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Fiber Bragg Grating Sensors and Interrogation Systems
Published in Krzysztof Iniewski, Ginu Rajan, Krzysztof Iniewski, Optical Fiber Sensors, 2017
An FBG interrogation system consisting of a bulk optical edge filter shows a critical alignment problem and thus reduced portability. In such situation, an all-fiber approach is obviously more attractive. Davis and Kersey have demonstrated a demodulation scheme using a WDM coupler for an FBG sensor (Figure 9.37a). The WDM coupler shows a monotonic change in the coupling ratio between the two output fiber ports. Figure 9.37b shows its transfer function. Taking the ratio of the difference and sum of the two outputs of the WDM coupler gives a drift compensated output for wavelength shift detection. The spectral slope of a WDM coupler is typically less and the slope steepness determines the sensitivity, and the minimum detectable wavelength shifts ±3 με and 0.5 με/√(Hz) are the static and dynamic strain resolution of the system as reported [170]. The system is simple and cost effective and can be implemented where high sensitivity is not a constraint.
Bio-Optical Characteristics in Relation to Phytoplankton Composition and Productivity in a Twin Arctic Fjord Ecosystem during Summer
Published in Neloy Khare, Climate Change in the Arctic, 2022
where 2.303, L, ODs, ODfsw and ODnull are the conversion factor from log10 to loge, optical (cuvette) path-length in metres, optical densities of sample, purified FSW and at null absorption wavelength, respectively. The spectral slope of CDOM absorption coefficient (S) was derived by non-linear exponential regression fitting of aCDOM coefficient versus wavelength (350–650 nm) as per the approach of Stedmon et al. (2000). The S is the exponential slope coefficient, which indicates decrease in absorption with respect to wavelength. The S value varies with the source of the CDOM.
Damage detection of a cable-stayed bridge based on combining effective intrinsic mode functions of empirical mode decomposition using the feature selection technique
Published in Inverse Problems in Science and Engineering, 2021
Hossein Babajanian Bisheh, Gholamreza Ghodrati Amiri, Masoud Nekooei, Ehsan Darvishan
The following descriptors represent the shape of the STFT using Equations (19)–(24), as listed in Table 3. The spectral roll off (S5) measures the bandwidth of the analysed block n of the samples.The spectral flux (S6) represents the amount of variation in the spectral shape. It is defined as the mean difference between successive STFT frames.The spectral decrease (S7) computes the steepness of the decrease of the spectral envelope over frequency.The spectral slope (S8) measures the slope of the spectral shape using a linear regression over the spectral amplitude values.The spectral crest factor (S9) is the ratio of the maximum value of the magnitude spectrum to the sum of the magnitude spectrum.The spectral flatness (S10) is obtained by comparing the geometric and arithmetic means of the spectrum.
Damage detection of a cable-stayed bridge using feature extraction and selection methods
Published in Structure and Infrastructure Engineering, 2019
Hossein Babajanian Bisheh, Gholamreza Ghodrati Amiri, Masoud Nekooei, Ehsan Darvishan
The following features describe the shape of the (magnitude spectrum of the) STFT using Equations (18)–(23), as listed in Table 3: The spectral roll off (S5) is a measure of the bandwidth of the analysed block n of the samples.The spectral flux (S6) measures the amount of change of the spectral shape. It is defined as the average difference between consecutive STFT frames.The spectral decrease (S7) estimates the steepness of the decrease of the spectral envelope over frequency.The spectral slope (S8) is similar to the spectral decrease, a measure of the slope of the spectral shape.Spectral Crest Factor (S9) compares peaks of the magnitude spectrum with the sum of this magnitude spectrum).Spectral Flatness (S10) is the ratio of geometric mean to the arithmetic mean of the magnitude spectrum.