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

Classification of Systems-I01:26

Classification of Systems-I

709
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:
709
Classification of Signals01:30

Classification of Signals

1.7K
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...
1.7K

You might also read

Related Articles

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

Sort by
Same author

Emotion Recognition Using PPG Signals of Smartwatch on Purpose of Threat Detection.

Sensors (Basel, Switzerland)·2025
Same author

Wi-Fi Fingerprint Indoor Localization by Semi-Supervised Generative Adversarial Network.

Sensors (Basel, Switzerland)·2024
Same author

Multiple Fingerprinting Localization by an Artificial Neural Network.

Sensors (Basel, Switzerland)·2022
Same author

Linear RGB-D SLAM for Structured Environments.

IEEE transactions on pattern analysis and machine intelligence·2021
Same author

Influence of Isometric Exercise Combined With Electromyostimulation on Inflammatory Cytokine Levels, Muscle Strength, and Knee Joint Function in Elderly Women With Early Knee Osteoarthritis.

Frontiers in physiology·2021
Same author

Psychophysical condition of adolescents in coronavirus disease 2019.

Journal of exercise rehabilitation·2021

Related Experiment Video

Updated: Apr 18, 2026

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.9K

Target tracking and classification from labeled and unlabeled data in wireless sensor networks.

Jaehyun Yoo1, Hyoun Jin Kim2

  • 1Department of Mechanical and Aerospace Engineering, Seoul National University, 599 Gwanangno, Gwanak-gu, Seoul KS013, Korea. yjh5455@gmail.com.

Sensors (Basel, Switzerland)
|January 24, 2015
PubMed
Summary

This study introduces a new method for tracking multiple targets using low-cost sensors and semi-supervised learning. The approach accurately identifies and locates targets, even when some sensor data is unavailable.

More Related Videos

Using a Real-Time Locating System to Measure Walking Activity Associated with Wandering Behaviors Among Institutionalized Older Adults
04:13

Using a Real-Time Locating System to Measure Walking Activity Associated with Wandering Behaviors Among Institutionalized Older Adults

Published on: February 8, 2019

7.4K
Flying Insect Detection and Classification with Inexpensive Sensors
05:16

Flying Insect Detection and Classification with Inexpensive Sensors

Published on: October 15, 2014

25.9K

Related Experiment Videos

Last Updated: Apr 18, 2026

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.9K
Using a Real-Time Locating System to Measure Walking Activity Associated with Wandering Behaviors Among Institutionalized Older Adults
04:13

Using a Real-Time Locating System to Measure Walking Activity Associated with Wandering Behaviors Among Institutionalized Older Adults

Published on: February 8, 2019

7.4K
Flying Insect Detection and Classification with Inexpensive Sensors
05:16

Flying Insect Detection and Classification with Inexpensive Sensors

Published on: October 15, 2014

25.9K

Area of Science:

  • Wireless Sensor Networks
  • Machine Learning
  • Target Tracking

Background:

  • Traditional target tracking relies on expensive sensors.
  • Low-cost distributed sensor networks offer a cost-effective alternative.
  • Integrating localization and classification is crucial for effective tracking.

Purpose of the Study:

  • To develop an integrated localization and classification method for target tracking in wireless sensor networks.
  • To utilize semi-supervised learning with low-cost sensors.
  • To ensure robust tracking performance even with missing labeled data.

Main Methods:

  • Employed semi-supervised learning using both labeled (seismic, PIR sensors) and unlabeled (RF signal strength) data.
  • Utilized Gaussian process for predicting target locations from unlabeled data.
  • Generated artificial labeled data using Support Vector Machine characteristics to handle missing labeled data.

Main Results:

  • Accurate estimation of identities and locations for multiple moving targets.
  • Demonstrated robustness of the tracking algorithm in the absence of labeled data.
  • Validated the effectiveness of the proposed artificial labeled data generation technique.

Conclusions:

  • The proposed semi-supervised learning approach enables accurate and robust target tracking using low-cost sensors.
  • The method effectively integrates localization and classification for enhanced surveillance.
  • Artificial labeled data generation provides a reliable solution for scenarios with missing sensor information.