Multimodal Sentiment Analysis (MSA) and Emotion Recognition in Conversations (ERC) have rapidly developed into pivotal tasks in artificial intelligence. Large Language Models (LLMs) offer powerful semantic reasoning and computational capabilities, showing great potential for understanding emotional content. However, when applied to multimodal sentiment data, LLMs face significant challenges, including the inability to directly process heterogeneous data, difficulties in coping with feature misalignment and suboptimal cross-modal fusion. To address these challenges, we propose a novel multimodal sentiment inference framework named ATGFB-MFF which grounded in fiber bundle theory. This method decomposes multimodal features into an adaptive text-guided shared semantic space and fiber offset spaces to achieve structured alignment and fusion. Then the fused features are converted into structured pseudo-token sequences for effective inference via frozen LLMs. We also introduce two loss functions respectively called shared space consistency loss and fiber offset regularization loss which are used to improve representation stability. Extensive experiments on four benchmark datasets demonstrate that ATGFB-MFF consistently outperforms state-of-the-art baselines. These results highlight the efficacy of geometric structural modeling in unlocking the potential of LLMs for multimodal sentiment inference.

Data-driven spectrum sensing is a key technology for addressing complex challenges in Cognitive Radio Networks (CRNs). Traditional methods are typically designed for simple single-band scenarios and perform poorly in practical wideband applications. In real-world systems, a single Secondary User (SU) is often restricted by energy, time, and hardware capabilities during real-time sensing. Consequently, only local and fragmented frequency information can be obtained. This partial sensing leads to a severe lack of training data. Additionally, the lack of historical records for emerging frequency bands, combined with data incompleteness due to resource constraints, creates training bottlenecks for data-driven models and limits the reliability of sensing. To address these challenges, this paper proposes a novel framework based on Collaborative Subgraph Learning and Hyperbolic Graph Neural Networks (GNNs). This approach enables Secondary Users to perform collaborative sensing through distributed subgraph learning. By utilizing GNNs to extract features and model multi-band correlations, a new distributed GNNs architecture is designed to efficiently detect wideband spectrum occupancy, even with partial observations. Within this framework, all frequency bands in the wideband spectrum pool are treated as a unified graph, while the bands observed by each SU form a subgraph. Subsequently, the complete spectrum graph is constructed through the joint training and aggregation of these subgraphs. By integrating hyperbolic geometry into GNNs, this method better captures the hierarchical structure of spectrum patterns, providing a more accurate and efficient sensing model. Experimental results demonstrate that, compared to the second-best HCNNs model, the proposed framework improves sensing accuracy by 3.8% on average across various test environments, while reducing key resource consumption by 18.4% on average.

Spectral–spatial feature modeling plays a crucial role in hyperspectral image (HSI) classification. However, existing models based on convolutional neural networks (CNNs) and Transformers still face a trade-off between feature modeling capability and computational efficiency. Although recent wavelet-based HSI classification methods have demonstrated the advantages of frequency-domain analysis, they typically rely on a single wavelet basis, which limits their ability to capture diverse spectral–spatial patterns across different frequency bands. To address these issues, we propose Wavelet Spectral-Spatial Mamba (WSSMamba) by combining wavelet transform with state space modeling for HSI classification. WSSMamba introduces an Adaptive Wavelet Fusion Module (AWFM) to perform multi-scale frequency domain decomposition using multiple wavelet bases. This allows the model to extract both low-frequency global structure and high-frequency local details. A Wavelet Feature Enhancement (WFE) module is also designed to improve feature discriminability by applying channel and spatial attention mechanisms. Furthermore, we propose a Spectral-Spatial Cross-Fusion Strategy (SSCFS), which uses multi-directional state modeling to dynamically integrate high-frequency information. Extensive experiments on benchmark datasets demonstrate that WSSMamba outperforms state-of-the-art methods in classification performance.

The industrial internet of things (IIoT) plays a crucial role in manufacturing, logistics, and equipment management. Graph neural networks (GNNs) can effectively model graph-structured data and have received widespread attention in IIoT applications. However, existing methods face key challenges. First, IIoT data typically contains sensitive information, making it difficult to conduct centralized training on dispersed data. Second, the current model fails to fully capture the complex interrelationships between different devices. To address the above issues, this article proposes a federated learning-based graph neural network model FedMRGNN for joint analysis of distributed IIoT. This model performs federated learning through model aggregation and parameter exchange, while protecting privacy through differential privacy mechanisms. Meanwhile, to better capture the complex relationships between devices, this article integrates multiscale feature extraction and residual connections into the model. Multiscale feature extraction can process graph data in parallel through multiple branches, each branch using convolutional kernels of different scales to extract node features, and utilizing multiscale pooling operations for local aggregation and dimensionality reduction. Residual connections can enhance the fusion ability of multiscale features and alleviate the problem of gradient vanishing in deep network training. In order to further verify the effectiveness of FedMRGNN, experimental verification was conducted on different datasets. The results show that FedMRGNN improves classification accuracy by 2.21%–6.40% compared to other baseline algorithms in most scenarios. In practical IIoT applications, improved classification accuracy can help predictive maintenance systems detect potential device failures in advance, thereby improving overall device operational efficiency.

The need for high-resolution (HR) remote sensing images has grown significantly in recent years as a result of the rapid advancement of fine-sensing technologies. However, increasing sensor resolution usually requires a costly investment. To tackle this challenge, super-resolution (SR) methods for remote sensing images have emerged as a cost-effective alternative to enhance the quality and usability of existing low-resolution (LR) images. Although many current methods have achieved some reconstruction results, they often suffer from problems such as transition smoothing and artifacts. To solve these problems, we propose an SR reconstruction model for detail recovery based on generative adversarial networks (GANs), referred to as DRGAN. Specifically, unlike the traditional residual-in-residual dense block network (RRDBNet), we propose a novel dense residual network (OSRRDBNet). It uses dynamic convolution and self-attention mechanisms to recover the rich detailed information in the image more effectively. In addition, we employ an average pooling layer to enhance the ability to capture HR image features. By conducting experiments on three different remote sensing datasets, DRGAN shows remarkable reconstruction results and successfully recovers the rich detail information in the images.

High resolution optical remote sensing images are the guarantee for remote sensing image analysis and application. However, many images suffer from blurring, distortion and low resolution due to camera hardware limitations and unstable image transmission. To address these challenges, we propose a Random Degradation and Adaptive Fusion-based super-resolution network (RDAF-GAN) for improving the clarity and detail of images. Specifically, unlike the traditional single degradation method, we design a comprehensive simulation model for remote sensing image degradation. It aims to generate low-resolution remote sensing images that are closer to the real scene. Subsequently, these generated low-resolution images are fed into RDAF-GAN for reconstruction to recover finer and more accurate image details. In addition, we propose an image fusion method based on local contrast. By adaptively adjusting the fusion weights, the perceived clarity and visual quality of the images are further enhanced. The experimental results validate that RDAF-GAN outperforms other state-of-the-art methods and consistently produces excellent results in a variety of situations.

Lane detection is crucial for autonomous driving systems (ADS), utilizing sensors like cameras and LiDAR to identify lanes and understand vehicle position, direction, and lane shape. It provides data support for the control system to make informed driving decisions. In this survey, we review recent advancements in lane detection, focusing on both 2D techniques and emerging 3D methods. We begin with an overview of the significance of lane detection in ADS, followed by an analysis of the evolution of 2D techniques over the past decade, covering traditional and deep learning approaches. We also examine recent advancements in 3D lane detection. Additionally, we summarize evaluation metrics and popular datasets in the field. Finally, we discuss current challenges and future directions in lane detection, aiming to provide valuable insights for researchers and developers in this technology.

3D lane detection provides richer spatial information than 2D lane detection planar position results. It improves vehicle perception in complex scenes, which is becoming increasingly important in intelligent driving. However, existing frameworks mainly focus on mapping front-view (FV) and bird’s-eye view (BEV) features and ignore the intrinsic correlations between different perspectives and scales. It can lead to incomplete feature extraction, affecting the perception accuracy of lane detection and adaptation ability to complex scenes. To alleviate these problems, we present a novel Freq-3DLane framework, an efficient end-to-end 3D lane detector. Instead of directly superimposing deeper and lower-level features, we propose a strategy for multi-scale information integration that exploits the frequency characteristics of features for image feature extraction. To enhance perception, we fuse image features at each scale through frequency processing to ensure that detailed information and global structure are fully utilized. Next, spatial transformation fusion captures the association between the FV and the BEV feature at any two-pixel position of both, thus enabling view feature transformation. In addition, attentional guidance enhances the lane semantic information to ensure recovery of the lane geometry for accurate 3D lane detection. Extensive results on two challenging benchmarks (Apollo 3D Lane Synthetic, and OpenLane) show that our model performs favorably against the state of the arts.