The demand for immersive and interactive communication has driven advancements in 3D video conferencing, yet achieving high-fidelity 3D talking face representation at low bitrates remains a challenge. Traditional 2D video compression techniques fail to preserve fine-grained geometric and appearance details, while implicit neural rendering methods like NeRF suffer from prohibitive computational costs. To address these challenges, we propose a lightweight, high-fidelity, low-bitrate 3D talking face compression framework that integrates FLAME-based parametric modeling with 3DGS neural rendering. Our approach transmits only essential facial metadata in real time, enabling efficient reconstruction with a Gaussian-based head model. Additionally, we introduce a compact representation and compression scheme, including Gaussian attribute compression and MLP optimization, to enhance transmission efficiency. Experimental results demonstrate that our method achieves superior rate-distortion performance, delivering high-quality facial rendering at extremely low bitrates, making it well-suited for real-time 3D video conferencing applications.

Abstract Traditional maritime search and rescue methods mainly rely on manual operation, which takes a long time to identify and results in poor search and rescue results. This paper applies computer vision technology to the field of maritime distress object detection and proposes an improved object detection algorithm YOLOv5s‐EFOE based on the YOLOv5s algorithm. Firstly, the authors change the detection head of the YOLOv5s algorithm to use a mixed channel strategy to build a more efficient decoupling head, reducing the number of 3 × 3 convolutional layers in the middle of the decoupling head to only one. The width of the head is scaled by the width multipliers of Backbone and Neck. Secondly, in conjunction with the SimOTA matching algorithm, the positive samples of the target to be tested are dynamically allocated to improve the recognition ability of different targets. Finally, considering the low pixel value of the maritime distress target from the perspective of unmanned aerial vehicles (UAV), the loss function CIoU in the original YOLOv5s is changed to EIoU. EIoU not only considers the distance and aspect ratio of the centre point, but also considers the true difference between the width and height of the prediction box and the real box, which improves the prediction accuracy of the anchor box. Experiments are conducted on a subset of the public dataset SeaDronesSee. The of the YOLOv5s‐EFOE model proposed in this paper reached 75.9%, reached 79.9%, and reached 44.5%. These indicators are superior to the original YOLOv5s model, YOLOv7 series models, and YOLOv8 series models. Compared with the original model, the YOLOv5s‐EFOE model has increased the by 5.4%, by 5.6%, and by 4.6%, improving the difficult to detect and missed detection situations. This model can be deployed on UAVs and can effectively identify maritime distress target, achieving the purpose of search and rescue.

Abstract In recent years, the task of maritime emergency rescue has increased, while the cost of time for traditional methods of search and rescue is pretty long with poor effect subject to the constraints of the complex circumstances around the sea, the effective conditions, and the support capability. This paper applies deep learning and proposes a YOLOv5s‐SwinDS algorithm for target detection in distress at sea. Firstly, the backbone network of the YOLOv5s algorithm is replaced by swin transformer, and a multi‐level feature fusion module is introduced to enhance the feature expression ability for maritime targets. Secondly, deformable convolutional networks v2 (DCNv2) is used instead of traditional convolution to improve the recognition capability for irregular targets when the neck network features are output. Finally, the CIoU loss function is replaced with SIoU to reduce the redundant box effectively while accelerating the convergence and regression of the predicted box. Experimenting on the publicly dataset SeaDronesSee, the , , and of YOLOv5s‐SwinDS model are 87.9%, 75.8%, 79.1% and 42.9%, respectively, which get higher results than the original YOLOv5s model, the YOLOv7 series of models, and the YOLOv8 series of models. The experiments verifies that the algorithm has good performance in detecting maritime distress targets.

Cooperative non-orthogonal multiple access (C-NOMA) and buffering are promising techniques to improve spectrum efficiency in the next generation of wireless networks. In this article, a buffer-aided cooperative NOMA network with direct links is studied. The throughput maximization problem is firstly formulated under the assumption of fixed power allocation and optimally solved by designing a mode selection policy. In order to further improve system throughput, the problem is extended into the case that power allocation and mode selection are jointly optimized. An optimal solution is obtained, while a sub-optimal one is also provided in order to decrease the implementation complexity. Furthermore, considering the case where the buffer has finite size and the users are delay-sensitive, a throughput-delay aware strategy is also presented. Moreover, it is shown that the proposed schemes outperform the baseline one in terms of throughput. Finally, simulations demonstrate that the sub-optimal solution achieves similar performance to the optimal one, while significantly reduces the implementation complexity.

Non-orthogonal multiple access (NOMA) has recently attracted the academic and industrial interest, due to offering higher spectral efficiency and connectivity compared to conventional orthogonal multiple access schemes. In this paper, buffer-aided relaying for a downlink NOMA system with direct links is proposed, while in order to take advantage the extra degrees of freedom, appropriate transmission modes are presented. Targeting at the throughput maximization, the corresponding optimization problem is formulated and solved and theoretical expressions are derived for the optimal mode selection policy and maximum throughput. Finally, simulation results illustrate the efficiency of the proposed scheme and its superiority compared with a previously presented baseline scheme.