Sensor Data Quality Upgrade Framework

Technology #ua17-237

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Researchers
Wolfgang Fink
Associate Professor, Electrical & Computer Engineering
Managed By
John Geikler
Asst. Director, Physical Science Licensing (520) 626-4605

Title: Sensor Data Quality Upgrade Framework (SQUF)

 

Invention: The Sensor Data Quality Upgrade Framework (SQUF) combines an artificial neural network and an optimization method to evaluate differences in data between low-quality and high-quality sensors. Once trained for a specific low-quality sensor, the SQUF can convert data from that sensor into higher quality data.

 

Background: The sensor industry is faced with rapid increases in smart sensor sales coupled with an equally rapid fall in prices. SQUF provides a platform for sensor manufacturers to provide extra value while riding these trends. One example is the wearable sensors market where lower quality sensors could be used to deliver higher quality results that approach expensive medical equipment, which is often not portable. The result would be medically relevant alerts and diagnoses on a continuous basis and at a fraction of the cost.

 

Applications:

  • Mobile healthcare
  • Internet of Things (IoT)
  • Autonomous systems
  • Environmental monitoring

 

Advantages:

  • Higher quality data
  • Less expensive sensors
  • Real-time results

 

Licensing Manager:

John Geikler

JohnG@tla.arizona.edu

(520) 626-4605