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Rapidly Varying Flow01:24

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Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
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The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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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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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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Signal-flow graphs offer a streamlined and intuitive approach to representing control systems, providing an alternative to traditional block diagrams. These graphs use branches to symbolize systems and nodes to represent signals, effectively illustrating the relationships and interactions within the system.
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Consider a control volume, such as a pipe with solid boundaries, through which fluid flows and changes direction due to the impulse exerted by the resulting force from the pipe walls. In steady flow, the mass of fluid entering the control volume at a given time, t, with velocity v1, is equal to the mass leaving after infinitesimal time dt, with velocity v2.
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Deep-Framework: A Distributed, Scalable, and Edge-Oriented Framework for Real-Time Analysis of Video Streams.

Alessandro Sassu1, Jose Francisco Saenz-Cogollo1, Maurizio Agelli1

  • 1Center for Advanced Studies, Research and Development in Sardinia (CRS4), Località Pixina Manna, Edificio 1, 09010 Pula, CA, Italy.

Sensors (Basel, Switzerland)
|July 2, 2021
PubMed
Summary

Deep-Framework is an open-source solution for real-time video analytics using deep learning on edge computing systems. It simplifies deploying complex AI models for scalable, efficient video data processing at the edge.

Keywords:
deep learningdistributed systemsedge computingreal-time video analyticssoftware framework

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Edge Computing

Background:

  • Edge computing is crucial for real-time video analytics due to increasing data demands.
  • Deep learning models for video analysis are computationally intensive, requiring efficient deployment solutions.

Purpose of the Study:

  • To introduce Deep-Framework, an open-source framework for developing edge-oriented, deep learning-based video analytics applications.
  • To address the need for scalable and flexible edge architectures for AI video processing.

Main Methods:

  • Developed a scalable multi-stream architecture using Docker for service orchestration and GPU resource allocation.
  • Provided Python interfaces for integrating popular deep learning frameworks.
  • Implemented high-level APIs using HTTP and WebRTC for data consumption.

Main Results:

  • Deep-Framework simplifies the deployment of deep learning models on edge devices.
  • The framework manages cluster configuration, service orchestration, and GPU allocation.
  • Enables real-time video data consumption on web-based clients.

Conclusions:

  • Deep-Framework offers a robust solution for edge-based deep learning video analytics.
  • It effectively abstracts complexities, making advanced AI accessible for real-time applications.
  • Facilitates the integration and deployment of AI video analytics at the network edge.