A Two-Dimensionally Coincident Second Difference Cosmic Ray Spike Removal Method for the Fully Automated Processing of Raman Spectra

2014 ◽  
Vol 68 (2) ◽  
pp. 185-191 ◽  
Author(s):  
H. Georg Schulze ◽  
Robin F.B. Turner
Keyword(s):  
Raman Spectra ◽  
Cosmic Ray ◽  
Removal Method ◽  
Automated Processing

A Model-Free, Fully Automated Baseline-Removal Method for Raman Spectra

2011 ◽  
Vol 65 (1) ◽  
pp. 75-84 ◽  
Author(s):  
H. Georg Schulze ◽  
Rod B. Foist ◽  
Kadek Okuda ◽  
André Ivanov ◽  
Robin F. B. Turner
Keyword(s):  
Raman Spectra ◽  
Removal Method ◽  
Model Free ◽  
Baseline Removal

Single-Step Preprocessing of Raman Spectra Using Convolutional Neural Networks

2020 ◽  
Vol 74 (4) ◽  
pp. 427-438 ◽  
Author(s):  
Joel Wahl ◽  
Mikael Sjödahl ◽  
Kerstin Ramser
Keyword(s):  
Raman Spectra ◽  
Background Noise ◽  
Simulated Data ◽  
Cosmic Ray ◽  
Absolute Error ◽  
Single Step ◽  
Proof Of Concept ◽  
Baseline Subtraction ◽  
Synthetic Spectra ◽  
Asymmetric Least Squares

Preprocessing of Raman spectra is generally done in three separate steps: (1) cosmic ray removal, (2) signal smoothing, and (3) baseline subtraction. We show that a convolutional neural network (CNN) can be trained using simulated data to handle all steps in one operation. First, synthetic spectra are created by randomly adding peaks, baseline, mixing of peaks and baseline with background noise, and cosmic rays. Second, a CNN is trained on synthetic spectra and known peaks. The results from preprocessing were generally of higher quality than what was achieved using a reference based on standardized methods (second-difference, asymmetric least squares, cross-validation). From 105 simulated observations, 91.4% predictions had smaller absolute error (RMSE), 90.3% had improved quality (SSIM), and 94.5% had reduced signal-to-noise (SNR) power. The CNN preprocessing generated reliable results on measured Raman spectra from polyethylene, paraffin and ethanol with background contamination from polystyrene. The result shows a promising proof of concept for the automated preprocessing of Raman spectra.

A new paradigm for signal processing of Raman spectra using a smoothing free algorithm: Coupling continuous wavelet transform with signal removal method

2013 ◽  
Vol 44 (4) ◽  
pp. 608-621 ◽  
Author(s):  
Ahmad Esmaielzadeh Kandjani ◽  
Matthew J. Griffin ◽  
Rajesh Ramanathan ◽  
Samuel J. Ippolito ◽  
Suresh K. Bhargava ◽  
...  
Keyword(s):  
Signal Processing ◽  
Wavelet Transform ◽  
Raman Spectra ◽  
Continuous Wavelet Transform ◽  
Continuous Wavelet ◽  
New Paradigm ◽  
Removal Method

Convergent-beam thickness determination: The advantages of digital imaging

1994 ◽  
Vol 52 ◽  
pp. 420-421
Author(s):  
Stuart McKernan ◽  
C. Barry Carter
Keyword(s):  
Intensity Variation ◽  
Convergent Beam Electron Diffraction ◽  
Digital Data ◽  
Thickness Determination ◽  
Frequent Use ◽  
Automated Processing ◽  
Beam Thickness ◽  
Convergent Beam ◽  
Remarkable Agreement

Convergent-beam electron diffraction (CBED) patterns contain an immense amount of information relating to the structure of the material from which they are obtained. The analysis of these patterns has progressed to the point that under appropriate, well specified conditions, the intensity variation within the CBED discs may be understood in a quantitative sense. Rossouw et al for example, have produced numerical simulations of zone-axis CBED patterns which show remarkable agreement with experimental patterns. Spence and co-workers have obtained the structure factor parameters for lowindex reflections using the intensity variation in 2-beam CBED patterns. Both of these examples involve the use of digital data. Perhaps the most frequent use for quantitative CBED analysis is the thickness determination described by Kelly et al. This analysis has been implemented in a variety of different ways; from real-time, in-situ analysis using the microscope controls, to measurements of photographic prints with a ruler, to automated processing of digitally acquired images. The potential advantages of this latter process will be presented.

Single Crystal Raman Spectra of the Phase Transition in KTCNQ

1982 ◽  
Vol 85 (1) ◽  
pp. 297-303 ◽  
Author(s):  
A. D. Bandrauk ◽  
K. D. Truong ◽  
S. Jandl
Keyword(s):  
Phase Transition ◽  
Single Crystal ◽  
Raman Spectra

DEVELOPMENT OF THE COSMIC RAY TECHNIQUES

1982 ◽  
Vol 43 (C8) ◽  
pp. C8-69-C8-88 ◽  
Author(s):  
B. Rossi
Keyword(s):  
Cosmic Ray
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