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Tashkent University of Information Technologies

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Tashkent University of Information Technologies named after Muhammad al-Khwarizmi is an educational institution that provides students with the academic knowledge necessary to become highly qualified specialists and actively shape the future of our country. The wide range of subjects offered by the university provides students with a modern education and, in turn, represents a solution to social and global challenges. Continuing education lays the foundation for lifelong exploration. The concept of innovation is seen as an important basis for the continuous improvement of the university. The university strives to maintain the quality of education at the level of world requirements, providing continuous training for teachers by supporting dialogue between teachers and students.

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Browsing Tashkent University of Information Technologies by Scopus Q "Q2"

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    Article
    Citation Count: 2
    Bi-Cubic Spline Based Temperature Measurement in the Thermal Field for Navigation and Time System
    (Tamkang Univ, 2019) Singh, Madhusudan; Zaynidinov, Hakimjon; Zaynutdinova, Mastura; Singh, Dhananjay; Department of Artificial Intelligence
    This paper highlights the use of Bi-cubic splines sets for measuring the temperatures at any point (x, y) on printed circuit boards (PCB). This has accomplished by approximating the system of bi-cubic splines of sets of temperatures measured at points on the PCB in a graphical view. The proposed approximation method is using Bi-cubic splines of modeling the temperature field T (x, y) and replace the continuous two variable function by a combination of single variable functions. While developing designs for the navigation and time system (NTS), there is a need for calculating and analyzing the heat generation processes in units in the NTS equipment, which is a factor in choosing design solutions for systems. The boards currently used can conduct full-fledged 3D simulations of heat transfers to PCB which are about 10 percent accurate compared to full-scale tests. It is always difficult to determine the temperatures at specific points on the PCB. Therefore the accurate numbers are only available at the boundaries of temperature zones.
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    Article
    Citation Count: 2
    Clustered Routing Using Chaotic Genetic Algorithm With Grey Wolf Optimization To Enhance Energy Efficiency in Sensor Networks
    (Mdpi, 2024) Khujamatov, Halimjon; Pitchai, Mohaideen; Shamsiev, Alibek; Mukhamadiyev, Abdinabi; Cho, Jinsoo; Department of Data Communication Networks and Systems
    As an alternative to flat architectures, clustering architectures are designed to minimize the total energy consumption of sensor networks. Nonetheless, sensor nodes experience increased energy consumption during data transmission, leading to a rapid depletion of energy levels as data are routed towards the base station. Although numerous strategies have been developed to address these challenges and enhance the energy efficiency of networks, the formulation of a clustering-based routing algorithm that achieves both high energy efficiency and increased packet transmission rate for large-scale sensor networks remains an NP-hard problem. Accordingly, the proposed work formulated an energy-efficient clustering mechanism using a chaotic genetic algorithm, and subsequently developed an energy-saving routing system using a bio-inspired grey wolf optimizer algorithm. The proposed chaotic genetic algorithm-grey wolf optimization (CGA-GWO) method is designed to minimize overall energy consumption by selecting energy-aware cluster heads and creating an optimal routing path to reach the base station. The simulation results demonstrate the enhanced functionality of the proposed system when associated with three more relevant systems, considering metrics such as the number of live nodes, average remaining energy level, packet delivery ratio, and overhead associated with cluster formation and routing.
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    Article
    Citation Count: 0
    Erirms Evaluation of the Reliability of Iot-Aided Remote Monitoring Systems of Low-Voltage Overhead Transmission Lines
    (Mdpi, 2024) Khujamatov, Halimjon; Davronbekov, Dilmurod; Khayrullaev, Alisher; Abdullaev, Mirjamol; Mukhiddinov, Mukhriddin; Cho, Jinsoo; Department of Data Communication Networks and Systems
    Researchers have studied instances of power line technical failures, the significant rise in the energy loss index in the line connecting the distribution transformer and consumer meters, and the inability to control unauthorized line connections. New, innovative, and scientific approaches are required to address these issues while enhancing the reliability and efficiency of electricity supply. This study evaluates the reliability of Internet of Things (IoT)-aided remote monitoring systems specifically designed for a low-voltage overhead transmission line. Many methods of analysis and comparison have been employed to examine the reliability of wireless sensor devices used in real-time remote monitoring. A reliability model was developed to evaluate the reliability of the monitoring system in various situations. Based on the developed models, it was found that the reliability indicators of the proposed monitoring system were 98% in 1 month. In addition, it has been proven that the reliability of the system remains high even when an optional sensor in the network fails. This study investigates various IoT technologies, their integration into monitoring systems, and their effectiveness in enhancing the reliability and efficiency of electrical transmission infrastructure. The analysis includes data from field deployments, case studies, and simulations to assess performance metrics, such as accuracy, latency, and fault detection capabilities.
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    Citation Count: 0
    Fog Computing Capabilities for Big Data Provisioning: Visualization Scenario
    (Mdpi, 2022) Khujamatov, Halimjon; Ahmad, Khaleel; Usmanova, Nargiza; Khoshimov, Jamshid; Alduailij, Mai; Alduailij, Mona; Department of Data Communication Networks and Systems
    With the development of Internet technologies, huge amounts of data are collected from various sources, and used 'anytime, anywhere' to enrich and change the life of the whole of society, attract ways to do business, and better perceive people's lives. Those datasets, called 'big data', need to be processed, stored, or retrieved, and special tools were developed to analyze this big data. At the same time, the ever-increasing development of the Internet of Things (IoT) requires IoT devices to be mobile, with adequate data processing performance. The new fog computing paradigm makes computing resources more accessible, and provides a flexible environment that will be widely used in next-generation networks, vehicles, etc., demonstrating enhanced capabilities and optimizing resources. This paper is devoted to analyzing fog computing capabilities for big data provisioning, while considering this technology's different architectural and functional aspects. The analysis includes exploring the protocols suitable for fog computing by implementing an experimental fog computing network and assessing its capabilities for providing big data, originating from both a real-time stream and batch data, with appropriate visualization of big data processing.