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

Cluster Sampling Method01:20

Cluster Sampling Method

12.0K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
12.0K
Probability Histograms01:17

Probability Histograms

11.8K
A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
11.8K
Aggregates Classification01:29

Aggregates Classification

350
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
350

You might also read

Related Articles

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

Sort by
Same author

Novel role of the lncRNA EPR as oncosuppressor in intestinal cancer.

bioRxiv : the preprint server for biology·2026
Same author

Immersive VR for the Assessment of Spatial Skills in Adolescents: Performance, Gender Effects, and Links to Computational Thinking.

IEEE transactions on visualization and computer graphics·2026
Same author

Navigating the Seas of AI: Effectiveness of Small Language Models on Edge Devices for Maritime Applications.

Sensors (Basel, Switzerland)·2026
Same author

Dynamic Reorganization of Developmental to Adult Genome Topology Controls the Initiation and Stabilization of the Human Muscle Stem Cell State.

bioRxiv : the preprint server for biology·2026
Same author

Tenascin-C from the tissue microenvironment promotes muscle stem cell maintenance and function through Annexin A2.

Communications biology·2025
Same author

Modulation of the JAK2-STAT3 pathway promotes expansion and maturation of human iPSC-derived myogenic progenitor cells.

Stem cell reports·2025

Related Experiment Video

Updated: Jul 27, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.0K

Automatic Passenger Counting on the Edge via Unsupervised Clustering.

Giorgio Delzanno1, Luca Caputo1, Daniele D'Agostino1

  • 1DIBRIS, University of Genoa, via Dodecaneso, 35, 16146 Genoa, Italy.

Sensors (Basel, Switzerland)
|June 10, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a real-time, edge-based passenger counting system using low-cost WiFi scanners. The system effectively handles MAC address randomization and processes data on the fly for accurate passenger detection.

Keywords:
IoTWSNartificial intelligenceedge computing

More Related Videos

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

3.9K
Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
09:11

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

2.2K

Related Experiment Videos

Last Updated: Jul 27, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.0K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

3.9K
Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
09:11

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

2.2K

Area of Science:

  • Computer Science
  • Electrical Engineering
  • Transportation Systems

Background:

  • Accurate real-time passenger counting is crucial for transportation management and efficiency.
  • Existing methods may face challenges with device privacy features like MAC address randomization.
  • Edge computing offers a promising approach for on-site, real-time data processing.

Purpose of the Study:

  • To develop and present a novel device- and network-based solution for automatic passenger counting.
  • To implement this solution on the edge for real-time operation.
  • To address the challenge of MAC address randomization in WiFi-based tracking.

Main Methods:

  • Utilized a low-cost WiFi scanner device capable of capturing 802.11 probe requests.
  • Developed custom algorithms to manage MAC address randomization.
  • Implemented a Python data-processing pipeline with a lightweight DBSCAN algorithm for on-the-fly analysis.
  • Employed multi-threading and multi-processing to enhance computational speed.

Main Results:

  • The proposed solution demonstrated promising experimental results across various mobile devices.
  • The system effectively captures and analyzes WiFi probe requests for passenger identification.
  • The edge computing approach enables real-time, on-site data processing.

Conclusions:

  • The presented edge computing solution offers an effective and low-cost method for automatic passenger counting.
  • The system's modular design allows for future extensions and improvements.
  • The approach successfully overcomes technical hurdles like MAC address randomization for enhanced accuracy.