Early detection of visual impairments remains a persistent challenge, especially due to the subtle and often unnoticed nature of early-stage symptoms. Recent works have attempted to transition clinical tests to home-based services or develop innovative diagnostic methods, but most approaches remain self-initiated and discrete. In this study, we focused on refractive disorders and explored the feasibility of using electrooculography (EOG) to detect changes in refractive power passively. Thirty-nine participants used optometry trial lenses to simulate different refractive conditions. Participants performed a series of visual tasks while their EOG signals were recorded. We trained classification models to predict simulated refractive power levels relative to baseline visual condition across multiple evaluation settings, including within-subject, temporal generalization, and across-subject scenarios. The findings reveal that refractive power classification models achieve a mean accuracy of $0.950 \pm 0.034$ in within-subject, within-condition scenarios. Within-subject models tested on data from a different time point showed highly variable performance. While some participants achieved promising results, overall accuracy remained low, with a mean of $0.159 \pm 0.285$. We employed three strategies to evaluate the across-subject models. Naive models performed poorly ($0.161 \pm 0.063$) and linear normalization provided limited improvement ($0.175 \pm 0.062$). However, the fine-tuning strategy substantially improved the model's performance ($0.785 \pm 0.123$). EOG signals contain useful information for refractive power classification, particularly in personalized contexts. However, generalizing across time and individuals remains challenging. Overall, this work offers valuable insights for advancing EOG-based systems aimed at passive, real-time monitoring of visual conditions.

Sparse wearable inertial measurement units (IMUs) have gained popularity for estimating 3D human motion. However, challenges such as pose ambiguity, data drift, and limited adaptability to diverse bodies persist. To address these issues, we propose UMotion, an uncertainty-driven, online fusing-all state estimation framework for 3D human shape and pose estimation, supported by six integrated, body-worn ultra-wideband (UWB) distance sensors with IMUs. UWB sensors measure inter-node distances to infer spatial relationships, aiding in resolving pose ambiguities and body shape variations when combined with anthropometric data. Unfortunately, IMUs are prone to drift, and UWB sensors are affected by body occlusions. Consequently, we develop a tightly coupled Unscented Kalman Filter (UKF) framework that fuses uncertainties from sensor data and estimated human motion based on individual body shape. The UKF iteratively refines IMU and UWB measurements by aligning them with uncertain human motion constraints in real-time, producing optimal estimates for each. Experiments on both synthetic and real-world datasets demonstrate the effectiveness of UMotion in stabilizing sensor data and the improvement over state of the art in pose accuracy. Code is available at: https://github.com/kk9six/umotion.

Refractive errors are among the most common visual impairments globally, yet their diagnosis often relies on active user participation and clinical oversight. This study explores a passive method for estimating refractive power using two eye movement recording techniques: electrooculography (EOG) and video-based eye tracking. Using a publicly available dataset recorded under varying diopter conditions, we trained Long Short-Term Memory (LSTM) models to classify refractive power from unimodal (EOG or video-based eye tracking) and multimodal configurations. In the context of eye movement analysis, EOG captures fine-grained electrical signals, while video-based tracking provides rich features such as pupil dynamics and gaze behavior, making the two modalities complementary. We assess performance in both subject-dependent and subject-independent settings to evaluate model personalization and generalizability across individuals. Results show that the multimodal model consistently outperforms unimodal models, achieving the highest average accuracy in both settings: 96.568% in the subject-dependent scenario and 9.344% in the subject-independent scenario. Statistical comparisons in the subject-dependent setting confirmed that both unimodal and multimodal models significantly exceeded the chance level. Among them, the multimodal model significantly outperformed the EOG and eye-tracking models. The strong performance of subject-dependent models highlights the potential for developing personalized models tailored to the target user for refractive power monitoring. However, generalization remains limited, with classification accuracy only marginally above chance in the subject-independent evaluations. Our findings demonstrate both the potential and current limitations of eye movement data-based refractive error estimation, contributing to the development of continuous, non-invasive screening methods using EOG signals and eye-tracking data.

Heavy navigation demands of incoming/outgoing ships and operational features in dry bulk export terminals (DBETs) call for effective and intelligent optimization methods to improve navigation channel traffic and reduce delays. Considering ship deballasting delays in DBETs and their influences on channel traffic flow, this paper proposes a channel traffic scheduling (CTS) optimization method based on deep reinforcement learning (DRL). The CTS problem is formulated into a Markov decision process with tailored state and action definitions. Practical constraints, such as tidal windows and dynamic switching traffic mode, are incorporated into action selection processes. In coping with large state-action spaces caused by practical applications, a hierarchical DRL framework is proposed to perform layered decision-making. Relying on the reward signal design, DRL agents can learn optimization policies to produce integrated scheduling plans of channel traffic and ship deballasting operations while minimizing ship mooring, unberthing, and deballasting delays. A proximal policy optimization method is developed for coupling training DRL agents. Numerical experiments demonstrate that the proposed method can converge faster to better scheduling strategies and efficiently generate high-quality CTS solutions for industrial-scale applications.

Wireless multimedia sensor networks (WMSNs) have been widely used in environmental monitoring, intelligent transportation, and other scenarios; however, their data have high-dimensional, large-scale, and multitype properties, as well as other characteristics. It is technically difficult to construct an appropriate index structure and query strategy while we perform a highly accurate search. This paper proposes a novel locality-sensitive hashing (LSH) fast Johnson-Lindenstrauss transform (FJLT)-fly locality-sensitive hashing (FLSH) algorithm for WMSN Internet of Things search. In this method, the projection method of FJLT and the winner-takes-all feature selection strategy in the fruity FLSH are considered. The method provides a new solution for the nearest neighbor search of high-dimensional data. We also discuss the distance-keeping property of our algorithm, and prove theoretically that the method proposed in this paper has better distance keeping performance than the traditional dimensionality reduction method. The experimental results show that the proposed algorithm has better generalization, accuracy of the search results, and time efficiency when using the Drosophila olfactory nerve to simulate the LSH process. This method effectively solves the problem of the approximate neighbor query of high-dimensional big data and can be effectively applied to search application on WMSN system.