scholarly journals Confidence Intervals for the Signal-to-Noise Ratio and Difference of Signal-to-Noise Ratios of Log-Normal Distributions

Stats ◽  
2019 ◽  
Vol 2 (1) ◽  
pp. 164-173 ◽  
Author(s):  
Warisa Thangjai ◽  
Sa-Aat Niwitpong
Keyword(s):  
Confidence Interval ◽  
Confidence Intervals ◽  
Coverage Probability ◽  
Average Length ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Normal Distributions ◽  
Noise Ratio ◽  
The Difference ◽  
Log Normal

In this article, we propose approaches for constructing confidence intervals for the single signal-to-noise ratio (SNR) of a log-normal distribution and the difference in the SNRs of two log-normal distributions. The performances of all of the approaches were compared, in terms of the coverage probability and average length, using Monte Carlo simulations for varying values of the SNRs and sample sizes. The simulation studies demonstrate that the generalized confidence interval (GCI) approach performed well, in terms of coverage probability and average length. As a result, the GCI approach is recommended for the confidence interval estimation for the SNR and the difference in SNRs of two log-normal distributions.

scholarly journals Novel Configurations of Ultrahigh Frequency (≤600 MHz) Analog Frontend for High Resolution Ultrasound Measurement

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2598
Author(s):  
Min Kim ◽  
Jinhyoung Park ◽  
Qifa Zhou ◽  
Koping Shung
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Signal To Noise Ratio ◽  
Ultrahigh Frequency ◽  
Ultrasound Measurement ◽  
Functional Responses ◽  
Signal To Noise ◽  
Coded Excitation ◽  
Noise Ratio ◽  
The Difference

In this article, an approach to designing and developing an ultrahigh frequency (≤600 MHz) ultrasound analog frontend with Golay coded excitation sequence for high resolution imaging applications is presented. For the purpose of visualizing specific structures or measuring functional responses of micron-sized biological samples, a higher frequency ultrasound is needed to obtain a decent spatial resolution while it lowers the signal-to-noise ratio, the difference in decibels between the signal level and the background noise level, due to the higher attenuation coefficient. In order to enhance the signal-to-noise ratio, conventional approach was to increase the transmit voltage level. However, it may cause damaging the extremely thin piezoelectric material in the ultrahigh frequency range. In this paper, we present a novel design of ultrahigh frequency (≤600 MHz) frontend system capable of performing pseudo Golay coded excitation by configuring four independently operating pulse generators in parallel and the consecutive delayed transmission from each channel. Compared with the conventional monocycle pulse approach, the signal-to-noise ratio of the proposed approach was improved by 7–9 dB without compromising the spatial resolution. The measured axial and lateral resolutions of wire targets were 16.4 µm and 10.6 µm by using 156 MHz 4 bit pseudo Golay coded excitation, respectively and 4.5 µm and 7.7 µm by using 312 MHz 4 bit pseudo Golay coded excitation, respectively.

scholarly journals The robustness of the differential quantizer in the case of the variable signal to noise ratio

2017 ◽  
Vol 14 (1) ◽  
pp. 149-160
Author(s):  
Lazar Cokic ◽  
Aleksandra Marjanovic ◽  
Sanja Vujnovic ◽  
Zeljko Djurovic
Keyword(s):  
Speech Signal ◽  
Signal To Noise Ratio ◽  
Noise Signal ◽  
Fixed Number ◽  
Signal To Noise ◽  
Input And Output ◽  
Noise Ratio ◽  
The Difference ◽  
Optimal Signal ◽  
Theoretical Overview

In this paper a short theoretical overview of differential quantizer and its implementations is given. Afterward, the effect of the order of prediction in differential quantizer and the effect of the difference in order of predictor in the input and output of differential quantizer is analyzed. Then it was proceeded with the examination of the robustness of the differential quantizer in the case in which a noise signal is brought to the input of the differential quantizer, instead of the clean speech signal. The analysis was conducted with a uniform distribution, as well as the noise with the gaussian distribution, and the obtained results were adequately commented on. Also, experimentally a limit was set which refers to the intensity of the noise and still enable results which are better that a regular uniform quantizer. The whole analysis is done by using the fixed number of bits in quantization, i.e. 12-bit quantizer is used in all the implementations of differential quantizer. In the conclusion of this paper there is a discussion about the possibility of implementing a differential quantizer which will be able to recognize which noise attacks the system, and in addition to that, in what form it adapts its coefficients so that it at any moment acquires the optimal signal to noise ratio.

BOOTSTRAP CONFIDENCE INTERVALS FOR STEADY-STATE AVAILABILITY

2004 ◽  
Vol 21 (03) ◽  
pp. 407-419 ◽  
Author(s):  
JAE-HAK LIM ◽  
SANG WOOK SHIN ◽  
DAE KYUNG KIM ◽  
DONG HO PARK
Keyword(s):  
Steady State ◽  
Confidence Interval ◽  
Confidence Intervals ◽  
Coverage Probability ◽  
Average Length ◽  
Repairable System ◽  
Bootstrap Confidence Interval ◽  
Bootstrap Confidence Intervals ◽  
Bootstrap Methods ◽  
Percentile Bootstrap

Steady-state availability, denoted by A, has been widely used as a measure to evaluate the reliability of a repairable system. In this paper, we develop new confidence intervals for steady-state availability based on four bootstrap methods; standard bootstrap confidence interval, percentile bootstrap confidence interval, bootstrap-t confidence interval, and bias-corrected and accelerated confidence interval. We also investigate the accuracy of these bootstrap confidence intervals by calculating the coverage probability and the average length of intervals.

scholarly journals Bayesian Confidence Intervals for Coefficients of Variation of PM10 Dispersion

2021 ◽  
Vol 5 (2) ◽  
pp. 139-154
Author(s):  
Warisa Thangjai ◽  
Sa-Aat Niwitpong ◽  
Suparat Niwitpong
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Confidence Intervals ◽  
Bayesian Approach ◽  
Normal Distributions ◽  
Coefficients Of Variation ◽  
The Difference ◽  
Log Normal ◽  
Log Normal Distribution ◽  
The Bayesian Approach

Herein, we propose the Bayesian approach for constructing the confidence intervals for both the coefficient of variation of a log-normal distribution and the difference between the coefficients of variation of two log-normal distributions. For the first case, the Bayesian approach was compared with large-sample, Chi-squared, and approximate fiducial approaches via Monte Carlo simulation. For the second case, the Bayesian approach was compared with the method of variance estimates recovery (MOVER), modified MOVER, and approximate fiducial approaches using Monte Carlo simulation. The results show that the Bayesian approach provided the best approach for constructing the confidence intervals for both the coefficient of variation of a log-normal distribution and the difference between the coefficients of variation of two log-normal distributions. To illustrate the performances of the confidence limit construction approaches with real data, they were applied to analyze real PM10 datasets from the Nan and Chiang Mai provinces in Thailand, the results of which are in agreement with the simulation results. Doi: 10.28991/esj-2021-01264 Full Text: PDF

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