Artificial Intelligence in the Protection and Inheritance of Cultural Landscape Heritage in Traditional Village

To continue to protect and inherit the cultural landscape heritage of traditional villages, starting from the perspective of artificial intelligence (AI), literature review methods are used, and related theories are collected. Then, Wuyuan County in Jiangxi Province in the traditional villages is taken as the research object. By analyzing the tourism income of this place from 2016 to 2020, the overall income of this county is relatively good. In fact, due to the weak protection of traditional villages in Wuyuan County, the lack of supervision awareness, the implementation of the “immigrant and relocation” policy, and the backward thinking of residents, the cultural landscape of traditional villages has collapsed and destroyed. Up to now, there are 113 ancient ancestral temples, 28 ancient mansion houses, 36 ancient private houses, 187 ancient bridges, and only 12 ancient villages. Finally, AI technology is applied to the cultural landscape of traditional villages. Through image restoration technology, traditional villages can be restored to a certain extent. Intelligent positioning and radio frequency (RF) technology can also realize real-time monitoring of traditional villages from the perspective of weather and service life to achieve the purpose of protecting cultural landscape heritage. Therefore, AI technology is applied in the protection and inheritance of traditional village cultural landscape heritage, which has great reference significance for the management of various historical and cultural heritage.

Application of Internet of Things intelligent image-positioning studio classroom in English teaching

The purpose is to minimize color overflow and color patch generation in intelligent images and promote the application of the Internet of Things (IoT) intelligent image-positioning studio classroom in English teaching. Here, the Convolutional Neural Network (CNN) algorithm is introduced to extract and classify features for intelligent images. Then, the extracted features can position images in real-time. Afterward, the performance of the CNN algorithm is verified through training. Subsequently, two classes in senior high school are selected for experiments, and the influences of IoT intelligent image-positioning studio classroom on students’ performance in the experimental class and control class are analyzed and compared. The results show that the introduction of the CNN algorithm can optimize the intelligent image, accelerate the image classification, reduce color overflow, brighten edge color, and reduce color patches, facilitating intelligent image editing and dissemination. The feasibility analysis proves the effectiveness of the IoT intelligent image-positioning studio classroom, which is in line with students’ language learning rules and interests and can involve students in classroom activities and encourage self-learning. Meanwhile, interaction and cooperation can help students master learning strategies efficiently. The experimental class taught with the IoT intelligent positioning studio has made significant progress in academic performance, especially, in the post-test. In short, the CNN algorithm can promote IoT technologies and is feasible in English teaching.

Towards using UAV for improving 5G cellular communication systems

Unmanned aerial vehicles (UAVs) have sparked a lot of interest in the wireless networking community as an emerging subject in aerial robotics. The UAV environment can be used to improve UAV communications in various ways. These smart devices cater for a broad variety of wireless technologies and applications because of UAV's inherent features related to versatile mobility in 3D space, autonomous operations as well as intelligent positioning. This study will investigate the convergence synergies between 5G/B5G mobile systems and UAV technologies, with the UAV being integrated into current cellular networks as a modern aerial user equipment (UE). In this integration, UAVs play the function of cellular flying customers, and are hence referred to as cellularly linked UAVs (a.k.a. UAVUE, drone-UE, 5G-connected drone, or aerial user). The major goal of this research is to provide a thorough analysis of the integration task, as well as major technical breakthroughs from 5G/B5G and current work in prototyping architecture and field trials that support cell-based UAVs. This study examines recent 3GPP standards advances as well as socio-economic challenges that must be addressed before this promising technology can be properly implemented. There are already some accessible issues clearing the way for potential study opportunities.

Mutual Positioning Method in Unknown Indoor Environment Based on Visual Image Semantics

In our society, realizing intelligent positioning in indoor environments is important to build a smart city. Currently, mutual positioning requirements in the unknown indoor environment are growing fast. However, in such environment, we can obtain neither outdoor radio signal nor the indoor images in advance for online positioning. Therefore, how to achieve mutual positioning becomes an interesting problem. In this paper, we propose a vision-based mutual positioning method in an unknown indoor environment. First, two users take images of the unknown indoor environment, use semantic segmentation network to identify the semantic targets contained in the images, and upload the generated semantic sequence to the user shared database in real time. Then, every time two users reupload a semantic sequence due to a change of location, it is necessary to retrieve whether another user has uploaded the same semantic sequence in the shared database. If the retrieval is successful, it means that two users have seen the same scene. Finally, two users select a target from the two user images taken based on the same scene to establish a three-dimensional coordinate system, respectively, calculate their own position coordinates in this coordinate system, and realize mutual positioning through position coordinate sharing. Experiment results show that our proposed method can successfully realize mutual positioning between two users in an unknown indoor environment, while ensuring high positioning accuracy.

Intelligent Positioning for a Commercial Mobile Platform in Seamless Indoor/Outdoor Scenes based on Multi-sensor Fusion

Many traffic occasions such as tunnels, subway stations and underground parking require accurate and continuous positioning. Navigation and timing services offered by the Global Navigation Satellite System (GNSS) is the most popular outdoor positioning method, but its signals are vulnerable to interference, leading to a degraded performance or even unavailability. The combination of magnetometer and Inertial Measurement Unit (IMU) is one of the commonly used indoor positioning methods. Within the proposed mobile platform for positioning in seamless indoor and outdoor scenes, the data of magnetometer and IMU are used to update the positioning when the GNSS signals are weak. Because the magnetometer is susceptible to environmental interference, an intelligent method for calculating heading angle by magnetometer is proposed, which can dynamically calculate and correct the heading angle of the mobile platform in a working environment. The results show that the proposed method of calculating heading angle by magnetometer achieved better performance with interference existence. Compared with the uncorrected heading angle, the corrected accuracy results could be improved by 60%, and the effect was more obvious when the interference was stronger. The error of overall positioning trajectory and true trajectory was within 2 m.