Improvement of NIES lidar network observations by adding Raman scatter measurement function

2012 ◽  
Author(s):  
Tomoaki Nishizawa ◽  
Nobuo Sugimoto ◽  
Ichiro Matsui ◽  
A. Shimizu
Keyword(s):  
Measurement Function ◽  
Raman Scatter

Nature connectedness:It's concept, measurement, function and intervention

2017 ◽  
Vol 25 (8) ◽  
pp. 1360 ◽  
Author(s):  
Ying YANG ◽  
Liuna GENG ◽  
Peng XIANG ◽  
Jing ZHANG ◽  
Lifang ZHU
Keyword(s):  
Measurement Function

scholarly journals First application of the optimal estimation method to retrieve temperature from pure rotational raman scatter lidar measurements

2018 ◽  
Vol 176 ◽  
pp. 01011
Author(s):  
S. Mahagammulla Gamage ◽  
A. Haefele ◽  
R.J. Sica
Keyword(s):  
Estimation Method ◽  
Optimal Estimation ◽  
Forward Model ◽  
Temperature Calibration ◽  
Lidar Measurements ◽  
Raman Scatter ◽  
Calibration Coefficients

We present the application of the Optimal Estimation Method (OEM) to retrieve atmospheric temperatures from pure rotational Raman (PRR) backscatter lidar measurements. A forward model (FM) is developed to retrieve temperature and tested using synthetic measurements. The OEM offers many advantages for this analysis, including eliminating the need to determine temperature calibration coefficients.

scholarly journals FRACTIONAL TREND OF THE VARIANCE IN CAVALIERI SAMPLING

2011 ◽  
Vol 19 (2) ◽  
pp. 71 ◽  
Author(s):  
Marta García-Fiñana ◽  
Luis M Cruz-Orive
Keyword(s):  
Fractional Derivatives ◽  
Real Data ◽  
Current Theory ◽  
Continuous Derivative ◽  
General Representation ◽  
Higher Order Terms ◽  
Measurement Function ◽  
Constant Distance ◽  
Derivatives Of ◽  
Maclaurin Formula

Cavalieri sampling is often used to estimate the volume of an object with systematic sections a constant distance T apart. The variance of the corresponding estimator can be expressed as the sum of the extension term (which gives the overall trend of the variance and is used to estimate it), the 'Zitterbewegung' (which oscillates about zero) and higher order terms. The extension term is of order T2m+2 for small T, where m is the order of the first non-continuous derivative of the measurement function f, (namely of the area function if the target is the volume). A key condition is that the jumps of the mth derivative f (m) of f are finite. When this is not the case, then the variance exhibits a fractional trend, and the current theory fails. Indeed, in practice the mentioned trend is often of order T2q+2, typically with 0 <q <1. We obtain a general representation of the variance, and thereby of the extension term, by means of a new Euler-MacLaurin formula involving fractional derivatives of f. We also present a new and general estimator of the variance, see Eq. 26a, b, and apply it to real data (white matter of a human brain).

scholarly journals The Luyten – Control Data Stellar Proper Motion Measuring Machine

1970 ◽  
Vol 7 ◽  
pp. 5-25
Author(s):  
James Newcomb
Keyword(s):  
Proper Motion ◽  
Time Scale ◽  
Proper Motions ◽  
Control Data ◽  
Stellar Proper Motions ◽  
Measurement Function ◽  
Measuring Machine ◽  
Motion Measurements

The discovery and measurement of stellar proper motions has always been associated with machines: for proper motion measurements involve four activities: observation, recording, comparison and measurement. Participation by the astronomer in these activities has step by step been replaced partically or wholly by machines. First the observation and recording functions changed from visual to photographic – with the fine guiding done by the astronomer; then the comparison by the blink microscope and the measurement by visually operated measuring machines. On a comparative time scale, the next step – automation of the comparison and measurement function – has been much money, time, and effort away from the previous steps, but as this presentation and other presentations at this conference will show, machines of varying degrees of automation and astronomer participation are now in operation.

Analysis of Trust Chain Transitivity in TCG

2012 ◽  
Vol 490-495 ◽  
pp. 2042-2046
Author(s):  
Ce Zhang ◽  
Gang Cui ◽  
Zhong Chuan Fu ◽  
Yong Dong Xu
Keyword(s):  
Trusted Computing ◽  
Key Factors ◽  
Loss Measurement ◽  
General Trust ◽  
Trust Chain ◽  
Trust Relation ◽  
Measurement Function ◽  
Chain Transitivity ◽  
Two Stages

Trust chain is fundamental technology of Trusted Computing facing many problems. This paper points out that trust chain is made of two stages, analyses different trust relation building mechanism of two stages, and also thinks and proves that trust loss during trust transitivity process comes from composition loss and so on, describes behavior of trust chain transitivity quantitatively. After discussing key factors affect the trust, this paper defines the general trust transitivity model and trust loss measurement function, and discusses trust loss of Stage1 in TCG.

Fluorescence Intensity Calibration Using the Raman Scatter Peak of Water

2009 ◽  
Vol 63 (8) ◽  
pp. 936-940 ◽  
Author(s):  
A. J. Lawaetz ◽  
C. A. Stedmon
Keyword(s):  
Fluorescence Intensity ◽  
Calibration Method ◽  
Simple Approach ◽  
Raman Peak ◽  
Great Intensity ◽  
Hazardous Chemicals ◽  
Intensity Factors ◽  
Raman Scatter ◽  
Intensity Calibration

Fluorescence data of replicate samples obtained from different fluorescence spectrometers or by the same spectrometer but with different instrument settings can have great intensity differences. In order to compare such data an intensity calibration must be applied. Here we explain a simple calibration method for fluorescence intensity using only the integrated area of a water Raman peak. By applying this method to data from three different instruments, we show that it is possible to remove instrument-dependent intensity factors, and we present results on a unified scale of Raman units. The method presented is a rapid and simple approach suitable for routine measurements with no need for hazardous chemicals.

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