 for all test sites. This strategy seemed very promising for landscape mapping of complex urban areas.

Multiscale Characterization of PM2.5 in Southern Taiwan based on Noise-assisted Multivariate Empirical Mode Decomposition and Time-dependent Intrinsic Correlation

NASA Astrophysics Data System (ADS)

Hsiao, Y. R.; Tsai, C.

2017-12-01

As the WHO Air Quality Guideline indicates, ambient air pollution exposes world populations under threat of fatal symptoms (e.g. heart disease, lung cancer, asthma etc.), raising concerns of air pollution sources and relative factors. This study presents a novel approach to investigating the multiscale variations of PM2.5 in southern Taiwan over the past decade, with four meteorological influencing factors (Temperature, relative humidity, precipitation and wind speed),based on Noise-assisted Multivariate Empirical Mode Decomposition(NAMEMD) algorithm, Hilbert Spectral Analysis(HSA) and Time-dependent Intrinsic Correlation(TDIC) method. NAMEMD algorithm is a fully data-driven approach designed for nonlinear and nonstationary multivariate signals, and is performed to decompose multivariate signals into a collection of channels of Intrinsic Mode Functions (IMFs). TDIC method is an EMD-based method using a set of sliding window sizes to quantify localized correlation coefficients for multiscale signals. With the alignment property and quasi-dyadic filter bank of NAMEMD algorithm, one is able to produce same number of IMFs for all variables and estimates the cross correlation in a more accurate way. The performance of spectral representation of NAMEMD-HSA method is compared with Complementary Empirical Mode Decomposition/ Hilbert Spectral Analysis (CEEMD-HSA) and Wavelet Analysis. The nature of NAMAMD-based TDICC analysis is then compared with CEEMD-based TDIC analysis and the traditional correlation analysis.

Reliable Quantitative Mineral Abundances of the Martian Surface using THEMIS

NASA Astrophysics Data System (ADS)

Smith, R. J.; Huang, J.; Ryan, A. J.; Christensen, P. R.

2013-12-01

The following presents a proof of concept that given quality data, Thermal Emission Imaging System (THEMIS) data can be used to derive reliable quantitative mineral abundances of the Martian surface using a limited mineral library. The THEMIS instrument aboard the Mars Odyssey spacecraft is a multispectral thermal infrared imager with a spatial resolution of 100 m/pixel. The relatively high spatial resolution along with global coverage makes THEMIS datasets powerful tools for comprehensive fine scale petrologic analyses. However, the spectral resolution of THEMIS is limited to 8 surface sensitive bands between 6.8 and 14.0 μm with an average bandwidth of ~ 1 μm, which complicates atmosphere-surface separation and spectral analysis. This study utilizes the atmospheric correction methods of both Bandfield et al. [2004] and Ryan et al. [2013] joined with the iterative linear deconvolution technique pioneered by Huang et al. [in review] in order to derive fine-scale quantitative mineral abundances of the Martian surface. In general, it can be assumed that surface emissivity combines in a linear fashion in the thermal infrared (TIR) wavelengths such that the emitted energy is proportional to the areal percentage of the minerals present. TIR spectra are unmixed using a set of linear equations involving an endmember library of lab measured mineral spectra. The number of endmembers allowed in a spectral library are restricted to a quantity of n-1 (where n = the number of spectral bands of an instrument), preserving one band for blackbody. Spectral analysis of THEMIS data is thus allowed only seven endmembers. This study attempts to prove that this limitation does not prohibit the derivation of meaningful spectral analyses from THEMIS data. Our study selects THEMIS stamps from a region of Mars that is well characterized in the TIR by the higher spectral resolution, lower spatial resolution Thermal Emission Spectrometer (TES) instrument (143 bands at 10 cm-1 sampling and 3x5 km pixel). Multiple atmospheric corrections are performed for one image using the methods of Bandfield et al. [2004] and Ryan et al. [2013]. 7x7 pixel areas were selected, averaged, and compared using each atmospherically corrected image to ensure consistency. Corrections that provided reliable data were then used for spectral analyses. Linear deconvolution is performed using an iterative spectral analysis method [Huang et al. in review] that takes an endmember spectral library, and creates mineral combinations based on prescribed mineral group selections. The script then performs a spectral mixture analysis on each surface spectrum using all possible mineral combinations, and reports the best modeled fit to the measured spectrum. Here we present initial results from Syrtis Planum where multiple atmospherically corrected THEMIS images were deconvolved to produce similar spectral analysis results, within the detection limit of the instrument. THEMIS mineral abundances are comparable to TES-derived abundances. References: Bandfield, JL et al. [2004], JGR 109, E10008 Huang, J et al., JGR, in review Ryan, AJ et al. [2013], AGU Fall Meeting

Simulation of time-dispersion spectral device with sample spectra accumulation

NASA Astrophysics Data System (ADS)

Zhdanov, Arseny; Khansuvarov, Ruslan; Korol, Georgy

2014-09-01

This research is conducted in order to design a spectral device for light sources power spectrum analysis. The spectral device should process radiation from sources, direct contact with radiation of which is either impossible or undesirable. Such sources include jet blast of an aircraft, optical radiation in metallurgy and textile industry. In proposed spectral device optical radiation is guided out of unfavorable environment via a piece of optical fiber with high dispersion. It is necessary for analysis to make samples of analyzed radiation as short pulses. Dispersion properties of such optical fiber cause spectral decomposition of input optical pulses. The faster time of group delay vary the stronger the spectral decomposition effect. This effect allows using optical fiber with high dispersion as a major element of proposed spectral device. Duration of sample must be much shorter than group delay time difference of a dispersive system. In the given frequency range this characteristic has to be linear. The frequency range is 400 … 500 THz for typical optical fiber. Using photonic-crystal fiber (PCF) gives much wider spectral range for analysis. In this paper we propose simulation of single pulse transmission through dispersive system with linear dispersion characteristic and quadratic-detected output responses accumulation. During simulation we propose studying influence of optical fiber dispersion characteristic angle on spectral measurement results. We also consider pulse duration and group delay time difference impact on output pulse shape and duration. Results show the most suitable dispersion characteristic that allow choosing the structure of PCF - major element of time-dispersion spectral analysis method and required number of samples for reliable assessment of measured spectrum.

NDVI and Panchromatic Image Correlation Using Texture Analysis

DTIC Science & Technology

2010-03-01

6 Figure 5. Spectral reflectance of vegetation and soil from 0.4 to 1.1 mm (From Perry...should help the classification methods to be able to classify kelp. Figure 5. Spectral reflectance of vegetation and soil from 0.4 to 1.1 mm...1988). Image processing software for imaging spectrometry analysis. Remote Sensing of Enviroment , 24: 201–210. Perry, C., & Lautenschlager, L. F

Real-time spectral analysis of HRV signals: an interactive and user-friendly PC system.

PubMed

Basano, L; Canepa, F; Ottonello, P

1998-01-01

We present a real-time system, built around a PC and a low-cost data acquisition board, for the spectral analysis of the heart rate variability signal. The Windows-like operating environment on which it is based makes the computer program very user-friendly even for non-specialized personnel. The Power Spectral Density is computed through the use of a hybrid method, in which a classical FFT analysis follows an autoregressive finite-extension of data; the stationarity of the sequence is continuously checked. The use of this algorithm gives a high degree of robustness of the spectral estimation. Moreover, always in real time, the FFT of every data block is computed and displayed in order to corroborate the results as well as to allow the user to interactively choose a proper AR model order.

Rapid screening of guar gum using portable Raman spectral identification methods.

PubMed

Srivastava, Hirsch K; Wolfgang, Steven; Rodriguez, Jason D

2016-01-25

Guar gum is a well-known inactive ingredient (excipient) used in a variety of oral pharmaceutical dosage forms as a thickener and stabilizer of suspensions and as a binder of powders. It is also widely used as a food ingredient in which case alternatives with similar properties, including chemically similar gums, are readily available. Recent supply shortages and price fluctuations have caused guar gum to come under increasing scrutiny for possible adulteration by substitution of cheaper alternatives. One way that the U.S. FDA is attempting to screen pharmaceutical ingredients at risk for adulteration or substitution is through field-deployable spectroscopic screening. Here we report a comprehensive approach to evaluate two field-deployable Raman methods--spectral correlation and principal component analysis--to differentiate guar gum from other gums. We report a comparison of the sensitivity of the spectroscopic screening methods with current compendial identification tests. The ability of the spectroscopic methods to perform unambiguous identification of guar gum compared to other gums makes them an enhanced surveillance alternative to the current compendial identification tests, which are largely subjective in nature. Our findings indicate that Raman spectral identification methods perform better than compendial identification methods and are able to distinguish guar gum from other gums with 100% accuracy for samples tested by spectral correlation and principal component analysis. Published by Elsevier B.V.

[Study of near infrared spectral preprocessing and wavelength selection methods for endometrial cancer tissue].

PubMed

Zhao, Li-Ting; Xiang, Yu-Hong; Dai, Yin-Mei; Zhang, Zhuo-Yong

2010-04-01

Near infrared spectroscopy was applied to measure the tissue slice of endometrial tissues for collecting the spectra. A total of 154 spectra were obtained from 154 samples. The number of normal, hyperplasia, and malignant samples was 36, 60, and 58, respectively. Original near infrared spectra are composed of many variables, for example, interference information including instrument errors and physical effects such as particle size and light scatter. In order to reduce these influences, original spectra data should be performed with different spectral preprocessing methods to compress variables and extract useful information. So the methods of spectral preprocessing and wavelength selection have played an important role in near infrared spectroscopy technique. In the present paper the raw spectra were processed using various preprocessing methods including first derivative, multiplication scatter correction, Savitzky-Golay first derivative algorithm, standard normal variate, smoothing, and moving-window median. Standard deviation was used to select the optimal spectral region of 4 000-6 000 cm(-1). Then principal component analysis was used for classification. Principal component analysis results showed that three types of samples could be discriminated completely and the accuracy almost achieved 100%. This study demonstrated that near infrared spectroscopy technology and chemometrics method could be a fast, efficient, and novel means to diagnose cancer. The proposed methods would be a promising and significant diagnosis technique of early stage cancer.

Automatic sub-pixel coastline extraction based on spectral mixture analysis using EO-1 Hyperion data

NASA Astrophysics Data System (ADS)

Hong, Zhonghua; Li, Xuesu; Han, Yanling; Zhang, Yun; Wang, Jing; Zhou, Ruyan; Hu, Kening

2018-06-01

Many megacities (such as Shanghai) are located in coastal areas, therefore, coastline monitoring is critical for urban security and urban development sustainability. A shoreline is defined as the intersection between coastal land and a water surface and features seawater edge movements as tides rise and fall. Remote sensing techniques have increasingly been used for coastline extraction; however, traditional hard classification methods are performed only at the pixel-level and extracting subpixel accuracy using soft classification methods is both challenging and time consuming due to the complex features in coastal regions. This paper presents an automatic sub-pixel coastline extraction method (ASPCE) from high-spectral satellite imaging that performs coastline extraction based on spectral mixture analysis and, thus, achieves higher accuracy. The ASPCE method consists of three main components: 1) A Water- Vegetation-Impervious-Soil (W-V-I-S) model is first presented to detect mixed W-V-I-S pixels and determine the endmember spectra in coastal regions; 2) The linear spectral mixture unmixing technique based on Fully Constrained Least Squares (FCLS) is applied to the mixed W-V-I-S pixels to estimate seawater abundance; and 3) The spatial attraction model is used to extract the coastline. We tested this new method using EO-1 images from three coastal regions in China: the South China Sea, the East China Sea, and the Bohai Sea. The results showed that the method is accurate and robust. Root mean square error (RMSE) was utilized to evaluate the accuracy by calculating the distance differences between the extracted coastline and the digitized coastline. The classifier's performance was compared with that of the Multiple Endmember Spectral Mixture Analysis (MESMA), Mixture Tuned Matched Filtering (MTMF), Sequential Maximum Angle Convex Cone (SMACC), Constrained Energy Minimization (CEM), and one classical Normalized Difference Water Index (NDWI). The results from the three test sites indicated that the proposed ASPCE method extracted coastlines more efficiently than did the compared methods, and its coastline extraction accuracy corresponded closely to the digitized coastline, with 0.39 pixels, 0.40 pixels, and 0.35 pixels in the three test regions, showing that the ASPCE method achieves an accuracy below 12.0 m (0.40 pixels). Moreover, in the quantitative accuracy assessment for the three test sites, the ASPCE method shows the best performance in coastline extraction, achieving a 0.35 pixel-level at the Bohai Sea, China test site. Therefore, the proposed ASPCE method can extract coastline more accurately than can the hard classification methods or other spectral unmixing methods.

Electron-boson spectral density function of correlated multiband systems obtained from optical data: Ba0.6K0.4Fe2As2 and LiFeAs.

PubMed

Hwang, Jungseek

2016-03-31

We introduce an approximate method which can be used to simulate the optical conductivity data of correlated multiband systems for normal and superconducting cases by taking advantage of a reversed process in comparison to a usual optical data analysis, which has been used to extract the electron-boson spectral density function from measured optical spectra of single-band systems, like cuprates. We applied this method to optical conductivity data of two multiband pnictide systems (Ba0.6K0.4Fe2As2 and LiFeAs) and obtained the electron-boson spectral density functions. The obtained electron-boson spectral density consists of a sharp mode and a broad background. The obtained spectral density functions of the multiband systems show similar properties as those of cuprates in several aspects. We expect that our method helps to reveal the nature of strong correlations in the multiband pnictide superconductors.

Spectral reflectance properties (0.4-2.5 μm) of secondary Fe-oxide, Fe-hydroxide, and Fe-sulphate-hydrate minerals associated with sulphide-bearing mine wastes

USGS Publications Warehouse

Crowley, J.K.; Williams, D.E.; Hammarstrom, J.M.; Piatak, N.; Chou, I.-Ming; Mars, J.C.

2003-01-01

Diffuse reflectance spectra of 15 mineral species commonly associated with sulphide-bearing mine wastes show diagnostic absorption bands related to electronic processes involving ferric and/or ferrous iron, and to vibrational processes involving water and hydroxyl. Many of these absorption bands are relatively broad and overlapping; however, spectral analysis methods, including continuum removal and derivative analysis, permit most of the minerals to be distinguished. Key spectral differences between the minerals are illustrated in a series of plots showing major absorption band centres and other spectral feature positions. Because secondary iron minerals are sensitive indicators of pH, Eh, relative humidity, and other environmental conditions, spectral mapping of mineral distributions promises to have important application to mine waste remediation studies.

Multispectral scanner system parameter study and analysis software system description, volume 2

NASA Technical Reports Server (NTRS)

Landgrebe, D. A. (Principal Investigator); Mobasseri, B. G.; Wiersma, D. J.; Wiswell, E. R.; Mcgillem, C. D.; Anuta, P. E.

1978-01-01

The author has identified the following significant results. The integration of the available methods provided the analyst with the unified scanner analysis package (USAP), the flexibility and versatility of which was superior to many previous integrated techniques. The USAP consisted of three main subsystems; (1) a spatial path, (2) a spectral path, and (3) a set of analytic classification accuracy estimators which evaluated the system performance. The spatial path consisted of satellite and/or aircraft data, data correlation analyzer, scanner IFOV, and random noise model. The output of the spatial path was fed into the analytic classification and accuracy predictor. The spectral path consisted of laboratory and/or field spectral data, EXOSYS data retrieval, optimum spectral function calculation, data transformation, and statistics calculation. The output of the spectral path was fended into the stratified posterior performance estimator.

Sensitivity test of derivative matrix isopotential synchronous fluorimetry and least squares fitting methods.

PubMed

Makkai, Géza; Buzády, Andrea; Erostyák, János

2010-01-01

Determination of concentrations of spectrally overlapping compounds has special difficulties. Several methods are available to calculate the constituents' concentrations in moderately complex mixtures. A method which can provide information about spectrally hidden components in mixtures is very useful. Two methods powerful in resolving spectral components are compared in this paper. The first method tested is the Derivative Matrix Isopotential Synchronous Fluorimetry (DMISF). It is based on derivative analysis of MISF spectra, which are constructed using isopotential trajectories in the Excitation-Emission Matrix (EEM) of background solution. For DMISF method, a mathematical routine fitting the 3D data of EEMs was developed. The other method tested uses classical Least Squares Fitting (LSF) algorithm, wherein Rayleigh- and Raman-scattering bands may lead to complications. Both methods give excellent sensitivity and have advantages against each other. Detection limits of DMISF and LSF have been determined at very different concentration and noise levels.

A SPECTRAL GRAPH APPROACH TO DISCOVERING GENETIC ANCESTRY1

PubMed Central

Lee, Ann B.; Luca, Diana; Roeder, Kathryn

2010-01-01

Mapping human genetic variation is fundamentally interesting in fields such as anthropology and forensic inference. At the same time, patterns of genetic diversity confound efforts to determine the genetic basis of complex disease. Due to technological advances, it is now possible to measure hundreds of thousands of genetic variants per individual across the genome. Principal component analysis (PCA) is routinely used to summarize the genetic similarity between subjects. The eigenvectors are interpreted as dimensions of ancestry. We build on this idea using a spectral graph approach. In the process we draw on connections between multidimensional scaling and spectral kernel methods. Our approach, based on a spectral embedding derived from the normalized Laplacian of a graph, can produce more meaningful delineation of ancestry than by using PCA. The method is stable to outliers and can more easily incorporate different similarity measures of genetic data than PCA. We illustrate a new algorithm for genetic clustering and association analysis on a large, genetically heterogeneous sample. PMID:20689656

[HRV-Spectral analysis of Pain, by 3D Evaluation and by Balance Index in the Pain Rehabilitation Field].

PubMed

Goto, Yukio

2015-07-01

Pain signaling is achieved by electrical impulses in the body; however, some electrical abnormalities can cause pain in the body without generating any visible symptoms. This phenomenon is sensed by the brain and a signal that may affect cardiac rhythms is immediately transmitted to the heart. To evaluate heart rate variability (HRV), the balance correction between an increase and decrease of heart rate was recorded in real time. Using a special method for spectral-analysis of the HRV, techniques for analyzing the essence of pain were developed, namely, the 'Balance index' and the '3D spectrum evaluation method'. Using these techniques, an alpha wave-like factor or a beta wave-like reaction can be obtained, and the nature and strength of pain can be displayed as spectral zones, as in a rainbow. The balance reaction can be shown by analyzing data in the frequency band using a 1/f-like spectral-analysis method. Additionally, emotional reactions can be detected using a 'Balance index' that can demonstrate imbalance responding to the pain. The mental state of the subject can also be inferred because this technique is adapted from the 1/f fluctuation theory related to the best balanced 1/f-sound wave in nature that comforts the human mind, similar to music (artificial sound wave). In this study, the variety and intensity of pain were determined from the frequency band resulting from the 1/f-spectral analysis of HRV fluctuation. These techniques could explain several situations related to medication or anesthesia and can be helpful in preventative treatment and/or explaining the differences in the effectiveness of various techniques for the rehabilitation of chronic pain.

Quantifying Vocal Mimicry in the Greater Racket-Tailed Drongo: A Comparison of Automated Methods and Human Assessment

PubMed Central

Agnihotri, Samira; Sundeep, P. V. D. S.; Seelamantula, Chandra Sekhar; Balakrishnan, Rohini

2014-01-01

Objective identification and description of mimicked calls is a primary component of any study on avian vocal mimicry but few studies have adopted a quantitative approach. We used spectral feature representations commonly used in human speech analysis in combination with various distance metrics to distinguish between mimicked and non-mimicked calls of the greater racket-tailed drongo, Dicrurus paradiseus and cross-validated the results with human assessment of spectral similarity. We found that the automated method and human subjects performed similarly in terms of the overall number of correct matches of mimicked calls to putative model calls. However, the two methods also misclassified different subsets of calls and we achieved a maximum accuracy of ninety five per cent only when we combined the results of both the methods. This study is the first to use Mel-frequency Cepstral Coefficients and Relative Spectral Amplitude - filtered Linear Predictive Coding coefficients to quantify vocal mimicry. Our findings also suggest that in spite of several advances in automated methods of song analysis, corresponding cross-validation by humans remains essential. PMID:24603717

Stability analysis of spectral methods for hyperbolic initial-boundary value systems

NASA Technical Reports Server (NTRS)

Gottlieb, D.; Lustman, L.; Tadmor, E.

1986-01-01

A constant coefficient hyperbolic system in one space variable, with zero initial data is discussed. Dissipative boundary conditions are imposed at the two points x = + or - 1. This problem is discretized by a spectral approximation in space. Sufficient conditions under which the spectral numerical solution is stable are demonstrated - moreover, these conditions have to be checked only for scalar equations. The stability theorems take the form of explicit bounds for the norm of the solution in terms of the boundary data. The dependence of these bounds on N, the number of points in the domain (or equivalently the degree of the polynomials involved), is investigated for a class of standard spectral methods, including Chebyshev and Legendre collocations.

Spectroradiometric calibration of the Thematic Mapper and Multispectral Scanner system. [White Sands, New Mexico

NASA Technical Reports Server (NTRS)

Palmer, J. M. (Principal Investigator); Slater, P. N.

1984-01-01

The newly built Caste spectropolarimeters gave satisfactory performance during tests in the solar radiometer and helicopter modes. A bandwidth normalization technique based on analysis of the moments of the spectral responsivity curves was used to analyze the spectral bands of the MSS and TM subsystems of LANDSAT 4 and 5 satellites. Results include the effective wavelength, the bandpass, the wavelength limits, and the normalized responsivity for each spectral channel. Temperature coefficients for TM PF channel 6 were also derived. The moments normalization method used yields sensor parameters whose derivation is independent of source characteristics (i.e., incident solar spectral irradiance, atmospheric transmittance, or ground reflectance). The errors expected using these parameters are lower than those expected using other normalization methods.

freeQuant: A Mass Spectrometry Label-Free Quantification Software Tool for Complex Proteome Analysis.

PubMed

Deng, Ning; Li, Zhenye; Pan, Chao; Duan, Huilong

2015-01-01

Study of complex proteome brings forward higher request for the quantification method using mass spectrometry technology. In this paper, we present a mass spectrometry label-free quantification tool for complex proteomes, called freeQuant, which integrated quantification with functional analysis effectively. freeQuant consists of two well-integrated modules: label-free quantification and functional analysis with biomedical knowledge. freeQuant supports label-free quantitative analysis which makes full use of tandem mass spectrometry (MS/MS) spectral count, protein sequence length, shared peptides, and ion intensity. It adopts spectral count for quantitative analysis and builds a new method for shared peptides to accurately evaluate abundance of isoforms. For proteins with low abundance, MS/MS total ion count coupled with spectral count is included to ensure accurate protein quantification. Furthermore, freeQuant supports the large-scale functional annotations for complex proteomes. Mitochondrial proteomes from the mouse heart, the mouse liver, and the human heart were used to evaluate the usability and performance of freeQuant. The evaluation showed that the quantitative algorithms implemented in freeQuant can improve accuracy of quantification with better dynamic range.

Autoregressive modeling for the spectral analysis of oceanographic data

NASA Technical Reports Server (NTRS)

Gangopadhyay, Avijit; Cornillon, Peter; Jackson, Leland B.

1989-01-01

Over the last decade there has been a dramatic increase in the number and volume of data sets useful for oceanographic studies. Many of these data sets consist of long temporal or spatial series derived from satellites and large-scale oceanographic experiments. These data sets are, however, often 'gappy' in space, irregular in time, and always of finite length. The conventional Fourier transform (FT) approach to the spectral analysis is thus often inapplicable, or where applicable, it provides questionable results. Here, through comparative analysis with the FT for different oceanographic data sets, the possibilities offered by autoregressive (AR) modeling to perform spectral analysis of gappy, finite-length series, are discussed. The applications demonstrate that as the length of the time series becomes shorter, the resolving power of the AR approach as compared with that of the FT improves. For the longest data sets examined here, 98 points, the AR method performed only slightly better than the FT, but for the very short ones, 17 points, the AR method showed a dramatic improvement over the FT. The application of the AR method to a gappy time series, although a secondary concern of this manuscript, further underlines the value of this approach.

Development and application of the maximum entropy method and other spectral estimation techniques

NASA Astrophysics Data System (ADS)

King, W. R.

1980-09-01

This summary report is a collection of four separate progress reports prepared under three contracts, which are all sponsored by the Office of Naval Research in Arlington, Virginia. This report contains the results of investigations into the application of the maximum entropy method (MEM), a high resolution, frequency and wavenumber estimation technique. The report also contains a description of two, new, stable, high resolution spectral estimation techniques that is provided in the final report section. Many examples of wavenumber spectral patterns for all investigated techniques are included throughout the report. The maximum entropy method is also known as the maximum entropy spectral analysis (MESA) technique, and both names are used in the report. Many MEM wavenumber spectral patterns are demonstrated using both simulated and measured radar signal and noise data. Methods for obtaining stable MEM wavenumber spectra are discussed, broadband signal detection using the MEM prediction error transform (PET) is discussed, and Doppler radar narrowband signal detection is demonstrated using the MEM technique. It is also shown that MEM cannot be applied to randomly sampled data. The two new, stable, high resolution, spectral estimation techniques discussed in the final report section, are named the Wiener-King and the Fourier spectral estimation techniques. The two new techniques have a similar derivation based upon the Wiener prediction filter, but the two techniques are otherwise quite different. Further development of the techniques and measurement of the technique spectral characteristics is recommended for subsequent investigation.

Exploring the potential of hyper-spectral imaging for the biogeochemical analysis of varved lake sediments

NASA Astrophysics Data System (ADS)

Butz, Christoph; Grosjean, Martin; Enters, Dirk; Tylmann, Wojciech

2014-05-01

Varved lake sediments have successfully been used to make inferences about past environmental and climate conditions from annual to multi-millennial scales. Among other proxies, concentrations of sedimentary photopigments have been used for temperature reconstructions. However, obtaining well calibrated annually resolved records from sediments still remains challenging. Most laboratory methods used to analyse lake sediments require physical subsampling and are destructive in the process. Hence, temporal resolution and number of data are limited by the amount of material available in the core. Furthermore, for very low sediment accumulation rates annual subsampling is often very difficult or even impossible. To address these problems we explore hyper-spectral imaging as a new method to analyse lake sediments based on their reflectance spectra in the visible and near infrared spectrum. In contrast to other fast and non-destructive methods like X-ray fluorescence, VIS/NIR reflectance spectrometry distinguishes between biogeochemical substances rather than single elements. Rein (2003) has shown that VIS-RS can be used to detect relative concentrations of sedimentary photopigments (e.g. chlorins, carotenoids) and clay minerals. This study presents an advanced approach using a hyper-spectral camera and remote sensing techniques to infer climate proxy data from reflectance spectra of varved lake sediments. Hyper-spectral imaging allows analysing an entire sediment core in a single measurement, producing a spectral dataset with very high spatial (30x30µm/pixel) and spectral resolutions (~1nm) and a higher spectral range (400-1000nm) compared to previously used spectrophotometers. This allows the analysis of data time series at sub-varve scales or spatial mapping of sedimentary substances (e.g. chlorophyll-a and diagenetic products) at very high resolution. The method is demonstrated on varved lake sediments from northern Poland showing the change of the relative concentrations of chlorin pigments within individual varve years. In a next step absolute concentrations of chlorins derived from HPLC measurements have been calibrated to the spectral data using a linear regression model. This results in a very high-resolution dataset of absolute sedimentary pigment concentrations. In a second example µXRF measurements are used to validate a spectral index for clay mineral detection.

Contrast enhancement of subcutaneous blood vessel images by means of visible and near-infrared hyper-spectral imaging

NASA Astrophysics Data System (ADS)

Katrašnik, Jaka; Bürmen, Miran; Pernuš, Franjo; Likar, Boštjan

2009-02-01

Visualization of subcutaneous veins is very difficult with the naked eye, but important for diagnosis of medical conditions and different medical procedures such as catheter insertion and blood withdrawal. Moreover, recent studies showed that the images of subcutaneous veins could be used for biometric identification. The majority of methods used for enhancing the contrast between the subcutaneous veins and surrounding tissue are based on simple imaging systems utilizing CMOS or CCD cameras with LED illumination capable of acquiring images from the near infrared spectral region, usually near 900 nm. However, such simplified imaging methods cannot exploit the full potential of the spectral information. In this paper, a new highly versatile method for enhancing the contrast of subcutaneous veins based on state-of-the-art high-resolution hyper-spectral imaging system utilizing the spectral region from 550 to 1700 nm is presented. First, a detailed analysis of the contrast between the subcutaneous veins and the surrounding tissue as a function of wavelength, for several different positions on the human arm, was performed in order to extract the spectral regions with the highest contrast. The highest contrast images were acquired at 1100 nm, however, combining the individual images from the extracted spectral regions by the proposed contrast enhancement method resulted in a single image with up to ten-fold better contrast. Therefore, the proposed method has proved to be a useful tool for visualization of subcutaneous veins.

A fully Bayesian method for jointly fitting instrumental calibration and X-ray spectral models

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

Xu, Jin; Yu, Yaming; Van Dyk, David A.

2014-10-20

Owing to a lack of robust principled methods, systematic instrumental uncertainties have generally been ignored in astrophysical data analysis despite wide recognition of the importance of including them. Ignoring calibration uncertainty can cause bias in the estimation of source model parameters and can lead to underestimation of the variance of these estimates. We previously introduced a pragmatic Bayesian method to address this problem. The method is 'pragmatic' in that it introduced an ad hoc technique that simplified computation by neglecting the potential information in the data for narrowing the uncertainty for the calibration product. Following that work, we use amore » principal component analysis to efficiently represent the uncertainty of the effective area of an X-ray (or γ-ray) telescope. Here, however, we leverage this representation to enable a principled, fully Bayesian method that coherently accounts for the calibration uncertainty in high-energy spectral analysis. In this setting, the method is compared with standard analysis techniques and the pragmatic Bayesian method. The advantage of the fully Bayesian method is that it allows the data to provide information not only for estimation of the source parameters but also for the calibration product—here the effective area, conditional on the adopted spectral model. In this way, it can yield more accurate and efficient estimates of the source parameters along with valid estimates of their uncertainty. Provided that the source spectrum can be accurately described by a parameterized model, this method allows rigorous inference about the effective area by quantifying which possible curves are most consistent with the data.« less

Brillouin Scattering Spectrum Analysis Based on Auto-Regressive Spectral Estimation

NASA Astrophysics Data System (ADS)

Huang, Mengyun; Li, Wei; Liu, Zhangyun; Cheng, Linghao; Guan, Bai-Ou

2018-06-01

Auto-regressive (AR) spectral estimation technology is proposed to analyze the Brillouin scattering spectrum in Brillouin optical time-domain refelectometry. It shows that AR based method can reliably estimate the Brillouin frequency shift with an accuracy much better than fast Fourier transform (FFT) based methods provided the data length is not too short. It enables about 3 times improvement over FFT at a moderate spatial resolution.

Determination of rare-earth elements in Luna 16 regolith sample by chemical spectral method

NASA Technical Reports Server (NTRS)

Stroganova, N. S.; Ryabukhin, V. A.; Laktinova, N. V.; Ageyeva, L. V.; Galkina, I. P.; Gatinskaya, N. G.; Yermakov, A. N.; Karyakin, A. V.

1974-01-01

An analysis was made of regolith from layer A of the Luna 16 sample for rare earth elements, by a chemical spectral method. Chemical and ion exchange concentrations were used to determine the content of 12 elements and Y at the level 0.001 to 0.0001 percent with 10 to 15 percent reproducibility of the emission determination. Results within the limits of reproducibility agree with data obtained by mass spectra, activation, and X-ray fluorescent methods.

Fluctuation-Enhanced Sensing for Biological Agent Detection and Identification

DTIC Science & Technology

2009-01-01

method for bacterium detection published earlier; sensing and evaluating the odors of microbes ; and spectral and amplitude distribution analysis of noise...REPORT TYPE 3. DATES COVERED 00-00-2009 to 00-00-2009 4. TITLE AND SUBTITLE Fluctuation-Enhanced Sensing for Biological Agent Detection and...evaluating the odors of microbes ; and spectral and amplitude distribution analysis of noise in light scattering to identify spores based on their

Robust Multipoint Water-Fat Separation Using Fat Likelihood Analysis

PubMed Central

Yu, Huanzhou; Reeder, Scott B.; Shimakawa, Ann; McKenzie, Charles A.; Brittain, Jean H.

2016-01-01

Fat suppression is an essential part of routine MRI scanning. Multiecho chemical-shift based water-fat separation methods estimate and correct for Bo field inhomogeneity. However, they must contend with the intrinsic challenge of water-fat ambiguity that can result in water-fat swapping. This problem arises because the signals from two chemical species, when both are modeled as a single discrete spectral peak, may appear indistinguishable in the presence of Bo off-resonance. In conventional methods, the water-fat ambiguity is typically removed by enforcing field map smoothness using region growing based algorithms. In reality, the fat spectrum has multiple spectral peaks. Using this spectral complexity, we introduce a novel concept that identifies water and fat for multiecho acquisitions by exploiting the spectral differences between water and fat. A fat likelihood map is produced to indicate if a pixel is likely to be water-dominant or fat-dominant by comparing the fitting residuals of two different signal models. The fat likelihood analysis and field map smoothness provide complementary information, and we designed an algorithm (Fat Likelihood Analysis for Multiecho Signals) to exploit both mechanisms. It is demonstrated in a wide variety of data that the Fat Likelihood Analysis for Multiecho Signals algorithm offers highly robust water-fat separation for 6-echo acquisitions, particularly in some previously challenging applications. PMID:21842498

Ocean wavenumber estimation from wave-resolving time series imagery

USGS Publications Warehouse

Plant, N.G.; Holland, K.T.; Haller, M.C.

2008-01-01

We review several approaches that have been used to estimate ocean surface gravity wavenumbers from wave-resolving remotely sensed image sequences. Two fundamentally different approaches that utilize these data exist. A power spectral density approach identifies wavenumbers where image intensity variance is maximized. Alternatively, a cross-spectral correlation approach identifies wavenumbers where intensity coherence is maximized. We develop a solution to the latter approach based on a tomographic analysis that utilizes a nonlinear inverse method. The solution is tolerant to noise and other forms of sampling deficiency and can be applied to arbitrary sampling patterns, as well as to full-frame imagery. The solution includes error predictions that can be used for data retrieval quality control and for evaluating sample designs. A quantitative analysis of the intrinsic resolution of the method indicates that the cross-spectral correlation fitting improves resolution by a factor of about ten times as compared to the power spectral density fitting approach. The resolution analysis also provides a rule of thumb for nearshore bathymetry retrievals-short-scale cross-shore patterns may be resolved if they are about ten times longer than the average water depth over the pattern. This guidance can be applied to sample design to constrain both the sensor array (image resolution) and the analysis array (tomographic resolution). ?? 2008 IEEE.

[Research on the measurement of flue-dust concentration in Vis, IR spectral region].

PubMed

Sun, Xiao-gang; Tang, Hong; Yuan, Gui-bin

2008-10-01

In the measurement of flue-dust concentration based on the transmission method, the dependent model algorithm was used to invert the flue-dust concentration in the visible, infrared and visible-infrared spectral regions respectively. By the analysis and comparison of the accuracy, linearity and sensitivity of the inversion flue-dust concentration, the optimal spectral region was determined. Meanwhile, the influence of the water droplet with different size distribution and volume concentration was simulated, and a method was proposed which has advantages of simplicity, rapidity, and suitability for on line measurement. Simulation experiments illustrate that the flue-dust concentration can be inverted very well in the visible-infrared spectral region, and it is feasible to use the ratio of the constrained light extinction method to overcome the influence of water droplet. The inverse results all remain satisfactory when 2% stochastic noise is added to the value of the light extinction.

Spectroscopically Enhanced Method and System for Multi-Factor Biometric Authentication

NASA Astrophysics Data System (ADS)

Pishva, Davar

This paper proposes a spectroscopic method and system for preventing spoofing of biometric authentication. One of its focus is to enhance biometrics authentication with a spectroscopic method in a multifactor manner such that a person's unique ‘spectral signatures’ or ‘spectral factors’ are recorded and compared in addition to a non-spectroscopic biometric signature to reduce the likelihood of imposter getting authenticated. By using the ‘spectral factors’ extracted from reflectance spectra of real fingers and employing cluster analysis, it shows how the authentic fingerprint image presented by a real finger can be distinguished from an authentic fingerprint image embossed on an artificial finger, or molded on a fingertip cover worn by an imposter. This paper also shows how to augment two widely used biometrics systems (fingerprint and iris recognition devices) with spectral biometrics capabilities in a practical manner and without creating much overhead or inconveniencing their users.

Accurate palm vein recognition based on wavelet scattering and spectral regression kernel discriminant analysis

NASA Astrophysics Data System (ADS)

Elnasir, Selma; Shamsuddin, Siti Mariyam; Farokhi, Sajad

2015-01-01

Palm vein recognition (PVR) is a promising new biometric that has been applied successfully as a method of access control by many organizations, which has even further potential in the field of forensics. The palm vein pattern has highly discriminative features that are difficult to forge because of its subcutaneous position in the palm. Despite considerable progress and a few practical issues, providing accurate palm vein readings has remained an unsolved issue in biometrics. We propose a robust and more accurate PVR method based on the combination of wavelet scattering (WS) with spectral regression kernel discriminant analysis (SRKDA). As the dimension of WS generated features is quite large, SRKDA is required to reduce the extracted features to enhance the discrimination. The results based on two public databases-PolyU Hyper Spectral Palmprint public database and PolyU Multi Spectral Palmprint-show the high performance of the proposed scheme in comparison with state-of-the-art methods. The proposed approach scored a 99.44% identification rate and a 99.90% verification rate [equal error rate (EER)=0.1%] for the hyperspectral database and a 99.97% identification rate and a 99.98% verification rate (EER=0.019%) for the multispectral database.

[A cloud detection algorithm for MODIS images combining Kmeans clustering and multi-spectral threshold method].

PubMed

Wang, Wei; Song, Wei-Guo; Liu, Shi-Xing; Zhang, Yong-Ming; Zheng, Hong-Yang; Tian, Wei

2011-04-01

An improved method for detecting cloud combining Kmeans clustering and the multi-spectral threshold approach is described. On the basis of landmark spectrum analysis, MODIS data is categorized into two major types initially by Kmeans method. The first class includes clouds, smoke and snow, and the second class includes vegetation, water and land. Then a multi-spectral threshold detection is applied to eliminate interference such as smoke and snow for the first class. The method is tested with MODIS data at different time under different underlying surface conditions. By visual method to test the performance of the algorithm, it was found that the algorithm can effectively detect smaller area of cloud pixels and exclude the interference of underlying surface, which provides a good foundation for the next fire detection approach.

Combined spectral-domain optical coherence tomography and hyperspectral imaging applied for tissue analysis: Preliminary results

NASA Astrophysics Data System (ADS)

Dontu, S.; Miclos, S.; Savastru, D.; Tautan, M.

2017-09-01

In recent years many optoelectronic techniques have been developed for improvement and the development of devices for tissue analysis. Spectral-Domain Optical Coherence Tomography (SD-OCT) is a new medical interferometric imaging modality that provides depth resolved tissue structure information with resolution in the μm range. However, SD-OCT has its own limitations and cannot offer the biochemical information of the tissue. These data can be obtained with hyperspectral imaging, a non-invasive, sensitive and real time technique. In the present study we have combined Spectral-Domain Optical Coherence Tomography (SD-OCT) with Hyperspectral imaging (HSI) for tissue analysis. The Spectral-Domain Optical Coherence Tomography (SD-OCT) and Hyperspectral imaging (HSI) are two methods that have demonstrated significant potential in this context. Preliminary results using different tissue have highlighted the capabilities of this technique of combinations.

Factor analysis as a tool for spectral line component separation 21cm emission in the direction of L1780

NASA Technical Reports Server (NTRS)

Toth, L. V.; Mattila, K.; Haikala, L.; Balazs, L. G.

1992-01-01

The spectra of the 21cm HI radiation from the direction of L1780, a small high-galactic latitude dark/molecular cloud, were analyzed by multivariate methods. Factor analysis was performed on HI (21cm) spectra in order to separate the different components responsible for the spectral features. The rotated, orthogonal factors explain the spectra as a sum of radiation from the background (an extended HI emission layer), and from the L1780 dark cloud. The coefficients of the cloud-indicator factors were used to locate the HI 'halo' of the molecular cloud. Our statistically derived 'background' and 'cloud' spectral profiles, as well as the spatial distribution of the HI halo emission distribution were compared to the results of a previous study which used conventional methods analyzing nearly the same data set.

[Road Extraction in Remote Sensing Images Based on Spectral and Edge Analysis].

PubMed

Zhao, Wen-zhi; Luo, Li-qun; Guo, Zhou; Yue, Jun; Yu, Xue-ying; Liu, Hui; Wei, Jing

2015-10-01

Roads are typically man-made objects in urban areas. Road extraction from high-resolution images has important applications for urban planning and transportation development. However, due to the confusion of spectral characteristic, it is difficult to distinguish roads from other objects by merely using traditional classification methods that mainly depend on spectral information. Edge is an important feature for the identification of linear objects (e. g. , roads). The distribution patterns of edges vary greatly among different objects. It is crucial to merge edge statistical information into spectral ones. In this study, a new method that combines spectral information and edge statistical features has been proposed. First, edge detection is conducted by using self-adaptive mean-shift algorithm on the panchromatic band, which can greatly reduce pseudo-edges and noise effects. Then, edge statistical features are obtained from the edge statistical model, which measures the length and angle distribution of edges. Finally, by integrating the spectral and edge statistical features, SVM algorithm is used to classify the image and roads are ultimately extracted. A series of experiments are conducted and the results show that the overall accuracy of proposed method is 93% comparing with only 78% overall accuracy of the traditional. The results demonstrate that the proposed method is efficient and valuable for road extraction, especially on high-resolution images.

Cloud cover analysis with Arctic Advanced Very High Resolution Radiometer data. II - Classification with spectral and textural measures

NASA Technical Reports Server (NTRS)

Key, J.

1990-01-01

The spectral and textural characteristics of polar clouds and surfaces for a 7-day summer series of AVHRR data in two Arctic locations are examined, and the results used in the development of a cloud classification procedure for polar satellite data. Since spatial coherence and texture sensitivity tests indicate that a joint spectral-textural analysis based on the same cell size is inappropriate, cloud detection with AVHRR data and surface identification with passive microwave data are first done on the pixel level as described by Key and Barry (1989). Next, cloud patterns within 250-sq-km regions are described, then the spectral and local textural characteristics of cloud patterns in the image are determined and each cloud pixel is classified by statistical methods. Results indicate that both spectral and textural features can be utilized in the classification of cloudy pixels, although spectral features are most useful for the discrimination between cloud classes.

A new analysis of heart rate variability in the assessment of fetal parasympathetic activity: An experimental study in a fetal sheep model.

PubMed

Garabedian, C; Champion, C; Servan-Schreiber, E; Butruille, L; Aubry, E; Sharma, D; Logier, R; Deruelle, P; Storme, L; Houfflin-Debarge, V; De Jonckheere, J

2017-01-01

Analysis of heart rate variability (HRV) is a recognized tool in the assessment of autonomic nervous system (ANS) activity. Indeed, both time and spectral analysis techniques enable us to obtain indexes that are related to the way the ANS regulates the heart rate. However, these techniques are limited in terms of the lack of thresholds of the numerical indexes, which is primarily due to high inter-subject variability. We proposed a new fetal HRV analysis method related to the parasympathetic activity of the ANS. The aim of this study was to evaluate the performance of our method compared to commonly used HRV analysis, with regard to i) the ability to detect changes in ANS activity and ii) inter-subject variability. This study was performed in seven sheep fetuses. In order to evaluate the sensitivity and specificity of our index in evaluating parasympathetic activity, we directly administered 2.5 mg intravenous atropine, to inhibit parasympathetic tone, and 5 mg propranolol to block sympathetic activity. Our index, as well as time analysis (root mean square of the successive differences; RMSSD) and spectral analysis (high frequency (HF) and low frequency (LF) spectral components obtained via fast Fourier transform), were measured before and after injection. Inter-subject variability was estimated by the coefficient of variance (%CV). In order to evaluate the ability of HRV parameters to detect fetal parasympathetic decrease, we also estimated the effect size for each HRV parameter before and after injections. As expected, our index, the HF spectral component, and the RMSSD were reduced after the atropine injection. Moreover, our index presented a higher effect size. The %CV was far lower for our index than for RMSSD, HF, and LF. Although LF decreased after propranolol administration, fetal stress index, RMSSD, and HF were not significantly different, confirming the fact that those indexes are specific to the parasympathetic nervous system. In conclusion, our method appeared to be effective in detecting parasympathetic inhibition. Moreover, inter-subject variability was much lower, and effect size higher, with our method compared to other HRV analysis methods.

Simultaneous separation by reversed-phase high-performance liquid chromatography and mass spectral identification of anthocyanins and flavonols in Shiraz grape skin.

PubMed

Downey, Mark O; Rochfort, Simone

2008-08-01

A limitation of large-scale viticultural trials is the time and cost of comprehensive compositional analysis of the fruit by high-performance liquid chromatography (HPLC). In addition, separate methods have generally been required to identify and quantify different classes of metabolites. To address these shortcomings a reversed-phase HPLC method was developed to simultaneously separate the anthocyanins and flavonols present in grape skins. The method employs a methanol and water gradient acidified with 10% formic acid with a run-time of 48 min including re-equilibration. Identity of anthocyanins and flavonols in Shiraz (Vitis vinifera L.) skin was confirmed by mass spectral analysis.

Use of a region of the visible and near infrared spectrum to predict mechanical properties of wet wood and standing trees

DOEpatents

Meglen, Robert R.; Kelley, Stephen S.

2003-01-01

In a method for determining the dry mechanical strength for a green wood, the improvement comprising: (a) illuminating a surface of the wood to be determined with a reduced range of wavelengths in the VIS-NIR spectra 400 to 1150 nm, said wood having a green moisture content; (b) analyzing the surface of the wood using a spectrometric method, the method generating a first spectral data of a reduced range of wavelengths in VIS-NIR spectra; and (c) using a multivariate analysis technique to predict the mechanical strength of green wood when dry by comparing the first spectral data with a calibration model, the calibration model comprising a second spectrometric method of spectral data of a reduced range of wavelengths in VIS-NIR spectra obtained from a reference wood having a green moisture content, the second spectral being correlated with a known mechanical strength analytical result obtained from the reference wood when dried and a having a dry moisture content.

Polychromatic spectral pattern analysis of ultra-weak photon emissions from a human body.

PubMed

Kobayashi, Masaki; Iwasa, Torai; Tada, Mika

2016-06-01

Ultra-weak photon emission (UPE), often designated as biophoton emission, is generally observed in a wide range of living organisms, including human beings. This phenomenon is closely associated with reactive oxygen species (ROS) generated during normal metabolic processes and pathological states induced by oxidative stress. Application of UPE extracting the pathophysiological information has long been anticipated because of its potential non-invasiveness, facilitating its diagnostic use. Nevertheless, its weak intensity and UPE mechanism complexity hinder its use for practical applications. Spectroscopy is crucially important for UPE analysis. However, filter-type spectroscopy technique, used as a conventional method for UPE analysis, intrinsically limits its performance because of its monochromatic scheme. To overcome the shortcomings of conventional methods, the authors developed a polychromatic spectroscopy system for UPE spectral pattern analysis. It is based on a highly efficient lens systems and a transmission-type diffraction grating with a highly sensitive, cooled, charge-coupled-device (CCD) camera. Spectral pattern analysis of the human body was done for a fingertip using the developed system. The UPE spectrum covers the spectral range of 450-750nm, with a dominant emission region of 570-670nm. The primary peak is located in the 600-650nm region. Furthermore, application of UPE source exploration was demonstrated with the chemiluminescence spectrum of melanin and coexistence with oxidized linoleic acid. Copyright © 2016 Elsevier B.V. All rights reserved.

Infrared micro-spectral imaging: distinction of tissue types in axillary lymph node histology

PubMed Central

Bird, Benjamin; Miljkovic, Milos; Romeo, Melissa J; Smith, Jennifer; Stone, Nicholas; George, Michael W; Diem, Max

2008-01-01

Background Histopathologic evaluation of surgical specimens is a well established technique for disease identification, and has remained relatively unchanged since its clinical introduction. Although it is essential for clinical investigation, histopathologic identification of tissues remains a time consuming and subjective technique, with unsatisfactory levels of inter- and intra-observer discrepancy. A novel approach for histological recognition is to use Fourier Transform Infrared (FT-IR) micro-spectroscopy. This non-destructive optical technique can provide a rapid measurement of sample biochemistry and identify variations that occur between healthy and diseased tissues. The advantage of this method is that it is objective and provides reproducible diagnosis, independent of fatigue, experience and inter-observer variability. Methods We report a method for analysing excised lymph nodes that is based on spectral pathology. In spectral pathology, an unstained (fixed or snap frozen) tissue section is interrogated by a beam of infrared light that samples pixels of 25 μm × 25 μm in size. This beam is rastered over the sample, and up to 100,000 complete infrared spectra are acquired for a given tissue sample. These spectra are subsequently analysed by a diagnostic computer algorithm that is trained by correlating spectral and histopathological features. Results We illustrate the ability of infrared micro-spectral imaging, coupled with completely unsupervised methods of multivariate statistical analysis, to accurately reproduce the histological architecture of axillary lymph nodes. By correlating spectral and histopathological features, a diagnostic algorithm was trained that allowed both accurate and rapid classification of benign and malignant tissues composed within different lymph nodes. This approach was successfully applied to both deparaffinised and frozen tissues and indicates that both intra-operative and more conventional surgical specimens can be diagnosed by this technique. Conclusion This paper provides strong evidence that automated diagnosis by means of infrared micro-spectral imaging is possible. Recent investigations within the author's laboratory upon lymph nodes have also revealed that cancers from different primary tumours provide distinctly different spectral signatures. Thus poorly differentiated and hard-to-determine cases of metastatic invasion, such as micrometastases, may additionally be identified by this technique. Finally, we differentiate benign and malignant tissues composed within axillary lymph nodes by completely automated methods of spectral analysis. PMID:18759967

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 ...

Analysis of spectral operators in one-dimensional domains

NASA Technical Reports Server (NTRS)

Maday, Y.

1985-01-01

Results are proven concerning certain projection operators on the space of all polynomials of degree less than or equal to N with respect to a class of one-dimensional weighted Sobolev spaces. The results are useful in the theory of the approximation of partial differential equations with spectral methods.

SAR image change detection using watershed and spectral clustering

NASA Astrophysics Data System (ADS)

Niu, Ruican; Jiao, L. C.; Wang, Guiting; Feng, Jie

2011-12-01

A new method of change detection in SAR images based on spectral clustering is presented in this paper. Spectral clustering is employed to extract change information from a pair images acquired on the same geographical area at different time. Watershed transform is applied to initially segment the big image into non-overlapped local regions, leading to reduce the complexity. Experiments results and system analysis confirm the effectiveness of the proposed algorithm.

A theoretical-experimental methodology for assessing the sensitivity of biomedical spectral imaging platforms, assays, and analysis methods.

PubMed

Leavesley, Silas J; Sweat, Brenner; Abbott, Caitlyn; Favreau, Peter; Rich, Thomas C

2018-01-01

Spectral imaging technologies have been used for many years by the remote sensing community. More recently, these approaches have been applied to biomedical problems, where they have shown great promise. However, biomedical spectral imaging has been complicated by the high variance of biological data and the reduced ability to construct test scenarios with fixed ground truths. Hence, it has been difficult to objectively assess and compare biomedical spectral imaging assays and technologies. Here, we present a standardized methodology that allows assessment of the performance of biomedical spectral imaging equipment, assays, and analysis algorithms. This methodology incorporates real experimental data and a theoretical sensitivity analysis, preserving the variability present in biomedical image data. We demonstrate that this approach can be applied in several ways: to compare the effectiveness of spectral analysis algorithms, to compare the response of different imaging platforms, and to assess the level of target signature required to achieve a desired performance. Results indicate that it is possible to compare even very different hardware platforms using this methodology. Future applications could include a range of optimization tasks, such as maximizing detection sensitivity or acquisition speed, providing high utility for investigators ranging from design engineers to biomedical scientists. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Biologically-inspired data decorrelation for hyper-spectral imaging

NASA Astrophysics Data System (ADS)

Picon, Artzai; Ghita, Ovidiu; Rodriguez-Vaamonde, Sergio; Iriondo, Pedro Ma; Whelan, Paul F.

2011-12-01

Hyper-spectral data allows the construction of more robust statistical models to sample the material properties than the standard tri-chromatic color representation. However, because of the large dimensionality and complexity of the hyper-spectral data, the extraction of robust features (image descriptors) is not a trivial issue. Thus, to facilitate efficient feature extraction, decorrelation techniques are commonly applied to reduce the dimensionality of the hyper-spectral data with the aim of generating compact and highly discriminative image descriptors. Current methodologies for data decorrelation such as principal component analysis (PCA), linear discriminant analysis (LDA), wavelet decomposition (WD), or band selection methods require complex and subjective training procedures and in addition the compressed spectral information is not directly related to the physical (spectral) characteristics associated with the analyzed materials. The major objective of this article is to introduce and evaluate a new data decorrelation methodology using an approach that closely emulates the human vision. The proposed data decorrelation scheme has been employed to optimally minimize the amount of redundant information contained in the highly correlated hyper-spectral bands and has been comprehensively evaluated in the context of non-ferrous material classification

Effective numerical method of spectral analysis of quantum graphs

NASA Astrophysics Data System (ADS)

Barrera-Figueroa, Víctor; Rabinovich, Vladimir S.

2017-05-01

We present in the paper an effective numerical method for the determination of the spectra of periodic metric graphs equipped by Schrödinger operators with real-valued periodic electric potentials as Hamiltonians and with Kirchhoff and Neumann conditions at the vertices. Our method is based on the spectral parameter power series method, which leads to a series representation of the dispersion equation, which is suitable for both analytical and numerical calculations. Several important examples demonstrate the effectiveness of our method for some periodic graphs of interest that possess potentials usually found in quantum mechanics.

Application of linear discriminant analysis and Attenuated Total Reflectance Fourier Transform Infrared microspectroscopy for diagnosis of colon cancer.

PubMed

Khanmohammadi, Mohammadreza; Bagheri Garmarudi, Amir; Samani, Simin; Ghasemi, Keyvan; Ashuri, Ahmad

2011-06-01

Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) microspectroscopy was applied for detection of colon cancer according to the spectral features of colon tissues. Supervised classification models can be trained to identify the tissue type based on the spectroscopic fingerprint. A total of 78 colon tissues were used in spectroscopy studies. Major spectral differences were observed in 1,740-900 cm(-1) spectral region. Several chemometric methods such as analysis of variance (ANOVA), cluster analysis (CA) and linear discriminate analysis (LDA) were applied for classification of IR spectra. Utilizing the chemometric techniques, clear and reproducible differences were observed between the spectra of normal and cancer cases, suggesting that infrared microspectroscopy in conjunction with spectral data processing would be useful for diagnostic classification. Using LDA technique, the spectra were classified into cancer and normal tissue classes with an accuracy of 95.8%. The sensitivity and specificity was 100 and 93.1%, respectively.

Meeting the Cool Neighbors. XII. An Optically Anchored Analysis of the Near-infrared Spectra of L Dwarfs

NASA Astrophysics Data System (ADS)

Cruz, Kelle L.; Núñez, Alejandro; Burgasser, Adam J.; Abrahams, Ellianna; Rice, Emily L.; Reid, I. Neill; Looper, Dagny

2018-01-01

Discrepancies between competing optical and near-infrared (NIR) spectral typing systems for L dwarfs have motivated us to search for a classification scheme that ties the optical and NIR schemes together, and addresses complexities in the spectral morphology. We use new and extant optical and NIR spectra to compile a sample of 171 L dwarfs, including 27 low-gravity β and γ objects, with spectral coverage from 0.6–2.4 μm. We present 155 new low-resolution NIR spectra and 19 new optical spectra. We utilize a method for analyzing NIR spectra that partially removes the broad-band spectral slope and reveals similarities in the absorption features between objects of the same optical spectral type. Using the optical spectra as an anchor, we generate near-infrared spectral average templates for L0–L8, L0–L4γ, and L0–L1β type dwarfs. These templates reveal that NIR spectral morphologies are correlated with the optical types. They also show the range of spectral morphologies spanned by each spectral type. We compare low-gravity and field-gravity templates to provide recommendations on the minimum required observations for credibly classifying low-gravity spectra using low-resolution NIR data. We use the templates to evaluate the existing NIR spectral standards and propose new ones where appropriate. Finally, we build on the work of Kirkpatrick et al. to provide a spectral typing method that is tied to the optical and can be used when only H or K band data are available. The methods we present here provide resolutions to several long-standing issues with classifying L dwarf spectra and could also be the foundation for a spectral classification scheme for cloudy exoplanets.

Multi-frequency data analysis in AFM by wavelet transform

NASA Astrophysics Data System (ADS)

Pukhova, V.; Ferrini, G.

2017-10-01

Interacting cantilevers in AFM experiments generate non-stationary, multi-frequency signals consisting of numerous excited flexural and torsional modes and their harmonics. The analysis of such signals is challenging, requiring special methodological approaches and a powerful mathematical apparatus. The most common approach to the signal analysis is to apply Fourier transform analysis. However, FT gives accurate spectra for stationary signals, and for signals changing their spectral content over time, FT provides only an averaged spectrum. Hence, for non-stationary and rapidly varying signals, such as those from interacting cantilevers, a method that shows the spectral evolution in time is needed. One of the most powerful techniques, allowing detailed time-frequency representation of signals, is the wavelet transform. It is a method of analysis that allows representation of energy associated to the signal at a particular frequency and time, providing correlation between the spectral and temporal features of the signal, unlike FT. This is particularly important in AFM experiments because signals nonlinearities contains valuable information about tip-sample interactions and consequently surfaces properties. The present work is aimed to show the advantages of wavelet transform in comparison with FT using as an example the force curve analysis in dynamic force spectroscopy.

Comment on "Astronomical constraints on the duration of the Early Jurassic Pliensbachian Stage and global climatic fluctuations" [Earth Planet. Sci. Lett. 455 (2016) 149-165

NASA Astrophysics Data System (ADS)

Smith, David G.; Bailey, Robin J.

2018-01-01

Astrochronology employs spectral analysis of stratigraphic data series to substantiate and quantify depositional cyclicity, and thus to establish a probable causal link between cases of rhythmic bedding and periodic orbitally-forced climate change. Vaughan et al. (2011 - not cited by Ruhl et al.) showed that the spectral methods conventionally used in cyclostratigraphy will generate false positive results - they will identify multiple cycles that are not present in the data. Tests with synthetic random datasets are both a simple and an essential way to prove this. Ruhl et al. (2016) used the methods to which these criticisms apply in their analysis of XRF-compositional data series from the Early Jurassic of the Mochras borehole, Wales. We use properly corrected methods to re-examine some of their data, showing that their spectral results are not valid, thus casting doubt on their proposed calibration of Pliensbachian time.

Multi-Dimensional High Order Essentially Non-Oscillatory Finite Difference Methods in Generalized Coordinates

NASA Technical Reports Server (NTRS)

Shu, Chi-Wang

1998-01-01

This project is about the development of high order, non-oscillatory type schemes for computational fluid dynamics. Algorithm analysis, implementation, and applications are performed. Collaborations with NASA scientists have been carried out to ensure that the research is relevant to NASA objectives. The combination of ENO finite difference method with spectral method in two space dimension is considered, jointly with Cai [3]. The resulting scheme behaves nicely for the two dimensional test problems with or without shocks. Jointly with Cai and Gottlieb, we have also considered one-sided filters for spectral approximations to discontinuous functions [2]. We proved theoretically the existence of filters to recover spectral accuracy up to the discontinuity. We also constructed such filters for practical calculations.

An Extended Spectral-Spatial Classification Approach for Hyperspectral Data

NASA Astrophysics Data System (ADS)

Akbari, D.

2017-11-01

In this paper an extended classification approach for hyperspectral imagery based on both spectral and spatial information is proposed. The spatial information is obtained by an enhanced marker-based minimum spanning forest (MSF) algorithm. Three different methods of dimension reduction are first used to obtain the subspace of hyperspectral data: (1) unsupervised feature extraction methods including principal component analysis (PCA), independent component analysis (ICA), and minimum noise fraction (MNF); (2) supervised feature extraction including decision boundary feature extraction (DBFE), discriminate analysis feature extraction (DAFE), and nonparametric weighted feature extraction (NWFE); (3) genetic algorithm (GA). The spectral features obtained are then fed into the enhanced marker-based MSF classification algorithm. In the enhanced MSF algorithm, the markers are extracted from the classification maps obtained by both SVM and watershed segmentation algorithm. To evaluate the proposed approach, the Pavia University hyperspectral data is tested. Experimental results show that the proposed approach using GA achieves an approximately 8 % overall accuracy higher than the original MSF-based algorithm.

Structural, spectral, NLO and MEP analysis of the [MgO2Ti2(OPri)6], [MgO2Ti2(OPri)2(acac)4] and [MgO2Ti2(OPri)2(bzac)4] by DFT method

NASA Astrophysics Data System (ADS)

Sayin, Koray; Karakaş, Duran

2015-06-01

Quantum chemical calculations are performed on [MgO2Ti2(OPri)6] and [MgO2Ti2(OPri)2(L)4] complexes. L is acetylacetonate (acac) and benzoylacetonate (bzac) anion. The crystal structures of these complexes have not been obtained as experimentally but optimized structures of these complexes are obtained as theoretically in this study. Universal force field (UFF) and DFT/B3LYP method are used to obtain optimized structures. Theoretical spectral analysis (IR, 1H and 13C NMR) is compared with their experimental values. A good agreement is found between experimental and theoretical spectral analysis. These results mean that the optimized structures of mentioned complexes are appropriate. Additionally, the active sites of mentioned complexes are determined by molecular electrostatic potential (MEP) diagrams and non-linear optical (NLO) properties are investigated.

Groupwise shape analysis of the hippocampus using spectral matching

NASA Astrophysics Data System (ADS)

Shakeri, Mahsa; Lombaert, Hervé; Lippé, Sarah; Kadoury, Samuel

2014-03-01

The hippocampus is a prominent subcortical feature of interest in many neuroscience studies. Its subtle morphological changes often predicate illnesses, including Alzheimer's, schizophrenia or epilepsy. The precise location of structural differences requires a reliable correspondence between shapes across a population. In this paper, we propose an automated method for groupwise hippocampal shape analysis based on a spectral decomposition of a group of shapes to solve the correspondence problem between sets of meshes. The framework generates diffeomorphic correspondence maps across a population, which enables us to create a mean shape. Morphological changes are then located between two groups of subjects. The performance of the proposed method was evaluated on a dataset of 42 hippocampus shapes and compared with a state-of-the-art structural shape analysis approach, using spherical harmonics. Difference maps between mean shapes of two test groups demonstrates that the two approaches showed results with insignificant differences, while Gaussian curvature measures calculated between matched vertices showed a better fit and reduced variability with spectral matching.

Optimization design of spectral discriminator for high-spectral-resolution lidar based on error analysis.

PubMed

Di, Huige; Zhang, Zhanfei; Hua, Hangbo; Zhang, Jiaqi; Hua, Dengxin; Wang, Yufeng; He, Tingyao

2017-03-06

Accurate aerosol optical properties could be obtained via the high spectral resolution lidar (HSRL) technique, which employs a narrow spectral filter to suppress the Rayleigh or Mie scattering in lidar return signals. The ability of the filter to suppress Rayleigh or Mie scattering is critical for HSRL. Meanwhile, it is impossible to increase the rejection of the filter without limitation. How to optimize the spectral discriminator and select the appropriate suppression rate of the signal is important to us. The HSRL technology was thoroughly studied based on error propagation. Error analyses and sensitivity studies were carried out on the transmittance characteristics of the spectral discriminator. Moreover, ratwo different spectroscopic methods for HSRL were described and compared: one is to suppress the Mie scattering; the other is to suppress the Rayleigh scattering. The corresponding HSRLs were simulated and analyzed. The results show that excessive suppression of Rayleigh scattering or Mie scattering in a high-spectral channel is not necessary if the transmittance of the spectral filter for molecular and aerosol scattering signals can be well characterized. When the ratio of transmittance of the spectral filter for aerosol scattering and molecular scattering is less than 0.1 or greater than 10, the detection error does not change much with its value. This conclusion implies that we have more choices for the high-spectral discriminator in HSRL. Moreover, the detection errors of HSRL regarding the two spectroscopic methods vary greatly with the atmospheric backscattering ratio. To reduce the detection error, it is necessary to choose a reasonable spectroscopic method. The detection method of suppressing the Rayleigh signal and extracting the Mie signal can achieve less error in a clear atmosphere, while the method of suppressing the Mie signal and extracting the Rayleigh signal can achieve less error in a polluted atmosphere.

SpecViz: Interactive Spectral Data Analysis

NASA Astrophysics Data System (ADS)

Earl, Nicholas Michael; STScI

2016-06-01

The astronomical community is about to enter a new generation of scientific enterprise. With next-generation instrumentation and advanced capabilities, the need has arisen to equip astronomers with the necessary tools to deal with large, multi-faceted data. The Space Telescope Science Institute has initiated a data analysis forum for the creation, development, and maintenance of software tools for the interpretation of these new data sets. SpecViz is a spectral 1-D interactive visualization and analysis application built with Python in an open source development environment. A user-friendly GUI allows for a fast, interactive approach to spectral analysis. SpecViz supports handling of unique and instrument-specific data, incorporation of advanced spectral unit handling and conversions in a flexible, high-performance interactive plotting environment. Active spectral feature analysis is possible through interactive measurement and statistical tools. It can be used to build wide-band SEDs, with the capability of combining or overplotting data products from various instruments. SpecViz sports advanced toolsets for filtering and detrending spectral lines; identifying, isolating, and manipulating spectral features; as well as utilizing spectral templates for renormalizing data in an interactive way. SpecViz also includes a flexible model fitting toolset that allows for multi-component models, as well as custom models, to be used with various fitting and decomposition routines. SpecViz also features robust extension via custom data loaders and connection to the central communication system underneath the interface for more advanced control. Incorporation with Jupyter notebooks via connection with the active iPython kernel allows for SpecViz to be used in addition to a user’s normal workflow without demanding the user drastically alter their method of data analysis. In addition, SpecViz allows the interactive analysis of multi-object spectroscopy in the same straight-forward, consistent way. Through the development of such tools, STScI hopes to unify astronomical data analysis software for JWST and other instruments, allowing for efficient, reliable, and consistent scientific results.

Spectral analysis software improves confidence in plant and soil water stable isotope analyses performed by isotope ratio infrared spectroscopy (IRIS).

PubMed

West, A G; Goldsmith, G R; Matimati, I; Dawson, T E

2011-08-30

Previous studies have demonstrated the potential for large errors to occur when analyzing waters containing organic contaminants using isotope ratio infrared spectroscopy (IRIS). In an attempt to address this problem, IRIS manufacturers now provide post-processing spectral analysis software capable of identifying samples with the types of spectral interference that compromises their stable isotope analysis. Here we report two independent tests of this post-processing spectral analysis software on two IRIS systems, OA-ICOS (Los Gatos Research Inc.) and WS-CRDS (Picarro Inc.). Following a similar methodology to a previous study, we cryogenically extracted plant leaf water and soil water and measured the δ(2)H and δ(18)O values of identical samples by isotope ratio mass spectrometry (IRMS) and IRIS. As an additional test, we analyzed plant stem waters and tap waters by IRMS and IRIS in an independent laboratory. For all tests we assumed that the IRMS value represented the "true" value against which we could compare the stable isotope results from the IRIS methods. Samples showing significant deviations from the IRMS value (>2σ) were considered to be contaminated and representative of spectral interference in the IRIS measurement. Over the two studies, 83% of plant species were considered contaminated on OA-ICOS and 58% on WS-CRDS. Post-analysis, spectra were analyzed using the manufacturer's spectral analysis software, in order to see if the software correctly identified contaminated samples. In our tests the software performed well, identifying all the samples with major errors. However, some false negatives indicate that user evaluation and testing of the software are necessary. Repeat sampling of plants showed considerable variation in the discrepancies between IRIS and IRMS. As such, we recommend that spectral analysis of IRIS data must be incorporated into standard post-processing routines. Furthermore, we suggest that the results from spectral analysis be included when reporting stable isotope data from IRIS. Copyright © 2011 John Wiley & Sons, Ltd.

Novel Spectral Representations and Sparsity-Driven Algorithms for Shape Modeling and Analysis

NASA Astrophysics Data System (ADS)

Zhong, Ming

In this dissertation, we focus on extending classical spectral shape analysis by incorporating spectral graph wavelets and sparsity-seeking algorithms. Defined with the graph Laplacian eigenbasis, the spectral graph wavelets are localized both in the vertex domain and graph spectral domain, and thus are very effective in describing local geometry. With a rich dictionary of elementary vectors and forcing certain sparsity constraints, a real life signal can often be well approximated by a very sparse coefficient representation. The many successful applications of sparse signal representation in computer vision and image processing inspire us to explore the idea of employing sparse modeling techniques with dictionary of spectral basis to solve various shape modeling problems. Conventional spectral mesh compression uses the eigenfunctions of mesh Laplacian as shape bases, which are highly inefficient in representing local geometry. To ameliorate, we advocate an innovative approach to 3D mesh compression using spectral graph wavelets as dictionary to encode mesh geometry. The spectral graph wavelets are locally defined at individual vertices and can better capture local shape information than Laplacian eigenbasis. The multi-scale SGWs form a redundant dictionary as shape basis, so we formulate the compression of 3D shape as a sparse approximation problem that can be readily handled by greedy pursuit algorithms. Surface inpainting refers to the completion or recovery of missing shape geometry based on the shape information that is currently available. We devise a new surface inpainting algorithm founded upon the theory and techniques of sparse signal recovery. Instead of estimating the missing geometry directly, our novel method is to find this low-dimensional representation which describes the entire original shape. More specifically, we find that, for many shapes, the vertex coordinate function can be well approximated by a very sparse coefficient representation with respect to the dictionary comprising its Laplacian eigenbasis, and it is then possible to recover this sparse representation from partial measurements of the original shape. Taking advantage of the sparsity cue, we advocate a novel variational approach for surface inpainting, integrating data fidelity constraints on the shape domain with coefficient sparsity constraints on the transformed domain. Because of the powerful properties of Laplacian eigenbasis, the inpainting results of our method tend to be globally coherent with the remaining shape. Informative and discriminative feature descriptors are vital in qualitative and quantitative shape analysis for a large variety of graphics applications. We advocate novel strategies to define generalized, user-specified features on shapes. Our new region descriptors are primarily built upon the coefficients of spectral graph wavelets that are both multi-scale and multi-level in nature, consisting of both local and global information. Based on our novel spectral feature descriptor, we developed a user-specified feature detection framework and a tensor-based shape matching algorithm. Through various experiments, we demonstrate the competitive performance of our proposed methods and the great potential of spectral basis and sparsity-driven methods for shape modeling.

Stability of compressible Taylor-Couette flow

NASA Technical Reports Server (NTRS)

Kao, Kai-Hsiung; Chow, Chuen-Yen

1991-01-01

Compressible stability equations are solved using the spectral collocation method in an attempt to study the effects of temperature difference and compressibility on the stability of Taylor-Couette flow. It is found that the Chebyshev collocation spectral method yields highly accurate results using fewer grid points for solving stability problems. Comparisons are made between the result obtained by assuming small Mach number with a uniform temperature distribution and that based on fully incompressible analysis.

Photoplethysmographic imaging via spectrally demultiplexed erythema fluctuation analysis for remote heart rate monitoring

NASA Astrophysics Data System (ADS)

Deglint, Jason; Chung, Audrey G.; Chwyl, Brendan; Amelard, Robert; Kazemzadeh, Farnoud; Wang, Xiao Yu; Clausi, David A.; Wong, Alexander

2016-03-01

Traditional photoplethysmographic imaging (PPGI) systems use the red, green, and blue (RGB) broadband measurements of a consumer digital camera to remotely estimate a patients heart rate; however, these broadband RGB signals are often corrupted by ambient noise, making the extraction of subtle fluctuations indicative of heart rate difficult. Therefore, the use of narrow-band spectral measurements can significantly improve the accuracy. We propose a novel digital spectral demultiplexing (DSD) method to infer narrow-band spectral information from acquired broadband RGB measurements in order to estimate heart rate via the computation of motion- compensated skin erythema fluctuation. Using high-resolution video recordings of human participants, multiple measurement locations are automatically identified on the cheeks of an individual, and motion-compensated broadband reflectance measurements are acquired at each measurement location over time via measurement location tracking. The motion-compensated broadband reflectance measurements are spectrally demultiplexed using a non-linear inverse model based on the spectral sensitivity of the camera's detector. A PPG signal is then computed from the demultiplexed narrow-band spectral information via skin erythema fluctuation analysis, with improved signal-to-noise ratio allowing for reliable remote heart rate measurements. To assess the effectiveness of the proposed system, a set of experiments involving human motion in a front-facing position were performed under ambient lighting conditions. Experimental results indicate that the proposed system achieves robust and accurate heart rate measurements and can provide additional information about the participant beyond the capabilities of traditional PPGI methods.

A comparison of digital multi-spectral imagery versus conventional photography for mapping seagrass in Indian River Lagoon, Florida

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

Virnstein, R.; Tepera, M.; Beazley, L.

1997-06-01

A pilot study is very briefly summarized in the article. The study tested the potential of multi-spectral digital imagery for discrimination of seagrass densities and species, algae, and bottom types. Imagery was obtained with the Compact Airborne Spectral Imager (casi) and two flight lines flown with hyper-spectral mode. The photogrammetric method used allowed interpretation of the highest quality product, eliminating limitations caused by outdated or poor quality base maps and the errors associated with transfer of polygons. Initial image analysis indicates that the multi-spectral imagery has several advantages, including sophisticated spectral signature recognition and classification, ease of geo-referencing, and rapidmore » mosaicking.« less

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.

Robust demarcation of basal cell carcinoma by dependent component analysis-based segmentation of multi-spectral fluorescence images.

PubMed

Kopriva, Ivica; Persin, Antun; Puizina-Ivić, Neira; Mirić, Lina

2010-07-02

This study was designed to demonstrate robust performance of the novel dependent component analysis (DCA)-based approach to demarcation of the basal cell carcinoma (BCC) through unsupervised decomposition of the red-green-blue (RGB) fluorescent image of the BCC. Robustness to intensity fluctuation is due to the scale invariance property of DCA algorithms, which exploit spectral and spatial diversities between the BCC and the surrounding tissue. Used filtering-based DCA approach represents an extension of the independent component analysis (ICA) and is necessary in order to account for statistical dependence that is induced by spectral similarity between the BCC and surrounding tissue. This generates weak edges what represents a challenge for other segmentation methods as well. By comparative performance analysis with state-of-the-art image segmentation methods such as active contours (level set), K-means clustering, non-negative matrix factorization, ICA and ratio imaging we experimentally demonstrate good performance of DCA-based BCC demarcation in two demanding scenarios where intensity of the fluorescent image has been varied almost two orders of magnitude. Copyright 2010 Elsevier B.V. All rights reserved.

Measurement analysis of two radials with a common-origin point and its application.

PubMed

Liu, Zhenyao; Yang, Jidong; Zhu, Weiwei; Zhou, Shang; Tan, Xuanping

2017-08-01

In spectral analysis, a chemical component is usually identified by its characteristic spectra, especially the peaks. If two components have overlapping spectral peaks, they are generally considered to be indiscriminate in current analytical chemistry textbooks and related literature. However, if the intensities of the overlapping major spectral peaks are additive, and have different rates of change with respect to variations in the concentration of the individual components, a simple method, named the 'common-origin ray', for the simultaneous determination of two components can be established. Several case studies highlighting its applications are presented. Copyright © 2017 John Wiley & Sons, Ltd.

Application and evaluation of ISVR method in QuickBird image fusion

NASA Astrophysics Data System (ADS)

Cheng, Bo; Song, Xiaolu

2014-05-01

QuickBird satellite images are widely used in many fields, and applications have put forward high requirements for the integration of the spatial information and spectral information of the imagery. A fusion method for high resolution remote sensing images based on ISVR is identified in this study. The core principle of ISVS is taking the advantage of radicalization targeting to remove the effect of different gain and error of satellites' sensors. Transformed from DN to radiance, the multi-spectral image's energy is used to simulate the panchromatic band. The linear regression analysis is carried through the simulation process to find a new synthetically panchromatic image, which is highly linearly correlated to the original panchromatic image. In order to evaluate, test and compare the algorithm results, this paper used ISVR and other two different fusion methods to give a comparative study of the spatial information and spectral information, taking the average gradient and the correlation coefficient as an indicator. Experiments showed that this method could significantly improve the quality of fused image, especially in preserving spectral information, to maximize the spectral information of original multispectral images, while maintaining abundant spatial information.

[On-Orbit Multispectral Sensor Characterization Based on Spectral Tarps].

PubMed

Li, Xin; Zhang, Li-ming; Chen, Hong-yao; Xu, Wei-wei

2016-03-01

The multispectral remote sensing technology has been a primary means in the research of biomass monitoring, climate change, disaster prediction and etc. The spectral sensitivity is essential in the quantitative analysis of remote sensing data. When the sensor is running in the space, it will be influenced by cosmic radiation, severe change of temperature, chemical molecular contamination, cosmic dust and etc. As a result, the spectral sensitivity will degrade by time, which has great implication on the accuracy and consistency of the physical measurements. This paper presents a characterization method of the degradation based on man-made spectral targets. Firstly, a degradation model is established in the paper. Then, combined with equivalent reflectance of spectral targets measured and inverted from image, the degradation characterization can be achieved. The simulation and on orbit experiment results showed that, using the proposed method, the change of center wavelength and band width can be monotored. The method proposed in the paper has great significance for improving the accuracy of long time series remote sensing data product and comprehensive utilization level of multi sensor data products.

Diverse Power Iteration Embeddings and Its Applications

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

Huang H.; Yoo S.; Yu, D.

2014-12-14

Abstract—Spectral Embedding is one of the most effective dimension reduction algorithms in data mining. However, its computation complexity has to be mitigated in order to apply it for real-world large scale data analysis. Many researches have been focusing on developing approximate spectral embeddings which are more efficient, but meanwhile far less effective. This paper proposes Diverse Power Iteration Embeddings (DPIE), which not only retains the similar efficiency of power iteration methods but also produces a series of diverse and more effective embedding vectors. We test this novel method by applying it to various data mining applications (e.g. clustering, anomaly detectionmore » and feature selection) and evaluating their performance improvements. The experimental results show our proposed DPIE is more effective than popular spectral approximation methods, and obtains the similar quality of classic spectral embedding derived from eigen-decompositions. Moreover it is extremely fast on big data applications. For example in terms of clustering result, DPIE achieves as good as 95% of classic spectral clustering on the complex datasets but 4000+ times faster in limited memory environment.« less

Prediction of the spectral reflectance of laser-generated color prints by combination of an optical model and learning methods.

PubMed

Nébouy, David; Hébert, Mathieu; Fournel, Thierry; Larina, Nina; Lesur, Jean-Luc

2015-09-01

Recent color printing technologies based on the principle of revealing colors on pre-functionalized achromatic supports by laser irradiation offer advanced functionalities, especially for security applications. However, for such technologies, the color prediction is challenging, compared to classic ink-transfer printing systems. The spectral properties of the coloring materials modified by the lasers are not precisely known and may strongly vary, depending on the laser settings, in a nonlinear manner. We show in this study, through the example of the color laser marking (CLM) technology, based on laser bleaching of a mixture of pigments, that the combination of an adapted optical reflectance model and learning methods to get the model's parameters enables prediction of the spectral reflectance of any printable color with rather good accuracy. Even though the pigment mixture is formulated from three colored pigments, an analysis of the dimensionality of the spectral space generated by CLM printing, thanks to a principal component analysis decomposition, shows that at least four spectral primaries are needed for accurate spectral reflectance predictions. A polynomial interpolation is then used to relate RGB laser intensities with virtual coordinates of new basis vectors. By studying the influence of the number of calibration patches on the prediction accuracy, we can conclude that a reasonable number of 130 patches are enough to achieve good accuracy in this application.

Spectral iterative method and convergence analysis for solving nonlinear fractional differential equation

NASA Astrophysics Data System (ADS)

Yarmohammadi, M.; Javadi, S.; Babolian, E.

2018-04-01

In this study a new spectral iterative method (SIM) based on fractional interpolation is presented for solving nonlinear fractional differential equations (FDEs) involving Caputo derivative. This method is equipped with a pre-algorithm to find the singularity index of solution of the problem. This pre-algorithm gives us a real parameter as the index of the fractional interpolation basis, for which the SIM achieves the highest order of convergence. In comparison with some recent results about the error estimates for fractional approximations, a more accurate convergence rate has been attained. We have also proposed the order of convergence for fractional interpolation error under the L2-norm. Finally, general error analysis of SIM has been considered. The numerical results clearly demonstrate the capability of the proposed method.

Extracting spectral contrast in Landsat Thematic Mapper image data using selective principal component analysis

USGS Publications Warehouse

Chavez, P.S.; Kwarteng, A.Y.

1989-01-01

A challenge encountered with Landsat Thematic Mapper (TM) data, which includes data from size reflective spectral bands, is displaying as much information as possible in a three-image set for color compositing or digital analysis. Principal component analysis (PCA) applied to the six TM bands simultaneously is often used to address this problem. However, two problems that can be encountered using the PCA method are that information of interest might be mathematically mapped to one of the unused components and that a color composite can be difficult to interpret. "Selective' PCA can be used to minimize both of these problems. The spectral contrast among several spectral regions was mapped for a northern Arizona site using Landsat TM data. Field investigations determined that most of the spectral contrast seen in this area was due to one of the following: the amount of iron and hematite in the soils and rocks, vegetation differences, standing and running water, or the presence of gypsum, which has a higher moisture retention capability than do the surrounding soils and rocks. -from Authors

Mapping minerals, amorphous materials, environmental materials, vegetation, water, ice and snow, and other materials: The USGS tricorder algorithm

NASA Technical Reports Server (NTRS)

Clark, Roger N.; Swayze, Gregg A.

1995-01-01

One of the challenges of Imaging Spectroscopy is the identification, mapping and abundance determination of materials, whether mineral, vegetable, or liquid, given enough spectral range, spectral resolution, signal to noise, and spatial resolution. Many materials show diagnostic absorption features in the visual and near infrared region (0.4 to 2.5 micrometers) of the spectrum. This region is covered by the modern imaging spectrometers such as AVIRIS. The challenge is to identify the materials from absorption bands in their spectra, and determine what specific analyses must be done to derive particular parameters of interest, ranging from simply identifying its presence to deriving its abundance, or determining specific chemistry of the material. Recently, a new analysis algorithm was developed that uses a digital spectral library of known materials and a fast, modified-least-squares method of determining if a single spectral feature for a given material is present. Clark et al. made another advance in the mapping algorithm: simultaneously mapping multiple minerals using multiple spectral features. This was done by a modified-least-squares fit of spectral features, from data in a digital spectral library, to corresponding spectral features in the image data. This version has now been superseded by a more comprehensive spectral analysis system called Tricorder.

Domain decomposition methods for systems of conservation laws: Spectral collocation approximations

NASA Technical Reports Server (NTRS)

Quarteroni, Alfio

1989-01-01

Hyperbolic systems of conversation laws are considered which are discretized in space by spectral collocation methods and advanced in time by finite difference schemes. At any time-level a domain deposition method based on an iteration by subdomain procedure was introduced yielding at each step a sequence of independent subproblems (one for each subdomain) that can be solved simultaneously. The method is set for a general nonlinear problem in several space variables. The convergence analysis, however, is carried out only for a linear one-dimensional system with continuous solutions. A precise form of the error reduction factor at each iteration is derived. Although the method is applied here to the case of spectral collocation approximation only, the idea is fairly general and can be used in a different context as well. For instance, its application to space discretization by finite differences is straight forward.

SpectralNET – an application for spectral graph analysis and visualization

PubMed Central

Forman, Joshua J; Clemons, Paul A; Schreiber, Stuart L; Haggarty, Stephen J

2005-01-01

Background Graph theory provides a computational framework for modeling a variety of datasets including those emerging from genomics, proteomics, and chemical genetics. Networks of genes, proteins, small molecules, or other objects of study can be represented as graphs of nodes (vertices) and interactions (edges) that can carry different weights. SpectralNET is a flexible application for analyzing and visualizing these biological and chemical networks. Results Available both as a standalone .NET executable and as an ASP.NET web application, SpectralNET was designed specifically with the analysis of graph-theoretic metrics in mind, a computational task not easily accessible using currently available applications. Users can choose either to upload a network for analysis using a variety of input formats, or to have SpectralNET generate an idealized random network for comparison to a real-world dataset. Whichever graph-generation method is used, SpectralNET displays detailed information about each connected component of the graph, including graphs of degree distribution, clustering coefficient by degree, and average distance by degree. In addition, extensive information about the selected vertex is shown, including degree, clustering coefficient, various distance metrics, and the corresponding components of the adjacency, Laplacian, and normalized Laplacian eigenvectors. SpectralNET also displays several graph visualizations, including a linear dimensionality reduction for uploaded datasets (Principal Components Analysis) and a non-linear dimensionality reduction that provides an elegant view of global graph structure (Laplacian eigenvectors). Conclusion SpectralNET provides an easily accessible means of analyzing graph-theoretic metrics for data modeling and dimensionality reduction. SpectralNET is publicly available as both a .NET application and an ASP.NET web application from . Source code is available upon request. PMID:16236170

SpectralNET--an application for spectral graph analysis and visualization.

PubMed

Forman, Joshua J; Clemons, Paul A; Schreiber, Stuart L; Haggarty, Stephen J

2005-10-19

Graph theory provides a computational framework for modeling a variety of datasets including those emerging from genomics, proteomics, and chemical genetics. Networks of genes, proteins, small molecules, or other objects of study can be represented as graphs of nodes (vertices) and interactions (edges) that can carry different weights. SpectralNET is a flexible application for analyzing and visualizing these biological and chemical networks. Available both as a standalone .NET executable and as an ASP.NET web application, SpectralNET was designed specifically with the analysis of graph-theoretic metrics in mind, a computational task not easily accessible using currently available applications. Users can choose either to upload a network for analysis using a variety of input formats, or to have SpectralNET generate an idealized random network for comparison to a real-world dataset. Whichever graph-generation method is used, SpectralNET displays detailed information about each connected component of the graph, including graphs of degree distribution, clustering coefficient by degree, and average distance by degree. In addition, extensive information about the selected vertex is shown, including degree, clustering coefficient, various distance metrics, and the corresponding components of the adjacency, Laplacian, and normalized Laplacian eigenvectors. SpectralNET also displays several graph visualizations, including a linear dimensionality reduction for uploaded datasets (Principal Components Analysis) and a non-linear dimensionality reduction that provides an elegant view of global graph structure (Laplacian eigenvectors). SpectralNET provides an easily accessible means of analyzing graph-theoretic metrics for data modeling and dimensionality reduction. SpectralNET is publicly available as both a .NET application and an ASP.NET web application from http://chembank.broad.harvard.edu/resources/. Source code is available upon request.

[Development of sample pretreatment techniques-rapid detection coupling methods for food security analysis].

PubMed

Huang, Yichun; Ding, Weiwei; Zhang, Zhuomin; Li, Gongke

2013-07-01

This paper summarizes the recent developments of the rapid detection methods for food security, such as sensors, optical techniques, portable spectral analysis, enzyme-linked immunosorbent assay, portable gas chromatograph, etc. Additionally, the applications of these rapid detection methods coupled with sample pretreatment techniques in real food security analysis are reviewed. The coupling technique has the potential to provide references to establish the selective, precise and quantitative rapid detection methods in food security analysis.

Analysis and application of Fourier transform spectroscopy in atmospheric remote sensing

NASA Technical Reports Server (NTRS)

Park, J. H.

1984-01-01

An analysis method for Fourier transform spectroscopy is summarized with applications to various types of distortion in atmospheric absorption spectra. This analysis method includes the fast Fourier transform method for simulating the interferometric spectrum and the nonlinear least-squares method for retrieving the information from a measured spectrum. It is shown that spectral distortions can be simulated quite well and that the correct information can be retrieved from a distorted spectrum by this analysis technique.

Fine structure of the low-frequency spectra of heart rate and blood pressure

PubMed Central

Kuusela, Tom A; Kaila, Timo J; Kähönen, Mika

2003-01-01

Background The aim of this study was to explore the principal frequency components of the heart rate and blood pressure variability in the low frequency (LF) and very low frequency (VLF) band. The spectral composition of the R–R interval (RRI) and systolic arterial blood pressure (SAP) in the frequency range below 0.15 Hz were carefully analyzed using three different spectral methods: Fast Fourier transform (FFT), Wigner-Ville distribution (WVD), and autoregression (AR). All spectral methods were used to create time–frequency plots to uncover the principal spectral components that are least dependent on time. The accurate frequencies of these components were calculated from the pole decomposition of the AR spectral density after determining the optimal model order – the most crucial factor when using this method – with the help of FFT and WVD methods. Results Spectral analysis of the RRI and SAP of 12 healthy subjects revealed that there are always at least three spectral components below 0.15 Hz. The three principal frequency components are 0.026 ± 0.003 (mean ± SD) Hz, 0.076 ± 0.012 Hz, and 0.117 ± 0.016 Hz. These principal components vary only slightly over time. FFT-based coherence and phase-function analysis suggests that the second and third components are related to the baroreflex control of blood pressure, since the phase difference between SAP and RRI was negative and almost constant, whereas the origin of the first component is different since no clear SAP–RRI phase relationship was found. Conclusion The above data indicate that spontaneous fluctuations in heart rate and blood pressure within the standard low-frequency range of 0.04–0.15 Hz typically occur at two frequency components rather than only at one as widely believed, and these components are not harmonically related. This new observation in humans can help explain divergent results in the literature concerning spontaneous low-frequency oscillations. It also raises methodological and computational questions regarding the usability and validity of the low-frequency spectral band when estimating sympathetic activity and baroreflex gain. PMID:14552660

Fine structure of the low-frequency spectra of heart rate and blood pressure.

PubMed

Kuusela, Tom A; Kaila, Timo J; Kähönen, Mika

2003-10-13

The aim of this study was to explore the principal frequency components of the heart rate and blood pressure variability in the low frequency (LF) and very low frequency (VLF) band. The spectral composition of the R-R interval (RRI) and systolic arterial blood pressure (SAP) in the frequency range below 0.15 Hz were carefully analyzed using three different spectral methods: Fast Fourier transform (FFT), Wigner-Ville distribution (WVD), and autoregression (AR). All spectral methods were used to create time-frequency plots to uncover the principal spectral components that are least dependent on time. The accurate frequencies of these components were calculated from the pole decomposition of the AR spectral density after determining the optimal model order--the most crucial factor when using this method--with the help of FFT and WVD methods. Spectral analysis of the RRI and SAP of 12 healthy subjects revealed that there are always at least three spectral components below 0.15 Hz. The three principal frequency components are 0.026 +/- 0.003 (mean +/- SD) Hz, 0.076 +/- 0.012 Hz, and 0.117 +/- 0.016 Hz. These principal components vary only slightly over time. FFT-based coherence and phase-function analysis suggests that the second and third components are related to the baroreflex control of blood pressure, since the phase difference between SAP and RRI was negative and almost constant, whereas the origin of the first component is different since no clear SAP-RRI phase relationship was found. The above data indicate that spontaneous fluctuations in heart rate and blood pressure within the standard low-frequency range of 0.04-0.15 Hz typically occur at two frequency components rather than only at one as widely believed, and these components are not harmonically related. This new observation in humans can help explain divergent results in the literature concerning spontaneous low-frequency oscillations. It also raises methodological and computational questions regarding the usability and validity of the low-frequency spectral band when estimating sympathetic activity and baroreflex gain.

A spectral analysis of the domain decomposed Monte Carlo method for linear systems

DOE PAGES

Slattery, Stuart R.; Evans, Thomas M.; Wilson, Paul P. H.

2015-09-08

The domain decomposed behavior of the adjoint Neumann-Ulam Monte Carlo method for solving linear systems is analyzed using the spectral properties of the linear oper- ator. Relationships for the average length of the adjoint random walks, a measure of convergence speed and serial performance, are made with respect to the eigenvalues of the linear operator. In addition, relationships for the effective optical thickness of a domain in the decomposition are presented based on the spectral analysis and diffusion theory. Using the effective optical thickness, the Wigner rational approxi- mation and the mean chord approximation are applied to estimate the leakagemore » frac- tion of random walks from a domain in the decomposition as a measure of parallel performance and potential communication costs. The one-speed, two-dimensional neutron diffusion equation is used as a model problem in numerical experiments to test the models for symmetric operators with spectral qualities similar to light water reactor problems. We find, in general, the derived approximations show good agreement with random walk lengths and leakage fractions computed by the numerical experiments.« less

Classification of narcotics in solid mixtures using principal component analysis and Raman spectroscopy.

PubMed

Ryder, Alan G

2002-03-01

Eighty-five solid samples consisting of illegal narcotics diluted with several different materials were analyzed by near-infrared (785 nm excitation) Raman spectroscopy. Principal Component Analysis (PCA) was employed to classify the samples according to narcotic type. The best sample discrimination was obtained by using the first derivative of the Raman spectra. Furthermore, restricting the spectral variables for PCA to 2 or 3% of the original spectral data according to the most intense peaks in the Raman spectrum of the pure narcotic resulted in a rapid discrimination method for classifying samples according to narcotic type. This method allows for the easy discrimination between cocaine, heroin, and MDMA mixtures even when the Raman spectra are complex or very similar. This approach of restricting the spectral variables also decreases the computational time by a factor of 30 (compared to the complete spectrum), making the methodology attractive for rapid automatic classification and identification of suspect materials.

Joint spatial-spectral hyperspectral image clustering using block-diagonal amplified affinity matrix

NASA Astrophysics Data System (ADS)

Fan, Lei; Messinger, David W.

2018-03-01

The large number of spectral channels in a hyperspectral image (HSI) produces a fine spectral resolution to differentiate between materials in a scene. However, difficult classes that have similar spectral signatures are often confused while merely exploiting information in the spectral domain. Therefore, in addition to spectral characteristics, the spatial relationships inherent in HSIs should also be considered for incorporation into classifiers. The growing availability of high spectral and spatial resolution of remote sensors provides rich information for image clustering. Besides the discriminating power in the rich spectrum, contextual information can be extracted from the spatial domain, such as the size and the shape of the structure to which one pixel belongs. In recent years, spectral clustering has gained popularity compared to other clustering methods due to the difficulty of accurate statistical modeling of data in high dimensional space. The joint spatial-spectral information could be effectively incorporated into the proximity graph for spectral clustering approach, which provides a better data representation by discovering the inherent lower dimensionality from the input space. We embedded both spectral and spatial information into our proposed local density adaptive affinity matrix, which is able to handle multiscale data by automatically selecting the scale of analysis for every pixel according to its neighborhood of the correlated pixels. Furthermore, we explored the "conductivity method," which aims at amplifying the block diagonal structure of the affinity matrix to further improve the performance of spectral clustering on HSI datasets.

Variables separation of the spectral BRDF for better understanding color variation in special effect pigment coatings.

PubMed

Ferrero, Alejandro; Rabal, Ana María; Campos, Joaquín; Pons, Alicia; Hernanz, María Luisa

2012-06-01

A type of representation of the spectral bidirectional reflectance distribution function (BRDF) is proposed that distinctly separates the spectral variable (wavelength) from the geometrical variables (spherical coordinates of the irradiation and viewing directions). Principal components analysis (PCA) is used in order to decompose the spectral BRDF in decorrelated spectral components, and the weight that they have at every geometrical configuration of irradiation/viewing is established. This method was applied to the spectral BRDF measurement of a special effect pigment sample, and four principal components with relevant variance were identified. These four components are enough to reproduce the great diversity of spectral reflectances observed at different geometrical configurations. Since this representation is able to separate spectral and geometrical variables, it facilitates the interpretation of the color variation of special effect pigments coatings versus the geometrical configuration of irradiation/viewing.

Scintillation Method of Analysis for Determination of Properties of Wear Particles in Lubricating Oils,

DTIC Science & Technology

1998-01-01

Scintillation Method of Analysis for Determination of Properties of Wear Particles in Lubricating Oils Andrey B. Alkhimov Applied Physics Insitute...lubricating oils; metrological properties ; scintillation spectral analysis; spectrometer; unit-to-unit diagnostics; wear particles. Introduction: In...filter. The use of air reduces the metrological properties of the method, but it saves the operators the trouble and expense of using argon and

SCPS: a fast implementation of a spectral method for detecting protein families on a genome-wide scale.

PubMed

Nepusz, Tamás; Sasidharan, Rajkumar; Paccanaro, Alberto

2010-03-09

An important problem in genomics is the automatic inference of groups of homologous proteins from pairwise sequence similarities. Several approaches have been proposed for this task which are "local" in the sense that they assign a protein to a cluster based only on the distances between that protein and the other proteins in the set. It was shown recently that global methods such as spectral clustering have better performance on a wide variety of datasets. However, currently available implementations of spectral clustering methods mostly consist of a few loosely coupled Matlab scripts that assume a fair amount of familiarity with Matlab programming and hence they are inaccessible for large parts of the research community. SCPS (Spectral Clustering of Protein Sequences) is an efficient and user-friendly implementation of a spectral method for inferring protein families. The method uses only pairwise sequence similarities, and is therefore practical when only sequence information is available. SCPS was tested on difficult sets of proteins whose relationships were extracted from the SCOP database, and its results were extensively compared with those obtained using other popular protein clustering algorithms such as TribeMCL, hierarchical clustering and connected component analysis. We show that SCPS is able to identify many of the family/superfamily relationships correctly and that the quality of the obtained clusters as indicated by their F-scores is consistently better than all the other methods we compared it with. We also demonstrate the scalability of SCPS by clustering the entire SCOP database (14,183 sequences) and the complete genome of the yeast Saccharomyces cerevisiae (6,690 sequences). Besides the spectral method, SCPS also implements connected component analysis and hierarchical clustering, it integrates TribeMCL, it provides different cluster quality tools, it can extract human-readable protein descriptions using GI numbers from NCBI, it interfaces with external tools such as BLAST and Cytoscape, and it can produce publication-quality graphical representations of the clusters obtained, thus constituting a comprehensive and effective tool for practical research in computational biology. Source code and precompiled executables for Windows, Linux and Mac OS X are freely available at http://www.paccanarolab.org/software/scps.

Novel full-spectral flow cytometry with multiple spectrally-adjacent fluorescent proteins and fluorochromes and visualization of in vivo cellular movement.

PubMed

Futamura, Koji; Sekino, Masashi; Hata, Akihiro; Ikebuchi, Ryoyo; Nakanishi, Yasutaka; Egawa, Gyohei; Kabashima, Kenji; Watanabe, Takeshi; Furuki, Motohiro; Tomura, Michio

2015-09-01

Flow cytometric analysis with multicolor fluoroprobes is an essential method for detecting biological signatures of cells. Here, we present a new full-spectral flow cytometer (spectral-FCM). Unlike conventional flow cytometer, this spectral-FCM acquires the emitted fluorescence for all probes across the full-spectrum from each cell with 32 channels sequential PMT unit after dispersion with prism, and extracts the signals of each fluoroprobe based on the spectral shape of each fluoroprobe using unique algorithm in high speed, high sensitive, accurate, automatic and real-time. The spectral-FCM detects the continuous changes in emission spectra from green to red of the photoconvertible protein, KikGR with high-spectral resolution and separates spectrally-adjacent fluoroprobes, such as FITC (Emission peak (Em) 519 nm) and EGFP (Em 507 nm). Moreover, the spectral-FCM can measure and subtract autofluorescence of each cell providing increased signal-to-noise ratios and improved resolution of dim samples, which leads to a transformative technology for investigation of single cell state and function. These advances make it possible to perform 11-color fluorescence analysis to visualize movement of multilinage immune cells by using KikGR-expressing mice. Thus, the novel spectral flow cytometry improves the combinational use of spectrally-adjacent various FPs and multicolor fluorochromes in metabolically active cell for the investigation of not only the immune system but also other research and clinical fields of use. © 2015 International Society for Advancement of Cytometry.

Bearing failure detection of micro wind turbine via power spectral density analysis for stator current signals spectrum

NASA Astrophysics Data System (ADS)

Mahmood, Faleh H.; Kadhim, Hussein T.; Resen, Ali K.; Shaban, Auday H.

2018-05-01

The failure such as air gap weirdness, rubbing, and scrapping between stator and rotor generator arise unavoidably and may cause extremely terrible results for a wind turbine. Therefore, we should pay more attention to detect and identify its cause-bearing failure in wind turbine to improve the operational reliability. The current paper tends to use of power spectral density analysis method of detecting internal race and external race bearing failure in micro wind turbine by estimation stator current signal of the generator. The failure detector method shows that it is well suited and effective for bearing failure detection.

Some spectral approximation of one-dimensional fourth-order problems

NASA Technical Reports Server (NTRS)

Bernardi, Christine; Maday, Yvon

1989-01-01

Some spectral type collocation method well suited for the approximation of fourth-order systems are proposed. The model problem is the biharmonic equation, in one and two dimensions when the boundary conditions are periodic in one direction. It is proved that the standard Gauss-Lobatto nodes are not the best choice for the collocation points. Then, a new set of nodes related to some generalized Gauss type quadrature formulas is proposed. Also provided is a complete analysis of these formulas including some new issues about the asymptotic behavior of the weights and we apply these results to the analysis of the collocation method.

Application of Fourier transform infrared spectroscopy with chemometrics on postmortem interval estimation based on pericardial fluids.

PubMed

Zhang, Ji; Li, Bing; Wang, Qi; Wei, Xin; Feng, Weibo; Chen, Yijiu; Huang, Ping; Wang, Zhenyuan

2017-12-21

Postmortem interval (PMI) evaluation remains a challenge in the forensic community due to the lack of efficient methods. Studies have focused on chemical analysis of biofluids for PMI estimation; however, no reports using spectroscopic methods in pericardial fluid (PF) are available. In this study, Fourier transform infrared (FTIR) spectroscopy with attenuated total reflectance (ATR) accessory was applied to collect comprehensive biochemical information from rabbit PF at different PMIs. The PMI-dependent spectral signature was determined by two-dimensional (2D) correlation analysis. The partial least square (PLS) and nu-support vector machine (nu-SVM) models were then established based on the acquired spectral dataset. Spectral variables associated with amide I, amide II, COO - , C-H bending, and C-O or C-OH vibrations arising from proteins, polypeptides, amino acids and carbohydrates, respectively, were susceptible to PMI in 2D correlation analysis. Moreover, the nu-SVM model appeared to achieve a more satisfactory prediction than the PLS model in calibration; the reliability of both models was determined in an external validation set. The study shows the possibility of application of ATR-FTIR methods in postmortem interval estimation using PF samples.

The software and algorithms for hyperspectral data processing

NASA Astrophysics Data System (ADS)

Shyrayeva, Anhelina; Martinov, Anton; Ivanov, Victor; Katkovsky, Leonid

2017-04-01

Hyperspectral remote sensing technique is widely used for collecting and processing -information about the Earth's surface objects. Hyperspectral data are combined to form a three-dimensional (x, y, λ) data cube. Department of Aerospace Research of the Institute of Applied Physical Problems of the Belarusian State University presents a general model of the software for hyperspectral image data analysis and processing. The software runs in Windows XP/7/8/8.1/10 environment on any personal computer. This complex has been has been written in C++ language using QT framework and OpenGL for graphical data visualization. The software has flexible structure that consists of a set of independent plugins. Each plugin was compiled as Qt Plugin and represents Windows Dynamic library (dll). Plugins can be categorized in terms of data reading types, data visualization (3D, 2D, 1D) and data processing The software has various in-built functions for statistical and mathematical analysis, signal processing functions like direct smoothing function for moving average, Savitzky-Golay smoothing technique, RGB correction, histogram transformation, and atmospheric correction. The software provides two author's engineering techniques for the solution of atmospheric correction problem: iteration method of refinement of spectral albedo's parameters using Libradtran and analytical least square method. The main advantages of these methods are high rate of processing (several minutes for 1 GB data) and low relative error in albedo retrieval (less than 15%). Also, the software supports work with spectral libraries, region of interest (ROI) selection, spectral analysis such as cluster-type image classification and automatic hypercube spectrum comparison by similarity criterion with similar ones from spectral libraries, and vice versa. The software deals with different kinds of spectral information in order to identify and distinguish spectrally unique materials. Also, the following advantages should be noted: fast and low memory hypercube manipulation features, user-friendly interface, modularity, and expandability.

A comparison of autonomous techniques for multispectral image analysis and classification

NASA Astrophysics Data System (ADS)

Valdiviezo-N., Juan C.; Urcid, Gonzalo; Toxqui-Quitl, Carina; Padilla-Vivanco, Alfonso

2012-10-01

Multispectral imaging has given place to important applications related to classification and identification of objects from a scene. Because of multispectral instruments can be used to estimate the reflectance of materials in the scene, these techniques constitute fundamental tools for materials analysis and quality control. During the last years, a variety of algorithms has been developed to work with multispectral data, whose main purpose has been to perform the correct classification of the objects in the scene. The present study introduces a brief review of some classical as well as a novel technique that have been used for such purposes. The use of principal component analysis and K-means clustering techniques as important classification algorithms is here discussed. Moreover, a recent method based on the min-W and max-M lattice auto-associative memories, that was proposed for endmember determination in hyperspectral imagery, is introduced as a classification method. Besides a discussion of their mathematical foundation, we emphasize their main characteristics and the results achieved for two exemplar images conformed by objects similar in appearance, but spectrally different. The classification results state that the first components computed from principal component analysis can be used to highlight areas with different spectral characteristics. In addition, the use of lattice auto-associative memories provides good results for materials classification even in the cases where some spectral similarities appears in their spectral responses.

Implementation of spectral clustering on microarray data of carcinoma using k-means algorithm

NASA Astrophysics Data System (ADS)

Frisca, Bustamam, Alhadi; Siswantining, Titin

2017-03-01

Clustering is one of data analysis methods that aims to classify data which have similar characteristics in the same group. Spectral clustering is one of the most popular modern clustering algorithms. As an effective clustering technique, spectral clustering method emerged from the concepts of spectral graph theory. Spectral clustering method needs partitioning algorithm. There are some partitioning methods including PAM, SOM, Fuzzy c-means, and k-means. Based on the research that has been done by Capital and Choudhury in 2013, when using Euclidian distance k-means algorithm provide better accuracy than PAM algorithm. So in this paper we use k-means as our partition algorithm. The major advantage of spectral clustering is in reducing data dimension, especially in this case to reduce the dimension of large microarray dataset. Microarray data is a small-sized chip made of a glass plate containing thousands and even tens of thousands kinds of genes in the DNA fragments derived from doubling cDNA. Application of microarray data is widely used to detect cancer, for the example is carcinoma, in which cancer cells express the abnormalities in his genes. The purpose of this research is to classify the data that have high similarity in the same group and the data that have low similarity in the others. In this research, Carcinoma microarray data using 7457 genes. The result of partitioning using k-means algorithm is two clusters.

Spectral algorithm for non-destructive damage localisation: Application to an ancient masonry arch model

NASA Astrophysics Data System (ADS)

Masciotta, Maria-Giovanna; Ramos, Luís F.; Lourenço, Paulo B.; Vasta, Marcello

2017-02-01

Structural monitoring and vibration-based damage identification methods are fundamental tools for condition assessment and early-stage damage identification, especially when dealing with the conservation of historical constructions and the maintenance of strategic civil structures. However, although the substantial advances in the field, several issues must still be addressed to broaden the application range of such tools and to assert their reliability. This study deals with the experimental validation of a novel method for non-destructive damage identification purposes. This method is based on the use of spectral output signals and has been recently validated by the authors through a numerical simulation. After a brief insight into the basic principles of the proposed approach, the spectral-based technique is applied to identify the experimental damage induced on a masonry arch through statically increasing loading. Once the direct and cross spectral density functions of the nodal response processes are estimated, the system's output power spectrum matrix is built and decomposed in eigenvalues and eigenvectors. The present study points out how the extracted spectral eigenparameters contribute to the damage analysis allowing to detect the occurrence of damage and to locate the target points where the cracks appear during the experimental tests. The sensitivity of the spectral formulation to the level of noise in the modal data is investigated and discussed. As a final evaluation criterion, the results from the spectrum-driven method are compared with the ones obtained from existing non-model based damage identification methods.

Spectral Electroencephalogram Analysis for the Evaluation of Encephalopathy Grade in Children With Acute Liver Failure.

PubMed

Press, Craig A; Morgan, Lindsey; Mills, Michele; Stack, Cynthia V; Goldstein, Joshua L; Alonso, Estella M; Wainwright, Mark S

2017-01-01

Spectral electroencephalogram analysis is a method for automated analysis of electroencephalogram patterns, which can be performed at the bedside. We sought to determine the utility of spectral electroencephalogram for grading hepatic encephalopathy in children with acute liver failure. Retrospective cohort study. Tertiary care pediatric hospital. Patients between 0 and 18 years old who presented with acute liver failure and were admitted to the PICU. None. Electroencephalograms were analyzed by spectral analysis including total power, relative δ, relative θ, relative α, relative β, θ-to-Δ ratio, and α-to-Δ ratio. Normal values and ranges were first derived using normal electroencephalograms from 70 children of 0-18 years old. Age had a significant effect on each variable measured (p < 0.03). Electroencephalograms from 33 patients with acute liver failure were available for spectral analysis. The median age was 4.3 years, 14 of 33 were male, and the majority had an indeterminate etiology of acute liver failure. Neuroimaging was performed in 26 cases and was normal in 20 cases (77%). The majority (64%) survived, and 82% had a good outcome with a score of 1-3 on the Pediatric Glasgow Outcome Scale-Extended at the time of discharge. Hepatic encephalopathy grade correlated with the qualitative visual electroencephalogram scores assigned by blinded neurophysiologists (rs = 0.493; p < 0.006). Spectral electroencephalogram characteristics varied significantly with the qualitative electroencephalogram classification (p < 0.05). Spectral electroencephalogram variables including relative Δ, relative θ, relative α, θ-to-Δ ratio, and α-to-Δ ratio all significantly varied with the qualitative electroencephalogram (p < 0.025). Moderate to severe hepatic encephalopathy was correlated with a total power of less than or equal to 50% of normal for children 0-3 years old, and with a relative θ of less than or equal to 50% normal for children more than 3 years old (p > 0.05). Spectral electroencephalogram classification correlated with outcome (p < 0.05). Spectral electroencephalogram analysis can be used to evaluate even young patients for hepatic encephalopathy and correlates with outcome. Spectral electroencephalogram may allow improved quantitative and reproducible assessment of hepatic encephalopathy grade in children with acute liver failure.

Method for estimating effects of unknown correlations in spectral irradiance data on uncertainties of spectrally integrated colorimetric quantities

NASA Astrophysics Data System (ADS)

Kärhä, Petri; Vaskuri, Anna; Mäntynen, Henrik; Mikkonen, Nikke; Ikonen, Erkki

2017-08-01

Spectral irradiance data are often used to calculate colorimetric properties, such as color coordinates and color temperatures of light sources by integration. The spectral data may contain unknown correlations that should be accounted for in the uncertainty estimation. We propose a new method for estimating uncertainties in such cases. The method goes through all possible scenarios of deviations using Monte Carlo analysis. Varying spectral error functions are produced by combining spectral base functions, and the distorted spectra are used to calculate the colorimetric quantities. Standard deviations of the colorimetric quantities at different scenarios give uncertainties assuming no correlations, uncertainties assuming full correlation, and uncertainties for an unfavorable case of unknown correlations, which turn out to be a significant source of uncertainty. With 1% standard uncertainty in spectral irradiance, the expanded uncertainty of the correlated color temperature of a source corresponding to the CIE Standard Illuminant A may reach as high as 37.2 K in unfavorable conditions, when calculations assuming full correlation give zero uncertainty, and calculations assuming no correlations yield the expanded uncertainties of 5.6 K and 12.1 K, with wavelength steps of 1 nm and 5 nm used in spectral integrations, respectively. We also show that there is an absolute limit of 60.2 K in the error of the correlated color temperature for Standard Illuminant A when assuming 1% standard uncertainty in the spectral irradiance. A comparison of our uncorrelated uncertainties with those obtained using analytical methods by other research groups shows good agreement. We re-estimated the uncertainties for the colorimetric properties of our 1 kW photometric standard lamps using the new method. The revised uncertainty of color temperature is a factor of 2.5 higher than the uncertainty assuming no correlations.

Hyper-spectral imaging: A promising tool for quantitative pigment analysis of varved lake sediments

NASA Astrophysics Data System (ADS)

Butz, Christoph; Grosjean, Martin; Tylmann, Wojciech

2015-04-01

Varved lake sediments are good archives for past environmental and climate conditions from annual to multi-millennial scales. Among other proxies, concentrations of sedimentary photopigments have been used for temperature reconstructions. However, obtaining well calibrated annually resolved records from sediments still remains challenging. Most laboratory methods used to analyse lake sediments require physical subsampling and are destructive in the process. Hence, temporal resolution and number of data are limited by the amount of material available in the core. Furthermore, for very low sediment accumulation rates annual subsampling is often very difficult or even impossible. To address these problems we explore hyper-spectral imaging as a non-destructive method to analyse lake sediments based on their reflectance spectra in the visible and near infrared spectrum. In contrast to other scanning methods like X-ray fluorescence, VIS/NIR reflectance spectrometry distinguishes between biogeochemical substances rather than single elements. Among others Rein (2003) has shown that VIS-RS can be used to detect relative concentrations of sedimentary photopigments (e.g. chlorins, carotenoids) and clay minerals. In this study hyper-spectral imaging is used to infer ecological proxy data from reflectance spectra of varved lake sediments. Hyper-spectral imaging permits the measurement of an entire sediment core in a single run at high spatial (30x30µm/pixel) and spectral resolutions (~2.8nm) within the visual to near infrared spectrum (400-1000nm). This allows the analysis of data time series and spatial mapping of sedimentary substances (e.g. chlorophylls/bacterio-chlorophylls and diagenetic products) at sub-varve scales. The method is demonstrated on two varved lake sediments from northern Poland showing the distributions of relative concentrations of two types of sedimentary pigments (Chlorophyll-a + derivatives and Bacterio-pheophytin-a) within individual varve years. The relative concentrations from the spectral data set have then been calibrated with absolute concentrations derived by High-Performance-Liquid-Chromatography (HPLC). This results in very high-resolution data sets of absolute sedimentary pigment concentrations suitable for the analysis of seasonal pigment variations.

Nondestructive evaluation of Bakwan paddy grains moisture content by means of spectrophotometry

NASA Astrophysics Data System (ADS)

Makky, M.; Putry, R. E.; Nakano, K.; Santosa

2018-03-01

Paddy grains moisture content (MC) strongly correlated to the physical properties of rice after being milled. Incorrect MC will cause higher percentage of broken rice and prompts the grains to be more fragile. In general, paddy grains with 13 – 14% MC are ideal for post-harvest processing. The objective of this study is to measure the MC of intact paddy grain from CV. Bakwan by means of non-destructive evaluation using NIR spectral assessment. Paddy grains samples with identical MC were put into 30 mm tube glass and measured using NIR spectrophotometer. The electromagnetic radiation absorbance under consideration upon spectral measurement fell between 1000 and 2500 nm. The grains’ actual MC was then measured by primary method, based on weight measurement i.e. oven method. In this study, the spectral data of the grains was then processed by means of Principal Component Analysis (PCA) before correlated with its MCs by Partial Least Square (PLS) method. The model calibration obtained correlation (r) of 0.983 and RMSEC of 1.684. Moreover, model validation produced correlation (r) of 0.973, RMSEP of 2.095, and bias of 0.2, indicating that the MC of paddy grains can be precisely identified by non-destructive evaluation using spectral analysis.

Analysis of wave motion in one-dimensional structures through fast-Fourier-transform-based wavelet finite element method

NASA Astrophysics Data System (ADS)

Shen, Wei; Li, Dongsheng; Zhang, Shuaifang; Ou, Jinping

2017-07-01

This paper presents a hybrid method that combines the B-spline wavelet on the interval (BSWI) finite element method and spectral analysis based on fast Fourier transform (FFT) to study wave propagation in One-Dimensional (1D) structures. BSWI scaling functions are utilized to approximate the theoretical wave solution in the spatial domain and construct a high-accuracy dynamic stiffness matrix. Dynamic reduction on element level is applied to eliminate the interior degrees of freedom of BSWI elements and substantially reduce the size of the system matrix. The dynamic equations of the system are then transformed and solved in the frequency domain through FFT-based spectral analysis which is especially suitable for parallel computation. A comparative analysis of four different finite element methods is conducted to demonstrate the validity and efficiency of the proposed method when utilized in high-frequency wave problems. Other numerical examples are utilized to simulate the influence of crack and delamination on wave propagation in 1D rods and beams. Finally, the errors caused by FFT and their corresponding solutions are presented.

In vivo imaging of scattering and absorption properties of exposed brain using a digital red-green-blue camera

NASA Astrophysics Data System (ADS)

Nishidate, Izumi; Yoshida, Keiichiro; Kawauchi, Satoko; Sato, Shunichi; Sato, Manabu

2014-03-01

We investigate a method to estimate the spectral images of reduced scattering coefficients and the absorption coefficients of in vivo exposed brain tissues in the range from visible to near-infrared wavelength (500-760 nm) based on diffuse reflectance spectroscopy using a digital RGB camera. In the proposed method, the multi-spectral reflectance images of in vivo exposed brain are reconstructed from the digital red, green blue images using the Wiener estimation algorithm. The Monte Carlo simulation-based multiple regression analysis for the absorbance spectra is then used to specify the absorption and scattering parameters of brain tissue. In this analysis, the concentration of oxygenated hemoglobin and that of deoxygenated hemoglobin are estimated as the absorption parameters whereas the scattering amplitude a and the scattering power b in the expression of μs'=aλ-b as the scattering parameters, respectively. The spectra of absorption and reduced scattering coefficients are reconstructed from the absorption and scattering parameters, and finally, the spectral images of absorption and reduced scattering coefficients are estimated. The estimated images of absorption coefficients were dominated by the spectral characteristics of hemoglobin. The estimated spectral images of reduced scattering coefficients showed a broad scattering spectrum, exhibiting larger magnitude at shorter wavelengths, corresponding to the typical spectrum of brain tissue published in the literature. In vivo experiments with exposed brain of rats during CSD confirmed the possibility of the method to evaluate both hemodynamics and changes in tissue morphology due to electrical depolarization.

Least-Squares Regression and Spectral Residual Augmented Classical Least-Squares Chemometric Models for Stability-Indicating Analysis of Agomelatine and Its Degradation Products: A Comparative Study.

PubMed

Naguib, Ibrahim A; Abdelrahman, Maha M; El Ghobashy, Mohamed R; Ali, Nesma A

2016-01-01

Two accurate, sensitive, and selective stability-indicating methods are developed and validated for simultaneous quantitative determination of agomelatine (AGM) and its forced degradation products (Deg I and Deg II), whether in pure forms or in pharmaceutical formulations. Partial least-squares regression (PLSR) and spectral residual augmented classical least-squares (SRACLS) are two chemometric models that are being subjected to a comparative study through handling UV spectral data in range (215-350 nm). For proper analysis, a three-factor, four-level experimental design was established, resulting in a training set consisting of 16 mixtures containing different ratios of interfering species. An independent test set consisting of eight mixtures was used to validate the prediction ability of the suggested models. The results presented indicate the ability of mentioned multivariate calibration models to analyze AGM, Deg I, and Deg II with high selectivity and accuracy. The analysis results of the pharmaceutical formulations were statistically compared to the reference HPLC method, with no significant differences observed regarding accuracy and precision. The SRACLS model gives comparable results to the PLSR model; however, it keeps the qualitative spectral information of the classical least-squares algorithm for analyzed components.

Plant leaf chlorophyll content retrieval based on a field imaging spectroscopy system.

PubMed

Liu, Bo; Yue, Yue-Min; Li, Ru; Shen, Wen-Jing; Wang, Ke-Lin

2014-10-23

A field imaging spectrometer system (FISS; 380-870 nm and 344 bands) was designed for agriculture applications. In this study, FISS was used to gather spectral information from soybean leaves. The chlorophyll content was retrieved using a multiple linear regression (MLR), partial least squares (PLS) regression and support vector machine (SVM) regression. Our objective was to verify the performance of FISS in a quantitative spectral analysis through the estimation of chlorophyll content and to determine a proper quantitative spectral analysis method for processing FISS data. The results revealed that the derivative reflectance was a more sensitive indicator of chlorophyll content and could extract content information more efficiently than the spectral reflectance, which is more significant for FISS data compared to ASD (analytical spectral devices) data, reducing the corresponding RMSE (root mean squared error) by 3.3%-35.6%. Compared with the spectral features, the regression methods had smaller effects on the retrieval accuracy. A multivariate linear model could be the ideal model to retrieve chlorophyll information with a small number of significant wavelengths used. The smallest RMSE of the chlorophyll content retrieved using FISS data was 0.201 mg/g, a relative reduction of more than 30% compared with the RMSE based on a non-imaging ASD spectrometer, which represents a high estimation accuracy compared with the mean chlorophyll content of the sampled leaves (4.05 mg/g). Our study indicates that FISS could obtain both spectral and spatial detailed information of high quality. Its image-spectrum-in-one merit promotes the good performance of FISS in quantitative spectral analyses, and it can potentially be widely used in the agricultural sector.

Plant Leaf Chlorophyll Content Retrieval Based on a Field Imaging Spectroscopy System

PubMed Central

Liu, Bo; Yue, Yue-Min; Li, Ru; Shen, Wen-Jing; Wang, Ke-Lin

2014-01-01

A field imaging spectrometer system (FISS; 380–870 nm and 344 bands) was designed for agriculture applications. In this study, FISS was used to gather spectral information from soybean leaves. The chlorophyll content was retrieved using a multiple linear regression (MLR), partial least squares (PLS) regression and support vector machine (SVM) regression. Our objective was to verify the performance of FISS in a quantitative spectral analysis through the estimation of chlorophyll content and to determine a proper quantitative spectral analysis method for processing FISS data. The results revealed that the derivative reflectance was a more sensitive indicator of chlorophyll content and could extract content information more efficiently than the spectral reflectance, which is more significant for FISS data compared to ASD (analytical spectral devices) data, reducing the corresponding RMSE (root mean squared error) by 3.3%–35.6%. Compared with the spectral features, the regression methods had smaller effects on the retrieval accuracy. A multivariate linear model could be the ideal model to retrieve chlorophyll information with a small number of significant wavelengths used. The smallest RMSE of the chlorophyll content retrieved using FISS data was 0.201 mg/g, a relative reduction of more than 30% compared with the RMSE based on a non-imaging ASD spectrometer, which represents a high estimation accuracy compared with the mean chlorophyll content of the sampled leaves (4.05 mg/g). Our study indicates that FISS could obtain both spectral and spatial detailed information of high quality. Its image-spectrum-in-one merit promotes the good performance of FISS in quantitative spectral analyses, and it can potentially be widely used in the agricultural sector. PMID:25341439

Temperature Distribution in a Composite of Opaque and Semitransparent Spectral Layers

NASA Technical Reports Server (NTRS)

Siegel, Robert

1997-01-01

The analysis of radiative transfer becomes computationally complex for a composite when there are multiple layers and multiple spectral bands. A convenient analytical method is developed for combined radiation and conduction in a composite of alternating semitransparent and opaque layers. The semi- transparent layers absorb, scatter, and emit radiation, and spectral properties with large scattering are included. The two-flux method is used, and its applicability is verified by comparison with a basic solution in the literature. The differential equation in the two-flux method Is solved by deriving a Green's function. The solution technique is applied to analyze radiation effects in a multilayer zirconia thermal barrier coating with internal radiation shields for conditions in an aircraft engine combustor. The zirconia radiative properties are modeled by two spectral bands. Thin opaque layers within the coating are used to decrease radiant transmission that can degrade the zirconia insulating ability. With radiation shields, the temperature distributions more closely approach the opaque limit that provides the lowest metal wall temperatures.

Comparative Analysis of Haar and Daubechies Wavelet for Hyper Spectral Image Classification

NASA Astrophysics Data System (ADS)

Sharif, I.; Khare, S.

2014-11-01

With the number of channels in the hundreds instead of in the tens Hyper spectral imagery possesses much richer spectral information than multispectral imagery. The increased dimensionality of such Hyper spectral data provides a challenge to the current technique for analyzing data. Conventional classification methods may not be useful without dimension reduction pre-processing. So dimension reduction has become a significant part of Hyper spectral image processing. This paper presents a comparative analysis of the efficacy of Haar and Daubechies wavelets for dimensionality reduction in achieving image classification. Spectral data reduction using Wavelet Decomposition could be useful because it preserves the distinction among spectral signatures. Daubechies wavelets optimally capture the polynomial trends while Haar wavelet is discontinuous and resembles a step function. The performance of these wavelets are compared in terms of classification accuracy and time complexity. This paper shows that wavelet reduction has more separate classes and yields better or comparable classification accuracy. In the context of the dimensionality reduction algorithm, it is found that the performance of classification of Daubechies wavelets is better as compared to Haar wavelet while Daubechies takes more time compare to Haar wavelet. The experimental results demonstrate the classification system consistently provides over 84% classification accuracy.

Research on marine and freshwater fish identification model based on hyper-spectral imaging technology

NASA Astrophysics Data System (ADS)

Fu, Yan; Guo, Pei-yuan; Xiang, Ling-zi; Bao, Man; Chen, Xing-hai

2013-08-01

With the gradually mature of hyper spectral image technology, the application of the meat nondestructive detection and recognition has become one of the current research focuses. This paper for the study of marine and freshwater fish by the pre-processing and feature extraction of the collected spectral curve data, combined with BP network structure and LVQ network structure, a predictive model of hyper spectral image data of marine and freshwater fish has been initially established and finally realized the qualitative analysis and identification of marine and freshwater fish quality. The results of this study show that hyper spectral imaging technology combined with the BP and LVQ Artificial Neural Network Model can be used for the identification of marine and freshwater fish detection. Hyper-spectral data acquisition can be carried out without any pretreatment of the samples, thus hyper-spectral imaging technique is the lossless, high- accuracy and rapid detection method for quality of fish. In this study, only 30 samples are used for the exploratory qualitative identification of research, although the ideal study results are achieved, we will further increase the sample capacity to take the analysis of quantitative identification and verify the feasibility of this theory.

Simulation of multi-element multispectral UV radiation source for optical-electronic system of minerals luminescence analysis

NASA Astrophysics Data System (ADS)

Peretyagin, Vladimir S.; Korolev, Timofey K.; Chertov, Aleksandr N.

2017-02-01

The problems of dressability the solid minerals are attracted attention of specialists, where the extraction of mineral raw materials is a significant sector of the economy. There are a significant amount of mineral ore dressability methods. At the moment the radiometric dressability methods are considered the most promising. One of radiometric methods is method photoluminescence. This method is based on the spectral analysis, amplitude and kinetic parameters luminescence of minerals (under UV radiation), as well as color parameters of radiation. The absence of developed scientific and methodological approaches of analysis irradiation area to UV radiation as well as absence the relevant radiation sources are the factors which hinder development and use of photoluminescence method. The present work is devoted to the development of multi-element UV radiation source designed for the solution problem of analysis and sorting minerals by their selective luminescence. This article is presented a method of theoretical modeling of the radiation devices based on UV LEDs. The models consider such factors as spectral component, the spatial and energy parameters of the LEDs. Also, this article is presented the results of experimental studies of the some samples minerals.

A target detection multi-layer matched filter for color and hyperspectral cameras

NASA Astrophysics Data System (ADS)

Miyanishi, Tomoya; Preece, Bradley L.; Reynolds, Joseph P.

2018-05-01

In this article, a method for applying matched filters to a 3-dimentional hyperspectral data cube is discussed. In many applications, color visible cameras or hyperspectral cameras are used for target detection where the color or spectral optical properties of the imaged materials are partially known in advance. Therefore, the use of matched filtering with spectral data along with shape data is an effective method for detecting certain targets. Since many methods for 2D image filtering have been researched, we propose a multi-layer filter where ordinary spatially matched filters are used before the spectral filters. We discuss a way to layer the spectral filters for a 3D hyperspectral data cube, accompanied by a detectability metric for calculating the SNR of the filter. This method is appropriate for visible color cameras and hyperspectral cameras. We also demonstrate an analysis using the Night Vision Integrated Performance Model (NV-IPM) and a Monte Carlo simulation in order to confirm the effectiveness of the filtering in providing a higher output SNR and a lower false alarm rate.

The original method for imaging of biological tissues in optical coherence tomography with usage of hyperchromatic lens

NASA Astrophysics Data System (ADS)

Egorov, D. I.

2017-06-01

Our study focuses on an analysis of the original method of investigation biological tissues in the spectral OCT (optical coherence tomography) with usage hyperchromatic lenses. Using hyperchromatic lens, i.e. the lens with uncorrected longitudinal color allows scanning in the depth of the object by changing the wavelength of the emitter. In this case, the depth of the scan will be determined not by the microlens depth of field, but the value of axial color. In our study, we demonstrated the advantages of this method of research on biological tissues existing. Spectral OCT schemes with the hyperchromatic lens could increase the depth of spectral scanning, eliminate the use of multi-channel systems with a set of microscope objectives, reduce the time of measurement. In our paper, we show the developed method of calculation of hyperchromatic lenses and hybrid hyperchromatic lens consisting of a diffractive and refractive component in spectral OCT systems. We also demonstrate the results of aberration calculation designed microscope lenses. We show examples of developed hyperchromatic lenses with the diffractive element and without it.

Monitoring of Water Spectral Pattern Reveals Differences in Probiotics Growth When Used for Rapid Bacteria Selection.

PubMed

Slavchev, Aleksandar; Kovacs, Zoltan; Koshiba, Haruki; Nagai, Airi; Bázár, György; Krastanov, Albert; Kubota, Yousuke; Tsenkova, Roumiana

2015-01-01

Development of efficient screening method coupled with cell functionality evaluation is highly needed in contemporary microbiology. The presented novel concept and fast non-destructive method brings in to play the water spectral pattern of the solution as a molecular fingerprint of the cell culture system. To elucidate the concept, NIR spectroscopy with Aquaphotomics were applied to monitor the growth of sixteen Lactobacillus bulgaricus one Lactobacillus pentosus and one Lactobacillus gasseri bacteria strains. Their growth rate, maximal optical density, low pH and bile tolerances were measured and further used as a reference data for analysis of the simultaneously acquired spectral data. The acquired spectral data in the region of 1100-1850nm was subjected to various multivariate data analyses - PCA, OPLS-DA, PLSR. The results showed high accuracy of bacteria strains classification according to their probiotic strength. Most informative spectral fingerprints covered the first overtone of water, emphasizing the relation of water molecular system to cell functionality.

Identification of substance in complicated mixture of simulants under the action of THz radiation on the base of SDA (spectral dynamics analysis) method

NASA Astrophysics Data System (ADS)

Trofimov, Vyacheslav A.; Varentsova, Svetlana A.; Krotkus, Arunas; Molis, Gediminas

2010-10-01

The SDA (Spectral Dynamics Analysis) - method (method of THz spectrum dynamics analysis in THz range of frequencies) is used for the detection and identification of substances with similar THz Fourier spectra (such substances are named usually as the simulants) in the two- or three-component medium. This method allows us to obtain the unique 2D THz signature of the substance - the spectrogram- and to analyze the dynamics of many spectral lines of the THz signal, passed through or reflected from substance, by one set of its integral measurements simultaneously; even measurements are made on short-term intervals (less than 20 ps). For long-term intervals (100 ps and more) the SDA method gives an opportunity to define the relaxation time for excited energy levels of molecules. This information gives new opportunity to identify the substance because the relaxation time is different for molecules of different substances. The restoration of the signal by its integral values is made on the base of SVD - Single Value Decomposition - technique. We consider three examples for PTFE mixed with small content of the L-Tartaric Acid and the Sucrose in pellets. A concentration of these substances is about 5%-10%. Our investigations show that the spectrograms and dynamics of spectral lines of THz pulse passed through the pure PTFE differ from the spectrograms of the compound medium containing PTFE and the L-Tartaric Acid or the Sucrose or both these substances together. So, it is possible to detect the presence of a small amount of the additional substances in the sample even their THz Fourier spectra are practically identical. Therefore, the SDA method can be very effective for the defense and security applications and for quality control in pharmaceutical industry. We also show that in the case of substances-simulants the use of auto- and correlation functions has much worse resolvability in a comparison with the SDA method.

Mapping and monitoring changes in vegetation communities of Jasper Ridge, CA, using spectral fractions derived from AVIRIS images

NASA Technical Reports Server (NTRS)

Sabol, Donald E., Jr.; Roberts, Dar A.; Adams, John B.; Smith, Milton O.

1993-01-01

An important application of remote sensing is to map and monitor changes over large areas of the land surface. This is particularly significant with the current interest in monitoring vegetation communities. Most of traditional methods for mapping different types of plant communities are based upon statistical classification techniques (i.e., parallel piped, nearest-neighbor, etc.) applied to uncalibrated multispectral data. Classes from these techniques are typically difficult to interpret (particularly to a field ecologist/botanist). Also, classes derived for one image can be very different from those derived from another image of the same area, making interpretation of observed temporal changes nearly impossible. More recently, neural networks have been applied to classification. Neural network classification, based upon spectral matching, is weak in dealing with spectral mixtures (a condition prevalent in images of natural surfaces). Another approach to mapping vegetation communities is based on spectral mixture analysis, which can provide a consistent framework for image interpretation. Roberts et al. (1990) mapped vegetation using the band residuals from a simple mixing model (the same spectral endmembers applied to all image pixels). Sabol et al. (1992b) and Roberts et al. (1992) used different methods to apply the most appropriate spectral endmembers to each image pixel, thereby allowing mapping of vegetation based upon the the different endmember spectra. In this paper, we describe a new approach to classification of vegetation communities based upon the spectra fractions derived from spectral mixture analysis. This approach was applied to three 1992 AVIRIS images of Jasper Ridge, California to observe seasonal changes in surface composition.

The Berkeley SuperNova Ia Program (BSNIP): Dataset and Initial Analysis

NASA Astrophysics Data System (ADS)

Silverman, Jeffrey; Ganeshalingam, M.; Kong, J.; Li, W.; Filippenko, A.

2012-01-01

I will present spectroscopic data from the Berkeley SuperNova Ia Program (BSNIP), their initial analysis, and the results of attempts to use spectral information to improve cosmological distance determinations to Type Ia supernova (SNe Ia). The dataset consists of 1298 low-redshift (z< 0.2) optical spectra of 582 SNe Ia observed from 1989 through the end of 2008. Many of the SNe have well-calibrated light curves with measured distance moduli as well as spectra that have been corrected for host-galaxy contamination. I will also describe the spectral classification scheme employed (using the SuperNova Identification code, SNID; Blondin & Tonry 2007) which utilizes a newly constructed set of SNID spectral templates. The sheer size of the BSNIP dataset and the consistency of the observation and reduction methods make this sample unique among all other published SN Ia datasets. I will also discuss measurements of the spectral features of about one-third of the spectra which were obtained within 20 days of maximum light. I will briefly describe the adopted method of automated, robust spectral-feature definition and measurement which expands upon similar previous studies. Comparisons of these measurements of SN Ia spectral features to photometric observables will be presented with an eye toward using spectral information to calculate more accurate cosmological distances. Finally, I will comment on related projects which also utilize the BSNIP dataset that are planned for the near future. This research was supported by NSF grant AST-0908886 and the TABASGO Foundation. I am grateful to Marc J. Staley for a Graduate Fellowship.

Identifying scales of pattern in ecological data: a comparison of lacunarity, spectral and wavelet analyses

Treesearch

Sari C. Saunders; Jiquan Chen; Thomas D. Drummer; Eric J. Gustafson; Kimberley D. Brosofske

2005-01-01

Identifying scales of pattern in ecological systems and coupling patterns to processes that create them are ongoing challenges. We examined the utility of three techniques (lacunarity, spectral, and wavelet analysis) for detecting scales of pattern of ecological data. We compared the information obtained using these methods for four datasets, including: surface...

Integrated spectral and image analysis of hyperspectral scattering data for prediction of apple fruit firmness and soluble solids content

USDA-ARS?s Scientific Manuscript database

Spectral scattering is useful for assessing the firmness and soluble solids content (SSC) of apples. In previous research, mean reflectance extracted from the hyperspectral scattering profiles was used for this purpose since the method is simple and fast and also gives relatively good predictions. T...

Modification of kaolinite surfaces through mechanochemical activation with quartz: A diffuse reflectance infrared fourier transform and chemometrics study.

PubMed

Carmody, Onuma; Frost, Ray L; Kristóf, János; Kokot, Serge; Kloprogge, J Theo; Makó, Eva

2006-12-01

Studies of kaolinite surfaces are of industrial importance. One useful method for studying the changes in kaolinite surface properties is to apply chemometric analyses to the kaolinite surface infrared spectra. A comparison is made between the mechanochemical activation of Kiralyhegy kaolinites with significant amounts of natural quartz and the mechanochemical activation of Zettlitz kaolinite with added quartz. Diffuse reflectance infrared Fourier transform (DRIFT) spectra were analyzed using principal component analysis (PCA) and multi-criteria decision making (MCDM) methods, the preference ranking organization method for enrichment evaluations (PROMETHEE) and geometrical analysis for interactive assistance (GAIA). The clear discrimination of the Kiralyhegy spectral objects on the two PC scores plots (400-800 and 800-2030 cm(-1)) indicated the dominance of quartz. Importantly, no ordering of any spectral objects appeared to be related to grinding time in the PC plots of these spectral regions. Thus, neither the kaolinite nor the quartz are systematically responsive to grinding time according to the spectral criteria investigated. The third spectral region (2600-3800 cm(-1), OH vibrations), showed apparent systematic ordering of the Kiralyhegy and, to a lesser extent, Zettlitz spectral objects with grinding time. This was attributed to the effect of the natural quartz on the delamination of kaolinite and the accompanying phenomena (i.e., formation of kaolinite spheres and water). The mechanochemical activation of kaolinite and quartz, through dry grinding, results in changes to the surface structure. Different grinding times were adopted to study the rate of destruction of the kaolinite and quartz structures. This relationship (i.e., grinding time) was classified using PROMETHEE and GAIA methodology.

Broadband radio jet emission and variability of γ-ray blazars

NASA Astrophysics Data System (ADS)

Nestoras, Ioannis

2015-07-01

AGN (Active Galactic Nuclei) and in particular their subclass blazars, are among the most energetic objects observed in the universe, featuring extreme phenomenological characteristics such as rapid broadband flux density and polarization variability, fast super--luminal motion, high degree of polarization and a broadband, double-humped spectral energy distribution (SED). The details of the emission processes and violent variability of blazars are still poorly understood. Variability studies give important clues about the size, structure, physics and dynamics of the emitting region making AGN/blazar monitoring programs of uttermost importance in providing the necessary constraints for understanding the origin of energy production. In this framework the F-gamma program was initiated, monitoring monthly 60 fermi detected AGN/blazars at 12 frequencies between 2.6 and 345GHz since 2007. For the thesis in hand observations and data analysis were performed within the realms of the F-gamma program, using the Effelsberg (EB) 100m and Pico Veleta (PV) 30m telescopes at 10 frequency bands ranging from 2.64 to 142GHz. The cm to short-mm variability/spectral characteristics are monitored for a sample of 59 sources for a period of five years enabling for the first time a detailed study of the observed flaring activity in both the light curve and spectral domains for such a large number of sources and such high cadence. Also the observing systems and methods are introduced as well as the data reduction techniques. The thesis at hand is structured as follows: Chapter 3 presents the reduction methods and post measurement corrections applied to the data such as pointing offsets, gain--elevation and sensitivity corrections as well as specific corrections applied for each of the Effelsberg and Pico Veleta observing systems respectively. Chapter 4 presents the analysis tools and methods that were used such as: variability characteristics, flare amplitudes with a new method for estimating the intrinsic standard deviation, flare time scales using Structure Function analysis, spectral indices and spectral peak estimations. Chapter 5 presents the results of the analysis performed upon the five year light curves. The significance of variability through a x^2 test is estimated as well as the flare amplitudes using the intrinsic variability of the light curves along with a new proposed k--index. The introduction of the k--index enables the characterization of the observed variability amplitudes across frequency, thus permitting us to limit the parameter space of various physical models. Also flare time scales, brightness temperatures and Doppler factors are reported. Chapter 6 presents the corresponding analysis in the spectral domain, including results for spectral indices and an S_max - v_max analysis. By determining the spectral peak of every spectra for a selected number of sources, it is possible to track the evolution of the flaring activity in the S_max - v_max plane, enabling us to discriminate between different underlying physical mechanisms that are in action. Finally Chapter 7 includes the overall discussion and a summary of results obtained.

Determination of carotid disease with the application of STFT and CWT methods.

PubMed

Hardalaç, Firat; Yildirim, Hanefi; Serhatlioğlu, Selami

2007-06-01

In this study, Doppler signals were recorded from the output of carotid arteries of 40 subjects and transferred to a personal computer (PC) by using a 16-bit sound card. Doppler difference frequencies were recorded from each of the subjects, and then analyzed by using short-time Fourier transform (STFT) and the continuous wavelet transform (CWT) methods to obtain their sonograms. These sonograms were then used to determine the relationships of applied methods with medical conditions. The sonograms that were obtained by CWT method gave better results for spectral resolution than the STFT method. The sonograms of CWT method offer net envelope and better imaging, so that the measurement of blood flow and brain pressure can be made more accurately. Simultaneously, receiver operating characteristic (ROC) analysis has been conducted for this study and the estimation performance of the spectral resolution for the STFT and CTW has been obtained. The STFT has shown a 80.45% success for the spectral resolution while CTW has shown a 89.90% success.

Model for spectral and chromatographic data

DOEpatents

Jarman, Kristin [Richland, WA; Willse, Alan [Richland, WA; Wahl, Karen [Richland, WA; Wahl, Jon [Richland, WA

2002-11-26

A method and apparatus using a spectral analysis technique are disclosed. In one form of the invention, probabilities are selected to characterize the presence (and in another form, also a quantification of a characteristic) of peaks in an indexed data set for samples that match a reference species, and other probabilities are selected for samples that do not match the reference species. An indexed data set is acquired for a sample, and a determination is made according to techniques exemplified herein as to whether the sample matches or does not match the reference species. When quantification of peak characteristics is undertaken, the model is appropriately expanded, and the analysis accounts for the characteristic model and data. Further techniques are provided to apply the methods and apparatuses to process control, cluster analysis, hypothesis testing, analysis of variance, and other procedures involving multiple comparisons of indexed data.

Stochastic model stationarization by eliminating the periodic term and its effect on time series prediction

NASA Astrophysics Data System (ADS)

Moeeni, Hamid; Bonakdari, Hossein; Fatemi, Seyed Ehsan

2017-04-01

Because time series stationarization has a key role in stochastic modeling results, three methods are analyzed in this study. The methods are seasonal differencing, seasonal standardization and spectral analysis to eliminate the periodic effect on time series stationarity. First, six time series including 4 streamflow series and 2 water temperature series are stationarized. The stochastic term for these series obtained with ARIMA is subsequently modeled. For the analysis, 9228 models are introduced. It is observed that seasonal standardization and spectral analysis eliminate the periodic term completely, while seasonal differencing maintains seasonal correlation structures. The obtained results indicate that all three methods present acceptable performance overall. However, model accuracy in monthly streamflow prediction is higher with seasonal differencing than with the other two methods. Another advantage of seasonal differencing over the other methods is that the monthly streamflow is never estimated as negative. Standardization is the best method for predicting monthly water temperature although it is quite similar to seasonal differencing, while spectral analysis performed the weakest in all cases. It is concluded that for each monthly seasonal series, seasonal differencing is the best stationarization method in terms of periodic effect elimination. Moreover, the monthly water temperature is predicted with more accuracy than monthly streamflow. The criteria of the average stochastic term divided by the amplitude of the periodic term obtained for monthly streamflow and monthly water temperature were 0.19 and 0.30, 0.21 and 0.13, and 0.07 and 0.04 respectively. As a result, the periodic term is more dominant than the stochastic term for water temperature in the monthly water temperature series compared to streamflow series.

Rapid analysis of pharmaceutical drugs using LIBS coupled with multivariate analysis.

PubMed

Tiwari, P K; Awasthi, S; Kumar, R; Anand, R K; Rai, P K; Rai, A K

2018-02-01

Type 2 diabetes drug tablets containing voglibose having dose strengths of 0.2 and 0.3 mg of various brands have been examined, using laser-induced breakdown spectroscopy (LIBS) technique. The statistical methods such as the principal component analysis (PCA) and the partial least square regression analysis (PLSR) have been employed on LIBS spectral data for classifying and developing the calibration models of drug samples. We have developed the ratio-based calibration model applying PLSR in which relative spectral intensity ratios H/C, H/N and O/N are used. Further, the developed model has been employed to predict the relative concentration of element in unknown drug samples. The experiment has been performed in air and argon atmosphere, respectively, and the obtained results have been compared. The present model provides rapid spectroscopic method for drug analysis with high statistical significance for online control and measurement process in a wide variety of pharmaceutical industrial applications.

Film thickness measurement based on nonlinear phase analysis using a Linnik microscopic white-light spectral interferometer.

PubMed

Guo, Tong; Chen, Zhuo; Li, Minghui; Wu, Juhong; Fu, Xing; Hu, Xiaotang

2018-04-20

Based on white-light spectral interferometry and the Linnik microscopic interference configuration, the nonlinear phase components of the spectral interferometric signal were analyzed for film thickness measurement. The spectral interferometric signal was obtained using a Linnik microscopic white-light spectral interferometer, which includes the nonlinear phase components associated with the effective thickness, the nonlinear phase error caused by the double-objective lens, and the nonlinear phase of the thin film itself. To determine the influence of the effective thickness, a wavelength-correction method was proposed that converts the effective thickness into a constant value; the nonlinear phase caused by the effective thickness can then be determined and subtracted from the total nonlinear phase. A method for the extraction of the nonlinear phase error caused by the double-objective lens was also proposed. Accurate thickness measurement of a thin film can be achieved by fitting the nonlinear phase of the thin film after removal of the nonlinear phase caused by the effective thickness and by the nonlinear phase error caused by the double-objective lens. The experimental results demonstrated that both the wavelength-correction method and the extraction method for the nonlinear phase error caused by the double-objective lens improve the accuracy of film thickness measurements.

Analysis on the optical aberration effect on spectral resolution of coded aperture spectroscopy

NASA Astrophysics Data System (ADS)

Hao, Peng; Chi, Mingbo; Wu, Yihui

2017-10-01

The coded aperture spectrometer can achieve high throughput and high spectral resolution by replacing the traditional single slit with two-dimensional array slits manufactured by MEMS technology. However, the sampling accuracy of coding spectrum image will be distorted due to the existence of system aberrations, machining error, fixing errors and so on, resulting in the declined spectral resolution. The influence factor of the spectral resolution come from the decode error, the spectral resolution of each column, and the column spectrum offset correction. For the Czerny-Turner spectrometer, the spectral resolution of each column most depend on the astigmatism, in this coded aperture spectroscopy, the uncorrected astigmatism does result in degraded performance. Some methods must be used to reduce or remove the limiting astigmatism. The curvature of field and the spectral curvature can be result in the spectrum revision errors.

The Python Spectral Analysis Tool (PySAT): A Powerful, Flexible, Preprocessing and Machine Learning Library and Interface

NASA Astrophysics Data System (ADS)

Anderson, R. B.; Finch, N.; Clegg, S. M.; Graff, T. G.; Morris, R. V.; Laura, J.; Gaddis, L. R.

2017-12-01

Machine learning is a powerful but underutilized approach that can enable planetary scientists to derive meaningful results from the rapidly-growing quantity of available spectral data. For example, regression methods such as Partial Least Squares (PLS) and Least Absolute Shrinkage and Selection Operator (LASSO), can be used to determine chemical concentrations from ChemCam and SuperCam Laser-Induced Breakdown Spectroscopy (LIBS) data [1]. Many scientists are interested in testing different spectral data processing and machine learning methods, but few have the time or expertise to write their own software to do so. We are therefore developing a free open-source library of software called the Python Spectral Analysis Tool (PySAT) along with a flexible, user-friendly graphical interface to enable scientists to process and analyze point spectral data without requiring significant programming or machine-learning expertise. A related but separately-funded effort is working to develop a graphical interface for orbital data [2]. The PySAT point-spectra tool includes common preprocessing steps (e.g. interpolation, normalization, masking, continuum removal, dimensionality reduction), plotting capabilities, and capabilities to prepare data for machine learning such as creating stratified folds for cross validation, defining training and test sets, and applying calibration transfer so that data collected on different instruments or under different conditions can be used together. The tool leverages the scikit-learn library [3] to enable users to train and compare the results from a variety of multivariate regression methods. It also includes the ability to combine multiple "sub-models" into an overall model, a method that has been shown to improve results and is currently used for ChemCam data [4]. Although development of the PySAT point-spectra tool has focused primarily on the analysis of LIBS spectra, the relevant steps and methods are applicable to any spectral data. The tool is available at https://github.com/USGS-Astrogeology/PySAT_Point_Spectra_GUI. [1] Clegg, S.M., et al. (2017) Spectrochim Acta B. 129, 64-85. [2] Gaddis, L. et al. (2017) 3rd Planetary Data Workshop, #1986. [3] http://scikit-learn.org/ [4] Anderson, R.B., et al. (2017) Spectrochim. Acta B. 129, 49-57.

A method based on IHS cylindrical transform model for quality assessment of image fusion

NASA Astrophysics Data System (ADS)

Zhu, Xiaokun; Jia, Yonghong

2005-10-01

Image fusion technique has been widely applied to remote sensing image analysis and processing, and methods for quality assessment of image fusion in remote sensing have also become the research issues at home and abroad. Traditional assessment methods combine calculation of quantitative indexes and visual interpretation to compare fused images quantificationally and qualitatively. However, in the existing assessment methods, there are two defects: on one hand, most imdexes lack the theoretic support to compare different fusion methods. On the hand, there is not a uniform preference for most of the quantitative assessment indexes when they are applied to estimate the fusion effects. That is, the spatial resolution and spectral feature could not be analyzed synchronously by these indexes and there is not a general method to unify the spatial and spectral feature assessment. So in this paper, on the basis of the approximate general model of four traditional fusion methods, including Intensity Hue Saturation(IHS) triangle transform fusion, High Pass Filter(HPF) fusion, Principal Component Analysis(PCA) fusion, Wavelet Transform(WT) fusion, a correlation coefficient assessment method based on IHS cylindrical transform is proposed. By experiments, this method can not only get the evaluation results of spatial and spectral features on the basis of uniform preference, but also can acquire the comparison between fusion image sources and fused images, and acquire differences among fusion methods. Compared with the traditional assessment methods, the new methods is more intuitionistic, and in accord with subjective estimation.

GPU implementation of the simplex identification via split augmented Lagrangian

NASA Astrophysics Data System (ADS)

Sevilla, Jorge; Nascimento, José M. P.

2015-10-01

Hyperspectral imaging can be used for object detection and for discriminating between different objects based on their spectral characteristics. One of the main problems of hyperspectral data analysis is the presence of mixed pixels, due to the low spatial resolution of such images. This means that several spectrally pure signatures (endmembers) are combined into the same mixed pixel. Linear spectral unmixing follows an unsupervised approach which aims at inferring pure spectral signatures and their material fractions at each pixel of the scene. The huge data volumes acquired by such sensors put stringent requirements on processing and unmixing methods. This paper proposes an efficient implementation of a unsupervised linear unmixing method on GPUs using CUDA. The method finds the smallest simplex by solving a sequence of nonsmooth convex subproblems using variable splitting to obtain a constraint formulation, and then applying an augmented Lagrangian technique. The parallel implementation of SISAL presented in this work exploits the GPU architecture at low level, using shared memory and coalesced accesses to memory. The results herein presented indicate that the GPU implementation can significantly accelerate the method's execution over big datasets while maintaining the methods accuracy.

Evaluation of 1H NMR metabolic profiling using biofluid mixture design.

PubMed

Athersuch, Toby J; Malik, Shahid; Weljie, Aalim; Newton, Jack; Keun, Hector C

2013-07-16

A strategy for evaluating the performance of quantitative spectral analysis tools in conditions that better approximate background variation in a metabonomics experiment is presented. Three different urine samples were mixed in known proportions according to a {3, 3} simplex lattice experimental design and analyzed in triplicate by 1D (1)H NMR spectroscopy. Fifty-four urinary metabolites were subsequently quantified from the sample spectra using two methods common in metabolic profiling studies: (1) targeted spectral fitting and (2) targeted spectral integration. Multivariate analysis using partial least-squares (PLS) regression showed the latent structure of the spectral set recapitulated the experimental mixture design. The goodness-of-prediction statistic (Q(2)) of each metabolite variable in a PLS model was calculated as a metric for the reliability of measurement, across the sample compositional space. Several metabolites were observed to have low Q(2) values, largely as a consequence of their spectral resonances having low s/n or strong overlap with other sample components. This strategy has the potential to allow evaluation of spectral features obtained from metabolic profiling platforms in the context of the compositional background found in real biological sample sets, which may be subject to considerable variation. We suggest that it be incorporated into metabolic profiling studies to improve the estimation of matrix effects that confound accurate metabolite measurement. This novel method provides a rational basis for exploiting information from several samples in an efficient manner and avoids the use of multiple spike-in authentic standards, which may be difficult to obtain.

Microburger Biochemistry: Extraction and Spectral Characterization of Myoglyobin from Hamburger

NASA Astrophysics Data System (ADS)

Bylkas, Sheri A.; Andersson, Laura A.

1997-04-01

This experiment provides a demonstration of useful biochemical methods at a Basic or Advanced Level, depending upon the available spectrophotometric equipment. The protocol combines protein extraction, ox-i-dation and reduction, and simple spectroscopic analysis, as well as gel filtration chromatography and generation/analysis of spectral scans. Mammalian myoglobin (Mb) is a monomeric O2-binding protein that functions in muscle to store oxygen. The single iron protoporphyrin IX (heme) group is bound to protein by the amino acid Histidine93. The common, stable forms, Met-Mb and Oxy-Mb are studied because in a non-living system, red Oxy-Mb is converted to brown Met-Mb as bound O2 molecule is released. Mb is easily extracted from steak, to illustrate and address why fresh meat is red and aged meat is brown; the protein has unique spectral properties that are diagnostic for characterization of sample identity. After application of heme redox chemical methods, the MetMb or OxyMb samples can be studied spectroscopically. The color change between Oxy-Mb and Met-Mb is dramatic (illustrating bright red fresh meat vs. brown older meat), and method(s) used in this laboratory are simple, inexpensive, and non-harmful to the student.

The correlation between acoustic and magnetic properties in the long working metal boiler drum with the parameters of the electron microscope

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

Ababkov, Nikolai, E-mail: n.ababkov@rambler.ru; Smirnov, Alexander, E-mail: galvas.kem@gmail.com

The present paper presents comparative analysis of measurement results of acoustic and magnetic properties in long working metal of boiler drums and the results obtained by methods of electronic microscopy. The structure of the metal sample from the fracture zone to the base metal (metal working sample long) and the center of the base metal before welding (weld metal sample) was investigated by electron microscopy. Studies performed by spectral acoustic, magnetic noise and electron microscopic methods were conducted on the same plots and the same samples of long working and weld metal of high-pressure boiler drums. The analysis of researchmore » results showed high sensitivity of spectral-acoustic and magnetic-noise methods to definition changes of microstructure parameters. Practical application of spectral-acoustic and magnetic noise NDT method is possible for the detection of irregularities and changes in structural and phase state of the long working and weld metal of boiler drums, made of a special molybdenum steel (such as 20M). The above technique can be used to evaluate the structure and physical-mechanical properties of the long working metal of boiler drums in the energy sector.« less

Desertification Assessment and Monitoring Based on Remote Sensing

NASA Astrophysics Data System (ADS)

Gao, Z.; del Barrio, G.; Li, X.

2016-08-01

The objective of Dragon 3 Project 10367 is the development of techniques research for desertification assessment and monitoring in China using remote sensing data in combination with climate and environmental-related data. The main achievements acquired during the last two years could be summarized as follows:(1) Photosynthetic vegetation (PV) and non-photosynthetic vegetation (NPV) were estimated in Otindag sandy land by comparison of the pixel-invariant (Spectral Mixture Analysis, SMA) and pixel-variable (Multi-Endmember Spectral Mixture Analysis, MESMA, Automated Monte Carlo Unmixing Analysis, AutoMCU) methods, based on GF-1 data and field measured spectral library.(2) Based on GF-1 data, SMA was applied to solve vegetation cover and transitional sandy land detection in Zhenglan Banner, Inner Mongolia, China.(3) By defined a new indictor, Moisture-responded NPP(MNPP), a new method for identification of degraded lands was put forward, and the land degradation in Xinlin Gol league, Inner Mongolia Autonomous Region, China was assessed preliminarily. (4) The 2dRUE proved to be a good indicator for land degradation, based on which, land degradation status in the general potential extent of desertification in China (PEDC) was assessed.

A new classification method for MALDI imaging mass spectrometry data acquired on formalin-fixed paraffin-embedded tissue samples.

PubMed

Boskamp, Tobias; Lachmund, Delf; Oetjen, Janina; Cordero Hernandez, Yovany; Trede, Dennis; Maass, Peter; Casadonte, Rita; Kriegsmann, Jörg; Warth, Arne; Dienemann, Hendrik; Weichert, Wilko; Kriegsmann, Mark

2017-07-01

Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) shows a high potential for applications in histopathological diagnosis, and in particular for supporting tumor typing and subtyping. The development of such applications requires the extraction of spectral fingerprints that are relevant for the given tissue and the identification of biomarkers associated with these spectral patterns. We propose a novel data analysis method based on the extraction of characteristic spectral patterns (CSPs) that allow automated generation of classification models for spectral data. Formalin-fixed paraffin embedded (FFPE) tissue samples from N=445 patients assembled on 12 tissue microarrays were analyzed. The method was applied to discriminate primary lung and pancreatic cancer, as well as adenocarcinoma and squamous cell carcinoma of the lung. A classification accuracy of 100% and 82.8%, resp., could be achieved on core level, assessed by cross-validation. The method outperformed the more conventional classification method based on the extraction of individual m/z values in the first application, while achieving a comparable accuracy in the second. LC-MS/MS peptide identification demonstrated that the spectral features present in selected CSPs correspond to peptides relevant for the respective classification. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann. Copyright © 2016 Elsevier B.V. All rights reserved.

[Main Components of Xinjiang Lavender Essential Oil Determined by Partial Least Squares and Near Infrared Spectroscopy].

PubMed

Liao, Xiang; Wang, Qing; Fu, Ji-hong; Tang, Jun

2015-09-01

This work was undertaken to establish a quantitative analysis model which can rapid determinate the content of linalool, linalyl acetate of Xinjiang lavender essential oil. Totally 165 lavender essential oil samples were measured by using near infrared absorption spectrum (NIR), after analyzing the near infrared spectral absorption peaks of all samples, lavender essential oil have abundant chemical information and the interference of random noise may be relatively low on the spectral intervals of 7100~4500 cm(-1). Thus, the PLS models was constructed by using this interval for further analysis. 8 abnormal samples were eliminated. Through the clustering method, 157 lavender essential oil samples were divided into 105 calibration set samples and 52 validation set samples. Gas chromatography mass spectrometry (GC-MS) was used as a tool to determine the content of linalool and linalyl acetate in lavender essential oil. Then the matrix was established with the GC-MS raw data of two compounds in combination with the original NIR data. In order to optimize the model, different pretreatment methods were used to preprocess the raw NIR spectral to contrast the spectral filtering effect, after analysizing the quantitative model results of linalool and linalyl acetate, the root mean square error prediction (RMSEP) of orthogonal signal transformation (OSC) was 0.226, 0.558, spectrally, it was the optimum pretreatment method. In addition, forward interval partial least squares (FiPLS) method was used to exclude the wavelength points which has nothing to do with determination composition or present nonlinear correlation, finally 8 spectral intervals totally 160 wavelength points were obtained as the dataset. Combining the data sets which have optimized by OSC-FiPLS with partial least squares (PLS) to establish a rapid quantitative analysis model for determining the content of linalool and linalyl acetate in Xinjiang lavender essential oil, numbers of hidden variables of two components were 8 in the model. The performance of the model was evaluated according to root mean square error of cross-validation (RMSECV), root mean square error of prediction (RMSEP). In the model, RESECV of linalool and linalyl acetate were 0.170 and 0.416, respectively; RM-SEP were 0.188 and 0.364. The results indicated that raw data was pretreated by OSC and FiPLS, the NIR-PLS quantitative analysis model with good robustness, high measurement precision; it could quickly determine the content of linalool and linalyl acetate in lavender essential oil. In addition, the model has a favorable prediction ability. The study also provide a new effective method which could rapid quantitative analysis the major components of Xinjiang lavender essential oil.

Optimization of data analysis for the in vivo neutron activation analysis of aluminum in bone.

PubMed

Mohseni, H K; Matysiak, W; Chettle, D R; Byun, S H; Priest, N; Atanackovic, J; Prestwich, W V

2016-10-01

An existing system at McMaster University has been used for the in vivo measurement of aluminum in human bone. Precise and detailed analysis approaches are necessary to determine the aluminum concentration because of the low levels of aluminum found in the bone and the challenges associated with its detection. Phantoms resembling the composition of the human hand with varying concentrations of aluminum were made for testing the system prior to the application to human studies. A spectral decomposition model and a photopeak fitting model involving the inverse-variance weighted mean and a time-dependent analysis were explored to analyze the results and determine the model with the best performance and lowest minimum detection limit. The results showed that the spectral decomposition and the photopeak fitting model with the inverse-variance weighted mean both provided better results compared to the other methods tested. The spectral decomposition method resulted in a marginally lower detection limit (5μg Al/g Ca) compared to the inverse-variance weighted mean (5.2μg Al/g Ca), rendering both equally applicable to human measurements. Copyright © 2016 Elsevier Ltd. All rights reserved.

A fast, automated, polynomial-based cosmic ray spike-removal method for the high-throughput processing of Raman spectra.

PubMed

Schulze, H Georg; Turner, Robin F B

2013-04-01

Raman spectra often contain undesirable, randomly positioned, intense, narrow-bandwidth, positive, unidirectional spectral features generated when cosmic rays strike charge-coupled device cameras. These must be removed prior to analysis, but doing so manually is not feasible for large data sets. We developed a quick, simple, effective, semi-automated procedure to remove cosmic ray spikes from spectral data sets that contain large numbers of relatively homogenous spectra. Although some inhomogeneous spectral data sets can be accommodated--it requires replacing excessively modified spectra with the originals and removing their spikes with a median filter instead--caution is advised when processing such data sets. In addition, the technique is suitable for interpolating missing spectra or replacing aberrant spectra with good spectral estimates. The method is applied to baseline-flattened spectra and relies on fitting a third-order (or higher) polynomial through all the spectra at every wavenumber. Pixel intensities in excess of a threshold of 3× the noise standard deviation above the fit are reduced to the threshold level. Because only two parameters (with readily specified default values) might require further adjustment, the method is easily implemented for semi-automated processing of large spectral sets.

Spectral imaging toolbox: segmentation, hyperstack reconstruction, and batch processing of spectral images for the determination of cell and model membrane lipid order.

PubMed

Aron, Miles; Browning, Richard; Carugo, Dario; Sezgin, Erdinc; Bernardino de la Serna, Jorge; Eggeling, Christian; Stride, Eleanor

2017-05-12

Spectral imaging with polarity-sensitive fluorescent probes enables the quantification of cell and model membrane physical properties, including local hydration, fluidity, and lateral lipid packing, usually characterized by the generalized polarization (GP) parameter. With the development of commercial microscopes equipped with spectral detectors, spectral imaging has become a convenient and powerful technique for measuring GP and other membrane properties. The existing tools for spectral image processing, however, are insufficient for processing the large data sets afforded by this technological advancement, and are unsuitable for processing images acquired with rapidly internalized fluorescent probes. Here we present a MATLAB spectral imaging toolbox with the aim of overcoming these limitations. In addition to common operations, such as the calculation of distributions of GP values, generation of pseudo-colored GP maps, and spectral analysis, a key highlight of this tool is reliable membrane segmentation for probes that are rapidly internalized. Furthermore, handling for hyperstacks, 3D reconstruction and batch processing facilitates analysis of data sets generated by time series, z-stack, and area scan microscope operations. Finally, the object size distribution is determined, which can provide insight into the mechanisms underlying changes in membrane properties and is desirable for e.g. studies involving model membranes and surfactant coated particles. Analysis is demonstrated for cell membranes, cell-derived vesicles, model membranes, and microbubbles with environmentally-sensitive probes Laurdan, carboxyl-modified Laurdan (C-Laurdan), Di-4-ANEPPDHQ, and Di-4-AN(F)EPPTEA (FE), for quantification of the local lateral density of lipids or lipid packing. The Spectral Imaging Toolbox is a powerful tool for the segmentation and processing of large spectral imaging datasets with a reliable method for membrane segmentation and no ability in programming required. The Spectral Imaging Toolbox can be downloaded from https://uk.mathworks.com/matlabcentral/fileexchange/62617-spectral-imaging-toolbox .

A Single Chiroptical Spectroscopic Method May Not Be Able To Establish the Absolute Configurations of Diastereomers: Dimethylesters of Hibiscus and Garcinia Acids

PubMed Central

Polavarapu, Prasad L.; Donahue, Emily A.; Shanmugam, Ganesh; Scalmani, Giovanni; Hawkins, Edward K.; Rizzo, Carmelo; Ibnusaud, Ibrahim; Thomas, Grace; Habel, Deenamma; Sebastian, Dellamol

2013-01-01

Electronic circular dichroism (ECD), optical rotatory dispersion (ORD), and vibrational circular dichroism (VCD) spectra of hibiscus acid dimethyl ester have been measured and analyzed in combination with quantum chemical calculations of corresponding spectra. These results, along with those reported previously for garcinia acid dimethyl ester, reveal that none of these three (ECD, ORD, or VCD) spectroscopic methods, in isolation, can unequivocally establish the absolute configurations of diastereomers. This deficiency is eliminated when a combined spectral analysis of either ECD and VCD or ORD and VCD methods is used. It is also found that the ambiguities in the assignment of absolute configurations of diastereomers may also be overcome when unpolarized vibrational absorption is included in the spectral analysis. PMID:21568330

Evaluation of algorithm methods for fluorescence spectra of cancerous and normal human tissues

NASA Astrophysics Data System (ADS)

Pu, Yang; Wang, Wubao; Alfano, Robert R.

2016-03-01

The paper focus on the various algorithms on to unravel the fluorescence spectra by unmixing methods to identify cancerous and normal human tissues from the measured fluorescence spectroscopy. The biochemical or morphologic changes that cause fluorescence spectra variations would appear earlier than the histological approach; therefore, fluorescence spectroscopy holds a great promise as clinical tool for diagnosing early stage of carcinomas and other deceases for in vivo use. The method can further identify tissue biomarkers by decomposing the spectral contributions of different fluorescent molecules of interest. In this work, we investigate the performance of blind source un-mixing methods (backward model) and spectral fitting approaches (forward model) in decomposing the contributions of key fluorescent molecules from the tissue mixture background when certain selected excitation wavelength is applied. Pairs of adenocarcinoma as well as normal tissues confirmed by pathologist were excited by selective wavelength of 340 nm. The emission spectra of resected fresh tissue were used to evaluate the relative changes of collagen, reduced nicotinamide adenine dinucleotide (NADH), and Flavin by various spectral un-mixing methods. Two categories of algorithms: forward methods and Blind Source Separation [such as Principal Component Analysis (PCA) and Independent Component Analysis (ICA), and Nonnegative Matrix Factorization (NMF)] will be introduced and evaluated. The purpose of the spectral analysis is to discard the redundant information which conceals the difference between these two types of tissues, but keep their diagnostically significance. The facts predicted by different methods were compared to the gold standard of histopathology. The results indicate that these key fluorophores within tissue, e.g. tryptophan, collagen, and NADH, and flavin, show differences of relative contents of fluorophores among different types of human cancer and normal tissues. The sensitivity, specificity, and receiver operating characteristic (ROC) are finally employed as the criteria to evaluate the efficacy of these methods in cancer detection. The underlying physical and biological basis for these optical approaches will be discussed with examples. This ex vivo preliminary trial demonstrates that these different criteria from different methods can distinguish carcinoma from normal tissues with good sensitivity and specificity while among them, we found that ICA appears to be the superior method in predication accuracy.

The fundamental parameter method applied to X-ray fluorescence analysis with synchrotron radiation

NASA Astrophysics Data System (ADS)

Pantenburg, F. J.; Beier, T.; Hennrich, F.; Mommsen, H.

1992-05-01

Quantitative X-ray fluorescence analysis applying the fundamental parameter method is usually restricted to monochromatic excitation sources. It is shown here, that such analyses can be performed as well with a white synchrotron radiation spectrum. To determine absolute elemental concentration values it is necessary to know the spectral distribution of this spectrum. A newly designed and tested experimental setup, which uses the synchrotron radiation emitted from electrons in a bending magnet of ELSA (electron stretcher accelerator of the university of Bonn) is presented. The determination of the exciting spectrum, described by the given electron beam parameters, is limited due to uncertainties in the vertical electron beam size and divergence. We describe a method which allows us to determine the relative and absolute spectral distributions needed for accurate analysis. First test measurements of different alloys and standards of known composition demonstrate that it is possible to determine exact concentration values in bulk and trace element analysis.

Deep learning on temporal-spectral data for anomaly detection

NASA Astrophysics Data System (ADS)

Ma, King; Leung, Henry; Jalilian, Ehsan; Huang, Daniel

2017-05-01

Detecting anomalies is important for continuous monitoring of sensor systems. One significant challenge is to use sensor data and autonomously detect changes that cause different conditions to occur. Using deep learning methods, we are able to monitor and detect changes as a result of some disturbance in the system. We utilize deep neural networks for sequence analysis of time series. We use a multi-step method for anomaly detection. We train the network to learn spectral and temporal features from the acoustic time series. We test our method using fiber-optic acoustic data from a pipeline.

Mass Spectral Library Quality Assurance by Inter-Library Comparison

NASA Astrophysics Data System (ADS)

Wallace, William E.; Ji, Weihua; Tchekhovskoi, Dmitrii V.; Phinney, Karen W.; Stein, Stephen E.

2017-04-01

A method to discover and correct errors in mass spectral libraries is described. Comparing across a set of highly curated reference libraries compounds that have the same chemical structure quickly identifies entries that are outliers. In cases where three or more entries for the same compound are compared, the outlier as determined by visual inspection was almost always found to contain the error. These errors were either in the spectrum itself or in the chemical descriptors that accompanied it. The method is demonstrated on finding errors in compounds of forensic interest in the NIST/EPA/NIH Mass Spectral Library. The target list of compounds checked was the Scientific Working Group for the Analysis of Seized Drugs (SWGDRUG) mass spectral library. Some examples of errors found are described. A checklist of errors that curators should look for when performing inter-library comparisons is provided.

Scaling earthquake ground motions for performance-based assessment of buildings

USGS Publications Warehouse

Huang, Y.-N.; Whittaker, A.S.; Luco, N.; Hamburger, R.O.

2011-01-01

The impact of alternate ground-motion scaling procedures on the distribution of displacement responses in simplified structural systems is investigated. Recommendations are provided for selecting and scaling ground motions for performance-based assessment of buildings. Four scaling methods are studied, namely, (1)geometric-mean scaling of pairs of ground motions, (2)spectrum matching of ground motions, (3)first-mode-period scaling to a target spectral acceleration, and (4)scaling of ground motions per the distribution of spectral demands. Data were developed by nonlinear response-history analysis of a large family of nonlinear single degree-of-freedom (SDOF) oscillators that could represent fixed-base and base-isolated structures. The advantages and disadvantages of each scaling method are discussed. The relationship between spectral shape and a ground-motion randomness parameter, is presented. A scaling procedure that explicitly considers spectral shape is proposed. ?? 2011 American Society of Civil Engineers.

Mass Spectral Library Quality Assurance by Inter-Library Comparison

PubMed Central

Wallace, W.E.; Ji, W.; Tchekhovskoi, D.V.; Phinney, K.W.; Stein, S.E.

2017-01-01

A method to discover and correct errors in mass spectral libraries is described. Comparing across a set of highly curated reference libraries compounds that have the same chemical structure quickly identifies entries that are outliers. In cases where three or more entries for the same compound are compared the outlier as determined by visual inspection was almost always found to contain the error. These errors were either in the spectrum itself or in the chemical descriptors that accompanied it. The method is demonstrated on finding errors in compounds of forensic interest in the NIST/EPA/NIH Mass Spectral Library. The target list of compounds checked was the Scientific Working Group for the Analysis of Seized Drugs (SWGDRUG) mass spectral library. Some examples of errors found are described. A checklist of errors that curators should look for when performing inter-library comparisons is provided. PMID:28127680

Early Oscillation Detection for Hybrid DC/DC Converter Fault Diagnosis

NASA Technical Reports Server (NTRS)

Wang, Bright L.

2011-01-01

This paper describes a novel fault detection technique for hybrid DC/DC converter oscillation diagnosis. The technique is based on principles of feedback control loop oscillation and RF signal modulations, and Is realized by using signal spectral analysis. Real-circuit simulation and analytical study reveal critical factors of the oscillation and indicate significant correlations between the spectral analysis method and the gain/phase margin method. A stability diagnosis index (SDI) is developed as a quantitative measure to accurately assign a degree of stability to the DC/DC converter. This technique Is capable of detecting oscillation at an early stage without interfering with DC/DC converter's normal operation and without limitations of probing to the converter.

Real-time new satellite product demonstration from microwave sensors and GOES-16 at NRL TC web

NASA Astrophysics Data System (ADS)

Cossuth, J.; Richardson, K.; Surratt, M. L.; Bankert, R.

2017-12-01

The Naval Research Laboratory (NRL) Tropical Cyclone (TC) satellite webpage (https://www.nrlmry.navy.mil/TC.html) provides demonstration analyses of storm imagery to benefit operational TC forecast centers around the world. With the availability of new spectral information provided by GOES-16 satellite data and recent research into improved visualization methods of microwave data, experimental imagery was operationally tested to visualize the structural changes of TCs during the 2017 hurricane season. This presentation provides an introduction into these innovative satellite analysis methods, NRL's next generation satellite analysis system (the Geolocated Information Processing System, GeoIPSTM), and demonstration the added value of additional spectral frequencies when monitoring storms in near-realtime.

Measurement of the refractive index of hemoglobin solutions for a continuous spectral region

PubMed Central

Wang, Jin; Deng, Zhichao; Wang, Xiaowan; Ye, Qing; Zhou, Wenyuan; Mei, Jianchun; Zhang, Chunping; Tian, Jianguo

2015-01-01

Determination of the refractive index of hemoglobin solutions over a wide wavelength range remains challenging. A famous detour approach is the Kramers-Kronig (KK) analysis which can resolve the real part of complex refractive index from the imaginary part. However, KK analysis is limited by the contradiction between the requirement of semi-infinite frequency range and limited measured range. In this paper, based on the Multi-curve fitting method (MFM), continuous refractive index dispersion (CRID) of oxygenated and deoxygenated hemoglobin solutions are measured using a homemade symmetrical arm-linked apparatus in the continuous wavelength range with spectral resolution of about 0.259nm. A novel method to obtain the CRID is proposed. PMID:26203379

Wavelet Filter Banks for Super-Resolution SAR Imaging

NASA Technical Reports Server (NTRS)

Sheybani, Ehsan O.; Deshpande, Manohar; Memarsadeghi, Nargess

2011-01-01

This paper discusses Innovative wavelet-based filter banks designed to enhance the analysis of super resolution Synthetic Aperture Radar (SAR) images using parametric spectral methods and signal classification algorithms, SAR finds applications In many of NASA's earth science fields such as deformation, ecosystem structure, and dynamics of Ice, snow and cold land processes, and surface water and ocean topography. Traditionally, standard methods such as Fast-Fourier Transform (FFT) and Inverse Fast-Fourier Transform (IFFT) have been used to extract Images from SAR radar data, Due to non-parametric features of these methods and their resolution limitations and observation time dependence, use of spectral estimation and signal pre- and post-processing techniques based on wavelets to process SAR radar data has been proposed. Multi-resolution wavelet transforms and advanced spectral estimation techniques have proven to offer efficient solutions to this problem.

Persistence of uranium emission in laser-produced plasmas

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

LaHaye, N. L.; Harilal, S. S., E-mail: hari@purdue.edu; Diwakar, P. K.

2014-04-28

Detection of uranium and other nuclear materials is of the utmost importance for nuclear safeguards and security. Optical emission spectroscopy of laser-ablated U plasmas has been presented as a stand-off, portable analytical method that can yield accurate qualitative and quantitative elemental analysis of a variety of samples. In this study, optimal laser ablation and ambient conditions are explored, as well as the spatio-temporal evolution of the plasma for spectral analysis of excited U species in a glass matrix. Various Ar pressures were explored to investigate the role that plasma collisional effects and confinement have on spectral line emission enhancement andmore » persistence. The plasma-ambient gas interaction was also investigated using spatially resolved spectra and optical time-of-flight measurements. The results indicate that ambient conditions play a very important role in spectral emission intensity as well as the persistence of excited neutral U emission lines, influencing the appropriate spectral acquisition conditions.« less

Energy-Discriminative Performance of a Spectral Micro-CT System

PubMed Central

He, Peng; Yu, Hengyong; Bennett, James; Ronaldson, Paul; Zainon, Rafidah; Butler, Anthony; Butler, Phil; Wei, Biao; Wang, Ge

2013-01-01

Experiments were performed to evaluate the energy-discriminative performance of a spectral (multi-energy) micro-CT system. The system, designed by MARS (Medipix All Resolution System) Bio-Imaging Ltd. (Christchurch, New Zealand), employs a photon-counting energy-discriminative detector technology developed by CERN (European Organization for Nuclear Research). We used the K-edge attenuation characteristic of some known materials to calibrate the detector’s photon energy discrimination. For tomographic analysis, we used the compressed sensing (CS) based ordered-subset simultaneous algebraic reconstruction techniques (OS-SART) to reconstruct sample images, which is effective to reduce noise and suppress artifacts. Unlike conventional CT, the principal component analysis (PCA) method can be applied to extract and quantify additional attenuation information from a spectral CT dataset. Our results show that the spectral CT has a good energy-discriminative performance and provides more attenuation information than the conventional CT. PMID:24004864

The study of active tectonic based on hyperspectral remote sensing

NASA Astrophysics Data System (ADS)

Cui, J.; Zhang, S.; Zhang, J.; Shen, X.; Ding, R.; Xu, S.

2017-12-01

As of the latest technical methods, hyperspectral remote sensing technology has been widely used in each brach of the geosciences. However, it is still a blank for using the hyperspectral remote sensing to study the active structrure. Hyperspectral remote sensing, with high spectral resolution, continuous spectrum, continuous spatial data, low cost, etc, has great potentialities in the areas of stratum division and fault identification. Blind fault identification in plains and invisible fault discrimination in loess strata are the two hot problems in the current active fault research. Thus, the study of active fault based on the hyperspectral technology has great theoretical significance and practical value. Magnetic susceptibility (MS) records could reflect the rhythm alteration of the formation. Previous study shown that MS has correlation with spectral feature. In this study, the Emaokou section, located to the northwest of the town of Huairen, in Shanxi Province, has been chosen for invisible fault study. We collected data from the Emaokou section, including spectral data, hyperspectral image, MS data. MS models based on spectral features were established and applied to the UHD185 image for MS mapping. The results shown that MS map corresponded well to the loess sequences. It can recognize the stratum which can not identity by naked eyes. Invisible fault has been found in this section, which is useful for paleoearthquake analysis. The faults act as the conduit for migration of terrestrial gases, the fault zones, especially the structurally weak zones such as inrtersections or bends of fault, may has different material composition. We take Xiadian fault for study. Several samples cross-fault were collected and these samples were measured by ASD Field Spec 3 spectrometer. Spectral classification method has been used for spectral analysis, we found that the spectrum of the fault zone have four special spectral region(550-580nm, 600-700nm, 700-800nm and 800-900nm), which different with the spectrum of the none-fault zone. It could help us welly located the fault zone. The located result correspond well to the physical prospecting method result. The above study shown that Hypersepctral remote sensing technology provide a new method for active study.

Thermal structure of Sikhote Alin and adjacent areas based on spectral analysis of the anomalous magnetic field

NASA Astrophysics Data System (ADS)

Didenko, A. N.; Nosyrev, M. Yu.; Shevchenko, B. F.; Gilmanova, G. Z.

2017-11-01

The depth of the base of the magnetoactive layer and the geothermal gradient in the Sikhote Alin crust are estimated based on a method determining the Curie depth point of magnetoactive masses by using spectral analysis of the anomalous magnetic field. A detailed map of the geothermal gradient is constructed for the first time for the Sikhote Alin and adjacent areas of the Central Asian belt. Analysis of this map shows that the zones with a higher geothermal gradient geographically fit the areas with a higher level of seismicity.

Multi-spectral fiber spectroscopy in 0,4-16μm range for biomedical applications(Conference Presentation)

NASA Astrophysics Data System (ADS)

Artyushenko, Viacheslav

2017-02-01

Various biomedical applications of fiber optics in a broad spectral range 0,4-16μm span from endoscopic imaging and Photo Dynamic Diagnostics (PDD) to laser power delivery for minimal invasive laser surgery, tissue coagulation and welding, Photo Dynamic Therapy (PDT), etc. Present review will highlight the latest results in advanced fiber solutions for a precise tissue diagnostics and control of some therapy methods - for so called "theranostic". Spectral fiber sensing for label free analysis of tissue composition helps to differentiate malignant and normal tissue to secure minimal invasive, but complete tumor removal or treatment. All key methods of Raman, fluorescence, diffuse reflection & MIR-absorption spectroscopy will be compared when used for the same spot of tissue - to select the most specific, sensitive and accurate method or to combine them for the synergy enhanced effect. The most informative spectral features for distinct organs/ tumor can be used to design special fiber sensors to be developed for portable and low cost applications with modern IT-features. Examples of multi-spectral tissue diagnostics promising for the future clinical applications will be presented to enable reduced mortality from cancer in the future. Translation of described methods into clinical practice will be discussed in comparison with the other method of optical diagnostics which should enhance modern medicine by less invasive, more precise and more effective methods of therapy to be fused with in-vivo diagnostics sensors & systems.

Hyperspectral small animal fluorescence imaging: spectral selection imaging

NASA Astrophysics Data System (ADS)

Leavesley, Silas; Jiang, Yanan; Patsekin, Valery; Hall, Heidi; Vizard, Douglas; Robinson, J. Paul

2008-02-01

Molecular imaging is a rapidly growing area of research, fueled by needs in pharmaceutical drug-development for methods for high-throughput screening, pre-clinical and clinical screening for visualizing tumor growth and drug targeting, and a growing number of applications in the molecular biology fields. Small animal fluorescence imaging employs fluorescent probes to target molecular events in vivo, with a large number of molecular targeting probes readily available. The ease at which new targeting compounds can be developed, the short acquisition times, and the low cost (compared to microCT, MRI, or PET) makes fluorescence imaging attractive. However, small animal fluorescence imaging suffers from high optical scattering, absorption, and autofluorescence. Much of these problems can be overcome through multispectral imaging techniques, which collect images at different fluorescence emission wavelengths, followed by analysis, classification, and spectral deconvolution methods to isolate signals from fluorescence emission. We present an alternative to the current method, using hyperspectral excitation scanning (spectral selection imaging), a technique that allows excitation at any wavelength in the visible and near-infrared wavelength range. In many cases, excitation imaging may be more effective at identifying specific fluorescence signals because of the higher complexity of the fluorophore excitation spectrum. Because the excitation is filtered and not the emission, the resolution limit and image shift imposed by acousto-optic tunable filters have no effect on imager performance. We will discuss design of the imager, optimizing the imager for use in small animal fluorescence imaging, and application of spectral analysis and classification methods for identifying specific fluorescence signals.

Application of surface enhanced Raman scattering and competitive adaptive reweighted sampling on detecting furfural dissolved in transformer oil

NASA Astrophysics Data System (ADS)

Chen, Weigen; Zou, Jingxin; Wan, Fu; Fan, Zhou; Yang, Dingkun

2018-03-01

Detecting the dissolving furfural in mineral oil is an essential technical method to evaluate the ageing condition of oil-paper insulation and the degradation of mechanical properties. Compared with the traditional detection method, Raman spectroscopy is obviously convenient and timesaving in operation. This study explored the method of applying surface enhanced Raman scattering (SERS) on quantitative analysis of the furfural dissolved in oil. Oil solution with different concentration of furfural were prepared and calibrated by high-performance liquid chromatography. Confocal laser Raman spectroscopy (CLRS) and SERS technology were employed to acquire Raman spectral data. Monte Carlo cross validation (MCCV) was used to eliminate the outliers in sample set, then competitive adaptive reweighted sampling (CARS) was developed to select an optimal combination of informative variables that most reflect the chemical properties of concern. Based on selected Raman spectral features, support vector machine (SVM) combined with particle swarm algorithm (PSO) was used to set up a furfural quantitative analysis model. Finally, the generalization ability and prediction precision of the established method were verified by the samples made in lab. In summary, a new spectral method is proposed to quickly detect furfural in oil, which lays a foundation for evaluating the ageing of oil-paper insulation in oil immersed electrical equipment.

Rapid quantitation of atorvastatin in process pharmaceutical powder sample using Raman spectroscopy and evaluation of parameters related to accuracy of analysis.

PubMed

Lim, Young-Il; Han, Janghee; Woo, Young-Ah; Kim, Jaejin; Kang, Myung Joo

2018-07-05

The purpose of this study was to determine the atorvastatin (ATV) content in process pharmaceutical powder sample using Raman spectroscopy. To establish the analysis method, the influence of the type of Raman measurements (back-scattering or transmission mode), preparation of calibration sample (simple admixing or granulation), sample pre-treatment (pelletization), and spectral pretreatment on the Raman spectra was investigated. The characteristic peak of the active compound was more distinctively detected in transmission Raman mode with a laser spot size of 4mm than in the back-scattering method. Preparation of calibration samples by wet granulation, identical to the actual manufacturing process, provided unchanged spectral patterns for the in process sample, with no changes and/or shifts in the spectrum. Pelletization before Raman analysis remarkably improved spectral reproducibility by decreasing the difference in density between the samples. Probabilistic quotient normalization led to accurate and consistent quantification of the ATV content in the calibration samples (standard error of cross validation: 1.21%). Moreover, the drug content in the granules obtained from five commercial batches were reliably quantified, with no statistical difference (p=0.09) with that obtained by HPLC assay. From these findings, we suggest that transmission Raman analysis may be a fast and non-invasive method for the quantification of ATV in actual manufacturing processes. Copyright © 2018 Elsevier B.V. All rights reserved.

Rapid quantitation of atorvastatin in process pharmaceutical powder sample using Raman spectroscopy and evaluation of parameters related to accuracy of analysis

NASA Astrophysics Data System (ADS)

Lim, Young-Il; Han, Janghee; Woo, Young-Ah; Kim, Jaejin; Kang, Myung Joo

2018-07-01

The purpose of this study was to determine the atorvastatin (ATV) content in process pharmaceutical powder sample using Raman spectroscopy. To establish the analysis method, the influence of the type of Raman measurements (back-scattering or transmission mode), preparation of calibration sample (simple admixing or granulation), sample pre-treatment (pelletization), and spectral pretreatment on the Raman spectra was investigated. The characteristic peak of the active compound was more distinctively detected in transmission Raman mode with a laser spot size of 4 mm than in the back-scattering method. Preparation of calibration samples by wet granulation, identical to the actual manufacturing process, provided unchanged spectral patterns for the in process sample, with no changes and/or shifts in the spectrum. Pelletization before Raman analysis remarkably improved spectral reproducibility by decreasing the difference in density between the samples. Probabilistic quotient normalization led to accurate and consistent quantification of the ATV content in the calibration samples (standard error of cross validation: 1.21%). Moreover, the drug content in the granules obtained from five commercial batches were reliably quantified, with no statistical difference (p = 0.09) with that obtained by HPLC assay. From these findings, we suggest that transmission Raman analysis may be a fast and non-invasive method for the quantification of ATV in actual manufacturing processes.

DFT analysis and spectral characteristics of Celecoxib a potent COX-2 inhibitor

NASA Astrophysics Data System (ADS)

Vijayakumar, B.; Kannappan, V.; Sathyanarayanamoorthi, V.

2016-10-01

Extensive quantum mechanical studies are carried out on Celecoxib (CXB), a new generation drug to understand the vibrational and electronic spectral characteristics of the molecule. The vibrational frequencies of CXB are computed by HF and B3LYP methods with 6-311++G (d, p) basis set. The theoretical scaled vibrational frequencies have been assigned and they agreed satisfactorily with experimental FT-IR and Raman frequencies. The theoretical maximum wavelength of absorption of CXB are calculated in water and ethanol by TD-DFT method and these values are compared with experimentally determined λmax values. The spectral and Natural bonds orbital (NBO) analysis in conjunction with spectral data established the presence of intra molecular interactions such as mesomeric, hyperconjugative and steric effects in CXB. The electron density at various positions and reactivity descriptors of CXB indicate that the compound functions as a nucleophile and establish that aromatic ring system present in the molecule is the site of drug action. Electronic distribution and HOMO - LUMO energy values of CXB are discussed in terms of intra-molecular interactions. Computed values of Mulliken charges and thermodynamic properties of CXB are reported.

Color analysis and image rendering of woodblock prints with oil-based ink

NASA Astrophysics Data System (ADS)

Horiuchi, Takahiko; Tanimoto, Tetsushi; Tominaga, Shoji

2012-01-01

This paper proposes a method for analyzing the color characteristics of woodblock prints having oil-based ink and rendering realistic images based on camera data. The analysis results of woodblock prints show some characteristic features in comparison with oil paintings: 1) A woodblock print can be divided into several cluster areas, each with similar surface spectral reflectance; and 2) strong specular reflection from the influence of overlapping paints arises only in specific cluster areas. By considering these properties, we develop an effective rendering algorithm by modifying our previous algorithm for oil paintings. A set of surface spectral reflectances of a woodblock print is represented by using only a small number of average surface spectral reflectances and the registered scaling coefficients, whereas the previous algorithm for oil paintings required surface spectral reflectances of high dimension at all pixels. In the rendering process, in order to reproduce the strong specular reflection in specific cluster areas, we use two sets of parameters in the Torrance-Sparrow model for cluster areas with or without strong specular reflection. An experiment on a woodblock printing with oil-based ink was performed to demonstrate the feasibility of the proposed method.

Multispectral Image Compression for Improvement of Colorimetric and Spectral Reproducibility by Nonlinear Spectral Transform

NASA Astrophysics Data System (ADS)

Yu, Shanshan; Murakami, Yuri; Obi, Takashi; Yamaguchi, Masahiro; Ohyama, Nagaaki

2006-09-01

The article proposes a multispectral image compression scheme using nonlinear spectral transform for better colorimetric and spectral reproducibility. In the method, we show the reduction of colorimetric error under a defined viewing illuminant and also that spectral accuracy can be improved simultaneously using a nonlinear spectral transform called Labplus, which takes into account the nonlinearity of human color vision. Moreover, we show that the addition of diagonal matrices to Labplus can further preserve the spectral accuracy and has a generalized effect of improving the colorimetric accuracy under other viewing illuminants than the defined one. Finally, we discuss the usage of the first-order Markov model to form the analysis vectors for the higher order channels in Labplus to reduce the computational complexity. We implement a multispectral image compression system that integrates Labplus with JPEG2000 for high colorimetric and spectral reproducibility. Experimental results for a 16-band multispectral image show the effectiveness of the proposed scheme.

Individual Sensitivity to Spectral and Temporal Cues in Listeners With Hearing Impairment

PubMed Central

Wright, Richard A.; Blackburn, Michael C.; Tatman, Rachael; Gallun, Frederick J.

2015-01-01

Purpose The present study was designed to evaluate use of spectral and temporal cues under conditions in which both types of cues were available. Method Participants included adults with normal hearing and hearing loss. We focused on 3 categories of speech cues: static spectral (spectral shape), dynamic spectral (formant change), and temporal (amplitude envelope). Spectral and/or temporal dimensions of synthetic speech were systematically manipulated along a continuum, and recognition was measured using the manipulated stimuli. Level was controlled to ensure cue audibility. Discriminant function analysis was used to determine to what degree spectral and temporal information contributed to the identification of each stimulus. Results Listeners with normal hearing were influenced to a greater extent by spectral cues for all stimuli. Listeners with hearing impairment generally utilized spectral cues when the information was static (spectral shape) but used temporal cues when the information was dynamic (formant transition). The relative use of spectral and temporal dimensions varied among individuals, especially among listeners with hearing loss. Conclusion Information about spectral and temporal cue use may aid in identifying listeners who rely to a greater extent on particular acoustic cues and applying that information toward therapeutic interventions. PMID:25629388

Ship Detection Based on Multiple Features in Random Forest Model for Hyperspectral Images

NASA Astrophysics Data System (ADS)

Li, N.; Ding, L.; Zhao, H.; Shi, J.; Wang, D.; Gong, X.

2018-04-01

A novel method for detecting ships which aim to make full use of both the spatial and spectral information from hyperspectral images is proposed. Firstly, the band which is high signal-noise ratio in the range of near infrared or short-wave infrared spectrum, is used to segment land and sea on Otsu threshold segmentation method. Secondly, multiple features that include spectral and texture features are extracted from hyperspectral images. Principal components analysis (PCA) is used to extract spectral features, the Grey Level Co-occurrence Matrix (GLCM) is used to extract texture features. Finally, Random Forest (RF) model is introduced to detect ships based on the extracted features. To illustrate the effectiveness of the method, we carry out experiments over the EO-1 data by comparing single feature and different multiple features. Compared with the traditional single feature method and Support Vector Machine (SVM) model, the proposed method can stably achieve the target detection of ships under complex background and can effectively improve the detection accuracy of ships.

Estimating Achievable Accuracy for Global Imaging Spectroscopy Measurement of Non-Photosynthetic Vegetation Cover

NASA Astrophysics Data System (ADS)

Dennison, P. E.; Kokaly, R. F.; Daughtry, C. S. T.; Roberts, D. A.; Thompson, D. R.; Chambers, J. Q.; Nagler, P. L.; Okin, G. S.; Scarth, P.

2016-12-01

Terrestrial vegetation is dynamic, expressing seasonal, annual, and long-term changes in response to climate and disturbance. Phenology and disturbance (e.g. drought, insect attack, and wildfire) can result in a transition from photosynthesizing "green" vegetation to non-photosynthetic vegetation (NPV). NPV cover can include dead and senescent vegetation, plant litter, agricultural residues, and non-photosynthesizing stem tissue. NPV cover is poorly captured by conventional remote sensing vegetation indices, but it is readily separable from substrate cover based on spectral absorption features in the shortwave infrared. We will present past research motivating the need for global NPV measurements, establishing that mapping seasonal NPV cover is critical for improving our understanding of ecosystem function and carbon dynamics. We will also present new research that helps determine a best achievable accuracy for NPV cover estimation. To test the sensitivity of different NPV cover estimation methods, we simulated satellite imaging spectrometer data using field spectra collected over mixtures of NPV, green vegetation, and soil substrate. We incorporated atmospheric transmittance and modeled sensor noise to create simulated spectra with spectral resolutions ranging from 10 to 30 nm. We applied multiple methods of NPV estimation to the simulated spectra, including spectral indices, spectral feature analysis, multiple endmember spectral mixture analysis, and partial least squares regression, and compared the accuracy and bias of each method. These results prescribe sensor characteristics for an imaging spectrometer mission with NPV measurement capabilities, as well as a "Quantified Earth Science Objective" for global measurement of NPV cover. Copyright 2016, all rights reserved.

Multispectral imaging of absorption and scattering properties of in vivo exposed rat brain using a digital red-green-blue camera.

PubMed

Yoshida, Keiichiro; Nishidate, Izumi; Ishizuka, Tomohiro; Kawauchi, Satoko; Sato, Shunichi; Sato, Manabu

2015-05-01

In order to estimate multispectral images of the absorption and scattering properties in the cerebral cortex of in vivo rat brain, we investigated spectral reflectance images estimated by the Wiener estimation method using a digital RGB camera. A Monte Carlo simulation-based multiple regression analysis for the corresponding spectral absorbance images at nine wavelengths (500, 520, 540, 560, 570, 580, 600, 730, and 760 nm) was then used to specify the absorption and scattering parameters of brain tissue. In this analysis, the concentrations of oxygenated hemoglobin and that of deoxygenated hemoglobin were estimated as the absorption parameters, whereas the coefficient a and the exponent b of the reduced scattering coefficient spectrum approximated by a power law function were estimated as the scattering parameters. The spectra of absorption and reduced scattering coefficients were reconstructed from the absorption and scattering parameters, and the spectral images of absorption and reduced scattering coefficients were then estimated. In order to confirm the feasibility of this method, we performed in vivo experiments on exposed rat brain. The estimated images of the absorption coefficients were dominated by the spectral characteristics of hemoglobin. The estimated spectral images of the reduced scattering coefficients had a broad scattering spectrum, exhibiting a larger magnitude at shorter wavelengths, corresponding to the typical spectrum of brain tissue published in the literature. The changes in the estimated absorption and scattering parameters during normoxia, hyperoxia, and anoxia indicate the potential applicability of the method by which to evaluate the pathophysiological conditions of in vivo brain due to the loss of tissue viability.

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.

Higher-Order Spectral Analysis of F-18 Flight Flutter Data

NASA Technical Reports Server (NTRS)

Silva, Walter A.; Dunn, Shane

2005-01-01

Royal Australian Air Force (RAAF) F/A-18 flight flutter test data is presented and analyzed using various techniques. The data includes high-quality measurements of forced responses and limit cycle oscillation (LCO) phenomena. Standard correlation and power spectral density (PSD) techniques are applied to the data and presented. Novel applications of experimentally-identified impulse responses and higher-order spectral techniques are also applied to the data and presented. The goal of this research is to develop methods that can identify the onset of nonlinear aeroelastic phenomena, such as LCO, during flutter testing.

Algorithms for Spectral Decomposition with Applications to Optical Plume Anomaly Detection

NASA Technical Reports Server (NTRS)

Srivastava, Askok N.; Matthews, Bryan; Das, Santanu

2008-01-01

The analysis of spectral signals for features that represent physical phenomenon is ubiquitous in the science and engineering communities. There are two main approaches that can be taken to extract relevant features from these high-dimensional data streams. The first set of approaches relies on extracting features using a physics-based paradigm where the underlying physical mechanism that generates the spectra is used to infer the most important features in the data stream. We focus on a complementary methodology that uses a data-driven technique that is informed by the underlying physics but also has the ability to adapt to unmodeled system attributes and dynamics. We discuss the following four algorithms: Spectral Decomposition Algorithm (SDA), Non-Negative Matrix Factorization (NMF), Independent Component Analysis (ICA) and Principal Components Analysis (PCA) and compare their performance on a spectral emulator which we use to generate artificial data with known statistical properties. This spectral emulator mimics the real-world phenomena arising from the plume of the space shuttle main engine and can be used to validate the results that arise from various spectral decomposition algorithms and is very useful for situations where real-world systems have very low probabilities of fault or failure. Our results indicate that methods like SDA and NMF provide a straightforward way of incorporating prior physical knowledge while NMF with a tuning mechanism can give superior performance on some tests. We demonstrate these algorithms to detect potential system-health issues on data from a spectral emulator with tunable health parameters.

Finding the patterns in complex specimens by improving the acquisition and analysis of x-ray spectromicroscopy data

NASA Astrophysics Data System (ADS)

Lerotic, Mirna

Soft x-ray spectromicroscopy provides spectral data on the chemical speciation of light elements at sub-100 nanometer spatial resolution. The high resolution imaging places a strong demand on the microscope stability and on the reproducibility of the scanned image field, and the volume of data necessitates the need for improved data analysis methods. This dissertation concerns two developments in extending the capability of soft x-ray transmission microscopes to carry out studies of chemical speciation at high spatial resolution. One development involves an improvement in x-ray microscope instrumentation: a new Stony Brook scanning transmission x-ray microscope which incorporates laser interferometer feedback in scanning stage positions. The interferometer is used to control the position between the sample and focusing optics, and thus improve the stability of the system. A second development concerns new analysis methods for the study of chemical speciation of complex specimens, such as those in biological and environmental science studies. When all chemical species in a specimen are known and separately characterized, existing approaches can be used to measure the concentration of each component at each pixel. In other cases (such as often occur in biology or environmental science), where the specimen may be too complicated or provide at least some unknown spectral signatures, other approaches must be used. We describe here an approach that uses principal component analysis (similar to factor analysis) to orthogonalize and noise-filter spectromicroscopy data. We then use cluster analysis (a form of unsupervised pattern matching) to classify pixels according to spectral similarity, to extract representative, cluster-averaged spectra with good signal-to-noise ratio, and to obtain gradations of concentration of these representative spectra at each pixel. The method is illustrated with a simulated data set of organic compounds, and a mixture of lutetium in hematite used to understand colloidal transport properties of radionuclides. Also, we describe here an extension of that work employing an angle distance measure; this measure provides better classification based on spectral signatures alone in specimens with significant thickness variations. The method is illustrated using simulated data, and also to examine sporulation in the bacterium Clostridium sp.

High-throughput spectral and lifetime-based FRET screening in living cells to identify small-molecule effectors of SERCA

PubMed Central

Schaaf, Tory M.; Peterson, Kurt C.; Grant, Benjamin D.; Bawaskar, Prachi; Yuen, Samantha; Li, Ji; Muretta, Joseph M.; Gillispie, Gregory D.; Thomas, David D.

2017-01-01

A robust high-throughput screening (HTS) strategy has been developed to discover small-molecule effectors targeting the sarco/endoplasmic reticulum calcium ATPase (SERCA), based on a fluorescence microplate reader that records both the nanosecond decay waveform (lifetime mode) and the complete emission spectrum (spectral mode), with high precision and speed. This spectral unmixing plate reader (SUPR) was used to screen libraries of small molecules with a fluorescence resonance energy transfer (FRET) biosensor expressed in living cells. Ligand binding was detected by FRET associated with structural rearrangements of green (GFP, donor) and red (RFP, acceptor) fluorescent proteins fused to the cardiac-specific SERCA2a isoform. The results demonstrate accurate quantitation of FRET along with high precision of hit identification. Fluorescence lifetime analysis resolved SERCA’s distinct structural states, providing a method to classify small-molecule chemotypes on the basis of their structural effect on the target. The spectral analysis was also applied to flag interference by fluorescent compounds. FRET hits were further evaluated for functional effects on SERCA’s ATPase activity via both a coupled-enzyme assay and a FRET-based calcium sensor. Concentration-response curves indicated excellent correlation between FRET and function. These complementary spectral and lifetime FRET detection methods offer an attractive combination of precision, speed, and resolution for HTS. PMID:27899691

Assessing and monitoring of urban vegetation using multiple endmember spectral mixture analysis

NASA Astrophysics Data System (ADS)

Zoran, M. A.; Savastru, R. S.; Savastru, D. M.

2013-08-01

During last years urban vegetation with significant health, biological and economical values had experienced dramatic changes due to urbanization and human activities in the metropolitan area of Bucharest in Romania. We investigated the utility of remote sensing approaches of multiple endmember spectral mixture analysis (MESMA) applied to IKONOS and Landsat TM/ETM satellite data for estimating fractional cover of urban/periurban forest, parks, agricultural vegetation areas. Because of the spectral heterogeneity of same physical features of urban vegetation increases with the increase of image resolution, the traditional spectral information-based statistical method may not be useful to classify land cover dynamics from high resolution imageries like IKONOS. So we used hierarchy tree classification method in classification and MESMA for vegetation land cover dynamics assessment based on available IKONOS high-resolution imagery of Bucharest town. This study employs thirty two endmembers and six hundred and sixty spectral models to identify all Earth's features (vegetation, water, soil, impervious) and shade in the Bucharest area. The mean RMS error for the selected vegetation land cover classes range from 0.0027 to 0.018. The Pearson correlation between the fraction outputs from MESMA and reference data from all IKONOS images 1m panchromatic resolution data for urban/periurban vegetation were ranging in the domain 0.7048 - 0.8287. The framework in this study can be applied to other urban vegetation areas in Romania.

Delineating gas bearing reservoir by using spectral decomposition attribute: Case study of Steenkool formation, Bintuni Basin

NASA Astrophysics Data System (ADS)

Haris, A.; Pradana, G. S.; Riyanto, A.

2017-07-01

Tectonic setting of the Bird Head Papua Island becomes an important model for petroleum system in Eastern part of Indonesia. The current exploration has been started since the oil seepage finding in Bintuni and Salawati Basin. The biogenic gas in shallow layer turns out to become an interesting issue in the hydrocarbon exploration. The hydrocarbon accumulation appearance in a shallow layer with dry gas type, appeal biogenic gas for further research. This paper aims at delineating the sweet spot hydrocarbon potential in shallow layer by applying the spectral decomposition technique. The spectral decomposition is decomposing the seismic signal into an individual frequency, which has significant geological meaning. One of spectral decomposition methods is Continuous Wavelet Transform (CWT), which transforms the seismic signal into individual time and frequency simultaneously. This method is able to make easier time-frequency map analysis. When time resolution increases, the frequency resolution will be decreased, and vice versa. In this study, we perform low-frequency shadow zone analysis in which the amplitude anomaly at a low frequency of 15 Hz was observed and we then compare it to the amplitude at the mid (20 Hz) and the high-frequency (30 Hz). The appearance of the amplitude anomaly at a low frequency was disappeared at high frequency, this anomaly disappears. The spectral decomposition by using CWT algorithm has been successfully applied to delineate the sweet spot zone.

Two-Flux Green's Function Analysis for Transient Spectral Radiation in a Composite

NASA Technical Reports Server (NTRS)

Siegel, Robert

1996-01-01

An analysis is developed for obtaining transient temperatures in a two-layer semitransparent composite with spectrally dependent properties. Each external boundary of the composite is subjected to radiation and convection. The two-flux radiative transfer equations are solved by deriving a Green's function. This yields the local radiative heat source needed to numerically solve the transient energy equation. An advantage of the two-flux method is that isotropic scattering is included without added complexity. The layer refractive indices are larger than one. This produces internal reflections at the boundaries and the internal interface; the reflections are assumed diffuse. Spectral results using the Green's function method are verified by comparing with numerical solutions using the exact radiative transfer equations. Transient temperature distributions are given to illustrate the effect of radiative heating on one side of a composite with external convective cooling. The protection of a material from incident radiation is illustrated by adding scattering to the layer adjacent to the radiative source.

Characterizing multivariate decoding models based on correlated EEG spectral features

PubMed Central

McFarland, Dennis J.

2013-01-01

Objective Multivariate decoding methods are popular techniques for analysis of neurophysiological data. The present study explored potential interpretative problems with these techniques when predictors are correlated. Methods Data from sensorimotor rhythm-based cursor control experiments was analyzed offline with linear univariate and multivariate models. Features were derived from autoregressive (AR) spectral analysis of varying model order which produced predictors that varied in their degree of correlation (i.e., multicollinearity). Results The use of multivariate regression models resulted in much better prediction of target position as compared to univariate regression models. However, with lower order AR features interpretation of the spectral patterns of the weights was difficult. This is likely to be due to the high degree of multicollinearity present with lower order AR features. Conclusions Care should be exercised when interpreting the pattern of weights of multivariate models with correlated predictors. Comparison with univariate statistics is advisable. Significance While multivariate decoding algorithms are very useful for prediction their utility for interpretation may be limited when predictors are correlated. PMID:23466267

Reformulation of Traditional Chamomile Oil: Quality Controls and Fingerprint Presentation Based on Cluster Analysis of Attenuated Total Reflectance–Infrared Spectral Data

PubMed Central

Sakhteman, Amirhossein; Faridi, Pouya; Daneshamouz, Saeid; Akbarizadeh, Amin Reza; Borhani-Haghighi, Afshin; Mohagheghzadeh, Abdolali

2017-01-01

Herbal oils have been widely used in Iran as medicinal compounds dating back to thousands of years in Iran. Chamomile oil is widely used as an example of traditional oil. We remade chamomile oils and tried to modify it with current knowledge and facilities. Six types of oil (traditional and modified) were prepared. Microbial limit tests and physicochemical tests were performed on them. Also, principal component analysis, hierarchical cluster analysis, and partial least squares discriminant analysis were done on the spectral data of attenuated total reflectance–infrared in order to obtain insight based on classification pattern of the samples. The results show that we can use modified versions of the chamomile oils (modified Clevenger-type apparatus method and microwave method) with the same content of traditional ones and with less microbial contaminations and better physicochemical properties. PMID:28585466

Reformulation of Traditional Chamomile Oil: Quality Controls and Fingerprint Presentation Based on Cluster Analysis of Attenuated Total Reflectance-Infrared Spectral Data.

PubMed

Zargaran, Arman; Sakhteman, Amirhossein; Faridi, Pouya; Daneshamouz, Saeid; Akbarizadeh, Amin Reza; Borhani-Haghighi, Afshin; Mohagheghzadeh, Abdolali

2017-10-01

Herbal oils have been widely used in Iran as medicinal compounds dating back to thousands of years in Iran. Chamomile oil is widely used as an example of traditional oil. We remade chamomile oils and tried to modify it with current knowledge and facilities. Six types of oil (traditional and modified) were prepared. Microbial limit tests and physicochemical tests were performed on them. Also, principal component analysis, hierarchical cluster analysis, and partial least squares discriminant analysis were done on the spectral data of attenuated total reflectance-infrared in order to obtain insight based on classification pattern of the samples. The results show that we can use modified versions of the chamomile oils (modified Clevenger-type apparatus method and microwave method) with the same content of traditional ones and with less microbial contaminations and better physicochemical properties.

MO-F-CAMPUS-I-04: Characterization of Fan Beam Coded Aperture Coherent Scatter Spectral Imaging Methods for Differentiation of Normal and Neoplastic Breast Structures

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

Morris, R; Albanese, K; Lakshmanan, M

Purpose: This study intends to characterize the spectral and spatial resolution limits of various fan beam geometries for differentiation of normal and neoplastic breast structures via coded aperture coherent scatter spectral imaging techniques. In previous studies, pencil beam raster scanning methods using coherent scatter computed tomography and selected volume tomography have yielded excellent results for tumor discrimination. However, these methods don’t readily conform to clinical constraints; primarily prolonged scan times and excessive dose to the patient. Here, we refine a fan beam coded aperture coherent scatter imaging system to characterize the tradeoffs between dose, scan time and image quality formore » breast tumor discrimination. Methods: An X-ray tube (125kVp, 400mAs) illuminated the sample with collimated fan beams of varying widths (3mm to 25mm). Scatter data was collected via two linear-array energy-sensitive detectors oriented parallel and perpendicular to the beam plane. An iterative reconstruction algorithm yields images of the sample’s spatial distribution and respective spectral data for each location. To model in-vivo tumor analysis, surgically resected breast tumor samples were used in conjunction with lard, which has a form factor comparable to adipose (fat). Results: Quantitative analysis with current setup geometry indicated optimal performance for beams up to 10mm wide, with wider beams producing poorer spatial resolution. Scan time for a fixed volume was reduced by a factor of 6 when scanned with a 10mm fan beam compared to a 1.5mm pencil beam. Conclusion: The study demonstrates the utility of fan beam coherent scatter spectral imaging for differentiation of normal and neoplastic breast tissues has successfully reduced dose and scan times whilst sufficiently preserving spectral and spatial resolution. Future work to alter the coded aperture and detector geometries could potentially allow the use of even wider fans, thereby making coded aperture coherent scatter imaging a clinically viable method for breast cancer detection. United States Department of Homeland Security; Duke University Medical Center - Department of Radiology; Carl E Ravin Advanced Imaging Laboratories; Duke University Medical Physics Graduate Program.« less

Preliminary results of the comparative study between EO-1/Hyperion and ALOS/PALSAR

NASA Astrophysics Data System (ADS)

Koizumi, E.; Furuta, R.; Yamamoto, A.

2011-12-01

[Introduction]Hyper-spectral remote sensing images have been used for land-cover classification due to their high spectral resolutions. Synthetic Aperture Radar (SAR) remote sensing data are also useful to probe surface condition because radar image reflects surface geometry, although there are not so many reports about the land-cover detection with combination use of both hyper-spectral data and SAR data. Among SAR sensors, L-band SAR is thought to be useful tool to find physical properties because its comparatively long wave length can through small objects on surface. We are comparing the result of land cover classification and/or physical values from hyper-spectral and L-band SAR data to find the relationship between these two quite different sensors and to confirm the possibility of the combined analysis of hyper-spectral and L-band SAR data, and in this presentation we will report the preliminary result of this study. There are only few sources of both hyper-spectral and L-band SAR data from the space in this time, however, several space organizations plan to launch new satellites on which hyper-spectral or L-band SAR equipments are mounted in next few years. So, the importance of the combined analysis will increase more than ever. [Target Area]We are performing and planning analyses on the following areas in this study. (a)South of Cairo, Nile river area, Egypt, for sand, sandstone, limestone, river, crops. (b)Mount Sakurajima, Japan, for igneous rock and other related geological property. [Methods and Results]EO-1 Hyperion data are analyzed in this study as hyper-spectral data. The Hyperion equipment has 242 channels but some of them include full noise or have no data. We selected channels for analysis by checking each channel, and select about 150 channels (depend on the area). Before analysis, the atmospheric correction of ATCOR-3 was applied for the selected channels. The corrected data were analyzed by unsupervised classification or principal component analysis (PCA). We also did the unsupervised classification with the several components from PCA. According to the analysis results, several classifications can be extracted for each category (vegetation, sand and rocks, and water). One of the interesting results is that there are a few classes for sand as those of other categories, and these classes seem to reflect artificial and natural surface changes that are some result of excavation or scratching. ALOS PALSAR data are analyzed as L-band SAR data. We selected the Dual Polarization data for each target area. The data were converted to backscattered images, and then calculated some image statistic values. The topographic information also calculates with SAR interferometry technique as reference. Comparing the Hyperion classification results with the result of the calculation of statistic values from PALSAR, there are some areas where relativities seem to be confirmed. To confirm the combined analysis between hyper-spectral and L-band SAR data to detect and classify the surface material, further studies are still required. We will continue to investigate more efficient analytic methods and to examine other functions like the adopted channels, the number of class in classification, the kind of statistic information, and so on, to refine the method.

Software algorithm and hardware design for real-time implementation of new spectral estimator

PubMed Central

2014-01-01

Background Real-time spectral analyzers can be difficult to implement for PC computer-based systems because of the potential for high computational cost, and algorithm complexity. In this work a new spectral estimator (NSE) is developed for real-time analysis, and compared with the discrete Fourier transform (DFT). Method Clinical data in the form of 216 fractionated atrial electrogram sequences were used as inputs. The sample rate for acquisition was 977 Hz, or approximately 1 millisecond between digital samples. Real-time NSE power spectra were generated for 16,384 consecutive data points. The same data sequences were used for spectral calculation using a radix-2 implementation of the DFT. The NSE algorithm was also developed for implementation as a real-time spectral analyzer electronic circuit board. Results The average interval for a single real-time spectral calculation in software was 3.29 μs for NSE versus 504.5 μs for DFT. Thus for real-time spectral analysis, the NSE algorithm is approximately 150× faster than the DFT. Over a 1 millisecond sampling period, the NSE algorithm had the capability to spectrally analyze a maximum of 303 data channels, while the DFT algorithm could only analyze a single channel. Moreover, for the 8 second sequences, the NSE spectral resolution in the 3-12 Hz range was 0.037 Hz while the DFT spectral resolution was only 0.122 Hz. The NSE was also found to be implementable as a standalone spectral analyzer board using approximately 26 integrated circuits at a cost of approximately $500. The software files used for analysis are included as a supplement, please see the Additional files 1 and 2. Conclusions The NSE real-time algorithm has low computational cost and complexity, and is implementable in both software and hardware for 1 millisecond updates of multichannel spectra. The algorithm may be helpful to guide radiofrequency catheter ablation in real time. PMID:24886214

Development and Evaluation of a Spectral Analysis Method to Eliminate Organic Interference with Cavity Ring-Down Measurements of Water Isotope Ratios.

NASA Astrophysics Data System (ADS)

Lin, Z.; Kim-Hak, D.; Popp, B. N.; Wallsgrove, N.; Kagawa-Viviani, A.; Johnson, J.

2017-12-01

Cavity ring-down spectroscopy (CRDS) is a technology based on the spectral absorption of gas molecules of interest at specific spectral regions. The CRDS technique enables the analysis of hydrogen and oxygen stable isotope ratios of water by directly measuring individual isotopologue absorption peaks such as H16OH, H18OH, and D16OH. Early work demonstrated that the accuracy of isotope analysis by CRDS and other laser-based absorption techniques could be compromised by spectral interference from organic compounds, in particular methanol and ethanol, which can be prevalent in ecologically-derived waters. There have been several methods developed by various research groups including Picarro to address the organic interference challenge. Here, we describe an organic fitter and a post-processing algorithm designed to improve the accuracy of the isotopic analysis of the "organic contaminated" water specifically for Picarro models L2130-i and L2140-i. To create the organic fitter, the absorption features of methanol around 7200 cm-1 were characterized and incorporated into spectral analysis. Since there was residual interference remaining after applying the organic fitter, a statistical model was also developed for post-processing correction. To evaluate the performance of the organic fitter and the postprocessing correction, we conducted controlled experiments on the L2130-i for two water samples with different isotope ratios blended with varying amounts of methanol (0-0.5%) and ethanol (0-5%). When the original fitter was not used for spectral analysis, the addition of 0.5% methanol changed the apparent isotopic composition of the water samples by +62‰ for δ18O values and +97‰ for δ2H values, and the addition of 5% ethanol changed the apparent isotopic composition by -0.5‰ for δ18O values and -3‰ for δ2H values. When the organic fitter was used for spectral analysis, the maximum methanol-induced errors were reduced to +4‰ for δ18O values and +5‰ for δ2H values, and the maximum ethanol-induced errors were unchanged. When the organic fitter was combined with the post-processing correction, up to 99.8% of the total methanol-induced errors and 96% of the total ethanol-induced errors could be corrected. The applicability of the algorithm to natural samples such as plant and soil waters will be investigated.

High-resolution CASSINI-VIMS mosaics of Titan and the icy Saturnian satellites

USGS Publications Warehouse

Jaumann, R.; Stephan, K.; Brown, R.H.; Buratti, B.J.; Clark, R.N.; McCord, T.B.; Coradini, A.; Capaccioni, F.; Filacchione, G.; Cerroni, P.; Baines, K.H.; Bellucci, G.; Bibring, J.-P.; Combes, M.; Cruikshank, D.P.; Drossart, P.; Formisano, V.; Langevin, Y.; Matson, D.L.; Nelson, R.M.; Nicholson, P.D.; Sicardy, B.; Sotin, Christophe; Soderbloom, L.A.; Griffith, C.; Matz, K.-D.; Roatsch, Th.; Scholten, F.; Porco, C.C.

2006-01-01

The Visual Infrared Mapping Spectrometer (VIMS) onboard the CASSINI spacecraft obtained new spectral data of the icy satellites of Saturn after its arrival at Saturn in June 2004. VIMS operates in a spectral range from 0.35 to 5.2 ??m, generating image cubes in which each pixel represents a spectrum consisting of 352 contiguous wavebands. As an imaging spectrometer VIMS combines the characteristics of both a spectrometer and an imaging instrument. This makes it possible to analyze the spectrum of each pixel separately and to map the spectral characteristics spatially, which is important to study the relationships between spectral information and geological and geomorphologic surface features. The spatial analysis of the spectral data requires the determination of the exact geographic position of each pixel on the specific surface and that all 352 spectral elements of each pixel show the same region of the target. We developed a method to reproject each pixel geometrically and to convert the spectral data into map projected image cubes. This method can also be applied to mosaic different VIMS observations. Based on these mosaics, maps of the spectral properties for each Saturnian satellite can be derived and attributed to geographic positions as well as to geological and geomorphologic surface features. These map-projected mosaics are the basis for all further investigations. ?? 2006 Elsevier Ltd. All rights reserved.

Wavelength Scanning with a Tilting Interference Filter for Glow-Discharge Elemental Imaging.

PubMed

Storey, Andrew P; Ray, Steven J; Hoffmann, Volker; Voronov, Maxim; Engelhard, Carsten; Buscher, Wolfgang; Hieftje, Gary M

2017-06-01

Glow discharges have long been used for depth profiling and bulk analysis of solid samples. In addition, over the past decade, several methods of obtaining lateral surface elemental distributions have been introduced, each with its own strengths and weaknesses. Challenges for each of these techniques are acceptable optical throughput and added instrumental complexity. Here, these problems are addressed with a tilting-filter instrument. A pulsed glow discharge is coupled to an optical system comprising an adjustable-angle tilting filter, collimating and imaging lenses, and a gated, intensified charge-coupled device (CCD) camera, which together provide surface elemental mapping of solid samples. The tilting-filter spectrometer is instrumentally simpler, produces less image distortion, and achieves higher optical throughput than a monochromator-based instrument, but has a much more limited tunable spectral range and poorer spectral resolution. As a result, the tilting-filter spectrometer is limited to single-element or two-element determinations, and only when the target spectral lines fall within an appropriate spectral range and can be spectrally discerned. Spectral interferences that result from heterogeneous impurities can be flagged and overcome by observing the spatially resolved signal response across the available tunable spectral range. The instrument has been characterized and evaluated for the spatially resolved analysis of glow-discharge emission from selected but representative samples.

Spectral imaging of histological and cytological specimens

NASA Astrophysics Data System (ADS)

Rothmann, Chana; Malik, Zvi

1999-05-01

Evaluation of cell morphology by bright field microscopy is the pillar of histopathological diagnosis. The need for quantitative and objective parameters for diagnosis has given rise to the development of morphometric methods. The development of spectral imaging for biological and medical applications introduced both fields to large amounts of information extracted from a single image. Spectroscopic analysis is based on the ability of a stained histological specimen to absorb, reflect, or emit photons in ways characteristic to its interactions with specific dyes. Spectral information obtained from a histological specimen is stored in a cube whose appellate signifies the two spatial dimensions of a flat sample (x and y) and the third dimension, the spectrum, representing the light intensity for every wavelength. The spectral information stored in the cube can be further processed by morphometric analysis and quantitative procedures. Such a procedure is spectral-similarity mapping (SSM), which enables the demarcation of areas occupied by the same type of material. SSM constructs new images of the specimen, revealing areas with similar stain-macromolecule characteristics and enhancing subcellular features. Spectral imaging combined with SSM reveals nuclear organization through the differentiation stages as well as in apoptotic and necrotic conditions and identifies specifically the nucleoli domains.

Spherical Harmonic Analysis of Particle Velocity Distribution Function: Comparison of Moments and Anisotropies using Cluster Data

NASA Technical Reports Server (NTRS)

Gurgiolo, Chris; Vinas, Adolfo F.

2009-01-01

This paper presents a spherical harmonic analysis of the plasma velocity distribution function using high-angular, energy, and time resolution Cluster data obtained from the PEACE spectrometer instrument to demonstrate how this analysis models the particle distribution function and its moments and anisotropies. The results show that spherical harmonic analysis produced a robust physical representation model of the velocity distribution function, resolving the main features of the measured distributions. From the spherical harmonic analysis, a minimum set of nine spectral coefficients was obtained from which the moment (up to the heat flux), anisotropy, and asymmetry calculations of the velocity distribution function were obtained. The spherical harmonic method provides a potentially effective "compression" technique that can be easily carried out onboard a spacecraft to determine the moments and anisotropies of the particle velocity distribution function for any species. These calculations were implemented using three different approaches, namely, the standard traditional integration, the spherical harmonic (SPH) spectral coefficients integration, and the singular value decomposition (SVD) on the spherical harmonic methods. A comparison among the various methods shows that both SPH and SVD approaches provide remarkable agreement with the standard moment integration method.

Comparison of multivariate analysis methods for extracting the paraffin component from the paraffin-embedded cancer tissue spectra for Raman imaging

NASA Astrophysics Data System (ADS)

Meksiarun, Phiranuphon; Ishigaki, Mika; Huck-Pezzei, Verena A. C.; Huck, Christian W.; Wongravee, Kanet; Sato, Hidetoshi; Ozaki, Yukihiro

2017-03-01

This study aimed to extract the paraffin component from paraffin-embedded oral cancer tissue spectra using three multivariate analysis (MVA) methods; Independent Component Analysis (ICA), Partial Least Squares (PLS) and Independent Component - Partial Least Square (IC-PLS). The estimated paraffin components were used for removing the contribution of paraffin from the tissue spectra. These three methods were compared in terms of the efficiency of paraffin removal and the ability to retain the tissue information. It was found that ICA, PLS and IC-PLS could remove the paraffin component from the spectra at almost the same level while Principal Component Analysis (PCA) was incapable. In terms of retaining cancer tissue spectral integrity, effects of PLS and IC-PLS on the non-paraffin region were significantly less than that of ICA where cancer tissue spectral areas were deteriorated. The paraffin-removed spectra were used for constructing Raman images of oral cancer tissue and compared with Hematoxylin and Eosin (H&E) stained tissues for verification. This study has demonstrated the capability of Raman spectroscopy together with multivariate analysis methods as a diagnostic tool for the paraffin-embedded tissue section.

[Quantitative Analysis of Heavy Metals in Water with LIBS Based on Signal-to-Background Ratio].

PubMed

Hu, Li; Zhao, Nan-jing; Liu, Wen-qing; Fang, Li; Zhang, Da-hai; Wang, Yin; Meng, De Shuo; Yu, Yang; Ma, Ming-jun

2015-07-01

There are many influence factors in the precision and accuracy of the quantitative analysis with LIBS technology. According to approximately the same characteristics trend of background spectrum and characteristic spectrum along with the change of temperature through in-depth analysis, signal-to-background ratio (S/B) measurement and regression analysis could compensate the spectral line intensity changes caused by system parameters such as laser power, spectral efficiency of receiving. Because the measurement dates were limited and nonlinear, we used support vector machine (SVM) for regression algorithm. The experimental results showed that the method could improve the stability and the accuracy of quantitative analysis of LIBS, and the relative standard deviation and average relative error of test set respectively were 4.7% and 9.5%. Data fitting method based on signal-to-background ratio(S/B) is Less susceptible to matrix elements and background spectrum etc, and provides data processing reference for real-time online LIBS quantitative analysis technology.

[Fast determination of induction period of motor gasoline using Fourier transform attenuated total reflection infrared spectroscopy].

PubMed

Liu, Ya-Fei; Yuan, Hong-Fu; Song, Chun-Feng; Xie, Jin-Chun; Li, Xiao-Yu; Yan, De-Lin

2014-11-01

A new method is proposed for the fast determination of the induction period of gasoline using Fourier transform attenuated total reflection infrared spectroscopy (ATR-FTIR). A dedicated analysis system with the function of spectral measurement, data processing, display and storage was designed and integrated using a Fourier transform infrared spectrometer module and chemometric software. The sample presentation accessory designed which has advantages of constant optical path, convenient sample injection and cleaning is composed of a nine times reflection attenuated total reflectance (ATR) crystal of zinc selenide (ZnSe) coated with a diamond film and a stainless steel lid with sealing device. The influence of spectral scanning number and repeated sample loading times on the spectral signal-to-noise ratio was studied. The optimum spectral scanning number is 15 times and the optimum sample loading number is 4 times. Sixty four different gasoline samples were collected from the Beijing-Tianjin area and the induction period values were determined as reference data by standard method GB/T 8018-87. The infrared spectra of these samples were collected in the operating condition mentioned above using the dedicated fast analysis system. Spectra were pretreated using mean centering and 1st derivative to reduce the influence of spectral noise and baseline shift A PLS calibration model for the induction period was established by correlating the known induction period values of the samples with their spectra. The correlation coefficient (R2), standard error of calibration (SEC) and standard error of prediction (SEP) of the model are 0.897, 68.3 and 91.9 minutes, respectively. The relative deviation of the model for gasoline induction period prediction is less than 5%, which meets the requirements of repeatability tolerance in GB method. The new method is simple and fast. It takes no more than 3 minutes to detect one sample. Therefore, the method is feasible for implementing fast determination of gasoline induction period, and of a positive meaning in the evaluation of fuel quality.

Dense grid sibling frames with linear phase filters

NASA Astrophysics Data System (ADS)

Abdelnour, Farras

2013-09-01

We introduce new 5-band dyadic sibling frames with dense time-frequency grid. Given a lowpass filter satisfying certain conditions, the remaining filters are obtained using spectral factorization. The analysis and synthesis filterbanks share the same lowpass and bandpass filters but have different and oversampled highpass filters. This leads to wavelets approximating shift-invariance. The filters are FIR, have linear phase, and the resulting wavelets have vanishing moments. The filters are designed using spectral factorization method. The proposed method leads to smooth limit functions with higher approximation order, and computationally stable filterbanks.

Definition of spectrally separable classes for soil survey research

NASA Technical Reports Server (NTRS)

Cipra, J. E.; Swain, P. H.; Gill, J. H.; Baumgardner, M. F.; Kristof, S. J.

1972-01-01

A procedure is outlined for defining spectral classes such that the differences between classes can be quantified. It also facilitates determination of a number of classes such that the classes are spectrally discriminable. This is accomplished by partitioning the data into many classes and then combining similar spectral classes on the basis of appropriate criteria. Multispectral data were collected over a 12-mile flightline in White County, Indiana, in connection with the 1971 Corn Blight Watch Experiment. Data were collected in May by the University of Michigan airborne scanning spectrometer at an altitude of 5000 feet. Spectral maps resulting from the analysis were compared to existing soil surveys of the National Cooperative Soil Survey. The method should help determine the extent to which spectral properties of soil surfaces can be associated with morphologic and topographic differences of interest to soil surveyors engaged in operational soil mapping.

Hyperspectral Image Classification via Multitask Joint Sparse Representation and Stepwise MRF Optimization.

PubMed

Yuan, Yuan; Lin, Jianzhe; Wang, Qi

2016-12-01

Hyperspectral image (HSI) classification is a crucial issue in remote sensing. Accurate classification benefits a large number of applications such as land use analysis and marine resource utilization. But high data correlation brings difficulty to reliable classification, especially for HSI with abundant spectral information. Furthermore, the traditional methods often fail to well consider the spatial coherency of HSI that also limits the classification performance. To address these inherent obstacles, a novel spectral-spatial classification scheme is proposed in this paper. The proposed method mainly focuses on multitask joint sparse representation (MJSR) and a stepwise Markov random filed framework, which are claimed to be two main contributions in this procedure. First, the MJSR not only reduces the spectral redundancy, but also retains necessary correlation in spectral field during classification. Second, the stepwise optimization further explores the spatial correlation that significantly enhances the classification accuracy and robustness. As far as several universal quality evaluation indexes are concerned, the experimental results on Indian Pines and Pavia University demonstrate the superiority of our method compared with the state-of-the-art competitors.

[Effect of near infrared spectrum on the precision of PLS model for oil yield from oil shale].

PubMed

Wang, Zhi-Hong; Liu, Jie; Chen, Xiao-Chao; Sun, Yu-Yang; Yu, Yang; Lin, Jun

2012-10-01

It is impossible to use present measurement methods for the oil yield of oil shale to realize in-situ detection and these methods unable to meet the requirements of the oil shale resources exploration and exploitation. But in-situ oil yield analysis of oil shale can be achieved by the portable near infrared spectroscopy technique. There are different correlativities of NIR spectrum data formats and contents of sample components, and the different absorption specialities of sample components shows in different NIR spectral regions. So with the proportioning samples, the PLS modeling experiments were done by 3 formats (reflectance, absorbance and K-M function) and 4 regions of modeling spectrum, and the effect of NIR spectral format and region to the precision of PLS model for oil yield from oil shale was studied. The results show that the best data format is reflectance and the best modeling region is combination spectral range by PLS model method and proportioning samples. Therefore, the appropriate data format and the proper characteristic spectral region can increase the precision of PLS model for oil yield form oil shale.

Breast tissue decomposition with spectral distortion correction: A postmortem study

PubMed Central

Ding, Huanjun; Zhao, Bo; Baturin, Pavlo; Behroozi, Farnaz; Molloi, Sabee

2014-01-01

Purpose: To investigate the feasibility of an accurate measurement of water, lipid, and protein composition of breast tissue using a photon-counting spectral computed tomography (CT) with spectral distortion corrections. Methods: Thirty-eight postmortem breasts were imaged with a cadmium-zinc-telluride-based photon-counting spectral CT system at 100 kV. The energy-resolving capability of the photon-counting detector was used to separate photons into low and high energy bins with a splitting energy of 42 keV. The estimated mean glandular dose for each breast ranged from 1.8 to 2.2 mGy. Two spectral distortion correction techniques were implemented, respectively, on the raw images to correct the nonlinear detector response due to pulse pileup and charge-sharing artifacts. Dual energy decomposition was then used to characterize each breast in terms of water, lipid, and protein content. In the meantime, the breasts were chemically decomposed into their respective water, lipid, and protein components to provide a gold standard for comparison with dual energy decomposition results. Results: The accuracy of the tissue compositional measurement with spectral CT was determined by comparing to the reference standard from chemical analysis. The averaged root-mean-square error in percentage composition was reduced from 15.5% to 2.8% after spectral distortion corrections. Conclusions: The results indicate that spectral CT can be used to quantify the water, lipid, and protein content in breast tissue. The accuracy of the compositional analysis depends on the applied spectral distortion correction technique. PMID:25281953

Application of Artificial Neural Networks and Singular-Spectral Analysis in Forecasting the Daily Traffic in the Moscow Metro

NASA Astrophysics Data System (ADS)

Ivanov, Victor; Osetrov, Evgenii

2018-02-01

In this paper, we investigate the possibility of applying various approaches to solving the problem of medium-term forecasting of daily passenger traffic volumes in the Moscow metro (MM): 1) on the basis of artificial neural networks (ANN); 2) using the singular-spectral analysis implemented in the package "Caterpillar"-SSA; 3) sharing the ANN and the "Caterpillar"-SSA approach. We demonstrate that the developed methods and algorithms allow us to conduct medium-term forecasting of passenger traffic in the MM with reasonable accuracy.

Time-resolved spectral analysis of Radachlorin luminescence in water

NASA Astrophysics Data System (ADS)

Belik, V. P.; Gadzhiev, I. M.; Semenova, I. V.; Vasyutinskii, O. S.

2017-05-01

We report results of spectral- and time-resolved study of Radachlorin photosensitizer luminescence in water in the spectral range of 950-1350nm and for determination of the photosensitizer triplet state and the singlet oxygen lifetimes responsible for singlet oxygen generation and degradation. At any wavelength within the explored spectral range the luminescence decay contained two major contributions: a fast decay at the ns time scale and a slow evolution at the μs time scale. The fast decay was attributed to electric dipole fluorescence transitions in photosensitizer molecules and the slow evolution to intercombination phosphorescence transitions in singlet oxygen and photosensitizer molecules. Relatively high-amplitude ns peak observed at all wavelengths suggests that singlet oxygen monitoring with spectral isolation methods alone, without additional temporal resolution can be controversial. In the applied experimental conditions the total phosphorescence signal at any wavelength contained a contribution from the photosensitizer triplet state decay, while at 1274nm the singlet oxygen phosphorescence dominated. The results obtained can be used for optimization of the methods of singlet oxygen monitoring and imaging.

A Modified Relative Spectral Mixture Analysis to Extract the Fractions of Major Land Cover Components

NASA Astrophysics Data System (ADS)

Jia, S.

2015-12-01

As an effective method of extracting land cover fractions based on spectral endmembers, spectral mixture analysis (SMA) has been applied using remotely sensed imagery in different spatial, temporal, and spectral resolutions. A number of studies focused on arid/semiarid ecosystem have used SMA to obtain the land cover fractions of GV, NPV/litter, and bare soil (BS) using MODIS reflectance products to understand ecosystem phenology, track vegetation dynamics, and evaluate the impact of major disturbances. However, several challenges remain in the application of SMA in studying ecosystem phenology, including obtaining high quality endmembers and increasing computational efficiency when considering to long time series that cover a broad spatial extent. Okin (2007) proposes a variation of SMA, named as relative spectra mixture analysis (RSMA) to address the latter challenge by calculating the relative change of fraction of GV, NPV/litter, and BS compared with a baseline date. This approach assumes that the baseline image contains the spectral information of the bare soil that can be used as an endmember for spectral mixture analysis though it is mixed with the spectral reflectance of other non-soil land cover types. Using the baseline image, one can obtain the change of fractions of GV, NPV/litter, BS, and snow compared with the baseline image. However, RSMA results depend on the selection of baseline date and the fractional components during this date. In this study, we modified the strategy of implementing RSMA by introducing a step of obtaining a soil map as the baseline image using multiple-endmember SMA (MESMA) before applying RSMA. The fractions of land cover components from this modified RSMA are also validated using the field observations from two study area in semiarid savanna and grassland of Queensland, Australia.

Application of factor analysis of infrared spectra for quantitative determination of beta-tricalcium phosphate in calcium hydroxylapatite.

PubMed

Arsenyev, P A; Trezvov, V V; Saratovskaya, N V

1997-01-01

This work represents a method, which allows to determine phase composition of calcium hydroxylapatite basing on its infrared spectrum. The method uses factor analysis of the spectral data of calibration set of samples to determine minimal number of factors required to reproduce the spectra within experimental error. Multiple linear regression is applied to establish correlation between factor scores of calibration standards and their properties. The regression equations can be used to predict the property value of unknown sample. The regression model was built for determination of beta-tricalcium phosphate content in hydroxylapatite. Statistical estimation of quality of the model was carried out. Application of the factor analysis on spectral data allows to increase accuracy of beta-tricalcium phosphate determination and expand the range of determination towards its less concentration. Reproducibility of results is retained.

Characterizing the performance of ecosystem models across time scales: A spectral analysis of the North American Carbon Program site-level synthesis

Treesearch

Michael C. Dietze; Rodrigo Vargas; Andrew D. Richardson; Paul C. Stoy; Alan G. Barr; Ryan S. Anderson; M. Altaf Arain; Ian T. Baker; T. Andrew Black; Jing M. Chen; Philippe Ciais; Lawrence B. Flanagan; Christopher M. Gough; Robert F. Grant; David Hollinger; R. Cesar Izaurralde; Christopher J. Kucharik; Peter Lafleur; Shugang Liu; Erandathie Lokupitiya; Yiqi Luo; J. William Munger; Changhui Peng; Benjamin Poulter; David T. Price; Daniel M. Ricciuto; William J. Riley; Alok Kumar Sahoo; Kevin Schaefer; Andrew E. Suyker; Hanqin Tian; Christina Tonitto; Hans Verbeeck; Shashi B. Verma; Weifeng Wang; Ensheng Weng

2011-01-01

Ecosystem models are important tools for diagnosing the carbon cycle and projecting its behavior across space and time. Despite the fact that ecosystems respond to drivers at multiple time scales, most assessments of model performance do not discriminate different time scales. Spectral methods, such as wavelet analyses, present an alternative approach that enables the...

Uncertainties in Atomic Data and Their Propagation Through Spectral Models. I.

NASA Technical Reports Server (NTRS)

Bautista, M. A.; Fivet, V.; Quinet, P.; Dunn, J.; Gull, T. R.; Kallman, T. R.; Mendoza, C.

2013-01-01

We present a method for computing uncertainties in spectral models, i.e., level populations, line emissivities, and emission line ratios, based upon the propagation of uncertainties originating from atomic data.We provide analytic expressions, in the form of linear sets of algebraic equations, for the coupled uncertainties among all levels. These equations can be solved efficiently for any set of physical conditions and uncertainties in the atomic data. We illustrate our method applied to spectral models of Oiii and Fe ii and discuss the impact of the uncertainties on atomic systems under different physical conditions. As to intrinsic uncertainties in theoretical atomic data, we propose that these uncertainties can be estimated from the dispersion in the results from various independent calculations. This technique provides excellent results for the uncertainties in A-values of forbidden transitions in [Fe ii]. Key words: atomic data - atomic processes - line: formation - methods: data analysis - molecular data - molecular processes - techniques: spectroscopic

Model-based spectral estimation of Doppler signals using parallel genetic algorithms.

PubMed

Solano González, J; Rodríguez Vázquez, K; García Nocetti, D F

2000-05-01

Conventional spectral analysis methods use a fast Fourier transform (FFT) on consecutive or overlapping windowed data segments. For Doppler ultrasound signals, this approach suffers from an inadequate frequency resolution due to the time segment duration and the non-stationarity characteristics of the signals. Parametric or model-based estimators can give significant improvements in the time-frequency resolution at the expense of a higher computational complexity. This work describes an approach which implements in real-time a parametric spectral estimator method using genetic algorithms (GAs) in order to find the optimum set of parameters for the adaptive filter that minimises the error function. The aim is to reduce the computational complexity of the conventional algorithm by using the simplicity associated to GAs and exploiting its parallel characteristics. This will allow the implementation of higher order filters, increasing the spectrum resolution, and opening a greater scope for using more complex methods.

Land cover mapping at Alkali Flat and Lake Lucero, White Sands, New Mexico, USA using multi-temporal and multi-spectral remote sensing data

NASA Astrophysics Data System (ADS)

Ghrefat, Habes A.; Goodell, Philip C.

2011-08-01

The goal of this research is to map land cover patterns and to detect changes that occurred at Alkali Flat and Lake Lucero, White Sands using multispectral Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Advanced Land Imager (ALI), and hyperspectral Hyperion and Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data. The other objectives of this study were: (1) to evaluate the information dimensionality limits of Landsat 7 ETM+, ASTER, ALI, Hyperion, and AVIRIS data with respect to signal-to-noise and spectral resolution, (2) to determine the spatial distribution and fractional abundances of land cover endmembers, and (3) to check ground correspondence with satellite data. A better understanding of the spatial and spectral resolution of these sensors, optimum spectral bands and their information contents, appropriate image processing methods, spectral signatures of land cover classes, and atmospheric effects are needed to our ability to detect and map minerals from space. Image spectra were validated using samples collected from various localities across Alkali Flat and Lake Lucero. These samples were measured in the laboratory using VNIR-SWIR (0.4-2.5 μm) spectra and X-ray Diffraction (XRD) method. Dry gypsum deposits, wet gypsum deposits, standing water, green vegetation, and clastic alluvial sediments dominated by mixtures of ferric iron (ferricrete) and calcite were identified in the study area using Minimum Noise Fraction (MNF), Pixel Purity Index (PPI), and n-D Visualization. The results of MNF confirm that AVIRIS and Hyperion data have higher information dimensionality thresholds exceeding the number of available bands of Landsat 7 ETM+, ASTER, and ALI data. ASTER and ALI data can be a reasonable alternative to AVIRIS and Hyperion data for the purpose of monitoring land cover, hydrology and sedimentation in the basin. The spectral unmixing analysis and dimensionality eigen analysis between the various datasets helped to uncover the most optimum spatial-spectral-temporal and radiometric-resolution sensor characteristics for remote sensing based on monitoring of seasonal land cover, surface water, groundwater, and alluvial sediment input changes within the basin. The results demonstrated good agreement between ground truth data and XRD analysis of samples, and the results of Matched Filtering (MF) mapping method.

Correlation of the earth's rotation rate and the secular change of the geomagnetic field. [power spectra/harmonic analysis

NASA Technical Reports Server (NTRS)

Jin, R. S.

1975-01-01

Power spectral density analysis using Burg's maximum entropy method was applied to the geomagnetic dipole field and its rate of change for the years 1901 to 1969. Both spectra indicate relative maxima at 0.015 cycles/year and its harmonics. These maxima correspond approximately to 66, 33, 22, 17, 13, 11, and 9-year spectral lines. The application of the same analysis techniques to the length-of-day (l.o.d) fluctuations for the period 1865 to 1961 reveal similar spectral characteristics. Although peaks were observed at higher harmonics of the fundamental frequency, the 22-year and 11-year lines are not attributed unambiguously to the solar magnetic cycle and the solar cycle. It is suggested that the similarity in the l.o.d fluctuations and the dipole field variations is related to the motion within the earth's fluid core during the past one hundred years.

A novel analysis method for near infrared spectroscopy based on Hilbert-Huang transform

NASA Astrophysics Data System (ADS)

Zhou, Zhenyu; Yang, Hongyu; Liu, Yun; Ruan, Zongcai; Luo, Qingming; Gong, Hui; Lu, Zuhong

2007-05-01

Near Infrared Imager (NIRI) has been widely used to access the brain functional activity non-invasively. We use a portable, multi-channel and continuous-wave NIR topography instrument to measure the concentration changes of each hemoglobin species and map cerebral cortex functional activation. By extracting some essential features from the BOLD signals, optical tomography is able to be a new way of neuropsychological studies. Fourier spectral analysis provides a common framework for examining the distribution of global energy in the frequency domain. However, this method assumes that the signal should be stationary, which limits its application in non-stationary system. The hemoglobin species concentration changes are of such kind. In this work we develop a new signal processing method using Hilbert-Huang transform to perform spectral analysis of the functional NIRI signals. Compared with wavelet based multi-resolution analysis (MRA), we demonstrated the extraction of task related signal for observation of activation in the prefrontal cortex (PFC) in vision stimulation experiment. This method provides a new analysis tool for functional NIRI signals. Our experimental results show that the proposed approach provides the unique method for reconstructing target signal without losing original information and enables us to understand the episode of functional NIRI more precisely.

WE-FG-207B-12: Quantitative Evaluation of a Spectral CT Scanner in a Phantom Study: Results of Spectral Reconstructions

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

Duan, X; Arbique, G; Guild, J

Purpose: To evaluate the quantitative image quality of spectral reconstructions of phantom data from a spectral CT scanner. Methods: The spectral CT scanner (IQon Spectral CT, Philips Healthcare) is equipped with a dual-layer detector and generates conventional 80-140 kVp images and variety of spectral reconstructions, e.g., virtual monochromatic (VM) images, virtual non-contrast (VNC) images, iodine maps, and effective atomic number (Z) images. A cylindrical solid water phantom (Gammex 472, 33 cm diameter and 5 cm thick) with iodine (2.0-20.0 mg I/ml) and calcium (50-600 mg/ml) rod inserts was scanned at 120 kVp and 27 mGy CTDIvol. Spectral reconstructions were evaluatedmore » by comparing image measurements with theoretical values calculated from nominal rod compositions provided by the phantom manufacturer. The theoretical VNC was calculated using water and iodine basis material decomposition, and the theoretical Z was calculated using two common methods, the chemical formula method (Z1) and the dual-energy ratio method (Z2). Results: Beam-hardening-like artifacts between high-attenuation calcium rods (≥300 mg/ml, >800 HU) influenced quantitative measurements, so the quantitative analysis was only performed on iodine rods using the images from the scan with all the calcium rods removed. The CT numbers of the iodine rods in the VM images (50∼150 keV) were close to theoretical values with average difference of 2.4±6.9 HU. Compared with theoretical values, the average difference for iodine concentration, VNC CT number and effective Z of iodine rods were −0.10±0.38 mg/ml, −0.1±8.2 HU, 0.25±0.06 (Z1) and −0.23±0.07 (Z2). Conclusion: The results indicate that the spectral CT scanner generates quantitatively accurate spectral reconstructions at clinically relevant iodine concentrations. Beam-hardening-like artifacts still exist when high-attenuation objects are present and their impact on patient images needs further investigation. YY is an employee of Philips Healthcare.« less

A comparison of CIE L*a*b* and spectral methods for the analysis of fading in sliced cured ham

NASA Astrophysics Data System (ADS)

Sheridan, C.; O'Farrell, M.; Lewis, E.; Flanagan, C.; Kerry, J.; Jackman, N.

2007-06-01

In the modern retail environment, the appearance of a product is frequently the only quality indicator available to consumers. This is especially true of products such as sliced ham that have been sealed into packages to maintain product freshness. It has been shown that sliced ham products undergo discolouration from their original pink colour to a pale grey colour when exposed to a combination of oxygen and light. This is unappealing to consumers who expect a pink colour for sliced ham. An investigation is made into a sensor that would monitor the initial colour status of cured ham before packaging in order to determine the amount of time left before the ham fades to an unsatisfactory colour. For this sensor to operate, appropriate analysis of the appearance of the meat is required. Two methods for the measurement of the fading were investigated—CIE L*a*b* measurements and analysis of the spectral reflectance of the colour of the ham. Several sliced ham products with differing amounts of fading were examined using both methods. It was observed that the products used covered a wide range of variation in colour. Reproducibility of CIE L*a*b* values proved to be quite difficult and significant overlapping of the L* (lightness) and a* (redness) values measured for pink and grey coloured ham was observed. The variations in these values can be attributed to differences in the intensity of reflected light for different products. L*a*b* measurements are sensitive to light intensity and pigment concentration. Analysis of the spectral reflectance readings did not encounter these problems as the spectral response was normalized (to reduce intensity errors) before data analysis was carried out on the spectral shape or 'pattern' using principal component analysis (PCA) and artificial neural networks (ANN). A classifier based on PCA and ANN was successfully implemented that can discriminate different stages of fading for the ham slices. A case study was carried out on ham slices that had two different initial colours—light and dark. The results of the case study show that the sensor system can better discriminate between a light initial colour and a dark faded colour than the CIE L*a*b* colour measurement system.

A comparative analysis of spectral exponent estimation techniques for 1/fβ processes with applications to the analysis of stride interval time series

PubMed Central

Schaefer, Alexander; Brach, Jennifer S.; Perera, Subashan; Sejdić, Ervin

2013-01-01

Background The time evolution and complex interactions of many nonlinear systems, such as in the human body, result in fractal types of parameter outcomes that exhibit self similarity over long time scales by a power law in the frequency spectrum S(f) = 1/fβ. The scaling exponent β is thus often interpreted as a “biomarker” of relative health and decline. New Method This paper presents a thorough comparative numerical analysis of fractal characterization techniques with specific consideration given to experimentally measured gait stride interval time series. The ideal fractal signals generated in the numerical analysis are constrained under varying lengths and biases indicative of a range of physiologically conceivable fractal signals. This analysis is to complement previous investigations of fractal characteristics in healthy and pathological gait stride interval time series, with which this study is compared. Results The results of our analysis showed that the averaged wavelet coefficient method consistently yielded the most accurate results. Comparison with Existing Methods: Class dependent methods proved to be unsuitable for physiological time series. Detrended fluctuation analysis as most prevailing method in the literature exhibited large estimation variances. Conclusions The comparative numerical analysis and experimental applications provide a thorough basis for determining an appropriate and robust method for measuring and comparing a physiologically meaningful biomarker, the spectral index β. In consideration of the constraints of application, we note the significant drawbacks of detrended fluctuation analysis and conclude that the averaged wavelet coefficient method can provide reasonable consistency and accuracy for characterizing these fractal time series. PMID:24200509

Detection of urban expansion in an urban-rural landscape with multitemporal QuickBird images

PubMed Central

Lu, Dengsheng; Hetrick, Scott; Moran, Emilio; Li, Guiying

2011-01-01

Accurately detecting urban expansion with remote sensing techniques is a challenge due to the complexity of urban landscapes. This paper explored methods for detecting urban expansion with multitemporal QuickBird images in Lucas do Rio Verde, Mato Grosso, Brazil. Different techniques, including image differencing, principal component analysis (PCA), and comparison of classified impervious surface images with the matched filtering method, were used to examine urbanization detection. An impervious surface image classified with the hybrid method was used to modify the urbanization detection results. As a comparison, the original multispectral image and segmentation-based mean-spectral images were used during the detection of urbanization. This research indicates that the comparison of classified impervious surface images with matched filtering method provides the best change detection performance, followed by the image differencing method based on segmentation-based mean spectral images. The PCA is not a good method for urban change detection in this study. Shadows and high spectral variation within the impervious surfaces represent major challenges to the detection of urban expansion when high spatial resolution images are used. PMID:21799706

Performance evaluation of the multiple-image optical compression and encryption method by increasing the number of target images

NASA Astrophysics Data System (ADS)

Aldossari, M.; Alfalou, A.; Brosseau, C.

2017-08-01

In an earlier study [Opt. Express 22, 22349-22368 (2014)], a compression and encryption method that simultaneous compress and encrypt closely resembling images was proposed and validated. This multiple-image optical compression and encryption (MIOCE) method is based on a special fusion of the different target images spectra in the spectral domain. Now for the purpose of assessing the capacity of the MIOCE method, we would like to evaluate and determine the influence of the number of target images. This analysis allows us to evaluate the performance limitation of this method. To achieve this goal, we use a criterion based on the root-mean-square (RMS) [Opt. Lett. 35, 1914-1916 (2010)] and compression ratio to determine the spectral plane area. Then, the different spectral areas are merged in a single spectrum plane. By choosing specific areas, we can compress together 38 images instead of 26 using the classical MIOCE method. The quality of the reconstructed image is evaluated by making use of the mean-square-error criterion (MSE).

Coherent spectroscopic methods for monitoring pathogens, genetically modified products and nanostructured materials in colloidal solution

NASA Astrophysics Data System (ADS)

Moguilnaya, T.; Suminov, Y.; Botikov, A.; Ignatov, S.; Kononenko, A.; Agibalov, A.

2017-01-01

We developed the new automatic method that combines the method of forced luminescence and stimulated Brillouin scattering. This method is used for monitoring pathogens, genetically modified products and nanostructured materials in colloidal solution. We carried out the statistical spectral analysis of pathogens, genetically modified soy and nano-particles of silver in water from different regions in order to determine the statistical errors of the method. We studied spectral characteristics of these objects in water to perform the initial identification with 95% probability. These results were used for creation of the model of the device for monitor of pathogenic organisms and working model of the device to determine the genetically modified soy in meat.

Absorption spectrum analysis based on singular value decomposition for photoisomerization and photodegradation in organic dyes

NASA Astrophysics Data System (ADS)

Kawabe, Yutaka; Yoshikawa, Toshio; Chida, Toshifumi; Tada, Kazuhiro; Kawamoto, Masuki; Fujihara, Takashi; Sassa, Takafumi; Tsutsumi, Naoto

2015-10-01

In order to analyze the spectra of inseparable chemical mixtures, many mathematical methods have been developed to decompose them into the components relevant to species from series of spectral data obtained under different conditions. We formulated a method based on singular value decomposition (SVD) of linear algebra, and applied it to two example systems of organic dyes, being successful in reproducing absorption spectra assignable to cis/trans azocarbazole dyes from the spectral data after photoisomerization and to monomer/dimer of cyanine dyes from those during photodegaradation process. For the example of photoisomerization, polymer films containing the azocarbazole dyes were prepared, which have showed updatable holographic stereogram for real images with high performance. We made continuous monitoring of absorption spectrum after optical excitation and found that their spectral shapes varied slightly after the excitation and during recovery process, of which fact suggested the contribution from a generated photoisomer. Application of the method was successful to identify two spectral components due to trans and cis forms of azocarbazoles. Temporal evolution of their weight factors suggested important roles of long lifetimed cis states in azocarbazole derivatives. We also applied the method to the photodegradation of cyanine dyes doped in DNA-lipid complexes which have shown efficient and durable optical amplification and/or lasing under optical pumping. The same SVD method was successful in the extraction of two spectral components presumably due to monomer and H-type dimer. During the photodegradation process, absorption magnitude gradually decreased due to decomposition of molecules and their decaying rates strongly depended on the spectral components, suggesting that the long persistency of the dyes in DNA-complex related to weak tendency of aggregate formation.

Spectral imaging: principles and applications.

PubMed

Garini, Yuval; Young, Ian T; McNamara, George

2006-08-01

Spectral imaging extends the capabilities of biological and clinical studies to simultaneously study multiple features such as organelles and proteins qualitatively and quantitatively. Spectral imaging combines two well-known scientific methodologies, namely spectroscopy and imaging, to provide a new advantageous tool. The need to measure the spectrum at each point of the image requires combining dispersive optics with the more common imaging equipment, and introduces constrains as well. The principles of spectral imaging and a few representative applications are described. Spectral imaging analysis is necessary because the complex data structure cannot be analyzed visually. A few of the algorithms are discussed with emphasis on the usage for different experimental modes (fluorescence and bright field). Finally, spectral imaging, like any method, should be evaluated in light of its advantages to specific applications, a selection of which is described. Spectral imaging is a relatively new technique and its full potential is yet to be exploited. Nevertheless, several applications have already shown its potential. (c) 2006 International Society for Analytical Cytology.

Spectral reflectance properties (0.4-2.5 um) of secondary Fe-oxide, Fe-hydroxide, and Fe-sulfate-hydrate minerals associated with sulfide-bearing mine waste

USGS Publications Warehouse

Crowley, J.K.; Williams, D.E.; Hammarstrom1, J.M.; Piatak, N.; Mars, J.C.; Chou, I-Ming

2006-01-01

Fifteen Fe-oxide, Fe-hydroxide, and Fe-sulphate-hydrate mineral species commonly associated with sulphide bearing mine wastes were characterized by using X-ray powder diffraction and scanning electron microscope methods. Diffuse reflectance spectra of the samples show diagnostic absorption features related to electronic processes involving ferric and/or ferrous iron, and to vibrational processes involving water and hydroxyl ions. Such spectral features enable field and remote sensing based studies of the mineral distributions. Because secondary minerals are sensitive indicators of pH, Eh, relative humidity, and other environmental conditions, spectral mapping of these minerals promises to have important applications to mine waste remediation studies. This report releases digital (ascii) spectra (spectral_data_files.zip) of the fifteen mineral samples to facilitate usage of the data with spectral libraries and spectral analysis software. The spectral data are provided in a two-column format listing wavelength (in micrometers) and reflectance, respectively.

A method to decompose spectral changes in Synechocystis PCC 6803 during light-induced state transitions.

PubMed

Acuña, Alonso M; Kaňa, Radek; Gwizdala, Michal; Snellenburg, Joris J; van Alphen, Pascal; van Oort, Bart; Kirilovsky, Diana; van Grondelle, Rienk; van Stokkum, Ivo H M

2016-12-01

Cyanobacteria have developed responses to maintain the balance between the energy absorbed and the energy used in different pigment-protein complexes. One of the relatively rapid (a few minutes) responses is activated when the cells are exposed to high light intensities. This mechanism thermally dissipates excitation energy at the level of the phycobilisome (PB) antenna before it reaches the reaction center. When exposed to low intensities of light that modify the redox state of the plastoquinone pool, the so-called state transitions redistribute energy between photosystem I and II. Experimental techniques to investigate the underlying mechanisms of these responses, such as pulse-amplitude modulated fluorometry, are based on spectrally integrated signals. Previously, a spectrally resolved fluorometry method has been introduced to preserve spectral information. The analysis method introduced in this work allows to interpret SRF data in terms of species-associated spectra of open/closed reaction centers (RCs), (un)quenched PB and state 1 versus state 2. Thus, spectral differences in the time-dependent fluorescence signature of photosynthetic organisms under varying light conditions can be traced and assigned to functional emitting species leading to a number of interpretations of their molecular origins. In particular, we present evidence that state 1 and state 2 correspond to different states of the PB-PSII-PSI megacomplex.

Spectrally optimal illuminations for diabetic retinopathy detection in retinal imaging

NASA Astrophysics Data System (ADS)

Bartczak, Piotr; Fält, Pauli; Penttinen, Niko; Ylitepsa, Pasi; Laaksonen, Lauri; Lensu, Lasse; Hauta-Kasari, Markku; Uusitalo, Hannu

2017-04-01

Retinal photography is a standard method for recording retinal diseases for subsequent analysis and diagnosis. However, the currently used white light or red-free retinal imaging does not necessarily provide the best possible visibility of different types of retinal lesions, important when developing diagnostic tools for handheld devices, such as smartphones. Using specifically designed illumination, the visibility and contrast of retinal lesions could be improved. In this study, spectrally optimal illuminations for diabetic retinopathy lesion visualization are implemented using a spectrally tunable light source based on digital micromirror device. The applicability of this method was tested in vivo by taking retinal monochrome images from the eyes of five diabetic volunteers and two non-diabetic control subjects. For comparison to existing methods, we evaluated the contrast of retinal images taken with our method and red-free illumination. The preliminary results show that the use of optimal illuminations improved the contrast of diabetic lesions in retinal images by 30-70%, compared to the traditional red-free illumination imaging.

Spectroscopic characterization of enzymatic flax retting: Factor analysis of FT-IR and FT-Raman data

NASA Astrophysics Data System (ADS)

Archibald, D. D.; Henrikssen, G.; Akin, D. E.; Barton, F. E.

1998-06-01

Flax retting is a chemical, microbial or enzymatic process which releases the bast fibers from the stem matrix so they can be suitable for mechanical processing before spinning into linen yarn. This study aims to determine the vibrational spectral features and sampling methods which can be used to evaluate the retting process. Flax stems were retted on a small scale using an enzyme mixture known to yield good retted flax. Processed stems were harvested at various time points in the process and the retting was evaluated by conventional methods including weight loss, color difference and Fried's test, a visual ranking of how the stems disintegrate in hot water. Spectroscopic measurements were performed on either whole stems or powders of the fibers that were mechanically extracted from the stems. Selected regions of spectra were baseline and amplitude corrected using a variant of the multiplicative signal correction method. Principal component regression and partial least-squares regression with full cross-validation were used to determine the spectral features and rate of spectral transformation by regressing the spectra against the retting time in hours. FT-Raman of fiber powders and FT-IR reflectance of whole stems were the simplest and most precise methods for monitoring the retting transformation. Raman tracks the retting by measuring the decrease in aromatic signal and subtle changes in the C-H stretching vibrations. The IR method uses complex spectral features in the fingerprint and carbonyl region, many of which are due to polysaccharide components. Both spectral techniques monitor the retting process with greater precision than the reference method.

[Research on discrimination of cabbage and weeds based on visible and near-infrared spectrum analysis].

PubMed

Zu, Qin; Zhao, Chun-Jiang; Deng, Wei; Wang, Xiu

2013-05-01

The automatic identification of weeds forms the basis for precision spraying of crops infest. The canopy spectral reflectance within the 350-2 500 nm band of two strains of cabbages and five kinds of weeds such as barnyard grass, setaria, crabgrass, goosegrass and pigweed was acquired by ASD spectrometer. According to the spectral curve characteristics, the data in different bands were compressed with different levels to improve the operation efficiency. Firstly, the spectrum was denoised in accordance with the different order of multiple scattering correction (MSC) method and Savitzky-Golay (SG) convolution smoothing method set by different parameters, then the model was built by combining the principal component analysis (PCA) method to extract principal components, finally all kinds of plants were classified by using the soft independent modeling of class analogy (SIMCA) taxonomy and the classification results were compared. The tests results indicate that after the pretreatment of the spectral data with the method of the combination of MSC and SG set with 3rd order, 5th degree polynomial, 21 smoothing points, and the top 10 principal components extraction using PCA as a classification model input variable, 100% correct classification rate was achieved, and it is able to identify cabbage and several kinds of common weeds quickly and nondestructively.

Pretreatment and integrated analysis of spectral data reveal seaweed similarities based on chemical diversity.

PubMed

Wei, Feifei; Ito, Kengo; Sakata, Kenji; Date, Yasuhiro; Kikuchi, Jun

2015-03-03

Extracting useful information from high dimensionality and large data sets is a major challenge for data-driven approaches. The present study was aimed at developing novel integrated analytical strategies for comprehensively characterizing seaweed similarities based on chemical diversity. The chemical compositions of 107 seaweed and 2 seagrass samples were analyzed using multiple techniques, including Fourier transform infrared (FT-IR) and solid- and solution-state nuclear magnetic resonance (NMR) spectroscopy, thermogravimetry-differential thermal analysis (TG-DTA), inductively coupled plasma-optical emission spectrometry (ICP-OES), CHNS/O total elemental analysis, and isotope ratio mass spectrometry (IR-MS). The spectral data were preprocessed using non-negative matrix factorization (NMF) and NMF combined with multivariate curve resolution-alternating least-squares (MCR-ALS) methods in order to separate individual component information from the overlapping and/or broad spectral peaks. Integrated analysis of the preprocessed chemical data demonstrated distinct discrimination of differential seaweed species. Further network analysis revealed a close correlation between the heavy metal elements and characteristic components of brown algae, such as cellulose, alginic acid, and sulfated mucopolysaccharides, providing a componential basis for its metal-sorbing potential. These results suggest that this integrated analytical strategy is useful for extracting and identifying the chemical characteristics of diverse seaweeds based on large chemical data sets, particularly complicated overlapping spectral data.

Spectral images browsing using principal component analysis and set partitioning in hierarchical tree

NASA Astrophysics Data System (ADS)

Ma, Long; Zhao, Deping

2011-12-01

Spectral imaging technology have been used mostly in remote sensing, but have recently been extended to new area requiring high fidelity color reproductions like telemedicine, e-commerce, etc. These spectral imaging systems are important because they offer improved color reproduction quality not only for a standard observer under a particular illuminantion, but for any other individual exhibiting normal color vision capability under another illuminantion. A possibility for browsing of the archives is needed. In this paper, the authors present a new spectral image browsing architecture. The architecture for browsing is expressed as follow: (1) The spectral domain of the spectral image is reduced with the PCA transform. As a result of the PCA transform the eigenvectors and the eigenimages are obtained. (2) We quantize the eigenimages with the original bit depth of spectral image (e.g. if spectral image is originally 8bit, then quantize eigenimage to 8bit), and use 32bit floating numbers for the eigenvectors. (3) The first eigenimage is lossless compressed by JPEG-LS, the other eigenimages were lossy compressed by wavelet based SPIHT algorithm. For experimental evalution, the following measures were used. We used PSNR as the measurement for spectral accuracy. And for the evaluation of color reproducibility, ΔE was used.here standard D65 was used as a light source. To test the proposed method, we used FOREST and CORAL spectral image databases contrain 12 and 10 spectral images, respectively. The images were acquired in the range of 403-696nm. The size of the images were 128*128, the number of bands was 40 and the resolution was 8 bits per sample. Our experiments show the proposed compression method is suitable for browsing, i.e., for visual purpose.

Extinction spectra of suspensions of microspheres: determination of the spectral refractive index and particle size distribution with nanometer accuracy.

PubMed

Gienger, Jonas; Bär, Markus; Neukammer, Jörg

2018-01-10

A method is presented to infer simultaneously the wavelength-dependent real refractive index (RI) of the material of microspheres and their size distribution from extinction measurements of particle suspensions. To derive the averaged spectral optical extinction cross section of the microspheres from such ensemble measurements, we determined the particle concentration by flow cytometry to an accuracy of typically 2% and adjusted the particle concentration to ensure that perturbations due to multiple scattering are negligible. For analysis of the extinction spectra, we employ Mie theory, a series-expansion representation of the refractive index and nonlinear numerical optimization. In contrast to other approaches, our method offers the advantage to simultaneously determine size, size distribution, and spectral refractive index of ensembles of microparticles including uncertainty estimation.

Spectral simulation of unsteady compressible flow past a circular cylinder

NASA Technical Reports Server (NTRS)

Don, Wai-Sun; Gottlieb, David

1990-01-01

An unsteady compressible viscous wake flow past a circular cylinder was successfully simulated using spectral methods. A new approach in using the Chebyshev collocation method for periodic problems is introduced. It was further proved that the eigenvalues associated with the differentiation matrix are purely imaginary, reflecting the periodicity of the problem. It was been shown that the solution of a model problem has exponential growth in time if improper boundary conditions are used. A characteristic boundary condition, which is based on the characteristics of the Euler equations of gas dynamics, was derived for the spectral code. The primary vortex shedding frequency computed agrees well with the results in the literature for Mach = 0.4, Re = 80. No secondary frequency is observed in the power spectrum analysis of the pressure data.

Application of Principal Component Analysis to NIR Spectra of Phyllosilicates: A Technique for Identifying Phyllosilicates on Mars

NASA Technical Reports Server (NTRS)

Rampe, E. B.; Lanza, N. L.

2012-01-01

Orbital near-infrared (NIR) reflectance spectra of the martian surface from the OMEGA and CRISM instruments have identified a variety of phyllosilicates in Noachian terrains. The types of phyllosilicates present on Mars have important implications for the aqueous environments in which they formed, and, thus, for recognizing locales that may have been habitable. Current identifications of phyllosilicates from martian NIR data are based on the positions of spectral absorptions relative to laboratory data of well-characterized samples and from spectral ratios; however, some phyllosilicates can be difficult to distinguish from one another with these methods (i.e. illite vs. muscovite). Here we employ a multivariate statistical technique, principal component analysis (PCA), to differentiate between spectrally similar phyllosilicate minerals. PCA is commonly used in a variety of industries (pharmaceutical, agricultural, viticultural) to discriminate between samples. Previous work using PCA to analyze raw NIR reflectance data from mineral mixtures has shown that this is a viable technique for identifying mineral types, abundances, and particle sizes. Here, we evaluate PCA of second-derivative NIR reflectance data as a method for classifying phyllosilicates and test whether this method can be used to identify phyllosilicates on Mars.

[Experimental Methods and Result Analysis of a Variety of Spectral Reflectance Properties of the Thin Oil Film].

PubMed

Ye, Zhou; Liu, Li; Wei, Chuan-xin; Gu, Qun; An, Ping-ao; Zhao, Yue-jiao; Yin, Da-yi

2015-06-01

In order to analysis the oil spill situation based on the obtained data in airborne aerial work, it's needed to get the spectral reflectance characteristics of the oil film of different oils and thickness as support and to select the appropriate operating band. An experiment is set up to measure the reflectance spectroscopy from ultraviolet to near-infrared for the film of five target samples, which means petrol, diesel, lubricating oil, kerosene and fossil, using spectral measurement device. The result is compared with the reflectance spectra of water in the same experimental environment, which shows that the spectral reflection characteristics of the oil film are related to the thickness and the type of the oil film. In case of the same thickness, the spectral reflectance curve of different types of film is far different, and for the same type of film, the spectral reflectance curve changes accordingly with the change of film thickness, therefore in terms of the single film, different film thickness can be distinguished by reflectance curves. It also shows that in terms of the same film thickness, the reflectance of diesel, kerosene, lubricants reaches peak around 380 nm wavelength, obviously different from the reflectance of water, and that the reflectance of crude oil is far less than that of water in more than 340 nm wavelength, and the obtained reflection spectrum can be used to distinguish between different types of oil film to some extent. The experiment covers main types of spilled oil, with data comprehensively covering commonly used detect spectral bands, and quantitative description of the spectral reflectance properties of film. It provides comprehensive theoretical and data support for the selection of airborne oil spill detection working band and the detection and analysis of water-surface oil spill.

Novel methods of time-resolved fluorescence data analysis for in-vivo tissue characterization: application to atherosclerosis.

PubMed

Jo, J A; Fang, Q; Papaioannou, T; Qiao, J H; Fishbein, M C; Dorafshar, A; Reil, T; Baker, D; Freischlag, J; Marcu, L

2004-01-01

This study investigates the ability of new analytical methods of time-resolved laser-induced fluorescence spectroscopy (TR-LIFS) data to characterize tissue in-vivo, such as the composition of atherosclerotic vulnerable plaques. A total of 73 TR-LIFS measurements were taken in-vivo from the aorta of 8 rabbits, and subsequently analyzed using the Laguerre deconvolution technique. The investigated spots were classified as normal aorta, thin or thick lesions, and lesions rich in either collagen or macrophages/foam-cells. Different linear and nonlinear classification algorithms (linear discriminant analysis, stepwise linear discriminant analysis, principal component analysis, and feedforward neural networks) were developed using spectral and TR features (ratios of intensity values and Laguerre expansion coefficients, respectively). Normal intima and thin lesions were discriminated from thick lesions (sensitivity >90%, specificity 100%) using only spectral features. However, both spectral and time-resolved features were necessary to discriminate thick lesions rich in collagen from thick lesions rich in foam cells (sensitivity >85%, specificity >93%), and thin lesions rich in foam cells from normal aorta and thin lesions rich in collagen (sensitivity >85%, specificity >94%). Based on these findings, we believe that TR-LIFS information derived from the Laguerre expansion coefficients can provide a valuable additional dimension for in-vivo tissue characterization.

Comparison of Portable and Bench-Top Spectrometers for Mid-Infrared Diffuse Reflectance Measurements of Soils.

PubMed

Hutengs, Christopher; Ludwig, Bernard; Jung, András; Eisele, Andreas; Vohland, Michael

2018-03-27

Mid-infrared (MIR) spectroscopy has received widespread interest as a method to complement traditional soil analysis. Recently available portable MIR spectrometers additionally offer potential for on-site applications, given sufficient spectral data quality. We therefore tested the performance of the Agilent 4300 Handheld FTIR (DRIFT spectra) in comparison to a Bruker Tensor 27 bench-top instrument in terms of (i) spectral quality and measurement noise quantified by wavelet analysis; (ii) accuracy of partial least squares (PLS) calibrations for soil organic carbon (SOC), total nitrogen (N), pH, clay and sand content with a repeated cross-validation analysis; and (iii) key spectral regions for these soil properties identified with a Monte Carlo spectral variable selection approach. Measurements and multivariate calibrations with the handheld device were as good as or slightly better than Bruker equipped with a DRIFT accessory, but not as accurate as with directional hemispherical reflectance (DHR) data collected with an integrating sphere. Variations in noise did not markedly affect the accuracy of multivariate PLS calibrations. Identified key spectral regions for PLS calibrations provided a good match between Agilent and Bruker DHR data, especially for SOC and N. Our findings suggest that portable FTIR instruments are a viable alternative for MIR measurements in the laboratory and offer great potential for on-site applications.

Boundary conditions in Chebyshev and Legendre methods

NASA Technical Reports Server (NTRS)

Canuto, C.

1984-01-01

Two different ways of treating non-Dirichlet boundary conditions in Chebyshev and Legendre collocation methods are discussed for second order differential problems. An error analysis is provided. The effect of preconditioning the corresponding spectral operators by finite difference matrices is also investigated.

Increasing sensitivity in the measurement of heart rate variability: the method of non-stationary RR time-frequency analysis.

PubMed

Melkonian, D; Korner, A; Meares, R; Bahramali, H

2012-10-01

A novel method of the time-frequency analysis of non-stationary heart rate variability (HRV) is developed which introduces the fragmentary spectrum as a measure that brings together the frequency content, timing and duration of HRV segments. The fragmentary spectrum is calculated by the similar basis function algorithm. This numerical tool of the time to frequency and frequency to time Fourier transformations accepts both uniform and non-uniform sampling intervals, and is applicable to signal segments of arbitrary length. Once the fragmentary spectrum is calculated, the inverse transform recovers the original signal and reveals accuracy of spectral estimates. Numerical experiments show that discontinuities at the boundaries of the succession of inter-beat intervals can cause unacceptable distortions of the spectral estimates. We have developed a measure that we call the "RR deltagram" as a form of the HRV data that minimises spectral errors. The analysis of the experimental HRV data from real-life and controlled breathing conditions suggests transient oscillatory components as functionally meaningful elements of highly complex and irregular patterns of HRV. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Predicting Gilthead Sea Bream (Sparus aurata) Freshness by a Novel Combined Technique of 3D Imaging and SW-NIR Spectral Analysis.

PubMed

Ivorra, Eugenio; Verdu, Samuel; Sánchez, Antonio J; Grau, Raúl; Barat, José M

2016-10-19

A technique that combines the spatial resolution of a 3D structured-light (SL) imaging system with the spectral analysis of a hyperspectral short-wave near infrared system was developed for freshness predictions of gilthead sea bream on the first storage days (Days 0-6). This novel approach allows the hyperspectral analysis of very specific fish areas, which provides more information for freshness estimations. The SL system obtains a 3D reconstruction of fish, and an automatic method locates gilthead's pupils and irises. Once these regions are positioned, the hyperspectral camera acquires spectral information and a multivariate statistical study is done. The best region is the pupil with an R² of 0.92 and an RMSE of 0.651 for predictions. We conclude that the combination of 3D technology with the hyperspectral analysis offers plenty of potential and is a very promising technique to non destructively predict gilthead freshness.

Predicting Gilthead Sea Bream (Sparus aurata) Freshness by a Novel Combined Technique of 3D Imaging and SW-NIR Spectral Analysis

PubMed Central

Ivorra, Eugenio; Verdu, Samuel; Sánchez, Antonio J.; Grau, Raúl; Barat, José M.

2016-01-01

A technique that combines the spatial resolution of a 3D structured-light (SL) imaging system with the spectral analysis of a hyperspectral short-wave near infrared system was developed for freshness predictions of gilthead sea bream on the first storage days (Days 0–6). This novel approach allows the hyperspectral analysis of very specific fish areas, which provides more information for freshness estimations. The SL system obtains a 3D reconstruction of fish, and an automatic method locates gilthead’s pupils and irises. Once these regions are positioned, the hyperspectral camera acquires spectral information and a multivariate statistical study is done. The best region is the pupil with an R2 of 0.92 and an RMSE of 0.651 for predictions. We conclude that the combination of 3D technology with the hyperspectral analysis offers plenty of potential and is a very promising technique to non destructively predict gilthead freshness. PMID:27775556

The spectroscopic properties of anticancer drug Apigenin investigated by using DFT calculations, FT-IR, FT-Raman and NMR analysis

NASA Astrophysics Data System (ADS)

Mariappan, G.; Sundaraganesan, N.; Manoharan, S.

2012-09-01

The FT-Raman and FT-Infrared spectra of solid Apigenin sample were measured in order to elucidate the spectroscopic properties of title molecule in the spectral range of 3500-50 cm-1 and 4000-400 cm-1, respectively. The recorded FT-IR and FT-Raman spectral measurements favor the calculated (by B3LYP/6-31G(d,p) method) structural parameters which are further supported by spectral simulation. Additional support is given by the collected 1H and 13C NMR spectra recorded with the sample dissolved in DMSO. The predicted chemical shifts at the B3LYP/6-31G(d) level obtained using the Gauge-Invariant Atomic Orbitals (GIAO) method with and without inclusion of solvent using the Polarizable Continuum Model (PCM). By using TD-DFT method, electronic absorption spectra of the title compound have been predicted and a good agreement with the TD-DFT method and the experimental one is determined. The UV-visible absorption spectra of the compound that dissolved in Ethanol, Methanol and DMSO were recorded in the range of 800-200 nm. The formation of hydrogen bond and the most possible interaction are explained using natural bond orbital (NBO) analysis. In addition, the molecular electrostatic potential (MEP), frontier molecular orbitals (FMO) analysis and atomic charges of the title compound were investigated using theoretical calculations. The results are discussed herein and compared with similar molecules whenever appropriate.

Python Spectral Analysis Tool (PySAT) for Preprocessing, Multivariate Analysis, and Machine Learning with Point Spectra

NASA Astrophysics Data System (ADS)

Anderson, R. B.; Finch, N.; Clegg, S.; Graff, T.; Morris, R. V.; Laura, J.

2017-06-01

We present a Python-based library and graphical interface for the analysis of point spectra. The tool is being developed with a focus on methods used for ChemCam data, but is flexible enough to handle spectra from other instruments.

Epsilon-Q: An Automated Analyzer Interface for Mass Spectral Library Search and Label-Free Protein Quantification.

PubMed

Cho, Jin-Young; Lee, Hyoung-Joo; Jeong, Seul-Ki; Paik, Young-Ki

2017-12-01

Mass spectrometry (MS) is a widely used proteome analysis tool for biomedical science. In an MS-based bottom-up proteomic approach to protein identification, sequence database (DB) searching has been routinely used because of its simplicity and convenience. However, searching a sequence DB with multiple variable modification options can increase processing time, false-positive errors in large and complicated MS data sets. Spectral library searching is an alternative solution, avoiding the limitations of sequence DB searching and allowing the detection of more peptides with high sensitivity. Unfortunately, this technique has less proteome coverage, resulting in limitations in the detection of novel and whole peptide sequences in biological samples. To solve these problems, we previously developed the "Combo-Spec Search" method, which uses manually multiple references and simulated spectral library searching to analyze whole proteomes in a biological sample. In this study, we have developed a new analytical interface tool called "Epsilon-Q" to enhance the functions of both the Combo-Spec Search method and label-free protein quantification. Epsilon-Q performs automatically multiple spectral library searching, class-specific false-discovery rate control, and result integration. It has a user-friendly graphical interface and demonstrates good performance in identifying and quantifying proteins by supporting standard MS data formats and spectrum-to-spectrum matching powered by SpectraST. Furthermore, when the Epsilon-Q interface is combined with the Combo-Spec search method, called the Epsilon-Q system, it shows a synergistic function by outperforming other sequence DB search engines for identifying and quantifying low-abundance proteins in biological samples. The Epsilon-Q system can be a versatile tool for comparative proteome analysis based on multiple spectral libraries and label-free quantification.

Spectrally resolved digital holography using a white light LED

NASA Astrophysics Data System (ADS)

Claus, D.; Pedrini, G.; Buchta, D.; Osten, W.

2017-06-01

This paper introduces the concept of spectrally resolved digital holography. The measurement principle and the analysis of the data will be discussed in detail. The usefulness of spectrally resolved digital holography is demonstrated for colour imaging and optical metrology with regards to the recovery of modulus information and phase information, respectively. The phase information will be used to measure the shape of an object via the application of the dual wavelength method. Based on the large degree of data available, multiple speckle de-correlated dual wavelength phase maps can be obtained, which when averaged result in a signal to noise ratio improvement.

Quantitative polarized light microscopy using spectral multiplexing interferometry.

PubMed

Li, Chengshuai; Zhu, Yizheng

2015-06-01

We propose an interferometric spectral multiplexing method for measuring birefringent specimens with simple configuration and high sensitivity. The retardation and orientation of sample birefringence are simultaneously encoded onto two spectral carrier waves, generated interferometrically by a birefringent crystal through polarization mixing. A single interference spectrum hence contains sufficient information for birefringence determination, eliminating the need for mechanical rotation or electrical modulation. The technique is analyzed theoretically and validated experimentally on cellulose film. System simplicity permits the possibility of mitigating system birefringence background. Further analysis demonstrates the technique's exquisite sensitivity as high as ∼20  pm for retardation measurement.

Measurement of energy contrast of amplified ultrashort pulses using cross-polarized wave generation and spectral interferometry.

PubMed

Iliev, Marin; Meier, Amanda K; Galloway, Benjamin; Adams, Daniel E; Squier, Jeff A; Durfee, Charles G

2014-07-28

We present a method using spectral interferometry (SI) to characterize a pulse in the presence of an incoherent background such as amplified spontaneous emission (ASE). The output of a regenerative amplifier is interfered with a copy of the pulse that has been converted using third-order cross-polarized wave generation (XPW). The ASE shows as a pedestal background in the interference pattern. The energy contrast between the short-pulse component and the ASE is retrieved. The spectra of the interacting beams are obtained through an improvement to the self-referenced spectral interferometry (SRSI) analysis.

Mapped Chebyshev Pseudo-Spectral Method for Dynamic Aero-Elastic Problem of Limit Cycle Oscillation

NASA Astrophysics Data System (ADS)

Im, Dong Kyun; Kim, Hyun Soon; Choi, Seongim

2018-05-01

A mapped Chebyshev pseudo-spectral method is developed as one of the Fourier-spectral approaches and solves nonlinear PDE systems for unsteady flows and dynamic aero-elastic problem in a given time interval, where the flows or elastic motions can be periodic, nonperiodic, or periodic with an unknown frequency. The method uses the Chebyshev polynomials of the first kind for the basis function and redistributes the standard Chebyshev-Gauss-Lobatto collocation points more evenly by a conformal mapping function for improved numerical stability. Contributions of the method are several. It can be an order of magnitude more efficient than the conventional finite difference-based, time-accurate computation, depending on the complexity of solutions and the number of collocation points. The method reformulates the dynamic aero-elastic problem in spectral form for coupled analysis of aerodynamics and structures, which can be effective for design optimization of unsteady and dynamic problems. A limit cycle oscillation (LCO) is chosen for the validation and a new method to determine the LCO frequency is introduced based on the minimization of a second derivative of the aero-elastic formulation. Two examples of the limit cycle oscillation are tested: nonlinear, one degree-of-freedom mass-spring-damper system and two degrees-of-freedom oscillating airfoil under pitch and plunge motions. Results show good agreements with those of the conventional time-accurate simulations and wind tunnel experiments.

Feature-based and statistical methods for analyzing the Deepwater Horizon oil spill with AVIRIS imagery

USGS Publications Warehouse

Rand, R.S.; Clark, R.N.; Livo, K.E.

2011-01-01

The Deepwater Horizon oil spill covered a very large geographical area in the Gulf of Mexico creating potentially serious environmental impacts on both marine life and the coastal shorelines. Knowing the oil's areal extent and thickness as well as denoting different categories of the oil's physical state is important for assessing these impacts. High spectral resolution data in hyperspectral imagery (HSI) sensors such as Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) provide a valuable source of information that can be used for analysis by semi-automatic methods for tracking an oil spill's areal extent, oil thickness, and oil categories. However, the spectral behavior of oil in water is inherently a highly non-linear and variable phenomenon that changes depending on oil thickness and oil/water ratios. For certain oil thicknesses there are well-defined absorption features, whereas for very thin films sometimes there are almost no observable features. Feature-based imaging spectroscopy methods are particularly effective at classifying materials that exhibit specific well-defined spectral absorption features. Statistical methods are effective at classifying materials with spectra that exhibit a considerable amount of variability and that do not necessarily exhibit well-defined spectral absorption features. This study investigates feature-based and statistical methods for analyzing oil spills using hyperspectral imagery. The appropriate use of each approach is investigated and a combined feature-based and statistical method is proposed.

Parametric Methods for Dynamic 11C-Phenytoin PET Studies.

PubMed

Mansor, Syahir; Yaqub, Maqsood; Boellaard, Ronald; Froklage, Femke E; de Vries, Anke; Bakker, Esther D M; Voskuyl, Rob A; Eriksson, Jonas; Schwarte, Lothar A; Verbeek, Joost; Windhorst, Albert D; Lammertsma, Adriaan A

2017-03-01

In this study, the performance of various methods for generating quantitative parametric images of dynamic 11 C-phenytoin PET studies was evaluated. Methods: Double-baseline 60-min dynamic 11 C-phenytoin PET studies, including online arterial sampling, were acquired for 6 healthy subjects. Parametric images were generated using Logan plot analysis, a basis function method, and spectral analysis. Parametric distribution volume (V T ) and influx rate ( K 1 ) were compared with those obtained from nonlinear regression analysis of time-activity curves. In addition, global and regional test-retest (TRT) variability was determined for parametric K 1 and V T values. Results: Biases in V T observed with all parametric methods were less than 5%. For K 1 , spectral analysis showed a negative bias of 16%. The mean TRT variabilities of V T and K 1 were less than 10% for all methods. Shortening the scan duration to 45 min provided similar V T and K 1 with comparable TRT performance compared with 60-min data. Conclusion: Among the various parametric methods tested, the basis function method provided parametric V T and K 1 values with the least bias compared with nonlinear regression data and showed TRT variabilities lower than 5%, also for smaller volume-of-interest sizes (i.e., higher noise levels) and shorter scan duration. © 2017 by the Society of Nuclear Medicine and Molecular Imaging.

Spectral analysis methods for vehicle interior vibro-acoustics identification

NASA Astrophysics Data System (ADS)

Hosseini Fouladi, Mohammad; Nor, Mohd. Jailani Mohd.; Ariffin, Ahmad Kamal

2009-02-01

Noise has various effects on comfort, performance and health of human. Sound are analysed by human brain based on the frequencies and amplitudes. In a dynamic system, transmission of sound and vibrations depend on frequency and direction of the input motion and characteristics of the output. It is imperative that automotive manufacturers invest a lot of effort and money to improve and enhance the vibro-acoustics performance of their products. The enhancement effort may be very difficult and time-consuming if one relies only on 'trial and error' method without prior knowledge about the sources itself. Complex noise inside a vehicle cabin originated from various sources and travel through many pathways. First stage of sound quality refinement is to find the source. It is vital for automotive engineers to identify the dominant noise sources such as engine noise, exhaust noise and noise due to vibration transmission inside of vehicle. The purpose of this paper is to find the vibro-acoustical sources of noise in a passenger vehicle compartment. The implementation of spectral analysis method is much faster than the 'trial and error' methods in which, parts should be separated to measure the transfer functions. Also by using spectral analysis method, signals can be recorded in real operational conditions which conduce to more consistent results. A multi-channel analyser is utilised to measure and record the vibro-acoustical signals. Computational algorithms are also employed to identify contribution of various sources towards the measured interior signal. These achievements can be utilised to detect, control and optimise interior noise performance of road transport vehicles.

Optimization of Immunolabeled Plasmonic Nanoparticles for Cell Surface Receptor Analysis

PubMed Central

Seekell, Kevin; Price, Hillel; Marinakos, Stella; Wax, Adam

2011-01-01

Noble metal nanoparticles hold great potential as optical contrast agents due to a unique feature, known as the plasmon resonance, which produces enhanced scattering and absorption at specific frequencies. The plasmon resonance also provides a spectral tunability that is not often found in organic fluorophores or other labeling methods. The ability to functionalize these nanoparticles with antibodies has led to their development as contrast agents for molecular optical imaging. In this review article, we present methods for optimizing the spectral agility of these labels. We discuss synthesis of gold nanorods, a plasmonic nanoparticle in which the plasmonic resonance can be tuned during synthesis to provide imaging within the spectral window commonly utilized in biomedical applications. We describe recent advances in our group to functionalize gold and silver nanoparticles using distinct antibodies, including EGFR, HER-2 and IGF-1, selected for their relevance to tumor imaging. Finally, we present characterization of these nanoparticle labels to verify their spectral properties and molecular specificity. PMID:21911063

Spectral data compression using weighted principal component analysis with consideration of human visual system and light sources

NASA Astrophysics Data System (ADS)

Cao, Qian; Wan, Xiaoxia; Li, Junfeng; Liu, Qiang; Liang, Jingxing; Li, Chan

2016-10-01

This paper proposed two weight functions based on principal component analysis (PCA) to reserve more colorimetric information in spectral data compression process. One weight function consisted of the CIE XYZ color-matching functions representing the characteristic of the human visual system, while another was made up of the CIE XYZ color-matching functions of human visual system and relative spectral power distribution of the CIE standard illuminant D65. The improvement obtained from the proposed two methods were tested to compress and reconstruct the reflectance spectra of 1600 glossy Munsell color chips and 1950 Natural Color System color chips as well as six multispectral images. The performance was evaluated by the mean values of color difference under the CIE 1931 standard colorimetric observer and the CIE standard illuminant D65 and A. The mean values of root mean square errors between the original and reconstructed spectra were also calculated. The experimental results show that the proposed two methods significantly outperform the standard PCA and another two weighted PCA in the aspects of colorimetric reconstruction accuracy with very slight degradation in spectral reconstruction accuracy. In addition, weight functions with the CIE standard illuminant D65 can improve the colorimetric reconstruction accuracy compared to weight functions without the CIE standard illuminant D65.

The method of trend analysis of parameters time series of gas-turbine engine state

NASA Astrophysics Data System (ADS)

Hvozdeva, I.; Myrhorod, V.; Derenh, Y.

2017-10-01

This research substantiates an approach to interval estimation of time series trend component. The well-known methods of spectral and trend analysis are used for multidimensional data arrays. The interval estimation of trend component is proposed for the time series whose autocorrelation matrix possesses a prevailing eigenvalue. The properties of time series autocorrelation matrix are identified.

An oil film information retrieval method overcoming the influence of sun glitter, based on AISA+ airborne hyper-spectral image

NASA Astrophysics Data System (ADS)

Zhan, Yuanzeng; Mao, Tianming; Gong, Fang; Wang, Difeng; Chen, Jianyu

2010-10-01

As an effective survey tool for oil spill detection, the airborne hyper-spectral sensor affords the potentiality for retrieving the quantitative information of oil slick which is useful for the cleanup of spilled oil. But many airborne hyper-spectral images are affected by sun glitter which distorts radiance values and spectral ratios used for oil slick detection. In 2005, there's an oil spill event leaking at oil drilling platform in The South China Sea, and an AISA+ airborne hyper-spectral image recorded this event will be selected for studying in this paper, which is affected by sun glitter terribly. Through a spectrum analysis of the oil and water samples, two features -- "spectral rotation" and "a pair of fixed points" can be found in spectral curves between crude oil film and water. Base on these features, an oil film information retrieval method which can overcome the influence of sun glitter is presented. Firstly, the radiance of the image is converted to normal apparent reflectance (NormAR). Then, based on the features of "spectral rotation" (used for distinguishing oil film and water) and "a pair of fixed points" (used for overcoming the effect of sun glitter), NormAR894/NormAR516 is selected as an indicator of oil film. Finally, by using a threshold combined with the technologies of image filter and mathematic morphology, the distribution and relative thickness of oil film are retrieved.

Terrain Extraction by Integrating Terrestrial Laser Scanner Data and Spectral Information

NASA Astrophysics Data System (ADS)

Lau, C. L.; Halim, S.; Zulkepli, M.; Azwan, A. M.; Tang, W. L.; Chong, A. K.

2015-10-01

The extraction of true terrain points from unstructured laser point cloud data is an important process in order to produce an accurate digital terrain model (DTM). However, most of these spatial filtering methods just utilizing the geometrical data to discriminate the terrain points from nonterrain points. The point cloud filtering method also can be improved by using the spectral information available with some scanners. Therefore, the objective of this study is to investigate the effectiveness of using the three-channel (red, green and blue) of the colour image captured from built-in digital camera which is available in some Terrestrial Laser Scanner (TLS) for terrain extraction. In this study, the data acquisition was conducted at a mini replica landscape in Universiti Teknologi Malaysia (UTM), Skudai campus using Leica ScanStation C10. The spectral information of the coloured point clouds from selected sample classes are extracted for spectral analysis. The coloured point clouds which within the corresponding preset spectral threshold are identified as that specific feature point from the dataset. This process of terrain extraction is done through using developed Matlab coding. Result demonstrates that a higher spectral resolution passive image is required in order to improve the output. This is because low quality of the colour images captured by the sensor contributes to the low separability in spectral reflectance. In conclusion, this study shows that, spectral information is capable to be used as a parameter for terrain extraction.

Direct glycan structure determination of intact N-linked glycopeptides by low-energy collision-induced dissociation tandem mass spectrometry and predicted spectral library searching.

PubMed

Pai, Pei-Jing; Hu, Yingwei; Lam, Henry

2016-08-31

Intact glycopeptide MS analysis to reveal site-specific protein glycosylation is an important frontier of proteomics. However, computational tools for analyzing MS/MS spectra of intact glycopeptides are still limited and not well-integrated into existing workflows. In this work, a new computational tool which combines the spectral library building/searching tool, SpectraST (Lam et al. Nat. Methods2008, 5, 873-875), and the glycopeptide fragmentation prediction tool, MassAnalyzer (Zhang et al. Anal. Chem.2010, 82, 10194-10202) for intact glycopeptide analysis has been developed. Specifically, this tool enables the determination of the glycan structure directly from low-energy collision-induced dissociation (CID) spectra of intact glycopeptides. Given a list of possible glycopeptide sequences as input, a sample-specific spectral library of MassAnalyzer-predicted spectra is built using SpectraST. Glycan identification from CID spectra is achieved by spectral library searching against this library, in which both m/z and intensity information of the possible fragmentation ions are taken into consideration for improved accuracy. We validated our method using a standard glycoprotein, human transferrin, and evaluated its potential to be used in site-specific glycosylation profiling of glycoprotein datasets from LC-MS/MS. In addition, we further applied our method to reveal, for the first time, the site-specific N-glycosylation profile of recombinant human acetylcholinesterase expressed in HEK293 cells. For maximum usability, SpectraST is developed as part of the Trans-Proteomic Pipeline (TPP), a freely available and open-source software suite for MS data analysis. Copyright © 2016 Elsevier B.V. All rights reserved.

FTIR-ATR infrared spectroscopy for the detection of ochratoxin A in dried vine fruit.

PubMed

Galvis-Sánchez, Andrea C; Barros, Antonio; Delgadillo, Ivonne

2007-11-01

A method of screening sultanas for ochratoxin A (OTA) contamination, using mid-infrared spectroscopy/Golden Gate single-reflection ATR (attenuated total reflection), is described. The main spectral characteristics of sultanas from different sources were identified in a preliminary acquisition and spectral analysis study. Principal component analysis (PCA) showed that samples of various origins had different spectral characteristics, especially in water content and the fingerprint region. A lack of reproducibility was observed in the spectra acquired on different days. However, spectral repeatability was greatly improved when water activity of the sample was set at 0.62. A calibration curve of OTA was constructed in the range 10-40 microg OTA kg(-1). Samples with OTA levels higher than 20 microg kg(-1) were separated from samples contaminated with a lower concentration (10 microg OTA kg(-1)) and from uncontaminated samples. The reported methodology is a reliable and simple technique for screening dried vine fruit for OTA.

Estimation of sub-pixel water area on Tibet plateau using multiple endmembers spectral mixture spectral analysis from MODIS data

NASA Astrophysics Data System (ADS)

Cui, Qian; Shi, Jiancheng; Xu, Yuanliu

2011-12-01

Water is the basic needs for human society, and the determining factor of stability of ecosystem as well. There are lots of lakes on Tibet Plateau, which will lead to flood and mudslide when the water expands sharply. At present, water area is extracted from TM or SPOT data for their high spatial resolution; however, their temporal resolution is insufficient. MODIS data have high temporal resolution and broad coverage. So it is valuable resource for detecting the change of water area. Because of its low spatial resolution, mixed-pixels are common. In this paper, four spectral libraries are built using MOD09A1 product, based on that, water body is extracted in sub-pixels utilizing Multiple Endmembers Spectral Mixture Analysis (MESMA) using MODIS daily reflectance data MOD09GA. The unmixed result is comparing with contemporaneous TM data and it is proved that this method has high accuracy.

Can Measurements of the Near-Infrared Solar Spectral Irradiance be Reconciled? A New Ground-Based Assessment Between 4,000 and 10,000 cm-1

NASA Astrophysics Data System (ADS)

Elsey, Jonathan; Coleman, Marc D.; Gardiner, Tom; Shine, Keith P.

2017-10-01

The near-infrared solar spectral irradiance (SSI) is of vital importance for understanding the Earth's radiation budget, and in Earth observation applications. Differences between previously published solar spectra (including the commonly used ATLAS3 spectrum) reach up to 10% at the low wavenumber end of the 4,000-10,000 cm-1 (2.5-1 μm) spectral region. The implications for the atmospheric sciences are significant, since this spectral region contains 25% of the incoming total solar irradiance. This work details an updated analysis of the CAVIAR SSI, featuring additional analysis techniques and an updated uncertainty budget using a Monte Carlo method. We report good consistency with ATLAS3 in the 7,000-10,000 cm-1 region where there is confidence in these results due to agreement with other spectra, but 7% lower in the 4,000-7,000 cm-1 region, in general agreement with several other analyses.

Influence of aerosols on surface reaching spectral irradiance and introduction to a new technique of estimating aerosol radiative forcing from high resolution spectral flux measurements

NASA Astrophysics Data System (ADS)

Rao, Roshan

2016-04-01

Aerosol radiative forcing estimates with high certainty are required in climate change studies. The approach in estimating the aerosol radiative forcing by using the chemical composition of aerosols is not effective as the chemical composition data with radiative properties are not widely available. We look into the approach where ground based spectral radiation flux measurement is made and along with an Radtiative transfer (RT) model, radiative forcing is estimated. Measurements of spectral flux were made using an ASD spectroradiometer with 350 - 1050 nm wavelength range and a 3nm resolution during around 54 clear-sky days during which AOD range was around 0.01 to 0.7. Simultaneous measurements of black carbon were also made using Aethalometer (Magee Scientific) which ranged from around 1.5 ug/m3 to 8 ug/m3. The primary study involved in understanding the sensitivity of spectral flux due to change in individual aerosol species (Optical properties of Aerosols and Clouds (OPAC) classified aerosol species) using the SBDART RT model. This made us clearly distinguish the influence of different aerosol species on the spectral flux. Following this, a new technique has been introduced to estimate an optically equivalent mixture of aerosol species for the given location. The new method involves matching different combinations of aerosol species in OPAC model and RT model as long as the combination which gives the minimum root mean squared deviation from measured spectral flux is obtained. Using the optically equivalent aerosol mixture and RT model, aerosol radiative forcing is estimated. Also an alternate method to estimate the spectral SSA is discussed. Here, the RT model, the observed spectral flux and spectral AOD is used. Spectral AOD is input to RT model and SSA is varied till the minimum root mean squared difference between observed and simulated spectral flux from RT model is obtained. The methods discussed are limited to clear sky scenes and its accuracy to derive an optically equivalent aerosol mixture reduces when diffuse component of flux increases. In our analysis, RT model clearly shows that direct component of spectral flux is more sensitive to different aerosol species than total spectral flux which is also supported by our observed data.

SVM-Based Spectral Analysis for Heart Rate from Multi-Channel WPPG Sensor Signals.

PubMed

Xiong, Jiping; Cai, Lisang; Wang, Fei; He, Xiaowei

2017-03-03

Although wrist-type photoplethysmographic (hereafter referred to as WPPG) sensor signals can measure heart rate quite conveniently, the subjects' hand movements can cause strong motion artifacts, and then the motion artifacts will heavily contaminate WPPG signals. Hence, it is challenging for us to accurately estimate heart rate from WPPG signals during intense physical activities. The WWPG method has attracted more attention thanks to the popularity of wrist-worn wearable devices. In this paper, a mixed approach called Mix-SVM is proposed, it can use multi-channel WPPG sensor signals and simultaneous acceleration signals to measurement heart rate. Firstly, we combine the principle component analysis and adaptive filter to remove a part of the motion artifacts. Due to the strong relativity between motion artifacts and acceleration signals, the further denoising problem is regarded as a sparse signals reconstruction problem. Then, we use a spectrum subtraction method to eliminate motion artifacts effectively. Finally, the spectral peak corresponding to heart rate is sought by an SVM-based spectral analysis method. Through the public PPG database in the 2015 IEEE Signal Processing Cup, we acquire the experimental results, i.e., the average absolute error was 1.01 beat per minute, and the Pearson correlation was 0.9972. These results also confirm that the proposed Mix-SVM approach has potential for multi-channel WPPG-based heart rate estimation in the presence of intense physical exercise.

Hilbert-Huang Transform: A Spectral Analysis Tool Applied to Sunspot Number and Total Solar Irradiance Variations, as well as Near-Surface Atmospheric Variables

NASA Astrophysics Data System (ADS)

Barnhart, B. L.; Eichinger, W. E.; Prueger, J. H.

2010-12-01

Hilbert-Huang transform (HHT) is a relatively new data analysis tool which is used to analyze nonstationary and nonlinear time series data. It consists of an algorithm, called empirical mode decomposition (EMD), which extracts the cyclic components embedded within time series data, as well as Hilbert spectral analysis (HSA) which displays the time and frequency dependent energy contributions from each component in the form of a spectrogram. The method can be considered a generalized form of Fourier analysis which can describe the intrinsic cycles of data with basis functions whose amplitudes and phases may vary with time. The HHT will be introduced and compared to current spectral analysis tools such as Fourier analysis, short-time Fourier analysis, wavelet analysis and Wigner-Ville distributions. A number of applications are also presented which demonstrate the strengths and limitations of the tool, including analyzing sunspot number variability and total solar irradiance proxies as well as global averaged temperature and carbon dioxide concentration. Also, near-surface atmospheric quantities such as temperature and wind velocity are analyzed to demonstrate the nonstationarity of the atmosphere.

A wavelet and least square filter based spatial-spectral denoising approach of hyperspectral imagery

NASA Astrophysics Data System (ADS)

Li, Ting; Chen, Xiao-Mei; Chen, Gang; Xue, Bo; Ni, Guo-Qiang

2009-11-01

Noise reduction is a crucial step in hyperspectral imagery pre-processing. Based on sensor characteristics, the noise of hyperspectral imagery represents in both spatial and spectral domain. However, most prevailing denosing techniques process the imagery in only one specific domain, which have not utilized multi-domain nature of hyperspectral imagery. In this paper, a new spatial-spectral noise reduction algorithm is proposed, which is based on wavelet analysis and least squares filtering techniques. First, in the spatial domain, a new stationary wavelet shrinking algorithm with improved threshold function is utilized to adjust the noise level band-by-band. This new algorithm uses BayesShrink for threshold estimation, and amends the traditional soft-threshold function by adding shape tuning parameters. Comparing with soft or hard threshold function, the improved one, which is first-order derivable and has a smooth transitional region between noise and signal, could save more details of image edge and weaken Pseudo-Gibbs. Then, in the spectral domain, cubic Savitzky-Golay filter based on least squares method is used to remove spectral noise and artificial noise that may have been introduced in during the spatial denoising. Appropriately selecting the filter window width according to prior knowledge, this algorithm has effective performance in smoothing the spectral curve. The performance of the new algorithm is experimented on a set of Hyperion imageries acquired in 2007. The result shows that the new spatial-spectral denoising algorithm provides more significant signal-to-noise-ratio improvement than traditional spatial or spectral method, while saves the local spectral absorption features better.

ANALYSIS OF VOLATILES AND SEMIVOLATILES BY DIRECT AQUEOUS INJECTION

EPA Science Inventory

Direct aqueous injection analysis (DAI) with gas chromatographic separation and ion trap mass spectral detection was used to analyze aqueous samples for g/L levels of 54 volatile and semivolatile compounds, and problematic non-purgeables and non-extractables. The method reduces ...

Detecting Weak Spectral Lines in Interferometric Data through Matched Filtering

NASA Astrophysics Data System (ADS)

Loomis, Ryan A.; Öberg, Karin I.; Andrews, Sean M.; Walsh, Catherine; Czekala, Ian; Huang, Jane; Rosenfeld, Katherine A.

2018-04-01

Modern radio interferometers enable observations of spectral lines with unprecedented spatial resolution and sensitivity. In spite of these technical advances, many lines of interest are still at best weakly detected and therefore necessitate detection and analysis techniques specialized for the low signal-to-noise ratio (S/N) regime. Matched filters can leverage knowledge of the source structure and kinematics to increase sensitivity of spectral line observations. Application of the filter in the native Fourier domain improves S/N while simultaneously avoiding the computational cost and ambiguities associated with imaging, making matched filtering a fast and robust method for weak spectral line detection. We demonstrate how an approximate matched filter can be constructed from a previously observed line or from a model of the source, and we show how this filter can be used to robustly infer a detection significance for weak spectral lines. When applied to ALMA Cycle 2 observations of CH3OH in the protoplanetary disk around TW Hya, the technique yields a ≈53% S/N boost over aperture-based spectral extraction methods, and we show that an even higher boost will be achieved for observations at higher spatial resolution. A Python-based open-source implementation of this technique is available under the MIT license at http://github.com/AstroChem/VISIBLE.

An advanced scanning method for space-borne hyper-spectral imaging system

NASA Astrophysics Data System (ADS)

Wang, Yue-ming; Lang, Jun-Wei; Wang, Jian-Yu; Jiang, Zi-Qing

2011-08-01

Space-borne hyper-spectral imagery is an important means for the studies and applications of earth science. High cost efficiency could be acquired by optimized system design. In this paper, an advanced scanning method is proposed, which contributes to implement both high temporal and spatial resolution imaging system. Revisit frequency and effective working time of space-borne hyper-spectral imagers could be greatly improved by adopting two-axis scanning system if spatial resolution and radiometric accuracy are not harshly demanded. In order to avoid the quality degradation caused by image rotation, an idea of two-axis rotation has been presented based on the analysis and simulation of two-dimensional scanning motion path and features. Further improvement of the imagers' detection ability under the conditions of small solar altitude angle and low surface reflectance can be realized by the Ground Motion Compensation on pitch axis. The structure and control performance are also described. An intelligent integration technology of two-dimensional scanning and image motion compensation is elaborated in this paper. With this technology, sun-synchronous hyper-spectral imagers are able to pay quick visit to hot spots, acquiring both high spatial and temporal resolution hyper-spectral images, which enables rapid response of emergencies. The result has reference value for developing operational space-borne hyper-spectral imagers.

Improvements to an earth observing statistical performance model with applications to LWIR spectral variability

NASA Astrophysics Data System (ADS)

Zhao, Runchen; Ientilucci, Emmett J.

2017-05-01

Hyperspectral remote sensing systems provide spectral data composed of hundreds of narrow spectral bands. Spectral remote sensing systems can be used to identify targets, for example, without physical interaction. Often it is of interested to characterize the spectral variability of targets or objects. The purpose of this paper is to identify and characterize the LWIR spectral variability of targets based on an improved earth observing statistical performance model, known as the Forecasting and Analysis of Spectroradiometric System Performance (FASSP) model. FASSP contains three basic modules including a scene model, sensor model and a processing model. Instead of using mean surface reflectance only as input to the model, FASSP transfers user defined statistical characteristics of a scene through the image chain (i.e., from source to sensor). The radiative transfer model, MODTRAN, is used to simulate the radiative transfer based on user defined atmospheric parameters. To retrieve class emissivity and temperature statistics, or temperature / emissivity separation (TES), a LWIR atmospheric compensation method is necessary. The FASSP model has a method to transform statistics in the visible (ie., ELM) but currently does not have LWIR TES algorithm in place. This paper addresses the implementation of such a TES algorithm and its associated transformation of statistics.

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

Miyazaki, Tadakuni; Harashima, Akira; Nakatani, Yukihiro

Coral reefs are the major sites for photo-synthesis and calcification in the present ocean. Estimating the production rate of calcification by the coral reefs or investigating the sink/source mechanism of CO{sub 2} by the coral reefs in the ocean, the distribution of the coral reefs in the world wide must be identified. Measuring the spectral signatures of underwater coral reefs and mapping of coral reefs by satellite remote sensing are described. The spectral signatures of different species of the coral reefs were measured using a spectroradiometer at off Kuroshima Island, Okinawa, Japan and investigated spectral difference between different species ofmore » the coral reefs. As well as the field experiments, laboratory experiments for measuring the spectral signatures of 9 different species of coral reefs were carried out with the same spectroradiometer. The spectral reflectance of each coral reef showed a significant result that a narrow absorption band exists in the spectral region between 660 and 680 nm, and very strong spectral reflectance from about 700 nm towards the longer wavelength range. On the other hand, absorption and the high reflectance region were not observed from the bottom sands or bare rocks underwater. These experiments suggested that there is a significant spectral difference between coral reefs and bottom sands or bare rocks and so the best spectral range for separating the coral reefs from other underwater objects in the ocean would be between 700 and 800 nm. As well as the basic spectral measurement either in the field or at the laboratory, SPOT satellite imageries were used to classify the underwater coral reefs. Classification methods used here were the principal component analysis, and the maximum likelihood. Finally, the evaluation of classification method for extracting the coral reefs was introduced.« less

Broadband Studies of Semsmic Sources at Regional and Teleseismic Distances Using Advanced Time Series Analysis Methods. Volume 1.

DTIC Science & Technology

1991-03-21

discussion of spectral factorability and motivations for broadband analysis, the report is subdivided into four main sections. In Section 1.0, we...estimates. The motivation for developing our multi-channel deconvolution method was to gain information about seismic sources, most notably, nuclear...with complex constraints for estimating the rupture history. Such methods (applied mostly to data sets that also include strong rmotion data), were

Method for evaluating moisture tensions of soils using spectral data

NASA Technical Reports Server (NTRS)

Peterson, John B. (Inventor)

1982-01-01

A method is disclosed which permits evaluation of soil moisture utilizing remote sensing. Spectral measurements at a plurality of different wavelengths are taken with respect to sample soils and the bidirectional reflectance factor (BRF) measurements produced are submitted to regression analysis for development therefrom of predictable equations calculated for orderly relationships. Soil of unknown reflective and unknown soil moisture tension is thereafter analyzed for bidirectional reflectance and the resulting data utilized to determine the soil moisture tension of the soil as well as providing a prediction as to the bidirectional reflectance of the soil at other moisture tensions.

The Spectral Image Processing System (SIPS) - Interactive visualization and analysis of imaging spectrometer data

NASA Technical Reports Server (NTRS)

Kruse, F. A.; Lefkoff, A. B.; Boardman, J. W.; Heidebrecht, K. B.; Shapiro, A. T.; Barloon, P. J.; Goetz, A. F. H.

1993-01-01

The Center for the Study of Earth from Space (CSES) at the University of Colorado, Boulder, has developed a prototype interactive software system called the Spectral Image Processing System (SIPS) using IDL (the Interactive Data Language) on UNIX-based workstations. SIPS is designed to take advantage of the combination of high spectral resolution and spatial data presentation unique to imaging spectrometers. It streamlines analysis of these data by allowing scientists to rapidly interact with entire datasets. SIPS provides visualization tools for rapid exploratory analysis and numerical tools for quantitative modeling. The user interface is X-Windows-based, user friendly, and provides 'point and click' operation. SIPS is being used for multidisciplinary research concentrating on use of physically based analysis methods to enhance scientific results from imaging spectrometer data. The objective of this continuing effort is to develop operational techniques for quantitative analysis of imaging spectrometer data and to make them available to the scientific community prior to the launch of imaging spectrometer satellite systems such as the Earth Observing System (EOS) High Resolution Imaging Spectrometer (HIRIS).

[Mahalanobis distance based hyperspectral characteristic discrimination of leaves of different desert tree species].

PubMed

Lin, Hai-jun; Zhang, Hui-fang; Gao, Ya-qi; Li, Xia; Yang, Fan; Zhou, Yan-fei

2014-12-01

The hyperspectral reflectance of Populus euphratica, Tamarix hispida, Haloxylon ammodendron and Calligonum mongolicum in the lower reaches of Tarim River and Turpan Desert Botanical Garden was measured by using the HR-768 field-portable spectroradiometer. The method of continuum removal, first derivative reflectance and second derivative reflectance were used to deal with the original spectral data of four tree species. The method of Mahalanobis Distance was used to select the bands with significant differences in the original spectral data and transform spectral data to identify the different tree species. The progressive discrimination analyses were used to test the selective bands used to identify different tree species. The results showed that The Mahalanobis Distance method was an effective method in feature band extraction. The bands for identifying different tree species were most near-infrared bands. The recognition accuracy of four methods was 85%, 93.8%, 92.4% and 95.5% respectively. Spectrum transform could improve the recognition accuracy. The recognition accuracy of different research objects and different spectrum transform methods were different. The research provided evidence for desert tree species classification, monitoring biodiversity and the analysis of area in desert by using large scale remote sensing method.

Non-opioid analgesic drug flupirtine: Spectral analysis, DFT computations, in vitro bioactivity and molecular docking study

NASA Astrophysics Data System (ADS)

Leenaraj, D. R.; Hubert Joe, I.

2017-06-01

Spectral features of non-opioid analgesic drug flupirtine have been explored by the Fourier transform infrared, Raman and Nuclear magnetic resonance spectroscopic techniques combined with density functional theory computations. The bioactive conformer of flupirtine is stabilized by an intramolecular Csbnd H⋯N hydrogen bonding resulting by the steric strain of hydrogen atoms. Natural bond orbital and natural population analysis support this result. The charge redistribution also has been analyzed. Antimicrobial activities of flupirtine have been screened by agar well disc diffusion and molecular docking methods, which exposes the importance of triaminopyridine in flupirtine.

Quantum walks with an anisotropic coin I: spectral theory

NASA Astrophysics Data System (ADS)

Richard, S.; Suzuki, A.; Tiedra de Aldecoa, R.

2018-02-01

We perform the spectral analysis of the evolution operator U of quantum walks with an anisotropic coin, which include one-defect models, two-phase quantum walks, and topological phase quantum walks as special cases. In particular, we determine the essential spectrum of U, we show the existence of locally U-smooth operators, we prove the discreteness of the eigenvalues of U outside the thresholds, and we prove the absence of singular continuous spectrum for U. Our analysis is based on new commutator methods for unitary operators in a two-Hilbert spaces setting, which are of independent interest.

New robust bilinear least squares method for the analysis of spectral-pH matrix data.

PubMed

Goicoechea, Héctor C; Olivieri, Alejandro C

2005-07-01

A new second-order multivariate method has been developed for the analysis of spectral-pH matrix data, based on a bilinear least-squares (BLLS) model achieving the second-order advantage and handling multiple calibration standards. A simulated Monte Carlo study of synthetic absorbance-pH data allowed comparison of the newly proposed BLLS methodology with constrained parallel factor analysis (PARAFAC) and with the combination multivariate curve resolution-alternating least-squares (MCR-ALS) technique under different conditions of sample-to-sample pH mismatch and analyte-background ratio. The results indicate an improved prediction ability for the new method. Experimental data generated by measuring absorption spectra of several calibration standards of ascorbic acid and samples of orange juice were subjected to second-order calibration analysis with PARAFAC, MCR-ALS, and the new BLLS method. The results indicate that the latter method provides the best analytical results in regard to analyte recovery in samples of complex composition requiring strict adherence to the second-order advantage. Linear dependencies appear when multivariate data are produced by using the pH or a reaction time as one of the data dimensions, posing a challenge to classical multivariate calibration models. The presently discussed algorithm is useful for these latter systems.

Hyperspectral laser-induced autofluorescence imaging of dental caries

NASA Astrophysics Data System (ADS)

Bürmen, Miran; Fidler, Aleš; Pernuš, Franjo; Likar, Boštjan

2012-01-01

Dental caries is a disease characterized by demineralization of enamel crystals leading to the penetration of bacteria into the dentine and pulp. Early detection of enamel demineralization resulting in increased enamel porosity, commonly known as white spots, is a difficult diagnostic task. Laser induced autofluorescence was shown to be a useful method for early detection of demineralization. The existing studies involved either a single point spectroscopic measurements or imaging at a single spectral band. In the case of spectroscopic measurements, very little or no spatial information is acquired and the measured autofluorescence signal strongly depends on the position and orientation of the probe. On the other hand, single-band spectral imaging can be substantially affected by local spectral artefacts. Such effects can significantly interfere with automated methods for detection of early caries lesions. In contrast, hyperspectral imaging effectively combines the spatial information of imaging methods with the spectral information of spectroscopic methods providing excellent basis for development of robust and reliable algorithms for automated classification and analysis of hard dental tissues. In this paper, we employ 405 nm laser excitation of natural caries lesions. The fluorescence signal is acquired by a state-of-the-art hyperspectral imaging system consisting of a high-resolution acousto-optic tunable filter (AOTF) and a highly sensitive Scientific CMOS camera in the spectral range from 550 nm to 800 nm. The results are compared to the contrast obtained by near-infrared hyperspectral imaging technique employed in the existing studies on early detection of dental caries.

Evolutionary Computing Methods for Spectral Retrieval

NASA Technical Reports Server (NTRS)

Terrile, Richard; Fink, Wolfgang; Huntsberger, Terrance; Lee, Seugwon; Tisdale, Edwin; VonAllmen, Paul; Tinetti, Geivanna

2009-01-01

A methodology for processing spectral images to retrieve information on underlying physical, chemical, and/or biological phenomena is based on evolutionary and related computational methods implemented in software. In a typical case, the solution (the information that one seeks to retrieve) consists of parameters of a mathematical model that represents one or more of the phenomena of interest. The methodology was developed for the initial purpose of retrieving the desired information from spectral image data acquired by remote-sensing instruments aimed at planets (including the Earth). Examples of information desired in such applications include trace gas concentrations, temperature profiles, surface types, day/night fractions, cloud/aerosol fractions, seasons, and viewing angles. The methodology is also potentially useful for retrieving information on chemical and/or biological hazards in terrestrial settings. In this methodology, one utilizes an iterative process that minimizes a fitness function indicative of the degree of dissimilarity between observed and synthetic spectral and angular data. The evolutionary computing methods that lie at the heart of this process yield a population of solutions (sets of the desired parameters) within an accuracy represented by a fitness-function value specified by the user. The evolutionary computing methods (ECM) used in this methodology are Genetic Algorithms and Simulated Annealing, both of which are well-established optimization techniques and have also been described in previous NASA Tech Briefs articles. These are embedded in a conceptual framework, represented in the architecture of the implementing software, that enables automatic retrieval of spectral and angular data and analysis of the retrieved solutions for uniqueness.

Real time automatic detection of bearing fault in induction machine using kurtogram analysis.

PubMed

Tafinine, Farid; Mokrani, Karim

2012-11-01

A proposed signal processing technique for incipient real time bearing fault detection based on kurtogram analysis is presented in this paper. The kurtogram is a fourth-order spectral analysis tool introduced for detecting and characterizing non-stationarities in a signal. This technique starts from investigating the resonance signatures over selected frequency bands to extract the representative features. The traditional spectral analysis is not appropriate for non-stationary vibration signal and for real time diagnosis. The performance of the proposed technique is examined by a series of experimental tests corresponding to different bearing conditions. Test results show that this signal processing technique is an effective bearing fault automatic detection method and gives a good basis for an integrated induction machine condition monitor.

Quantitative methods and detection techniques in hyperspectral imaging involving medical and other applications

NASA Astrophysics Data System (ADS)

Roy, Ankita

2007-12-01

This research using Hyperspectral imaging involves recognizing targets through spatial and spectral matching and spectral un-mixing of data ranging from remote sensing to medical imaging kernels for clinical studies based on Hyperspectral data-sets generated using the VFTHSI [Visible Fourier Transform Hyperspectral Imager], whose high resolution Si detector makes the analysis achievable. The research may be broadly classified into (I) A Physically Motivated Correlation Formalism (PMCF), which places both spatial and spectral data on an equivalent mathematical footing in the context of a specific Kernel and (II) An application in RF plasma specie detection during carbon nanotube growing process. (III) Hyperspectral analysis for assessing density and distribution of retinopathies like age related macular degeneration (ARMD) and error estimation enabling the early recognition of ARMD, which is treated as an ill-conditioned inverse imaging problem. The broad statistical scopes of this research are two fold-target recognition problems and spectral unmixing problems. All processes involve experimental and computational analysis of Hyperspectral data sets is presented, which is based on the principle of a Sagnac Interferometer, calibrated to obtain high SNR levels. PMCF computes spectral/spatial/cross moments and answers the question of how optimally the entire hypercube should be sampled and finds how many spatial-spectral pixels are required precisely for a particular target recognition. Spectral analysis of RF plasma radicals, typically Methane plasma and Argon plasma using VFTHSI has enabled better process monitoring during growth of vertically aligned multi-walled carbon nanotubes by instant registration of the chemical composition or density changes temporally, which is key since a significant correlation can be found between plasma state and structural properties. A vital focus of this dissertation is towards medical Hyperspectral imaging applied to retinopathies like age related macular degeneration targets taken with a Fundus imager, which is akin to the VFTHSI. Detection of the constituent components in the diseased hyper-pigmentation area is also computed. The target or reflectance matrix is treated as a highly ill-conditioned spectral un-mixing problem, to which methodologies like inverse techniques, principal component analysis (PCA) and receiver operating curves (ROC) for precise spectral recognition of infected area. The region containing ARMD was easily distinguishable from the spectral mesh plots over the entire band-pass area. Once the location was detected the PMCF coefficients were calculated by cross correlating a target of normal oxygenated retina with the deoxygenated one. The ROCs generated using PMCF shows 30% higher detection probability with improved accuracy than ROCs based on Spectral Angle Mapper (SAM). By spectral unmixing methods, the important endmembers/carotenoids of the MD pigment were found to be Xanthophyl and lutein, while beta-carotene which showed a negative correlation in the unconstrained inverse problem is a supplement given to ARMD patients to prevent the disease and does not occur in the eye. Literature also shows degeneration of meso-zeaxanthin. Ophthalmologists may assert the presence of ARMD and commence the diagnosis process if the Xanthophyl pigment have degenerated 89.9%, while the lutein has decayed almost 80%, as found deduced computationally. This piece of current research takes it to the next level of precise investigation in the continuing process of improved clinical findings by correlating the microanatomy of the diseased fovea and shows promise of an early detection of this disease.

Near and mid infrared spectroscopy and multivariate data analysis in studies of oxidation of edible oils.

PubMed

Wójcicki, Krzysztof; Khmelinskii, Igor; Sikorski, Marek; Sikorska, Ewa

2015-11-15

Infrared spectroscopic techniques and chemometric methods were used to study oxidation of olive, sunflower and rapeseed oils. Accelerated oxidative degradation of oils at 60°C was monitored using peroxide values and FT-MIR ATR and FT-NIR transmittance spectroscopy. Principal component analysis (PCA) facilitated visualization and interpretation of spectral changes occurring during oxidation. Multivariate curve resolution (MCR) method found three spectral components in the NIR and MIR spectral matrix, corresponding to the oxidation products, and saturated and unsaturated structures. Good quantitative relation was found between peroxide value and contribution of oxidation products evaluated using MCR--based on NIR (R(2) = 0.890), MIR (R(2) = 0.707) and combined NIR and MIR (R(2) = 0.747) data. Calibration models for prediction peroxide value established using partial least squares (PLS) regression were characterized for MIR (R(2) = 0.701, RPD = 1.7), NIR (R(2) = 0.970, RPD = 5.3), and combined NIR and MIR data (R(2) = 0.954, RPD = 3.1). Copyright © 2015 Elsevier Ltd. All rights reserved.

Post-processing of auditory steady-state responses to correct spectral leakage.

PubMed

Felix, Leonardo Bonato; de Sá, Antonio Mauricio Ferreira Leite Miranda; Mendes, Eduardo Mazoni Andrade Marçal; Moraes, Márcio Flávio Dutra

2009-06-30

Auditory steady-state responses (ASSRs) are electrical manifestations of brain due to high rate sound stimulation. These evoked responses can be used to assess the hearing capabilities of a subject in an objective, automatic fashion. Usually, the detection protocol is accomplished by frequency-domain techniques, such as magnitude-squared coherence, whose estimation is based on the fast Fourier transform (FFT) of several data segments. In practice, the FFT-based spectrum may spread out the energy of a given frequency to its side bins and this escape of energy in the spectrum is called spectral leakage. The distortion of the spectrum due to leakage may severely compromise statistical significance of objective detection. This work presents an offline, a posteriori method for spectral leakage minimization in the frequency-domain analysis of ASSRs using coherent sampling criterion and interpolation in time. The technique was applied to the local field potentials of 10 Wistar rats and the results, together with those from simulated data, indicate that a leakage-free analysis of ASSRs is possible for any dataset if the methods showed in this paper were followed.

Dimensionality-varied deep convolutional neural network for spectral-spatial classification of hyperspectral data

NASA Astrophysics Data System (ADS)

Qu, Haicheng; Liang, Xuejian; Liang, Shichao; Liu, Wanjun

2018-01-01

Many methods of hyperspectral image classification have been proposed recently, and the convolutional neural network (CNN) achieves outstanding performance. However, spectral-spatial classification of CNN requires an excessively large model, tremendous computations, and complex network, and CNN is generally unable to use the noisy bands caused by water-vapor absorption. A dimensionality-varied CNN (DV-CNN) is proposed to address these issues. There are four stages in DV-CNN and the dimensionalities of spectral-spatial feature maps vary with the stages. DV-CNN can reduce the computation and simplify the structure of the network. All feature maps are processed by more kernels in higher stages to extract more precise features. DV-CNN also improves the classification accuracy and enhances the robustness to water-vapor absorption bands. The experiments are performed on data sets of Indian Pines and Pavia University scene. The classification performance of DV-CNN is compared with state-of-the-art methods, which contain the variations of CNN, traditional, and other deep learning methods. The experiment of performance analysis about DV-CNN itself is also carried out. The experimental results demonstrate that DV-CNN outperforms state-of-the-art methods for spectral-spatial classification and it is also robust to water-vapor absorption bands. Moreover, reasonable parameters selection is effective to improve classification accuracy.

Potential of far-ultraviolet absorption spectroscopy as a highly sensitive qualitative and quantitative analysis method for polymer films, part I: classification of commercial food wrap films.

PubMed

Sato, Harumi; Higashi, Noboru; Ikehata, Akifumi; Koide, Noriko; Ozaki, Yukihiro

2007-07-01

The aim of the present study is to propose a totally new technique for the utilization of far-ultraviolet (UV) spectroscopy in polymer thin film analysis. Far-UV spectra in the 120-300 nm region have been measured in situ for six kinds of commercial polymer wrap films by use of a novel type of far-UV spectrometer that does not need vacuum evaporation. These films can be straightforwardly classified into three groups, polyethylene (PE) films, polyvinyl chloride (PVC) films, and polyvinylidene chloride (PVDC) films, by using the raw spectra. The differences in the wavelength of the absorption band due to the sigma-sigma* transition of the C-C bond have been used for the classification of the six kinds of films. Using this method, it was easy to distinguish the three kinds of PE films and to separate the two kinds of PVDC films. Compared with other spectroscopic methods, the advantages of this technique include nondestructive analysis, easy spectral measurement, high sensitivity, and simple spectral analysis. The present study has demonstrated that far-UV spectroscopy is a very promising technique for polymer film analysis.

Effects of surface preparation on quality of aluminum alloy weldments

NASA Technical Reports Server (NTRS)

Kizer, D.; Saperstein, Z.

1968-01-01

Study of surface preparations and surface contamination effects on the welding of 2014 aluminum involves several methods of surface analysis to identify surface properties conducive to weld defects. These methods are radioactive evaporation, spectral reflectance mass spectroscopy, gas chromatography and spark emission spectroscopy.

Guided filter and principal component analysis hybrid method for hyperspectral pansharpening

NASA Astrophysics Data System (ADS)

Qu, Jiahui; Li, Yunsong; Dong, Wenqian

2018-01-01

Hyperspectral (HS) pansharpening aims to generate a fused HS image with high spectral and spatial resolution through integrating an HS image with a panchromatic (PAN) image. A guided filter (GF) and principal component analysis (PCA) hybrid HS pansharpening method is proposed. First, the HS image is interpolated and the PCA transformation is performed on the interpolated HS image. The first principal component (PC1) channel concentrates on the spatial information of the HS image. Different from the traditional PCA method, the proposed method sharpens the PAN image and utilizes the GF to obtain the spatial information difference between the HS image and the enhanced PAN image. Then, in order to reduce spectral and spatial distortion, an appropriate tradeoff parameter is defined and the spatial information difference is injected into the PC1 channel through multiplying by this tradeoff parameter. Once the new PC1 channel is obtained, the fused image is finally generated by the inverse PCA transformation. Experiments performed on both synthetic and real datasets show that the proposed method outperforms other several state-of-the-art HS pansharpening methods in both subjective and objective evaluations.

Estimating maximum instantaneous distortion from inlet total pressure rms and PSD measurements. [Root Mean Square and Power Spectral Density methods

NASA Technical Reports Server (NTRS)

Melick, H. C., Jr.; Ybarra, A. H.; Bencze, D. P.

1975-01-01

An inexpensive method is developed to determine the extreme values of instantaneous inlet distortion. This method also provides insight into the basic mechanics of unsteady inlet flow and the associated engine reaction. The analysis is based on fundamental fluid dynamics and statistical methods to provide an understanding of the turbulent inlet flow and quantitatively relate the rms level and power spectral density (PSD) function of the measured time variant total pressure fluctuations to the strength and size of the low pressure regions. The most probable extreme value of the instantaneous distortion is then synthesized from this information in conjunction with the steady state distortion. Results of the analysis show the extreme values to be dependent upon the steady state distortion, the measured turbulence rms level and PSD function, the time on point, and the engine response characteristics. Analytical projections of instantaneous distortion are presented and compared with data obtained by a conventional, highly time correlated, 40 probe instantaneous pressure measurement system.

Spectral Regression Discriminant Analysis for Hyperspectral Image Classification

NASA Astrophysics Data System (ADS)

Pan, Y.; Wu, J.; Huang, H.; Liu, J.

2012-08-01

Dimensionality reduction algorithms, which aim to select a small set of efficient and discriminant features, have attracted great attention for Hyperspectral Image Classification. The manifold learning methods are popular for dimensionality reduction, such as Locally Linear Embedding, Isomap, and Laplacian Eigenmap. However, a disadvantage of many manifold learning methods is that their computations usually involve eigen-decomposition of dense matrices which is expensive in both time and memory. In this paper, we introduce a new dimensionality reduction method, called Spectral Regression Discriminant Analysis (SRDA). SRDA casts the problem of learning an embedding function into a regression framework, which avoids eigen-decomposition of dense matrices. Also, with the regression based framework, different kinds of regularizes can be naturally incorporated into our algorithm which makes it more flexible. It can make efficient use of data points to discover the intrinsic discriminant structure in the data. Experimental results on Washington DC Mall and AVIRIS Indian Pines hyperspectral data sets demonstrate the effectiveness of the proposed method.

Automated processing for proton spectroscopic imaging using water reference deconvolution.

PubMed

Maudsley, A A; Wu, Z; Meyerhoff, D J; Weiner, M W

1994-06-01

Automated formation of MR spectroscopic images (MRSI) is necessary before routine application of these methods is possible for in vivo studies; however, this task is complicated by the presence of spatially dependent instrumental distortions and the complex nature of the MR spectrum. A data processing method is presented for completely automated formation of in vivo proton spectroscopic images, and applied for analysis of human brain metabolites. This procedure uses the water reference deconvolution method (G. A. Morris, J. Magn. Reson. 80, 547(1988)) to correct for line shape distortions caused by instrumental and sample characteristics, followed by parametric spectral analysis. Results for automated image formation were found to compare favorably with operator dependent spectral integration methods. While the water reference deconvolution processing was found to provide good correction of spatially dependent resonance frequency shifts, it was found to be susceptible to errors for correction of line shape distortions. These occur due to differences between the water reference and the metabolite distributions.

[Application of AOTF in spectral analysis. 3. Application of AOTF in atomic emission spectral analysis].

PubMed

Chen, Ze-yong; Peng, Rong-fei; Zhang, Zhan-xia

2002-06-01

An atomic emission spectrometer based on acousto-optic tunable filter (AOTF) was self-constructed and was used to evaluate its practical use in atomic emission analysis. The AOTF used was of model TEAF5-0.36-0.52-S (Brimrose, USA) and the frequency of the direct digital RF synthesizer ranges from 100 MHz to 200 MHz. ICP and PMT were used as light source and detector respectively. The software, written in Visual C++ and running on the Windows 98 platform, is of an utility program system having two data banks and multiwindows. The wavelength calibration was performed with 14 emission lines of Ca, Y, Li, Eu, Sr and Ba using a tenth-order polynomial for line fitting method. The absolute error of the peak position was less than 0.1 nm, and the peak deviation was only 0.04 nm as the PMT varied from 337.5 V to 412.5 V. The scanning emission spectra and the calibration curves of Ba, Y, Eu, Sc and Sr are presented. Their average correlation coefficient was 0.9991 and their detection limits were in the range of 0.051 to 0.97 micrograms.mL-1 respectively. The detection limit can be improved under optimized operating conditions. However, the spectral resolution is only 2.1 nm at the wavelength of 488 nm. Evidently, this poor spectral resolution would restrict the application of AOTF in atomic emission spectral analysis, unless an enhancing techniques is integrated in it.

The relation between periods’ identification and noises in hydrologic series data

NASA Astrophysics Data System (ADS)

Sang, Yan-Fang; Wang, Dong; Wu, Ji-Chun; Zhu, Qing-Ping; Wang, Ling

2009-04-01

SummaryIdentification of dominant periods is a typical and important issue in hydrologic series data analysis, since it is the basis of building effective stochastic models, understanding complex hydrologic processes, etc. However it is still a difficult task due to the influence of many interrelated factors, such as noises in hydrologic series data. In this paper, firstly the great influence of noises on periods' identification has been analyzed. Then, based on two conventional methods of hydrologic series analysis: wavelet analysis (WA) and maximum entropy spectral analysis (MESA), a new method of periods' identification of hydrologic series data, main series spectral analysis (MSSA), has been put forward, whose main idea is to identify periods of the main series on the basis of reducing hydrologic noises. Various methods (include fast Fourier transform (FFT), MESA and MSSA) have been applied to both synthetic series and observed hydrologic series. Results show that conventional methods (FFT and MESA) are not as good as expected due to the great influence of noises. However, this influence is not so strong while using the new method MSSA. In addition, by using the new de-noising method proposed in this paper, which is suitable for both normal noises and skew noises, the results are more reasonable, since noises separated from hydrologic series data generally follow skew probability distributions. In conclusion, based on comprehensive analyses, it can be stated that the proposed method MSSA could improve periods' identification by effectively reducing the influence of hydrologic noises.

Reduced quantum dynamics with arbitrary bath spectral densities: hierarchical equations of motion based on several different bath decomposition schemes.

PubMed

Liu, Hao; Zhu, Lili; Bai, Shuming; Shi, Qiang

2014-04-07

We investigated applications of the hierarchical equation of motion (HEOM) method to perform high order perturbation calculations of reduced quantum dynamics for a harmonic bath with arbitrary spectral densities. Three different schemes are used to decompose the bath spectral density into analytical forms that are suitable to the HEOM treatment: (1) The multiple Lorentzian mode model that can be obtained by numerically fitting the model spectral density. (2) The combined Debye and oscillatory Debye modes model that can be constructed by fitting the corresponding classical bath correlation function. (3) A new method that uses undamped harmonic oscillator modes explicitly in the HEOM formalism. Methods to extract system-bath correlations were investigated for the above bath decomposition schemes. We also show that HEOM in the undamped harmonic oscillator modes can give detailed information on the partial Wigner transform of the total density operator. Theoretical analysis and numerical simulations of the spin-Boson dynamics and the absorption line shape of molecular dimers show that the HEOM formalism for high order perturbations can serve as an important tool in studying the quantum dissipative dynamics in the intermediate coupling regime.

Reduced quantum dynamics with arbitrary bath spectral densities: Hierarchical equations of motion based on several different bath decomposition schemes

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

Liu, Hao; Zhu, Lili; Bai, Shuming

2014-04-07

We investigated applications of the hierarchical equation of motion (HEOM) method to perform high order perturbation calculations of reduced quantum dynamics for a harmonic bath with arbitrary spectral densities. Three different schemes are used to decompose the bath spectral density into analytical forms that are suitable to the HEOM treatment: (1) The multiple Lorentzian mode model that can be obtained by numerically fitting the model spectral density. (2) The combined Debye and oscillatory Debye modes model that can be constructed by fitting the corresponding classical bath correlation function. (3) A new method that uses undamped harmonic oscillator modes explicitly inmore » the HEOM formalism. Methods to extract system-bath correlations were investigated for the above bath decomposition schemes. We also show that HEOM in the undamped harmonic oscillator modes can give detailed information on the partial Wigner transform of the total density operator. Theoretical analysis and numerical simulations of the spin-Boson dynamics and the absorption line shape of molecular dimers show that the HEOM formalism for high order perturbations can serve as an important tool in studying the quantum dissipative dynamics in the intermediate coupling regime.« less

Power-law statistics of neurophysiological processes analyzed using short signals

NASA Astrophysics Data System (ADS)

Pavlova, Olga N.; Runnova, Anastasiya E.; Pavlov, Alexey N.

2018-04-01

We discuss the problem of quantifying power-law statistics of complex processes from short signals. Based on the analysis of electroencephalograms (EEG) we compare three interrelated approaches which enable characterization of the power spectral density (PSD) and show that an application of the detrended fluctuation analysis (DFA) or the wavelet-transform modulus maxima (WTMM) method represents a useful way of indirect characterization of the PSD features from short data sets. We conclude that despite DFA- and WTMM-based measures can be obtained from the estimated PSD, these tools outperform the standard spectral analysis when characterization of the analyzed regime should be provided based on a very limited amount of data.

Separation of β-amyloid binding and white matter uptake of 18F-flutemetamol using spectral analysis

PubMed Central

Heurling, Kerstin; Buckley, Christopher; Vandenberghe, Rik; Laere, Koen Van; Lubberink, Mark

2015-01-01

The kinetic components of the β-amyloid ligand 18F-flutemetamol binding in grey and white matter were investigated through spectral analysis, and a method developed for creation of parametric images separating grey and white matter uptake. Tracer uptake in grey and white matter and cerebellar cortex was analyzed through spectral analysis in six subjects, with (n=4) or without (n=2) apparent β-amyloid deposition, having undergone dynamic 18F-flutemetamol scanning with arterial blood sampling. The spectra were divided into three components: slow, intermediate and fast basis function rates. The contribution of each of the components to total volume of distribution (VT) was assessed for different tissue types. The slow component dominated in white matter (average 90%), had a higher contribution to grey matter VT in subjects with β-amyloid deposition (average 44%) than without (average 6%) and was absent in cerebellar cortex, attributing the slow component of 18F-flutemetamol uptake in grey matter to β-amyloid binding. Parametric images of voxel-based spectral analysis were created for VT, the slow component and images segmented based on the slow component contribution; confirming that grey matter and white matter uptake can be discriminated on voxel-level using a threshold for the contribution from the slow component to VT. PMID:26550542

Time-frequency analysis in optical coherence tomography for technical objects examination

NASA Astrophysics Data System (ADS)

StrÄ kowski, Marcin R.; Kraszewski, Maciej; Trojanowski, Michał; Pluciński, Jerzy

2014-05-01

Optical coherence tomography (OCT) is one of the most advanced optical measurement techniques for complex structure visualization. The advantages of OCT have been used for surface and subsurface defect detection in composite materials, polymers, ceramics, non-metallic protective coatings, and many more. Our research activity has been focused on timefrequency spectroscopic analysis in OCT. It is based on time resolved spectral analysis of the backscattered optical signal delivered by the OCT. The time-frequency method gives spectral characteristic of optical radiation backscattered or backreflected from the particular points inside the tested device. This provides more information about the sample, which are useful for further analysis. Nowadays, the applications of spectroscopic analysis for composite layers characterization or tissue recognition have been reported. During our studies we have found new applications of spectroscopic analysis. We have used this method for thickness estimation of thin films, which are under the resolution of OCT. Also, we have combined the spectroscopic analysis with polarization sensitive OCT (PS-OCT). This approach enables to obtain a multiorder retardation value directly and may become a breakthrough in PS-OCT measurements of highly birefringent media. In this work, we present the time-frequency spectroscopic algorithms and their applications for OCT. Also, the theoretical simulations and measurement validation of this method are shown.

Reconstruction of hyperspectral image using matting model for classification

NASA Astrophysics Data System (ADS)

Xie, Weiying; Li, Yunsong; Ge, Chiru

2016-05-01

Although hyperspectral images (HSIs) captured by satellites provide much information in spectral regions, some bands are redundant or have large amounts of noise, which are not suitable for image analysis. To address this problem, we introduce a method for reconstructing the HSI with noise reduction and contrast enhancement using a matting model for the first time. The matting model refers to each spectral band of an HSI that can be decomposed into three components, i.e., alpha channel, spectral foreground, and spectral background. First, one spectral band of an HSI with more refined information than most other bands is selected, and is referred to as an alpha channel of the HSI to estimate the hyperspectral foreground and hyperspectral background. Finally, a combination operation is applied to reconstruct the HSI. In addition, the support vector machine (SVM) classifier and three sparsity-based classifiers, i.e., orthogonal matching pursuit (OMP), simultaneous OMP, and OMP based on first-order neighborhood system weighted classifiers, are utilized on the reconstructed HSI and the original HSI to verify the effectiveness of the proposed method. Specifically, using the reconstructed HSI, the average accuracy of the SVM classifier can be improved by as much as 19%.

The MEM of spectral analysis applied to L.O.D.

NASA Astrophysics Data System (ADS)

Fernandez, L. I.; Arias, E. F.

The maximum entropy method (MEM) has been widely applied for polar motion studies taking advantage of its performance on the management of complex time series. The authors used the algorithm of the MEM to estimate Cross Spectral function in order to compare interannual Length-of-Day (LOD) time series with Southern Oscillation Index (SOI) and Sea Surface Temperature (SST) series, which are close related to El Niño-Southern Oscillation (ENSO) events.

Marker-Based Hierarchical Segmentation and Classification Approach for Hyperspectral Imagery

NASA Technical Reports Server (NTRS)

Tarabalka, Yuliya; Tilton, James C.; Benediktsson, Jon Atli; Chanussot, Jocelyn

2011-01-01

The Hierarchical SEGmentation (HSEG) algorithm, which is a combination of hierarchical step-wise optimization and spectral clustering, has given good performances for hyperspectral image analysis. This technique produces at its output a hierarchical set of image segmentations. The automated selection of a single segmentation level is often necessary. We propose and investigate the use of automatically selected markers for this purpose. In this paper, a novel Marker-based HSEG (M-HSEG) method for spectral-spatial classification of hyperspectral images is proposed. First, pixelwise classification is performed and the most reliably classified pixels are selected as markers, with the corresponding class labels. Then, a novel constrained marker-based HSEG algorithm is applied, resulting in a spectral-spatial classification map. The experimental results show that the proposed approach yields accurate segmentation and classification maps, and thus is attractive for hyperspectral image analysis.

A GIHS-based spectral preservation fusion method for remote sensing images using edge restored spectral modulation

NASA Astrophysics Data System (ADS)

Zhou, Xiran; Liu, Jun; Liu, Shuguang; Cao, Lei; Zhou, Qiming; Huang, Huawen

2014-02-01

High spatial resolution and spectral fidelity are basic standards for evaluating an image fusion algorithm. Numerous fusion methods for remote sensing images have been developed. Some of these methods are based on the intensity-hue-saturation (IHS) transform and the generalized IHS (GIHS), which may cause serious spectral distortion. Spectral distortion in the GIHS is proven to result from changes in saturation during fusion. Therefore, reducing such changes can achieve high spectral fidelity. A GIHS-based spectral preservation fusion method that can theoretically reduce spectral distortion is proposed in this study. The proposed algorithm consists of two steps. The first step is spectral modulation (SM), which uses the Gaussian function to extract spatial details and conduct SM of multispectral (MS) images. This method yields a desirable visual effect without requiring histogram matching between the panchromatic image and the intensity of the MS image. The second step uses the Gaussian convolution function to restore lost edge details during SM. The proposed method is proven effective and shown to provide better results compared with other GIHS-based methods.

Trade-off studies of a hyperspectral infrared sounder on a geostationary satellite.

PubMed

Wang, Fang; Li, Jun; Schmit, Timothy J; Ackerman, Steven A

2007-01-10

Trade-off studies on spectral coverage, signal-to-noise ratio (SNR), and spectral resolution for a hyperspectral infrared (IR) sounder on a geostationary satellite are summarized. The data density method is applied for the vertical resolution analysis, and the rms error between true and retrieved profiles is used to represent the retrieval accuracy. The effects of spectral coverage, SNR, and spectral resolution on vertical resolution and retrieval accuracy are investigated. The advantages of IR and microwave sounder synergy are also demonstrated. When focusing on instrument performance and data processing, the results from this study show that the preferred spectral coverage combines long-wave infrared (LWIR) with the shorter middle-wave IR (SMidW). Using the appropriate spectral coverage, a hyperspectral IR sounder with appropriate SNR can achieve the required science performance (1 km vertical resolution, 1 K temperature, and 10% relative humidity retrieval accuracy). The synergy of microwave and IR sounders can improve the vertical resolution and retrieval accuracy compared to either instrument alone.

The Impact of Quantitative Data Provided by a Multi-spectral Digital Skin Lesion Analysis Device on Dermatologists'Decisions to Biopsy Pigmented Lesions.

PubMed

Farberg, Aaron S; Winkelmann, Richard R; Tucker, Natalie; White, Richard; Rigel, Darrell S

2017-09-01

BACKGROUND: Early diagnosis of melanoma is critical to survival. New technologies, such as a multi-spectral digital skin lesion analysis (MSDSLA) device [MelaFind, STRATA Skin Sciences, Horsham, Pennsylvania] may be useful to enhance clinician evaluation of concerning pigmented skin lesions. Previous studies evaluated the effect of only the binary output. OBJECTIVE: The objective of this study was to determine how decisions dermatologists make regarding pigmented lesion biopsies are impacted by providing both the underlying classifier score (CS) and associated probability risk provided by multi-spectral digital skin lesion analysis. This outcome was also compared against the improvement reported with the provision of only the binary output. METHODS: Dermatologists attending an educational conference evaluated 50 pigmented lesions (25 melanomas and 25 benign lesions). Participants were asked if they would biopsy the lesion based on clinical images, and were asked this question again after being shown multi-spectral digital skin lesion analysis data that included the probability graphs and classifier score. RESULTS: Data were analyzed from a total of 160 United States board-certified dermatologists. Biopsy sensitivity for melanoma improved from 76 percent following clinical evaluation to 92 percent after quantitative multi-spectral digital skin lesion analysis information was provided ( p <0.0001). Specificity improved from 52 percent to 79 percent ( p <0.0001). The positive predictive value increased from 61 percent to 81 percent ( p <0.01) when the quantitative data were provided. Negative predictive value also increased (68% vs. 91%, p<0.01), and overall biopsy accuracy was greater with multi-spectral digital skin lesion analysis (64% vs. 86%, p <0.001). Interrater reliability improved (intraclass correlation 0.466 before, 0.559 after). CONCLUSION: Incorporating the classifier score and probability data into physician evaluation of pigmented lesions led to both increased sensitivity and specificity, thereby resulting in more accurate biopsy decisions.

Determination of design and operation parameters for upper atmospheric research instrumentation to yield optimum resolution with deconvolution, appendix 2

NASA Technical Reports Server (NTRS)

Ioup, George E.; Ioup, Juliette W.

1988-01-01

This thesis reviews the technique established to clear channels in the Power Spectral Estimate by applying linear combinations of well known window functions to the autocorrelation function. The need for windowing the auto correlation function is due to the fact that the true auto correlation is not generally used to obtain the Power Spectral Estimate. When applied, the windows serve to reduce the effect that modifies the auto correlation by truncating the data and possibly the autocorrelation has on the Power Spectral Estimate. It has been shown in previous work that a single channel has been cleared, allowing for the detection of a small peak in the presence of a large peak in the Power Spectral Estimate. The utility of this method is dependent on the robustness of it on different input situations. We extend the analysis in this paper, to include clearing up to three channels. We examine the relative positions of the spikes to each other and also the effect of taking different percentages of lags of the auto correlation in the Power Spectral Estimate. This method could have application wherever the Power Spectrum is used. An example of this is beam forming for source location, where a small target can be located next to a large target. Other possibilities extend into seismic data processing. As the method becomes more automated other applications may present themselves.

Methodology for processing pressure traces used as inputs for combustion analyses in diesel engines

NASA Astrophysics Data System (ADS)

Rašić, Davor; Vihar, Rok; Žvar Baškovič, Urban; Katrašnik, Tomaž

2017-05-01

This study proposes a novel methodology for designing an optimum equiripple finite impulse response (FIR) filter for processing in-cylinder pressure traces of a diesel internal combustion engine, which serve as inputs for high-precision combustion analyses. The proposed automated workflow is based on an innovative approach of determining the transition band frequencies and optimum filter order. The methodology is based on discrete Fourier transform analysis, which is the first step to estimate the location of the pass-band and stop-band frequencies. The second step uses short-time Fourier transform analysis to refine the estimated aforementioned frequencies. These pass-band and stop-band frequencies are further used to determine the most appropriate FIR filter order. The most widely used existing methods for estimating the FIR filter order are not effective in suppressing the oscillations in the rate- of-heat-release (ROHR) trace, thus hindering the accuracy of combustion analyses. To address this problem, an innovative method for determining the order of an FIR filter is proposed in this study. This method is based on the minimization of the integral of normalized signal-to-noise differences between the stop-band frequency and the Nyquist frequency. Developed filters were validated using spectral analysis and calculation of the ROHR. The validation results showed that the filters designed using the proposed innovative method were superior compared with those using the existing methods for all analyzed cases. Highlights • Pressure traces of a diesel engine were processed by finite impulse response (FIR) filters with different orders • Transition band frequencies were determined with an innovative method based on discrete Fourier transform and short-time Fourier transform • Spectral analyses showed deficiencies of existing methods in determining the FIR filter order • A new method of determining the FIR filter order for processing pressure traces was proposed • The efficiency of the new method was demonstrated by spectral analyses and calculations of rate-of-heat-release traces

A comparative analysis of spectral exponent estimation techniques for 1/f(β) processes with applications to the analysis of stride interval time series.

PubMed

Schaefer, Alexander; Brach, Jennifer S; Perera, Subashan; Sejdić, Ervin

2014-01-30

The time evolution and complex interactions of many nonlinear systems, such as in the human body, result in fractal types of parameter outcomes that exhibit self similarity over long time scales by a power law in the frequency spectrum S(f)=1/f(β). The scaling exponent β is thus often interpreted as a "biomarker" of relative health and decline. This paper presents a thorough comparative numerical analysis of fractal characterization techniques with specific consideration given to experimentally measured gait stride interval time series. The ideal fractal signals generated in the numerical analysis are constrained under varying lengths and biases indicative of a range of physiologically conceivable fractal signals. This analysis is to complement previous investigations of fractal characteristics in healthy and pathological gait stride interval time series, with which this study is compared. The results of our analysis showed that the averaged wavelet coefficient method consistently yielded the most accurate results. Class dependent methods proved to be unsuitable for physiological time series. Detrended fluctuation analysis as most prevailing method in the literature exhibited large estimation variances. The comparative numerical analysis and experimental applications provide a thorough basis for determining an appropriate and robust method for measuring and comparing a physiologically meaningful biomarker, the spectral index β. In consideration of the constraints of application, we note the significant drawbacks of detrended fluctuation analysis and conclude that the averaged wavelet coefficient method can provide reasonable consistency and accuracy for characterizing these fractal time series. Copyright © 2013 Elsevier B.V. All rights reserved.

Temporal scaling of groundwater level fluctuations near a stream

USGS Publications Warehouse

Schilling, K.E.; Zhang, Y.-K.

2012-01-01

Temporal scaling in stream discharge and hydraulic heads in riparian wells was evaluated to determine the feasibility of using spectral analysis to identify potential surface and groundwater interaction. In floodplains where groundwater levels respond rapidly to precipitation recharge, potential interaction is established if the hydraulic head (h) spectrum of riparian groundwater has a power spectral density similar to stream discharge (Q), exhibiting a characteristic breakpoint between high and low frequencies. At a field site in Walnut Creek watershed in central Iowa, spectral analysis of h in wells located 1 m from the channel edge showed a breakpoint in scaling very similar to the spectrum of Q (~20 h), whereas h in wells located 20 and 40 m from the channel showed temporal scaling from 1 to 10,000 h without a well-defined breakpoint. The spectral exponent (??) in the riparian zone decreased systematically from the channel into the floodplain as groundwater levels were increasingly dominated by white noise groundwater recharge. The scaling pattern of hydraulic head was not affected by land cover type, although the number of analyses was limited and site conditions were variable among sites. Spectral analysis would not replace quantitative tracer or modeling studies, but the method may provide a simple means of confirming potential interaction at some sites. ?? 2011, The Author(s). Ground Water ?? 2011, National Ground Water Association.

Temporal scaling of groundwater level fluctuations near a stream.

PubMed

Schilling, Keith E; Zhang, You-Kuan

2012-01-01

Temporal scaling in stream discharge and hydraulic heads in riparian wells was evaluated to determine the feasibility of using spectral analysis to identify potential surface and groundwater interaction. In floodplains where groundwater levels respond rapidly to precipitation recharge, potential interaction is established if the hydraulic head (h) spectrum of riparian groundwater has a power spectral density similar to stream discharge (Q), exhibiting a characteristic breakpoint between high and low frequencies. At a field site in Walnut Creek watershed in central Iowa, spectral analysis of h in wells located 1 m from the channel edge showed a breakpoint in scaling very similar to the spectrum of Q (∼20 h), whereas h in wells located 20 and 40 m from the channel showed temporal scaling from 1 to 10,000 h without a well-defined breakpoint. The spectral exponent (β) in the riparian zone decreased systematically from the channel into the floodplain as groundwater levels were increasingly dominated by white noise groundwater recharge. The scaling pattern of hydraulic head was not affected by land cover type, although the number of analyses was limited and site conditions were variable among sites. Spectral analysis would not replace quantitative tracer or modeling studies, but the method may provide a simple means of confirming potential interaction at some sites. © 2011, The Author(s). Ground Water © 2011, National Ground Water Association.

Spectral methods and their implementation to solution of aerodynamic and fluid mechanic problems

NASA Technical Reports Server (NTRS)

Streett, C. L.

1987-01-01

Fundamental concepts underlying spectral collocation methods, especially pertaining to their use in the solution of partial differential equations, are outlined. Theoretical accuracy results are reviewed and compared with results from test problems. A number of practical aspects of the construction and use of spectral methods are detailed, along with several solution schemes which have found utility in applications of spectral methods to practical problems. Results from a few of the successful applications of spectral methods to problems of aerodynamic and fluid mechanic interest are then outlined, followed by a discussion of the problem areas in spectral methods and the current research under way to overcome these difficulties.

Spectral method for the correction of the Cerenkov light effect in plastic scintillation detectors: A comparison study of calibration procedures and validation in Cerenkov light-dominated situations

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

Guillot, Mathieu; Gingras, Luc; Archambault, Louis

2011-04-15

Purpose: The purposes of this work were: (1) To determine if a spectral method can accurately correct the Cerenkov light effect in plastic scintillation detectors (PSDs) for situations where the Cerenkov light is dominant over the scintillation light and (2) to develop a procedural guideline for accurately determining the calibration factors of PSDs. Methods: The authors demonstrate, by using the equations of the spectral method, that the condition for accurately correcting the effect of Cerenkov light is that the ratio of the two calibration factors must be equal to the ratio of the Cerenkov light measured within the two differentmore » spectral regions used for analysis. Based on this proof, the authors propose two new procedures to determine the calibration factors of PSDs, which were designed to respect this condition. A PSD that consists of a cylindrical polystyrene scintillating fiber (1.6 mm{sup 3}) coupled to a plastic optical fiber was calibrated by using these new procedures and the two reference procedures described in the literature. To validate the extracted calibration factors, relative dose profiles and output factors for a 6 MV photon beam from a medical linac were measured with the PSD and an ionization chamber. Emphasis was placed on situations where the Cerenkov light is dominant over the scintillation light and on situations dissimilar to the calibration conditions. Results: The authors found that the accuracy of the spectral method depends on the procedure used to determine the calibration factors of the PSD and on the attenuation properties of the optical fiber used. The results from the relative dose profile measurements showed that the spectral method can correct the Cerenkov light effect with an accuracy level of 1%. The results obtained also indicate that PSDs measure output factors that are lower than those measured with ionization chambers for square field sizes larger than 25x25 cm{sup 2}, in general agreement with previously published Monte Carlo results. Conclusions: The authors conclude that the spectral method can be used to accurately correct the Cerenkov light effect in PSDs. The authors confirmed the importance of maximizing the difference of Cerenkov light production between calibration measurements. The authors also found that the attenuation of the optical fiber, which is assumed to be constant in the original formulation of the spectral method, may cause a variation of the calibration factors in some experimental setups.« less

Spectral contents readout of birefringent sensor

NASA Technical Reports Server (NTRS)

Redner, Alex S.

1989-01-01

The technical objective of this research program was to develop a birefringent sensor, capable of measuring strain/stress up to 2000 F and a readout system based on Spectral Contents analysis. As a result of the research work, a data acquisition system was developed, capable of measuring strain birefringence in a sensor at 2000 F, with multi-point static and dynamic capabilities. The system uses a dedicated spectral analyzer for evaluation of stress-birefringence and a PC-based readout. Several sensor methods were evaluated. Fused silica was found most satisfactory. In the final evaluation, measurements were performed up to 2000 F and the system performance exceeded expectations.

Spectral analysis techniques for characterizing cadmium zinc telluride polarization modulators

NASA Astrophysics Data System (ADS)

FitzGerald, William R.; Taherion, Saeid; Kumar, F. Joseph; Giles, David; Hore, Dennis K.

2018-04-01

The low frequency electro-optic characteristics of cadmium zinc telluride are demonstrated in the mid-infrared, in the spectral range 2.5-11 μm. Conventional methods for characterizing the dynamic response by monitoring the amplitude of the time-varying light intensity do not account for spatial variation in material properties. In such cases, a more revealing method involves monitoring two distinct frequency components in order to characterize the dynamic and static contributions to the optical retardation. We demonstrate that, while this method works well for a ZnSe photo-elastic modulator, it does not fully capture the response of a cadmium zinc telluride electro-optic modulator. Ultimately, we show that acquiring the full waveform of the optical response enables a model to be created that accounts for inhomogeneity in the material that results in an asymmetric response with respect to the polarity of the driving voltage. This technique is applicable to broadband and fixed-wavelength applications in a variety of spectral ranges.

Spectral reflectance and emissivity features of broad leaf plants: Prospects for remote sensing in the thermal infrared (8.0-14.0 μm)

USGS Publications Warehouse

Ribeiro da Luz, Beatriz; Crowley, James K.

2007-01-01

In contrast to visible and short-wave infrared data, thermal infrared spectra of broad leaf plants show considerable spectral diversity, suggesting that such data eventually could be utilized to map vegetation composition. However, remotely measuring the subtle emissivity features of leaves still presents major challenges. To be successful, sensors operating in the 8–14 μm atmospheric window must have high signal-to-noise and a small enough instantaneous field of view to allow measurements of only a few leaf surfaces. Methods for atmospheric compensation, temperature–emissivity separation, and spectral feature analysis also will need to be refined to allow the recognition, and perhaps, exploitation of leaf thermal infrared spectral properties.

Leaf flavonoids of Albizia lebbeck.

PubMed

el-Mousallamy, A M

1998-06-01

Two new tri-O-glycoside flavonols: kaempferol and quercetin 3-O-alpha-rhamnopyranosyl(1-->6)-beta-glucopyranosyl(1-->6)-beta- galactopyranosides, were identified from the leaves of Albizia lebbeck. Structures were established by conventional methods of analysis and confirmed by ESI-MS, 1H and 13C-NMR spectral analysis.

An explorative chemometric approach applied to hyperspectral images for the study of illuminated manuscripts

NASA Astrophysics Data System (ADS)

Catelli, Emilio; Randeberg, Lise Lyngsnes; Alsberg, Bjørn Kåre; Gebremariam, Kidane Fanta; Bracci, Silvano

2017-04-01

Hyperspectral imaging (HSI) is a fast non-invasive imaging technology recently applied in the field of art conservation. With the help of chemometrics, important information about the spectral properties and spatial distribution of pigments can be extracted from HSI data. With the intent of expanding the applications of chemometrics to the interpretation of hyperspectral images of historical documents, and, at the same time, to study the colorants and their spatial distribution on ancient illuminated manuscripts, an explorative chemometric approach is here presented. The method makes use of chemometric tools for spectral de-noising (minimum noise fraction (MNF)) and image analysis (multivariate image analysis (MIA) and iterative key set factor analysis (IKSFA)/spectral angle mapper (SAM)) which have given an efficient separation, classification and mapping of colorants from visible-near-infrared (VNIR) hyperspectral images of an ancient illuminated fragment. The identification of colorants was achieved by extracting and interpreting the VNIR spectra as well as by using a portable X-ray fluorescence (XRF) spectrometer.

Determination of lateral size distribution of type-II ZnTe/ZnSe stacked submonolayer quantum dots via spectral analysis of optical signature of the Aharanov-Bohm excitons

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

Ji, Haojie; Dhomkar, Siddharth; Roy, Bidisha

2014-10-28

For submonolayer quantum dot (QD) based photonic devices, size and density of QDs are critical parameters, the probing of which requires indirect methods. We report the determination of lateral size distribution of type-II ZnTe/ZnSe stacked submonolayer QDs, based on spectral analysis of the optical signature of Aharanov-Bohm (AB) excitons, complemented by photoluminescence studies, secondary-ion mass spectroscopy, and numerical calculations. Numerical calculations are employed to determine the AB transition magnetic field as a function of the type-II QD radius. The study of four samples grown with different tellurium fluxes shows that the lateral size of QDs increases by just 50%, evenmore » though tellurium concentration increases 25-fold. Detailed spectral analysis of the emission of the AB exciton shows that the QD radii take on only certain values due to vertical correlation and the stacked nature of the QDs.« less

Comparison of spectral estimators for characterizing fractionated atrial electrograms

PubMed Central

2013-01-01

Background Complex fractionated atrial electrograms (CFAE) acquired during atrial fibrillation (AF) are commonly assessed using the discrete Fourier transform (DFT), but this can lead to inaccuracy. In this study, spectral estimators derived by averaging the autocorrelation function at lags were compared to the DFT. Method Bipolar CFAE of at least 16 s duration were obtained from pulmonary vein ostia and left atrial free wall sites (9 paroxysmal and 10 persistent AF patients). Power spectra were computed using the DFT and three other methods: 1. a novel spectral estimator based on signal averaging (NSE), 2. the NSE with harmonic removal (NSH), and 3. the autocorrelation function average at lags (AFA). Three spectral parameters were calculated: 1. the largest fundamental spectral peak, known as the dominant frequency (DF), 2. the DF amplitude (DA), and 3. the mean spectral profile (MP), which quantifies noise floor level. For each spectral estimator and parameter, the significance of the difference between paroxysmal and persistent AF was determined. Results For all estimators, mean DA and mean DF values were higher in persistent AF, while the mean MP value was higher in paroxysmal AF. The differences in means between paroxysmals and persistents were highly significant for 3/3 NSE and NSH measurements and for 2/3 DFT and AFA measurements (p<0.001). For all estimators, the standard deviation in DA and MP values were higher in persistent AF, while the standard deviation in DF value was higher in paroxysmal AF. Differences in standard deviations between paroxysmals and persistents were highly significant in 2/3 NSE and NSH measurements, in 1/3 AFA measurements, and in 0/3 DFT measurements. Conclusions Measurements made from all four spectral estimators were in agreement as to whether the means and standard deviations in three spectral parameters were greater in CFAEs acquired from paroxysmal or in persistent AF patients. Since the measurements were consistent, use of two or more of these estimators for power spectral analysis can be assistive to evaluate CFAE more objectively and accurately, which may lead to improved clinical outcome. Since the most significant differences overall were achieved using the NSE and NSH estimators, parameters measured from their spectra will likely be the most useful for detecting and discerning electrophysiologic differences in the AF substrate based upon frequency analysis of CFAE. PMID:23855345

Improvements in Virtual Sensors: Using Spatial Information to Estimate Remote Sensing Spectra

NASA Technical Reports Server (NTRS)

Oza, Nikunj C.; Srivastava, Ashok N.; Stroeve, Julienne

2005-01-01

Various instruments are used to create images of the Earth and other objects in the universe in a diverse set of wavelength bands with the aim of understanding natural phenomena. Sometimes these instruments are built in a phased approach, with additional measurement capabilities added in later phases. In other cases, technology may mature to the point that the instrument offers new measurement capabilities that were not planned in the original design of the instrument. In still other cases, high resolution spectral measurements may be too costly to perform on a large sample and therefore lower resolution spectral instruments are used to take the majority of measurements. Many applied science questions that are relevant to the earth science remote sensing community require analysis of enormous amounts of data that were generated by instruments with disparate measurement capabilities. In past work [1], we addressed this problem using Virtual Sensors: a method that uses models trained on spectrally rich (high spectral resolution) data to "fill in" unmeasured spectral channels in spectrally poor (low spectral resolution) data. We demonstrated this method by using models trained on the high spectral resolution Terra MODIS instrument to estimate what the equivalent of the MODIS 1.6 micron channel would be for the NOAA AVHRR2 instrument. The scientific motivation for the simulation of the 1.6 micron channel is to improve the ability of the AVHRR2 sensor to detect clouds over snow and ice. This work contains preliminary experiments demonstrating that the use of spatial information can improve our ability to estimate these spectra.

Using spectral methods to obtain particle size information from optical data: applications to measurements from CARES 2010

NASA Astrophysics Data System (ADS)

Atkinson, Dean B.; Pekour, Mikhail; Chand, Duli; Radney, James G.; Kolesar, Katheryn R.; Zhang, Qi; Setyan, Ari; O'Neill, Norman T.; Cappa, Christopher D.

2018-04-01

Multi-wavelength in situ aerosol extinction, absorption and scattering measurements made at two ground sites during the 2010 Carbonaceous Aerosols and Radiative Effects Study (CARES) are analyzed using a spectral deconvolution method that allows extraction of particle-size-related information, including the fraction of extinction produced by the fine-mode particles and the effective radius of the fine mode. The spectral deconvolution method is typically applied to analysis of remote sensing measurements. Here, its application to in situ measurements allows for comparison with more direct measurement methods and validation of the retrieval approach. Overall, the retrieved fine-mode fraction and effective radius compare well with other in situ measurements, including size distribution measurements and scattering and absorption measurements made separately for PM1 and PM10, although there were some periods during which the different methods yielded different results. One key contributor to differences between the results obtained is the alternative, spectrally based definitions of fine and coarse modes from the optical methods, relative to instruments that use a physically defined cut point. These results indicate that for campaigns where size, composition and multi-wavelength optical property measurements are made, comparison of the results can result in closure or can identify unusual circumstances. The comparison here also demonstrates that in situ multi-wavelength optical property measurements can be used to determine information about particle size distributions in situations where direct size distribution measurements are not available.

Using spectral methods to obtain particle size information from optical data: applications to measurements from CARES 2010

DOE PAGES

Atkinson, Dean B.; Pekour, Mikhail; Chand, Duli; ...

2018-04-23

Here, multi-wavelength in situ aerosol extinction, absorption and scattering measurements made at two ground sites during the 2010 Carbonaceous Aerosols and Radiative Effects Study (CARES) are analyzed using a spectral deconvolution method that allows extraction of particle-size-related information, including the fraction of extinction produced by the fine-mode particles and the effective radius of the fine mode. The spectral deconvolution method is typically applied to analysis of remote sensing measurements. Here, its application to in situ measurements allows for comparison with more direct measurement methods and validation of the retrieval approach. Overall, the retrieved fine-mode fraction and effective radius compare wellmore » with other in situ measurements, including size distribution measurements and scattering and absorption measurements made separately for PM 1 and PM 10, although there were some periods during which the different methods yielded different results. One key contributor to differences between the results obtained is the alternative, spectrally based definitions of fine and coarse modes from the optical methods, relative to instruments that use a physically defined cut point. These results indicate that for campaigns where size, composition and multi-wavelength optical property measurements are made, comparison of the results can result in closure or can identify unusual circumstances. The comparison here also demonstrates that in situ multi-wavelength optical property measurements can be used to determine information about particle size distributions in situations where direct size distribution measurements are not available.« less

Using spectral methods to obtain particle size information from optical data: applications to measurements from CARES 2010

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

Atkinson, Dean B.; Pekour, Mikhail; Chand, Duli

Here, multi-wavelength in situ aerosol extinction, absorption and scattering measurements made at two ground sites during the 2010 Carbonaceous Aerosols and Radiative Effects Study (CARES) are analyzed using a spectral deconvolution method that allows extraction of particle-size-related information, including the fraction of extinction produced by the fine-mode particles and the effective radius of the fine mode. The spectral deconvolution method is typically applied to analysis of remote sensing measurements. Here, its application to in situ measurements allows for comparison with more direct measurement methods and validation of the retrieval approach. Overall, the retrieved fine-mode fraction and effective radius compare wellmore » with other in situ measurements, including size distribution measurements and scattering and absorption measurements made separately for PM 1 and PM 10, although there were some periods during which the different methods yielded different results. One key contributor to differences between the results obtained is the alternative, spectrally based definitions of fine and coarse modes from the optical methods, relative to instruments that use a physically defined cut point. These results indicate that for campaigns where size, composition and multi-wavelength optical property measurements are made, comparison of the results can result in closure or can identify unusual circumstances. The comparison here also demonstrates that in situ multi-wavelength optical property measurements can be used to determine information about particle size distributions in situations where direct size distribution measurements are not available.« less

Using spectral methods to obtain particle size information from optical data: applications to measurements from CARES 2010

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

Atkinson, Dean B.; Pekour, Mikhail; Chand, Duli

Multi-wavelength in situ aerosol extinction, absorption and scattering measurements made at two ground sites during the 2010 Carbonaceous Aerosols and Radiative Effects Study (CARES) are analyzed using a spectral deconvolution method that allows extraction of particle-size-related information, including the fraction of extinction produced by the fine-mode particles and the effective radius of the fine mode. The spectral deconvolution method is typically applied to analysis of remote sensing measurements. Here, its application to in situ measurements allows for comparison with more direct measurement methods and validation of the retrieval approach. Overall, the retrieved fine-mode fraction and effective radius compare well withmore » other in situ measurements, including size distribution measurements and scattering and absorption measurements made separately for PM 1 and PM 10, although there were some periods during which the different methods yielded different results. One key contributor to differences between the results obtained is the alternative, spectrally based definitions of fine and coarse modes from the optical methods, relative to instruments that use a physically defined cut point. These results indicate that for campaigns where size, composition and multi-wavelength optical property measurements are made, comparison of the results can result in closure or can identify unusual circumstances. The comparison here also demonstrates that in situ multi-wavelength optical property measurements can be used to determine information about particle size distributions in situations where direct size distribution measurements are not available.« less

Modal analysis of 2-D sedimentary basin from frequency domain decomposition of ambient vibration array recordings

NASA Astrophysics Data System (ADS)

Poggi, Valerio; Ermert, Laura; Burjanek, Jan; Michel, Clotaire; Fäh, Donat

2015-01-01

Frequency domain decomposition (FDD) is a well-established spectral technique used in civil engineering to analyse and monitor the modal response of buildings and structures. The method is based on singular value decomposition of the cross-power spectral density matrix from simultaneous array recordings of ambient vibrations. This method is advantageous to retrieve not only the resonance frequencies of the investigated structure, but also the corresponding modal shapes without the need for an absolute reference. This is an important piece of information, which can be used to validate the consistency of numerical models and analytical solutions. We apply this approach using advanced signal processing to evaluate the resonance characteristics of 2-D Alpine sedimentary valleys. In this study, we present the results obtained at Martigny, in the Rhône valley (Switzerland). For the analysis, we use 2 hr of ambient vibration recordings from a linear seismic array deployed perpendicularly to the valley axis. Only the horizontal-axial direction (SH) of the ground motion is considered. Using the FDD method, six separate resonant frequencies are retrieved together with their corresponding modal shapes. We compare the mode shapes with results from classical standard spectral ratios and numerical simulations of ambient vibration recordings.

Analysis of doxorubicin distribution in MCF-7 cells treated with drug-loaded nanoparticles by combination of two fluorescence-based techniques, confocal spectral imaging and capillary electrophoresis.

PubMed

Gautier, Juliette; Munnier, Emilie; Soucé, Martin; Chourpa, Igor; Douziech Eyrolles, Laurence

2015-05-01

The intracellular distribution of the antiancer drug doxorubicin (DOX) was followed qualitatively by fluorescence confocal spectral imaging (FCSI) and quantitatively by capillary electrophoresis (CE). FCSI permits the localization of the major fluorescent species in cell compartments, with spectral shifts indicating the polarity of the respective environment. However, distinction between drug and metabolites by FCSI is difficult due to their similar fluorochromes, and direct quantification of their fluorescence is complicated by quantum yield variation between different subcellular environments. On the other hand, capillary electrophoresis with fluorescence detection (CE-LIF) is a quantitative method capable of separating doxorubicin and its metabolites. In this paper, we propose a method for determining drug and metabolite concentration in enriched nuclear and cytosolic fractions of cancer cells by CE-LIF, and we compare these data with those of FCSI. Significant differences in the subcellular distribution of DOX are observed between the drug administered as a molecular solution or as a suspension of drug-loaded iron oxide nanoparticles coated with polyethylene glycol. Comparative analysis of the CE-LIF vs FCSI data may lead to a tentative calibration of this latter method in terms of DOX fluorescence quantum yields in the nucleus and more or less polar regions of the cytosol.

Spectral studies of cosmic X-ray sources

NASA Astrophysics Data System (ADS)

Blissett, R. J.

1980-01-01

The conventional "indirect" method of reduction and data analysis of spectral data from non-dispersive X-ray detectors, by the fitting of assumed spectral models, is examined. The limitations of this procedure are presented, and alternative schemes are considered in which the derived spectra are not biased to an astrophysical source model. A new method is developed in detail to directly restore incident photon spectra from the detected count histograms. This Spectral Restoration Technique allows an increase in resolution, to a degree dependent on the statistical precision of the data. This is illustrated by numerical simulations. Proportional counter data from Ariel 5 are analysed using this technique. The results obtained for the sources Cas A and the Crab Nebula are consistent with previous analyses and show that increases in resolution of up to a factor three are possible in practice. The source Cyg X-3 is closely examined. Complex spectral variability is found, with the continuum and iron-line emission modulated with the 4.8 hour period of the source. The data suggest multi-component emission in the source. Comparing separate Ariel 5 observations and published data from other experiments, a correlation between the spectral shape and source intensity is evident. The source behaviour is discussed with reference to proposed source models. Data acquired by the low-energy detectors on-board HEAO-1 are analysed using the Spectral Restoration Technique. This treatment explicitly demonstrates the existence of oxygen K-absorption edges in the soft X-ray spectra of the Crab Nebula and Sco X-1. These results are considered with reference to current theories of the interstellar medium. The thesis commences with a review of cosmic X-ray sources and the mechanisms responsible for their spectral signatures, and continues with a discussion of the instruments appropriate for spectral studies in X-ray astronomy.

Effect of gold photocathode contamination on a flat spectral response X-ray diode

NASA Astrophysics Data System (ADS)

Wang, Kun-lun; Zhang, Si-qun; Zhou, Shao-tong; Huang, Xian-bin; Ren, Xiao-dong; Dan, Jia-kun; Xu, Qiang

2018-03-01

A detector with an approximately flat spectral response is important for diagnosing intense thermal X-ray flux. A flat-spectral-response X-ray diode (FSR-XRD) utilizes a gold photocathode X-ray diode and a specially configured gold filter to give rise to a nearly flat spectral response in the photon energy range of 100-4000 eV. It has been observed that the spectral responses of several FSR-XRDs changed after a few shots of z-pinch experiments on the Primary Test Stand facility. This paper presents an analysis of the changes by fitting the spectral responses of the gold photocathodes using a model with a free parameter which characterizes the thickness of the contamination. The spectral responses of FSR-XRDs were calibrated with synchrotron radiation, and several cleaning methods were tested with the calibration. Considering the results of model and cleaning, it may be anticipated that contamination was the major reason of the response changing. Contamination worsened the flatness of the spectral response of the FSR-XRD and decreased the averaged response, hence it is important to avoid contamination. Current results indicate a requirement of further study of the contamination.

Characterization of Interfacial Chemistry of Adhesive/Dentin Bond Using FTIR Chemical Imaging With Univariate and Multivariate Data Processing

PubMed Central

Wang, Yong; Yao, Xiaomei; Parthasarathy, Ranganathan

2008-01-01

Fourier transform infrared (FTIR) chemical imaging can be used to investigate molecular chemical features of the adhesive/dentin interfaces. However, the information is not straightforward, and is not easily extracted. The objective of this study was to use multivariate analysis methods, principal component analysis and fuzzy c-means clustering, to analyze spectral data in comparison with univariate analysis. The spectral imaging data collected from both the adhesive/healthy dentin and adhesive/caries-affected dentin specimens were used and compared. The univariate statistical methods such as mapping of intensities of specific functional group do not always accurately identify functional group locations and concentrations due to more or less band overlapping in adhesive and dentin. Apart from the ease with which information can be extracted, multivariate methods highlight subtle and often important changes in the spectra that are difficult to observe using univariate methods. The results showed that the multivariate methods gave more satisfactory, interpretable results than univariate methods and were conclusive in showing that they can discriminate and classify differences between healthy dentin and caries-affected dentin within the interfacial regions. It is demonstrated that the multivariate FTIR imaging approaches can be used in the rapid characterization of heterogeneous, complex structure. PMID:18980198

A new multiscale noise tuning stochastic resonance for enhanced fault diagnosis in wind turbine drivetrains

NASA Astrophysics Data System (ADS)

Hu, Bingbing; Li, Bing

2016-02-01

It is very difficult to detect weak fault signatures due to the large amount of noise in a wind turbine system. Multiscale noise tuning stochastic resonance (MSTSR) has proved to be an effective way to extract weak signals buried in strong noise. However, the MSTSR method originally based on discrete wavelet transform (DWT) has disadvantages such as shift variance and the aliasing effects in engineering application. In this paper, the dual-tree complex wavelet transform (DTCWT) is introduced into the MSTSR method, which makes it possible to further improve the system output signal-to-noise ratio and the accuracy of fault diagnosis by the merits of DTCWT (nearly shift invariant and reduced aliasing effects). Moreover, this method utilizes the relationship between the two dual-tree wavelet basis functions, instead of matching the single wavelet basis function to the signal being analyzed, which may speed up the signal processing and be employed in on-line engineering monitoring. The proposed method is applied to the analysis of bearing outer ring and shaft coupling vibration signals carrying fault information. The results confirm that the method performs better in extracting the fault features than the original DWT-based MSTSR, the wavelet transform with post spectral analysis, and EMD-based spectral analysis methods.

Morphological, spectral and chromatography analysis and forensic comparison of PET fibers.

PubMed

Farah, Shady; Tsach, Tsadok; Bentolila, Alfonso; Domb, Abraham J

2014-06-01

Poly(ethylene terephthalate) (PET) fiber analysis and comparison by spectral and polymer molecular weight determination was investigated. Plain fibers of PET, a common textile fiber and plastic material was chosen for this study. The fibers were analyzed for morphological (SEM and AFM), spectral (IR and NMR), thermal (DSC) and molecular weight (MS and GPC) differences. Molecular analysis of PET fibers by Gel Permeation Chromatography (GPC) allowed the comparison of fibers that could not be otherwise distinguished with high confidence. Plain PET fibers were dissolved in hexafluoroisopropanol (HFIP) and analyzed by GPC using hexafluoroisopropanol:chloroform 2:98 v/v as eluent. 14 PET fiber samples, collected from various commercial producers, were analyzed for polymer molecular weight by GPC. Distinct differences in the molecular weight of the different fiber samples were found which may have potential use in forensic fiber comparison. PET fibers with average molecular weights between about 20,000 and 70,000 g mol(-1) were determined using fiber concentrations in HFIP as low as 1 μg mL(-1). This GPC analytical method can be applied for exclusively distinguish between PET fibers using 1 μg of fiber. This method can be extended to forensic comparison of other synthetic fibers such as polyamides and acrylics. Copyright © 2014 Elsevier B.V. All rights reserved.

Effectiveness of Spectral Similarity Measures to Develop Precise Crop Spectra for Hyperspectral Data Analysis

NASA Astrophysics Data System (ADS)

Chauhan, H.; Krishna Mohan, B.

2014-11-01

The present study was undertaken with the objective to check effectiveness of spectral similarity measures to develop precise crop spectra from the collected hyperspectral field spectra. In Multispectral and Hyperspectral remote sensing, classification of pixels is obtained by statistical comparison (by means of spectral similarity) of known field or library spectra to unknown image spectra. Though these algorithms are readily used, little emphasis has been placed on use of various spectral similarity measures to select precise crop spectra from the set of field spectra. Conventionally crop spectra are developed after rejecting outliers based only on broad-spectrum analysis. Here a successful attempt has been made to develop precise crop spectra based on spectral similarity. As unevaluated data usage leads to uncertainty in the image classification, it is very crucial to evaluate the data. Hence, notwithstanding the conventional method, the data precision has been performed effectively to serve the purpose of the present research work. The effectiveness of developed precise field spectra was evaluated by spectral discrimination measures and found higher discrimination values compared to spectra developed conventionally. Overall classification accuracy for the image classified by field spectra selected conventionally is 51.89% and 75.47% for the image classified by field spectra selected precisely based on spectral similarity. KHAT values are 0.37, 0.62 and Z values are 2.77, 9.59 for image classified using conventional and precise field spectra respectively. Reasonable higher classification accuracy, KHAT and Z values shows the possibility of a new approach for field spectra selection based on spectral similarity measure.

Characterizing CDOM Spectral Variability Across Diverse Regions and Spectral Ranges

NASA Astrophysics Data System (ADS)

Grunert, Brice K.; Mouw, Colleen B.; Ciochetto, Audrey B.

2018-01-01

Satellite remote sensing of colored dissolved organic matter (CDOM) has focused on CDOM absorption (aCDOM) at a reference wavelength, as its magnitude provides insight into the underwater light field and large-scale biogeochemical processes. CDOM spectral slope, SCDOM, has been treated as a constant or semiconstant parameter in satellite retrievals of aCDOM despite significant regional and temporal variabilities. SCDOM and other optical metrics provide insights into CDOM composition, processing, food web dynamics, and carbon cycling. To date, much of this work relies on fluorescence techniques or aCDOM in spectral ranges unavailable to current and planned satellite sensors (e.g., <300 nm). In preparation for anticipated future hyperspectral satellite missions, we take the first step here of exploring global variability in SCDOM and fit deviations in the aCDOM spectra using the recently proposed Gaussian decomposition method. From this, we investigate if global variability in retrieved SCDOM and Gaussian components is significant and regionally distinct. We iteratively decreased the spectral range considered and analyzed the number, location, and magnitude of fitted Gaussian components to understand if a reduced spectral range impacts information obtained within a common spectral window. We compared the fitted slope from the Gaussian decomposition method to absorption-based indices that indicate CDOM composition to determine the ability of satellite-derived slope to inform the analysis and modeling of large-scale biogeochemical processes. Finally, we present implications of the observed variability for remote sensing of CDOM characteristics via SCDOM.

Spectral Dynamics of Resting State fMRI Within the Ventral Tegmental Area and Dorsal Raphe Nuclei in Medication-Free Major Depressive Disorder in Young Adults.

PubMed

Wohlschläger, Afra; Karne, Harish; Jordan, Denis; Lowe, Mark J; Jones, Stephen E; Anand, Amit

2018-01-01

Background: Dorsal raphe nucleus (DRN) and ventral tegmental area (VTA) are major brainstem monamine nuclei consisting of serotonin and dopamine neurons respectively. Animal studies show that firing patterns in both nuclei are altered when animals exhibit depression like behaviors. Functional MRI studies in humans have shown reduced VTA activation and DRN connectivity in depression. This study for the first time aims at investigating the functional integrity of local neuronal firing concurrently in both the VTA and DRN in vivo in humans using spectral analysis of resting state low frequency fluctuation fMRI. Method: A total of 97 medication-free subjects-67 medication-free young patients (ages 18-30) with major depressive disorder and 30 closely matched healthy controls were included in the study to detect aberrant dynamics in DRN and VTA. For the investigation of altered localized dynamics we conducted power spectral analysis and above this spectral cross correlation between the two groups. Complementary to this, spectral dependence of permutation entropy, an information theoretical measure, was compared between groups. Results: Patients displayed significant spectral slowing in VTA vs. controls ( p = 0.035, corrected). In DRN, spectral slowing was less pronounced, but the amount of slowing significantly correlated with 17-item Hamilton Depression Rating scores of depression severity ( p = 0.038). Signal complexity as assessed via permutation entropy showed spectral alterations inline with the results on spectral slowing. Conclusion: Our results indicate that altered functional dynamics of VTA and DRN in depression can be detected from regional fMRI signal. On this basis, impact of antidepressant treatment and treatment response can be assessed using these markers in future studies.

Method of photon spectral analysis

DOEpatents

Gehrke, Robert J.; Putnam, Marie H.; Killian, E. Wayne; Helmer, Richard G.; Kynaston, Ronnie L.; Goodwin, Scott G.; Johnson, Larry O.

1993-01-01

A spectroscopic method to rapidly measure the presence of plutonium in soils, filters, smears, and glass waste forms by measuring the uranium L-shell x-ray emissions associated with the decay of plutonium. In addition, the technique can simultaneously acquire spectra of samples and automatically analyze them for the amount of americium and .gamma.-ray emitting activation and fission products present. The samples are counted with a large area, thin-window, n-type germanium spectrometer which is equally efficient for the detection of low-energy x-rays (10-2000 keV), as well as high-energy .gamma. rays (>1 MeV). A 8192- or 16,384 channel analyzer is used to acquire the entire photon spectrum at one time. A dual-energy, time-tagged pulser, that is injected into the test input of the preamplifier to monitor the energy scale, and detector resolution. The L x-ray portion of each spectrum is analyzed by a linear-least-squares spectral fitting technique. The .gamma.-ray portion of each spectrum is analyzed by a standard Ge .gamma.-ray analysis program. This method can be applied to any analysis involving x- and .gamma.-ray analysis in one spectrum and is especially useful when interferences in the x-ray region can be identified from the .gamma.-ray analysis and accommodated during the x-ray analysis.