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Related Concept Videos

Echo01:06

Echo

505
The human ear cannot distinguish between two sources of sound if they happen to reach within a specific time interval, typically 0.1 seconds apart. More than this, and they are perceived as separate sources.
Imagine the sound is reflected back to the ears. Assuming that the source is very close to the human, the difference between hearing the two sounds—the emitted sound and the reflected sound—may be more than the minimum time for perceiving distinct sounds. If this is the case,...
505

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Related Experiment Video

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Ultrasonic Through-Metal Communication Based on Deep-Learning-Assisted Echo Cancellation.

Jinya Zhang1, Min Jiang2, Jingyi Zhang2

  • 1Engineering Training Center, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.

Sensors (Basel, Switzerland)
|April 13, 2024
PubMed
Summary

A novel deep learning algorithm, dual-path recurrent neural network (DPRNN), effectively cancels echoes in ultrasonic through-metal communication. This advanced echo cancellation significantly improves signal quality for reliable data transmission.

Keywords:
deep-learningecho cancellationultrasonic communication

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Area of Science:

  • Acoustics
  • Signal Processing
  • Machine Learning

Background:

  • Ultrasound offers efficient wireless transmission through metal barriers, bypassing Faraday shielding limitations.
  • Echoes in ultrasonic channels pose a significant challenge to high-quality communication, typically addressed by channel equalizers or pre-distorting filters.

Purpose of the Study:

  • To investigate the efficacy of a deep learning algorithm, the dual-path recurrent neural network (DPRNN), for echo cancellation in ultrasonic through-metal communication systems.

Main Methods:

  • A hardware-software system was built using ultrasonic transducers and an FPGA module.
  • The DPRNN algorithm was applied to signals with varying signal-to-noise ratios (SNR) at a 2 Mbps transmission rate.
  • Performance was evaluated against traditional methods like LMS, RLS, and PNLMS.

Main Results:

  • The DPRNN approach achieved over 20 dB SNR improvement across all tested signal-to-noise ratios.
  • Successful image transmission through a 50 mm aluminum plate yielded a 24.8 dB PSNR and 95% SSIM.
  • DPRNN demonstrated superior efficiency compared to LMS, RLS, and PNLMS echo cancellation methods.

Conclusions:

  • The DPRNN algorithm is a powerful tool for echo cancellation in ultrasonic through-metal transmission.
  • This deep learning approach significantly enhances the performance and reliability of ultrasonic communication systems penetrating metal barriers.