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Engineering Commons

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Electrical and Computer Engineering

TÜBİTAK

2023

Feature extraction

Articles 1 - 2 of 2

Full-Text Articles in Engineering

A Practical Low-Dimensional Feature Vector Generation Method Based On Wavelet Transform For Psychophysiological Signals, Erdem Erkan, Yasemi̇n Erkan Nov 2023

A Practical Low-Dimensional Feature Vector Generation Method Based On Wavelet Transform For Psychophysiological Signals, Erdem Erkan, Yasemi̇n Erkan

Turkish Journal of Electrical Engineering and Computer Sciences

High-dimensional feature vectors entail computational cost and computational complexity. However, a successful classification can be obtained with an optimally sized feature vector consisting of distinctive features. With the widespread use of the internet and mobile devices, the need for systems with low computational costs is increasing day by day. In this study, starting from the idea that each motor imagery is represented as a subject-specific pattern in the brain, we propose a new and practical method that can generate a low-dimensional feature vector based on wavelet transform. The feature vector is obtained from the correlation between each trial and each …


An Exploratory Study On The Effect Of Applying Various Artificial Neural Networks To The Classification Of Lower Limb Injury, Rachel Yun, May Salama, Lamiaa Elrefaei Mar 2023

An Exploratory Study On The Effect Of Applying Various Artificial Neural Networks To The Classification Of Lower Limb Injury, Rachel Yun, May Salama, Lamiaa Elrefaei

Turkish Journal of Electrical Engineering and Computer Sciences

This paper explores the application of a deep neural network (DNN) framework to human gait analysis for injury classification. The paper aims to identify whether a subject is healthy or has an injury of the ankle, knee, hip, or heel solely based on ground reaction force plate measurements. We consider how three DNNs-the multi-layer perceptron (MLP), fully convolutional network (FCN), and residual network (ResNet)-can be applied to gait analysis when the number of trainable network parameters far exceeds the number of training samples, and benchmark their performance in this context against that of shallow neural networks. The DNN architectures outperformed …