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

15.4K
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...
15.4K
Area Computation by the Alternative Coordinate Method01:24

Area Computation by the Alternative Coordinate Method

841
The alternative coordinate method, also known as the Shoelace Formula, is a technique for determining the area of a traverse using Cartesian coordinates. This method relies on the sequential arrangement of x and y coordinates for each point of the shape, ensuring accuracy and ease of application.In this approach, each corner's x and y coordinates are listed as fractions, with the x-coordinate as the numerator and the y-coordinate as the denominator. These coordinates are arranged sequentially...
841
Manipulation and Analysis01:21

Manipulation and Analysis

333
GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
333
Cluster Sampling Method01:20

Cluster Sampling Method

11.0K
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...
11.0K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

535
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
535
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

580
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
580

You might also read

Related Articles

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

Sort by
Same author

Digital Twin Prospects in IoT-Based Human Movement Monitoring Model.

Sensors (Basel, Switzerland)·2025
Same author

A Survey on Free-Space Optical Communication with RF Backup: Models, Simulations, Experience, Machine Learning, Challenges and Future Directions.

Sensors (Basel, Switzerland)·2025
Same author

A Study of Downlink Power-Domain Non-Orthogonal Multiple Access Performance in Tactile Internet Employing Sensors and Actuators.

Sensors (Basel, Switzerland)·2024
Same author

Cyber-Physical Distributed Intelligent Motor Fault Detection.

Sensors (Basel, Switzerland)·2024
Same author

Enhancing Handover for 5G mmWave Mobile Networks Using Jump Markov Linear System and Deep Reinforcement Learning.

Sensors (Basel, Switzerland)·2022
Same author

A Smart City Lighting Case Study on an OpenStack-Powered Infrastructure.

Sensors (Basel, Switzerland)·2015

Related Experiment Video

Updated: Apr 22, 2026

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

14.2K

Multivariate spatial condition mapping using subtractive fuzzy cluster means.

Hakilo Sabit1, Adnan Al-Anbuky2

  • 1Electrical and Electronic Engineering, Auckland University of Technology, 24 St Paul Street, Auckland 1010, New Zealand. hsabit@aut.ac.nz.

Sensors (Basel, Switzerland)
|October 15, 2014
PubMed
Summary

This study introduces a new subtractive fuzzy clustering algorithm for wireless sensor networks (WSNs) in a sensor cloud. The algorithm enables efficient distributed data stream mining comparable to centralized methods.

More Related Videos

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.3K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.2K

Related Experiment Videos

Last Updated: Apr 22, 2026

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

14.2K
Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.3K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.2K

Area of Science:

  • Computer Science
  • Data Science
  • Network Engineering

Background:

  • Wireless sensor networks (WSNs) are crucial for continuous spatio-temporal monitoring of physical phenomena.
  • Understanding event-phenomena relationships requires robust and continuous data acquisition.
  • WSNs offer inherent robustness suitable for long-term environmental or event monitoring.

Purpose of the Study:

  • To introduce a novel subtractive fuzzy clustering algorithm for data stream mining in wireless sensor systems.
  • To apply this algorithm within a sensor cloud architecture for distributed data stream mining.
  • To evaluate the performance of the proposed algorithm against established data mining techniques.

Main Methods:

  • Development of a subtractive fuzzy clustering algorithm tailored for data streams.
  • Implementation within a sensor cloud data stream mining architecture.
  • Benchmarking against k-means and Fuzzy C-Means (FCM) algorithms using standard datasets.

Main Results:

  • The subtractive fuzzy clustering algorithm demonstrates high-quality distributed data stream mining capabilities.
  • Performance is comparable to centralized data stream mining approaches.
  • The proposed method effectively handles data streams from WSNs in a cloud environment.

Conclusions:

  • The developed subtractive fuzzy clustering algorithm is effective for distributed data stream mining in sensor clouds.
  • This approach offers a viable alternative to centralized methods for WSN data analysis.
  • The sensor cloud data stream mining framework enhances the utility of WSN data for event characterization.