Automatic Phase‐Picking Method for Detecting Earthquakes Based on the Signal‐to‐Noise‐Ratio Concept

2019 ◽  
Vol 91 (1) ◽  
pp. 334-342
Author(s):  
Jihua Fu ◽  
Xu Wang ◽  
Zhitao Li ◽  
Hao Meng ◽  
Jianjun Wang ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Ratio Method ◽  
Mean Deviation ◽  
Signal To Noise ◽  
Short Term ◽  
Low Snr ◽  
Automatic Phase ◽  
Noise Ratio ◽  
Automatic Phase Picking

Abstract The automatic phase‐picking detection of earthquakes is a challenge under the background of big data and strong noise circumstances. The short‐term average/long‐term average (STA/LTA) ratio is widely used to detect earthquake due to its simplicity and robustness. However, STA/LTA‐based methods may not perform well with noisy data. Based on the signal‐to‐noise‐ratio (SNR) concept, a short‐term power/long‐term power (STP/LTP) ratio method is proposed. The characteristic function and the detection thresholds of the STP/LTP method are given physical meanings. Through a sample analysis, the STP/LTP detection results of both the P and S phases are better than the results of the STA/LTA by means of mean deviation, standard deviations, distributions of detection results, error rate, and missed rate on different SNR levels. In general, the STP/LTP method inherits the simple characteristics of the STA/LTA method, and it is suitable for phase picking of low‐SNR seismic data.

scholarly journals A MODWT-Based Algorithm for the Identification and Removal of Jumps/Short-Term Distortions in Displacement Measurements Used for Structural Health Monitoring

IoT ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 60-72
Author(s):  
Davi V. Q. Rodrigues ◽  
Delong Zuo ◽  
Changzhi Li
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Signal To Noise Ratio ◽  
Structural Condition ◽  
Signal To Noise ◽  
Short Term ◽  
Structural Health ◽  
Noise Ratio ◽  
Displacement Measurements

Researchers have made substantial efforts to improve the measurement of structural reciprocal motion using radars in the last years. However, the signal-to-noise ratio of the radar’s received signal still plays an important role for long-term monitoring of structures that are susceptible to excessive vibration. Although the prolonged monitoring of structural deflections may provide paramount information for the assessment of structural condition, most of the existing structural health monitoring (SHM) works did not consider the challenges to handle long-term displacement measurements when the signal-to-noise ratio of the measurement is low. This may cause discontinuities in the detected reciprocal motion and can result in wrong assessments during the data analyses. This paper introduces a novel approach that uses a wavelet-based multi-resolution analysis to correct short-term distortions in the calculated displacements even when previously proposed denoising techniques are not effective. Experimental results are presented to validate and demonstrate the feasibility of the proposed algorithm. The advantages and limitations of the proposed approach are also discussed.

scholarly journals Assessing Stability of Crutch Users by Non-Contact Methods

2021 ◽  
Vol 18 (6) ◽  
pp. 3001
Author(s):  
Achilles Vairis ◽  
Suzana Brown ◽  
Maurice Bess ◽  
Kyu Hyun Bae ◽  
Jonathan Boyack
Keyword(s):  
Signal To Noise Ratio ◽  
Time Derivative ◽  
Assistive Devices ◽  
Signal To Noise ◽  
Physically Disabled ◽  
Significant Difference ◽  
Novice Users ◽  
Noise Ratio ◽  
Physically Disabled People

Enhancing gait stability in people who use crutches is paramount for their health. With the significant difference in gait compared to users who do not require an assistive device, the use of standard gait analysis tools to measure movement for temporary crush users and physically disabled people proves to be more challenging. In this paper, a novel approach based on video analysis is proposed as non-contact low-cost solution to the more expensive alternative with the data collected from processed videos, two values are calculated: the Signal to Noise Ratio (SNR) of acceleration, and the Signal to Noise Ratio of the jerk (time derivative of acceleration), to assess the user’s stability while they walk with crutches. The adopted methodology has been tested on a total of 10 participants. Five are temporary users of assistive devices with one being a long-term user and the other four novice users, and five are disabled participants who use those assistive devices permanently. Preliminary results show differences between novice users, long-term users, and physically disabled users. The approach is promising and could improve the assessment of crutch user stability, allowing for the correction of gait for individuals while using an inexpensive non-contact setup and preventing unnecessary falls.

scholarly journals Convolution Network with Custom Loss Function for the Denoising of Low SNR Raman Spectra

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4623
Author(s):  
Sinead Barton ◽  
Salaheddin Alakkari ◽  
Kevin O’Dwyer ◽  
Tomas Ward ◽  
Bryan Hennelly
Keyword(s):  
Raman Spectra ◽  
Loss Function ◽  
Signal To Noise Ratio ◽  
Biomedical Science ◽  
Clinical Tool ◽  
Signal To Noise ◽  
Low Snr ◽  
Low Intensity ◽  
Spectral Peaks ◽  
Noise Ratio

Raman spectroscopy is a powerful diagnostic tool in biomedical science, whereby different disease groups can be classified based on subtle differences in the cell or tissue spectra. A key component in the classification of Raman spectra is the application of multi-variate statistical models. However, Raman scattering is a weak process, resulting in a trade-off between acquisition times and signal-to-noise ratios, which has limited its more widespread adoption as a clinical tool. Typically denoising is applied to the Raman spectrum from a biological sample to improve the signal-to-noise ratio before application of statistical modeling. A popular method for performing this is Savitsky–Golay filtering. Such an algorithm is difficult to tailor so that it can strike a balance between denoising and excessive smoothing of spectral peaks, the characteristics of which are critically important for classification purposes. In this paper, we demonstrate how Convolutional Neural Networks may be enhanced with a non-standard loss function in order to improve the overall signal-to-noise ratio of spectra while limiting corruption of the spectral peaks. Simulated Raman spectra and experimental data are used to train and evaluate the performance of the algorithm in terms of the signal to noise ratio and peak fidelity. The proposed method is demonstrated to effectively smooth noise while preserving spectral features in low intensity spectra which is advantageous when compared with Savitzky–Golay filtering. For low intensity spectra the proposed algorithm was shown to improve the signal to noise ratios by up to 100% in terms of both local and overall signal to noise ratios, indicating that this method would be most suitable for low light or high throughput applications.

Detection and Recognition of Stop Consonants by Normal.Hearing and Hearing Impaired Listeners

1992 ◽  
Vol 35 (4) ◽  
pp. 942-949 ◽  
Author(s):  
Christopher W. Turner ◽  
David A. Fabry ◽  
Stephanie Barrett ◽  
Amy R. Horwitz
Keyword(s):  
Signal To Noise Ratio ◽  
Recognition Performance ◽  
Normal Hearing ◽  
Hearing Impaired ◽  
Stop Consonants ◽  
Signal To Noise ◽  
Short Term ◽  
Masking Noise ◽  
Noise Ratio ◽  
Detection And Recognition

This study examined the possibility that hearing-impaired listeners, in addition to displaying poorer-than-normal recognition of speech presented in background noise, require a larger signal-to-noise ratio for the detection of the speech sounds. Psychometric functions for the detection and recognition of stop consonants were obtained from both normal-hearing and hearing-impaired listeners. Expressing the speech levels in terms of their short-term spectra, the detection of consonants for both subject groups occurred at the same signal-to-noise ratio. In contrast, the hearing-impaired listeners displayed poorer recognition performance than the normal-hearing listeners. These results imply that the higher signal-to-noise ratios required for a given level of recognition by some subjects with hearing loss are not due in part to a deficit in detection of the signals in the masking noise, but rather are due exclusively to a deficit in recognition.

scholarly journals BIT ERROR RATE ANALYSIS IN DIFFERENT TERRAINS FOR LTE

2019 ◽  
Vol 7 (7) ◽  
pp. 331-339
Author(s):  
Dilshad Mahjabeen ◽  
Moshiur Rahman Tarafder ◽  
T Saikat Ahmed
Keyword(s):  
Rural Area ◽  
Bit Error Rate ◽  
Error Rate ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Rate Analysis ◽  
Receiving Antenna ◽  
Noise Ratio ◽  
Clear Idea

Focus of this paper is mainly evaluating the performance of Long Term Evolution (LTE) system in different terrains such as urban, suburban and rural area. The performance parameters such as, Bit Error Rate (BER) and the Data Throughput are reported in terms of Signal to Noise Ratio (SNR). The system parameters taken into consideration are signal to noise ratio (SNR), number of receiving antenna (RxAn), reference channel and duplex mode. All of the simulations were performed in MATLAB, version 2014a simulink. The results are presented in table and graph which gives clear idea of the effect of environment on signal and receiver sensitivity. Also bit-error-rate, an important parameter in case of receiving signal, is analyzed with respect to SNR values. A comparative analysis of bit-error-rate is performed between three areas for same conditions which proves that LTE signal is well suited in a rural area than that of a suburban and urban area.

scholarly journals A Blind Spectrum Sensing Method Based on Deep Learning

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2270 ◽  
Author(s):  
Kai Yang ◽  
Zhitao Huang ◽  
Xiang Wang ◽  
Xueqiong Li
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Spectrum Sensing ◽  
Short Term Memory ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Noise Ratio

Spectrum sensing is one of the technologies that is used to solve the current problem of low utilization of spectrum resources. However, when the signal-to-noise ratio is low, current spectrum sensing methods cannot well-handle a situation in which the prior information of the licensed user signal is lacking. In this paper, a blind spectrum sensing method based on deep learning is proposed that uses three kinds of neural networks together, namely convolutional neural networks, long short-term memory, and fully connected neural networks. Experiments show that the proposed method has better performance than an energy detector, especially when the signal-to-noise ratio is low. At the same time, this paper also analyzes the effect of different long short-term memory layers on detection performance, and explores why the deep-learning-based detector can achieve better performance.

scholarly journals Data-mining Based Expert Platform for the Spectral Inspection

2014 ◽  
Vol 10 (S306) ◽  
pp. 292-294
Author(s):  
Haijun Tian ◽  
Yang Xu ◽  
Yang Tu ◽  
Yanxia Zhang ◽  
Yongheng Zhao ◽  
...  
Keyword(s):  
Data Mining ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Sky Surveys ◽  
Low Snr ◽  
Effective Role ◽  
Noise Ratio

AbstractWe propose and preliminarily implement a data-mining based platform to assist experts to inspect the increasing amount of spectra with low signal to noise ratio (SNR) generated by large sky surveys. The platform includes three layers: data-mining layer, data-node layer and expert layer. It is similar to the GalaxyZoo project and it is VO-compatible. The preliminary experiment suggests that this platform can play an effective role in managing the spectra and assisting the experts to inspect a large number of spectra with low SNR.

scholarly journals Improvement of Noise Uncertainty and Signal-To-Noise Ratio Wall in Spectrum Sensing Based on Optimal Stochastic Resonance

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 841 ◽  
Author(s):  
Di He ◽  
Xin Chen ◽  
Ling Pei ◽  
Lingge Jiang ◽  
Wenxian Yu
Keyword(s):  
Stochastic Resonance ◽  
Spectrum Sensing ◽  
Signal To Noise Ratio ◽  
Multipath Fading ◽  
Signal To Noise ◽  
Noise Uncertainty ◽  
Low Snr ◽  
Dynamic Stochastic ◽  
Snr Wall ◽  
Noise Ratio

Noise uncertainty and signal-to-noise ratio (SNR) wall are two very serious problems in spectrum sensing of cognitive radio (CR) networks, which restrict the applications of some conventional spectrum sensing methods especially under low SNR circumstances. In this study, an optimal dynamic stochastic resonance (SR) processing method is introduced to improve the SNR of the receiving signal under certain conditions. By using the proposed method, the SNR wall can be enhanced and the sampling complexity can be reduced, accordingly the noise uncertainty of the received signal can also be decreased. Based on the well-studied overdamped bistable SR system, the theoretical analyses and the computer simulations verify the effectiveness of the proposed approach. It can extend the application scenes of the conventional energy detection especially under some serious wireless conditions especially low SNR circumstances such as deep wireless signal fading, signal shadowing and multipath fading.

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