The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Rajeev Srivastava - One of the best experts on this subject based on the ideXlab platform.
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weibull Probability Distribution Function based matched filter approach for retinal blood vessels segmentation
2017Co-Authors: Nagendra Pratap Singh, Rajeev SrivastavaAbstract:Retinal blood vessels contain an important information that is useful for computer-aided diagnosis of various retinal pathologies such as hypertension, diabetes, glaucoma, etc. Therefore, a retinal blood vessel segmentation is a prominent task. In this paper, a novel Weibull Probability Distribution Function-based matched filter approach is introduced to improve the performance of retinal blood vessel segmentation with respect to prominent matched filter approaches and other matched filter-based approaches existing in literature. Moreover, to enhance the quality of input retinal images in pre-processing step, the concept of principal component analysis (PCA)-based gray scale conversion and contrast-limited adaptive histogram equalization (CLAHE) are used. To design a proposed matched filter, the appropriate value of parameters are selected on the basis of an exhaustive experimental analysis. The proposed approach has been tested on 20 retinal images of test set taken from the DRIVE database and confirms that the proposed approach achieved better performance with respect to other prominent matched filter-based approaches.
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retinal blood vessels segmentation by using gumbel Probability Distribution Function based matched filter
Computer Methods and Programs in Biomedicine, 2016Co-Authors: Nagendra Pratap Singh, Rajeev SrivastavaAbstract:Graphical abstractDisplay Omitted HighlightsA novel matched filter approach with the Gumbel PDF as its kernel is proposed.Pre-processing includes PCA based gray-scale conversion and contrast enhancement.Post-processing includes the entropy based optimal thresholding and length filtering.On the basis of exhaustive experiment select the appropriate value of parameters. Background and objectiveRetinal blood vessel segmentation is a prominent task for the diagnosis of various retinal pathology such as hypertension, diabetes, glaucoma, etc. In this paper, a novel matched filter approach with the Gumbel Probability Distribution Function as its kernel is introduced to improve the performance of retinal blood vessel segmentation. MethodsBefore applying the proposed matched filter, the input retinal images are pre-processed. During pre-processing stage principal component analysis (PCA) based gray scale conversion followed by contrast limited adaptive histogram equalization (CLAHE) are applied for better enhancement of retinal image. After that an exhaustive experiments have been conducted for selecting the appropriate value of parameters to design a new matched filter. The post-processing steps after applying the proposed matched filter include the entropy based optimal thresholding and length filtering to obtain the segmented image. ResultsFor evaluating the performance of proposed approach, the quantitative performance measures, an average accuracy, average true positive rate (ATPR), and average false positive rate (AFPR) are calculated. The respective values of the quantitative performance measures are 0.9522, 0.7594, 0.0292 for DRIVE data set and 0.9270, 0.7939, 0.0624 for STARE data set. To justify the effectiveness of proposed approach, receiver operating characteristic (ROC) curve is plotted and the average area under the curve (AUC) is calculated. The average AUC for DRIVE and STARE data sets are 0.9287 and 0.9140 respectively. ConclusionsThe obtained experimental results confirm that the proposed approach performance better with respect to other prominent Gaussian Distribution Function and Cauchy PDF based matched filter approaches.
Khee Gan Lee - One of the best experts on this subject based on the ideXlab platform.
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joint bayesian estimation of quasar continua and the lyα forest flux Probability Distribution Function
The Astrophysical Journal, 2017Co-Authors: Khee Gan Lee, Joseph F Hennawi, Annachristina EilersAbstract:Author(s): Eilers, Anna-Christina; Hennawi, Joseph F; Lee, Khee-Gan | Abstract: We present a new Bayesian algorithm making use of Markov Chain Monte Carlo sampling that allows us to simultaneously estimate the unknown continuum level of each quasar in an ensemble of high-resolution spectra, as well as their common Probability Distribution Function (PDF) for the transmitted Ly$\alpha$ forest flux. This fully automated PDF regulated continuum fitting method models the unknown quasar continuum with a linear Principal Component Analysis (PCA) basis, with the PCA coefficients treated as nuisance parameters. The method allows one to estimate parameters governing the thermal state of the intergalactic medium (IGM), such as the slope of the temperature-density relation $\gamma-1$, while marginalizing out continuum uncertainties in a fully Bayesian way. Using realistic mock quasar spectra created from a simplified semi-numerical model of the IGM, we show that this method recovers the underlying quasar continua to a precision of $\simeq7\%$ and $\simeq10\%$ at $z=3$ and $z=5$, respectively. Given the number of principal component spectra, this is comparable to the underlying accuracy of the PCA model itself. Most importantly, we show that we can achieve a nearly unbiased estimate of the slope $\gamma-1$ of the IGM temperature-density relation with a precision of $\pm8.6\%$ at $z=3$, $\pm6.1\%$ at $z=5$, for an ensemble of ten mock high-resolution quasar spectra. Applying this method to real quasar spectra and comparing to a more realistic IGM model from hydrodynamical simulations would enable precise measurements of the thermal and cosmological parameters governing the IGM, albeit with somewhat larger uncertainties given the increased flexibility of the model.
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joint bayesian estimation of quasar continua and the lyα forest flux Probability Distribution Function
The Astrophysical Journal, 2017Co-Authors: Khee Gan Lee, Joseph F Hennawi, Annachristina EilersAbstract:Author(s): Eilers, AC; Hennawi, JF; Lee, KG | Abstract: We present a new Bayesian algorithm making use of Markov Chain Monte Carlo sampling that allows us to simultaneously estimate the unknown continuum level of each quasar in an ensemble of high-resolution spectra, as well as their common Probability Distribution Function (PDF) for the transmitted Lyα forest flux. This fully automated PDF regulated continuum fitting method models the unknown quasar continuum with a linear principal component analysis (PCA) basis, with the PCA coefficients treated as nuisance parameters. The method allows one to estimate parameters governing the thermal state of the intergalactic medium (IGM), such as the slope of the temperature-density relation γ -1, while marginalizing out continuum uncertainties in a fully Bayesian way. Using realistic mock quasar spectra created from a simplified semi-numerical model of the IGM, we show that this method recovers the underlying quasar continua to a precision of ≃7% and ≃10% at z = 3 and z = 5, respectively. Given the number of principal component spectra, this is comparable to the underlying accuracy of the PCA model itself. Most importantly, we show that we can achieve a nearly unbiased estimate of the slope γ - 1 of the IGM temperature-density relation with a precision of ± 8.6% at z = 3 and ± 6.1% at z = 5, for an ensemble of ten mock high-resolution quasar spectra. Applying this method to real quasar spectra and comparing to a more realistic IGM model from hydrodynamical simulations would enable precise measurements of the thermal and cosmological parameters governing the IGM, albeit with somewhat larger uncertainties, given the increased flexibility of the model.
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joint bayesian estimation of quasar continua and the lyman alpha forest flux Probability Distribution Function
arXiv: Astrophysics of Galaxies, 2017Co-Authors: Annachristina Eilers, Joseph F Hennawi, Khee Gan LeeAbstract:We present a new Bayesian algorithm making use of Markov Chain Monte Carlo sampling that allows us to simultaneously estimate the unknown continuum level of each quasar in an ensemble of high-resolution spectra, as well as their common Probability Distribution Function (PDF) for the transmitted Ly$\alpha$ forest flux. This fully automated PDF regulated continuum fitting method models the unknown quasar continuum with a linear Principal Component Analysis (PCA) basis, with the PCA coefficients treated as nuisance parameters. The method allows one to estimate parameters governing the thermal state of the intergalactic medium (IGM), such as the slope of the temperature-density relation $\gamma-1$, while marginalizing out continuum uncertainties in a fully Bayesian way. Using realistic mock quasar spectra created from a simplified semi-numerical model of the IGM, we show that this method recovers the underlying quasar continua to a precision of $\simeq7\%$ and $\simeq10\%$ at $z=3$ and $z=5$, respectively. Given the number of principal component spectra, this is comparable to the underlying accuracy of the PCA model itself. Most importantly, we show that we can achieve a nearly unbiased estimate of the slope $\gamma-1$ of the IGM temperature-density relation with a precision of $\pm8.6\%$ at $z=3$, $\pm6.1\%$ at $z=5$, for an ensemble of ten mock high-resolution quasar spectra. Applying this method to real quasar spectra and comparing to a more realistic IGM model from hydrodynamical simulations would enable precise measurements of the thermal and cosmological parameters governing the IGM, albeit with somewhat larger uncertainties given the increased flexibility of the model.
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igm constraints from the sdss iii boss dr9 lyα forest transmission Probability Distribution Function
The Astrophysical Journal, 2015Co-Authors: Khee Gan Lee, Joseph F Hennawi, David N Spergel, David H Weinberg, David W Hogg, Matteo Viel, James S Bolton, S Bailey, Matthew M PieriAbstract:The Ly$\alpha$ forest transmission Probability Distribution Function (PDF) is an established probe of the intergalactic medium (IGM) astrophysics, especially the temperature-density relationship of the IGM. We measure the transmission PDF from 3393 Baryon Oscillations Spectroscopic Survey (BOSS) quasars from SDSS Data Release 9, and compare with mock spectra that include careful modeling of the noise, continuum, and astrophysical uncertainties. The BOSS transmission PDFs, measured at $\langle z \rangle = [2.3,2.6,3.0]$, are compared with PDFs created from mock spectra drawn from a suite of hydrodynamical simulations that sample the IGM temperature-density relationship, $\gamma$, and temperature at mean-density, $T_0$, where $T(\Delta) = T_0 \Delta^{\gamma-1}$. We find that a significant population of partial Lyman-limit systems with a column-density Distribution slope of $\beta_\mathrm{pLLS} \sim -2$ are required to explain the data at the low-transmission end of transmission PDF, while uncertainties in the mean Ly$\alpha$ forest transmission affect the high-transmission end. After modelling the LLSs and marginalizing over mean-transmission uncertainties, we find that $\gamma=1.6$ best describes the data over our entire redshift range, although constraints on $T_0$ are affected by systematic uncertainties. Within our model framework, isothermal or inverted temperature-density relationships ($\gamma \leq 1$) are disfavored at a significance of over 4$\sigma$, although this could be somewhat weakened by cosmological and astrophysical uncertainties that we did not model.
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igm constraints from the sdss iii boss dr9 ly alpha forest flux Probability Distribution Function
arXiv: Cosmology and Nongalactic Astrophysics, 2014Co-Authors: Khee Gan Lee, Joseph F Hennawi, David N Spergel, David H Weinberg, David W Hogg, Matteo Viel, James S Bolton, S Bailey, Matthew M Pieri, W CarithersAbstract:The Ly$\alpha$ forest transmission Probability Distribution Function (PDF) is an established probe of the intergalactic medium (IGM) astrophysics, especially the temperature-density relationship of the IGM. We measure the transmission PDF from 3393 Baryon Oscillations Spectroscopic Survey (BOSS) quasars from SDSS Data Release 9, and compare with mock spectra that include careful modeling of the noise, continuum, and astrophysical uncertainties. The BOSS transmission PDFs, measured at $\langle z \rangle = [2.3,2.6,3.0]$, are compared with PDFs created from mock spectra drawn from a suite of hydrodynamical simulations that sample the IGM temperature-density relationship, $\gamma$, and temperature at mean-density, $T_0$, where $T(\Delta) = T_0 \Delta^{\gamma-1}$. We find that a significant population of partial Lyman-limit systems with a column-density Distribution slope of $\beta_\mathrm{pLLS} \sim -2$ are required to explain the data at the low-transmission end of transmission PDF, while uncertainties in the mean Ly$\alpha$ forest transmission affect the high-transmission end. After modelling the LLSs and marginalizing over mean-transmission uncertainties, we find that $\gamma=1.6$ best describes the data over our entire redshift range, although constraints on $T_0$ are affected by systematic uncertainties. Within our model framework, isothermal or inverted temperature-density relationships ($\gamma \leq 1$) are disfavored at a significance of over 4$\sigma$, although this could be somewhat weakened by cosmological and astrophysical uncertainties that we did not model.
Peter C Chu - One of the best experts on this subject based on the ideXlab platform.
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Probability Distribution Function of the upper equatorial Pacific current speeds,” Geophys
2016Co-Authors: Peter C ChuAbstract:[1] The Probability Distribution Function (PDF) of the upper (0–50 m) tropical Pacific current speeds (w), constructed from hourly ADCP data (1990–2007) at six stations for the Tropical Atmosphere Ocean project, satisfies the two-parameter Weibull Distribution reasonably well with different characteristics between El Nino and La Nina events: In the western Pacific, the PDF of w has a larger peakedness during the La Nina events than during the El Nino events; and vice versa in the eastern Pacific. However, the PDF of w for the lower layer (100–200 m) does not fit theWeibull Distribution so well as the upper layer. This is due to the different stochastic differential equations between upper and lower layers in the tropical Pacific. For the upper layer, the stochastic differential equations, established on the base of the Ekman dynamics, have analytical solution, i.e., the Rayleigh Distribution (simplest form of the Weibull Distribution), for constant eddy viscosity K. Knowledge on PDF ofw during the El Nino and LaNina events will improve the ensemble horizontal flux calculation, which contribute
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Probability Distribution Function of the upper equatorial pacific current speeds
Geophysical Research Letters, 2008Co-Authors: Peter C ChuAbstract:[1] The Probability Distribution Function (PDF) of the upper (0–50 m) tropical Pacific current speeds (w), constructed from hourly ADCP data (1990–2007) at six stations for the Tropical Atmosphere Ocean project, satisfies the two-parameter Weibull Distribution reasonably well with different characteristics between El Nino and La Nina events: In the western Pacific, the PDF of w has a larger peakedness during the La Nina events than during the El Nino events; and vice versa in the eastern Pacific. However, the PDF of w for the lower layer (100–200 m) does not fit the Weibull Distribution so well as the upper layer. This is due to the different stochastic differential equations between upper and lower layers in the tropical Pacific. For the upper layer, the stochastic differential equations, established on the base of the Ekman dynamics, have analytical solution, i.e., the Rayleigh Distribution (simplest form of the Weibull Distribution), for constant eddy viscosity K. Knowledge on PDF of w during the El Nino and La Nina events will improve the ensemble horizontal flux calculation, which contributes to the climate studies.
Zekai şen - One of the best experts on this subject based on the ideXlab platform.
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theoretical derivation of wind power Probability Distribution Function and applications
Applied Energy, 2012Co-Authors: Abdusselam Altunkaynak, Tarkan Erdik, Ismail Dabanli, Zekai şenAbstract:The instantaneous wind power contained in the air current is directly proportional with the cube of the wind speed. In practice, there is a record of wind speeds in the form of a time series. It is, therefore, necessary to develop a formulation that takes into consideration the statistical parameters of such a time series. The purpose of this paper is to derive the general wind power formulation in terms of the statistical parameters by using the perturbation theory, which leads to a general formulation of the wind power expectation and other statistical parameter expressions such as the standard deviation and the coefficient of variation. The formulation is very general and can be applied specifically for any wind speed Probability Distribution Function. Its application to two-parameter Weibull Probability Distribution of wind speeds is presented in full detail. It is concluded that provided wind speed is distributed according to a Weibull Distribution, the wind power could be derived based on wind speed data. It is possible to determine wind power at any desired risk level, however, in practical studies most often 5% or 10% risk levels are preferred and the necessary simple procedure is presented for this purpose in this paper.
Nagendra Pratap Singh - One of the best experts on this subject based on the ideXlab platform.
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weibull Probability Distribution Function based matched filter approach for retinal blood vessels segmentation
2017Co-Authors: Nagendra Pratap Singh, Rajeev SrivastavaAbstract:Retinal blood vessels contain an important information that is useful for computer-aided diagnosis of various retinal pathologies such as hypertension, diabetes, glaucoma, etc. Therefore, a retinal blood vessel segmentation is a prominent task. In this paper, a novel Weibull Probability Distribution Function-based matched filter approach is introduced to improve the performance of retinal blood vessel segmentation with respect to prominent matched filter approaches and other matched filter-based approaches existing in literature. Moreover, to enhance the quality of input retinal images in pre-processing step, the concept of principal component analysis (PCA)-based gray scale conversion and contrast-limited adaptive histogram equalization (CLAHE) are used. To design a proposed matched filter, the appropriate value of parameters are selected on the basis of an exhaustive experimental analysis. The proposed approach has been tested on 20 retinal images of test set taken from the DRIVE database and confirms that the proposed approach achieved better performance with respect to other prominent matched filter-based approaches.
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retinal blood vessels segmentation by using gumbel Probability Distribution Function based matched filter
Computer Methods and Programs in Biomedicine, 2016Co-Authors: Nagendra Pratap Singh, Rajeev SrivastavaAbstract:Graphical abstractDisplay Omitted HighlightsA novel matched filter approach with the Gumbel PDF as its kernel is proposed.Pre-processing includes PCA based gray-scale conversion and contrast enhancement.Post-processing includes the entropy based optimal thresholding and length filtering.On the basis of exhaustive experiment select the appropriate value of parameters. Background and objectiveRetinal blood vessel segmentation is a prominent task for the diagnosis of various retinal pathology such as hypertension, diabetes, glaucoma, etc. In this paper, a novel matched filter approach with the Gumbel Probability Distribution Function as its kernel is introduced to improve the performance of retinal blood vessel segmentation. MethodsBefore applying the proposed matched filter, the input retinal images are pre-processed. During pre-processing stage principal component analysis (PCA) based gray scale conversion followed by contrast limited adaptive histogram equalization (CLAHE) are applied for better enhancement of retinal image. After that an exhaustive experiments have been conducted for selecting the appropriate value of parameters to design a new matched filter. The post-processing steps after applying the proposed matched filter include the entropy based optimal thresholding and length filtering to obtain the segmented image. ResultsFor evaluating the performance of proposed approach, the quantitative performance measures, an average accuracy, average true positive rate (ATPR), and average false positive rate (AFPR) are calculated. The respective values of the quantitative performance measures are 0.9522, 0.7594, 0.0292 for DRIVE data set and 0.9270, 0.7939, 0.0624 for STARE data set. To justify the effectiveness of proposed approach, receiver operating characteristic (ROC) curve is plotted and the average area under the curve (AUC) is calculated. The average AUC for DRIVE and STARE data sets are 0.9287 and 0.9140 respectively. ConclusionsThe obtained experimental results confirm that the proposed approach performance better with respect to other prominent Gaussian Distribution Function and Cauchy PDF based matched filter approaches.