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High-precision Electromagnetic Flowmeter with Empty Pipe Detection via Complex Programmable Logic Device-based Waveform Recognition

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A Generalized Transformer-Based Pulse Detection Algorithm.

Dario Dematties^1,2, Chenyu Wen^3,4, Shi-Li Zhang⁵

¹Northwestern Argonne Institute of Science and Engineering, Northwestern University, 2205 Tech Drive Suite 1-160, Evanston, 60208 Illinois, United States.

|August 30, 2022

Summary

This summary is machine-generated.

A new machine learning method, pulse detection transformer (PETR), objectively identifies pulse-like signals in noisy data, improving single molecule analysis without user-defined thresholds.

Keywords:

artificial neural network generalized algorithm machine learning nanopore sensing spike recognition transformer

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Area of Science:

Single molecule analysis
Nanopore sensing
Machine learning applications

Background:

Pulse-like signals are common in single molecule analysis, such as in nanopore experiments.
Detecting these signals in noisy data is challenging, with current methods relying on subjective thresholds.

Purpose of the Study:

To develop an objective, generalized machine learning method for pulse detection.
To improve the accuracy and objectivity of pulse recognition in time-sequential data.

Main Methods:

A novel machine learning model, the pulse detection transformer (PETR), was developed.
PETR determines precise start and end time points for individual pulses.
The method was validated on simulated and experimental nanopore translocation data.

Main Results:

PETR demonstrated competitive performance in pulse detection using standard metrics.
The method successfully identified pulse segments without requiring user-defined thresholds.
The generalized output of PETR was shown to be compatible with downstream feature extraction algorithms.

Conclusions:

PETR offers an objective and generalized approach to pulse detection in noisy signals.
This method enhances the processing of single molecule analysis data, particularly from nanopore experiments.
The model's adaptability supports various downstream applications in data analysis.