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Jia Uddin
BRAC University
Computer Science & IT Electrical & Electronics Engineering

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Recognition of emotional states using EEG signals based on time-frequency analysis and SVM classifier Fabian Parsia George; Istiaque Mannafee Shaikat; Prommy Sultana Ferdawoos Hossain; Mohammad Zavid Parvez; Jia Uddin
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 2: April 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (495.011 KB) | DOI: 10.11591/ijece.v9i2.pp1012-1020

Abstract

The recognition of emotions is a vast significance and a high developing field of research in the recent years. The applications of emotion recognition have left an exceptional mark in various fields including education and research. Traditional approaches used facial expressions or voice intonation to detect emotions, however, facial gestures and spoken language can lead to biased and ambiguous results. This is why, researchers have started to use electroencephalogram (EEG) technique which is well defined method for emotion recognition. Some approaches used standard and pre-defined methods of the signal processing area and some worked with either fewer channels or fewer subjects to record EEG signals for their research. This paper proposed an emotion detection method based on time-frequency domain statistical features. Box-and-whisker plot is used to select the optimal features, which are later feed to SVM classifier for training and testing the DEAP dataset, where 32 participants with different gender and age groups are considered. The experimental results show that the proposed method exhibits 92.36% accuracy for our tested dataset. In addition, the proposed method outperforms than the state-of-art methods by exhibiting higher accuracy.
Optimizing Apple Lossless Audio Codec Algorithm using NVIDIA CUDA Architecture Rafid Ahmed; Md. Sazzadul Islam; Jia Uddin
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 1: February 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (197.373 KB) | DOI: 10.11591/ijece.v8i1.pp70-75

Abstract

As majority of the compression algorithms are implementations for CPU architecture, the primary focus of our work was to exploit the opportunities of GPU parallelism in audio compression. This paper presents an implementation of Apple Lossless Audio Codec (ALAC) algorithm by using NVIDIA GPUs Compute Unified Device Architecture (CUDA) Framework. The core idea was to identify the areas where data parallelism could be applied and parallel programming model CUDA could be used to execute the identified parallel components on Single Instruction Multiple Thread (SIMT) model of CUDA. The dataset was retrieved from European Broadcasting Union, Sound Quality Assessment Material (SQAM). Faster execution of the algorithm led to execution time reduction when applied to audio coding for large audios. This paper also presents the reduction of power usage due to running the parallel components on GPU. Experimental results reveal that we achieve about 80-90% speedup through CUDA on the identified components over its CPU implementation while saving CPU power consumption.
Co-Authors Fabian Parsia George Istiaque Mannafee Shaikat Md. Sazzadul Islam Mohammad Zavid Parvez Prommy Sultana Ferdawoos Hossain Rafid Ahmed