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>

Speech in noise testing before and after grommet insertion

2012 ◽  
Vol 126 (10) ◽  
pp. 1010-1015 ◽  
Author(s):  
V Possamai ◽  
G Kirk ◽  
A Scott ◽  
D Skinner
Keyword(s):  
Small Group ◽  
Background Noise ◽  
Signal To Noise Ratio ◽  
Sound Field ◽  
Noise Signal ◽  
Signal To Noise ◽  
Speech In Noise ◽  
Before And After ◽  
Noise Test ◽  
Noise Ratio

AbstractObjectives:To assess the feasibility of designing and implementing a speech in noise test in children before and after grommet insertion, and to analyse the results of such a test in a small group of children.Methods:Twelve children aged six to nine years who were scheduled to undergo grommet insertion were identified. They underwent speech in noise testing before and after grommet insertion. This testing used Arthur Boothroyd word lists read at 60 dB in four listening conditions presented in a sound field: firstly in quiet conditions, then in signal to noise ratios of +10 (50 dB background noise), 0 (60 dB) and −10 (70 dB).Results:Mean phoneme scores were: in quiet conditions, 28.1 pre- and 30 post-operatively (p = 0.04); in 50 dB background noise (signal to noise ratio +10), 24.2 pre- and 29 post-operatively (p < 0.01); in 60 dB background noise (signal to noise ratio 0), 22.6 pre- and 27.5 post-operatively (p = 0.06); and in 70 dB background noise (signal to noise ratio −10), 13.9 pre- and 21 post-operatively (p = 0.05).Conclusion:This small study suggests that speech in noise testing is feasible in this scenario. Our small group of children demonstrated a significant improvement in speech in noise scores following grommet insertion. This is likely to translate into a significant advantage in the educational environment.

scholarly journals Super-Resolution Reconstruction of Cell Pseudo-Color Image Based on Raman Technology

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4076
Author(s):  
Yang ◽  
Zhu ◽  
Wang ◽  
Yang ◽  
Wu ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Raman Spectrum ◽  
Background Noise ◽  
Color Image ◽  
Signal To Noise Ratio ◽  
Super Resolution ◽  
Reconstruction Algorithm ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Pseudo Color

Raman spectroscopy visualization is a challenging task due to the interference of complex background noise and the number of selected measurement points. In this paper, a super-resolution image reconstruction algorithm for Raman spectroscopy is studied to convert raw Raman data into pseudo-color super-resolution imaging. Firstly, the Raman spectrum data of a single measurement point is measured multiple times to calculate the mean value to remove the random background noise, and innovatively introduce the Retinex algorithm and the median filtering algorithm which improve the signal-to-noise ratio. The novel method of using deep neural network performs a super-resolution reconstruction operation on the gray image. An adaptive guided filter that automatically adjusts the filter radius and penalty factor is proposed to highlight the contour of the cell, and the super-resolution reconstruction of the pseudo-color image of the Raman spectrum is realized. The average signal-to-noise ratio of the reconstructed pseudo-color image sub-band reaches 14.29 db, and the average value of information entropy reaches 4.30 db. The results show that the Raman-based cell pseudo-color image super-resolution reconstruction algorithm is an effective tool to effectively remove noise and high-resolution visualization. The contrast experiments show that the pseudo-color image Kullback–Leiber (KL) entropy of the color image obtained by the method is small, the boundary is obvious, and the noise is small, which provide technical support for the development of sophisticated single-cell imaging Raman spectroscopy instruments.

Uncertainty and Signal-to-Noise Ratio for Unsteady Background Noise

2018 ◽  
Vol 66 (2) ◽  
pp. 131-141 ◽  
Author(s):  
Wongyu Choi ◽  
Michael B. Pate ◽  
James F. Sweeney
Keyword(s):  
Background Noise ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Noise Ratio

Effects of Speech Rate, Background Noise, and Simulated Hearing Loss on Speech Rate Judgment and Speech Intelligibility in Young Listeners

2009 ◽  
Vol 20 (01) ◽  
pp. 028-039 ◽  
Author(s):  
Elizabeth M. Adams ◽  
Robert E. Moore
Keyword(s):  
Hearing Loss ◽  
Research Design ◽  
Background Noise ◽  
Speech Intelligibility ◽  
Signal To Noise Ratio ◽  
Speech Rate ◽  
Normal Hearing ◽  
Moderate Correlation ◽  
Signal To Noise ◽  
Noise Ratio

Purpose: To study the effect of noise on speech rate judgment and signal-to-noise ratio threshold (SNR50) at different speech rates (slow, preferred, and fast). Research Design: Speech rate judgment and SNR50 tasks were completed in a normal-hearing condition and a simulated hearing-loss condition. Study Sample: Twenty-four female and six male young, normal-hearing participants. Results: Speech rate judgment was not affected by background noise regardless of hearing condition. Results of the SNR50 task indicated that, as speech rate increased, performance decreased for both hearing conditions. There was a moderate correlation between speech rate judgment and SNR50 with the various speech rates, such that as judgment of speech rate increased from too slow to too fast, performance deteriorated. Conclusions: These findings can be used to support the need for counseling patients and their families about the potential advantages to using average speech rates or rates that are slightly slowed while conversing in the presence of background noise.

scholarly journals A generalised background correction algorithm for a Halo Doppler lidar and its application to data from Finland

2015 ◽  
Vol 8 (10) ◽  
pp. 11139-11170
Author(s):  
A. J. Manninen ◽  
E. J. O'Connor ◽  
V. Vakkari ◽  
T. Petäjä
Keyword(s):  
Background Noise ◽  
Signal To Noise Ratio ◽  
Background Correction ◽  
Data Availability ◽  
Horizontal Wind ◽  
Limiting Factor ◽  
High Temporal Resolution ◽  
Signal To Noise ◽  
Uncertainty Estimates ◽  
Noise Ratio

Abstract. Current commercially available Doppler lidars provide an economical and robust solution for measuring vertical and horizontal wind velocities, together with the ability to provide co- and cross-polarised backscatter profiles. The high temporal resolution of these instruments allow turbulent properties to be obtained from studying the variation in velocities. However, the instrument specifications mean that certain characteristics, especially the background noise behaviour, become a limiting factor for the instrument sensitivity in regions where the aerosol load is low. Turbulent calculations require an accurate estimate of the contribution from velocity uncertainty estimates, which are directly related to the signal-to-noise ratio. Any bias in the signal-to-noise ratio will propagate through as a bias in turbulent properties. In this paper we present a method to correct for artefacts in the background noise behaviour of commercially available Doppler lidars and reduce the signal-to-noise ratio threshold used to discriminate between noise, and cloud or aerosol signals. We show that, for Doppler lidars operating continuously at a number of locations in Finland, the data availability can be increased by as much as 50 % after performing this background correction and subsequent reduction in the threshold. The reduction in bias also greatly improves subsequent calculations of turbulent properties in weak signal regimes.

scholarly journals A post-processing technique to estimate the signal-to-noise ratio and remove echosounder background noise

2007 ◽  
Vol 64 (6) ◽  
pp. 1282-1291 ◽  
Author(s):  
Alex De Robertis ◽  
Ian Higginbottom
Keyword(s):  
Background Noise ◽  
Signal To Noise Ratio ◽  
Processing Technique ◽  
Post Processing ◽  
Acoustic Measurements ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Noise Correction ◽  
Changes Over Time ◽  
User Intervention

Abstract De Robertis, A., and Higginbottom, I. 2007. A post-processing technique to estimate the signal-to-noise ratio and remove echosounder background noise. – ICES Journal of Marine Science, 64: 1282–1291. A simple and effective post-processing technique to estimate echosounder background-noise levels and signal-to-noise ratios (SNRs) during active pinging is developed. Similar to other methods of noise estimation during active pinging, this method assumes that some portion of the sampled acoustic signal is dominated by background noise, with a negligible contribution from the backscattered transmit signal. If this assumption is met, the method will provide robust and accurate estimates of background noise equivalent to that measured by the receiver if the transmitter were disabled. It provides repeated noise estimates over short intervals of time without user intervention, which is beneficial in cases where background noise changes over time. In situations where background noise is dominant in a portion of the recorded signal, it is straightforward to make first-order corrections for the effects of noise and to estimate the SNR to evaluate the effects of background noise on acoustic measurements. Noise correction and signal-to-noise-based thresholds have the potential to improve inferences from acoustic measurements in lower signal-to-noise situations, such as when surveying from noisy vessels, using multifrequency techniques, surveying at longer ranges, and when working with weak acoustic targets such as invertebrates and fish lacking swimbladders.

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