Bioprocess Fault Detection by Nonlinear Multivariate Analysis: Application of an Artificial Autoassociative Neural Network and Wavelet Filter Bank

1998 ◽  
Vol 14 (1) ◽  
pp. 79-87 ◽  
Author(s):  
H. Shimizu ◽  
K. Yasuoka ◽  
K. Uchiyama ◽  
S. Shioya
Keyword(s):  
Neural Network ◽  
Multivariate Analysis ◽  
Fault Detection ◽  
Filter Bank ◽  
Wavelet Filter ◽  
Autoassociative Neural Network ◽  
Wavelet Filter Bank ◽  
Analysis Application ◽  
Nonlinear Multivariate Analysis

Dynamic synapse neural network (DSNN): A new configuration based on wavelet filter bank and genetic algorithm

2001 ◽  
Vol 109 (5) ◽  
pp. 2491-2491
Author(s):  
Hassan Heidari Namarvar ◽  
Jim‐Shih Liaw ◽  
Theodore W. Berger
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Filter Bank ◽  
Wavelet Filter ◽  
Dynamic Synapse ◽  
Wavelet Filter Bank

Signal-adapted wavelet filter bank design

1998 ◽  
Vol 34 (5) ◽  
pp. 434 ◽  
Author(s):  
Susu Yao
Keyword(s):  
Filter Bank ◽  
Wavelet Filter ◽  
Wavelet Filter Bank

Measurement of reverberation time of a passenger car utilizing the wavelet filter bank

2005 ◽  
Vol 219 (3) ◽  
pp. 329-336 ◽  
Author(s):  
Sang-Kwon Lee ◽  
Dong-June Yu
Keyword(s):  
Filter Bank ◽  
Bandpass Filter ◽  
Wavelet Filter ◽  
Reverberation Time ◽  
Passenger Cars ◽  
Passenger Car ◽  
Concert Hall ◽  
Low Level ◽  
Wavelet Filter Bank

A few researchers have tried to find the measurement of the reverberation time of a passenger car. However, this has proved to be extremely difficult because the reverberation time of a passenger car is too short to measure using the traditional bandpass filter. If the reverberation time is very short, the product of the reverberation time ( T) and the bandwidth ( B) of the traditional bandpass filter is very small. The low level of the product BT required for the measurement of the reverberation time using the traditional bandpass filter is 16. In order to overcome this problem, the wavelet filter bank has been developed. In the paper, this new wavelet filter is employed to measure the reverberation times of five different classes of passenger car. The low level of the product BT required for the measurement of reverberation time using the wavelet filter is 4. Therefore, it was possible to measure the reverberation times of five passenger cars successfully using the new wavelet filter bank. It is found that the reverberation times measured in most passenger cars are around 0.04. It is a very short reverberation time compared with those of general acoustic rooms like a concert hall.

scholarly journals Hypertension Diagnosis Index for Discrimination of High-Risk Hypertension ECG Signals Using Optimal Orthogonal Wavelet Filter Bank

2019 ◽  
Vol 16 (21) ◽  
pp. 4068 ◽  
Author(s):  
Jaypal Singh Rajput ◽  
Manish Sharma ◽  
U. Rajendra Acharya
Keyword(s):  
Blood Pressure ◽  
High Risk ◽  
Filter Bank ◽  
Early Stage ◽  
Fractional Dimension ◽  
Wavelet Filter ◽  
Orthogonal Wavelet ◽  
Ecg Signals ◽  
Wavelet Filter Bank ◽  
Hypertension Diagnosis

Hypertension (HT) is an extreme increment in blood pressure that can prompt a stroke, kidney disease, and heart attack. HT does not show any symptoms at the early stage, but can lead to various cardiovascular diseases. Hence, it is essential to identify it at the beginning stages. It is tedious to analyze electrocardiogram (ECG) signals visually due to their low amplitude and small bandwidth. Hence, to avoid possible human errors in the diagnosis of HT patients, an automated ECG-based system is developed. This paper proposes the computerized segregation of low-risk hypertension (LRHT) and high-risk hypertension (HRHT) using ECG signals with an optimal orthogonal wavelet filter bank (OWFB) system. The HRHT class is comprised of patients with myocardial infarction, stroke, and syncope ECG signals. The ECG-data are acquired from physionet’s smart health for accessing risk via ECG event (SHAREE) database, which contains recordings of a total 139 subjects. First, ECG signals are segmented into epochs of 5 min. The segmented epochs are then decomposed into six wavelet sub-bands (WSBs) using OWFB. We extract the signal fractional dimension (SFD) and log-energy (LOGE) features from all six WSBs. Using Student’s t-test ranking, we choose the high ranked WSBs of LOGE and SFD features. We develop a novel hypertension diagnosis index (HDI) using two features (SFD and LOGE) to discriminate LRHT and HRHT classes using a single numeric value. The performance of our developed system is found to be encouraging, and we believe that it can be employed in intensive care units to monitor the abrupt rise in blood pressure while screening the ECG signals, provided this is tested with an extensive independent database.

Palm print image recovery and de-noising via multi-channel wavelet filter bank

2006 ◽  
Vol 30 (5) ◽  
pp. 310-314
Author(s):  
Y. -H. Qiao ◽  
F. -Q. Li ◽  
A. -K. Qiao ◽  
J. -H. Zhang ◽  
K. Liang ◽  
...  
Keyword(s):  
Filter Bank ◽  
Image Recovery ◽  
Wavelet Filter ◽  
Wavelet Filter Bank ◽  
Palm Print
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