Wavelet network-based classification of transients using dominant frequency signature

2008 ◽  
Vol 78 (1) ◽  
pp. 21-29 ◽  
Author(s):  
Saibal Chatterjee ◽  
Sivaji Chakravorti ◽  
Chinmoy Kanti Roy ◽  
Debangshu Dey
Keyword(s):  
Dominant Frequency ◽  
Wavelet Network

scholarly journals A Deep Convolutional Neural Wavelet Network for Classification of Medical Images

2018 ◽  
Vol 14 (11) ◽  
pp. 1488-1498
Author(s):  
Ramzi Ben Ali ◽  
Ridha Ejbali ◽  
Mourad Zaied
Keyword(s):  
Medical Images ◽  
Wavelet Network

Detection and classification of voltage disturbances using a Fuzzy-ARTMAP-wavelet network

2011 ◽  
Vol 81 (12) ◽  
pp. 2057-2065 ◽  
Author(s):  
José G.M.S. Decanini ◽  
Mauro S. Tonelli-Neto ◽  
Fernando C.V. Malange ◽  
Carlos R. Minussi
Keyword(s):  
Fuzzy Artmap ◽  
Wavelet Network

Classification of impulse fault response patterns in transformers using cascaded wavelet network

2009 ◽  
Vol 1 (2/3) ◽  
pp. 243 ◽  
Author(s):  
P. Rajamani ◽  
D. Dey ◽  
B. Chatterjee ◽  
S. Chakravorti
Keyword(s):  
Response Patterns ◽  
Wavelet Network

Wavelet network-based detection and classification of transients

2001 ◽  
Vol 50 (5) ◽  
pp. 1425-1435 ◽  
Author(s):  
L. Angrisani ◽  
P. Daponte ◽  
M. D'Apuzzo
Keyword(s):  
Wavelet Network

scholarly journals Classification of Dementia in EEG with a Two-Layered Feed Forward Artificial Neural Network

2021 ◽  
Vol 11 (3) ◽  
pp. 7135-7139
Author(s):  
G. Anuradha ◽  
D. N. Jamal
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Dementia With Lewy Bodies ◽  
Lewy Bodies ◽  
Normal Subjects ◽  
Dominant Frequency ◽  
Abnormal Behavior ◽  
Feed Forward ◽  
Artificial Neural

Dementia has become a global public health issue. The current study is focused on diagnosing dementia with Electro Encephalography (EEG). The detection of the advancement of the disease is carried out by detecting the abnormal behavior in EEG measurements. Assessment and evaluation of EEG abnormalities is conducted for all the subjects in order to detect dementia. EEG feature analysis, namely dominant frequency, dominant frequency variability, and frequency prevalence, is done for abnormal and normal subjects and the results are compared. For dementia with Lewy bodies, in 85% of the epochs, the dominant frequency is present in the delta range whereas for normal subjects it lies in the alpha range. The dominant frequency variability in 75% of the epochs is above 4Hz for dementia with Lewy bodies, and in normal subjects at 72% of the epochs, the dominant frequency variability is less than 2Hz. It is observed that these features are sufficient to diagnose dementia with Lewy bodies. The classification of Lewy body dementia is done by using a feed-forward artificial neural network wich proved to have a 94.4% classification accuracy. The classification with the proposed feed-forward neural network has better accuracy, sensitivity, and specificity than the already known methods.

scholarly journals Ground penetrating radar use in flood prevention

2019 ◽  
Vol 67 (6) ◽  
pp. 1955-1965 ◽  
Author(s):  
Sylwia Tomecka-Suchoń
Keyword(s):  
Ground Penetrating Radar ◽  
Dominant Frequency ◽  
Automatic Method ◽  
Geophysical Method ◽  
Flood Prevention ◽  
The Stability ◽  
Curvature Gradient ◽  
Ground Penetrating ◽  
Weak Zones

Abstract The main goal of the work is to create an automatic method of locating weak zones within flood embankments structure based on ground penetrating radar (GPR) measurements. The presented research shows the possibilities of using advanced methods of GPR signal processing and its analysis with the help of signal attributes for detecting zones threatening the stability of the structure of flood embankments. Obtained results may help in quick detection of potential weak zones of the embankments and consequently give means to ameliorate them, which may prevent damage to the embankments during rise in the level of river water. The presented analyses were carried out on GPR data obtained for the flood banks of the Rudawa River (Kraków, Poland) in the area of their visible degradation. The use of signal attributes, such as Energy, instantaneous frequency, similarity, curvature gradient, dominant frequency, allowed initial indication of anomalous zones threatening the stability of embankment. Advanced processing supported by the use of advanced filters such as GLCM, Grubbs filter threshold and Convolve Prewitt helped in the analysis of the structure of the embankments. Artificial neural networks (ANNs) in the supervised and unsupervised variants were used to perform the automatic classification of weakened zones within the embankments. The results demonstrated the usefulness of GPR geophysical method through integration of ANN in the analysis of the data.

Classification of Impulse Fault Patterns in Transformers Using Wavelet Network

2008 ◽  
Author(s):  
P. Rajamani ◽  
D. Dey ◽  
B. Chatterjee ◽  
S. Chakravorti
Keyword(s):  
Wavelet Network
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