With the popularization of mobile devices, the demand for mobile-end image classification has been growing increasingly. Traditional deep learning models are difficult to operate efficiently on mobile devices due to their large number of parameters and complex computations. This study takes daily items as the research objects and adopts MobileNet, a lightweight convolutional neural network, to achieve fast classification suitable for mobile devices by simplifying the network structure and applying transfer learning. Experiments were conducted to compare the accuracy difference between transfer learning and training from scratch, and to analyze the impact of different learning rates and batch sizes on model performance. The results show that the MobileNet model based on transfer learning not only ensures the classification accuracy but also significantly reduces the computational cost. It has high practicality in entry-level daily item classification tasks and provides a simplified and feasible solution for mobile-end image classification applications.

In the past decade, with the rapid growth of mobile internet, cloud computing, and big data technology, data has shown explosive growth in different fields. In the era of big data, people have more information to utilize, but the difficulty of obtaining effective information is also greater than before. Therefore, it is necessary to study parallel computing models and performance optimization for big data processing. Exploring the value behind big data using data processing techniques has become a current research focus in the field of data. Given the importance of parallel applications of artificial intelligence (AI) and big data, it is crucial to focus on analyzing the High Performance Computing (HPC) that integrates the two. The complexity and diversity of storage structures, computer architecture, as well as the large volume and complex data of big data processing problems, pose significant challenges for the application of high-performance computers in the field of big data processing. Big data not only provides AI with an increasingly rich set of training data, but also puts higher demands on the computing power of computer systems. Faced with the problems of large scale and complex computation of big data, this paper proposes a multi strategy parallel genetic algorithm (GA) based on machine learning (ML) for optimizing HPC.

With the rapid development of computer hardware and network systems, virtual reality technology has penetrated into many industries, representing a new application trend from server virtualization to storage virtualization. Virtual reality technology is the core of new research. This article uses MATLAB as the underlying computing environment to study the design of virtual reality interactive applications based on wireless communication technology. This paper proposes an RLMCom model for high-speed communication between domains, which covers the old network protocol stack and domain forwarding, and uses shared memory directly between domains to send and receive data, thereby reducing network communication paths. At the same time, the RLMCom model also provides compatibility with traditional network programming interfaces, allowing users to use high-speed RLMCom communications without modifying existing applications. The experimental results of this work show that the RLMCom model can significantly improve the communication efficiency between departments. The communication throughput rate based on the RLMCom model has reached 258% of TCP communication, and the delay time is about 1/5 of that of ordinary TCP communication.

This article delves into the communication quality monitoring system driven by big data under the C/S (client/server) architecture, aiming to provide a comprehensive, real-time, and efficient security monitoring solution for factory production and manufacturing environments. The system integrates LoRa wireless communication technology, big data processing and analysis technology, and innovative dynamic expansion algorithms to achieve real-time monitoring and rapid response to various safety hazards in the production environment. The core of the system lies in the construction of LoRa wireless communication network, which adopts an innovative umbrella shaped network structure to ensure stable and fast transmission of sensor monitoring data to the server. By using the STM32 development board with LoRa module, dynamic expansion of hardware integration modules has been achieved, enhancing the flexibility and scalability of the system. At the level of data transmission and processing, introducing Kafka distributed message queue as a data cache effectively alleviates the pressure of processing massive real-time data. Combining the Spark Streaming streaming data processing framework, a distributed data processing model was constructed to achieve real-time consumption and efficient parallel processing of messages in Kafka queues. At the same time, the designed dynamic extension algorithm model can automatically persist the data of new monitoring points, ensuring the system's rapid adaptation to data changes. In terms of data storage and visualization, a data persistence layer architecture for a big data platform has been constructed. Real time data, historical data, and log data are stored separately in Redis, MySQL, and HDFS systems through a data streaming model, improving data storage efficiency and system performance. Based on Tomcat network server and SSM architecture, a B/S structured web server visualization platform has been developed, which supports users to remotely query the security status of the production environment through computer or mobile browser, and receive alarm notifications in case of abnormalities. The system integration test results show that the communication quality monitoring system performs excellently in wireless communication quality, data processing speed, data storage efficiency, and data visualization, and can meet the complex monitoring needs of factory production and manufacturing environments, providing solid technical support for enterprise safety production. However, there is still room for improvement in real-time data collection loss rate, alarm function completeness, and mobile access experience. Future research will focus on optimizing data transmission software models, adding multi-channel alarm functions, and developing mobile apps to enhance user experience.

Base station location selection and network optimization are critical to improving the performance of wireless communication networks in terms of latency reduction. To this end, the article proposes leveraging a convolutional neural network (CNN) to improve the accuracy of base station location selection and network latency reduction. The CNN method, based on a three-dimensional representation including signal strength data set, network topology data set, and transmission path data set, is used to select base station location and optimize the multihop relay network for latency reduction. The article presents a following method: location selection and network optimization for the wireless communication network. First, it collects the experimental data set of base station location selection and network optimization, and then uses the training data to train the CNN model to extract features. Once the training is done, the article further optimizes the network parameters and configurations, and ultimately obtains the optimal base station location and network configuration while minimizing network latency. As a result, simulation results indicate that the CNN model has remarkable performance in base station location selection, as well as in network optimization. In summary, the feature extraction and processing ability of CNN are powerful, enabling it to effectively capture factors leading to delay, hence improving the performance of base station location selection and network optimization. The article also demonstrates that the CNN model can be adjusted according to different environments and scenario settings through dynamic tuning.

With the wide application of digital image processing technology in various fields, how to ensure the privacy and security of image data has become an urgent problem to be solved. To this end, this study selected the chaotic encryption algorithm, focusing on its encryption performance on the real low-light image dataset RENOIR on the ARM platform. After choosing a specific ARM hardware and software environment, a series of encryption experiments were performed using the RENOIR dataset. The experimental evaluation criteria include PSNR value of encrypted image, information entropy and encryption execution time on ARM platform. Preliminary results show that the chaotic encryption algorithm can effectively protect image content in this environment, has high randomness and unpredictability, and its execution efficiency meets the needs of practical applications.

With the progress of society and technology and the acceleration of people's life rhythm, nowadays, location-based services play an indispensable role in people's lives. For mobile users using mobile devices such as smartphones, location-based services are an integral part of life. Location-based services usually obtain the location information (geographic coordinates, or geographic coordinates) of mobile terminal users through the wireless communication network (such as GSM network, CDMA network) or external positioning (such as GPS) of mobile telecommunication operators. The accelerated pace of life also means more and more trips and transportation. In many cases, people would like to meet friends or family at a particular place. At the same time, they hope to observe each other's location and route during the journey and even hope to be able to chat by text or voice when they go to their destination together. The purpose of this project is to develop a map application program, which allows users to share real-time location, communication, and chats with friends or family members. Besides, if the user establishes a meeting place with friends, the application can make routes for the "travel together" people respectively, and the user can see the routes of friends on the map. This project collects useful insights and design ideas, and refers to and studies related applications to help the development of products in this project. In the future, more in-depth research can also be carried out to improve based on this project.

Air transportation has the characteristics of fast transportation, high transportation efficiency and independent of ground conditions, so the economic benefits brought by air transportation greatly exceed those of other transportation modes. However, the safety of the air transportation system is constantly challenged. In recent years, the natural environment has changed due to rapid economic development, and meteorological disasters have occurred from time to time. In addition, flight cancellations due to airline aircraft turnarounds, technical aircraft failures, and traffic jams at airport terminals have become commonplace. To address the problem of insufficient stability of China's air transportation system, cities and flights with civil airports existing by the third quarter of 2022 are taken as the research objects, and the network model of China's air transportation system is constructed by using Ucinet software with the cities where airports are located as nodes and regular routes between airports as connecting edges. Then, robustness simulation experiments are conducted by random and intentional disruptions to analyze the extent to which the network can maintain its normal operation after the nodes in the network are down. The conclusions show that the robustness of the Chinese air transport network is better than that of the intentional disturbance in the case of random disturbance. Finally, suggestions for countermeasures that can improve the stability of the air transportation system are given.