scholarly journals A Novel Method for Laser Peak Detection with Subpixel Accuracy for the Rail Corrugation Measurement

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Mehran Torabi ◽  
S. Mohammad Mousavi G ◽  
Davood Younesian
Keyword(s):  
Detection Algorithm ◽  
Peak Detection ◽  
Prototype System ◽  
Noise Elimination ◽  
Rail Corrugation ◽  
Detection Algorithms ◽  
Laser Stripe ◽  
Subpixel Accuracy ◽  
Peak Detection Algorithm ◽  
Novel Method

In this article, a new wavelet-based laser peak detection algorithm is proposed having subpixel accuracy. The algorithm provides an accurate and rapid measurement platform for the rail surface corrugation with no need to any image noise elimination. The proposed rail Corrugation Measurement System (CMS) is based on the laser triangulation principle, and the accuracy of such system is mainly affected by the laser peak detection in the captured image. The intensity of each row or column of the image is taken as a 1-D discrete signal. Intensity distribution of a laser stripe in this signal follows a Gaussian pattern contaminated by the white noise. Against usual peak detection algorithms with need to prenoise-filtering process, the proposed method based on the wavelet transform is able to perform these tasks efficiently and robustly. Present wavelet-based methods for the peak detection are at pixel level, but for achieving high accuracy subpixel detection is proposed. Experiments show that the capability of the proposed method for laser peak detection is more accurate and faster than the filter-based methods, especially for low S/N ratios. Also, this technique can be utilized for any application in laser peak detection with subpixel accuracy. A prototype system based on the proposed method for the rail corrugation measurement has been designed and manufactured. Results of the rail corrugation measurement guarantee capability of the proposed methodology for accurate measurement of the rail corrugation and its potential for industrial application.

scholarly journals The mountaineer's method for peak detection in photoplethysmographic signals

2019 ◽  
pp. 42-50
Author(s):  
Erick Javier Argüello-Prada
Keyword(s):  
Detection Rate ◽  
Motion Artifact ◽  
Detection Algorithm ◽  
Peak Detection ◽  
Motion Artifacts ◽  
Baseline Drift ◽  
Detection Algorithms ◽  
Peak Detection Algorithm ◽  
Drift Conditions ◽  
Low Amplitude

Several efforts have been made to develop algorithms for accurate peak detection in photoplethysmographic (PPG) signals. Most of those algorithms have been specifically conceived to perform under high motion artifact and baseline drift conditions. However, little has been done regarding peak detection in low-amplitude PPG signals. In an attempt to address this issue, a simple and real-time peak detection algorithm for PPG signals was proposed. In comparison with two other well-established peak detection algorithms, the proposed method was able to achieve over than 98% sensitivity and less than 3% failed detection rate, even when the amplitude of the PPG signal dropped to 0.2 V. Still, further work is needed to improve its robustness to motion artifacts.

Fiber Bragg Grating Spectrum Peak-Detection Algorithm Based on Difference of Gaussian

2021 ◽  
Vol 58 (7) ◽  
pp. 0706002
Author(s):  
蔺彦章 Lin Yanzhang ◽  
刘毅 Liu Yi ◽  
潘玉恒 Pan Yuheng ◽  
李国燕 Li Guoyan
Keyword(s):  
Fiber Bragg Grating ◽  
Detection Algorithm ◽  
Peak Detection ◽  
Bragg Grating ◽  
Peak Detection Algorithm ◽  
Difference Of Gaussian

Research of Peak-Detection Algorithm Based on Absolute Wavelength Calibration of Excimer Laser

2018 ◽  
Vol 45 (7) ◽  
pp. 0701003
Author(s):  
袁靖超 Yuan Jingchao ◽  
赵江山 Zhao Jiangshan ◽  
李慧 Li Hui ◽  
刘广义 Liu Guangyi
Keyword(s):  
Excimer Laser ◽  
Detection Algorithm ◽  
Peak Detection ◽  
Wavelength Calibration ◽  
Peak Detection Algorithm

scholarly journals Low Resource Complexity R-peak Detection Based on Triangle Template Matching and Moving Average Filter

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3997 ◽  
Author(s):  
Tam Nguyen ◽  
Xiaoli Qin ◽  
Anh Dinh ◽  
Francis Bui
Keyword(s):  
Template Matching ◽  
Moving Average ◽  
Detection Algorithm ◽  
Peak Detection ◽  
Moving Average Filter ◽  
Ecg Signals ◽  
Low Resource ◽  
Peak Detection Algorithm ◽  
Robustness To Noise ◽  
Average Filter

A novel R-peak detection algorithm suitable for wearable electrocardiogram (ECG) devices is proposed with four objectives: robustness to noise, low latency processing, low resource complexity, and automatic tuning of parameters. The approach is a two-pronged algorithm comprising (1) triangle template matching to accentuate the slope information of the R-peaks and (2) a single moving average filter to define a dynamic threshold for peak detection. The proposed algorithm was validated on eight ECG public databases. The obtained results not only presented good accuracy, but also low resource complexity, all of which show great potential for detection R-peaks in ECG signals collected from wearable devices.

scholarly journals Fully Automated R-peak Detection Algorithm (FLORA) for fetal magnetoencephalographic data

2019 ◽  
Vol 173 ◽  
pp. 35-41 ◽  
Author(s):  
Katrin Sippel ◽  
Julia Moser ◽  
Franziska Schleger ◽  
Hubert Preissl ◽  
Wolfgang Rosenstiel ◽  
...  
Keyword(s):  
Detection Algorithm ◽  
Peak Detection ◽  
Peak Detection Algorithm

scholarly journals An Adaptive and Time-Efficient ECG R-Peak Detection Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Qin Qin ◽  
Jianqing Li ◽  
Yinggao Yue ◽  
Chengyu Liu
Keyword(s):  
Detection Algorithm ◽  
Peak Detection ◽  
Time Interval ◽  
Detection Accuracy ◽  
Ecg Signal ◽  
Peak Detection Algorithm ◽  
Wavelet Multiresolution Analysis ◽  
The Mean ◽  
Electrocardiogram Ecg ◽  
Mean Time

R-peak detection is crucial in electrocardiogram (ECG) signal analysis. This study proposed an adaptive and time-efficient R-peak detection algorithm for ECG processing. First, wavelet multiresolution analysis was applied to enhance the ECG signal representation. Then, ECG was mirrored to convert large negative R-peaks to positive ones. After that, local maximums were calculated by the first-order forward differential approach and were truncated by the amplitude and time interval thresholds to locate the R-peaks. The algorithm performances, including detection accuracy and time consumption, were tested on the MIT-BIH arrhythmia database and the QT database. Experimental results showed that the proposed algorithm achieved mean sensitivity of 99.39%, positive predictivity of 99.49%, and accuracy of 98.89% on the MIT-BIH arrhythmia database and 99.83%, 99.90%, and 99.73%, respectively, on the QT database. By processing one ECG record, the mean time consumptions were 0.872 s and 0.763 s for the MIT-BIH arrhythmia database and QT database, respectively, yielding 30.6% and 32.9% of time reduction compared to the traditional Pan-Tompkins method.

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