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Epileptic seizure is the abnormal synchronous neuronal activity that occurs in human brain. The early detection of epileptic seizure helps in improving patient's mental health. In this work, an Electroencephalogram based methodology of automated epileptic seizure detection using Flexible Analytical Wavelet Transform is presented. In the proposed methodology, Electroencephalogram signals are decomposed...
The main objective of this paper is prominent feature extraction from the EEG Signal using different wavelets and select the best suitable wavelet which gives excellent result and with the help of these features the EEG signal is classified into normal and abnormal category using Neural Networks. The EEG signal data is collected from the database available online (https://www.physionet.org/physiobank/database/)...
This paper presents a method for detecting anomalous power consumption patterns attacks, using a discrete wavelet transform, as well as the variance fractal dimension (VFD) and an artificial neural network (ANN) for a smart grid. The main procedure of the proposed algorithm consists of the following steps: (i) Finding normal and anomalous patterns of power consumption to train the proposed method,...
Recently, Deep belief networks(DBNs) have been applied in classification and regression, proved to be superior to general algorithms. But its powerful deep feature extraction ability has not yet been fully played so that a novel algorithm, multi-scale DBNs fusing wavelet transform(WT), is proposed in this paper. Based on the advantages of predicting high frequency components from WT by DBN confirmed,...
The physiological and pathological information obtained by the single-point or complex multi-point pressure sensor is still less. In this paper, we adopt the pulse image sensor which can reflect the change of the pulse-taking skin surface particularly and comprehensively, we use the MM-3 pulse model (Group A) as the subjects produced by Shanghai University of Traditional Chinese Medicine to study...
Electrocardiography (ECG) is a widely used noninvasive clinical tool for the diagnosis of cardiovascular disease. However, the accuracy of ECG analysis significantly affect the diagnostic error rate of cardiovascular diseases. Therefore, in recent year, many Neural Network (NN)-based approaches were proposed to automatically analyze the ECG signal. However, these methods suffer from long computing...
Electrocardiogram (ECG) is nearly a periodic signal widely used for the detection and diagnosis of cardiac abnormalities. Recently with the inception of computer based techniques, automated analysis of shape and pattern of ECG waveform has facilitated physician to obtain fast and accurate diagnosis of cardiac disorders. Abnormalities related to sinus rhythms can be detected by using ECG signal beat...
The paper presents a novel technique proposed for protection of multi bus Extra High Voltage (EHV) transmission system having multiple series compensation in line sections. It is found that conventional methods fail to protect such system under wide range of operating scenarios involving faults in series compensated transmission lines. The protective scheme presented here is in the algorithm which...
Fault classification in distance protection of transmission lines, with considering the wide variation in the fault operating conditions, has been very challenging task. In this paper, an approach is presented to classify the fault in transmission line based on Empirical Mode Decomposition (EMD) using instantaneous power for each phase of only one terminal. For decision making stage of proposed methodology,...
Fault detection and diagnosis (FDD) of power systems have become important issues due to the high power quality (PQ) demands for modern systems. For this purpose, wavelet transform is invoked to extract features of different transient disturbances. Then, an artificial neural network (ANN) as a powerful intelligent method is employed to automatically classify the disturbances based on their features...
Wavelet transform extracts the features of a signal and image via shifting and weighting methods. This transform has either advantages or disadvantages on image processing applications. One of important disadvantage of wavelet transform is limited orientation problem. This problem has been solved by different orientation with ridgelet transform. Ripplet-II transform is defined by recently generalising...
A cavitating two-phase flow of water in a pipe with area shrinkage was experimentally investigated, acquiring at high sampling rate pressure signals and images of the cavitating flow field. The time series of the pressure fluctuations was analyzed in terms of power spectral density and related to the cavitation regimes. Furthermore, the fluctuations of the pressure measurements were also decomposed...
This paper presents a new approach to detect and classify the power quality disturbance using Wavelet Transform (WT) based Optimized Artificial Neural Network (ANN). The proposed algorithm extracts the energy based feature vector consisting of approximation and detail coefficients of WT. ANN based classifier is used to classify the power quality (PQ) disturbances. Six different types of PQ disturbances...
This paper is about a new palm-print based biometric authentication system. It is low-cost as it employs a scanner coupled to a PC, for acquiring the image of the human palm. The histogram of the palm-image is cross wavelet transformed(XWT) with respect to that of a reference palmimage. Several features are extracted from the resulting spectrum, and a trained ANN is used to identify the subject, based...
Glaucoma disease detection from retinal images using classifiers like Least Square-Support Vector Machine classifier, Random forest, Dual Sequential Minimal Optimization classifier, Naive bayes classifier and Artificial neural networks. The textual features obtained from retinal images are used for this classification. Energy distributions over wavelet sub bands provide these features. The proposed...
Glaucoma disease detection from retinal images using classifiers like least square-Support Vector Machine classifier, random forest, dual Sequential Minimal Optimization classifier, naive bayes classifier and artificial neural networks. The textual features obtained from retinal images are used for this classification. Energy distributions over wavelet sub bands provides these features. The proposed...
This paper gives a new approach for recognition of handwritten Devanagari characters. Twenty handwritten characters from 100 people resulting 2000 characters are used for the experimentation. The handwritten characters written of paper is scanned, preprocessed and on every individual characters wavelet transform is applied so as to get decomposed images of characters. Statistical parameters are computed...
Detection and classification of electrocardiogram (ECG) signals is critically linked to the diagnosis of cardiac abnormalities. In this paper, a novel approach for ECG classification is presented using features based on wavelet subband energy coefficients. The ECG signals are decomposed into time-frequency representation using wavelet transform and then wavelet coefficients are used to calculate some...
In this paper, a potential application of Stock-ewell transforms (S-transform) is proposed to classify the ECG beats of the MIT-BIH database arrhythmias. Feature extraction is the important component of designing the system based on pattern recognition since even the best classifier will not perform better if the good features are not chosen properly. In this study, S-transform is used to extract...
Recently, vast research attention has been put to develop automated procedures for pavement inspection and evaluation. The current work concentrates on developing a multi-stage expert system for pavement distress detection and classification. Mixture of Wavelet modulus and Three Dimensional Radon Transform (3DRT) are used for knowledge generation. The features and parameters of the peaks are finally...
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