scholarly journals The two-spin model of a tracking chamber: a phase-space perspective

2021 ◽  
Author(s):  
Luigi Barletti
Keyword(s):  
Phase Space ◽  
Spin Model ◽  
Space Representation ◽  
Dimensional Model ◽  
Wigner Matrix ◽  
One Dimensional ◽  
Classical Localization ◽  
Relationship Of ◽  
The Relationship ◽  
Phase Space Representation

AbstractWe study the dynamics of classical localization in a simple, one-dimensional model of a tracking chamber. The emitted particle is represented by a superposition of Gaussian wave packets moving in opposite directions, and the detectors are two spins in fixed, opposite positions with respect to the central emitter. At variance with other similar studies, we give here a phase-space representation of the dynamics in terms of the Wigner matrix of the system. This allows a better visualization of the phenomenon and helps in its interpretation. In particular, we discuss the relationship of the localization process with the properties of entanglement possessed by the system.

The one-dimensional Hulthén potential in the quantum phase space representation

Open Physics ◽  
2014 ◽  
Vol 12 (2) ◽  
Author(s):  
Jerzy Stanek
Keyword(s):  
Phase Space ◽  
Wigner Function ◽  
Quantum Phase ◽  
Space Representation ◽  
Hulthén Potential ◽  
One Dimensional ◽  
Hulthen Potential ◽  
Analytic Expression ◽  
The One ◽  
Phase Space Representation

AbstractThe analytic expression of the Wigner function for bound eigenstates of the Hulthén potential in quantum phase space is obtained and presented by plotting this function for a few quantum states. In addition, the correct marginal distributions of the Wigner function in spherical coordinates are determined analytically.

Phase-Space Wave Functions of Diatomic System in One-Dimensional Nanomaterials

2011 ◽  
Vol 474-476 ◽  
pp. 1179-1182
Author(s):  
Jun Lu
Keyword(s):  
Phase Space ◽  
Exact Solutions ◽  
Potential Function ◽  
Wave Functions ◽  
Space Representation ◽  
One Dimensional ◽  
Empirical Potential ◽  
Diatomic System ◽  
Phase Space Representation ◽  
Space Wave

The exact solutions of the stationary Schrödinger equations for the diatomic system with an empirical potential function in one-dimensional nanomaterials are solved within the framework of the quantum phase-space representation established by Torres-Vega and Frederick. The wave functions in position and momentum representations can be obtained through the Fourier-like projection transformation from the phase-space wave functions.

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

scholarly journals Non-negative sparse Laplacian regularized latent multi-view subspace clustering

2021 ◽  
Vol 15 ◽  
pp. 174830262110249
Author(s):  
Cong-Zhe You ◽  
Zhen-Qiu Shu ◽  
Hong-Hui Fan
Keyword(s):  
Subspace Clustering ◽  
Structural Features ◽  
Space Representation ◽  
Graph Regularization ◽  
Structure Information ◽  
Cluster Data ◽  
Latent Space ◽  
Low Dimensional ◽  
Relationship Of ◽  
The Relationship

Recently, in the area of artificial intelligence and machine learning, subspace clustering of multi-view data is a research hotspot. The goal is to divide data samples from different sources into different groups. We proposed a new subspace clustering method for multi-view data which termed as Non-negative Sparse Laplacian regularized Latent Multi-view Subspace Clustering (NSL2MSC) in this paper. The method proposed in this paper learns the latent space representation of multi view data samples, and performs the data reconstruction on the latent space. The algorithm can cluster data in the latent representation space and use the relationship of different views. However, the traditional representation-based method does not consider the non-linear geometry inside the data, and may lose the local and similar information between the data in the learning process. By using the graph regularization method, we can not only capture the global low dimensional structural features of data, but also fully capture the nonlinear geometric structure information of data. The experimental results show that the proposed method is effective and its performance is better than most of the existing alternatives.

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.

scholarly journals Estimation for Two-Dimensional Nonsymmetric Coherently Distributed Source in L-Shaped Arrays

2018 ◽  
Vol 2018 ◽  
pp. 1-16
Author(s):  
Tao Wu ◽  
Zhenghong Deng ◽  
Qingyue Gu ◽  
Jiwei Xu
Keyword(s):  
Expectation Maximization ◽  
Two Dimensional ◽  
Signal Distribution ◽  
Distributed Source ◽  
One Dimensional ◽  
Least Squares Estimators ◽  
Iterative Calculation ◽  
Symmetric Source ◽  
Relationship Of ◽  
The Relationship

We explore the estimation of a two-dimensional (2D) nonsymmetric coherently distributed (CD) source using L-shaped arrays. Compared with a symmetric source, the modeling and estimation of a nonsymmetric source are more practical. A nonsymmetric CD source is established through modeling the deterministic angular signal distribution function as a summation of Gaussian probability density functions. Parameter estimation of the nonsymmetric distributed source is proposed under an expectation maximization (EM) framework. The proposed EM iterative calculation contains three steps in each cycle. Firstly, the nominal azimuth angles and nominal elevation angles of Gaussian components in the nonsymmetric source are obtained from the relationship of rotational invariance matrices. Then, angular spreads can be solved through one-dimensional (1D) searching based on nominal angles. Finally, the powers of Gaussian components are obtained by solving least-squares estimators. Simulations are conducted to verify the effectiveness of the nonsymmetric CD model and estimation technique.

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