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Energy Optimization in Ultrasound Tomography Through Sensor Reduction Supported by Machine Learning Algorithms

Author

Listed:
  • Bartłomiej Baran

    (Research & Development Centre Netrix S.A., 20-704 Lublin, Poland)

  • Tomasz Rymarczyk

    (Research & Development Centre Netrix S.A., 20-704 Lublin, Poland
    Institute of Public Administration and Business, WSEI University, 20-209 Lublin, Poland)

  • Dariusz Majerek

    (Faculty of Mathematics and Information Technology, Lublin University of Technology, 20-618 Lublin, Poland)

  • Piotr Szyszka

    (Faculty of Mathematics and Information Technology, Lublin University of Technology, 20-618 Lublin, Poland)

  • Dariusz Wójcik

    (Research & Development Centre Netrix S.A., 20-704 Lublin, Poland
    Institute of Public Administration and Business, WSEI University, 20-209 Lublin, Poland)

  • Tomasz Cieplak

    (Faculty of Management, Lublin University of Technology, 20-618 Lublin, Poland)

  • Marcin Gąsior

    (Faculty of Management, Lublin University of Technology, 20-618 Lublin, Poland)

  • Marcin Marczuk

    (Institute of Public Administration and Business, WSEI University, 20-209 Lublin, Poland)

  • Edmund Wąsik

    (Institute of Public Administration and Business, WSEI University, 20-209 Lublin, Poland)

  • Konrad Gauda

    (Institute of Public Administration and Business, WSEI University, 20-209 Lublin, Poland)

Abstract

This paper focuses on reducing energy consumption in ultrasound tomography by utilizing machine learning techniques. The core idea is to investigate the feasibility of minimizing the number of measurement sensors without sacrificing prediction accuracy. This article evaluates the quality of reconstructions derived from data collected through two or three measurement channels. In subsequent steps, machine learning models are developed to predict the number, location, and size of the objects. A reliable object detection method is introduced, requiring less information than traditional signal analysis from multiple channels. Various machine learning models were tested and compared to validate the approach, with most demonstrating high accuracy or R 2 scores in their respective tasks. By reducing the number of sensors, the goal is to lower energy usage while maintaining high precision in localization. This study contributes to the ongoing research on energy efficiency in sensing and localization, especially in environments where resource optimization is crucial, such as remote or resource-limited settings.

Suggested Citation

  • Bartłomiej Baran & Tomasz Rymarczyk & Dariusz Majerek & Piotr Szyszka & Dariusz Wójcik & Tomasz Cieplak & Marcin Gąsior & Marcin Marczuk & Edmund Wąsik & Konrad Gauda, 2024. "Energy Optimization in Ultrasound Tomography Through Sensor Reduction Supported by Machine Learning Algorithms," Energies, MDPI, vol. 17(21), pages 1-15, October.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:21:p:5406-:d:1510083
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    References listed on IDEAS

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    1. Dariusz Wójcik & Tomasz Rymarczyk & Bartosz Przysucha & Michał Gołąbek & Dariusz Majerek & Tomasz Warowny & Manuchehr Soleimani, 2023. "Energy Reduction with Super-Resolution Convolutional Neural Network for Ultrasound Tomography," Energies, MDPI, vol. 16(3), pages 1-14, January.
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