In recent years, RF fingerprinting (RFF) has emerged as a promising technology for wireless device authentication. However, temporal variations in device load and temperature, along with channel effects, lead to inconsistencies in RFF distributions between training and testing phases. As a result, deep learning (DL)-based recognition models often suffer from degraded performance. To address this problem, we propose the first test-time-adaptation (TTA) approach to improve the domain generalization ability of RFF recognition models. We first analyze the causes of time-varying RFF distribution shifts, such as carrier frequency offset (CFO), and develop a physical impairment-based data augmentation strategy. Based on this, we further propose a physically information-aware prototype to guide the model for TTA. Our method requires no model retraining or labeled test samples, and is a lightweight, nonparametric solution. Finally, our approach is extensively evaluated using mobile phones with the IEEE 802.11 orthogonal frequency division multiplexing (OFDM) system, which demonstrates that our scheme can effectively improve RFF average recognition performance by about 7.8%.

Text-to-3D Scene Retrieval (T3SR) aims to retrieve 3D scenes that match users' linguistic queries, enabling intuitive access to 3D scene repositories. Existing approaches rely on joint embedding learning with large amounts of paired text–scene data, which is expensive to collect and often fails to generalize under open-vocabulary queries and diverse scene distributions. In this paper, we propose Struct-Align, a foundation-model-driven framework for zero-shot T3SR that eliminates the need for paired training data. Our key insight is to reformulate T3SR as a single-modality structural alignment problem by converting both 3D scenes and textual queries into a shared, schema-aligned textual representation compatible with pretrained text embedding models. To reliably derive such representations from complex 3D environments, we introduce a role-decomposed scene structuring pipeline that mitigates generative instability and produces semantically consistent scene depictions. To address the inherent semantic asymmetry between query and scene representations, we further propose a locality-aware structural matching strategy that explicitly localizes query intent and performs instance- and relation-level alignment within query-relevant substructures. Extensive experiments on multiple benchmarks demonstrate that Struct-Align outperforms both training-based and zero-shot baselines while exhibiting strong robustness to domain shift.

The emergence of abundant non-stationary radio signal (NSRS) data presents significant opportunities for applications in wireless communications, radar systems, remote sensing, and healthcare. While deep learning models have shown promise in capturing sequence dependencies, deriving generic and fine-grained representations of NSRS data remains challenging due to its complex, dynamic nature and the scarcity of labeled data. The NSRS data are often frequency-sensitive and exhibit minuscule inter-class distances, posing significant challenges for precise classification. To address these issues, we propose a novelDualModalityPatchContrastive (DMPC) framework. This framework leverages a stochastic patching paradigm for diverse local pattern extraction and a time-frequency cross-view optimization for frequency-sensitive feature mining. Furthermore, an Attentive Patch Aggregation (APA) mechanism enhances fine-grained inference under few-shot conditions through patch-level feature voting. Extensive experiments demonstrate the effectiveness of our approach in addressing the unique challenges of NSRS data.

Fabric defect detection is a crucial step in the textile manufacturing process and serves as a key factor in ensuring high-quality fabric production. However, fabric defect images exhibit significant diversity, with defects often complex and irregularly distributed. Existing fabric defect detection algorithms face challenges such as low detection accuracy and slow processing speeds. In this paper, we propose a novel fabric defect detection algorithm based on YOLOv11, named YOLO-FEE. To reduce model parameters and computational overhead, we integrate a FasterNet module into YOLOv11. To enhance the model’s ability to represent discriminative features, we design the EMA module, which leverages a multi-scale attention mechanism to effectively capture detailed information at various scales. Furthermore, to improve the model’s stability and detection accuracy, we adopt the EIoU bounding-box loss function, which refines the IoU calculation method to more precisely evaluate the overlap between predicted and ground-truth bounding boxes. Experimental results demonstrate that even without applying quantization or other acceleration techniques, our approach shows significant potential to enhance the speed of inference and overall performance.

Industrial cyber-physical systems (ICPSs) have significantly improved production efficiency, e.g., Siemens applied ICPS in its Amberg factory, which achieved 75% production automation. However, the security problems involved have not been completely solved, e.g., the source location privacy (SLP). Currently, with the expansion of the ICPS network size and the increasing number of devices, the existing defense process uses these device nodes as false nodes to enhance the security of the ICPS. However, this approach also results in additional energy consumption and transmission delays. To address the above challenges, we propose a solution to mix false sources and phantom strategies [i.e., phantom-backbone–fake (PBF)]. First, a relay phantom node selection algorithm is proposed because adversaries are prone to track source nodes through fixed routes. We aim to optimize the path from the source node to the relay node and then to the sink node to minimize transmission delays. Ultimately, we devise a comprehensive strategy that considers both energy efficiency and distance indicators for the selection of appropriate false nodes. Through simulation and analysis, we demonstrate that our approach improves security and overall network performance.

In Earth observation, cloud severely affects the interpretation of optical satellites generated high-resolution images. Cloud-free optical images are vital for downstream tasks such as semantic segmentation and object detection. Thus, the elimination of clouds from optical imagery has emerged as a significant topic in remote sensing. Currently, most existing methods are proposed to leverage the texture information from auxiliary synthetic aperture radar (SAR) images to restore cloud-free images via direct channel merging. However, such a unified feature extraction approach often neglects the inherent distribution disparity between SAR and optical images—the result of differing imaging principles-potentially leading to significant feature loss. To this end, we introduce a network by jointing SAR and optical images multimodal and cross-attention network (MMCANet) to effectively extract multiscale contextual features from SAR imagery and integrate them with optical features. Specifically, instead of simple concatenation of the channels of SAR and optical images, we obtain high-dimensional features from them through independent feature extractors. The integration of these features is facilitated by a cross-attention mechanism that provides a more fine-grained amalgamation of information. Meanwhile, an atrous spatial pyramid pooling (ASPP) module is introduced into the integration of high-level features, which captures multiscale contextual information around clouded areas. In addition, we propose four advanced remote sensing image restoration algorithms that approach image restoration as a series of subtasks, gradually eliminating clouds to enhance performance. Comprehensive assessments show that MMCANet performs well on the SEN 12 MS-CR dataset with peak signal-to-noise ratio (PSNR) of 39.8871, structural similarity index (SSIM) of 0.9672, mean absolute error (MAE) of 0.0081, and spectral angle mapper (SAM) of 2.9884.

The rapid growth of the Internet of Things (IoT) has led to the widespread adoption of the IoT networks in numerous digital applications. To counter physical threats in these systems, automatic modulation classification (AMC) has emerged as an effective approach for identifying the modulation format of signals in noisy environments. However, identifying those threats can be particularly challenging due to the scarcity of labeled data, which is a common issue in various IoT applications, such as anomaly detection for unmanned aerial vehicles (UAVs) and intrusion detection in the IoT networks. Few-shot learning (FSL) offers a promising solution by enabling models to grasp the concepts of new classes using only a limited number of labeled samples. However, prevalent FSL techniques are primarily tailored for tasks in the computer vision domain and are not suitable for the wireless signal domain. Instead of designing a new FSL model, this work suggests a novel approach that enhances wireless signals to be more efficiently processed by the existing state-of-the-art (SOTA) FSL models. We present the semantic-consistent signal pretransformation (ScSP), a parameterized transformation architecture that ensures signals with identical semantics exhibit similar representations. ScSP is designed to integrate seamlessly with various SOTA FSL models for signal modulation recognition and supports commonly used deep learning backbones. Our evaluation indicates that ScSP boosts the performance of numerous SOTA FSL models, while preserving flexibility.

The recent flourish of deep learning on various tasks is largely accredited to the rich and high-quality labeled data. Nonetheless, collecting sufficient labeled samples is not very practical for many real applications. Few-shot Learning (FSL) provides a promising solution that allows a model to learn the concept of novel classes with a few labeled samples. However, many existing FSL methods are only designed for computer vision tasks and are not suitable for radio signal recognition. This paper calls for a radically different approach to FSL: in contrast to developing a new FSL model, we should focus on transforming the radio signal to be better processed by the state-of-the-art (SOTA) FSL model. We propose Modulated Signal Pre-transformation (MSP), a parameterized radio signal transformation framework that encourages the signals having the same semantics to have similar representations. MSP currently adapts to various SOTA FSL models for signal modulation recognition and can support the mainstream deep learning backbone. Evaluation results show that MSP improves the performance gains for many SOTA FSL models while maintaining flexibility.

Many efforts paid attention to the multi-modal task, of which image captioning is a classic work. Especially the Clip model improves the performance of image captioning; meantime, its few-shot and zero-shot problems have become a significant research project. In this work, aiming at the image captioning task, we design the new few-shot and zero-shot settings different from popular directions. The direction focuses on the impact of the exited dataset for captioning model ability. According to analysis, we discover the frequency of the word combination can directly influence the performance of the captioning model. Based on this, we define the new few-shot and zero-shot settings. In terms of this, a Cycle-based captioning framework based on data augmentation is proposed to overcome this problem, of which the novelty switcher module is the critical component. Finally, experiments demonstrate that our framework can achieve state-of-the-art performance on both traditional, few-shot and zero-shot settings.

In Earth observation activities, cloud severely affects the interpretation of the high-resolution imagery, generated by optical satellites. Therefore, removing clouds from optical imagery becomes a topic of interest in the remote sensing field. Currently, most methods use auxiliary Synthetic Aperture Radar (SAR) images to reconstruct optical images by merging SAR and optical images into a deep learning network. However, the speckle noise of the SAR image is not taken into the consideration during feature fusion processing, leading to the blurry edges in the reconstructed optical images. To get fine-grained optical images, we propose a novel cloud removal framework based on the edge fusion of SAR and optical images. Firstly, the edge feature of SAR images is extracted by the GRHED. As the prior knowledge, it can provide fine-grained edge information for subsequent reconstruction work. Then channels from three modal data are stacked to guide the reconstruction of optical images by exploiting their correlations and interactions. Furthermore, a structural similarity (SSIM) loss function is introduced to optimize the training network and improve the coherence of the image structure. Experimental results confirm its advantages on the SEN12MS-CR dataset.

Electronic Stability Program (ESP) is widely used in modern vehicles. Its safety and stability largely depend on the strength and reliability of the MEMS gyroscope. However, the tight coupling between this sensor and the environment brings significant safety hazards to the vehicle. In this study, we describe the physical vulnerability of gyroscopes to high-frequency acoustics and introduce methods for finding resonant frequencies. We devised two methods to inject the attack signal into audio files to make the acoustic attack more stealthy. The realized attack is non-intrusive and does not require tampering with the ESP hardware device, making attack detection more difficult. We also consider a neural network-based defense strategy and verify its effectiveness. The construction of the vehicle simulation system and the above experiments are completed in the co-simulation environment of Carsim and Simulink.