With growing awareness of user privacy and the widespread adoption of encryption tools such as SSL/TLS, VPN, and Tor, traditional traffic classification methods based on port analysis and deep packet inspection (DPI) have become increasingly ineffective. While conventional machine learning approaches can classify traffic using flow statistical features, they remain limited by their dependence on manual feature engineering and poor generalization capability. Deep learning techniques overcome these constraints by automatically extracting features and enabling early traffic classification. However, existing deep learning solutions still exhibit notable shortcomings: most rely exclusively on either flow-level or packet-level features, neglecting the complementary information present in the other dimension; furthermore, packet-level methods often mix header and payload data, risking violation of user privacy. To address these issues, this paper proposes a novel encrypted traffic classification method that effectively integrates both flow-level and packet-level features without relying on payload content. By introducing new techniques for feature extraction and fusion at both levels, our approach captures complex nonlinear relationships among features. Extensive evaluation on four public datasets demonstrates that the proposed method achieves high accuracy and significantly outperforms single-modal models.

Telemedicine for 3D images on mobile devices presents promising development opportunities. Being constrained by computing power and storage capacity on mobile devices, the processing performance of 3D medical images is insufficient for more demanding tasks. Using virtual mobile infrastructure technology to utilize cloud resources is a common solution. But it encounters the challenge of poor performance in data transmission, image rendering and image coding. This paper presents a GPU and VPU enabled Open Virtual Mobile Infrastructure (OpenVMI) for 3D image rendering to solve the challenge. It makes two improvements. First, a bespoke GPU driver is developed in the Android Docker, optimizing the transmission workflow for data transmission and image rendering. Second, a Video Process Unit (VPU) is added to the hardware layer to code rendered results in H.264 format, replacing CPU coding which consumes a large amount of CPU resources. By adopting the OpenVMI, the telemedicine training system proposed in this paper presents an easy-to-set up, cheap and low latency solution that is particularly helpful for telemedicine training in remote and underdeveloped areas. Performance experiments suggest that the OpenVMI delivers better performance than existing state-of-the-art systems, even in mobile devices with weaker hardware capabilities. Concurrency experiment suggests that a single host server can support up to 24 concurrent training sessions, which makes the OpenVMI very helpful for telemedicine training that demands high concurrency. The OpenVMI-based solution proposed in this paper is not restricted to the use of telemedicine training, but also suitable for other application areas such as Virtual Reality and Augmented Reality in mobile environments.

Aiming for overcoming the constraints in graphic rendering of mobile devices used in telemedicine training for 3D images, this paper presents a GPU-enabled mobile telemedicine training system for graphic rendering. Two improvements are made to seamlessly display interactive 3D images of human organs, bones and blood vessels with realtime rendering. Firstly, instead of multiple stages of instruction translation between OpenGL ES and OpenGL, a bespoke GPU driver is added in Virtual Android to invoke GPU resources directly. Secondly, a Video Process Unit (VPU) is added to the hardware layer replacing CPU to code rendered results in H.264 format, reducing CPU load significantly. Test results suggest that the system can deliver consistent performance even in mobile devices of weak capability and a single server can support up to 24 concurrent virtual Android operating systems, each of which connects to 5 clients. The framework proposed by this paper is not only suitable for telemedicine training, but also for other application areas such as Virtual Reality and Augmented Reality in mobile environment.

Voxel-based structures in 3D detection have achieved rapid advancement due to their superior capability for feature extraction. However, the accuracy is usually low because the point cloud is divided into a grid. In order to overcome the above problems and improve detection accuracy, we propose a flexible two-stage 3D object detection architecture, which adopts two branches to refine generated proposals, aggregating voxel features and raw point features simultaneously. We also design a new gating mechanism to achieve fusion features from different levels. In addition, we propose a novel feature aggregation module to reduce the semantic gap between the features of the two types. First, a transformer based on raw points is employed as an encoder to aggregate the contextual information. Then, the point-based channel-wise self-attention mechanism serves as a decoder to aggregate the global features. Experiment results on the KITTI 3D dataset and Waymo Open datest demonstrate that our approach outperforms the state-of-the-art methods and exhibits excellent scalability.