With the rapid advancement of mobile communication technologies, privacy preservation in VANETs has emerged as a pivotal research frontier. Previous authentication protocols often face challenges such as key leakage risks and high computational overhead. Physical Unclonable Functions (PUFs), as a lightweight hardware primitive, offer a promising solution for enhancing security in VANETs. Recently, Xie et al. designed an anonymous authentication protocol for VANETs. Unfortunately, our analysis reveals that their protocol cannot resist ephemeral key leakage attacks. To address these limitations, we propose an enhanced PUF-based anonymous authentication protocol, referred to as iXDZ. Our protocol leverages PUF challenge-response mechanisms to generate real-time vehicle keys, eliminating the need to store long-term keys on vehicles and thereby preventing physical key extraction attacks. Our protocol not only resists ephemeral key leakage attacks but also utilizes the uniqueness, unpredictability, and tamper-resistance of PUF to defend against RSU capture attacks and various physical attacks, meeting the diverse security requirements of VANETs. Furthermore, it is anonymous and lightweight, protecting user privacy and enhancing overall network trust. We rigorously demonstrate the security of iXDZ under the random oracle model and supplement the proof utilizing the Scyther formal analysis tool. Performance evaluations reveal that iXDZ significantly enhances security while reducing computational and communication overhead. Additionally, a case study highlights that iXDZ achieves a 33.3% reduction in execution time compared to the original protocol.

Deep learning (DL) methods have shown great potential for remote sensing image change detection recently, but still suffer from several limitations. Within the identical semantic concept, significant but irrelevant changes in surface texture, color and spatial shifting of building objects resulted from variations in imaging physical factors, causes feature inconsistency of building objects in bitemporal sences. Conventional DL methods lack the capability to effectively distinguish real changes from irrelevant changes, leading to some false detections. This letter propose a novel framework, the multi-scale residual alignment transformer (AlignFormer), to mitigate the above issues. Specifically, inspired by deformable attention mechanism, we firstly design an adaptive feature alignment module (AFAM) to suppress the inconsistency of feature pairs, where the regions of building can be adaptively focused on and the spatial-temporal dependencies of relevant building objects in feature pairs effectively captured, via scheme of flexibly sampling Keys/Values for each given Query. Besides, we utilize an extremely tiny Swin Transformer as the backbone of differencing-based framework for obtaining hierarchical features. Moreover, inspired by residual learning strategy, three AFAMs are integrated into the framework to form the multi-scale residual architecture for the coarse-to-fine alignment of the paired features. Experimental results confirm the superiority of our proposed method over several state-of-the-art algorithms. Our code will be released at https://github.com/lilei-aircas/AlignFormer_CD.

Zixuan Li, Yutao Zeng, Yuxin Zuo, Weicheng Ren, Wenxuan Liu, Miao Su, Yucan Guo, Yantao Liu, Xiang Li, Zhilei Hu, Long Bai, Wei Li, Yidan Liu, Pan Yang, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

As a newly emerging advance in deep generative models, diffusion models have achieved state-of-the-art results in many fields, including computer vision, natural language processing, and molecule design. The remote sensing (RS) community has also noticed the powerful ability of diffusion models and quickly applied them to a variety of tasks for image processing. Given the rapid increase in research on diffusion models in the field of RS, it is necessary to conduct a comprehensive review of existing diffusion model-based RS papers, to help researchers recognize the potential of diffusion models and provide some directions for further exploration. Specifically, this article first introduces the theoretical background of diffusion models, and then systematically reviews the applications of diffusion models in RS, including image generation, enhancement, and interpretation. Finally, the limitations of existing RS diffusion models and worthy research directions for further exploration are discussed and summarized.

Hyperspectral anomaly detection (HAD) is a challenging task since samples are unavailable for training. Although unsupervised learning methods have been developed, they often train the model using an original hyperspectral image (HSI) and require retraining on different HSIs, which may limit the feasibility of HAD methods in practical applications. To tackle this problem, we propose a dual-frequency autoencoder (DFAE) detection model in which the original HSI is transformed into high-frequency components (HFCs) and low-frequency components (LFCs) before detection. A novel spectral rectification is first proposed to alleviate the spectral variation problem and generate the LFCs of HSI. Meanwhile, the HFCs are extracted by the Laplacian operator. Subsequently, the proposed DFAE model is learned to detect anomalies from the LFCs and HFCs in parallel. Finally, the learned model is well-generalized for anomaly detection from other hyperspectral datasets. While breaking the dilemma of limited generalization in the sample-free HAD task, the proposed DFAE can enhance the background–anomaly separability, providing a better performance gain. Experiments on real datasets demonstrate that the DFAE method exhibits competitive performance compared with other advanced HAD methods.

This paper proposes an energy router topology based on virtual generator theory. The AC and DC interfaces in this paper are controlled by a virtual synchronous generator (VSG)and virtual DC generator (VDG)to achieve coordinated control of AC and DC interface energy, respectively. The mathematical models of virtual DC generator and virtual AC generator control are also established separately, and AC and DC-side coordinated control was realized. Three kinds of working conditions were analyzed to verify the proposed energy router topology. The MATLAB simulation results verify the correctness and effectiveness of the proposed circuit topology and control strategy.

Virtual synchronous generator (VSG) is a grid-connected inverter control algorithm developed rapidly in recent years, which can provide inertia and dynamic frequency support for the grid. This article mainly studies the VSG control of grid-connected inverter based on microgrid, the VSG algorithm is adopted in the power outer loop, and designs the voltage and full-order sliding mode control current double loop to improve the stability of the system. With this method, the robustness of the system is enhanced, and the stable operation of the system is achieved while dynamic performance ensured. The feasibility and effectiveness of the above control algorithms are verified by building a simulation model in MATLAB software.

The paper firstly establishes a passivity-based control (PBC) of a double-fed inductor generator (DFIG). A new control method of the DFIG is proposed with the outer speed loop controlled by a full-order terminal sliding mode (TSM) controller to undertake maximum wind power point tracking (MPPT), while the inner current control loop is a PBC designed to manage the three-phase PWM rectifier. Simulation results show that the controller design is effective and that the system exhibits satisfactory performance.

Existing recommender systems mostly focus on recommending individual items which users may be interested in. User-generated item lists on the other hand have become a popular feature in many applications. E.g., Goodreads provides users with an interface for creating and sharing interesting book lists. These user-generated item lists complement the main functionality of the corresponding application, and intuitively become an alternative way for users to browse and discover interesting items to be consumed. Unfortunately, existing recommender systems are not designed for recommending user-generated item lists. In this work, we study properties of these user-generated item lists and propose a Bayesian ranking model, called LIRE for recommending them. The proposed model takes into consideration users' previous interactions with both item lists and with individual items. Furthermore, we propose in LIRE a novel way of weighting items within item lists based on both position of items, and personalized list consumption pattern. Through extensive experiments on a real item list dataset from Goodreads, we demonstrate the effectiveness of our proposed LIRE model.