An improved signal filtering strategy based on EMD algorithm for ultrahigh precision grating encoder

AbstractIn the paper an Equivalent Water Thickness (EWT) determination as a way of observing gravity variations is described. Since raw data acquired directly from Gravity Recovery and Climate Experiment - GRACE satellites is unsuitable for analysis due to stripes occurrence, a filtering algorithm must be used. In this paper, authors are testing two isotropic (Gauss, CNES/GRGS) filters and two anisotropic filters (Wiener- -Kolomogorov, ANS). Correlation, amplitude ratio, and modification were determined as well as maps were generated.

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Compressive Sensing: Analysis of Signals in Radio Astronomy

Open Astronomy ◽

10.1515/astro-2017-0165 ◽

2013 ◽

Vol 22 (4) ◽

Author(s):

G. Gaigals ◽

M. Greitāns ◽

A. Andziulis

Keyword(s):

Compressive Sensing ◽

Radio Astronomy ◽

Signal Analysis ◽

Signal Filtering ◽

Nyquist Criterion

AbstractThe compressive sensing (CS) theory says that for some kind of signals there is no need to keep or transfer all the data acquired accordingly to the Nyquist criterion. In this work we investigate if the CS approach is applicable for recording and analysis of radio astronomy (RA) signals. Since CS methods are applicable for the signals with sparse (and compressible) representations, the compressibility of RA signals is verified. As a result, we identify which RA signals can be processed using CS, find the parameters which can improve or degrade CS application to RA results, describe the optimum way how to perform signal filtering in CS applications. Also, a range of virtual LabVIEW instruments are created for the signal analysis with the CS theory.

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Dynamic measurements of CO diffusing capacity using discrete samples of alveolar gas

Journal of Applied Physiology ◽

10.1152/jappl.1983.54.1.73 ◽

1983 ◽

Vol 54 (1) ◽

pp. 73-79 ◽

Cited By ~ 9

Author(s):

B. L. Graham ◽

J. T. Mink ◽

D. J. Cotton

Keyword(s):

Response Time ◽

Normal Subjects ◽

Diffusing Capacity ◽

Dynamic Measurements ◽

Signal Filtering ◽

Single Breath ◽

Discrete Sample ◽

Yield Information ◽

Co Concentration ◽

Sample Method

It has been shown that measurements of the diffusing capacity of the lung for CO made during a slow exhalation [DLCO(exhaled)] yield information about the distribution of the diffusing capacity in the lung that is not available from the commonly measured single-breath diffusing capacity [DLCO(SB)]. Current techniques of measuring DLCO(exhaled) require the use of a rapid-responding (less than 240 ms, 10–90%) CO meter to measure the CO concentration in the exhaled gas continuously during exhalation. DLCO(exhaled) is then calculated using two sample points in the CO signal. Because DLCO(exhaled) calculations are highly affected by small amounts of noise in the CO signal, filtering techniques have been used to reduce noise. However, these techniques reduce the response time of the system and may introduce other errors into the signal. We have developed an alternate technique in which DLCO(exhaled) can be calculated using the concentration of CO in large discrete samples of the exhaled gas, thus eliminating the requirement of a rapid response time in the CO analyzer. We show theoretically that this method is as accurate as other DLCO(exhaled) methods but is less affected by noise. These findings are verified in comparisons of the discrete-sample method of calculating DLCO(exhaled) to point-sample methods in normal subjects, patients with emphysema, and patients with asthma.

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