Polarization analysis and separation of P- and SV-wavefields with adaptive sliding windows

2014 ◽  
Author(s):  
Baotong Liu
Keyword(s):  
Polarization Analysis ◽  
Sliding Windows

Imaging magnetic microstructure with SEMPA

1993 ◽  
Vol 51 ◽  
pp. 1032-1033
Author(s):  
M. H. Kelley ◽  
J. Unguris ◽  
R. J. Celotta ◽  
D. T. Pierce
Keyword(s):  
Electron Microscopy ◽  
Thin Films ◽  
Scanning Electron Microscopy ◽  
Sandwich Structure ◽  
Magnetic Coupling ◽  
Polarization Analysis ◽  
Secondary Electrons ◽  
Magnetic Microstructure ◽  
Escape Depth ◽  
Scanning Electron

By measuring the spin polarization of secondary electrons generated in a scanning electron microscope, scanning electron microscopy with polarization analysis (SEMPA) can directly image the magnitude and direction of a material’s magnetization. Because the escape depth of the secondaries is only on the order of 1 nm, SEMPA is especially well-suited for investigating the magnetization of ultra-thin films and surfaces. We have exploited this feature of SEMPA to study the magnetic microstrcture and magnetic coupling in ferromagnetic multilayers where the layers may only be a few atomic layers thick. For example, we have measured the magnetic coupling in Fe/Cr/Fe(100) and Fe/Ag/Fe(100) trilayers and have found that the coupling oscillates between ferromagnetic and antiferromagnetic as a function of the Cr or Ag spacer thickness.The SEMPA apparatus has been described in detail elsewhere. The sample consisted of a magnetic sandwich structure with a wedge-shaped interlayer as shown in Fig. 1.

POLARIZATION ANALYSIS OF THE MAGNETIC DIFFUSE SCATTERING OF Na3Cr2P3O12

1988 ◽  
Vol 49 (C8) ◽  
pp. C8-199-C8-200
Author(s):  
N. Fanjat ◽  
O. Schaerpf ◽  
J. L. Soubeyroux ◽  
A. J. Dianoux ◽  
G. Lucazeau
Keyword(s):  
Diffuse Scattering ◽  
Polarization Analysis

Anomaly Detection via Mining Numerical Workflow Relations from Logs

2020 ◽  
Author(s):  
Bo Zhang ◽  
Hongyu Zhang ◽  
Pablo Moscato
Keyword(s):  
Distributed Systems ◽  
Anomaly Detection ◽  
Text Messages ◽  
Distributed File System ◽  
Log Data ◽  
Sliding Windows ◽  
Novel Approach ◽  
Hadoop Distributed File System ◽  
Blue Gene ◽  
Online Anomaly Detection

<div>Complex software intensive systems, especially distributed systems, generate logs for troubleshooting. The logs are text messages recording system events, which can help engineers determine the system's runtime status. This paper proposes a novel approach named ADR (stands for Anomaly Detection by workflow Relations) that employs matrix nullspace to mine numerical relations from log data. The mined relations can be used for both offline and online anomaly detection and facilitate fault diagnosis. We have evaluated ADR on log data collected from two distributed systems, HDFS (Hadoop Distributed File System) and BGL (IBM Blue Gene/L supercomputers system). ADR successfully mined 87 and 669 numerical relations from the logs and used them to detect anomalies with high precision and recall. For online anomaly detection, ADR employs PSO (Particle Swarm Optimization) to find the optimal sliding windows' size and achieves fast anomaly detection.</div><div>The experimental results confirm that ADR is effective for both offline and online anomaly detection. </div>

An Impulsive Interference Depression Method Based on Polarization Analysis

2013 ◽  
Vol 34 (11) ◽  
pp. 2548-2553
Author(s):  
Wei Liu ◽  
Sheng-chun Piao ◽  
Han-hao Zhu
Keyword(s):  
Polarization Analysis ◽  
Impulsive Interference

A new visual/inertial integrated navigation algorithm based on sliding-window factor graph optimisation

2020 ◽  
pp. 1-17
Author(s):  
Haiying Liu ◽  
Jingqi Wang ◽  
Jianxin Feng ◽  
Xinyao Wang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Prior Information ◽  
Sliding Window ◽  
Factor Graph ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Sliding Windows ◽  
Navigation Algorithm ◽  
Almost All

Abstract Visual–Inertial Navigation Systems (VINS) plays an important role in many navigation applications. In order to improve the performance of VINS, a new visual/inertial integrated navigation method, named Sliding-Window Factor Graph optimised algorithm with Dynamic prior information (DSWFG), is proposed. To bound computational complexity, the algorithm limits the scale of data operations through sliding windows, and constructs the states to be optimised in the window with factor graph; at the same time, the prior information for sliding windows is set dynamically to maintain interframe constraints and ensure the accuracy of the state estimation after optimisation. First, the dynamic model of vehicle and the observation equation of VINS are introduced. Next, as a contrast, an Invariant Extended Kalman Filter (InEKF) is constructed. Then, the DSWFG algorithm is described in detail. Finally, based on the test data, the comparison experiments of Extended Kalman Filter (EKF), InEKF and DSWFG algorithms in different motion scenes are presented. The results show that the new method can achieve superior accuracy and stability in almost all motion scenes.

scholarly journals The relationship between Google search interest for pulmonary symptoms and COVID-19 cases using dynamic conditional correlation analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Halit Cinarka ◽  
Mehmet Atilla Uysal ◽  
Atilla Cifter ◽  
Elif Yelda Niksarlioglu ◽  
Aslı Çarkoğlu
Keyword(s):  
Case Reports ◽  
Time Varying ◽  
Conditional Correlation ◽  
Web Based ◽  
Specific Symptom ◽  
Sliding Windows ◽  
Dynamic Conditional Correlation ◽  
The United Kingdom ◽  
Correlation Models ◽  
Google Search

AbstractThis study aims to evaluate the monitoring and predictive value of web-based symptoms (fever, cough, dyspnea) searches for COVID-19 spread. Daily search interests from Turkey, Italy, Spain, France, and the United Kingdom were obtained from Google Trends (GT) between January 1, 2020, and August 31, 2020. In addition to conventional correlational models, we studied the time-varying correlation between GT search and new case reports; we used dynamic conditional correlation (DCC) and sliding windows correlation models. We found time-varying correlations between pulmonary symptoms on GT and new cases to be significant. The DCC model proved more powerful than the sliding windows correlation model. This model also provided better at time-varying correlations (r ≥ 0.90) during the first wave of the pandemic. We used a root means square error (RMSE) approach to attain symptom-specific shift days and showed that pulmonary symptom searches on GT should be shifted separately. Web-based search interest for pulmonary symptoms of COVID-19 is a reliable predictor of later reported cases for the first wave of the COVID-19 pandemic. Illness-specific symptom search interest on GT can be used to alert the healthcare system to prepare and allocate resources needed ahead of time.

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