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Related Concept Videos

IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

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IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
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Neural Network-Based LoRa Received Signal Strength Indicator Fingerprint Identification for Indoor Localization of

Chandan Barai1,2, Meem Sarkar3, Ushnish Sarkar1,2

  • 1Homi Bhabha National Institute, 2nd Floor, BARC Training School Complex, Anushaktinagar, Mumbai 400094, Maharashtra, India.

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Summary
This summary is machine-generated.

This study introduces a robust indoor localization system for Industry 4.0 using Long-Range (LoRa) signals and a Multi-layer Perceptron (MLP) neural network. The framework achieves high accuracy for mobile robots in complex industrial environments.

Keywords:
LoRaMLPRSSI fingerprintSSIMentropy of fingerprint databasetime series RSSI preprocessing

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Area of Science:

  • Robotics
  • Wireless Communication
  • Artificial Intelligence

Background:

  • Indoor localization is crucial for Industry 4.0 automation and smart environments.
  • Existing methods face challenges in signal-dense, congested indoor settings.
  • Robust and low-power positioning systems are needed for mobile robots.

Purpose of the Study:

  • To present an indoor self-localization framework for mobile robots.
  • To evaluate a Received Signal Strength Indicator (RSSI) fingerprinting technique using Long-Range (LoRa) technology.
  • To develop a Multi-layer Perceptron (MLP) neural network for precise positional prediction.

Main Methods:

  • Utilized LoRa technology for RSSI fingerprinting in challenging indoor environments.
  • Employed Structural Similarity Index Measure (SSIM) to optimize communication parameters (spreading factor).
  • Calculated RSSI database entropy to ensure fingerprint stability and used an MLP for location classification in a grid-based setup.

Main Results:

  • Achieved 91.8% validation accuracy with the MLP during training.
  • Demonstrated high precision in classifying grid regions within a signal-dense environment.
  • The LoRa-MLP integration proved effective for high-accuracy localization of slow-moving robots.

Conclusions:

  • The proposed LoRa-MLP framework offers a robust, low-power solution for indoor positioning systems (IPSs).
  • This method provides highly accurate, high-level localization data suitable for applications like radiation mapping.
  • The integration is well-suited for modern industrial infrastructure requiring precise mobile robot navigation.