Feng Chen

1. In recent years, Unmanned Surface Vehicles (USVs) have demonstrated flexible application potential in inland water operations, such as environmental monitoring, hydrological surveying, emergency...

Advances in microelectronic components and high‐speed networks have enabled the widespread application of virtual reality (VR) technology in education. However, insufficient attention to knowledge visualization in VR learning has resulted in disorganized knowledge structures, comprehension difficulties, and mismatches between user experience and learning achievement. Therefore, we propose a Knowledge Cube (KC)‐based visualization method to standardize knowledge encoding in VR learning. During courseware development, the instructor defines discrete knowledge as Events, organizes them into Event Groups, and populates data into a KC model to generate VR courseware. In subsequent VR learning, when learners search for task‐relevant knowledge using the provided retrieval method, the KC model presents the corresponding events within interactive scenarios according to its predefined structure. Comparative experiments on different knowledge visualization methods revealed that, in VR learning, the KC method outperforms other VR approaches in both learning performance and efficiency. This method effectively guided learners to focus on the learning content and optimized the knowledge encoding in VR learning. This provides an operational framework for knowledge encoding in VR courseware design and emphasizes the importance of supporting effective learning behaviors over merely pursuing immersion, presenting a new perspective for refining the design approach of VR courseware.

The increasing demand for low-latency and high-reliability computation in vehicular networks has accelerated the development of Mobile Edge Computing (MEC). However, traditional edge infrastructures such as roadside units (RSUs) often lack flexibility in handling emergent or large-scale computational workloads. To address this challenge, we propose a distributed computation offloading framework that leverages Unmanned Aerial Vehicles (UAVs) as mobile edge nodes to assist task vehicles. UAVs equipped with computation resources can provide computation support for multiple task vehicles. Furthermore, we formulate a delay minimization problem under energy constraints of vehicles and UAVs, and solve it using a Lagrangian dual optimization method. Simulation results demonstrate that the proposed offloading strategy significantly reduces the total task processing delay and outperforms conventional local-only or partially offloaded schemes.

Space-time-block-code (STBC) and wideband permutation index differential-chaos-shift-keying (PI-DCSK) modulation can effectively mitigate the multipath fading. By integrating STBC with PI-DCSK, we propose an M-ary space-time-block-code shift index differential-chaos-shift-keying (STBC-SI-DCSK) scheme. This scheme leverages low correlation between chaotic signals to minimize inter antenna interference and achieve full diversity gain. Simulation results show that the proposed scheme improves bit-error-rate (BER) by about 10 dB and 5 dB over single-input single-output (SISO)-aided PI-DCSK (SISO-PI-DCSK) and STBC-PI-DCSK, respectively, at a BER of $10^{-4}$. The proposed STBC-SI-DCSK scheme also demonstrates superior BER performance in ultra-wide bandwidth (UWB) channels while simplifying the permutation operation. This results in a transceiver complexity comparable to that of SISO-PI-DCSK, making it well-suited for wireless local area network (WLAN) applications.

With the rapid development of mobile networks and devices, real-time video transmission has become increasingly important worldwide. Constrained by the bandwidth limitations of single networks, extensive research has shifted towards video transmission in multi-network environments. However, differences in bandwidth and latency in heterogeneous networks (such as LTE and Wi-Fi) lead to high latency and packet loss issues, severely affecting video quality and user experience. This paper proposes a Forward Error Correction (FEC)-based Low-Delay Multipath Scheduling algorithm (LDMP-FEC). This algorithm combines the Gilbert model with a continuous Markov chain to adaptively adjust FEC redundancy, thereby enhancing data integrity. Through the FEC Recovery Priority Scheduling (FEC-RPS) algorithm, it dynamically optimizes the transmission order of data packets, reducing the number of out-of-order packets (OFO-packets) and end-to-end latency. Experimental results show that LDMP-FEC significantly reduces the number of out-of-order packets in heterogeneous network environments, improving performance by 50% compared to the round-robin and MinRtt algorithms, while maintaining end-to-end latency within 150 ms. Under various packet loss conditions, LDMP-FEC sustains a playable frame rate (PFR) above 90% and a Peak Signal-to-Noise Ratio (PSNR) exceeding 35 dB, providing an efficient and reliable solution for real-time video and other low-latency applications.

Detection-based tracking is one of the main methods of multi-object tracking. It can achieve good tracking performance when using excellent detectors but it may associate wrong targets when facing overlapping and low-confidence detections. To address this issue, this paper proposes a novel multi-object tracker (SCTracker) by exploiting shape constraint and confidence. In the data association stage, an Intersection of Union distance with shape constraints is developed to calculate the cost matrix between tracks and detections, which can reduce the track of the wrong target with the similar position but inconsistent shape. Moreover, the detection confidence is calculated in the update stage of the Kalman Filter to improve the track performance with the inaccurate detection result. Experimental results on MOT 17 dataset show that the proposed SCTracker can improve the tracking performance of multi-object tracking as compared to the state-of-the-art methods.

The safety of autonomous driving is closely linked to accurate depth perception. With the continuous development of autonomous driving, depth completion has become one of the crucial methods in this field. However, current depth completion methods have major shortcomings in small objects. To solve this problem, this paper proposes an end-to-end architecture with adaptive spatial feature fusion by encoder–decoder (ASFF-ED) module for depth completion. The architecture is built on the basis of the network architecture proposed in this paper, and is able to extract depth information adaptively with different weights on the specified feature map, which effectively solves the problem of insufficient depth accuracy of small objects. At the same time, this paper also proposes a depth map visualization method with a semi-quantitative visualization, which makes the depth information more intuitive to display. Compared with the currently available depth map visualization methods, this method has stronger quantitative analysis and horizontal comparison ability. Through experiments of ablation study and comparison, the results show that the method proposed in this paper exhibits a lower root-mean-squared error (RMSE) and better small object detection performance on the KITTI dataset.

Anomaly detection is represented as an unsupervised learning to identify deviated images from normal images. In general, there are two main challenges of anomaly detection tasks, i.e., the class imbalance and the unexpectedness of anomalies. In this paper, we propose a multiresolution feature guidance method based on Transformer named GTrans for unsupervised anomaly detection and localization. In GTrans, an Anomaly Guided Network (AGN) pre-trained on ImageNet is developed to provide surrogate labels for features and tokens. Under the tacit knowledge guidance of the AGN, the anomaly detection network named Trans utilizes Transformer to effectively establish a relationship between features with multiresolution, enhancing the ability of the Trans in fitting the normal data manifold. Due to the strong generalization ability of AGN, GTrans locates anomalies by comparing the differences in spatial distance and direction of multi-scale features extracted from the AGN and the Trans. Our experiments demonstrate that the proposed GTrans achieves state-of-the-art performance in both detection and localization on the MVTec AD dataset. GTrans achieves image-level and pixel-level anomaly detection AUROC scores of 99.0% and 97.9% on the MVTec AD dataset, respectively.