Estimated Signal-to-noise Ratio Values Research Articles

An automatic method for accurate signal-to-noise ratio estimation and baseline correction of Raman spectra of environmental microplastics

In this study, we introduced a k-iterative double sliding-window (DSW^k) method for the estimation of spectral noise, signal-to-noise ratio (SNR), and baseline correction. The performance was evaluated using simulated spectra and compared against other commonly employed methods. Convergent evaluation determined that a k value of 20 strikes an optimal balance between convergence and computational intensity. The DSW^k method demonstrated outstanding performance across different spectral types (flat baseline, baseline with elevation, baseline with fluctuation, baseline with elevation and fluctuation) coupled with SNR values from 10 to 1000, achieving results that ranged from 1.01 to 1.08 times of the reference value in estimating spectral noise. It also showed that the estimated SNR values are 0.89 to 0.93 times of the reference value, demonstrating a 74.5 % − 131.7 % improvement over the conventional method in spectra with elevated and/or fluctuating baselines. Additionally, the DSW^k method proved effective in correcting baselines and identifying polymers in environmental samples of polyethylene (PE), polypropylene (PP), and polystyrene (PS), despite the limitation of reducing the peak height in spectra with low SNR. This method offers the potential to enhance the automatic and accurate evaluation of spectral quality and could assist in the development of guidelines for more rapid parameter adjustments in Raman measurements.

Empirical SNR-based model of the displacement accuracy for ground-based radar measurements

Ground-based radar interferometry offers significant capabilities for the observation of engineering structures and natural objects displacements. The quality of the recorded displacement is significantly influenced by the intensity of the reflected radar signal. In order to determine the actual accuracy of measurement performed in the field, two comparative experiments were carried out. In both cases, referential values were specified using precise instruments: a laser interferometer and a precise total station. This way, the error value equal to 0.1–0.2 mm declared by the manufacturer was confirmed in the field conditions. Furthermore, the relationship between SNR (signal-to-noise ratio) parameter and the actual measurement accuracy was analysed based on the acquired results. Due to the different ways of expressing the parameter, i.e. thermal and estimated SNR values, specific for the applied radar system, both of them were analysed. Finally, the model which allows determining the actual accuracy of displacement measurement in the field was proposed. It includes the SNR value and the distance to the object, so the accuracy can be reliably estimated upon these basic parameters without the need for additional measurement techniques.

SNR Weighting for Shear Wave Speed Reconstruction in Tomoelastography.

In tomoelastography, to achieve a final wave speed map by combining reconstructions obtained from all spatial directions and excitation frequencies, the use of weights is inevitable. Here, a new weighting scheme, which maximizes the signal-to-noise ratio (SNR) of the final wave speed map, has been proposed. To maximize the SNR of the final wave speed map, the use of squares of estimated SNR values of reconstructed individual maps has been proposed. Therefore, derivations of the SNR of the reconstructed wave speed maps have become necessary. Considering the noise on the complex MRI signal, the SNR of the reconstructed wave speed map was formulated by an analytical approach assuming a high SNR, and the results were verified using Monte Carlo simulations (MCSs). It has been assumed that the noise remains approximately Gaussian when the image SNR is high enough, despite the nonlinear operations in tomoelastography inversion. Hence, the SNR threshold was determined by comparing the SNR computed by MCSs and analytical approximations. The weighting scheme was evaluated for accuracy, spatial resolution and SNR performances on simulated phantoms. MR elastography (MRE) experiments on two different phantoms were conducted. Wave speed maps were generated for simulated 3D human abdomen MRE data and experimental human abdomen MRE data. The simulation results demonstrated that the SNR-weighted inversion improved the SNR performance of the wave speed map by a factor of two compared to the performance of the original (i.e., amplitude-weighted) reconstruction. In the case of a low SNR, no bias occurred in the wave speed map when SNR weighting was used, whereas 10% bias occurred when the original weighting (i.e., amplitude weighting) was used. Thus, while not altering the accuracy or spatial resolution of the wave speed map with the proposed weighting method, the SNR of the wave speed map has been significantly improved.

Blind Signal-to-Noise Ratio Estimation of Speech Based on Vector Quantizer Classifiers and Decision Level Fusion

A blind approach for estimating the signal to noise ratio (SNR) of a speech signal corrupted by additive noise is proposed. The method is based on a pattern recognition paradigm using various linear predictive based features, a vector quantizer classifier and estimation combination. Blind SNR estimation is very useful in speaker identification systems in which a confidence metric is determined along with the speaker identity. The confidence metric is partially based on the mismatch between the training and testing conditions of the speaker identification system and SNR estimation is very important in evaluating the degree of this mismatch. The aim is to correctly estimate SNR values from 0 to 30 dB, a range that is both practical and crucial for speaker identification systems. Experiments consider (1) artificially generated additive white Gaussian noise, pink noise and bandpass noise and (2) fifteen noise types from the NOISEX database. Four features are combined to get the best results. The average SNR estimation error depends on the type of noise in that a relatively low error results for pink noise and jet cockpit noise and a high error results for destroyer operations room noise and military vehicle noise. For both artificially generated noise and the NOISEX data, the error is lower than what is achieved by the IMCRA method that uses SNR estimation for speech enhancement. Combining the four features with IMCRA lowers the error for 8 of the 15 noise types from NOISEX.

Autoregressive linear least square single scanning electron microscope image signal-to-noise ratio estimation.

A technique based on linear Least Squares Regression (LSR) model is applied to estimate signal-to-noise ratio (SNR) of scanning electron microscope (SEM) images. In order to test the accuracy of this technique on SNR estimation, a number of SEM images are initially corrupted with white noise. The autocorrelation function (ACF) of the original and the corrupted SEM images are formed to serve as the reference point to estimate the SNR value of the corrupted image. The LSR technique is then compared with the previous three existing techniques known as nearest neighbourhood, first-order interpolation, and the combination of both nearest neighborhood and first-order interpolation. The actual and the estimated SNR values of all these techniques are then calculated for comparison purpose. It is shown that the LSR technique is able to attain the highest accuracy compared to the other three existing techniques as the absolute difference between the actual and the estimated SNR value is relatively small. SCANNING 38:771-782, 2016. © 2016 Wiley Periodicals, Inc.

Enriched the Spectrum Sensing Performance of Estimated SNR based Detector in Cognitive Radio Networks

Spectrum sensing is one of the vital functions, plays an important role in cognitive radio networks (CRN). This paper discuss to enrich the sensing performance of propose estimated signal to noise ratio (SNR) based detector. Estimated SNR detector consists two detectors, energy detector (ED) & ED with ADT detector, out of these two detectors only one will perform sensing operation at a time. Which detector will be work, selection will be done on condition between estimated SNR value (Se) and threshold (γ). Results show that proposed ESNR_ADT scheme outperforms the cyclostationary based sensing method by 30.5 % at – 10 dB SNR. It is also shown that the proposed scheme takes smaller detection time period as compare to cyclostationary detection in the order of 5.2 ms at 20 dB SNR. Furthermore, the scheme was associated with cooperative spectrum sensing (CSS) where each CRs work together in order to take final decision. Finally, it was analyzed that proposed ESNR_ADT sensing scheme with CSS improves detection performance in the order of 0.9 at low SNR value of – 18.5 dB approximately.

A Robust Detector Using SNR with Adaptive Threshold Scheme in Cognitive Radio Networks

Spectrum sensing is a key functionalities of Cognitive radio networks (CRN). There are several methods to improve sensing techniques. In this paper, we present a robust detector using signal to noise ratio (SNR) with adaptive threshold (ADT) scheme in Cognitive Radio Networks (CRN). This technique has two detectors, energy detector (ED)&EDwithADT detector, out of these two detectors only one will perform sensing operation at a time. Selection of detector depends on condition between estimated SNR value (Se) and threshold (γ). Numerical results show that proposed ESNR_ADT scheme optimizes detection performance and outperforms the cyclostationary based sensing method and adaptive spectrum sensing (SS) by 30.5 % and 30 % at – 10 dB signal to noise ratio (SNR) respectively. It is also shown that the proposed scheme yields lesser sensing time than cyclostationary detection and adaptive SS scheme in the order of 5.2 ms and 1.0 ms at - 20 dB SNR respectively.

Error vector magnitude to SNR conversion for nondata-aided receivers

Error vector magnitude (EVM) is one of the widely accepted figure of merits used to evaluate the quality of communication systems. In the literature, EVM has been related to signal-to-noise ratio (SNR) for data-aided receivers, where preamble sequences or pilots are used to measure the EVM, or under the assumption of high SNR values. In this paper, this relation is examined for nondata-aided receivers and is shown to perform poorly, especially for low SNR values or high modulation orders. The EVM for nondata-aided receivers is then evaluated and its value is related to the SNR for quadrature amplitude modulation (QAM) and pulse amplitude modulation (PAM) signals over additive white Gaussian noise (AWGN) channels and Rayleigh fading channels, and for systems with IQ imbalances. The results show that derived equations can be used to reliably estimate SNR values using EVM measurements that are made based on detected data symbols. Thus, presented work can be quite useful for measurement devices such as vector signal analyzers (VSA), where EVM measurements are readily available.

Differences in MEG/EEG Epileptic Spike Yields Explained by Regional Differences in Signal-to-Noise Ratios

Controversy remains regarding the preferred modality, magnetoencephalography (MEG) or EEG, for the presurgical evaluation of patients with epilepsy. In general, it appears that the spike yields for MEG and EEG are similar in patients with temporal lobe epilepsy, and that for neocortical epilepsy the MEG spike yields may be larger than for EEG. In general, MEG/EEG spike yields depend on factors such as (1) the number of sensors, (2) the source depth and orientation, (3) the background activity, and (4) the smearing of the potential fields due to variations in skull resistivity in EEG. Because the contribution of all these factors are of the same order of magnitude, the authors took them all into account to predict the signal-to-noise ratio (SNR) of hypothetical spikes in different brain areas. In this study, it was assumed that spike sensitivity (and therefore the spike yield) increases with SNR. The estimated SNR values at temporal areas were comparable for MEG and EEG, which is in agreement with clinical findings that spike yields in temporal lobe epilepsy are similar. Furthermore, the SNR of MEG was substantially higher in the frontal area, indicating that in frontal lobe epilepsy MEG may be highly relevant to prescreening of epilepsy patients. This model-based approach indicates that SNR mapping clarifies differences between MEG and EEG findings that are difficult to understand on the basis of patient studies only.

Signal-to-noise measurements in magnitude images from NMR phased arrays.

A method is proposed to estimate signal-to-noise ratio (SNR) values in phased array magnitude images, based on a region-of-interest (ROI) analysis. It is shown that the SNR can be found by correcting the measured signal intensity for the noise bias effects and by evaluating the noise variance as the mean square value of all the pixel intensities in a chosen background ROI, divided by twice the number of receivers used. Estimated SNR values are shown to vary spatially within a bound of 20% with respect to the true SNR values as a result of noise correlations between receivers.