Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Vector Algebra: Method of Components01:08

Vector Algebra: Method of Components

13.9K
It is cumbersome to find the magnitudes of vectors using the parallelogram rule or using the graphical method to perform mathematical operations like addition, subtraction, and multiplication. There are two ways to circumvent this algebraic complexity. One way is to draw the vectors to scale, as in navigation, and read approximate vector lengths and angles (directions) from the graphs. The other way is to use the method of components.
In many applications, the magnitudes and directions of...
13.9K
Magnetic Vector Potential01:15

Magnetic Vector Potential

606
In electrostatics, the electric field can be written as the negative gradient of the potential. In magnetostatics, the zero divergence of the magnetic field ensures that the magnetic field can be expressed as the curl of a vector potential. This potential is known as the magnetic vector potential.
Consider an ideal solenoid with n turns per unit length and radius R. If I is the current through the solenoid, the magnetic field inside the solenoid is expressed as the product of vacuum...
606
Vector Components in the Cartesian Coordinate System01:29

Vector Components in the Cartesian Coordinate System

19.6K
Vectors are usually described in terms of their components in a coordinate system. Even in everyday life, we naturally invoke the concept of orthogonal projections in a rectangular coordinate system. For example, if someone gives you directions for a particular location, you will be told to go a few km in a direction like east, west, north, or south, along with the angle in which you are supposed to move. In a rectangular (Cartesian) xy-coordinate system in a plane, a point in a plane is...
19.6K
Magnetic Moment of an Electron01:23

Magnetic Moment of an Electron

1.3K
Electrons revolving around a nucleus are analogous to a circular current carrying loop. This current produces a magnetic dipole moment proportional to the electron's orbital angular momentum. Since the orbital angular momentum is quantized in terms of the reduced Planck's constant, the dipole moment is quantized in the Bohr Magneton. The value of the Bohr magneton is 9.27 x 10-24 Am2. Electrons also have an intrinsic spin angular momentum, and the associated spin magnetic moment is...
1.3K
Divergence and Curl of Magnetic Field01:26

Divergence and Curl of Magnetic Field

2.9K
The magnetic field due to a volume current distribution given by the Biot–Savart Law can be expressed as follows:
2.9K
Magnetic Field Lines01:19

Magnetic Field Lines

4.1K
The representation of magnetic fields by magnetic field lines is very useful in visualizing the strength and direction of the magnetic field. Each of the magnetic field lines forms a closed loop. The field lines emerge from the north pole (N), loop around to the south pole (S), and continue through the bar magnet back to the north pole.
Magnetic field lines follow several hard-and-fast rules:
4.1K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A chip-scale atomic beam for nonclassical light.

Science advances·2026
Same author

Prism coupling and visualization of surface nanoscale axial photonic structures.

Communications engineering·2026
Same author

Dispersive-wave-agile optical frequency division.

Nature photonics·2025
Same author

A cold-atom beam clock based on coherent population trapping.

Applied physics letters·2024
Same author

Reduction of light shifts in Ramsey spectroscopy with a combined error signal.

Applied physics letters·2024
Same author

Monolithic optical resonator for ultrastable laser and photonic millimeter-wave synthesis.

Communications physics·2024
Same journal

Correlated clustering and projection for dimensionality reduction.

Machine learning: science and technology·2026
Same journal

An Attention-based Spatio-Temporal Neural Operator for Evolving Physics.

Machine learning: science and technology·2026
Same journal

MDCrow: automating molecular dynamics workflows with large language models.

Machine learning: science and technology·2026
Same journal

CAP: Commutative algebra prediction of protein-nucleic acid binding affinities.

Machine learning: science and technology·2026
Same journal

FDDM: Unsupervised Medical Image Translation with a Frequency-Decoupled Diffusion Model.

Machine learning: science and technology·2026
Same journal

Depthwise-Dilated Convolutional Adapters for Medical Object Tracking and Segmentation Using the Segment Anything Model 2.

Machine learning: science and technology·2026
See all related articles

Related Experiment Video

Updated: Jun 23, 2025

Spectral and Angle-Resolved Magneto-Optical Characterization of Photonic Nanostructures
08:01

Spectral and Angle-Resolved Magneto-Optical Characterization of Photonic Nanostructures

Published on: November 21, 2019

7.1K

Application of kernel principal component analysis for optical vector atomic magnetometry.

James A McKelvy1, Irina Novikova2, Eugeniy E Mikhailov2

  • 1Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, United States of America.

Machine Learning: Science and Technology
|June 25, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a machine learning method for atomic magnetometers to determine magnetic field direction. The algorithm accurately predicts field angles using electromagnetically induced transparency (EIT) spectra.

Keywords:
kernel principal component analysissupport vector regression machinesunsupervised machine learningvector magnetometry

More Related Videos

Co-localizing Kelvin Probe Force Microscopy with Other Microscopies and Spectroscopies: Selected Applications in Corrosion Characterization of Alloys
12:18

Co-localizing Kelvin Probe Force Microscopy with Other Microscopies and Spectroscopies: Selected Applications in Corrosion Characterization of Alloys

Published on: June 27, 2022

2.6K
High-Throughput Analysis of Optical Mapping Data Using ElectroMap
07:36

High-Throughput Analysis of Optical Mapping Data Using ElectroMap

Published on: June 4, 2019

9.3K

Related Experiment Videos

Last Updated: Jun 23, 2025

Spectral and Angle-Resolved Magneto-Optical Characterization of Photonic Nanostructures
08:01

Spectral and Angle-Resolved Magneto-Optical Characterization of Photonic Nanostructures

Published on: November 21, 2019

7.1K
Co-localizing Kelvin Probe Force Microscopy with Other Microscopies and Spectroscopies: Selected Applications in Corrosion Characterization of Alloys
12:18

Co-localizing Kelvin Probe Force Microscopy with Other Microscopies and Spectroscopies: Selected Applications in Corrosion Characterization of Alloys

Published on: June 27, 2022

2.6K
High-Throughput Analysis of Optical Mapping Data Using ElectroMap
07:36

High-Throughput Analysis of Optical Mapping Data Using ElectroMap

Published on: June 4, 2019

9.3K

Area of Science:

  • Physics
  • Spectroscopy
  • Machine Learning

Background:

  • Vector atomic magnetometers utilizing electromagnetically induced transparency (EIT) offer high precision magnetic field measurements.
  • Determining the precise longitudinal angle of a magnetic field from EIT spectra remains a challenge.

Purpose of the Study:

  • To develop a practical methodology for accurately recovering the longitudinal angle of a local magnetic field using EIT spectra.
  • To enhance the capabilities of EIT-based atomic rubidium magnetometers for vector magnetic field measurements.

Main Methods:

  • An unsupervised machine learning algorithm employing nonlinear dimensionality reduction (kernel principal component analysis - KPCA) was developed.
  • KPCA was used for feature extraction from EIT spectra, reducing data to a single coordinate in a lower-dimensional space.
  • A supervised support vector regression (SVR) machine modeled the relationship between KPCA features and magnetic field direction.

Main Results:

  • The KPCA-SVR algorithm achieved an accuracy of within 1 degree for predicting the longitudinal angle of the magnetic field.
  • The method demonstrated a resolution of 70 nT for the magnitude of the absolute magnetic field.
  • The algorithm effectively streamlined the angle determination process from EIT spectroscopic measurements.

Conclusions:

  • The developed KPCA-SVR algorithm provides an accurate and efficient method for vector magnetic field determination using EIT magnetometers.
  • This approach enhances the competitiveness of EIT magnetometers compared to conventional vector magnetometry techniques.
  • The combination of scalar and angular sensitivity makes this method highly valuable for precision magnetic field measurements.