The world is currently facing many unrests and challenges due to the emergence of the COVID-19 Epidemic. The management of medical resources is considered one of the most important challenges posed by the emergence of this epidemic. The intensive care unit (ICU) plays an important role in saving the life of a COVID-19 patient, and therefore work has been done in this research to find models to predict the patient's need to enter ICU or not. The prediction models depend on Machine Learning (ML). Three models will be built to predict the state at which the patient needs to enter the ICU or not. The proposed predictor models based on three types of Supervised machine learning Naive Bayes, K -nearest neighbor (K-NN), and Support Vector Machine (SVM) according to the scarce datasets. Predictor model trained based on Extracted features from patients' X-ray images.

Fifth-generation (5G) cellular networks are state-of-the-art wireless technologies revolutionizing all wireless systems. The fundamental goals of 5G are to increase network capacity, improve data rates, and reduce end-to-end latency. Therefore, 5G can support many devices connected to the Internet and realize the Internet of Things (IoT) vision. Though 5 G provides significant features for mobile wireless networks, some challenges still need to be addressed. Although 5 G offers valuable capabilities for mobile wireless networks, specific issues still need to be resolved. This article thoroughly introduces 5G technology, detailing its needs, infrastructure, features, and difficulties. In addition, it summarizes all the requirements and specifications of the 5G network based on the 3rd Generation Partnership Project (3GPP) Releases 15–17. Finally, this study discusses the key specifications challenges of 5G wireless networks.

Asset localization implies adding more features in wireless sensor networks, such as object searching and tracking capability. Ultra Wideband (UWB) wireless technology offers greater resistance to multipath fading, interference, and potentially lowers power consumption. Therefore UWB technology inherently enables the accurate localization of assets. In this paper, localization method based on Time of Arrival (TOA) scheme using IEEE802.15.3a channel model is implemented using Matlab and tested. In this method only two reference nodes are used for localization, which decreases the overall power consumption through reducing number of packets exchanged in the system while reducing packets collision probability.

Low voltage low power 4-bits 90Ms/s, 40uw, with DNL (+0.19/−0.4)LSB and INL (+0.47/−0.46)LSB is designed using 0.13um UMC CMOS technology operated with 1.2V voltage supply. The converter is composed of three stages the first, second stages produce 1.5bit/stage and last stage produce 2 bit/stage. Using Bottom-Plate Switching and fully digital error correction which corrects errors due to capacitor mismatch, charge injection, and comparator offsets. The calibration is performed without any additional analog circuitry, and the conversion does not need extra clock cycles.

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