One of critical problem causing death of humankind is heart disease. To help Medical doctors to find patient heart conditions and monitoring their body’s signals, several electronic devices have been developed over several decades. Among different methods used for these purposes, Ballistocardiography (BCG) has an interesting measurement feature that no electrodes are needed to be attached to th...

Removing and reducing noise and improving the quality of image is very active research area in image processing. Various applications require various kinds of images as genesis of information for various works. Whenever an image is converted degradation occurs at the output. The output image processed through image enhancement for the removal of degradation. The main aim of Image enhancement is...

This paper deals with a novel approach towards Automatic Emotion Classification from human utterances. Discrete Wavelet Transform (DWT) is used for feature extraction from speech signals. Malayalam (One of the south Indian languages) is used for the experiment. We have used an elicited dataset of 500 utterances recorded from 10 male and 8 female speakers. Using Artificial Neural Network we have...

In today’s era Heart Rate Variability becomes an important characteristic to determine the condition of heart. That’s why the calculation of HRV and classification to generate rules is necessary. Human Heart Generates the electrical signal. ECG is used to detect the heart beat. ECG signal contains lots of noise. To classify the signals first to decompose the signals using wavelet transform. Man...

This paper proposed ship classification based on covariance of discrete wavelet using probability Neural Network A set of ship profiles are used to build a covariance matrix by discrete wavelet transform using Neural Network. It is found that this method for ship classifier design offers good class discriminacy when trained with 5 ship classes. This method can discriminate noisy ship very well....

In this article, a new technique for features extraction from SAR interferograms is presented. The technique combines the properties of auto-associative neural networks with those of more traditional approaches such as discrete Fourier transform or discrete wavelet transform. The feature extraction is chained to another neural module performing the estimation of the fault parameters characteriz...

Transformer protection is critical issue in power system as the issue lies in the accurate and rapid discrimination of magnetizing inrush current from internal fault current. Artificial neural network has been proposed and has demonstrated the capability of solving the transformer monitoring and fault detection problem using an inexpensive, reliable, and noninvasive procedure. This paper gives ...

This paper addressed a framework of a traffic prediction model which could eliminate the noises caused by random travel conditions. In the meantime, this model can also quantitatively calculate the influence of special factors. This framework combined several artificial intelligence technologies such as wavelet transform, neural network, and fuzzy logic. In addition to developing the prediction...