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Ateya Abdelhamied A.,Soliman Naglaa F.,Alkanhel Reem,Alhussan Amel A.,Muthanna Ammar,Koucheryavy Andrey 대한전기학회 2023 Journal of Electrical Engineering & Technology Vol.18 No.3
Internet of Things (IoT) is one of the promising technologies, announced as one of the primary use cases of the fifth-generation cellular systems (5G). It has many applications that cover many fields, moving from indoor applications, e.g., smart homes, smart metering, and healthcare applications, to outdoor applications, including smart agriculture, smart city, and surveillance applications. This produces massive heterogeneous traffic that loads the IoT network and other integrated communication networks, e.g., 5G, which represents a significant challenge in designing IoT networks; especially, with dense deployment scenarios. To this end, this work considers developing a novel artificial intelligence (AI)-based framework for predicting traffic over IoT networks with dense deployment. This facilitates traffic management and avoids network congestion. The developed AI algorithm is a deep learning model based on the convolutional neural network, which is a lightweight algorithm to be implemented by a distributed edge computing node, e.g., a fog node, with limited computing capabilities. The considered IoT model deploys distributed edge computing to enable dense deployment, increase network availability, reliability, and energy efficiency, and reduce communication latency. The developed framework has been evaluated, and the results are introduced to validate the proposed prediction model.
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Abbass Mohammed Y.,Sadic Nevein,Ashiba Huda I.,Hassan Emad S.,El-Dolil Sami,Soliman Naglaa F.,Algarni Abeer D.,Alabdulkreem Eatedal A.,Algarni Fatimah,El-Banby Ghada M.,Abdel-Rahman Mohamed R.,Aldosar 대한전기학회 2022 Journal of Electrical Engineering & Technology Vol.17 No.5
Infrared (IR) image sequences are acquired with certain types of cameras. These cameras give the sequence of images according to the heat distribution. With time, some deterioration of the quality of the sequence occurs due the thermal noise eff ect generated in the camera. This thermal noise eff ect leads to some sort of non-uniformity in the obtained image sequence. Hence, it is necessary to perform some sort of non-uniformity correction in the video sequence according to the fi rst frame. This type of non-uniformity correction is scene-based. This paper introduces a scene-based non-uniformity correction technique that depends mainly on histogram matching. The noise eff ect on each frame in the sequence leads to some drift in the histogram of that frame. Hence, the proposed technique depends on the histogram matching concept to correct the histogram of each frame in the sequence based on the histogram of the fi rst frame that is free from the thermal noise eff ect. Diff erent image quality metrics including entropy, contrast, edge intensity, average gradient, and correlation with the fi rst frame are adopted to assess the quality of the obtained frames after adjustment. It is required in the frames to be corrected to reduce entropy, edge intensity and average gradient as these metrics are increased with the presence of thermal noise eff ect on all pixels represented as much details and unnecessary information. In addition, the contrast of the video sequences should be increased to determine objects in a better way. The correlation of the corrected frames with the fi rst one should be increased to reduce the noise eff ect. Simulation results reveal enhanced quality of the obtained video sequences after processing with the proposed technique.
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