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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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Related Experiment Video

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Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization
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Hybrid Wireless Fingerprint Indoor Localization Method Based on a Convolutional Neural Network.

Zhenyu Liu1, Bin Dai2, Xiang Wan3

  • 1School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China. zhenyuliu@gdut.edu.cn.

Sensors (Basel, Switzerland)
|October 27, 2019
PubMed
Summary

This study introduces a Hybrid Wireless fingerprint (HW-fingerprint) and Convolutional Neural Network (CNN) to improve indoor location accuracy. The new method enhances Received Signal Strength Indicator (RSSI) fingerprinting, overcoming environmental changes for better localization services.

Keywords:
RSSIWiFiconvolutional neural networkfingerprintindoor location

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Geographic Information Science

Background:

  • Indoor location services rely heavily on Received Signal Strength Indicator (RSSI) fingerprint quality.
  • Environmental changes degrade indoor location accuracy, posing a significant challenge.

Purpose of the Study:

  • To propose a novel Hybrid Wireless fingerprint (HW-fingerprint) and Convolutional Neural Network (CNN) based localization method.
  • To enhance the robustness and accuracy of indoor localization against environmental variations.

Main Methods:

  • Constructed a Ratio fingerprint using RSSI ratios from key access points (APs).
  • Developed a Hybrid Wireless fingerprint (HW-fingerprint) by combining Ratio and RSSI fingerprints.
  • Employed a Convolutional Neural Network (CNN) architecture to extract features from HW-fingerprints for localization.

Main Results:

  • HW-fingerprint improved average daily location accuracy by 3.39% (KNN), 8.03% (SVM), and 9.03% (CNN).
  • The CNN method demonstrated superior performance, outperforming SVM by 4.19% and KNN by 16.37% in average daily location accuracy.
  • Tested HW-fingerprint in a real indoor scene over 15 days, confirming its effectiveness.

Conclusions:

  • The proposed HW-fingerprint combined with CNN significantly enhances indoor localization accuracy and robustness.
  • This approach offers a promising solution for reliable indoor positioning systems facing dynamic environmental conditions.