In recent years, the rise of location-based service applications such as cashier-less shopping, mobile advertisement targeting, and geo-based augmented reality (AR) has been remarkable. These applications offer convenient and interactive experiences by utilizing indoor localization technology. One popular research area in indoor localization is passive fingerprinting localization based on Channel State Information (CSI), which uses general-purpose Wi-Fi platforms and “unconscious cooperative sensing” to achieve device-free localization. However, existing studies face challenges related to inadequate fingerprint richness, limited distinguishability, and inconsistent fingerprint features in real-world dynamic environments. To address these challenges, we prpose MFFLoc in this paper. MFFLoc extracts and processes amplitude and phase information from CSI in a 2D manner. It then fuses the amplitude and phase information using multimodal fusion representation, resulting in rich and distinguishable fused fingerprint features. This approach allows MFFLoc to achieve satisfactory accuracy with just one communication link, reducing deployment costs. To overcome the issue of inconsistent fingerprint features in dynamic environments, MFFLoc proposes an unsupervised domain adaptation method. It employs a dual-flow structure, with one flow operating in the source domain and the other in the target domain. The adaptation layer, with correlated weights, remains unshared between the two flows. Meta-learning is also used to automatically determine the most suitable adaptation layer. Through extensive 6-day experiments conducted in a dynamic indoor environment, MFFLoc showcases superior performance compared to state-of-the-art systems. It demonstrates higher localization accuracy and robustness, making it a promising solution for indoor localization applications.

Positive emotional experiences can improve learning efficiency and cognitive ability, stimulate students' interest in learning, and improve teacher-student relationships. However, positive emotions in the classroom are primarily identified through teachers' observations and postclass questionnaires or interviews. The expression intensity of students, which is extremely important for fine-grained emotion analysis, is not considered. Hence, a novel method based on smile intensity estimation using sequence-relative key-frame labeling is presented. This method aims to recognize the positive emotion levels of a student in an end-to-end framework. First, the intensity label is generated robustly for each frame in the expression sequence based on the relative key frames to address the lack of annotations for smile intensity. Then, a deep-asymmetric convolutional neural network learns the expression model through dual neural networks, to enhance the stability of the network model and avoid the extreme attention region learned. Further, dual neural networks and the dual attention mechanism are integrated using the intensity label based on the relative key frames as the supervised information. Thus, diverse features are effectively extracted and subtle appearance differences between different smiles are perceived based on different perspectives. Finally, comparative experiments for the convergence speed, model-training parameters, confusion matrix, and classification probability are performed. The proposed method was applied to a real classroom scene to analyze the emotions of students. Numerous experiments validated that the proposed method is promising for analyzing the differences in the positive emotion of students while learning in a classroom.

In recent years, device-free passive localization leveraging Wi-Fi Channel State Information (CSI) has emerged as a prominent technique for indoor positioning, yet the nonlinear interactions and signal superposition among multiple targets, coupled with occlusion and shadowing effects, significantly complicate the localization task, rendering multi-target device-free passive localization a substantial challenge in the field. In this paper, we propose a novel device-free passive multi-target indoor localization approach based on multi-label learning and unsupervised domain adaptation, denoted as MLDA-MultiLoc. It segments the localization area into multiple training point regions, reformulating the multi-target problem as a multi-label classification task. MLDA-MultiLoc employs a fusion representation model that capitalizes on the spatio-temporal redundancy of CSI amplitude and phase, effectively mapping these features into a unified representation domain. This model is optimized to enhance the discriminative power of the fusion fingerprint (HDFF) by maximizing spatial metrics. Acknowledging the nonlinear influence of multiple targets on CSI, MLDA-MultiLoc incorporates a Fusion Generation Network to synthesize multi-target fingerprints from multiple single-target fingerprints, creating virtual samples for multi-target scenarios. This process facilitates the training of a deep learning-based multi-label classifier, leveraging multi-label learning for robust parameter optimization. Furthermore, MLDA-MultiLoc introduces an unsupervised domain adaptation technique that utilizes a meta-learning dual-stream structure. This method effectively bridges the gap between virtual and real fingerprint samples, ensuring accurate multi-target localization in complex, dynamic indoor settings. Extensive experiments have confirmed the superiority of MLDA-MultiLoc over existing state-of-the-art systems, showcasing its effectiveness in real-world indoor environments.

Unmanned aerial vehicles (UAV) can capture multiview images of large-scale scenes, and then using aerial light field (ALF)-rendering technology, they can render high-quality, large-scale 3D scenes. However, the reconstruction of large-scale scenes poses challenges, such as rendering distortion and high memory consumption. In this article, we study the multiview image-capturing and novel view-rendering method of UAV sampling to address these issues. First, we present an ALF sampling model using the spectral analysis of light field and obtain an exact spectrum expression of the ALF. Through the spectral support of ALF, we determine the bandwidth and calculate the minimum required UAV sampling rate. Finally, we demonstrate that our sampling and rendering methods can improve the rendering quality of UAV 3D reconstruction and reduce the minimum sampling rate.