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Related Concept Videos

Reducing Line Loss01:18

Reducing Line Loss

193
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
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Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

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Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
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Downsampling01:20

Downsampling

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When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
255
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

142
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
142
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

754
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?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of...
754
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

101
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Updated: Sep 12, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Optimizing FCN for devices with limited resources using quantization and sparsity enhancement.

Muhammad Faizan-Khan1, Nisar Ali2, Raja Hashim Ali3

  • 1Departament d'Enginyeria Electrònica, Elèctrica i Automàtica, Universitat Rovira i Virgili, Tarragona, Spain. muhammadfaizan.khan@urv.cat.

Scientific Reports
|August 4, 2025
PubMed
Summary

This study optimizes fully convolutional networks (FCNs) for real-time use on limited devices. Full-layer quantization and retraining significantly boost sparsity up to 40% while maintaining 89.3% pixel accuracy.

Keywords:
Deep learningFixed-point quantizationFully convolutional networkSensitivity analysis

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Resource-limited devices pose challenges for deploying complex deep learning models like fully convolutional networks (FCNs).
  • Prior research focused on quantizing architectures like VGG-16, with limited exploration of comprehensive layer-wise quantization in FCN-8.

Purpose of the Study:

  • To optimize FCNs for real-time deployment on resource-constrained devices.
  • To investigate comprehensive layer-wise quantization techniques for the FCN-8 architecture.

Main Methods:

  • Proposed an innovative approach using full-layer quantization with an error minimization algorithm.
  • Employed sensitivity analysis to optimize fixed-point representation of network weights.
  • Utilized retraining to maintain network performance post-quantization.

Main Results:

  • Achieved significant network sparsity, up to 40%, under extreme quantization conditions.
  • Preserved network performance, yielding 89.3% pixel accuracy.
  • Demonstrated effectiveness in both image classification and semantic segmentation tasks.

Conclusions:

  • Full-layer quantization and retraining are effective for reducing network complexity.
  • This approach successfully maintains high accuracy in FCNs for real-time applications on resource-limited devices.