OptRPC: A novel and optimized recurrence plot-based system for ECG beat classification

2022 ◽  
Vol 72 ◽  
pp. 103328
Author(s):  
Mainul Islam Labib ◽  
Abdullah-Al Nahid
Keyword(s):  
Recurrence Plot ◽  
Beat Classification

Recurrence Characteristics Analysis of Near-Surface Wind Speed Signal with Different Sampling Frequencies

2014 ◽  
Vol 599-601 ◽  
pp. 1605-1609 ◽  
Author(s):  
Ming Zeng ◽  
Zhan Xie Wu ◽  
Qing Hao Meng ◽  
Jing Hai Li ◽  
Shu Gen Ma
Keyword(s):  
Wind Speed ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Recurrence Plot ◽  
Sampling Frequency ◽  
Near Surface ◽  
Gas Leakage ◽  
Time Period ◽  
Characteristics Analysis ◽  
Quantification Analysis

The wind is the main factor to influence the propagation of gas in the atmosphere. Therefore, the wind signal obtained by anemometer will provide us valuable clues for searching gas leakage sources. In this paper, the Recurrence Plot (RP) and Recurrence Quantification Analysis (RQA) are applied to analyze the influence of recurrence characteristics of the wind speed time series under the condition of the same place, the same time period and with the sampling frequency of 1hz, 2hz, 4.2hz, 5hz, 8.3hz, 12.5hz and 16.7hz respectively. Research results show that when the sampling frequency is higher than 5hz, the trends of recurrence nature of different groups are basically unchanged. However, when the sampling frequency is set below 5hz, the original trend of recurrence nature is destroyed, because the recurrence characteristic curves obtained using different sampling frequencies appear cross or overlapping phenomena. The above results indicate that the anemometer will not be able to fully capture the detailed information in wind field when its sampling frequency is lower than 5hz. The recurrence characteristics analysis of the wind speed signals provides an important basis for the optimal selection of anemometer.

scholarly journals Response Identification in a Vibration Energy-Harvesting System with Quasi-Zero Stiffness and Two Potential Wells

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3926
Author(s):  
Joanna Iwaniec ◽  
Grzegorz Litak ◽  
Marek Iwaniec ◽  
Jerzy Margielewicz ◽  
Damian Gąska ◽  
...  
Keyword(s):  
Energy Harvesting ◽  
Recurrence Plot ◽  
Piezoelectric Transducers ◽  
Vibration Energy ◽  
Vibration Energy Harvesting ◽  
Potential Wells ◽  
Frequency Responses ◽  
Voltage Output ◽  
Quantification Analysis ◽  
Energy Harvesting System

In this paper, the frequency broadband effect in vibration energy harvesting was studied numerically using a quasi-zero stiffness resonator with two potential wells and piezoelectric transducers. Corresponding solutions were investigated for system excitation harmonics at various frequencies. Solutions for the higher voltage output were collected in specific branches of the power output diagram. Both the resonant solution synchronized with excitation and the frequency responses of the subharmonic spectra were found. The selected cases were illustrated and classified using a phase portrait, a Poincaré section, and recurrence plot (RP) approaches. Select recurrence quantification analysis (RQA) measures were used to characterize the discussed solutions.

Few-shot modulation recognition using Recurrence Plot Algorithm

2021 ◽  
Author(s):  
Woojin Yun ◽  
Jiyeon Park ◽  
Hyeongyun Kim ◽  
Haewoon Nam
Keyword(s):  
Recurrence Plot ◽  
Modulation Recognition

Automatic Classification of ECG Using Multi-Classifiers with a New Fusion Method

2014 ◽  
Vol 687-691 ◽  
pp. 3917-3922
Author(s):  
Yi Chang Wang ◽  
Feng Qi Yan ◽  
Yu Fang
Keyword(s):  
Traditional Method ◽  
Heart Beat ◽  
Vector Model ◽  
Support Vector ◽  
Beat Classification ◽  
Computer Aided Analysis ◽  
Important Basis ◽  
Voting Method ◽  
Better Than

ECG signal contains abundant information of human heart activity. It is important basis of doctors’ diagnose. With the development of computer technology, computer aided analysis has been widely applied in the field of ECG analysis. Most of the traditional method is based on single classifier and too complex. Also, the accuracy is not high. This paper focuses on ECG heart beat classification, extracting different types of feature, training different classifiers by vector model and support vector machine (SVM), merging the result of multiple classifiers. In this paper, we used the advanced voting method (voting by weight) to fusion the result of different classifier, having compared it with the traditional voting method.It performed better than traditional method in term of accuracy

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