In the upcoming 6G era, edge artificial intelligence (AI), as a key technology, will be able to deliver AI processes anytime and anywhere by the deploying of AI models on edge devices. As a hot issue in public safety, person re-identification (Re-ID) also needs its models to be urgently deployed on edge devices to realize real-time and accurate recognition. However, due to complex scenarios and other practical reasons, the performance of the re-identification model is poor in practice. This is especially the case in public places, where most people have similar characteristics, and there are environmental differences, as well other such characteristics that cause problems for identification, and which make it difficult to search for suspicious persons. Therefore, a novel end-to-end suspicious person re-identification framework deployed on edge devices that focuses on real public scenarios is proposed in this paper. In our framework, the video data are cut images and are input into the You only look once (YOLOv5) detector to obtain the pedestrian position information. An omni-scale network (OSNet) is applied through which to conduct the pedestrian attribute recognition and re-identification. Broad learning systems (BLSs) and cycle-consistent adversarial networks (CycleGAN) are used to remove the noise data and unify the style of some of the data obtained under different shooting environments, thus improving the re-identification model performance. In addition, a real-world dataset of the railway station and actual problem requirements are provided as our experimental targets. The HUAWEI Atlas 500 was used as the edge equipment for the testing phase. The experimental results indicate that our framework is effective and lightweight, can be deployed on edge devices, and it can be applied for suspicious person re-identification in public places.

Partial differential equations (PDEs) usually apply for modeling complex physical phenomena in the real world, and the corresponding solution is the key to interpreting these problems. Generally, traditional solving methods suffer from inefficiency and time consumption. At the same time, the current rise in machine learning algorithms, represented by the Deep Operator Network (DeepONet), could compensate for these shortcomings and effectively predict the solutions of PDEs by learning the operators from the data. The current deep learning-based methods focus on solving one-dimensional PDEs, but the research on higher-dimensional problems is still in development. Therefore, this paper proposes an efficient scheme to predict the solution of two-dimensional PDEs with improved DeepONet. In order to construct the data needed for training, the functions are sampled from a classical function space and produce the corresponding two-dimensional data. The difference method is used to obtain the numerical solutions of the PDEs and form a point-value data set. For training the network, the matrix representing two-dimensional functions is processed to form vectors and adapt the DeepONet model perfectly. In addition, we theoretically prove that the discrete point division of the data ensures that the model loss is guaranteed to be in a small range. This method is verified for predicting the two-dimensional Poisson equation and heat conduction equation solutions through experiments. Compared with other methods, the proposed scheme is simple and effective.

The rise of the Internet of Things (IoT) and the Internet of Vehicles (IoV) has accelerated the realization of smart cities, where cameras as interconnected consumer electronics (CE) are deployed across cities to capture target images. The widespread deployment of monitoring equipment has prompted us to focus on the target re-identification (Re-ID) issue. One major challenge about this issue is that the identified targets are often obscured by different obstacles, which leads to bad performance. In practical applications, the occluded Re-ID task is very significant to complete. Previous approaches have focused on improving the occluded Re-ID performance but have neglected the lightweight problem, which makes the model difficult to deploy in the real world. Therefore, this paper proposes a lightweight framework that ensures occluded Re-ID performance and deploys at the edge to solve the problem of long transmission time and high latency caused by wireless and cloud technology in CE. This framework tackles occluded target Re-ID issues by integrating omni-scale features with human keypoint estimation and multi-head attention mechanism (OHMA). To solve the vehicle Re-ID problem, we use the cutout method to simulate an occlusion scene due to the lack of occluded vehicle data. Then, The multi-head attention mechanism combines with the omni-scale network (OSNet) to learn vehicles subtle features. To deal with occluded pedestrians, human keypoint estimation focuses on non-occluded areas of pedestrian images by paying attention to visible information about the human body. The generated heatmaps fuse omni-scale feature maps to explore better feature representations. In addition, the HUAWEI Atlas 200I DK A2 is used to simulate real edge devices and evaluate the experiments on both public and real-world private datasets. The results demonstrate that our framework improves the occluded Re-ID performance while ensuring lightweight. Compared with the previous methods, OHMA displays advantages in occlusion scenes.