Ultrasonic Total Focusing Imaging Method of Multilayer Composite Structures Using the Root-Mean-Square (RMS) Velocity

Multilayer composite structures have been widely used in industrial manufacturing, and nondestructive testing of these multilayer structures is to ensure their reliable quality and performance. Currently, ultrasonic total focusing method (TFM) imaging using full-matrix capture (FMC) technology has been proven to sense small defects in a single homogeneous medium and improve the imaging signal-to-noise ratio. However, these algorithms cannot be accurately applied to imaging of multilayer composite structures, due to the acoustic impedance variation and because reflection and refraction occur at the interface between the layers, which makes it very difficult to calculate the ultrasonic propagation path and time. To solve this problem, a root-mean-square (RMS) velocity algorithm for total focusing imaging of multilayer structures is proposed in the article. Based on the theory of RMS velocity for processing of seismic data, the approximated delays can be easily and quickly calculated by a hyperbolic time-distance relationship under circumstances of short lateral distance and horizontal layers. The performance of the proposed algorithm is evaluated by total focusing imaging of a two-layer medium with drilled holes and conducted by the finite element simulation. To further improve imaging efficiency, the partial data in the full-matrix data were used for imaging which is the simplified matrix focusing method (SFM). The results verify that the proposed methods are capable of total focusing imaging of two-layered structures. However, the imaging performance and efficiency of these algorithms are different.

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Sparse ECG Denoising with Generalized Minimax Concave Penalty

Sensors ◽

10.3390/s19071718 ◽

2019 ◽

Vol 19 (7) ◽

pp. 1718 ◽

Cited By ~ 5

Author(s):

Zhongyi Jin ◽

Anming Dong ◽

Minglei Shu ◽

Yinglong Wang

Keyword(s):

Root Mean Square ◽

Signal To Noise Ratio ◽

High Amplitude ◽

Mean Square ◽

Ecg Signals ◽

Reduction Techniques ◽

Ecg Denoising ◽

Important Diagnostic Tool ◽

Sparsity Recovery ◽

Norm Penalty

The electrocardiogram (ECG) is an important diagnostic tool for cardiovascular diseases. However, ECG signals are susceptible to noise, which may degenerate waveform and cause misdiagnosis. In this paper, the ECG noise reduction techniques based on sparse recovery are investigated. A novel sparse ECG denoising framework combining low-pass filtering and sparsity recovery is proposed. Two sparsity recovery algorithms are developed based on the traditional ℓ 1 -norm penalty and the novel generalized minimax concave (GMC) penalty, respectively. Compared with the ℓ 1 -norm penalty, the non-differentiable non-convex GMC penalty has the potential to strongly promote sparsity while maintaining the convexity of the cost function. Moreover, the GMC punishes large values less severely than ℓ 1 -norm, which is utilized to overcome the drawback of underestimating the high-amplitude components for the ℓ 1 -norm penalty. The proposed methods are evaluated on ECG signals from the MIT-BIH Arrhythmia database. The results show that underestimating problem is overcome by the proposed GMC-based method. The GMC-based method shows significant improvement with respect to the average of output signal-to-noise ratio improvement ( S N R i m p ), the average of root mean square error (RMSE) and the percent root mean square difference (PRD) over almost any given SNR compared with the classical methods, thus providing promising approaches for ECG denoising.

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Extraction of Voiced Regions of Speech from Emotional Speech Signals Using Wavelet-Pitch Method

Periodica Polytechnica Electrical Engineering and Computer Science ◽

10.3311/ppee.15373 ◽

2021 ◽

Author(s):

Lakshmi Srinivas Dendukuri ◽

Shaik Jakeer Hussain

Keyword(s):

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Signal To Noise Ratio ◽

Speech Signals ◽

Mean Square ◽

Emotional Speech ◽

Statistical Parameters ◽

Zero Crossing ◽

Previous Frame

Extraction of voiced regions of speech is one of the latest topics in speech domain for various speech applications. Emotional speech signals contain most of the information in voiced regions of speech. In this particular work, voiced regions of speech are extracted from emotional speech signals using wavelet-pitch method. Daubechies wavelet (Db4) is applied on the speech frames after downsampling the speech signals. Autocorrelation function is performed on the extracted approximation coefficients of each speech frame and corresponding pitch values are obtained. A local threshold is defined on obtained pitch values to extract voiced regions. The threshold values are different for male and female speakers, as male pitch values are low compared to the female pitch values in general. The obtained pitch values are scaled down and are compared with the thresholds to extract the voiced frames. The transition frames between the voiced and unvoiced frames are also extracted if the previous frame is voiced frame, to preserve the emotional content in extracted frames. The extracted frames are reshaped to have desired emotional speech signal. Signal to Noise Ratio (SNR), Normalized Root Mean Square Error (NRMSE) and statistical parameters are used as evaluation metrics. This particular work provides better SNR and Normalized Root Mean Square Error values compared to the zero crossing-energy and residual signal based methods in voiced region extraction. Db4 wavelet provides better results compared to Haar and Db2 wavelets in extracting voiced regions using wavelet-pitch method from emotional speech signals.

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Variability in ground motions: Root mean square acceleration and peak acceleration for the 1971 San Fernando, California, earthquake

Bulletin of the Seismological Society of America ◽

10.1785/bssa0730020615 ◽

1983 ◽

Vol 73 (2) ◽

pp. 615-632

Author(s):

Martin W. McCann ◽

David M. Boore

Keyword(s):

Standard Deviation ◽

Root Mean Square ◽

Ground Motion ◽

Ground Motions ◽

Propagation Path ◽

Embedment Depth ◽

Mean Square ◽

Stable Measure ◽

Building Effects ◽

San Fernando

abstract Data from the 1971 San Fernando, California, earthquake provided the opportunity to study the variation of ground motions on a local scale. The uncertainty in ground motion was analyzed by studying the residuals about a regression with distance and by utilizing the network of strong-motion instruments in three local geographic regions in the Los Angeles area. Our objectives were to compare the uncertainty in the peak ground acceleration (PGA) and root mean square acceleration (RMSa) about regressions on distance, and to isolate components of the variance. We find that the RMSa has only a slightly lower logarithmic standard deviation than the PGA and conclude that the RMSa does not provide a more stable measure of ground motion than does the PGA (as is commonly assumed). By conducting an analysis of the residuals, we have estimated contributions to the scatter in high-frequency ground motion due to phenomena local to the recording station, building effects defined by the depth of instrument embedment, and propagation-path effects. We observe a systematic decrease in both PGA and RMSa with increasing embedment depth. After removing this effect, we still find a significant variation (a standard deviation equivalent to a factor of up to 1.3) in the ground motions within small regions (circles of 0.5 km radius). We conclude that detailed studies which account for local site effects, including building effects, could reduce the uncertainty in ground motion predictions (as much as a factor of 1.3) attributable to these components. However, an irreducible component of the scatter in attenuation remains due to the randomness of stress release along faults during earthquakes. In a recent paper, Joyner and Boore (1981) estimate that the standard deviation associated with intra-earthquake variability corresponds to a factor of 1.35.

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Empirical Mode Decomposition (EMD) Based Denoising Method for Heart Sound Signal and Its Performance Analysis

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v6i5.11344 ◽

2016 ◽

Vol 6 (5) ◽

pp. 2197 ◽

Cited By ~ 1

Author(s):

Amy Hamidah Salman ◽

Nur Ahmadi ◽

Richard Mengko ◽

Armein Z. R. Langi ◽

Tati L. R. Mengko

Keyword(s):

Root Mean Square ◽

Empirical Mode Decomposition ◽

Heart Sound ◽

Signal To Noise Ratio ◽

Sound Signal ◽

Mean Square ◽

Denoising Method ◽

Mode Decomposition ◽

Mean Square Difference ◽

Heart Sound Signal

<p>In this paper, a denoising method for heart sound signal based on empirical mode decomposition (EMD) is proposed. To evaluate the performance of the proposed method, extensive simulations are performed using synthetic normal and abnormal heart sound data corrupted with white, colored, exponential and alpha-stable noise under different SNR input values. The performance is evaluated in terms of signal-to-noise ratio (SNR), root mean square error (RMSE), and percent root mean square difference (PRD), and compared with wavelet transform (WT) and total variation (TV) denoising methods. The simulation results show that the proposed method outperforms two other methods in removing three types of noises.</p>

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Self-Organization Characteristics of Lunar Regolith Inferred by Yutu-2 Lunar Penetrating Radar

Remote Sensing ◽

10.3390/rs13153017 ◽

2021 ◽

Vol 13 (15) ◽

pp. 3017

Author(s):

Xiang Zhang ◽

Wenmin Lv ◽

Lei Zhang ◽

Jinhai Zhang ◽

Yangting Lin ◽

...

Keyword(s):

Root Mean Square ◽

Structural Changes ◽

Structural Characteristics ◽

Lunar Regolith ◽

Homogeneous Medium ◽

Self Organization ◽

Mean Square ◽

Organization Model ◽

Correlation Distance ◽

Regolith Layer

Most previous studies tend to simplify the lunar regolith as a homogeneous medium. However, the lunar regolith is not completely homogeneous, because there are weak reflections from the lunar regolith layer. In this study, we examined the weak heterogeneity of the lunar regolith layer using a self-organization model by matching the reflection pattern of both the lunar regolith layer and the top of the ejecta layer. After a series of numerical experiments, synthetic results show great consistency with the observed Chang’E-4 lunar penetrating radar data and provide some constraints on the range of controlling parameters of the exponential self-organization model. The root mean square permittivity perturbation is estimated to be about 3% and the correlation distance is about 5–10 cm. Additionally, the upper layer of ejecta has about 1–2 rocks per square meter, and the rock diameter is about 20–30 cm. These parameters are helpful for further study of structural characteristics and the evolution process of the lunar regolith. The relatively small correlation distance and root mean square perturbation in the regolith indicate that the regolith is mature. The weak reflections within the regolith are more likely to be due to structural changes rather than material composition changes.

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Multi-defect detection based on ultrasonic Lamb wave sign phase coherence factor imaging method

Insight - Non-Destructive Testing and Condition Monitoring ◽

10.1784/insi.2021.63.11.659 ◽

2021 ◽

Vol 63 (11) ◽

pp. 659-666

Author(s):

Meng-Ke Zhang ◽

Guo-Peng Fan ◽

Wen-Fa Zhu ◽

Shu-Bin Zheng ◽

Xiao-Dong Chai ◽

...

Keyword(s):

Lamb Wave ◽

Performance Indicator ◽

Signal To Noise Ratio ◽

Scattered Signal ◽

Imaging Method ◽

Signal To Noise ◽

Full Matrix ◽

Wave Imaging ◽

Array Performance ◽

Scattering Signal

The ultrasonic Lamb wave total focusing method (TFM) only uses the amplitude of the defective scattered signal for virtual focused imaging, while ignoring the phase information of the scattered signal and the dispersion characteristics of the Lamb wave, resulting in low imaging resolution and easily produced artefacts in imaging. To solve this problem, an ultrasonic Lamb wave imaging method based on phase coherence is proposed in this paper and the sign coherence factor (SCF) in the full matrix scattering signal is extracted. Moreover, the method uses the SCF to weight the amplitude of the full matrix scattering signal, suppresses the side lobes of the defect echo signal and the Lamb wave dispersion effect, improves the ultrasonic Lamb wave imaging resolution and weakens the artefacts. Finally, single- and multiplehole defects in aluminium plates are detected for experimental validation using an ultrasonic phased array. The array performance indicator and signal-to-noise ratio are used as indicators for quantitative assessment of imaging performance. The results show that compared with the TFM imaging, the SCF imaging can effectively suppress the noise and scattered signal side lobes, improve the array performance indicator (API) by 69.1% and improve the signal-to-noise ratio (SNR) by 73.9%. In addition, the SCF imaging can effectively weaken the interference of scattered signals between multiple through-hole defects, resulting in fewer artefacts in imaging.

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Signal-To-Noise Ratio Calculations to Validate Sensor Positioning for Facial Muscle Assessment Using Noninvasive Facial Electromyography

Facial Plastic Surgery ◽

10.1055/s-0041-1725168 ◽

2021 ◽

Author(s):

Konstantin Frank ◽

Nicholas Moellhoff ◽

Antonia Kaiser ◽

Michael Alfertshofer ◽

Robert H. Gotkin ◽

...

Keyword(s):

Facial Expression ◽

Root Mean Square ◽

Muscle Activity ◽

Signal To Noise Ratio ◽

Mean Square ◽

Facial Muscle ◽

Signal To Noise ◽

Facial Electromyography ◽

Baseline Noise ◽

Noise Ratio

AbstractThe evaluation of neuromodulator treatment outcomes can be performed by noninvasive surface-derived facial electromyography (fEMG) which can detect cumulative muscle fiber activity deep to the skin. The objective of the present study is to identify the most reliable facial locations where the motor unit action potentials (MUAPs) of various facial muscles can be quantified during fEMG measurements. The study population consisted of five males and seven females (31.0 [12.9] years, body mass index of 22.15 [1.6] kg/m2). Facial muscle activity was assessed in several facial regions in each patient for their respective muscle activity utilizing noninvasive surface-derived fEMG. Variables of interest were the average root mean square of three performed muscle contractions (= signal) (µV), mean root mean square between those contraction with the face in a relaxed facial expression (= baseline noise) (µV), and the signal to noise ratio (SNR). A total of 1,709 processed fEMG signals revealed one specific reliable location in each investigated region based on each muscle's anatomy, on the highest value of the SNR, on the lowest value for the baseline noise, and on the practicability to position the sensor while performing a facial expression. The results of this exploratory study may help guiding future researchers and practitioners in designing study protocols and measuring individual facial MUAP when utilizing fEMG. The locations presented herein were selected based on the measured parameters (SNR, signal, baseline noise) and on the practicability and reproducibility of sensor placement.

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The Optimum Wavelet Base of Wavelet Analysis in Coal Rock Microseismic Signals

Advances in Mechanical Engineering ◽

10.1155/2014/537415 ◽

2014 ◽

Vol 6 ◽

pp. 537415

Author(s):

Shoufeng Tang ◽

Minming Tong ◽

Xinmin He

Keyword(s):

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Signal To Noise Ratio ◽

Wavelet Denoising ◽

Mean Square ◽

Wavelet Base ◽

Wavelet Bases ◽

Coal Rock ◽

Microseismic Signal

Coal rock rupture microseismic signal is characterized by time-varying, nonstationary, unpredictability, and transient property. Wavelet transform is an important method in microseismic signals processing. However, different wavelet bases yield different results when analyzing the same signal. To study the comparability of different wavelet bases in analyzing microseismic signals, the current paper uses the microseismic signals released from coal rock bursting as the research subject. Through the analysis of the properties of commonly used wavelet basis functions and the characteristics of coal rock microseismic signals, the current study found that Coiflet and Symlet wavelets are suitable for analyzing coal rock microseismic signals. Sym 8 and Coif 2 wavelets were found to be suitable for analyzing and denoising coal rock microseismic signals. After Sym 8 wavelet denoising, signal-to-noise ratio (SNR) and the root mean square error were 30.4184 and 1.3109 E–07, respectively. After Coif 2 wavelet denoising, the SNR and the root mean square error values were 35.2176 and 1.0312 E–07, respectively. The results will aid in the analysis and extraction of coal rock microseismic signals.

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