Complex Polarization Analysis Based on Windowed Hilbert Transform and Its Application

2005 ◽  
Vol 48 (4) ◽  
pp. 960-967 ◽  
Author(s):  
Yun CHEN ◽  
Zhong-Jie ZHANG ◽  
Xiao-Bo TIAN
Keyword(s):  
Hilbert Transform ◽  
Polarization Analysis ◽  
Complex Polarization

Ground roll attenuation by robust complex polarization analysis

2010 ◽  
Author(s):  
Chen Haifeng ◽  
Luo Guoan ◽  
Hou Aiyuan ◽  
Zhao Gulling ◽  
Lei Na
Keyword(s):  
Polarization Analysis ◽  
Ground Roll ◽  
Complex Polarization

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

Model of multicomponent narrow-band periodical non-stationary random signal

2020 ◽  
Vol 2020 (48) ◽  
pp. 17-24
Author(s):  
I.M. Javorskyj ◽  
◽  
R.M. Yuzefovych ◽  
P.R. Kurapov ◽  
◽  
...  
Keyword(s):  
Time Series ◽  
Real Time ◽  
Hilbert Transform ◽  
Spectral Properties ◽  
Narrow Band ◽  
Analytic Signal ◽  
Random Signal ◽  
Hilbert Transformation ◽  
The Hilbert Transform

The correlation and spectral properties of a multicomponent narrowband periodical non-stationary random signal (PNRS) and its Hilbert transformation are considered. It is shown that multicomponent narrowband PNRS differ from the monocomponent signal. This difference is caused by correlation of the quadratures for the different carrier harmonics. Such features of the analytic signal must be taken into account when we use the Hilbert transform for the analysis of real time series.

A Bayesian View on the Hilbert Transform and the Kramers-Kronig Transform of Electrochemical Impedance Data: Probabilistic Estimates and Quality Scores

2020 ◽  
Author(s):  
Jiapeng Liu ◽  
Ting Hei Wan ◽  
Francesco Ciucci
Keyword(s):  
Power Generation ◽  
Electrochemical Impedance Spectroscopy ◽  
Hilbert Transform ◽  
Electrochemical Impedance ◽  
The Self ◽  
Impedance Data ◽  
The Hilbert Transform ◽  
Validation Tests ◽  
Self Consistency ◽  
Mathematical Relations

<p>Electrochemical impedance spectroscopy (EIS) is one of the most widely used experimental tools in electrochemistry and has applications ranging from energy storage and power generation to medicine. Considering the broad applicability of the EIS technique, it is critical to validate the EIS data against the Hilbert transform (HT) or, equivalently, the Kramers–Kronig relations. These mathematical relations allow one to assess the self-consistency of obtained spectra. However, the use of validation tests is still uncommon. In the present article, we aim at bridging this gap by reformulating the HT under a Bayesian framework. In particular, we developed the Bayesian Hilbert transform (BHT) method that interprets the HT probabilistic. Leveraging the BHT, we proposed several scores that provide quick metrics for the evaluation of the EIS data quality.<br></p>

Display of Hilbert Transform Using Polarization Filter

1989 ◽  
Vol 18 (2) ◽  
pp. 48-50
Author(s):  
Kallol Bhattacharya ◽  
D. K. Basu ◽  
Ajay Ghosh ◽  
A. K. Chakrabortt
Keyword(s):  
Hilbert Transform ◽  
Polarization Filter
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