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

Computed Tomography01:10

Computed Tomography

7.6K
Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
7.6K
Electron Microscope Tomography and Single-particle Reconstruction01:07

Electron Microscope Tomography and Single-particle Reconstruction

2.0K
Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
Electron Tomography
Electron tomography can be performed either in TEM or STEM (scanning transmission...
2.0K

You might also read

Related Articles

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

Sort by
Same author

Nondestructive Localization of Subvisual Defects in Laser-Induced Graphene via Machine-Learning-Assisted Electrical Resistance Tomography.

ACS omega·2026
Same author

Mapping defect distribution in transparent single-walled carbon nanotube film with electrical resistance tomography.

Scientific reports·2025
Same author

Self-Powered Dye-Sensitized Solar-Cell-Based Synaptic Devices for Multi-Scale Time-Series Data Processing in Physical Reservoir Computing.

ACS applied materials & interfaces·2024
Same author

Improving the Performance of a Triboelectric Nanogenerator by Using an Asymmetric TiO<sub>2</sub>/PDMS Composite Layer.

Nanomaterials (Basel, Switzerland)·2023
Same author

Light-Driven Flying Balloons Based on Hybrids of Carbon Nanotubes and Cellulose Nanofibers.

Materials (Basel, Switzerland)·2022
Same author

Freestanding Translucent ZnO-Cellulose Nanocomposite Films for Ultraviolet Sensor Applications.

Nanomaterials (Basel, Switzerland)·2022

Related Experiment Video

Updated: May 5, 2026

Enhancing Electrode Location Assessment in Cochlear Implantation via Computed Tomography Image Fusion
03:58

Enhancing Electrode Location Assessment in Cochlear Implantation via Computed Tomography Image Fusion

Published on: January 17, 2025

970

Task-Oriented Inference Framework for Lightweight and Energy-Efficient Object Localization in Electrical Impedance

Takashi Ikuno1, Reiji Kaneko1

  • 1Department of Applied Electronics, Graduate School of Advanced Engineering, Tokyo University of Science, Tokyo 125-8585, Japan.

Sensors (Basel, Switzerland)
|May 4, 2026
PubMed
Summary
This summary is machine-generated.

A new task-oriented framework for Electrical Impedance Tomography (EIT) enables efficient object localization without complex image reconstruction. This lightweight approach significantly reduces energy consumption for low-power EIT applications.

Keywords:
edge computingelectrical impedance tomographyenergy efficiencyobject localizationtask-oriented inference

More Related Videos

Array Tomography Workflow for the Targeted Acquisition of Volume Information using Scanning Electron Microscopy
09:47

Array Tomography Workflow for the Targeted Acquisition of Volume Information using Scanning Electron Microscopy

Published on: July 15, 2021

6.2K
Monitoring Lung Function with Electrical Impedance Tomography in the Intensive Care Unit
05:56

Monitoring Lung Function with Electrical Impedance Tomography in the Intensive Care Unit

Published on: September 6, 2024

7.4K

Related Experiment Videos

Last Updated: May 5, 2026

Enhancing Electrode Location Assessment in Cochlear Implantation via Computed Tomography Image Fusion
03:58

Enhancing Electrode Location Assessment in Cochlear Implantation via Computed Tomography Image Fusion

Published on: January 17, 2025

970
Array Tomography Workflow for the Targeted Acquisition of Volume Information using Scanning Electron Microscopy
09:47

Array Tomography Workflow for the Targeted Acquisition of Volume Information using Scanning Electron Microscopy

Published on: July 15, 2021

6.2K
Monitoring Lung Function with Electrical Impedance Tomography in the Intensive Care Unit
05:56

Monitoring Lung Function with Electrical Impedance Tomography in the Intensive Care Unit

Published on: September 6, 2024

7.4K

Area of Science:

  • Biomedical Engineering
  • Computational Imaging
  • Sensor Technology

Background:

  • Electrical Impedance Tomography (EIT) offers non-invasive sensing but faces computational challenges for real-time applications.
  • High computational costs of inverse reconstruction limit EIT's use in resource-constrained and edge computing environments.

Purpose of the Study:

  • To develop a lightweight, task-oriented framework for object localization in EIT, bypassing computationally intensive inverse problems.
  • To enhance energy efficiency and speed for low-power EIT systems.

Main Methods:

  • Utilized finite element method (FEM) simulations to generate training data for Opposite and Adjacent current injection configurations.
  • Developed a feedforward neural network to estimate radial and angular object positions as probability distributions.
  • Evaluated performance based on injection configuration, model depth, and Wasserstein distance.

Main Results:

  • The Opposite injection configuration achieved perfect radial estimation accuracy (1.00) with an optimized four-layer neural network.
  • The Opposite configuration yielded more localized probability distributions compared to the Adjacent configuration.
  • The proposed framework reduced energy consumption by approximately 70% (from 3.09 to 0.96 Wh) compared to traditional methods like EIDORS.

Conclusions:

  • The task-oriented framework provides reliable, high-speed, and energy-efficient object localization for EIT.
  • This approach is well-suited for low-power EIT applications in mobile and embedded systems.
  • Bypassing iterative reconstruction significantly improves energy efficiency and computational feasibility.