Jungpil Shin

TL;DR: This paper deals with the problem of fault detection and diagnosis in sensors considering erratic, drift, hard-over, spike, and stuck faults, and shows that an increase in the number of features hardly increases the total accuracy of the classifier, but using ten features gives the highest accuracy for fault classification in an SVM.

TL;DR: The purpose of this research was to see if Kinect sensor can recognize numeric and alphabetic characters written with the hand in the air, and it takes some time to master them both.

TL;DR: In this paper, the authors present a survey of the main aspects of automatic speaker recognition, such as speaker identification, verification, diarization, and performance of current speaker recognition systems.

TL;DR: In this proposed model, Deep Convolutional Neural Network (DCNN) is used for extracting efficient hand features to recognize the American Sign Language (ASL) using hand gestures and the Multi-class Support Vector Machine (MCSVM) is use for identifying the hand sign.

TL;DR: In this paper, a media-pipe hands algorithm was used for estimating hand joints from RGB images of hands obtained from a web camera and two types of features were generated from the estimated coordinates of the joints obtained for classification: the distances between the joint points and the angles between vectors and 3D axes.