Classification of Underwater Echo Based on Fractal Theory and Learning Vector Quantization Neural Network

2011 ◽  
Vol 148-149 ◽  
pp. 1365-1369
Author(s):  
Pu Hua Tang ◽  
Mu Rong Zhou ◽  
Ying Yong Bu
Keyword(s):  
Neural Network ◽  
Fractal Dimension ◽  
Wavelet Transform ◽  
Vector Quantization ◽  
Continuous Wavelet Transform ◽  
Fractal Theory ◽  
Learning Vector Quantization ◽  
Experimental Results ◽  
Continuous Wavelet

A classification method for underwater echo is introduced, which based on fractal theory and learning vector quantization (LVQ) neural network. The fractal dimension was extracted from the underwater echo by continuous wavelet transform. Combining with accumulative energy as input of a LVQ neural network, neural network was used to classify four kinds of underwater echo. The experimental results showed this method is effective and reliable.

Neuro-Classification of Currency Fatigue Levels Based on Acoustic Cepstrum Patterns

2000 ◽  
Vol 4 (1) ◽  
pp. 18-23 ◽  
Author(s):  
Masaru Teranishi ◽  
◽  
sigeru Omatu ◽  
Toshihisa Kosaka ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Vector Quantization ◽  
Acoustic Signal ◽  
Learning Vector Quantization ◽  
Experimental Results ◽  
New Method ◽  
Competitive Neural Network ◽  
Good Classification

This paper proposes a new method to classify currencies into different fatigue levels. Acoustic cepstrum patterns obtained from an acoustic signal generated by a currency passing through a banking machine are used for classification. The acoustic cepstrum patterns are fed to a competitive neural network with the Learning Vector Quantization (LVQ) algorithm, and classified the currency into three fatigue levels. The experimental results show that the proposed method is useful for classification of fatigue levels of currencies, and the LVQ algorithm performs a good classification.

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.

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