Semiclassical phase space representation of the density matrix

1978 ◽  
Vol 69 (8) ◽  
pp. 3545-3547 ◽  
Author(s):  
Neil Snider
Keyword(s):  
Phase Space ◽  
Density Matrix ◽  
Space Representation ◽  
Phase Space Representation

The Structure of Electronic Moyal Functions, and their Connection with Experiment

2001 ◽  
Vol 215 (10) ◽  
Author(s):  
H. L. Schmider
Keyword(s):  
Phase Space ◽  
Density Matrix ◽  
Inelastic Scattering ◽  
Particle Density ◽  
Structural Features ◽  
Space Representation ◽  
Compton Profile ◽  
Direct Connection ◽  
Atoms And Molecules ◽  
Phase Space Representation

The Moyal Function is a phase-space representation of the reduced one-particle density matrix that is particularly close to experimental quantities. We discuss basic structural features of this function for atoms and molecules, and point out a direct connection to the “non-diagonal Compton profile” obtained from coherent inelastic scattering by Schülke

Phase space representation of pseudo oscillator

1969 ◽  
Vol 29 (5) ◽  
pp. 245-246 ◽  
Author(s):  
A.K. Jaiswal ◽  
C.L. Mehta
Keyword(s):  
Phase Space ◽  
Space Representation ◽  
Phase Space Representation

Automated classification of human lung sound signals using phase space representation of intrinsic mode function

2021 ◽  
Author(s):  
Sibghatullah I. Khan ◽  
Vikram Palodiya ◽  
Lavanya Poluboyina
Keyword(s):  
Phase Space ◽  
Human Lung ◽  
Feature Space ◽  
Normal Lung ◽  
Space Representation ◽  
Support Vector ◽  
Lung Sounds ◽  
Lung Sound ◽  
Phase Space Representation

Abstract Bronchiectasis and chronic obstructive pulmonary disease (COPD) are common human lung diseases. In general, the expert pulmonologistcarries preliminary screening and detection of these lung abnormalities by listening to the adventitious lung sounds. The present paper is an attempt towards the automatic detection of adventitious lung sounds ofBronchiectasis,COPD from normal lung sounds of healthy subjects. For classification of the lung sounds into a normaland adventitious category, we obtain features from phase space representation (PSR). At first, the empirical mode decomposition (EMD) is applied to lung sound signals to obtain intrinsic mode functions (IMFs). The IMFs are then further processed to construct two dimensional (2D) and three dimensional (3D) PSR. The feature space includes the 95% confidence ellipse area and interquartile range (IQR) of Euclidian distances computed from 2D and 3D PSRs, respectively. The process is carried out for the first four IMFs correspondings to normal and adventitious lung sound signals. The computed features depicta significant ability to discriminate the two categories of lung sound signals.To perform classification, we use the least square support vector machine with two kernels, namely, polynomial and radial basis function (RBF).Simulation outcomes on ICBHI 2017 lung sound dataset show the ability of the proposed method in effectively classifying normal and adventitious lung sound signals. LS-SVM is employing RBF kernel provides the highest classification accuracy of 97.67 % over feature space constituted by first, second, and fourth IMF.

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