Volumetric video (VV) has emerged as a prominent medium within the realm of extended reality (XR) with advancements in computer graphics and depth capture hardware. Users can fully immersive themselves in VV with the ability to switch their viewport in six degree of freedom (DOF), including three rotational dimensions (yaw, pitch, and roll) and three translational dimensions (X, Y, and Z). Different from traditional 2-D videos that are composed of pixel matrices, VVs employ point clouds, meshes, or voxels to represent a volumetric scene, resulting in significantly larger data sizes. While previous works have successfully achieved VV streaming in video-on-demand scenarios, the live streaming of VV remains an unresolved challenge due to the limited network bandwidth and stringent latency constraints. In this article, we proposeLiveVV, a holistic live VV streaming system that integrates multiview capture, scene segmentation and reuse, adaptive transmission, and real-time rendering.LiveVVfeatures lightweight VV capture modules for easy deployment, processes static and dynamic content separately to reduce bandwidth consumption, and incorporates a VV adaptive bitrate streaming algorithm (VABR) to ensure fluent playback with high-quality experience. Real-world implementation and evaluation demonstrate thatLiveVVachieves live VV streaming at 24 FPS frame rate with less than 350-ms latency on average, meeting the requirements of real-life application.

Real-world network configurations play a critical role in network management and research tasks. While valuable, data holders often hesitate to share them due to business and privacy concerns. Existing methods are deficient in concealing the implicit information that can be inferred from configurations, such as topology and routing paths. To address this, we present ConfMask, a novel framework designed to systematically anonymize network topology and routing paths in configurations. Our approach tackles key privacy, utility, and scalability challenges, which arise from the strong dependency between different datasets and complex routing protocols. Our anonymization algorithm is scalable to large networks and effectively mitigates de-anonymization risk. Moreover, it maintains essential network properties such as reachability, waypointing and multi-path consistency, making it suitable for a wide range of downstream tasks. Compared to existing dataplane anonymization algorithm (i.e., NetHide), ConfMask reduces ~75% specification differences between the original and the anonymized networks.

Volumetric video emerges as a new attractive video paradigm in recent years since it provides an immersive and interactive 3D viewing experience with six degree-of-freedom (DoF). Unlike traditional 2D or panoramic videos, volumetric videos require dense point clouds, voxels, meshes, or huge neural models to depict volumetric scenes, which results in a prohibitively high bandwidth burden for video delivery. Users' behavior analysis, especially the viewport and gaze analysis, then plays a significant role in prioritizing the content streaming within users' viewport and degrading the remaining content to maximize user QoE with limited bandwidth. Although understanding user behavior is crucial, to the best of our best knowledge, there are no available 3D volumetric video viewing datasets containing fine-grained user interactivity features, not to mention further analysis and behavior prediction.

Wind and Photovoltaic (PV) based hybrid standalone systems for supplying electricity to remote consumers are becoming increasingly popular around the world. In general, power generation from both PV and wind is always variable due to irradiance and speed fluctuations. Furthermore, the load is unpredictably variable and changes at random. As a result, one of the main challenges in such standalone micro grids is maintaining power quality. To maintain power quality in such standalone systems, a novel controller should be developed. Furthermore, due to the unpredictable random changes in wind speed, solar irradiance, and load, a battery bank must be integrated into the standalone system via a bidirectional converter. Furthermore, the interconnection of multiple wind turbines and PV panels can create a powerful hybrid system. This paper examines a 1 MW stand-alone power generation system using numerous PV and wind systems. Maximum power points tracking devices with perturbed and observe algorithms are used to optimize the utilization of individual PV modules and wind turbines. Because of its superior priority over conventional PI controllers, a Neuro-Fuzzy integrated controller-based novel control scheme for both inverter and bidirectional converter is proposed to achieve precise response while maintaining power quality. This paper discusses extensive results obtained by establishing hardware-in-the-loop on the OPAL-RT platform to evaluate the responses of proposed controllers. The experimental results also included to provide strength to the paper.