Research and Design of Dynamic Migration Access Control Technology Based on Heterogeneous Network

With the continuous development of wireless networks, the amount of privacy services in heterogeneous mobile networks is increasing, such as information storage, user access, and so on. Access control security issues for heterogeneous mobile radio network, this paper proposes a dynamic migration access control technology based on heterogeneous network. Through the system architecture of the mutual trust system, we can understand the real-time mobile node failure or abnormal state. To make the service can be terminated for the node. And adopt the 802.1X authentication way to improve the security of the system. Finally, it by combining the actual running test data, the trust update algorithm of the system is optimized to reduce the actual security threats in the environment. Experiments show that the system’s anti-attack, the success rate of access, bit error rate is in line with the expected results. This system can effectively reduce the system authentication information is illegally obtained after the network security protection mechanism failure and reduce the risk of user data leakage

Research and Design of Dynamic Migration Access Control Technology Based on Heterogeneous Network

With the continuous development of wireless networks, the amount of privacy services in heterogeneous mobile networks is increasing, such as information storage, user access, and so on. Access control security issues for heterogeneous mobile radio network, this paper proposes a dynamic migration access control technology based on heterogeneous network. Through the system architecture of the mutual trust system, we can understand the real-time mobile node failure or abnormal state. To make the service can be terminated for the node. And adopt the 802.1X authentication way to improve the security of the system. Finally, it by combining the actual running test data, the trust update algorithm of the system is optimized to reduce the actual security threats in the environment. Experiments show that the system’s anti-attack, the success rate of access, bit error rate is in line with the expected results. This system can effectively reduce the system authentication information is illegally obtained after the network security protection mechanism failure and reduce the risk of user data leakage

Algorithm for Target Detection in Smart City Combined with Depth Learning and Feature Extraction

The target detection algorithms have the problems of low detection accuracy and susceptibility to occlusion in existing smart cities. In response to this phenomenon, this paper presents an algorithm for target detection in a smart city combined with depth learning and feature extraction. It proposes an adaptive strategy is introduced to optimize the algorithm search windows based on the traditional SSD algorithm, which according to the target operating conditions change, strengthening the algorithm to enhance the accuracy of the objective function which is combined with the weighted correlation feature fusion method, and this method is a combination of appearance depth features and depth features. Experimental results show that this algorithm has a better antiblocking ability and detection accuracy compared with the conventional SSD algorithms. In addition, it has better stability in a changing environment

A 2D image 3D reconstruction function adaptive denoising algorithm

To address the issue of image denoising algorithms blurring image details during the denoising process, we propose an adaptive denoising algorithm for the 3D reconstruction of 2D images. This algorithm takes into account the inherent visual characteristics of human eyes and divides the image into regions based on the entropy value of each region. The background region is subject to threshold denoising, while the target region undergoes processing using an adversarial generative network. This network effectively handles 2D target images with noise and generates a 3D model of the target. The proposed algorithm aims to enhance the noise immunity of 2D images during the 3D reconstruction process and ensure that the constructed 3D target model better preserves the original image’s detailed information. Through experimental testing on 2D images and real pedestrian videos contaminated with noise, our algorithm demonstrates stable preservation of image details. The reconstruction effect is evaluated in terms of noise reduction and the fidelity of the 3D model to the original target. The results show an average noise reduction exceeding 95% while effectively retaining most of the target’s feature information in the original image. In summary, our proposed adaptive denoising algorithm improves the 3D reconstruction process by preserving image details that are often compromised by conventional denoising techniques. This has significant implications for enhancing image quality and maintaining target information fidelity in 3D models, providing a promising approach for addressing the challenges associated with noise reduction in 2D images during 3D reconstruction