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Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
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Updated: Jan 26, 2026

Fabrication, Operation and Flow Visualization in Surface-acoustic-wave-driven Acoustic-counterflow Microfluidics
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Acoustic Sensor Data Flow for Cultural Heritage Monitoring and Safeguarding.

Panagiotis Kasnesis1, Nicolaos-Alexandros Tatlas2, Stelios A Mitilineos3

  • 1Department of Electrical and Electronic Engineering, University of West Attica, 12244 Athens, Greece. pkasnesis@uniwa.gr.

Sensors (Basel, Switzerland)
|April 10, 2019
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Summary

This study introduces Wireless Acoustic Sensors Networks and deep learning to protect cultural heritage sites. The system effectively identifies audio sources, aiding preservation efforts and providing valuable insights for cultural experts.

Keywords:
acoustic sensorscultural heritagedeep learningontologiessemantic rulessensor linked datasensor signal processingstate of preservation monitoring

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Area of Science:

  • Heritage Preservation
  • Sensor Networks
  • Artificial Intelligence

Background:

  • Cultural heritage sites are vital for national identity and economy, necessitating robust preservation strategies.
  • Advanced monitoring systems using sensors are crucial for collecting data to protect these valuable assets.
  • Existing methods may lack the specificity needed for nuanced site safeguarding.

Purpose of the Study:

  • To present a novel framework utilizing acoustic sensors for safeguarding cultural heritage sites.
  • To develop and deploy Wireless Acoustic Sensors Networks (WASN) for real-time audio data collection.
  • To leverage deep learning for efficient audio source identification and data integration.

Main Methods:

  • Deployment of Wireless Acoustic Sensors Networks (WASN) to capture audio signals at cultural heritage sites.
  • Transfer of audio data to a modular cloud platform for processing.
  • Application of a deep learning algorithm (achieving an f1-score of 0.838) for identifying audio sources relevant to site materials.
  • Utilizing the STORM Audio Signal ontology and semantic rules for data fusion with spatiotemporal information.

Main Results:

  • Successful identification of audio sources of interest with high accuracy (f1-score: 0.838).
  • Development of a data flow framework integrating acoustic sensing, deep learning, and ontologies.
  • Generation of valuable, publicly available insights for cultural experts in Linked Open Data format.

Conclusions:

  • Acoustic sensor networks combined with deep learning offer an effective solution for cultural heritage site preservation.
  • The developed framework provides actionable intelligence for cultural experts by analyzing audio signatures.
  • This approach enhances the protection of cultural heritage by integrating diverse data streams semantically.