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To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
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Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
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Fumos: Neural Compression and Progressive Refinement for Continuous Point Cloud Video Streaming.

Zhicheng Liang, Junhua Liu, Mallesham Dasari

    IEEE Transactions on Visualization and Computer Graphics
    |March 4, 2024
    PubMed
    Summary

    Fumos enhances point cloud video streaming by using neural compression and progressive refinement to avoid stalls and maintain quality. This system significantly improves bandwidth efficiency and user experience in VR/AR applications.

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    Area of Science:

    • Computer Science
    • Multimedia Systems
    • Virtual Reality

    Background:

    • Point cloud video (PCV) streaming for immersive 6-DoF VR/AR is bandwidth-intensive.
    • Current FoV-adaptive streaming struggles with accurate prediction and causes stalls, degrading user quality of experience (QoE).
    • Existing systems face challenges in bandwidth efficiency and maintaining high fidelity for PCV.

    Purpose of the Study:

    • To introduce Fumos, a novel system for PCV streaming that avoids playback stalls.
    • To maintain high perceptual quality and compression rates in PCV streaming.
    • To address the research gap in inter-frame redundancy utilization and progressive streaming mechanisms.

    Main Methods:

    • Developed a Neural compression framework with inter-frame coding (N-PCC) for bandwidth efficiency and fidelity.
    • Implemented a progressive refinement streaming framework for continuous playback and quality upgrades.
    • Employed system-level adaptation with Lyapunov optimization for joint long-term user QoE optimization.

    Main Results:

    • Fumos significantly outperforms Draco, achieving over 260x decoding rate acceleration.
    • The N-PCC framework shows remarkable BD-Rate gains: 91.7% against G-PCC and 51.7% against V-PCC.
    • Fumos successfully preserves interactive experience by avoiding stalls while maintaining high quality and compression.

    Conclusions:

    • Fumos offers a novel solution to the bandwidth and QoE challenges in PCV streaming.
    • The proposed N-PCC and progressive streaming frameworks are effective for efficient and high-quality PCV delivery.
    • Fumos enables a superior user experience in 6-DoF VR/AR applications through optimized streaming.