Jove
Visualize
Contact Us

Related Concept Videos

Classification of Signals01:30

Classification of Signals

532
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
532
Classification of Systems-I01:26

Classification of Systems-I

215
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
215
Classification of Systems-II01:31

Classification of Systems-II

177
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
177
Force Classification01:22

Force Classification

1.3K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.3K
Methods of Classification and Identification01:28

Methods of Classification and Identification

37
Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...
37
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

1.4K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
1.4K

You might also read

Related Articles

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

Sort by
Same author

Image of the month: Ulcerated ileal mass in an 11-year-old male.

JPGN reports·2025
Same author

People with type 2 diabetes experiences of using WhatsApp-based diabetes self-management education and support: The process evaluation.

Journal of evaluation in clinical practice·2024
Same author

WhatsApp-based intervention for people with type 2 diabetes: A randomized controlled trial.

Nursing & health sciences·2024
Same author

Methanol poisonings from contaminated hand sanitizers identified by the United States Food and Drug Administration.

Clinical toxicology (Philadelphia, Pa.)·2024
Same author

Comparative Analysis of Resident Space Object (RSO) Detection Methods.

Sensors (Basel, Switzerland)·2023
Same author

Challenges, coping and resilience in caring for children with disability among immigrant parents: A mixed methods study.

Journal of advanced nursing·2022
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 Experiment Video

Updated: Jul 20, 2025

Surface Mapping of Earth-like Exoplanets using Single Point Light Curves
06:48

Surface Mapping of Earth-like Exoplanets using Single Point Light Curves

Published on: May 10, 2020

3.6K

Classification of Low Earth Orbit (LEO) Resident Space Objects' (RSO) Light Curves Using a Support Vector Machine

Randa Qashoa1, Regina Lee1

  • 1Department of Earth and Space Science, York University, Toronto, ON M3J 1P3, Canada.

Sensors (Basel, Switzerland)
|July 29, 2023
PubMed
Summary

This study introduces a new method for classifying low Earth orbit (LEO) resident space objects (RSOs) using light curve data. Deep learning with Long Short-Term Memory (LSTM) achieved 92% accuracy, outperforming traditional machine learning.

Keywords:
Space Situational Awarenesslight curvelong short-term memorylow Earth orbitresident space objectsupport vector machine

More Related Videos

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.2K
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.0K

Related Experiment Videos

Last Updated: Jul 20, 2025

Surface Mapping of Earth-like Exoplanets using Single Point Light Curves
06:48

Surface Mapping of Earth-like Exoplanets using Single Point Light Curves

Published on: May 10, 2020

3.6K
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.2K
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.0K

Area of Science:

  • Space Situational Awareness (SSA)
  • Astrodynamics and Orbital Mechanics
  • Machine Learning Applications in Space Science

Background:

  • Light curves of Resident Space Objects (RSOs) are crucial for inferring object type, attitude, and shape in Space Situational Awareness (SSA).
  • While geostationary orbit (GEO) RSO light curve analysis is established, low Earth orbit (LEO) RSO light curves are challenging due to their short duration (minutes).
  • Analyzing LEO RSO light curves is vital given the high concentration of objects in this orbit.

Purpose of the Study:

  • To develop and evaluate a novel approach for classifying observational LEO RSO light curves.
  • To compare the effectiveness of a conventional machine learning model (SVM) against a deep learning model (LSTM) for LEO light curve classification.

Main Methods:

  • Feature extraction from LEO light curves using wavelet scattering transformation.
  • Classification using a Support Vector Machine (SVM) as a baseline conventional machine learning approach.
  • Classification using a Long Short-Term Memory (LSTM) deep learning technique for comparison.

Main Results:

  • The Long Short-Term Memory (LSTM) deep learning model achieved a 92% accuracy in classifying LEO RSO light curves.
  • LSTM significantly outperformed the Support Vector Machine (SVM) in accuracy for this classification task.
  • The study demonstrates the effectiveness of the proposed feature extraction and deep learning approach for LEO RSO analysis.

Conclusions:

  • The developed method, utilizing wavelet scattering and LSTM, proves the viability of classifying RSOs by object type and spin rate from LEO light curves.
  • Deep learning techniques like LSTM offer a powerful solution for analyzing short, complex LEO light curve data.
  • This research enhances capabilities in Space Situational Awareness for objects in low Earth orbit.