The training method of Spiking Neural Networks (SNNs) is an essential problem, and how to integrate local and global learning is a worthy research interest. However, the current integration methods do not consider the network conditions suitable for local and global learning, and thus fail to balance their advantages. In this paper, we propose an Excitation-Inhibition Mechanism-assisted Hybrid Learning(EIHL) algorithm that adjusts the network connectivity by using the excitation-inhibition mechanism and then switches between local and global learning according to the network connectivity. The experimental results on CIFAR10/100 and DVS-CIFAR10 demonstrate that the EIHL not only has better accuracy performance than other methods but also has excellent sparsity advantage. Especially, the Spiking VGG11 is trained by EIHL, STBP, and STDP on DVS_CIFAR10, respectively. The accuracy of the Spiking VGG11 model on EIHL is 62.45%, which is 4.35% higher than STBP and 11.40% higher than STDP, and the sparsity is 18.74%, which is 18.74% higher than the other two methods. Moreover, the excitation-inhibition mechanism used in our method also offers a new perspective on the field of SNN learning.

The SOME/IP (Scalable service-Oriented Middle-ware over IP) is a communication middleware designed to meet the low latency and high bandwidth requirements of in-vehicle networks in smart cars. However, existing implementations of SOME/IP often rely on the kernel networking stack of the operating system, which significantly limits its ability to provide real-time guarantees for in-vehicle networks. To address this issue, this paper introduces XDP (eXpress Data Path) into SOME/IP, utilizing eBPF (extended Berkeley Packet Filter) to efficiently process packets before they reach the kernel networking stack, thus avoiding the hot path in network transmission. We have implemented XDP in the vSomeIP middleware (referred to as X vSomeIP) and conducted a series of experimental evaluations to verify its performance improvement. The experimental results show that XvSomeIP can effectively reduce packet processing latency by up to 30%. Moreover, as the size of network packets increases, the advantages of X vSomeIP in terms of network throughput become more pronounced. When the packet size is 1024 bytes, the throughput and maximum send/receive capabilities are improved by about 4 times.

Mobile robots have become extensively involved in human activities, taking on arduous tasks and providing significant assistance. Robot capabilities have been continuously enhanced, from simple chassis control to path planning and SLAM. Mixed criticality systems enable mobile robots to handle tasks of varying criticality by integrating multiple operating systems, allowing them to accomplish a wide range of tasks. However, besides improving robot computing performance, we should remember that robots are designed to serve humans. Reliability, usability, and affordability are all critical factors for robot design.We introduce SmartKit, a mixed criticality system (MCS) for mobile robots. Leveraging the efficiency in hardware utilization brought by virtualization, SmartKit can execute tasks of different criticality efficiently and securely. This paper will present the software and hardware architecture of SmartKit and provide performance and functionality validation of the robot system.

With the increasing prevalence of mobile robots, there is a growing demand for powerful system software. Integrating multiple operating systems into a single platform has become necessary, and virtualization offers a cost-effective solution for managing multiple OSes. While several types of hypervisors for embedded systems have been proposed to manage guest OSes, significant work is still needed to make hypervisors applicable in robot systems. This paper introduces SmartVisor, a microkernel-based hypervisor designed explicitly for robotic systems. Our work focuses on designing the virtual machine management module to improve robot performance and user experience. The goal is to ensure that the hypervisor-based robot meets the demands of real-world scenarios for robot developers and users.

The software of artificial organ is more and more complex, but it lacks real-time operating system to manage and schedule its resources. In this paper, we propose a lightweight real-time operating system (RTOS) Cyborgan OS based on the SmartOSEK OS. Cyborgan OS optimizes and improves it from the code size, context switch, low power consumption, and partial dynamic update, making it suitable for the artificial organ control system. Finally, we use the heart blood pump model to analyze the task allocation and execution sequence as well as the code size of the whole program. In this application, the maximum space occupied by the code is only 15 kB, which is suitable for most microcontrollers.