The symbiotic Internet of Things (IoT) computing paradigm leverages high-speed transmission technologies, such as 6G, to execute large-scale model computations while preserving data privacy, thereby mitigating significant economic losses from data breaches. This paradigm has emerged as a pivotal focus in industrial intelligent manufacturing research. However, within this framework, edge devices must concurrently support both large-scale model computations and high-precision industrial core tasks, creating critical challenges in system efficiency and stability that impede technological advancement. To address these issues, this article introduces a novel Quality of Experience (QoE)-driven heuristic task resource scheduling algorithm that employs Composite Differential Evolution (CoDE) integrated with a tabular Kolmogorov–Arnold Network (KANTab), specifically designed for the comprehensive processes of industrial intelligent manufacturing systems. This methodology enables precise simulation of extensive industrial task requirements and efficient allocation of computational resources under constrained conditions, effectively resolving the resource allocation conflict between large-scale model computation tasks at the edge and primary tasks on terminal devices. We evaluate our approach on a custom-built large-scale task demand dataset from refrigerator manufacturing and demonstrate that it achieves superior overall performance compared to state-of-the-art algorithms.

Neural Radiance Field (NeRF) represents a significant advancement in computer vision, offering implicit neural network-based scene representation and novel view synthesis capabilities. Its applications span diverse fields including robotics, urban mapping, autonomous navigation, virtual reality/augmented reality, etc., some of which are considered high-risk AI applications. However, despite its widespread adoption, the robustness and security of NeRF remain largely unexplored. In this study, we contribute to this area by introducing the Illusory Poisoning Attack against Neural Radiance Fields (IPA-NeRF). This attack involves embedding a hidden backdoor view into NeRF, allowing it to produce predetermined outputs, i.e. illusory, when presented with the specified backdoor view while maintaining normal performance with standard inputs. Our attack is specifically designed to deceive users or downstream models at a particular position while ensuring that any abnormalities in NeRF remain undetectable from other viewpoints. Experimental results demonstrate the effectiveness of our Illusory Poisoning Attack, successfully presenting the desired illusory on the specified viewpoint without impacting other views. Notably, we achieve this attack by introducing small perturbations solely to the training set. The code can be found at https://github.com/jiang-wenxiang/IPA-NeRF.

Cameras onboard autonomous, as a critial component of the sensor system of automatic driving, plays a vital role in perception of driving and road environment. However, in some bad weather or unpredictable situations, the image quality obtained by the in-vehicle sensing camera is not ideal, which will become an extremely unsafe factor for autonomous driving. In order to improve the safety of self-driving vehicles, we proposed a novel high-quality image of invehicle cameras generation approach CE-GAN, a conditional generative adversarial network that attempt to leverage the point cloud data from on-board lidar to compensate the defect of visible image to improve the image quality of on-board cameras. Inspired by the generative adversarial networks, our method establishes an adversarial game between the generator and the discriminator We designed specifically loss function for different reasons for image quality impairment including partially obscured and fogged. Consequently, extensive experiments show that CE-GAN renders better performance in detail texture, compared with conventional Cycle-GAN, pix2pix methods without assistance of LiDAR data.

Realtime foreground/background segmentation based on sequence video was of great significance for autonomous vehicles perception, edge device application and higher level data analysis. A new fast background subtraction method for dynamic objects detection was proposed by using the digital features of the whole block of pixels. The algorithm considered that the change of the current pixel was closely related to the surrounding pixels, took the current pixel and its eight neighboring pixels as a whole block, and used the digital features - average and variance to reflect the pixel level of the block and establish the background model. At the same time, a local remodeling method was proposed, which made the algorithm can process and eliminate ghost quickly. The results based on CDnet2014 dataset showed that our algorithm could adapt to various dynamic objects detection scenarios and initialize fast under the condition of low hardware cost, and provided a good overall performance.