Accurate vehicle navigation plays a critical role in vehicle-to-everything (V2X) applications, including connected transportation systems, intelligent traffic management, and autonomous driving. To address the stringent demands of these scenarios, the integration of the global navigation satellite system (GNSS) with the visual-inertial navigation system (VINS) has emerged as a pivotal advancement. Despite these strides, navigation systems remain susceptible to abnormal data. This data, originating from unpredictable external environments and internal device fallibility, poses a threat of substantial errors and system drift. In this paper, we present RF-Nav, a robust fusion-based GNSS-VINS navigation system with enhanced data processing and dynamic factor correction. The framework innovates with a dual-pronged approach: it first applies adaptive gamma correction with bilateral filtering and contrast-limited adaptive histogram equalization (AGCBF-CLAHE) to refine raw images; then, it deploys a long short-term memory (LSTM) denoising network enhanced with an advanced wavelet threshold for IMU data refinement. This dual enhancement of visual and IMU data integrity is further bolstered by a dynamic factor confidence correction mechanism, rooted in factor graph optimization (FGO), designed to counteract the adverse effects of abnormal data. Extensive experiments on large-scale public and real-field dataset demonstrate that RF-Nav exhibits superior robustness and accuracy in various environments.

Foundation models, such as the Segment Anything Model (SAM), have demonstrated impressive generalization across various image segmentation tasks. However, they encounter challenges when applied to medical imaging, primarily due to the lack of domain-specific expertise and the limited availability of annotated data. Existing methods for adapting SAM typically rely on expert-driven prompt design and extensive fine-tuning, which hinder their effectiveness in medical imaging, particularly for rare and complex anatomical structures. To overcome these challenges, we propose MASG-SAM, an innovative framework designed for efficient few-shot medical image segmentation. MASG-SAM integrates three key innovations: the Hierarchical Attention Enhancement (HAE), Boundary Feature Enhancement (BFE), and Dynamic Semantic Fusion (DSF) modules. The HAE module optimizes attention distribution across hierarchical feature maps, enhancing feature diversity and reducing feature drift, thereby improving segmentation of both global and local features in complex medical images. The BFE module introduces a boundary-sensitive mechanism that enhances edge detection, enabling precise segmentation of overlapping or difficult-to-delineate anatomical structures. Finally, the DSF module leverages Contrastive Language-Image Pretraining (CLIP) to inject domain-specific medical semantic knowledge. By adaptively refining feature fusion during training, DSF combines semantic guidance with spatial adjustments, progressively improving segmentation accuracy, particularly in data-scarce scenarios. Experiments conducted on four publicly available medical datasets show that MASG-SAM outperforms state-of-the-art methods, achieving high segmentation accuracy with minimal labeled data. Our framework significantly enhances the adaptability and accuracy of SAM in complex medical imaging tasks.

Modern industrial equipment’s growing complexity makes fault propagation a critical factor impacting operational efficiency and safety. Understanding the mechanisms and influencing factors of equipment fault propagation is therefore vital for enhancing reliability, optimizing maintenance strategies, and ensuring production safety. Based on the laws governing fault propagation within industrial equipment components, this study identifies three key influencing factors: interaction frequency, connection rate, and failure probability. It provides definitions and calculation methods for each. Utilizing these three factors and the SIRC infectious disease model, we construct a model to represent the fault propagation process in advanced industrial equipment. This model aims to mitigate fault propagation risk, thereby improving equipment reliability and the overall efficiency of the production system.

The essence of the cross-view geo-localization task is to accurately identify the same object across images captured from different viewpoints. Due to variations in image acquisition methods and viewing angles, the content information of the images can differ significantly, which may result in localization failure. Therefore, cross-view geo-localization remains a challenging task. To solve this issue, a joint representation learning network based on feature center region diffusion and edge radiation is proposed in this article. First, to extract the crucial information from the global features, we design the central diffusion module that identifies important regions within the features and enhances feature robustness. Additionally, we design an edge radiation mechanism that expands the receptive field and further highlights crucial information in the image to support the central diffusion module in achieving more stable performance. On this basis, we propose an adaptive triple InfoNCE loss function to assist network training, improving the discriminability of the extracted features. Finally, the proposed network is tested on two mainstream datasets, and experimental results demonstrate that the proposed model outperforms the state-of-the-art methods, which can prove its effectiveness.

The low resolution of thermal imaging from unmanned aerial vehicles (UAVs) poses a substantial obstacle to the understanding and analysis of ground targets. Utilizing readily available high-resolution visible images presents a promising solution to improve the quality of thermal UAV images. However, current methods primarily focus on simple degradation conditions, neglecting the complexity of real-world degradation scenarios, such as blur and noise, which fail to meet the demands of practical applications. In this paper, we introduce a Degradation-aware Cross-modality Mamba (DC-Mamba) framework to super-resolve (SR) thermal UAV images by integrating degradation information with cross-modality cues. Our approach begins with a self-supervised learning framework that extracts degradation information directly from input images. This information guides the restoration process through the designed degradation-aware modules, which enhance model sensitivity to distorted regions. Additionally, we incorporate a vision-focused state-space module (SSM) to capture long-term spatial dependencies, thereby improving feature adaptability. To address modality disparities, we develop a cross-modality feature integration framework that leverages visible cues at three levels (interaction, refinement, and enhancement) to improve thermal image reconstruction quality. Extensive experiments demonstrate that the proposed method outperforms current state-of-the-art SR methods, providing more realistic details and superior performance across multiple evaluation metrics.

The internet of things (IoT) is revolutionizing various industries by enabling the creation of smart systems for forest management, promoting the emergence of the forestry Internet of Things (IoFT). However, existing IoT node deployment methods often overlook geographic constraints, while solar-powered IoFT nodes face limitations in forests due to tree obstruction and maintenance challenges. Furthermore, to achieve cost-effective monitoring, it is essential to consider both coverage and network costs. In this paper, we propose a new metric, the coverage benefit ratio (CBR), which balances coverage and cost, ensuring long-term stable operation of forest monitoring systems while reducing maintenance costs and environmental impact. We first formulate the optimal deployment model to find the minimum-cost IoFT. Then, we propose develop a low complexity algorithm to solve the defined NP-hard optimization problem. Simulation results demonstrate that the effectiveness and progressiveness of the proposed method.

In this paper, an energy management strategy for electric vehicles equipped with fuel cell (FC), battery (BAT), and supercapacitor (SC) is considered, aiming at improving the whole performance under a framework of vehicle to network application. In detail, based on wavelet transform and equivalent consumption minimization strategy (ECMS), the demand power of vehicles is optimized to enhance the lifespan of fuel cell, fuel economy, and dynamic performance of electric vehicles. The wavelet transform is used to separate the high-frequency power in order to provide a peak power and recycle the braking energy. The equivalent consumption minimization strategy is used to distribute the low-frequency power to fuel cell and battery for minimizing the hydrogen consumption. Obtained results are studied using an advanced vehicle simulator, and its effectiveness of the strategy is confirmed, which provides a fundamental control method for the IOV application.