ABSTRACT
A dramatic increase in user and capacity demands has been noted after the COVID-19 pandemic. These challenges have damaged the 5G communication system mobility. Therefore, developing mobility and enhancing capacity transmission of 5G advanced services are the focused research gaps in the current era. In this paper, the free space optics (FSO) link is modeled with wavelength division multiplexing (WDM) technology based optical fiber system, purposing to enhance the 5G capabilities in multi-channel, high distance, and bidirectional transmissions. In addition, the presented hybrid FSO-WDM supported optical fiber network is analyzed for 4, 8, and 16 × 10 Gbps downlink and uplink transmission. The paper also includes the mathematical discussion of merged fiber length (SMF = 30 km) and FSO (600 m) with improved mobility management. In another contribution, the tolerance against Rayleigh backscattering (RB) noises is developed through various wavelengths of downlink and uplink channels. Finally, we perform the simulation analysis and reliability of the proposed structure for the 5G advanced communication system. © 2023 by the authors.
ABSTRACT
Fruit disease recognition is quickly becoming a hot topic in the field of computer vision. The presence of plant diseases not only reduces fruit production but also causes a significant loss to the national economy. Citrus fruits help to strengthen the immune system, allowing it to fight off diseases such as COVID-19. Manual inspection of fruit diseases with the naked eye takes time and is difficult;therefore, a computer based method is always required for accurate recognition of plant diseases. Several deep learning techniques for recognizing citrus fruit diseases have been introduced in the literature. Existing techniques had several issues, including redundant features, convolutional neural network (CNN) model selection, low contrast images, and long computational times. In this paper, single stream convolutional neural network architecture is proposed for recognizing citrus fruit diseases. In the first step, data augmentation is performed using four contrast enhancement operations: shadow removal, adjusting pixel intensity, improving brightness, and improving local contrast. The MobileNet-V2 CNN model is selected and fine-tuned in the second step. Using the transfer learning process, the fine-tuned model is trained on the augmented citrus dataset. The newly trained model is used for deep feature extraction;however, analysis shows that the extracted deep features contain little redundant information. As a result, an improved Whale Optimization Algorithm (IWOA) is used in the third step. The best features are then classified using machine learning classifiers in the final step. The augmented citrus fruits, leaves, and hybrid dataset were used in the experimental process and achieved an accuracy of 99.4, 99.5, and 99.7%. When compared to existing techniques, the proposed architecture outperformed them in terms of accuracy and time.