Computer-vision-based space circular target detection has a wide range of applications in visual measurement, object detection, and other fields. The space circular target is projected into an ellipse in the camera for localization. Traditional methods based on monocular vision use a precise calculation model to calculate the center coordinate and normal vector of the space circular target according to the image’s elliptic parameters. However, this accurate calculation method has the disadvantage of poor anti-interference ability in practical application. Aiming at the shortcomings of the above traditional calculation method, this paper proposes an optimization method for fitting the circular target in 3D space, where the image ellipse is projected back into 3D space and then detects the center coordinate and normal vector of the space circular target. Unlike the traditional method, this approach is not sensitive to the image’s elliptic parameters; it has stronger noise resistance performance and notable application value. The feasibility and effectiveness of the proposed method were verified by both simulation and practical experimental results.

An improved network calculus (NC)‐based model is proposed to analyze the upper‐bound latency for time‐sensitive networking (TSN). It aims to address two key issues existing in the benchmark NC model: on the one hand, the inaccurate description of the arrival curve (AC) for simultaneously arriving data packets; on the other hand, the design of the network service curve (SC) does not consider the interdependencies between nodes. In the simulation experiment section, the pessimism of theoretical analysis is reduced by comparing the two models, and the validity of the improved NC‐based model is verified using the OMNeT++ platform.

Traffic scheduling plays a critical role in Time-Sensitive Networking (TSN) for ensuring high reliability and deterministic latency. In this paper, we propose a novel window-based scheduling approach for the Time-Aware Shaper (TAS). By allowing packets to wait in egress queues before forwarding, our approach relaxes the strict timing constraints imposed by existing packet-based schedulers. We employ a generalized Network Calculus (NC) framework built on an End-to-End (E2E) network model, to analyze the upper-bound latency, which is then used to assess the schedulability of Time-Critical (TC) traffic. Inspired by the Proportional–Integral–Derivative (PID) closed-loop control architecture, we introduce an Incremental PID-based Search (IPS) algorithm to optimize schedulability, where the P, I, and D terms are leveraged to scale update steps, maintain search momentum, and dampen the oscillations, respectively. To accommodate various traffic classes, throughput constraints for non-TC traffic are incorporated as bounds on window lengths. Simulation experiments were performed on a multi-node network topology carrying large traffic volumes. Under optimal PID settings, the proposed IPS algorithm was evaluated against the well-validated Simulated Annealing (SA) method under a unified scheduling framework with identical decision variables and constraints to ensure a fair comparison. Results show that IPS consistently achieves higher schedulability and requires fewer iterations for flow counts ranging from 100 to 600. Furthermore, a real-time simulation platform based on OMNeT++ was developed, and the effectiveness of the proposed wait-allowed scheduling model was validated through optimized GCL configurations.

An improved Network Calculus (NC)-based model is proposed to analyze the upper-bound latency for Time-sensitive Networking (TSN). It aims to address two key issues existing in the benchmark NC model: on the one hand, the inaccurate description of the Arrival Curve (AC) for simultaneously arriving data packets; on the other hand, the design of the network Service Curve (SC) does not consider the interdependencies between nodes. In the simulation experiment section, the pessimism of theoretical analysis is reduced by comparing the two models, and the validity of the improved NC-based model is verified using the OMNeT++ platform.