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.

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.

On the one-point probability density function for the wind velocity in the neutral atmospheric surface layer

1998 ◽  
Vol 366 ◽  
pp. 351-365
Author(s):  
A. WENZEL ◽  
M. BALDAUF
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Turbulent Flows ◽  
Wind Velocity ◽  
Joint Probability ◽  
Second Moments ◽  
New Equation ◽  
The One ◽  
Strain Interaction

The differential equation describing the one-point joint probability density function for the wind velocity given by Lundgren (1967) in neutral turbulent flows is extended by a term which also takes into consideration the pressure–mean strain interaction. For the new equation a solution is given describing the one-point probability density function for the wind velocity fluctuations if the profile of the mean wind velocity is logarithmic. The properties of this solution are discussed to identify the differences to a Gaussian having the same first and second moments.

scholarly journals Mathematical approach to human character

2020 ◽  
Author(s):  
Takuya Yabu
Keyword(s):  
Probability Density Function ◽  
Probability Distribution ◽  
Probability Density ◽  
Density Function ◽  
Negative Emotions ◽  
The Other ◽  
Mathematical Approach ◽  
Fixed Probability ◽  
To Receive

I thought about whether to receive positive or negative emotions from an event from the perspective of human character. Regarding the human character, I define it as a process of selecting one's emotion x so that the received emotion x becomes x=0 with respect to the event X and the reaction of the other party when one's thoughts and reactions occur as the accompanying reactions. Mathematically modeled it, the probability density function of how much to select an emotion has a fixed probability distribution. I also described how to deal with one's character as an application of this model.

scholarly journals Unified Formulation of Single- and Multimoment Normalizations of the Raindrop Size Distribution Based on the Gamma Probability Density Function

2014 ◽  
Vol 53 (1) ◽  
pp. 166-179 ◽  
Author(s):  
Nan Yu ◽  
Guy Delrieu ◽  
Brice Boudevillain ◽  
Pieter Hazenberg ◽  
Remko Uijlenhoet
Keyword(s):  
Time Series ◽  
Probability Density Function ◽  
Probability Density ◽  
Size Distribution ◽  
Density Function ◽  
Power Law ◽  
General Distribution ◽  
Raindrop Size Distribution ◽  
Raindrop Size ◽  
The One

AbstractThis study offers a unified formulation of single- and multimoment normalizations of the raindrop size distribution (DSD), which have been proposed in the framework of scaling analyses in the literature. The key point is to consider a well-defined “general distribution” g(x) as the probability density function (pdf) of the raindrop diameter scaled by a characteristic diameter Dc. The two-parameter gamma pdf is used to model the g(x) function. This theory is illustrated with a 3-yr DSD time series collected in the Cévennes region, France. It is shown that three DSD moments (M2, M3, and M4) make it possible to satisfactorily model the DSDs, both for individual spectra and for time series of spectra. The formulation is then extended to the one- and two-moment normalization by introducing single and dual power-law models. As compared with previous scaling formulations, this approach explicitly accounts for the prefactors of the power-law models to yield a unique and dimensionless g(x), whatever the scaling moment(s) considered. A parameter estimation procedure, based on the analysis of power-law regressions and the self-consistency relationships, is proposed for those normalizations. The implementation of this method with different scaling DSD moments (rain rate and/or radar reflectivity) yields g(x) functions similar to the one obtained with the three-moment normalization. For a particular rain event, highly consistent g(x) functions can be obtained during homogeneous rain phases, whatever the scaling moments used. However, the g(x) functions may present contrasting shapes from one phase to another. This supports the idea that the g(x) function is process dependent and not “unique” as hypothesized in the scaling theory.

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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