Improved algorithm for delay extraction using Hilbert transform

2015 ◽  
Author(s):  
Jia-Yi Wu ◽  
Qian-Hua Jian ◽  
Mu-Shui Zhang ◽  
Hong-Zhou Tan
Keyword(s):  
Hilbert Transform ◽  
Delay Extraction ◽  
Improved Algorithm

REDUCTION OF DYNAMIC LOSSES IN THE ACTIVE ONE-PHASE FOUR-QUADRANT CONVERTER WITH THE IMPROVED ALGORITHM OF HYSTERESIS MODULATION

2018 ◽  
Vol 2018 (51) ◽  
pp. 88-94 ◽  
Author(s):  
O.A. Plakhtiy ◽  
◽  
V.P. Nerubatskiy ◽  
D.L. Sushko ◽  
V.E. Kavun ◽  
...  
Keyword(s):  
Four Quadrant ◽  
Improved Algorithm

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.

An Improved Algorithm for Universal Sensor Registration

2020 ◽  
Author(s):  
Daniel Sigalov ◽  
Aharon Gal ◽  
Boaz Vigdor
Keyword(s):  
Sensor Registration ◽  
Improved Algorithm

Improved algorithm for solving multi-route planning—based on travel route planning of USA state city

2014 ◽  
Author(s):  
Liyao Yang ◽  
Yihao Guo ◽  
Qian Chen ◽  
Kailian Lin ◽  
Shiyu Lin ◽  
...  
Keyword(s):  
Route Planning ◽  
Improved Algorithm

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>

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