Jove
Visualize
Contact Us

Related Concept Videos

Cluster Sampling Method01:20

Cluster Sampling Method

13.7K
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...
13.7K
Parallel Processing01:20

Parallel Processing

458
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
458
Multimachine Stability01:25

Multimachine Stability

371
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
371
Machines: Problem Solving II01:30

Machines: Problem Solving II

540
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
540
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

948
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
948
Machines: Problem Solving I01:22

Machines: Problem Solving I

572
A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
572

You might also read

Related Articles

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

Sort by
Same author

A Multi-Source Sensor Dataset for Spain: Integrating Air Quality, Meteorological, Mobility and Calendar Records.

Sensors (Basel, Switzerland)·2026
Same author

Near-Real-Time Integration of Multi-Source Seismic Data.

Sensors (Basel, Switzerland)·2026
Same author

Towards a Sustainable City for Cyclists: Promoting Safety through a Mobile Sensing Application.

Sensors (Basel, Switzerland)·2021
Same author

A Decision Support System for Water Optimization in Anti-Frost Techniques by Sprinklers.

Sensors (Basel, Switzerland)·2020
Same author

Using Machine-Learning Algorithms for Eutrophication Modeling: Case Study of Mar Menor Lagoon (Spain).

International journal of environmental research and public health·2020
Same author

Three Dimensional UAV Positioning for Dynamic UAV-to-Car Communications.

Sensors (Basel, Switzerland)·2020
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: Dec 1, 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

7.2K

Evaluation of Clustering Algorithms on GPU-Based Edge Computing Platforms.

José M Cecilia1, Juan-Carlos Cano1, Juan Morales-García2

  • 1Computer Engineering Department (DISCA), Universitat Politécnica de Valencia (UPV), 46022 Valencia, Spain.

Sensors (Basel, Switzerland)
|November 11, 2020
PubMed
Summary
This summary is machine-generated.

Internet of Things (IoT) generates vast "dark data." This study optimizes AI clustering algorithms on edge computing platforms with GPUs, achieving significant speed-ups and energy savings compared to high-performance computing. This enables efficient analysis for intelligent IoT applications.

Keywords:
GPU computingIoT applicationscloud computingclustering algorithmsedge computingintelligent systemslow-power

More Related Videos

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
12:11

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

8.5K
Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios
06:02

Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios

Published on: October 6, 2020

2.5K

Related Experiment Videos

Last Updated: Dec 1, 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

7.2K
Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
12:11

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

8.5K
Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios
06:02

Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios

Published on: October 6, 2020

2.5K

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Edge Computing

Background:

  • The Internet of Things (IoT) generates massive datasets, often unanalyzed ('dark data'), hindering the development of intelligent applications.
  • Analyzing large IoT datasets requires computationally intensive methods like clustering algorithms, typically run on energy-consuming High-Performance Computing (HPC) clusters.
  • Existing HPC solutions for IoT data analysis face challenges including high latency and privacy concerns.

Purpose of the Study:

  • To analyze emergent edge computing architectures for efficient IoT data processing.
  • To evaluate the performance and energy efficiency of low-power GPUs on edge platforms for AI workloads.
  • To compare the energy-performance ratio of edge computing against traditional HPC cloud solutions.

Main Methods:

  • Implemented and optimized three clustering algorithms (k-means, FM, FCM) for both edge and cloud platforms.
  • Utilized Nvidia's AGX Xavier edge platform with low-power GPUs for performance and power consumption analysis.
  • Compared edge-based GPU acceleration against sequential execution and HPC cloud counterparts.

Main Results:

  • Achieved speed-up factors of up to 11x for GPU-accelerated clustering algorithms on edge platforms compared to sequential versions.
  • Demonstrated significant energy savings, up to 150%, when using edge computing with GPUs versus HPC platforms.
  • Quantified the energy-performance benefits of edge computing architectures for large-scale IoT data analysis.

Conclusions:

  • Edge computing, particularly with integrated GPUs, offers a viable and energy-efficient solution for analyzing large IoT datasets.
  • The optimized clustering algorithms show substantial performance gains and reduced energy consumption at the network edge.
  • This approach facilitates the creation of next-generation intelligent IoT applications by overcoming the limitations of traditional cloud-based analysis.