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Energy and Power Signals01:17

Energy and Power Signals

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In an electrical system with a resistor, voltage and current signals facilitate the measurement of power and energy across the resistor. For a continuous-time signal, the total energy over a time interval is defined as the integral of the square of the signal's magnitude over that interval. Mathematically, this is expressed as:
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Maximum Power Transfer01:16

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Numerous practical applications within engineering disciplines, such as telecommunications, necessitate optimizing power delivery to a connected load. This pursuit, however, entails inherent internal losses, which can either equal or exceed the power supplied to the load. The Thevenin equivalent circuit is helpful in finding the maximum power a linear circuit can deliver to a load. It is assumed in this context that the load resistance can be adjusted.
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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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Distributed Loads: Problem Solving01:21

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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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Related Experiment Video

Updated: Aug 30, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

645

Dimension learning based chimp optimizer for energy efficient wireless sensor networks.

Preeti1, Ranjit Kaur2, Damanpreet Singh3

  • 1Department of Electronics and Communication Engineering, Punjabi University, Patiala, India. preeti_ece@pbi.ac.in.

Scientific Reports
|September 2, 2022
PubMed
Summary

An Improved Chimp Optimizer Algorithm (IChoA) enhances energy efficiency in wireless sensor networks (WSNs). This new method balances exploration and exploitation, extending network lifetime for IoT applications.

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Optimization Algorithms

Background:

  • Wireless Sensor Networks (WSNs) are crucial for smart infrastructure, including IoT and 5G.
  • WSNs face significant challenges in energy management and network lifetime extension due to their distributed nature.
  • Existing optimization algorithms struggle with the complexity of energy constraints in WSNs.

Purpose of the Study:

  • To develop an improved optimization algorithm for energy-constrained WSNs.
  • To enhance the performance of the Chimp Optimizer Algorithm (ChOA) for WSN applications.
  • To address the critical need for robust algorithms in managing WSN energy.

Main Methods:

  • An Improved Chimp Optimizer Algorithm (IChoA) was developed by integrating ChOA with a Dimension Learning-based Hunting (DLH) search technique.
  • The DLH strategy was employed to maintain population diversity and balance exploration-exploitation.
  • IChoA was evaluated on 29-CEC-2017 test suites and specific energy-constrained WSN problems.

Main Results:

  • The proposed IChoA demonstrated superior performance in solving complex optimization problems.
  • IChoA effectively addressed energy constraint issues in WSNs.
  • Experimental results showed IChoA to be more effective than recent comparative methods.

Conclusions:

  • The Improved Chimp Optimizer Algorithm (IChoA) is a robust and effective method for energy management in WSNs.
  • IChoA offers a promising solution for extending the network lifetime of wireless sensor networks.
  • The integration of DLH significantly enhances the optimization capabilities for WSN challenges.