An improved signal-to-noise ratio of a cool imaging photon detector for Fabry - Perot interferometer measurements of low-intensity air glow

1997 ◽  
Vol 8 (4) ◽  
pp. 456-458 ◽  
Author(s):  
T P Davies ◽  
P L Dyson
Keyword(s):  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Photon Detector ◽  
Low Intensity ◽  
Fabry Perot ◽  
Noise Ratio

scholarly journals Convolution Network with Custom Loss Function for the Denoising of Low SNR Raman Spectra

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4623
Author(s):  
Sinead Barton ◽  
Salaheddin Alakkari ◽  
Kevin O’Dwyer ◽  
Tomas Ward ◽  
Bryan Hennelly
Keyword(s):  
Raman Spectra ◽  
Loss Function ◽  
Signal To Noise Ratio ◽  
Biomedical Science ◽  
Clinical Tool ◽  
Signal To Noise ◽  
Low Snr ◽  
Low Intensity ◽  
Spectral Peaks ◽  
Noise Ratio

Raman spectroscopy is a powerful diagnostic tool in biomedical science, whereby different disease groups can be classified based on subtle differences in the cell or tissue spectra. A key component in the classification of Raman spectra is the application of multi-variate statistical models. However, Raman scattering is a weak process, resulting in a trade-off between acquisition times and signal-to-noise ratios, which has limited its more widespread adoption as a clinical tool. Typically denoising is applied to the Raman spectrum from a biological sample to improve the signal-to-noise ratio before application of statistical modeling. A popular method for performing this is Savitsky–Golay filtering. Such an algorithm is difficult to tailor so that it can strike a balance between denoising and excessive smoothing of spectral peaks, the characteristics of which are critically important for classification purposes. In this paper, we demonstrate how Convolutional Neural Networks may be enhanced with a non-standard loss function in order to improve the overall signal-to-noise ratio of spectra while limiting corruption of the spectral peaks. Simulated Raman spectra and experimental data are used to train and evaluate the performance of the algorithm in terms of the signal to noise ratio and peak fidelity. The proposed method is demonstrated to effectively smooth noise while preserving spectral features in low intensity spectra which is advantageous when compared with Savitzky–Golay filtering. For low intensity spectra the proposed algorithm was shown to improve the signal to noise ratios by up to 100% in terms of both local and overall signal to noise ratios, indicating that this method would be most suitable for low light or high throughput applications.

Research on Signal-to-Noise Ratio Effect in Spatial Scanning Optical Fiber Fabry-Perot Sensing Demodulation System

2015 ◽  
Vol 35 (11) ◽  
pp. 1106003
Author(s):  
江俊峰 Jiang Junfeng ◽  
邹盛亮 Zou Shengliang ◽  
王双 Wang Shuang ◽  
刘铁根 Liu Tiegen ◽  
刘琨 Liu Kun ◽  
...  
Keyword(s):  
Optical Fiber ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Ratio Effect ◽  
Fabry Perot ◽  
Noise Ratio

scholarly journals Fabry–Perot interferometer as a solar background noise suppressor: application to daytime lidar

2012 ◽  
Vol 55 (2) ◽  
Author(s):  
Karnam Raghunath ◽  
Karnam Ramesh ◽  
Sanama Narayana Reddy
Keyword(s):  
Background Noise ◽  
Noise Suppression ◽  
Performance Indicator ◽  
Signal To Noise Ratio ◽  
Interference Filter ◽  
Signal To Noise ◽  
Night Time ◽  
Fabry Perot ◽  
Noise Ratio ◽  
Identical Test

<p>Continuous atmospheric probing by a lidar is a requirement for many applications. However, due to high solar background noise during the daytime, lidar operations are mostly restricted to night-time. While many techniques are in practice, like reducing the receiver field of view, changing the view angle, introducing a narrow band Interference Filter (IF), these are applied to circumvent problems, rather than to suppress the noise. Using a Fabry-Perot interferometer as a narrow passband filter for solar background noise suppression is a known technique, and its potential is exploited in our system. An optical-fiber-coupled lidar system with its transmitter injection seeded was developed and has been operated during the daytime at Gadanki (13.6˚N, 79.2˚ E). The signal-to-noise ratio of the return signal is used as the performance indicator, to evaluate the improvements. Signal-to-noise ratios with and without the Fabry-Perot interferometer are measured with near identical test set-ups. The signal-to-noise ratio enhancement factor is ca. 4, in agreement with the theoretical value. The performance is compared when the receiver fields of view are changed.</p>

Improving the signal-to-noise ratio in a fiber-optic Fabry–Pérot acoustic sensor

2019 ◽  
Vol 16 (6) ◽  
pp. 065106 ◽  
Author(s):  
Hamed Moradi ◽  
Fahimeh Hosseinibalam ◽  
Smaeyl Hassanzadeh
Keyword(s):  
Fiber Optic ◽  
Signal To Noise Ratio ◽  
Acoustic Sensor ◽  
Signal To Noise ◽  
Fabry Perot ◽  
Noise Ratio

Determination of the Best Emulsion for the Microscopy of Crystals

1981 ◽  
Vol 39 ◽  
pp. 32-33
Author(s):  
David A. Grano ◽  
Kenneth H. Downing
Keyword(s):  
Spatial Frequency ◽  
Information Transfer ◽  
Signal To Noise Ratio ◽  
Experimental Situation ◽  
Photographic Emulsion ◽  
Modulation Transfer ◽  
Signal To Noise ◽  
Frequency Space ◽  
Noise Ratio

The retrieval of high-resolution information from images of biological crystals depends, in part, on the use of the correct photographic emulsion. We have been investigating the information transfer properties of twelve emulsions with a view toward 1) characterizing the emulsions by a few, measurable quantities, and 2) identifying the “best” emulsion of those we have studied for use in any given experimental situation. Because our interests lie in the examination of crystalline specimens, we've chosen to evaluate an emulsion's signal-to-noise ratio (SNR) as a function of spatial frequency and use this as our critereon for determining the best emulsion.The signal-to-noise ratio in frequency space depends on several factors. First, the signal depends on the speed of the emulsion and its modulation transfer function (MTF). By procedures outlined in, MTF's have been found for all the emulsions tested and can be fit by an analytic expression 1/(1+(S/S0)2). Figure 1 shows the experimental data and fitted curve for an emulsion with a better than average MTF. A single parameter, the spatial frequency at which the transfer falls to 50% (S0), characterizes this curve.

Experiments in Bright-Field Imaging with Hollow-Cone Illumination

1981 ◽  
Vol 39 ◽  
pp. 226-227
Author(s):  
W. Kunath ◽  
K. Weiss ◽  
E. Zeitler
Keyword(s):  
Signal To Noise Ratio ◽  
Carbon Support ◽  
Electronic System ◽  
Optic Axis ◽  
Hollow Cone ◽  
Signal To Noise ◽  
Bright Field ◽  
Noise Ratio ◽  
Single Field ◽  
Field Imaging

Bright-field images taken with axial illumination show spurious high contrast patterns which obscure details smaller than 15 ° Hollow-cone illumination (HCI), however, reduces this disturbing granulation by statistical superposition and thus improves the signal-to-noise ratio. In this presentation we report on experiments aimed at selecting the proper amount of tilt and defocus for improvement of the signal-to-noise ratio by means of direct observation of the electron images on a TV monitor.Hollow-cone illumination is implemented in our microscope (single field condenser objective, Cs = .5 mm) by an electronic system which rotates the tilted beam about the optic axis. At low rates of revolution (one turn per second or so) a circular motion of the usual granulation in the image of a carbon support film can be observed on the TV monitor. The size of the granular structures and the radius of their orbits depend on both the conical tilt and defocus.

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