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A linear circuit is characterized by its output having a direct proportionality to its input, adhering to the linearity property, which encompasses the principles of homogeneity (scaling) and additivity. Homogeneity dictates that when the input, also referred to as the excitation, is multiplied by a constant factor, the output, known as the response, is correspondingly scaled by the same constant factor. For instance, if the current is multiplied by a constant 'k,' the voltage likewise...
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Short-Dipole Sensor Response Linearization Through Physics-Informed Neural Networks.

Alessandro Fasse1, Romain Meyer2, Esra Neufeld1

  • 1Foundation for Research on Information Technologies in Society (IT'IS), Zurich, Switzerland.

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|May 22, 2025
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Summary
This summary is machine-generated.

This study introduces a physics-informed neural network (PINN) for rapid, accurate linearization of short-dipole diode sensors used in electromagnetic field measurements. This AI-driven approach overcomes limitations posed by complex 5G signals, enabling on-the-fly parameter computation.

Keywords:
artificial intelligence (AI)dosimetryelectromagnetic field probesphysics‐informed neural networksensor calibrationspecific absorption rate (SAR)

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Area of Science:

  • Electromagnetic field metrology
  • Sensor technology
  • Artificial intelligence in scientific instrumentation

Background:

  • Short-dipole diode sensors are crucial for measuring high-frequency electromagnetic fields, especially for near-field applications and exposure limit compliance.
  • Traditional sensor linearization methods are time-consuming and computationally expensive, particularly with the advent of diverse 5G communication signals.
  • Existing methods require signal-specific calibration through measurement or simulation, becoming impractical for the complexity of modern wireless technologies.

Purpose of the Study:

  • To develop an efficient and accurate method for linearizing short-dipole diode sensor responses across a wide dynamic range.
  • To address the challenges posed by complex modulation schemes in 5G and future communication systems.
  • To enable on-the-fly computation of linearization parameters, reducing computational cost and storage requirements.

Main Methods:

  • Developed an accelerated sensor model simulation technique with enhanced accuracy.
  • Created a comprehensive dataset of probe parameters and signal characteristic configurations.
  • Trained a physics-informed neural network (PINN) using readily accessible signal characteristics for on-the-fly linearization parameter determination.

Main Results:

  • The AI-based approach achieves linearization errors below 0.4 dB for peak specific absorption rate (SAR) values.
  • Determination of linearization parameters is accelerated by a factor of approximately 34,000.
  • Storage requirements are reduced by approximately 350,000 times, enabling on-site computation.

Conclusions:

  • The developed PINN offers a significant advancement in sensor linearization for high-frequency electromagnetic field measurements.
  • This AI-driven method effectively handles complex signal modulations, overcoming limitations of traditional approaches.
  • The approach ensures physical model accuracy while drastically improving computational efficiency and accessibility.