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Due to the different acquisition conditions, large variations in the feature distributions of two temporal domains generally exist, known as temporal domain shift. The temporal domain shift is primarily influenced by coupled dual-factor: global style variations (such as illumination and weather conditions) and local style variations (such as the inherent phenological properties of land cover classes). In this article, we first formulate the temporal domain shift problem as an issue of dual-factor coupled interference on feature distributions in remote sensing (RS) community. To address this issue, we propose a semantic-guided style transfer (SGST) framework seamlessly integrating global feature alignment with local feature semantic matching. We use an adaptive segmentation model to provide pseudosegmentation maps and feed them into the style transfer model as semantic guidance. Under semantic guidance, a semantic-constrained style normalization (SCSN) module is designed to achieve style transfer at both global and local levels. Furthermore, a dual learning approach is introduced to make the style transfer model and the adaptive segmentation model promote each other. As a result, the style transfer model generates high-quality style-transferred images, and the adaptive segmentation model progressively predicts more accurate pseudosegmentation maps. Extensive experiments demonstrate the superiority of our proposed framework over state-of-the-art methods in terms of both perceptual quality and quantitative performance.

The airflow sensor enabled by triboelectric nanogenerator (TENG) is significant for intelligent lab safety and human–computer interaction applications. However, the reported airflow/wind sensor focuses on enhancing the sensing materials and structures, lack of high resolution, and smart signal analysis. Herein, we present a self-powered micro-airflow sensor and its artificial intelligence (AI) system, applied for lab safety and human–computer interaction. The as-fabricated sensor has a high sensitivity of $0.6258~\mu \text{A}$ /(m/s) and a linearity of 0.9968. Attributing to the Venturi effect, the minimum detection velocity of the sensor is 0.13 m/s. Given the sensor performance, we develop a real-time pipeline gas leak location system with an AI user interface, which achieves a potential low detect error $\le 2.9$ cm. In addition, we successfully explore other applications, including human exit–entry counting, ventilation alarm, and breath-based smart aid communication. Above all, the airflow sensor exhibits tremendous potential in the AI and Internet of Things.

Nowadays, light detection and ranging (LiDAR) plays an important role in environmental perception technology; however, LiDAR cannot detect some blocked objects, and it can even detect some incorrect and unimportant objects as target objects due to the sparsity and disorder of the point cloud and the limited receiving area. Indeed, the detection accuracy can be improved by setting multiple cameras and LiDAR, but the cost can be unacceptable. In order to resolve these issues, a transformer-based architecture that can connect voxels with each other by neighborhood sparse attention is proposed. A standardized transformer, however, cannot be applied directly to sparse point cloud space because it contains a large number of empty voxels. Hence, a scheme of neighborhood sparse voxel attention to window attention (NSAW) is introduced, which is an efficient and accurate single stride transformer for connecting 3-D backbone to 2-D bird-eye-view (BEV) feature map. In this architecture, neighborhood window attention (NWA) and neighborhood voxel attention (NVA) are proposed to effectively extract features from no-empty voxels, which can achieve 84.30% average precision with 40 recall points (AP3D(R40)) and bring 2.68% AP3D(R40) improvement for Voxel region-based convolutional neural network (R-CNN) on the moderate car class on the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) test split. On the premise of computational efficiency, the proposed NSAW demonstrates quite a few improvements over the traditional baselines on KITTI3D dataset.