Mobile Networks and Applications最新文献

With the rapid development of information technology, 5G intelligent network is gradually widely used in all walks of life. The higher education sector is also facing the need for digital transformation, aimed at improving management efficiency and teaching quality. This study aims to explore the application potential of 5G intelligent network combined with ambient light sensing equipment in integrated management of higher education, in order to enhance the intelligent level of campus management, optimize the allocation of teaching resources, and improve the interactive experience of teachers and students. The research adopts the method of combining experimental design and case analysis, and realizes real-time transmission and processing through 5G network based on data acquisition of ambient light sensing equipment. The system functions include intelligent lighting control, classroom management optimization and teacher-student interaction enhancement. The research finds that the environmental light sensing system based on 5G intelligent network significantly improves the efficiency of campus management, effectively reduces resource waste, and the teacher-student interaction system improves teaching participation and satisfaction. The combination of 5G intelligent network and ambient light sensing equipment shows significant application value in the comprehensive management of higher education. It not only improves management efficiency and resource utilization, but also improves teaching quality and teacher and student experience.

Traditional urban green space planning methods often rely on site survey and manual mapping, which is inefficient and costly. In order to solve these problems, a new urban green space planning method is proposed based on remote sensing image enhancement technology and wireless sensing technology. In this paper, remote sensing image technology is used to obtain high-resolution urban green space images, and image enhancement algorithm is used to improve the clarity and recognition of the images. Then through the wireless sensor network in the green space, real-time environmental data is collected. Finally, combined with remote sensing data and sensor data, a comprehensive analysis is carried out to develop a reasonable green space planning scheme. The results show that the urban green space planning method based on remote sensing image enhancement and wireless sensing technology not only improves the accuracy and efficiency of green space monitoring, but also significantly reduces the cost. The real-time environmental data obtained by wireless sensors can more accurately reflect the ecological status of green space, and contribute to scientific planning and management of urban green space. Therefore, the combination of remote sensing image enhancement and wireless sensing technology provides an efficient and low-cost new way for urban green space planning.

In sports training and competition, the traditional methods of athlete posture monitoring often rely on complex equipment and expensive technology, which is difficult to be widely used. This study aims to explore a posture monitoring and correction technology based on wireless sensing and computer vision algorithms to provide a low-cost, efficient and easy-to-use solution. In this study, wireless sensors are used to collect real-time data of athletes during training and competition, and computer vision algorithms are combined to analyze athletes' posture. The wireless sensors include an inertial measurement unit (IMU) that captures the athlete's movement trajectory and changes in Angle. Using computer vision technology, the video images of athletes are obtained by cameras, and the posture recognition and dynamic analysis are carried out. Data fusion method combines sensor data with visual data to improve the accuracy and reliability of posture monitoring. The experimental results show that the posture monitoring system based on wireless sensing and computer vision algorithm can accurately identify and evaluate the athlete's posture. The system can feedback athletes' postural deviation in real time, provide effective correction suggestions, and significantly improve athletes' postural performance.

With the popularity of yoga around the world, the number of sports injuries caused by incorrect postures is also increasing. Traditional monitoring methods rely on manual observation and static data analysis, which is difficult to detect and prevent injury timely and accurately. This study aims to explore how to realize real-time monitoring and analysis of yoga practitioners' movement posture through wireless sensor network (WSN) combined with machine vision technology, so as to effectively prevent sports injuries. In this paper, a monitoring system based on WSN is constructed, which arranges sensor nodes in the key parts (such as joints) of exercisers to collect real-time motion data. Combined with machine vision technology, the collected data is processed and analyzed to identify incorrect motion posture. The system transmits data through wireless network, uses algorithms to analyze the attitude, and provides real-time feedback. The experimental results show that the WSN based monitoring system can efficiently collect the movement data of yoga practitioners, and accurately identify the incorrect posture through machine vision technology. Compared with the traditional method, this system significantly improves the timeliness and accuracy of monitoring.

This research paper investigates the potential of combining musical education with innovative technologies like Virtual Reality (VR), Biofeedback, and social robots to enhance student mental health. To optimize these interventions and ascertain how are they helpful in improving the role of musical education on mental health a reinforcement learning technique namely the Q-learning approach is used. VR is used for immersive learning and creates engaging and varied practice sessions. Biofeedback for real-time adjustment and defining personalized music therapy. Social robots are used to enhance group dynamics by facilitating positive group interactions. The study begins by selecting a group of students of diverse backgrounds from different educational institutions and evaluating their baseline mental health. These students were then engaged in musical education sessions like listening to music, learning musical instruments, and group activities assisted by the proposed technologies. Secondly, a monitoring mechanism is implemented that continuously monitors student’s mental health and collects feedback data. Thirdly, the collected data is analyzed using the Q-learning technique, which uses a trial-and-error approach to formulate optimal policy for musical education. It works by storing Q-value, a value that represents the expected future rewards for taking specific actions in a given state. The Q-values are updated at each step of the intervention and are based on the temporal difference error, which compares the expected reward with the actual reward obtained until the Q-value converges. The results analysis of student’s mental health following the intervention showed that stress levels decreased by an average of 25%, anxiety levels decreased by 20%, and depression levels decreased by 15%. Reductions in these metrics imply the positive impact of musical education intervention and highlight the importance of musical education in school curricula.

With the intensification of the aging of the population, the traditional way of supporting the elderly can no longer meet the increasing demand for health monitoring. The system uses a variety of wireless sensors to collect health-related data of the elderly, and realizes real-time data transmission and storage through the Internet of Things technology. Data mining algorithm is used to analyze and process the collected data and extract valuable information to provide personalized health management and early warning services. The experimental results show that the system can monitor the health status of the elderly in real time and accurately. Through data mining technology, the system can effectively identify abnormal situations and issue early warnings in time to ensure the health and safety of the elderly. The user interface of the system is friendly and easy to operate, which is suitable for the elderly. The system not only improves the quality of life of the elderly, but also provides strong technical support for the healthy old-age care of the family and the society.

With the development of wireless sensor network (WSN) technology, image restoration technology based on WSN has gradually become a research hotspot. This paper aims to study the image restoration technology based on adaptive segmentation and wireless sensor network, and explore its application in museum VR platform to improve the accuracy and efficiency of image restoration. The image data is transmitted through wireless sensor network, and the collaborative processing ability of sensor nodes is used to restore the image. In this paper, the museum VR platform is built, and the research is simulated and tested. The experimental results show that the image restoration technology based on adaptive segmentation and wireless sensor network has a significant improvement in image quality and recovery speed. Compared with traditional methods, this technology can better maintain the details and texture of the image, and has higher stability and anti-interference ability, which can not only improve the virtual experience of users, but also provide strong support for the protection of cultural relics and digital management.

This investigation addresses a range of critical challenges within the domain of mechanical engineering and anticipates their potential impacts. The study’s goals include developing methods for detecting tool breakage in integrated milling-turning machines, evaluating the service life of punching machine components, and determining the durability of molds in forging equipment, alongside other complex issues. The primary aim is to devise a specialized equipment health diagnostic system, designed for complex industrial environments. Industry consultation has revealed that effective monitoring strategies and threshold values must be tailored to the specific characteristics of each piece of equipment and their respective sectors. Despite the metal processing industry lagging roughly a decade behind the semiconductor sector in adopting intelligent monitoring systems, it encounters similar hurdles. These include shrinking labor demographics necessitating increased reliance on shift-based external labor, higher turnover rates impacting the retention of skilled workers for essential tasks such as tool replacements and machinery maintenance. Furthermore, there is a pressing need to maintain traceability for the usage history of molds and punching heads, especially to meet aerospace industry regulations. In response, the sector aims to accomplish two primary goals for its critical production machinery: firstly, to implement diagnostic tools for evaluating the wear and overall quality of tools and molds; secondly, to shift from time-based to condition-based maintenance protocols, adaptable to the frequent mold changes required for varied product fabrication.

AI-powered health checkers and apps for automated medical diagnosis have a lot of promise for a variety of applications. During pandemics, they can lessen the need for in-person patient-doctor interactions and offer vital medical advice in neglected rural areas. In this work, we demonstrate the creation of an expert system driven by machine learning on the web. This technology helps medical practitioners make better diagnostic decisions by supporting them and by offering accurate health forecasts and suggestions to the general population. Due to the lack of authentic medical datasets focusing on symptoms, we collected information from reputable medical sources. This enabled us to prioritize the medical diagnostic process, resulting in the compilation of a comprehensive list of illnesses and associated symptoms. This dataset played a key role in developing our health checker, which consisted of four primary parts: FrontEnd, Authentication module, BackEnd housing the machine learning module, and the Database. We constructed a dataset encompassing up to 415712 synthetic patients, 75 symptoms and risk factors, and 22 cough-related diagnoses. This dataset enabled the training and testing of supervised machine learning models to identify the most effective algorithm for implementation. The accuracy, performance and generalization ability of the utilized machine learning models were assessed using metrics including accuracy, F1-score and cross validation. Our work not only advances machine learning models but also addresses the pressing need for reliable medical datasets. The outcome of our efforts is a robust health checker, set to bring positive changes to diagnostic processes and healthcare accessibility as well as generalization and real-world applicability of our models. This highlights the critical role of dataset quality, especially with our ‘third dataset’ showcasing unparalleled performance across diverse medical scenarios with an accuracy superior to 99% and F1 score superior to 99% also for all the models. Stratified fivefold cross-validation also demonstrates positive results with an average accuracy and an average F1 score exceeding 99% for all models, thereby enhancing the reliability of our model evaluations and boosting confidence in the obtained metrics. In conclusion, our work propels the advancement of machine learning models, specifically addressing the imperative for reliable medical datasets. The result is a symptom-based health checker that demonstrates resilience, positioned to potentially contribute to advancements in diagnostics and improve accessibility to healthcare services.

Construction is an important sector of human activity that significantly impacts the environment. The impact of this sector can be analysed from different perspectives, such as consumption of natural resources, waste generation, energy intensity, and environmental change. The sector is increasingly promoting using renewable materials, energy-efficient practices, and planning those respects ecological processes and biodiversity. Against this background, it is important to take coordinated action across the sector and move to net-zero carbon standards through immediate action to raise awareness, implement innovation, and improve carbon management and reporting processes. Tools supporting the reduction of the adverse impacts of construction activities include artificial intelligence tools. The construction industry has long been considered a conservative and traditional industry but is now experiencing a technological revolution. Gradually, artificial intelligence (AI) principles and tools are beginning to be integrated into the various lifecycle processes of construction projects. This paper analyses the AI tools used to analyse carbon footprinting in the construction sector in terms of selected functionalities. The results of the research will form the basis for the development of a strategic plan for the development of AI within the research activities at the Faculty of Civil Engineering in Košice.