Optimized Replication of ADC-Based Particle Counting Algorithm with Reconfigurable Multi-Variables in...

Area of Science:

• Embedded Systems
• Sensor Technology
• Digital Twins

Background:

• Digital twin applications are expanding, driving cost optimization research.
• Low-power embedded devices are explored for cost-effective digital twin implementations.
• Replicating multi-sensing device performance with single-sensing devices is a key challenge.

Purpose of the Study:

• To achieve similar particle count results from a single-sensing device as a multi-sensing device.
• To develop a simplified particle count algorithm without prior knowledge of the multi-sensing acquisition method.
• To reduce computational complexity and improve efficiency in particle counting.

Main Methods:

• Applied data filtering to suppress noise and baseline drift in raw sensor data.
• Simplified the multi-threshold particle count determination algorithm for look-up table utilization.
• Developed a novel, simplified particle count calculation algorithm.

Main Results:

• Reduced optimal multi-threshold search time by an average of 87%.
• Decreased root mean square error by 58.5% compared to existing methods.
• Confirmed similar particle count distribution shapes between single and multi-sensing devices.

Conclusions:

• The simplified algorithm effectively replicates multi-sensing particle count data using a single device.
• The method offers significant improvements in speed and accuracy for embedded digital twin applications.
• This approach enables cost-effective, high-performance particle sensing solutions.