With the continuous emergence of Internet of Vehicles (IoV) applications, the demand for computational resources of many resource-intensive applications in IoV has shown an explosive growth trend, which poses a serious challenge to the limited computational resources of the vehicles themselves. This paper designs a federated learning structure with a two-layer game for vehicular networks, using intelligent roadside terminals for federated optimization. Meanwhile, this paper proposes a Federated Learning and Cloud-Edge Gaming with Incentive-Driven (FL-CEGID) algorithm for dynamic task offloading in IoV. Our proposed algorithm optimizes vehicle and computing resource allocation as well as cache updates through a hierarchical distributed approach, which has separate vehicle and edge intelligence strategies for offloading decisions and caching strategies. The experimental results show that our proposed FL-CEGID has significant improvements in transmission capacity, transmission delay, and advantages in different key tasks and times in IoV compared to other schemes.

High-resolution and clear fundus images are essential to help physicians diagnose lesions. However, the imaging quality of acquired fundus images often has errors due to differences in operator experience and equipment limitations. To address this problem, this paper proposes a super-resolution network for retinal fundus images based on generative adversarial networks (GANs). The network aims to improve the resolution of fundus images and restore fine retinal structure and lesion details. First, based on the analysis of the ophthalmic mirror system, we designed a new degradation model to simulate the effects of various unfavorable factors on fundus images, and thus constructed a batch of fundus image datasets for fundus image super-resolution work. In order to enhance the network's ability to extract local information, we introduced a texture reply block based on coordinate attention. Meanwhile, in order to capture the fundus image features at different scales, we also add a multi-scale feature extraction block to realize the fusion of multi-scale features. Experimental results show that our network is able to reconstruct high-quality fundus images, and the proposed method outperforms other super-resolution deblurring methods in both PSNR and SSIM metrics. This result provides strong support for accurate diagnosis of fundus images.

With the layer-by-layer addition of epidemic prevention and control, as well as the long-term low efficiency and insufficient control of man-drive luggage/cargo tractors, unmanned tractor has become the first landing application scene of autonomous vehicle in the background of the construction of smart airport. Apart from other vehicles, multiple trailers are towed behind the tractor, so the path planning of the tractor needs to consider the driving path of the rear trailers at the same time. In this paper, a two-dimensional mathematical model of tractor trailer group is established based on the Ackerman principle. And the pose of trailer is predicted by coordinate transformation and rotation displacement matrix. The calculation process of trailer drawbar angle is optimized by Fourier series and Gaussian filtering, and the relevant parameters are determined by comparing with the simulation results. A trajectory is designed for simulation to obtain the key data of the driving characteristic of the tractor trailer group, and the validity of the calculation result is verified.

This paper proposes a pixel-level grasp configuration prediction method based on deep neural network. The method utilizes RGB images as inputs, combines with a deep neural network model, and outputs the object's grasp configuration at the pixel level. This paper adopts a new region-level AGA model to model the grasping properties of objects. The model solves the angle conflict during training and simplifies the process of marking the real grasping posture. A pose estimation network based on Deeplabv3 is designed to predict the OAR model on RGB images. Pixel-level mapping avoids the loss of real grasping posture and overcomes the limitations of current deep learning technologies by avoiding discrete sampling of grasp candidates and long computation times. Finally, experiments are conducted on the Cornell Grasp Dataset to verify the proposed method. The results show that the proposed method can accurately predict the grasp configuration of objects at the pixel level and has good predictive and generalization abilities.

Summary As a multi‐dimensional complex system, Cyber physical systems (CPS) integrates computing, network, and physical environment. How to effectively fuse the local detection results is the key to improve the sensing performance in CPS. In order to improve the spectrum sensing performance of cognitive radio, a soft fusion‐based cooperative spectrum sensing using particle swarm optimization (PSO) for cognitive radio networks is proposed. To find the optimal weighting coefficient of soft fusion, the traditional PSO algorithm is investigated and improved. To further reduce the local convergence of PSO and accelerate the convergence speed of the algorithm, the immune algorithm is introduced and chaotic sequence mechanism is applied to ensure the diversity of the particles. Moreover, the inertia weight is adjusted adaptively according to the convergence of particles so as to maintain the tradeoff between the search ability and the convergence of particles. Simulation results show that the proposed algorithm can achieve fast convergence in CPS. Compared with other typical methods, it can obtain better sensing performance under different signal‐to‐noise ratio environments and the constraint of false alarm probability.

Tencent, one of the biggest Internet companies in China, contains billions of users and over 600-PB data, and leverages thousands of SSDs in the storage system to improve system performance and obtain energy savings. Existing commercial SSDs however fail to meet the needs of the ultra largescale applications due to not matching the service patterns. In order to address this problem and deliver high performance, we propose an application-aware and software-defined SSD scheme for Tencent applications, called TSSD. TSSD explores and exploits the business characteristics of Tencent, which facilitates the efficient use of SSDs. TSSD is software-defined by packaging each flash chip as a fully independent and concurrent storage unit. Each concurrent unit can be mounted as a character device, which allows the application layer to manage the flash chips in a more efficient manner, while optimizing the data layout. TSSD further employs a host-target FTL (TFTL) that uses a dedicated interface in the application layer, which efficiently connects the application layer with flash chips. Application layer hence becomes more accurately by using the flash memory chip-level information from TFTL, including the storage utilization, the degree of wear, etc. Moreover, TFTL is a programmable FTL and provides a programmable interface to the application layer. According to the running states of SSDs and workload information, TSSD makes use of the programmable interface to efficiently improve the performance of the FTL, wear leveling, and garbage collection for the specified applications. Extensive experiments use the real-world datasets from the commercial storage systems of Tencent. The results demonstrate that TSSD significantly improves the storage system performance and meets the needs of the Tencent's large-scale business applications.