Analysis of Acoustic Signal in the MICRO Discharges Using Continuous Wavelet Transform

2006 ◽  
Vol 9 (2) ◽  
Author(s):  
Toshiyuki Nakamiya ◽  
Daiki Sasahara ◽  
Kenji Ebihara ◽  
Tomoaki Ikegami ◽  
Ryoichi Tsuda
Keyword(s):  
Wavelet Transform ◽  
Energy Distribution ◽  
Power Supply ◽  
Acoustic Signal ◽  
Continuous Wavelet Transform ◽  
Dominant Frequency ◽  
Continuous Wavelet ◽  
Electrode Current ◽  
Current Voltage ◽  
Frequency Components

AbstractTo examine the tracking phenomenon that was one of the main causes of fire breaking, fundamental experiments were carried out. To one of the electrodes AC high voltage was applied. The following samples: the mesh plate, the flat ribbon cable and the ignition plug were prepared as the electrode. Current, voltage waveforms of micro discharge and the sound signal detected by the condenser microphone were stored in the Hi-coder memory. In this paper, Continuous Wavelet Transform (CWT) was applied to determine the acoustic sound of the micro discharge and to study its dominant frequency components. Additionally, the energy distribution of acoustic signal was examined by CWT, when the frequency of power supply increased from 10 kHz to 30 kHz.

scholarly journals Automatic ECG Classification Using Continuous Wavelet Transform and Convolutional Neural Network

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 119
Author(s):  
Tao Wang ◽  
Changhua Lu ◽  
Yining Sun ◽  
Mei Yang ◽  
Chun Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Convolutional Neural Network ◽  
Continuous Wavelet Transform ◽  
Continuous Wavelet ◽  
Time Frequency ◽  
Ecg Signals ◽  
Rr Interval ◽  
Frequency Components ◽  
Fully Connected

Early detection of arrhythmia and effective treatment can prevent deaths caused by cardiovascular disease (CVD). In clinical practice, the diagnosis is made by checking the electrocardiogram (ECG) beat-by-beat, but this is usually time-consuming and laborious. In the paper, we propose an automatic ECG classification method based on Continuous Wavelet Transform (CWT) and Convolutional Neural Network (CNN). CWT is used to decompose ECG signals to obtain different time-frequency components, and CNN is used to extract features from the 2D-scalogram composed of the above time-frequency components. Considering the surrounding R peak interval (also called RR interval) is also useful for the diagnosis of arrhythmia, four RR interval features are extracted and combined with the CNN features to input into a fully connected layer for ECG classification. By testing in the MIT-BIH arrhythmia database, our method achieves an overall performance of 70.75%, 67.47%, 68.76%, and 98.74% for positive predictive value, sensitivity, F1-score, and accuracy, respectively. Compared with existing methods, the overall F1-score of our method is increased by 4.75~16.85%. Because our method is simple and highly accurate, it can potentially be used as a clinical auxiliary diagnostic tool.

Instantaneous Frequencies of Continuous Blood Pressure a Comparison of the Power Spectrum, the Continuous Wavelet Transform and the Hilbert–Huang Transform

2017 ◽  
Vol 09 (04) ◽  
pp. 1750009
Author(s):  
Kathrine Knai ◽  
Geir Kulia ◽  
Marta Molinas ◽  
Nils Kristian Skjaervold
Keyword(s):  
Blood Pressure ◽  
Wavelet Transform ◽  
Power Spectrum ◽  
Continuous Wavelet Transform ◽  
Continuous Wavelet ◽  
Frequency Shifts ◽  
Hilbert Huang Transform ◽  
Frequency Components ◽  
Instantaneous Frequencies ◽  
Continuous Blood Pressure

Continuous biological signals, like blood pressure recordings, exhibit nonlinear and nonstationary properties which must be considered during their analysis. Heart rate variability analyses have identified several frequency components and their autonomic origin. There is need for more knowledge on the time-changing properties of these frequencies. The power spectrum, continuous wavelet transform and Hilbert–Huang transform are applied on a continuous blood pressure signal to investigate how the different methods compare to each other. The Hilbert–Huang transform shows high ability to analyze such data, and can, by identifying instantaneous frequency shifts, provide new insights into the nature of these kinds of data.

Application of Two-Dimensional Continuous Wavelet Transform in Demodulating Fringe Pattern with Two Frequency Components

2010 ◽  
Vol 47 (7) ◽  
pp. 071001
Author(s):  
蔡义祥 Cai Yixiang ◽  
陈文静 Chen Wenjing ◽  
李思坤 Li Sikun ◽  
赵玥 Zhao Yue ◽  
许罗鹏 Xu Luopeng
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Fringe Pattern ◽  
Continuous Wavelet ◽  
Two Dimensional ◽  
Frequency Components

scholarly journals RAINFALL ANALYSIS IN KLANG RIVER BASIN USING CONTINUOUS WAVELET TRANSFORM

2016 ◽  
Vol 10 (1) ◽  
pp. 3-10 ◽  
Author(s):  
Celso A. G. Santos ◽  
Richarde Marques Silva ◽  
Seyed Ahmad Akrami
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
River Basin ◽  
Continuous Wavelet Transform ◽  
Rainfall Data ◽  
Accurate Information ◽  
Rainfall Time Series ◽  
Continuous Wavelet ◽  
Rainfall Analysis ◽  
Frequency Components

The rainfall characteristics within Klang River basin is analyzed by the continuous wavelet transform using monthly rainfall data (1997–2009) from a raingauge and also using daily rainfall data (1998–2013) from the Tropical Rainfall Measuring Mission (TRMM). The wavelet power spectrum showed that some frequency components were presented within the rainfall time series, but the observed time series is short to provide accurate information, thus the daily TRMM rainfall data were used. In such analysis, two main frequency components, i.e., 6 and 12 months, showed to be present during the entire period of 16 years. Such semiannual and annual frequencies were confirmed by the global wavelet power spectra. Finally, the modulation in the 8–16-month and 256–512-day bands were examined by an average of all scales between 8 and 16 months, and 256 and 512 days, respectively, giving a measure of the average monthly/daily variance versus time, where the periods with low or high variance could be identified.

scholarly journals RAINFALL ANALYSIS IN KLANG RIVER BASIN USING CONTINUOUS WAVELET TRANSFORM

2016 ◽  
Vol 10 (1) ◽  
pp. 3-10
Author(s):  
Celso A. G. Santos ◽  
Richarde Marques Silva ◽  
Seyed Ahmad Akrami
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
River Basin ◽  
Continuous Wavelet Transform ◽  
Rainfall Data ◽  
Accurate Information ◽  
Rainfall Time Series ◽  
Continuous Wavelet ◽  
Rainfall Analysis ◽  
Frequency Components

The rainfall characteristics within Klang River basin is analyzed by the continuous wavelet transform using monthly rainfall data (1997–2009) from a raingauge and also using daily rainfall data (1998–2013) from the Tropical Rainfall Measuring Mission (TRMM). The wavelet power spectrum showed that some frequency components were presented within the rainfall time series, but the observed time series is short to provide accurate information, thus the daily TRMM rainfall data were used. In such analysis, two main frequency components, i.e., 6 and 12 months, showed to be present during the entire period of 16 years. Such semiannual and annual frequencies were confirmed by the global wavelet power spectra. Finally, the modulation in the 8–16-month and 256–512-day bands were examined by an average of all scales between 8 and 16 months, and 256 and 512 days, respectively, giving a measure of the average monthly/daily variance versus time, where the periods with low or high variance could be identified.

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