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Related Concept Videos

Aliasing01:18

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Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
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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...
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Accurate Peak Detection for Optical Sensing with Reduced Sampling Rate and Calculation Complexity.

Jiun-Yu Sung1, Jin-Kai Chen1, Shien-Kuei Liaw1

  • 1Department of Electronic and Computer Engineering and Graduate Institute of Electro-Optical Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan.

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Summary

A new three-point peak detection method for fiber Bragg grating (FBG) sensing systems offers improved accuracy over traditional methods. This efficient approach reduces processing complexity, making it suitable for large-scale Internet of Things (IoT) and artificial intelligence (AI) applications.

Keywords:
fiber Bragg grating (FBG)filteringfittingoptical sensingpeak detectionregression

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Area of Science:

  • Optoelectronics and Photonics
  • Optical Sensing Technologies
  • Signal Processing for Sensors

Background:

  • Fiber Bragg gratings (FBGs) are crucial components in optical sensing due to their sensitivity and integration potential.
  • Wavelength interrogation is a common technique for FBG sensing, where peak detection accuracy is critical for system performance.
  • Existing methods like Direct Maximum Value Readout (DMVR) suffer from noise and resolution limitations, while advanced methods increase processing complexity.

Purpose of the Study:

  • To develop an efficient and low-complexity peak detection algorithm for FBG sensing systems.
  • To address the need for reduced processing demands in massive sensor deployments for IoT and AI.
  • To improve the accuracy of wavelength peak detection in FBG-based sensing.

Main Methods:

  • Proposed and studied an efficient three-point peak detection estimator for FBG wavelength interrogation.
  • Compared the performance of the proposed method against Direct Maximum Value Readout (DMVR) and curve fitting methods.
  • Conducted a proof-of-concept experiment for temperature sensing using the developed method.

Main Results:

  • The proposed three-point peak detection method achieves performance comparable to complex curve fitting schemes.
  • Demonstrated a significant 34% accuracy improvement compared to the conventional DMVR method.
  • The new method offers reduced processing complexity, suitable for large-scale sensor networks.

Conclusions:

  • The three-point peak detection estimator provides an effective balance between accuracy and processing complexity for FBG sensing.
  • This method is a viable solution for enhancing the performance of optical sensing systems in IoT and AI contexts.
  • The proposed technique represents a practical advancement in FBG wavelength interrogation signal processing.