scholarly journals Improved Methods of Signal Processing Used in Low-Coherent Systems

2008 ◽  
Vol 114 (6A) ◽  
pp. A-127-A-131 ◽  
Author(s):  
M. Jedrzejewska-Szczerska
2011 ◽  
Vol 29 (8) ◽  
pp. 1098-1104 ◽  
Author(s):  
Jianqiang Li ◽  
Zhenning Tao ◽  
Huijian Zhang ◽  
Weizhen Yan ◽  
Takeshi Hoshida ◽  
...  

2017 ◽  
Vol 10 (12) ◽  
pp. 5063-5073 ◽  
Author(s):  
Jesse L. Ambrose

Abstract. Atmospheric Hg measurements are commonly carried out using Tekran® Instruments Corporation's model 2537 Hg vapor analyzers, which employ gold amalgamation preconcentration sampling and detection by thermal desorption (TD) and atomic fluorescence spectrometry (AFS). A generally overlooked and poorly characterized source of analytical uncertainty in those measurements is the method by which the raw Hg atomic fluorescence (AF) signal is processed. Here I describe new software-based methods for processing the raw signal from the Tekran® 2537 instruments, and I evaluate the performances of those methods together with the standard Tekran® internal signal processing method. For test datasets from two Tekran® instruments (one 2537A and one 2537B), I estimate that signal processing uncertainties in Hg loadings determined with the Tekran® method are within ±[1 % +  1.2 pg] and ±[6 % + 0.21 pg], respectively. I demonstrate that the Tekran® method can produce significant low biases (≥  5 %) not only at low Hg sample loadings (<  5 pg) but also at tropospheric background concentrations of gaseous elemental mercury (GEM) and total mercury (THg) (∼  1 to 2 ng m−3) under typical operating conditions (sample loadings of 5–10 pg). Signal processing uncertainties associated with the Tekran® method can therefore represent a significant unaccounted for addition to the overall  ∼  10 to 15 % uncertainty previously estimated for Tekran®-based GEM and THg measurements. Signal processing bias can also add significantly to uncertainties in Tekran®-based gaseous oxidized mercury (GOM) and particle-bound mercury (PBM) measurements, which often derive from Hg sample loadings < 5 pg. In comparison, estimated signal processing uncertainties associated with the new methods described herein are low, ranging from within ±0.053 pg, when the Hg thermal desorption peaks are defined manually, to within ±[2 % + 0.080 pg] when peak definition is automated. Mercury limits of detection (LODs) decrease by 31 to 88 % when the new methods are used in place of the Tekran® method. I recommend that signal processing uncertainties be quantified in future applications of the Tekran® 2537 instruments.


2011 ◽  
Vol 29 (20) ◽  
pp. 3070-3082 ◽  
Author(s):  
M. Selmi ◽  
C. Gosset ◽  
M. Noelle ◽  
P. Ciblat ◽  
Y. Jaouen

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Keith E. Holbert ◽  
Kang Lin

Economic constraints are driving the electric power industry to seek improved methods for monitoring, control, and diagnostics. To increase plant availability, various techniques have been implemented in industry to assess equipment condition to prevent system inoperability. The availability of a large number of measured signals and additional component information and the increasing number of signal processing options to analyze sampled data motivate the assimilation of such diverse information into a plantwide condition monitor. The use of fuzzy logic is described herein for the purpose of performing the decision making regarding the system status and the possible need for component maintenance. Fuzzy-logic-based diagnostic monitoring is applied to data acquired from instrumentation within operating facilities.


2017 ◽  
Author(s):  
Jesse L. Ambrose

Abstract. Atmospheric Hg measurements are commonly carried out using Tekran® Instruments Corporation's model 2537 Hg vapor analyzers, which employ gold amalgamation pre-concentration sampling and detection by thermal desorption/atomic fluorescence spectrometry. A generally overlooked and poorly characterized source of analytical uncertainty in those measurements is the method by which the raw Hg atomic fluorescence signal is processed. Here I describe new software-based methods for processing the raw signal from the Tekran® 2537 instruments, and I evaluate the performances of those methods together with the standard Tekran® internal signal processing method. For test datasets from two Tekran® instruments (one 2537A and one 2537B), I estimate that signal processing uncertainties in Hg loadings determined with the Tekran® method are within ±[6 % + 0.94 pg]. I demonstrate that the Tekran® method produces significant low biases (≥ 5 %) not only at low Hg sample loadings (


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