The convergence of federated learning (FL) and blockchain in edge-end-cloud systems offers promising opportunities for privacy-preserving collaborative intelligence. However, existing blockchain-enhanced FL (BFL) approaches remain vulnerable to malicious participants and lack robust protection for model updates. To address these issues, we propose SEPP-FLBC, a Secure and Efficient Privacy Protection framework based on Federated Learning and Blockchain Committees. SEPP-FLBC introduces a novel blockchain committee consensus mechanism to validate model updates and defend against unreliable nodes. It further employs a refined multi-party communication paradigm to facilitate indirect and secure data interactions, reducing the risk of information leakage. Additionally, differential privacy noise is applied to model updates to enhance resistance to inference attacks. A formal convergence analysis is conducted to ensure model stability and minimize overhead. Extensive experiments on benchmark datasets demonstrate that SEPP-FLBC achieves superior accuracy while maintaining strong privacy guarantees and communication efficiency, outperforming state-of-the-art BFL methods in both security and performance.

For metal foreign object detection (MFOD) in wireless power transfer (WPT) system, the electromagnetic induction coil detection method has the advantages of simple structure, strong environmental adaptability, and low cost. Whether it is a passive detection scheme or an active detection scheme, the detection effect is closely related to the mutual inductance between the detection coil and the metal foreign object. Thus, the structure of the detection coil needs to be optimized. It is very meaningful to optimize the structure of the detection coil for the purpose of improving mutual inductance. In this paper, a rack-shaped detection coil is designed. Firstly, theoretical analysis is used to compare the mutual inductance between the single-turn rack and the circular metal foreign object and the mutual inductance between the rectangular detection coil and the circular metal foreign object. Secondly, simulation analysis is used to compare the mutual inductance between the rack coil pair and the metal foreign object and the mutual inductance between the rectangular coil pair and the metal foreign object. Finally, experiment shows that the single-layer rack coil can effectively detect 1 RMB coin and double-layer rack coil can accurately locate the coin on the basis of effective detection when the WPT system power is 1.5kW and the vertical distance between the detection coil and the primary coil is 15mm. 1 jiao RMB coin is the minimum size of the object that can be detected accurately by the detection system.

This paper presents an algorithm based on the method of supervised machine learning and multi-keyframes to achieve markerless augmented reality (AR) application when there is a locally planar object in the scene. The main goal is to solve the problem of AR tracking in outdoor environment by only using vision and natural features. Instead of tracking fiducial markers, we track natural keypoints, during which the point correspondences are established from the classification perspective. The tracking range is able to be extended by employing many reference images. These results in a promising algorithm that successfully tested on a touring guide system, which provides views of virtual original appearance superimposed to ruins of Yuanmingyuan archaeological site of China. Comparisons are also made between ARToolkit and the proposed algorithm in indoor environment. Experimental results demonstrated that our algorithm is characterized by fairly robustness and high time efficiency in both indoor and outdoor application.