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The Hall Effect

The Hall Effect

Edwin H. Hall, in the year 1879, devised an experiment that could be used to identify the polarity of the predominant charge carriers in a conducting material. From a historical perspective, this experiment was the first to demonstrate that the charge carriers in most metals are negative.

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Related Experiment Video

Updated: Jan 13, 2026

Optimization, Test and Diagnostics of Miniaturized Hall Thrusters

Optimization, Test and Diagnostics of Miniaturized Hall Thrusters

Published on: February 16, 2019

Machine Learning-Based Position Detection Using Hall-Effect Sensor Arrays on Resource-Constrained Microcontroller.

Zalán Németh¹, Chan Hwang See¹, Keng Goh¹

¹School of Computing, Engineering and the Built Environment, Edinburgh Napier University, Edinburgh EH10 5DT, UK.

Sensors (Basel, Switzerland)

|October 29, 2025

Summary

This summary is machine-generated.

This study introduces a novel electromagnetic levitation system using TinyML for precise object positioning. It achieves optical-level accuracy with reduced cost and complexity, making advanced magnetic levitation more accessible.

Keywords:

Hall-effect sensor array TinyML electromagnetic levitation system machine learning microcontroller

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Last Updated: Jan 13, 2026

Optimization, Test and Diagnostics of Miniaturized Hall Thrusters

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Published on: February 16, 2019

Easy and Accurate Mechano-profiling on Micropost Arrays

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Published on: November 17, 2015

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

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Published on: March 13, 2017

Area of Science:

Physics
Engineering
Machine Learning

Background:

Conventional magnetic levitation systems often rely on complex and expensive optical tracking methods for position detection.
There is a need for cost-effective and integrated solutions for real-time position sensing in electromagnetic levitation.

Purpose of the Study:

To develop and validate a TinyML-based electromagnetic levitation system that replaces traditional optical tracking.
To demonstrate the feasibility of using microcontroller-optimized neural networks for accurate real-time position detection in magnetic levitation.

Main Methods:

Designing and optimizing an array of electromagnets using finite-element analysis.
Implementing a microcontroller-optimized neural network for processing Hall-effect sensor data to predict object position.
Training supervised multi-output regression models using quantized and full-precision implementations on spatially sampled data.

Main Results:

Achieved a mean absolute error of 0.0263-0.0381 mm in position detection.
Demonstrated stable system operation at control frequencies of 850-1000 Hz.
Validated sub-30 μm accuracy using standard microcontrollers and minimal hardware.

Conclusions:

Machine learning, specifically TinyML, offers a viable and cost-effective alternative to optical position detection in magnetic levitation.
The proposed system eliminates the need for external tracking devices and high-performance computing, lowering implementation barriers.
This research paves the way for wider adoption of advanced magnetic levitation in research and industrial applications.