Yu Fei, Lin Yue, Yao Wei, Cai Shuo, Lin Hairong, Li Yi
School of Computer and Communication Engineering, ChangSha University Of Science and Technology, Changsha, 410114, China.
School of Electronic Information, Central South University, Changsha, 410083, China.
Neural Netw. 2025 Feb;182:106904. doi: 10.1016/j.neunet.2024.106904. Epub 2024 Nov 17.
In Industrial Internet of Things (IIoT) production and operation processes, a substantial amount of video data is generated, often containing sensitive personal and commercial information. This paper proposed three new multiscroll Hopfield neural network (MHNN) systems by utilizing an improved segmented nonlinear non-ideal magnetic-controlled memristor model for electromagnetic radiation. Through dynamical methods, the constructed neural network's multidimensional multiscroll attractors and initial offset boosting behavior are analyzed. The observed initial offset boosting behavior demonstrates the system has extreme multistability. Secondly, a video encryption application based on the MHNN system is implemented on the Raspberry Pi platform. This approach encrypts each frame of the extracted video image using a novel encryption algorithm through frame-by-frame encryption, achieving significant encryption results with an information entropy calculation result of 7.9973. This provides strong protection for video data generated in IIoT. Finally, the proposed MHNN system is implemented on Field-Programmable Gate Array (FPGA) digital hardware platform.
在工业物联网（IIoT）的生产和运营过程中，会产生大量视频数据，这些数据通常包含敏感的个人和商业信息。本文通过利用一种改进的分段非线性非理想磁控忆阻器模型来处理电磁辐射，提出了三种新型多涡卷霍普菲尔德神经网络（MHNN）系统。通过动力学方法，分析了所构建神经网络的多维多涡卷吸引子和初始偏移增强行为。观察到的初始偏移增强行为表明该系统具有极端多稳定性。其次，在树莓派平台上实现了基于MHNN系统的视频加密应用。该方法通过逐帧加密，使用一种新颖的加密算法对提取的视频图像的每一帧进行加密，信息熵计算结果为7.9973，取得了显著的加密效果。这为工业物联网中生成的视频数据提供了强有力的保护。最后，所提出的MHNN系统在现场可编程门阵列（FPGA）数字硬件平台上得以实现。
