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Upsampling01:22

Upsampling

216
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
216
Fast Fourier Transform01:10

Fast Fourier Transform

287
The Fast Fourier Transform (FFT) is a computational algorithm designed to compute the Discrete Fourier Transform (DFT) efficiently. By breaking down the calculations into smaller, manageable sections, the FFT significantly reduces the computational complexity involved. Direct computation of an N-point DFT requires N2 complex multiplications, whereas the FFT algorithm needs only (N/2)log⁡2N multiplications, offering a much faster performance.
The computational efficiency of the FFT becomes...
287
Scaling01:26

Scaling

235
In designing and analyzing filters, resonant circuits, or circuit analysis at large, working with standard element values like 1 ohm, 1 henry, or 1 farad can be convenient before scaling these values to more realistic figures. This approach is widely utilized by not employing realistic element values in numerous examples and problems; it simplifies mastering circuit analysis through convenient component values. The complexity of calculations is thereby reduced, with the understanding that...
235

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Updated: Jun 14, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

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Fully Scalable Fuzzy Neural Network for Data Processing.

Łukasz Apiecionek1

  • 1Faculty of Computer Science, Kazimierz Wielki University in Bydgoszcz, Jana Karola Chodkiewicza 30, 85-064 Bydgoszcz, Poland.

Sensors (Basel, Switzerland)
|August 29, 2024
PubMed
Summary

This research introduces a new artificial neural network (ANN) using Ordered Fuzzy Numbers (OFNs) for efficient Industry 4.0 data processing. This AI solution enables edge computing on small devices, reducing cloud reliance and facilitating IoT applications.

Keywords:
Industry 4.0artificial neural networkdata processingfuzzy logic

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Conventional deep neural networks require significant computational resources.
  • Industry 4.0 applications increasingly demand efficient AI solutions for data processing.
  • Edge computing on small devices is hindered by the computational demands of traditional AI.

Purpose of the Study:

  • To develop an artificial neural network (ANN) with reduced computational power requirements.
  • To enable the deployment of AI at the network edge for Industry 4.0 and IoT applications.
  • To present a novel approach using Ordered Fuzzy Numbers (OFNs) for efficient AI.

Main Methods:

  • Development of a novel artificial neural network architecture.
  • Application of Ordered Fuzzy Numbers (OFNs) in the ANN design.
  • Testing and validation on a real-world system for anomaly detection.

Main Results:

  • The proposed ANN demonstrates significantly lower computational power demands compared to conventional deep neural networks.
  • Successful anomaly detection and prediction in a monitored real system.
  • The OFN-based ANN is suitable for edge deployment on resource-constrained devices.

Conclusions:

  • The developed ANN offers a computationally efficient solution for Industry 4.0 data processing.
  • This technology facilitates the implementation of AI in small-scale solutions, including the Internet of Things.
  • The approach enables edge AI, reducing the need for cloud-based data analysis.