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Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
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Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
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A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
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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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Related Experiment Video

Updated: Nov 10, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Latency-Optimal Computational Offloading Strategy for Sensitive Tasks in Smart Homes.

Yanyan Wang1, Lin Wang1, Ruijuan Zheng1

  • 1School of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China.

Sensors (Basel, Switzerland)
|April 3, 2021
PubMed
Summary
This summary is machine-generated.

We introduce a new computational offloading strategy for edge cloud computing (ECC) to minimize delay in smart homes. This back-pressure algorithm (BMDCO) enhances system stability and reduces task computation delays.

Keywords:
back-pressure algorithmcomputational offloadingedge cloud computinglyapunov driftsmart home

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Smart Home Technology

Background:

  • Smart homes generate substantial computational demands.
  • Edge cloud computing (ECC) offers a solution for managing these demands.
  • Efficient computational offloading is crucial for smart home performance.

Purpose of the Study:

  • To propose a novel computational offloading strategy for ECC systems.
  • To minimize task computation delay and ensure system stability.
  • To enhance the performance of smart home devices through optimized offloading.

Main Methods:

  • Developed a computational offloading strategy for minimizing delay (BMDCO) based on the back-pressure algorithm.
  • Constructed a system model with local smart device and edge processor task queues.
  • Formulated an offloading strategy using Lyapunov drift optimization to minimize queue length.

Main Results:

  • The BMDCO algorithm demonstrates theoretical stability with a deduced upper bound for all system queues.
  • Simulation results confirm the stability and superior performance of the BMDCO algorithm.
  • The proposed algorithm effectively reduces computation delay compared to existing methods.

Conclusions:

  • The BMDCO algorithm provides a stable and efficient solution for computational offloading in smart homes.
  • This strategy significantly improves smart home device performance by minimizing delay.
  • The findings highlight the effectiveness of back-pressure algorithms in edge cloud computing environments.