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data-driven-eit-imaging-using-recurrent-neural-networks

Increasing the Reliability of Absolute EIT Imaging using an LSTM-VAE Model Approach

https://github.com/spatialaudio/data-driven-eit-imaging-using-recurrent-neural-networks

Science Score: 44.0%

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Last synced: 6 months ago · JSON representation ·

Repository

Increasing the Reliability of Absolute EIT Imaging using an LSTM-VAE Model Approach

Basic Info
  • Host: GitHub
  • Owner: spatialaudio
  • License: mit
  • Language: Jupyter Notebook
  • Default Branch: main
  • Homepage:
  • Size: 88.3 MB
Statistics
  • Stars: 3
  • Watchers: 6
  • Forks: 1
  • Open Issues: 0
  • Releases: 0
Created over 2 years ago · Last pushed about 2 years ago
Metadata Files
Readme License Citation

README.md

Improved Data-Driven EIT Imaging for Temporal Sequences using Recurrent Neural Networks

Abstract: Data-driven reconstruction techniques using deep neural network (DNN) architectures are applied more frequently in the field of electrical impedance tomography (EIT). The solution of the underlying ill-posed inverse problem may benefit from the possibilities of machine learning (ML). This contribution demonstrates, how knowledge on recurring sequences of EIT measurements (e.g. breathing cycles) may be used to improve the reconstruction. A combination of a Long Short-Term Memory (LSTM) and an Variational Autoencoder (VAE) is used.

Owner

  • Name: spatialaudio.net
  • Login: spatialaudio
  • Kind: organization

Citation (citation.cff) 

cff-version: 1.2.0
title: >-
  Improved Data-Driven EIT Imaging for Temporal Sequences using Recurrent Neural Networks
message: >-
  If you use this repository, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Jacob
    family-names: Thönes
    email: jacob.thoenes@uni-rostock.de
    affiliation: Universität Rostock
    orcid: 'https://orcid.org/0000-0003-2826-5281'
repository-code: 'https://github.com/spatialaudio/Data-Driven-EIT-Imaging-using-Recurrent-Neural-Networks'
url: 'https://github.com/spatialaudio/EIT_abs_rec_VAE-LSTM'
keywords:
  - EIT
  - VAE
  - LSTM
license: MIT
version: 1.0
date-released: '2023-12-30'

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Dependencies

requirements.txt pypi
  • matplotlib ==3.7.2
  • numpy ==1.25.0
  • pyeit ==1.2.4
  • sciopy ==0.7
  • tensorflow ==2.14.0
  • tqdm ==4.65.0