This paper introduces a 3D shape reconstruction technique for interventional devices in endovascular surgery, utilizing a flexible magnetic tip guidewire that preserves the fundamental attributes of standard guidewires. We developed a model that correlates the magnetic tip's shape with the surrounding magnetic field distribution to estimate the shape through the magnetic field. The inherently nonlinear relationship between the magnetic field distribution and the shape of the magnetic guidewire presents challenges for direct shape estimation. To address this, we incorporated image and physical constraints to streamline the estimation process. This method shows high accuracy and stability in shape estimation, with root mean square error (RMSE) and Hausdorff distance (HD) both below 1 mm, which is better than other existing estimation methods. Notably, the interventional guidewire requires no embedded sensors or wiring, and the fluoroscopic images used are standard in clinical practice. The reconstruction process is non-disruptive to clinical procedures, suggesting broad applicability in vascular interventional navigation.

In this paper, we focus on the task offloading problem for zero-trust vehicular fog computing (VFC) to promote trustworthy collaborative computing among vehicles. We propose a blockchain-enabled zero-trust VFC framework (BlockZT-VFC) to continuously verify and dynamically authorize the vehicle nodes. To overcome the reliability and efficiency issues in BlockZT-VFC, a multi-attribute task offloading and group-based continuous verification (MTOCV) scheme is designed. In particular, multiple attributes are extracted from both vehicles and tasks, ensuring that tasks are offloaded to FVs with authorized trustworthiness, and the group-based continuous verification efficiently guarantees the resulting authenticity by evaluating independent probability mathematically. Experimental simulations demonstrate an 18% increase in throughput and a 34% decrease in latency across two different scenarios, showcasing the superiority of our mechanism in improving system performance and reliability.

The drone is applied extensively, and the usage shows the rapidly growing trend. Due to technical problems or external malicious interference, the crash problem of drone is increasingly serious. The location and recycle of crashed drone is a key problem to be solved. We use the machine learning algorithm to assess the accuracy of drone crash site in the complex scene, and make the data statistics and test in combination with the true scene. After the further summary, this paper puts forward a complete drone location, search and recycling program, to provide the guidance scheme for the reuse of the crashed drone.