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

Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

634
A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of...
634
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

199
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
199
Transformers in Distribution System01:27

Transformers in Distribution System

103
Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
103
Semiconductors01:22

Semiconductors

708
There is variation in the electrical conductivity of materials - metals, semiconductors, and insulators that are showcased with the help of the energy band diagrams.
Metals such as copper (Cu), zinc (Zn), or lead (Pb) have low resistivity and feature conduction bands that are either not fully occupied or overlap with the valence band, making a bandgap non-existent. This allows electrons in the highest energy levels of the valence band to easily transition to the conduction band upon gaining...
708

You might also read

Related Articles

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

Sort by
Same author

Reinforcement Learning-Based Control for Collaborative Robotic Brain Retraction.

Sensors (Basel, Switzerland)·2025
Same author

Pangea: An MLOps Tool for Automatically Generating Infrastructure and Deploying Analytic Pipelines in Edge, Fog and Cloud Layers.

Sensors (Basel, Switzerland)·2022
Same author

Energy and thermal modelling of an office building to develop an artificial neural networks model.

Scientific reports·2022
Same author

Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions.

Neural computing & applications·2021
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Jul 9, 2025

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem
10:15

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem

Published on: February 3, 2021

3.8K

Analysing Edge Computing Devices for the Deployment of Embedded AI.

Asier Garcia-Perez1, Raúl Miñón1, Ana I Torre-Bastida2

  • 1Digital, TECNALIA, Basque Research and Technology Alliance (BRTA), Parque Tecnológico de Álava Albert Einstein 28, 01510 Vitoria-Gasteiz, Álava, Spain.

Sensors (Basel, Switzerland)
|December 9, 2023
PubMed
Summary
This summary is machine-generated.

Edge computing processes data near the source, essential for the Internet of Things. Utilizing artificial intelligence accelerators like Tensor Processing Units significantly boosts edge device performance beyond CPU-only capabilities.

Keywords:
TPUTensorFlow Litedeviceedge computingmetricsmodel

More Related Videos

Artificial Intelligence Approaches to Assessing Primary Cilia
08:58

Artificial Intelligence Approaches to Assessing Primary Cilia

Published on: May 1, 2021

3.5K
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

3.8K

Related Experiment Videos

Last Updated: Jul 9, 2025

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem
10:15

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem

Published on: February 3, 2021

3.8K
Artificial Intelligence Approaches to Assessing Primary Cilia
08:58

Artificial Intelligence Approaches to Assessing Primary Cilia

Published on: May 1, 2021

3.5K
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

3.8K

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Network Engineering

Background:

  • The proliferation of Internet of Things (IoT) devices generates vast amounts of data.
  • Traditional cloud computing faces limitations in latency, efficiency, and real-time response for IoT applications.
  • Edge computing emerges as a solution to process data closer to the source, addressing cloud limitations.

Purpose of the Study:

  • To analyze and compare the performance of various Edge Computing devices for deploying artificial intelligence algorithms.
  • To evaluate the impact of artificial intelligence accelerators, specifically Tensor Processing Units (TPUs), on edge device performance.
  • To guide the selection of optimal edge devices based on specific AI requirements.

Main Methods:

  • Conducting a detailed experiment comparing multiple edge devices, AI models, and performance metrics.
  • Deploying artificial intelligence algorithms on selected edge computing hardware.
  • Observing and measuring the performance of artificial intelligence accelerators, such as TPUs.

Main Results:

  • The Jetson Nano demonstrates strong performance when utilizing only its CPU.
  • The integration of a Tensor Processing Unit (TPU) significantly enhances the performance of edge devices.
  • Specific performance gains vary depending on the edge device, AI model, and accelerator used.

Conclusions:

  • Edge computing, combined with AI, offers a powerful solution for real-time data processing and autonomous decision-making.
  • While CPU-based processing on devices like the Jetson Nano is viable, AI accelerators like TPUs provide substantial performance improvements.
  • The choice of edge device and the inclusion of AI accelerators are critical for meeting demanding AI application requirements at the network edge.