Mobile Networks and Applications最新文献

With the development of wireless iot devices, light imaging detection combined with artificial intelligence algorithms provides new possibilities for the optimization of strength training. The application of wireless sensor network makes data acquisition and real-time monitoring more efficient and convenient. In this study, a wireless sensor network was used to collect motion data during strength training, and the dynamic posture of athletes was monitored in real time by optical imaging technology. By feeding the collected data into a deep learning algorithm, the athlete's training performance is analyzed, potential risks are identified and personalized training recommendations are made. The experiment was carried out in multiple training scenarios and compared with traditional strength training monitoring methods. The experimental results show that the light imaging detection technology based on wireless Internet of Things can accurately identify the attitude deviation in motion, provide real-time feedback, and significantly improve the training effect and safety of athletes. In the process of strength training optimization, the algorithm can effectively analyze the data and improve the training scheme, which proves the effectiveness of artificial intelligence algorithm based on wireless Internet of Things devices combined with optical imaging detection technology in the process of strength training optimization.

With the rapid development of Internet of Things technology, optical image enhancement, as a technology to improve the quality of visual information, has shown great potential in art communication and display. This study aims to explore the effect of combining wireless network-based sensing technology and image segmentation method in light image enhancement, analyze its practical application in sensory communication art, and improve the visual expression and information transmission efficiency of artistic works. Wireless sensor network is used to collect light, temperature and other related data in the environment, and the data is processed and transmitted by mobile terminal. Combined with advanced image segmentation algorithm, the acquired images are processed to achieve dynamic enhancement under different lighting conditions. In the experiment, a variety of art works were selected for comparative analysis, and the effect was evaluated by combining subjective evaluation with objective indicators. The research results show that, based on the data support of wireless network sensors, image segmentation technology significantly improves the visual effect of art works in different environments. Under low light conditions, the enhanced image details are richer, the overall expressive force of art works is improved, and the audience’s aesthetic experience is also improved.

The traditional network transmission mode faces challenges in the real-time and reliability of teaching resources, especially in the environment of Internet of Things and wireless network. With the rapid development of artificial intelligence technology, this paper aims to study the application of optical network transmission technology based on machine learning and wireless network in artificial intelligence online education system, so as to improve the transmission efficiency of educational information and user experience, and promote the learning effect. In this paper, machine learning algorithm is used to analyze and optimize data flow in wireless and mobile networks in real time. Meanwhile, high speed and low latency of optical networks are utilized for data transmission. By building experimental models and testing them in real educational Settings, we evaluate the performance of the system under various network conditions. The experimental results show that the online education system combined with machine learning and wireless optical network transmission is significantly better than the traditional methods in terms of data transmission speed, delay and stability. Especially in the high concurrent user environment, the system can effectively reduce the data packet loss rate and improve the learning experience of users.

With the rapid development of information technology, museums, as an important place for cultural dissemination, need to improve the audience's sense of experience with the help of emerging technologies. This paper aims to explore the application of artificial intelligence system based on wireless network and optical sensing and recognition technology in interactive virtual reality (VR) design of museums, in order to enhance the interaction between visitors and exhibits through technical means, and enhance the visiting experience. An artificial intelligence system with integrated wireless sensor network was developed to collect real-time environmental data through optical sensors and transmit the data to the central processing unit through wireless network. Then VR technology was used to build an interactive display platform, and the audience could interact with the exhibition content in real time through mobile devices or VR glasses. The study also used user experience questionnaires and data analysis to evaluate the effectiveness of the system. The experimental results show that compared with the traditional exhibition methods, the interactive exhibition system has significantly improved the audience's participation and satisfaction. Therefore, the application of the artificial intelligence system based on wireless network and light sensing recognition in the interactive VR design of the museum successfully realizes the dynamic interaction between the exhibition content and the audience, which not only enhances the attraction of the museum, but also opens up a new path for cultural transmission.

With the rapid development of Internet of Things (IoT) technology, the application of wireless sensor networks in the field of health monitoring has attracted increasing attention. Especially in swimming training, monitoring athletes’ heart rate changes is of great significance to prevent sports injuries and optimize training programs. This study aims to build a heart rate monitoring system based on wireless sensor network to monitor the heart rate of swimmers in real time, so as to provide scientific basis for health prevention and improve the training effect. In this paper, a wireless sensor network system is designed, which is composed of several heart rate sensors and receiving devices, and uses Bluetooth low power technology to realize data transmission. The heart rate sensor is fixed to the athlete’s swimsuit for stable operation in the water. Through the data acquisition module, the heart rate data is acquired in real time and sent to the central processing unit for analysis. The cloud computing platform is used to store and process data, so that coaches and athletes can get training feedback at any time. By analyzing the collected data, it is found that the influence of different swimming styles on the heart rate is significantly different. By monitoring the trend of heart rate change, the fatigue state of athletes can be identified in time and provide reference for training adjustment.

With the continuous development of furniture design, the machining accuracy and surface quality of die steel have been paid more and more attention. The traditional grinding process has problems such as low efficiency and unstable quality, so it is urgent to introduce advanced technical means to improve the intelligent level of the processing process. This study aims to explore the application of the die steel grinding process based on machine vision and wireless sensor network equipment in furniture design, and improve the efficiency and quality of the grinding process through real-time monitoring and data analysis. A grinding monitoring platform integrating machine vision system and wireless sensor network was developed. A machine vision system is used to capture critical image data during the grinding process in real time, while a wireless sensor network is used to collect and transmit grinding parameters, including temperature, vibration and acoustic emission signals. By analyzing the acquired data, the optimized grinding parameters and control strategy are worked out. The experimental results show that the grinding process using machine vision and wireless sensor network has improved the relevant parameters compared with the traditional methods. The real-time monitoring capability of the system significantly reduces the failure rate during grinding and provides a more stable and reliable die steel processing solution for furniture design.

This paper aims to discuss the application of image tracking and segmentation algorithm based on laser sensor and wireless network equipment in moving object detection. Traditional target detection methods have problems in dynamic environment. Therefore, we propose a new integrated method, which combines the high-precision detection ability of laser sensor and the real-time data transmission advantage of wireless sensor network to improve the accuracy and response speed of moving target detection. A laser sensor is used to capture the 3D position information of the target, and an image processing algorithm is used for real-time tracking and segmentation. The acquired data is transmitted wirelessly to a central server for further analysis and processing. A network consisting of multiple wireless sensor nodes is constructed to test the detection performance under different environmental conditions. The results show that the combination of laser sensor and wireless network can significantly improve the detection rate and tracking accuracy of moving targets. Compared with traditional methods, our new algorithm also shows good performance in response time and data transmission efficiency.

With the improvement of health awareness and the development of science and technology, personalized intelligent fitness program has gradually become a research hotspot. The aim of this study is to develop an intelligent personalized fitness training scheme based on wireless network sensor and optical measurement technology. Through integrated wireless network sensors, real-time monitoring of the user's heart rate, movement frequency and posture; Optical measurement technology is used to accurately capture the user's movement trajectory and posture. The research designed and deployed a data acquisition system combining multiple sensors to transmit data to a central processing unit via wireless network, use advanced algorithms for data analysis, and generate personalized fitness training feedback and recommendations. The experimental results show that the system can accurately capture the user's motion state and provide real-time feedback, thus significantly improving the training effect. The intelligent personalized fitness training scheme based on wireless network sensor and optical measurement has high practicality and effectiveness, can meet the personalized fitness needs of users, and improve the scientific and security of fitness training.

With the development of wireless sensor network (WSN) technology, the application of image processing in green landscape design has ushered in new opportunities. This paper aims to explore the application of high-resolution image processing technology based on spatial optical characteristics and wireless sensor networks in the simulation of green landscape design, so as to improve the accuracy and efficiency of landscape design. In this paper, the structure and working principle of wireless sensor network are analyzed, and the influence of spatial optical characteristics on image acquisition and processing is studied. Then, combining with high resolution image processing technology, an image processing method based on wireless sensor network and spatial optical characteristics is proposed. The effectiveness of this method is verified by comparing the simulation and practical application of several landscape design cases. The research shows that the high-resolution image processing technology based on wireless sensor network can significantly improve the clarity and detail performance of the image, and realize real-time data acquisition and processing in a large range. The method has shown excellent simulation results and application prospects in many practical landscape design projects.

The traditional real-time condition monitoring methods of high-intensity training often rely on complex hardware equipment or manual observation, which has the problem of insufficient real-time and accuracy. The system uses infrared light sensor to acquire athletes' physiological data, and transmits it to the central processing unit combined with wireless network. Image recognition technology is used to analyze sensor data and images of training scenes to monitor the status of athletes in real time. A prototype system is designed and tested, and its performance is evaluated by experiments. The experimental results show that the designed system is efficient and accurate in real-time condition monitoring of high-intensity training. The application of wireless network significantly improves the speed and stability of data transmission and ensures the real-time performance of the system. Image recognition algorithm can effectively identify and analyze the key actions and state changes in the training process. The image recognition system based on infrared light sensor and wireless network developed in this research can significantly improve the real-time condition monitoring ability of high-intensity training. The system has advantages in real-time, accuracy and data transmission stability, and has a wide application prospect.