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All Journal Bulletin of Electrical Engineering and Informatics
Eduard Babulak
Liberty University
Electrical & Electronics Engineering

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Journal : Bulletin of Electrical Engineering and Informatics

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Flying object tracking and classification of military versus nonmilitary aircraft Ehsan Akbari Sekehravani; Eduard Babulak; Mehdi Masoodi
Bulletin of Electrical Engineering and Informatics Vol 9, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (984.791 KB) | DOI: 10.11591/eei.v9i4.1843

Abstract

Tracking of moving objects in a sequence of images is one of the important and functional branches of machine vision technology. Detection and tracking of a flying object with unknown features are important issues in detecting and tracking objects. This paper consists of two basic parts. The first part involves tracking multiple flying objects. At first, flying objects are detected and tracked, using the particle filter algorithm. The second part is to classify tracked objects (military or nonmilitary), based on four criteria; Size (center of mass) of objects, object speed vector, the direction of motion of objects, and thermal imagery identifies the type of tracked flying objects. To demonstrate the efficiency and the strength of the algorithm and the above system, several scenarios in different videos have been investigated that include challenges such as the number of objects (aircraft), different paths, the diverse directions of motion, different speeds and various objects. One of the most important challenges is the speed of processing and the angle of imaging.
On detecting and identifying faulty internet of things devices and outages Feng Wang; Eduard Babulak; Yongning Tang
Bulletin of Electrical Engineering and Informatics Vol 10, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i6.2698

Abstract

As internet of things (IoT) devices play an integral role in our everyday life, it is critical to monitor the health of the IoT devices. However, fault detection in IoT is much more challenging compared with that in traditional wired networks. Traditional observing and polling are not appropriate for detecting faults in resource-constrained IoT devices. Because of the dynamic feature of IoT devices, these detection methods are inadequate for IoT fault detection. In this paper, we propose two methods that can monitor the health status of IoT devices through monitoring the network traffic of these devices. Based on the collected traffic or flow entropy, these methods can determine the health status of IoT devices by comparing captured traffic behavior with normal traffic patterns. Our measurements show that the two methods can effectively detect and identify malfunctioned or defective IoT devices.
Co-Authors Ehsan Akbari Sekehravani Feng Wang Mehdi Masoodi Yongning Tang