Signal-to-noise Ratio Performance Research Articles

Disturbance extracted methods for auxiliary power quality monitor-based voltage sag localization in distribution network

It is of great significance to extract the voltage sag disturbances with high signal-to-noise ratio (SNR) for safety, environmental and economic interests, which can be applied to locate the voltage sag source using the widely deployed auxiliary power quality monitor (αPQM) in distribution network. Two classical disturbance extraction methods (DEM) and the respective advantages, disadvantages and applicable scenarios are summarized. To overcome their shortcomings at the requirements for fault duration and sampling frequency caused by the small storage space of αPQM, three new DEMs with higher SNR are proposed based on the sine wave reconstruction. The extracted disturbances are analyzed in terms of SNR using theoretical signals. Compared with the classical DEMs, the new ones show better SNR performance at sampling frequency, disturbance duration and residual voltage, as well as harmonics and noise. Besides, the test results using the simulation data of the IEEE 34 nodes distribution network show that they outperform classical DEMs in adapting to voltage sag localization algorithms. The sampling frequency of αPQM is recommended to be an integer multiple of the system frequency. In order to adapt αPQM and more DEMs to locate voltage sag, the longest possible steady-state waveforms are suggested to be stored in the case of redundant storage space.

Dependence of Bit Error Rates on HAMR Transition Curvatures

Transition curvature limits the performance of heat-assisted magnetic recording (HAMR), especially when bit length (BL) is reduced below 10 nm. In this article, in order to quantify this issue, the dependence of signal-to-noise ratios (SNRs) and bit error rates (BERs) on HAMR transition curvatures is investigated utilizing previously obtained curvature values. An artificial recording method is used and a Viterbi algorithm is applied to decode the readback signals and calculate BERs. A pseudorandom binary sequence (PRBS) is used. Two BLs are tested: 7.5 and 8.5 nm. Different reader widths (RWs), shield-to-shield distances, and magnetic fly heights are studied in order to seek optimized reader configurations. Generally, transition curvatures have a negative effect on SNR and BER performance, but this effect can be mitigated by the reduction of certain reader dimensions.

Robust Beamforming Optimization for Self-Sustainable Intelligent Reflecting Surface Assisted Wireless Networks

We focus on an intelligent reflecting surface (IRS)-assisted multiple-input single-output (MISO) system where the IRS sustains its operations by harvesting energy from the access point (AP) in the power splitting (PS) protocol. We aim to minimize the AP’s transmit power subject to the receivers’ signal-to-noise ratio (SNR) and the IRS’s energy budget constraints. A two-stage optimization framework is proposed to jointly optimize the AP’s active beamforming, the IRS’s passive beamforming, and the reflection amplitude. Given the reflection amplitude, we employ alternating optimization to update the beamforming strategies. Then, we determine the lower and upper bounds of the reflection amplitude in closed-form expressions, which help to update the reflection amplitude in a bisection method. We further extend our study to the robust case with uncertain channels. Our analysis reveals that the robust counterpart can be solved by the same optimization framework. Extensive simulations reveal that our algorithm is efficacy to balance the IRS’s energy budget and the receiver’s SNR performance. With uncertain channel information, a larger size of the IRS does not always ensure a higher performance improvement to information transmissions.

LoRa Signal Synchronization and Detection at Extremely Low Signal-to-Noise Ratios

In recent years, LoRa has been deployed in countless Internet of Things (IoT) applications across the globe. However, as LoRa is a proprietary technology, research into its physical-layer performance has been challenging. Implementing LoRa on software-defined radio (SDR) platforms yields valuable insight into the physical layer of the LoRa standard and paves the way for improvements in packet reception capabilities for LoRa receivers. This article presents an independently developed packet reception algorithm, which drastically improves the physical performance of LoRa communication links. The advanced signal presence detection, synchronization, and symbol detection strategies are shown to significantly increase packet reception ratios in extremely adverse noise conditions. Multiple algorithm variations are presented and compared in terms of bit error rate (BER) performance and computational cost. In comparison to a theoretical system with perfect channel state information, the simulated BER performance of the best performing algorithm only requires an increase of 1.6 dB in signal-to-noise ratio (SNR) to exhibit the same performance. Finally, SDR implementations of the algorithms exhibit average SNR performance gains up to 4.7 dB when compared to commercially available hardware.

Hybrid motion artifact detection and correction approach for functional near-infrared spectroscopy measurements.

.SignificanceFunctional near-infrared spectroscopy (fNIRS) is a promising optical neuroimaging technique, measuring the hemodynamic signals from the cortex. However, improving signal quality and reducing artifacts arising from oscillation and baseline shift (BS) are still challenging up to now for fNIRS applications.AimConsidering the advantages and weaknesses of the different algorithms to reduce the artifact effect in fNIRS signals, we propose a hybrid artifact detection and correction approach.ApproachFirst, distinct artifact detection was realized through an fNIRS detection strategy. Then the artifacts were divided into three categories: BS, slight oscillation, and severe oscillation. A comprehensive correction was applied through three main steps: severe artifact correction by cubic spline interpolation, BS removal by spline interpolation, and slight oscillation reduction by dual-threshold wavelet-based method.ResultsUsing fNIRS data acquired during whole night sleep monitoring, we compared the performance of our approach with existing algorithms in signal-to-noise ratio (SNR) and Pearson’s correlation coefficient (). We found that the proposed method showed improvements in performance in SNR and with strong stability.ConclusionsThese results suggest that the new hybrid artifact detection and correction method enhances the viability of fNIRS as a functional neuroimaging modality.

TCVQ-SVM algorithm for narrowband spectrum sensing

Spectrum sensing is viewed as the basic and crucial technology for cognitive radio. To improve the accuracy of spectrum sensing in low signal to noise ratio (SNR), this paper presents an efficient TCVQ-SVM method based on machine learning for narrowband spectrum sensing. Firstly, trace of covariance matrix and variance of quadratic covariance matrix (TCVQ) is extracted as feature vectors and combined as training samples of spectrum sensing. Then, the classification model can be achieved by training samples based on support vector machine (SVM), which can avoid setting threshold and adjusting classification hyperplane by its self-learning ability. Lastly, the result of spectrum sensing can be obtained. By utilizing trace and variance as input features of SVM, the algorithm can make full use of the eigenvalue difference and structure characteristic of the received signal, and at the same time, achieve good performance in low SNR. Theoretical analysis reveals that the proposed method has low computational complexity. Simulation results and experiments on the hardware platform illustrate that the proposed algorithm is effective and robust.

Performance analysis of LYSO–SiPM detection module for X-ray communication during spacecraft reentry blackout

In view of the demand for high-energy and high-bandwidth signal detection in X-ray communication (XCOM) during spacecraft reentry blackout, a detection module using fast-decay LYSO scintillator coupled with Silicon photomultiplier (SiPM) was proposed in this study. The theoretical model based on the Monte Carlo simulation and Personick’ theory was established and the detector prototype was built. Moreover, the signal to noise ratio (SNR) and bit error rate (BER) performance were investigated. Results present that the SNR of 19.8 dB, 29.9 dB and 35.5 dB could be reached for X-ray with the energy of 17 keV, 59.5 keV and 122 keV respectively. This energy response characteristic is much suitable for the X-ray signal detection during spacecraft reentry blackout. Additionally, the deviation of SNR corresponding to the BER between the theoretical and experimental results does not exceed 0.3 dB when the data rate is 100 kbps, which proves the validity of the theoretical model. Furthermore, the numerical analysis shows that the scheme demonstrates high SNR and low BER performance in high bandwidth and low signal receiving power, which explains the superiorities in X-ray signal detection of the design. This work provides the theoretical basis and design reference for the LYSO–SiPM X-ray signal detection module.

Improving distortion correction for isotropic high-resolution 3D diffusion MRI by optimizing Jacobian modulation.

To improve distortion correction for isotropic high-resolution whole-brain 3D diffusion MRI when in a time-saving acquisition scenario. Data were acquired using simultaneous multi-slab (SMSlab) acquisitions, with a b = 0 image pair encoded by reversed polarity gradients (RPG) for phase encoding (PE) and diffusion weighted images encoded by a single PE direction. Eddy current-induced distortions were corrected first. During the following susceptibility distortion correction, image deformation was first corrected by the field map estimated from the b = 0 image pair. Intensity variation was subsequently corrected by Jacobian modulation. Two Jacobian modulation methods were compared. They calculated the Jacobian modulation map from the field map, or from the deformation corrected b = 0 image pair, termed as JField and JRPG , respectively. A modified version of the JRPG method, with proper smoothing, was further proposed for improved correction performance, termed as JRPG-smooth . Compared to JField modulation, less remaining distortions are observed when using the JRPG and JRPG-smooth methods, especially in areas with large B0 field inhomogeneity. The original JRPG method causes signal-to-noise ratio (SNR) deficiency problem, which manifests as degraded SNR of the diffusion weighted images, while the JRPG-smooth method maintains the original image SNR. Less estimation errors of diffusion metrics are observed when using the JRPG-smooth method. This study improves the distortion correction for isotropic high-resolution whole-brain 3D diffusion MRI by optimizing Jacobian modulation. The optimized method outperforms the conventional JField method regarding intensity variation correction and accuracy of diffusion metrics estimation, and outperforms the original JRPG method regarding SNR performance.

Optimized 64-channel array configurations for accelerated simultaneous multislice acquisitions in 3T cardiac MRI.

Three 64-channel cardiac coils with different detector array configurations were designed and constructed to evaluate acceleration capabilities in simultaneous multislice (SMS) imaging for 3T cardiac MRI. Three 64-channel coil array configurations obtained from a simulation-guided design approach were constructed and systematically evaluated regarding their encoding capabilities for accelerated SMS cardiac acquisitions at 3T. Array configuration AUni-sized consists of uniformly distributed equally sized loops in an overlapped arrangement, BGapped uses a gapped array design with symmetrically distributed equally sized loops, and CDense has non-uniform loop density and size, where smaller elements were centered over the heart and larger elements were placed surrounding the target region. To isolate the anatomic variation from differences in the coil configurations, all three array coils were built with identical semi-adjustable housing segments. The arrays' performance was compared using bench-level measurements and imaging performance tests, including signal-to-noise ratio (SNR) maps, array element noise correlation, and SMS acceleration capabilities. Additionally, all cardiac array coils were evaluated on a healthy volunteer. The array configuration CDense with the non-uniformly distributed loop density showed the best overall cardiac imaging performance in both SNR and SMS encoding power, when compared to the other constructed arrays. The diffusion weighted cardiac acquisitions on a healthy volunteer support the favorable accelerated SNR performance of this array configuration. Our results indicate that optimized highly parallel cardiac arrays, such as the 64-channel coil with a non-uniform loop size and density improve highly accelerated SMS cardiac MRI in comparison to symmetrically distributed loop array designs.

The SNR Performance of Cooperative Diversity in HF Skywave Communication Systems

High Frequency (HF) communication systems uses the ionosphere to reflect signals from the source (S) to the destination (D) on earth. Variation of the height of the earth’s ionosphere layer causes the received signal also to fluctuate and affects the signal-to-noise ratio (SNR) at the destination. To maintain the SNR remains good value at the destination, cooperative communication system can be a solution. In cooperative communication system, all sources cooperate and coordinate with the relay (R) to improve the quality of the signal at the destination. In this communication system, the destination receives two signals, each derived from source and from relay. This paper divides the simulation observation time into 3 categories, namely midnight to dawn (00:00 and 04:00), morning to noon (08:00 and 12:00) and afternoon to evening (16:00 and 20: 00). In all categories of midnight to dawn, morning to noon and afternoon to evening, cooperative communication systems with maximal ratio combining (S - R - D) provide greater diversity gain than non cooperative communication systems (S - R - D).

Effects of Wireless Remote Microphone on Speech Recognition in Noise for Hearing Aid Users in China.

Objective: This study was aimed at evaluating improvements in speech-in-noise recognition ability as measured by signal-to-noise ratio (SNR) with the use of wireless remote microphone technology. These microphones transmit digital signals via radio frequency directly to hearing aids and may be a valuable assistive listening device for the hearing-impaired population of Mandarin speakers in China.Methods: Twenty-three adults (aged 19–80 years old) and fourteen children (aged 8–17 years old) with bilateral sensorineural hearing loss were recruited. The Mandarin Hearing in Noise Test was used to test speech recognition ability in adult subjects, and the Mandarin Hearing in Noise Test for Children was used for children. The subjects’ perceived SNR was measured using sentence recognition ability at three different listening distances of 1.5, 3, and 6 m. At each distance, SNR was obtained under three device settings: hearing aid microphone alone, wireless remote microphone alone, and hearing aid microphone and wireless remote microphone simultaneously.Results: At each test distance, for both adult and pediatric groups, speech-in-noise recognition thresholds were significantly lower with the use of the wireless remote microphone in comparison with the hearing aid microphones alone (P < 0.05), indicating better SNR performance with the wireless remote microphone. Moreover, when the wireless remote microphone was used, test distance had no effect on speech-in-noise recognition for either adults or children.Conclusion: Wireless remote microphone technology can significantly improve speech recognition performance in challenging listening environments for Mandarin speaking hearing aid users in China.

A self-decoupled 32-channel receive array for human-brain MRI at 10.5 T.

Receive array layout, noise mitigation, and B0 field strength are crucial contributors to SNR and parallel-imaging performance. Here, we investigate SNR and parallel-imaging gains at 10.5 T compared with 7 T using 32-channel receive arrays at both fields. A self-decoupled 32-channel receive array for human brain imaging at 10.5 T (10.5T-32Rx), consisting of 31 loops and one cloverleaf element, was co-designed and built in tandem with a 16-channel dual-row loop transmitter. Novel receive array design and self-decoupling techniques were implemented. Parallel imaging performance, in terms of SNR and noise amplification (g-factor), of the 10.5T-32Rx was compared with the performance of an industry-standard 32-channel receiver at 7 T (7T-32Rx) through experimental phantom measurements. Compared with the 7T-32Rx, the 10.5T-32Rx provided 1.46 times the central SNR and 2.08 times the peripheral SNR. Minimum inverse g-factor value of the 10.5T-32Rx (min[1/g] = 0.56) was 51% higher than that of the 7T-32Rx (min[1/g] = 0.37) with R = 4 × 4 2D acceleration, resulting in significantly enhanced parallel-imaging performance at 10.5 T compared with 7 T. The g-factor values of 10.5 T-32 Rx were on par with those of a 64-channel receiver at 7 T (eg, 1.8 vs 1.9, respectively, with R = 4 × 4 axial acceleration). Experimental measurements demonstrated effective self-decoupling of the receive array as well as substantial gains in SNR and parallel-imaging performance at 10.5 T compared with 7 T.

Evaluating the image quality of combined positron emission tomography-magnetic resonance images acquired in the pelvic radiotherapy position

Positron emission tomography-magnetic resonance (PET-MR) scanners could improve radiotherapy planning through combining PET and MR functional imaging. This depends on acquiring high quality and quantitatively accurate images in the radiotherapy position. This study evaluated PET-MR image quality using a flat couch and coil bridge for pelvic radiotherapy. MR and PET image quality phantoms were imaged in three setups: phantom on the PET-MR couch with anterior coil on top (diagnostic), phantom on a flat couch with coil on top (couch), and phantom on the flat couch with coil on a coil bridge (radiotherapy). PET images were also acquired in each setup without the anterior coil. PET attenuation correction of the flat couch and coil bridge were generated using kilovoltage computed tomography (CT) images and of the anterior coil using megavoltage CT images. MR image quality was substantially affected, with MR signal to noise ratio (SNR) relative to the diagnostic setup of 89% ± 2% (mean ± standard error of the mean, couch) and 54% ± 1% (radiotherapy), likely due to the increased distance between the patient and receive coils. The reduction impacted the low-contrast detectability score: 23 ± 1 (diagnostic), 19.7 ± 0.3 (couch) and 15 ± 1 (radiotherapy). All other MR metrics agreed within one standard error. PET quantitative accuracy was also affected, with measured activity with anterior coil being different to diagnostic without anterior coil by −16.7% ± 0.2% (couch) and −17.7 ± 0.1% (radiotherapy), without attenuation correction modification. Including the couch and coil bridge attenuation correction reduced this difference to −7.5% ± 0.1%, and including the anterior coil reduced this to −2.7% ± 0.1%. This was better than the diagnostic setup with anterior coil (difference −8.3% ± 0.2%). This translated into greater PET SNR performance for the fully corrected radiotherapy setup compared to diagnostic with coil. However contrast recovery was unchanged by the modified attenuation correction, with the diagnostic setup remaining ∼2% better. Quantitative PET in the radiotherapy setup is possible if appropriate attenuation correction is used. Pelvic radiotherapy PET-MR imaging protocols will need to consider the impact on PET-MR image quality.

Underwater Ambient-Noise Removing GAN Based on Magnitude and Phase Spectra

In this paper, an underwater ambient noise (UWAN) removing framework based on generative adversarial network (GAN) to recover underlying ship-engine data is proposed. Unlike previously reported GAN-based denoising techniques, the proposed underwater ambient-noise removing GAN (UWAR-GAN) is modelled using both, log magnitudes as well as phase information with significant time-frequency resolution. The as-developed network integrates U-net generator with articulate of skip connections, a patch discriminator and an additional adversarial correlation loss function. The proposed technique has been rigorously evaluated using publicly available real-time and simulated noisy data. Moreover, the UWAR-GAN shows excellent performance in SNR (signal-to-noise ratio), SSIM (structural similarity), MOS (mean opinion score) and RMSE (root mean square error) with 45.1%, 40.7%, 64.3% and 61.1% improvement, respectively, when compared with statistical method such as Wiener. Similarly, in comparison to GAN-based method, the proposed framework still outperforms with an improvement of 26.5%, 39.1%, 7.6% and 35.3% for SNR, SSIM, MOS and RMSE, respectively. Finally, the UWAR-GAN is assessed on very low SNR such as −10 dB with four common UWAN models, which infers that the proposed framework can be a robust candidate for the real-time underwater applications.

Quaternion-Based Two-Dimensional DOA Estimation for Coherent Underwater Sources Without Eigendecomposition

For scenarios of coherent underwater sources at low signal-to-noise ratio (SNR), a novel quaternion-based DOA algorithm without eigendecomposition is proposed using a linear vector-hydrophone array. We construct four quaternion models by judiciously arranging the received data to fully utilize the statistical information of the incident signals. To avoid the high computational complexity caused by the eigenvalue decomposition (EVD), we introduce the computationally efficient propagator method (PM) to estimate the elevation angles of the observed signals. In the quaternion algebra framework, we statistically eliminate the additive noise, which makes the PM method exhibit a robust performance in low SNR. By fully exploiting the direction information embedded in the velocity components, we achieve a high-resolution two-dimensional (2-D) DOA estimation result with a linear vector-hydrophone array. The simulations demonstrate that the proposed method offers stable estimation performance compared with the existing non-quaternion schemes without the need for any pair matching between the estimated azimuth and elevation angles.

Vibration Detection in Distributed Acoustic Sensor With Threshold-Based Technique: A Statistical View and Analysis

Detecting vibrations with high probability and low false alarm probability is crucial for prompting distributed acoustic sensors (DASs) to real applications. It is known that detection performance mainly depends on signal-to-noise ratio (SNR) and many efforts have been made to improve it. However, the relationship between SNR and detection performance has not been quantitatively analyzed so far. Threshold-based vibration detection is a simple and commonly used technique, but how to set the decision threshold in DAS is still an open question. In this work, for the first time, we propose a model to quantify the relationship between SNR and detection performance and provide a method for setting the decision threshold. Firstly, we build a model to differentiate vibrations from the background noise based on their short-time average energy. This model reveals that setting decision threshold requires perfect knowledge of noise power, which is a difficult task in DAS since noise power varies frequently with time and position. To solve this problem, secondly, we propose a noise-irrelevant threshold setting method based on autocorrelation-energy. Finally, experimental validation is performed on a DAS system along 47.4km sensing fiber with 5m spatial resolution. Results of autocorrelation-energy-based method show 100% and 98.1% detection probability for two vibrations with $1.12 \times {10^{ - 7}}$ false alarm probability in a one-hour measurement period.

A CMOS Image Sensor with a Novel Passive Pixel Array and High Precision Current Amplifier for a Compact Digital X-ray Detector

An active pixel sensor (APS) in a digital X-ray detector is the dominant circuitry for a CMOS image sensor (CIS) despite its lower fill factor (FF) compared to that of a passive pixel sensor (PPS). Although the PPS provides higher FF, its overall signal-to-noise ratio (SNR) is lower than that of the APS. The required high resolution and small focal plane can be achieved by reducing the number of transistors and contacts per pixel. We proposed a novel passive pixel array and a high precision current amplifier to improve the dynamic range (DR) without minimizing the sensitivity for diagnostic compact digital X-ray detector applications. The PPS can be an alternative to improve the FF. However, size reduction of the feedback capacitor causes degradation of SNR performance. This paper proposes a novel PPS based on readout and amplification circuits with a high precision current amplifier to minimize performance degradation. The expected result was attained with a 0.35-μm CMOS process parameter with power supply voltage of 3.3 V. The proposed PPS has a saturation signal of 1.5 V, dynamic range of 63.5 dB, and total power consumption of 13.47 mW. Therefore, the proposed PPS readout circuit improves the dynamic range without sacrificing the sensitivity.

Adaptive Magnetic Anomaly Detection Method Using Support Vector Machine

Magnetic anomaly detection (MAD) has been widely used for detecting the magnetic targets in many areas. In low signal-to-noise ratio (SNR) situations, the magnetic anomaly is usually buried in the magnetic noise, which leads to a decrease in the detection performance of traditional MAD methods. In order to improve the detection probability of MAD in low SNR, we propose an adaptive method of MAD using support vector machine (SVM). First, we design a detection model using SVM with radial basis function (RBF) kernel function. Meanwhile, we analyze the magnetic anomaly signal and determined the orthonormal basis function (OBF) energy and magnetic entropy as the features of the magnetic anomaly, which are as the input of the model. Then, the model is trained based on the data set with the magnetic signals. Finally, the trained model is applied to the raw magnetic signal to detect the anomaly. Compared to the traditional methods, the proposed method has a better performance and adaptive ability. The proposed method is validated by the real magnetic noise. The results show that the proposed method can reduce the case of false alarm and improve the detection performance in low SNR.

A study on the SNR performance analysis of laser-generated bidirectional thermal wave radar imaging inspection for hybrid C/GFRP laminate defects

In this paper, the detection capability of laser-generated bidirectional thermal wave radar imaging (BTWRI) inspection for carbon/glass fiber reinforced polymer (C/GFRP) defects was quantitatively evaluated by signal-to-noise ratio (SNR) analysis of characteristic images. A low-power experimental system was devised, and the C/GFRP specimens with multi-scale diameter-to-depth ratio were tested. An improved imaging algorithm (analytical chirp correlation, ACC) was presented by applying analytical bidirectional chirp signal to extract the correlation phase and amplitude of thermal wave radar signal corresponding to the expected delay time. The optimal ACC detection image is obtained by the SNR comparison of characteristic images frame by frame. The conventional imaging inspection results of using cross-correlation (CC), cross-correlation phase (CCP), chirp lock-in (CLI), and Hilbert transform mean (HTM) algorithms were performed for SNR comparison. Experimental results indicate that the ACC algorithm displays almost the same detection capability as the traditional CCP algorithm by a simpler calculation process, and the optimal ACC detection image has a significant enhancement on SNR performance for C/GFRP subsurface defects compared with conventional imaging algorithms.

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.