Named Data Networking as an alternative network for 5G network traffic is required to be able to provide better performance compared to other networks such as internet protocol networks. In NDN wireless, it is known t...
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ISBN:
(数字)9798350377057
ISBN:
(纸本)9798350377064
Named Data Networking as an alternative network for 5G network traffic is required to be able to provide better performance compared to other networks such as internet protocol networks. In NDN wireless, it is known that there is producer mobility and consumer mobility, so optimal mobility management is needed, including the handover process. In this paper the author proposes a simple RSSI-based 5G handover algorithm with distance as a reference RSSI value. This algorithm is a preparation for the handover algorithm on the NDN wireless network. By using the assumed distance value MS to eNB as the basic value for calculating RSSI, handover determination is determined by comparing the RSSI value to the RSSI threshold value. With the scenario of different numbers of users and cluster sizes, from the handover process involving 8 eNB. It can be calculated how big the handover probability is for each user. From the simulation results, it can be concluded that the more users and the larger the cluster size, the greater the handover probability value. This is linear, with the wider the cluster, the probability of getting an RSSI signal from the original eNB becomes smaller, making it possible to get an RSSI signal from neighboring eNB, so that a handover occurs.
The COVID-19 pandemic has initiated a global health emergency, with an exigent need for effective cure. Progressively, drug repurposing is emerging a promise solution as it saves the time, cost and labor. However, the...
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The COVID-19 pandemic has initiated a global health emergency, with an exigent need for effective cure. Progressively, drug repurposing is emerging a promise solution as it saves the time, cost and labor. However, the number of drug candidates that have been identified as being repurposed for the treatment of COVID-19 are still insufficient, so more effective and thorough drug exploring strategies are required. In this study, we joint the molecular docking with machine learning regression approaches to find some prospective therapeutic candidates for COVID-19 treatment. We screened the 5903 approved drugs for their inhibition by targeting the main protease 3CL of SARS-CoV-2, which is responsible to replicate the virus. Molecular docking is used to calculate the binding affinities of these drugs to the main protease 3CL. We employed several machine learning regression approaches for QSAR modeling to find out some potential drugs with high binding affinities. Our outcomes demonstrated that the Decision Tree Regression (DTR) model with best scores of R2 and RMSE, is the most suitable model to explore the potential drugs. We shortlisted six favorable drugs with their respective Zinc IDs ZINC (3873365, 85432544, 203757351, 85536956, 8214470, and 261494640) within the range of -15 kcal/mol to -13 kcal/mol. These drugs have novel repurposing potential, except for one antiviral ZINC203757351 compound that has already been identified in other studies. We further examined the physiochemical and pharmacokinetic properties of these most potent drugs and their best binding interaction to specific target protease 3CLpro. Our study provides an efficient framework to explore the potential drugs against COVID-19, and establishes the impending combination of molecular docking with machine learning regression approaches to accelerate the identification of potential therapeutic candidates. Our verdicts contribute to the larger goal of finding effective cures for COVID-19, which is an a
We fabricated integrated silicon coupled-ring resonators and measured photonic band structures for synthetic frequency lattices with different configurations. A phase difference between two modulation signals introduc...
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With the advent of the Internet of Things, a world was born in which everything could be uniquely identified and monitored, tracked, and managed by computer programs. Items can self-configure using a predefined commun...
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Smart cities require the use of many different types of sensors to make the communication, and distance sensors are one of the most commonly used elements in transportation systems and related infrastructures. The int...
Smart cities require the use of many different types of sensors to make the communication, and distance sensors are one of the most commonly used elements in transportation systems and related infrastructures. The introduction of increasingly advanced autonomous systems in many areas of smart cities requires high measurement precision of the sensors used. High precision is essential for proper operation, long-term use, and safety in machine-to-machine or machine-to-human interactions. This paper presents a comparison of the accuracy of distance measurements for two commercially available single-beam LiDARs and two ultrasonic sensors. The aim of the research was to develop a calibration method in order to improve the accuracy of distance sensors. Based on the collected distance measurements, the sensors were calibrated using selected machine learning algorithms. The results of the experiments show the effectiveness of the proposed calibration methods, which yield an average mean absolute error (MAE) of 1.76 E-05 meters (m) and a root mean square error (RMSE) of 1.33 E-04 m for the tested sensors.
In this article, the problem of linear precoding and radar receive beamforming design for joint radar-communication (JRC) systems is studied. A multiple antenna base station (BS) that serves multiple single-antenna us...
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Perspiration is a physiological response in high-stress situations, that also plays a key role in thermoregulation and stress management. Understanding perspiration patterns is used for assessing physiological respons...
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This work presents a numerical investigation of acoustic cavitation in Newtonian fluid based on frequency ultrasound. The model of ultrasonic cavitation dynamics is formulated by a modified Rayleigh-Plesset equation a...
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Emotions plays a potential role in human computer interaction which are having an obligatory models of cognitive measures. The emotions are dominated by the human physiological communication channels. Those emotions c...
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Unmanned aerial vehicles (UAVs) have garnered significant attention from the research community during the last decade, due to their diverse capabilities and potential applications. One of the most critical functions ...
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ISBN:
(数字)9798350368833
ISBN:
(纸本)9798350368840
Unmanned aerial vehicles (UAVs) have garnered significant attention from the research community during the last decade, due to their diverse capabilities and potential applications. One of the most critical functions that drones must execute efficiently is navigation in real-world environments. This paper presents a decentralized approach for enabling unmanned aerial vehicles (UAVs) to navigate safely in unknown environments and avoid obstacles. Leveraging the Optimal Reciprocal Collision Avoidance (ORCA) algorithm, implemented in the Robot Operating System (ROS), our method facilitates conflict detection and resolution in 2D environments. Through simulations using ROS, Gazebo, and Iris drones, we validate the effectiveness of our approach in scenarios with initial trajectory conflicts. Our work addresses the pressing need for UAVs to autonomously plan and execute safe flights, laying the groundwork for enhanced UAV capabilities in various real-world applications. The simulation results demonstrate the efficiency and robustness of our approach.
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