First geometrical path length probability density function derivation of the skylight from high-resolution oxygen A-band spectroscopy: 2. Derivation of the Lévy index for the skylight transmitted by midlatitude clouds

1999 ◽  
Vol 104 (D4) ◽  
pp. 4101-4116 ◽  
Author(s):  
K. Pfeilsticker
Keyword(s):  
High Resolution ◽  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Path Length

Multiscale Aspects and Resolution Robustness of Turbulent Scalar Fields and Interfaces

2007 ◽  
Author(s):  
Siarhei Piatrovich ◽  
Haris J. Catrakis
Keyword(s):  
Reynolds Number ◽  
Scalar Field ◽  
High Resolution ◽  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Scalar Fields ◽  
Experimental Database ◽  
Geometrical Properties ◽  
Interfacial Surface

This study focuses on fundamental issues regarding multiscale and multiresolution geometrical properties of turbulent scalar fields and interfaces. The probability density function of the scalar field is examined in terms of geometrical properties of the turbulent interfaces using a high-resolution experimental database of fully-developed turbulent scalar fields in jets at a Reynolds number of Re = 20,000. The pdf is found to exhibit significant robustness to resolution scale. The multiscale properties of the volume of fluid regions enclosed by outer turbulent interfaces are also investigated. The enclosed interfacial volume appears to be significantly robust to the resolution scale as well. An explanation for this behavior is proposed in terms of the opposite effects of protrusions of the scalar interface compared to indentations, which provide positive and negative contributions to the volume respectively. This is in contrast to the interfacial surface area for which protrusions and indentations both have additive contributions.

scholarly journals Two approaches to design of forward adaptive piecewise uniform scalar quantizers

2013 ◽  
Vol 26 (1) ◽  
pp. 61-68
Author(s):  
Jelena Nikolic ◽  
Zoran Peric
Keyword(s):  
High Resolution ◽  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
The Other ◽  
Speech Signals ◽  
High Quality ◽  
Region Partition ◽  
The One ◽  
Scalar Quantizers

In this paper, two forward adaptive piecewise uniform scalar quantizers are proposed for high-quality quantization of speech signals modeled by the Laplacian probability density function. In designing both forward adaptive piecewise uniform scalar quantizers an equidistant support region partition is assumed and a distribution of the number of reproduction levels per segments is optimized. The proposed models differ in the approach of determining the reproduction levels. In particular, one model defines the reproduction levels as the cell centroids and the other one as the cell midpoints. We show that, in the high-resolution case, the proposed quantizers provide approximately the same performance being close to the one of the forward adaptive nonlinear scalar compandor with equal number of quantization levels.

scholarly journals Optimization of the Photon Path Length Probability Density Function-Simultaneous (PPDF-S) Method and Evaluation of CO2 Retrieval Performance Under Dense Aerosol Conditions

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1262 ◽  
Author(s):  
Chisa Iwasaki ◽  
Ryoichi Imasu ◽  
Andrey Bril ◽  
Sergey Oshchepkov ◽  
Yukio Yoshida ◽  
...  
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Atmospheric Aerosol ◽  
Path Length ◽  
Stable Solution ◽  
Simulation Studies ◽  
Path Modification ◽  
Aerosol Types ◽  
Photon Path

The photon path length probability density function-simultaneous (PPDF-S) algorithm is effective for retrieving column-averaged concentrations of carbon dioxide (XCO2) and methane (XCH4) from Greenhouse gases Observing Satellite (GOSAT) spectra in Short Wavelength InfraRed (SWIR). Using this method, light-path modification attributable to light reflection/scattering by atmospheric clouds/aerosols is represented by the modification of atmospheric transmittance according to PPDF parameters. We optimized PPDF parameters for a more accurate XCO2 retrieval under aerosol dense conditions based on simulation studies for various aerosol types and surface albedos. We found a more appropriate value of PPDF parameters referring to the vertical profile of CO2 concentration as a measure of a stable solution. The results show that the constraint condition of a PPDF parameter that represents the light reflectance effect by aerosols is sufficiently weak to affect XCO2 adversely. By optimizing the constraint, it was possible to obtain a stable solution of XCO2. The new optimization was applied to retrieval analysis of the GOSAT data measured in Western Siberia. First, we assumed clear sky conditions and retrieved XCO2 from GOSAT data obtained near Yekaterinburg in the target area. The retrieved XCO2 was validated through a comparison with ground-based Fourier Transform Spectrometer (FTS) measurements made at the Yekaterinburg observation site. The validation results showed that the retrieval accuracy was reasonable. Next, we applied the optimized method to dense aerosol conditions when biomass burning was active. The results demonstrated that optimization enabled retrieval, even under smoky conditions, and that the total number of retrieved data increased by about 70%. Furthermore, the results of the simulation studies and the GOSAT data analysis suggest that atmospheric aerosol types that affected CO2 analysis are identifiable by the PPDF parameter value. We expect that we will be able to suggest a further improved algorithm after the atmospheric aerosol types are identified.

scholarly journals A Model of Extracting Building from High Resolution Remote Sensing Image Based on Bayesian Convolutional Neural Networks

2018 ◽  
Author(s):  
Xiaoxia Yang ◽  
Chengming Zhang ◽  
Shuai Gao ◽  
Fan Yu ◽  
Dejuan Song ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
High Resolution ◽  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Feature Vector ◽  
Remote Sensing Image ◽  
Conditional Probability Density ◽  
Conditional Probability Density Function

When extract building from high resolution remote sensing image with meter/sub-meter accuracy, the shade of trees and interference of roads are the main factors of reducing the extraction accuracy. Proposed a Bayesian Convolutional Neural Networks(BCNET) model base on standard fully convolutional networks(FCN) to solve these problems. First take building with no shade or artificial removal of shade as Sample-A, woodland as Sample-B, road as Sample-C. Set up 3 sample libraries. Learn these sample libraries respectively, get their own set of feature vector; Mixture Gauss model these feature vector set, evaluate the conditional probability density function of mixture of noise object and roofs; Improve the standard FCN from the 2 aspect:(1) Introduce atrous convolution. (2) Take conditional probability density function as the activation function of the last convolution. Carry out experiment using unmanned aerial vehicle(UVA) image, the results show that BCNET model can effectively eliminate the influence of trees and roads, the building extraction accuracy can reach 97%.

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