The basic aim of this paper is to introduce and describe an efficient numerical scheme based on spectral approach coupled with Chebyshev wavelets for the approximate solutions of Klein-Gordon and Sine-Gordon equations...The basic aim of this paper is to introduce and describe an efficient numerical scheme based on spectral approach coupled with Chebyshev wavelets for the approximate solutions of Klein-Gordon and Sine-Gordon equations. The main characteristic is that, it converts the given problem into a system of algebraic equations that can be solved easily with any of the usual methods. To show the accuracy and the efficiency of the method, several benchmark problems are implemented and the comparisons are given with other methods existing in the recent literature. The results of numerical tests confirm that the proposed method is superior to other existing ones and is highly accurate展开更多
Ahmad et al. in their paper [1] for the first time proposed to apply sharp function for classification of images. In continuation of their work, in this paper we investigate the use of sharp function as an edge detect...Ahmad et al. in their paper [1] for the first time proposed to apply sharp function for classification of images. In continuation of their work, in this paper we investigate the use of sharp function as an edge detector through well known diffusion models. Further, we discuss the formulation of weak solution of nonlinear diffusion equation and prove uniqueness of weak solution of nonlinear problem. The anisotropic generalization of sharp operator based diffusion has also been implemented and tested on various types of images.展开更多
Automated Speech Emotion Recognition (SER) becomes more popular and has increased applicability.SER concentrates on the automatic identification of the emotional state of a humanbeing using speech signals. It mainly d...Automated Speech Emotion Recognition (SER) becomes more popular and has increased applicability.SER concentrates on the automatic identification of the emotional state of a humanbeing using speech signals. It mainly depends upon the in-depth analysis of the speech signal,extracts features containing emotional details from the speech signal, and utilises patternrecognition techniques for emotional state identification. The major problem in automatic SERis to extract discriminate, powerful, and emotional salient features from the acoustical content ofspeech signals. The proposed model aims to detect and classify three emotional states of speechsuch as happy, neutral, and sad. The presented model makes use of Convolution neural network– Gated Recurrent unit (CNN-GRU) based feature extraction technique which derives a set offeature vectors. A comprehensive simulation takes place using the Berlin German Database andSJTU Chinese Database which comprises numerous audio files under a collection of differentemotion labels.展开更多
文摘The basic aim of this paper is to introduce and describe an efficient numerical scheme based on spectral approach coupled with Chebyshev wavelets for the approximate solutions of Klein-Gordon and Sine-Gordon equations. The main characteristic is that, it converts the given problem into a system of algebraic equations that can be solved easily with any of the usual methods. To show the accuracy and the efficiency of the method, several benchmark problems are implemented and the comparisons are given with other methods existing in the recent literature. The results of numerical tests confirm that the proposed method is superior to other existing ones and is highly accurate
文摘Ahmad et al. in their paper [1] for the first time proposed to apply sharp function for classification of images. In continuation of their work, in this paper we investigate the use of sharp function as an edge detector through well known diffusion models. Further, we discuss the formulation of weak solution of nonlinear diffusion equation and prove uniqueness of weak solution of nonlinear problem. The anisotropic generalization of sharp operator based diffusion has also been implemented and tested on various types of images.
文摘Automated Speech Emotion Recognition (SER) becomes more popular and has increased applicability.SER concentrates on the automatic identification of the emotional state of a humanbeing using speech signals. It mainly depends upon the in-depth analysis of the speech signal,extracts features containing emotional details from the speech signal, and utilises patternrecognition techniques for emotional state identification. The major problem in automatic SERis to extract discriminate, powerful, and emotional salient features from the acoustical content ofspeech signals. The proposed model aims to detect and classify three emotional states of speechsuch as happy, neutral, and sad. The presented model makes use of Convolution neural network– Gated Recurrent unit (CNN-GRU) based feature extraction technique which derives a set offeature vectors. A comprehensive simulation takes place using the Berlin German Database andSJTU Chinese Database which comprises numerous audio files under a collection of differentemotion labels.
