Based on advanced ZigBee and GPRS technology, which collects and transmits data, risk evaluation system of gas pipelines network realize the real time risk analysis and management. This paper introduces the methods of wireless data transmission, gives the architecture of software platform, discusses a processing method for real-time data, and analyzes risk assessment and evaluation model. This has extremely application prospect.
Aiming at the problems of poor transmission effect and serious data loss in digital engraving, this paper puts forward a digital modeling model from the perspective of spatial expression. First, in-depth analysis of the original digital art design can not solve the problem of digital carving accuracy, and analysis of the reasons for the poor calculation accuracy of digital carving. Using wireless network technology and WIFI technology to obtain digital information of digital engraving, through the Internet design scheme statistics, according to the digital features to judge the form and result of engraving, remove irrelevant spatial information. Then, according to the Internet data monitoring, the change rate and engraving method of the engraving data are calculated, and compared with the actual engraving requirements, the parameters and indicators of digital art design are adjusted. MATLAB simulation test analysis shows that in the case of wireless communication and Internet monitoring, the digital engraving model of spatial expression technique can improve the accuracy of artistic design, and the accuracy rate is greater than the actual design requirements. According to the design requirements of different wireless networks and network communications, the time and compliance rate of digital engraving can meet the needs of artistic design.
While existing research on immersive technologies such as virtual, augmented, and mixed reality has been shifting from improving the quality of experience (QoE) of a single user to multi-user scenarios, which are naturally required by the burgeoning Metaverse, there is little work on building the underlying infrastructure to support multi-user immersive applications, especially for geo-distributed users. In this position paper, we propose a research infrastructure, dubbed CoMIC, to fill this critical gap, by offering a collaborative, visual-first, and hologram-based computing space and a QoE-driven, multi-site, and immersive communication framework. With its rudimentary prototype, we demonstrate the effectiveness of CoMIC through a case study of multi-user volumetric video streaming over mmWave. We conclude this paper with a discussion of numerous unique opportunities that can be enabled by CoMIC.
Volumetric video is a medium that captures the three-dimensional (3D) shape and movement of real-life objects or people. However, pre-recorded volumetric video is limited in terms of interactivity. We introduce a novel authoring system called Volumivive, which enables the creation of interactive experiences using volumetric video, enhancing the dynamic capabilities and interactivity of the medium. We provide four interaction methods that allow users to manipulate and engage with digital objects within the volumetric video. These interactive experiences can be used in both augmented reality (AR) and virtual reality (VR) settings, providing users with a more immersive and interactive experience.
By combining various emerging technologies, mobile extended reality (XR) blends the real world with virtual content to create a spectrum of immersive experiences. Although Web-based XR can offer attractive features such as better accessibility, cross-platform compatibility, and instant updates, its performance may not be on par with its standalone counterpart. As a low-level bytecode, WebAssembly has the potential to drastically accelerate Web-based XR by enabling near-native execution speed. However, little has been known about how well Web-based XR performs with WebAssembly acceleration. To bridge this crucial gap, we conduct a first-of-its-kind systematic and empirical study to analyze the performance of Web-based XR expedited by WebAssembly on four diverse platforms with five different browsers. Our measurement results reveal that although WebAssemlby can accelerate different XR tasks in various contexts, there remains a substantial performance disparity between Web-based and standalone XR. We hope our findings can foster the realization of an immersive Web that is accessible to a wider audience with various emerging technologies.
Volumetric videos allow viewers to exercise 6-DoF (degrees of freedom) movement when watching them. Due to their true 3D nature, streaming volumetric videos is highly bandwidth demanding. In this work, we present to our knowledge a first volumetric video streaming system that leverages deep super resolution (SR) to boost the video quality on commodity mobile devices. We propose a series of judicious optimizations to make SR efficient on mobile devices.
Although existing work has demonstrated the feasibility of streaming volumetric content to a single user, there exist many appealing applications (e.g., classroom education and collaborative design) that involve multiple users who watch the same volumetric content simultaneously. In this paper, we first perform a scaling experiment to demonstrate the challenges of streaming high-quality volumetric videos to multiple users and reveal the viewport-similarity opportunity that we can leverage to effectively optimize the network resource utilization using multicast over mmWave. We then develop a holistic research agenda for improving the performance and quality of experience for multi-user volumetric video streaming on commodity devices. Our proposed research includes joint viewport prediction and blockage mitigation for multiple users, multicast grouping based on viewport similarity, customized mmWave beam design for efficient multicast, and mmWave-aware multi-user video rate adaptation. Finally, we discuss the open challenges of building a practical system with the proposed research roadmap.
While Positive-Unlabeled (PU) learning is vital in many real-world scenarios, its application to graph data still remains under-explored. We unveil that a critical challenge for PU learning on graph lies on the edge heterophily, which directly violates the irreducibility assumption for Class-Prior Estimation (class prior is essential for building PU learning algorithms) and degenerates the latent label inference on unlabeled nodes during classifier training. In response to this challenge, we introduce a new method, named Graph PU Learning with Label Propagation Loss (GPL). Specifically, GPL considers learning from PU nodes along with an intermediate heterophily reduction, which helps mitigate the negative impact of the heterophilic structure. We formulate this procedure as a bilevel optimization that reduces heterophily in the inner loop and efficiently learns a classifier in the outer loop. Extensive experiments across a variety of datasets have shown that GPL significantly outperforms baseline methods, confirming its effectiveness and superiority.
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