Special Issue "Deep Neural Networks and Their Applications, Volume II"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 31 May 2021.

Special Issue Editor

Dr. Shyam Prasad Adhikari
Website
Guest Editor
Department of Electronics Engineering, Jeonbuk National University, Jeonju 567-54896, Korea
Interests: neural networks, computer vision, deep learning, deep learning applications, memristor, memristor applications, hardware neural networks

Special Issue Information

Dear Colleagues,

By virtue of the success of recent deep neural network technologies, Artificial Intelligence has recently received great attention from almost all fields of academia and industries. Though the current success of Artificial Intelligence arose with the software version of neural networks, it is gradually extending to hardware implementations and human–computer interfaces. This Special Issue aims to provide a platform to researchers from both software and hardware of Artificial Intelligence to share cutting-edge developments in the field. The scope of this Special Issue is deep learning, neuromorphics, and brain–computer interfaces.

We solicit original research papers as well as review articles, including but not limited to the following key words:

  • Artificial Intelligence
  • Brain–computer interface (BCI)
  • Brain signal processing for BCI
  • Deep learning (AI) algorithm
  • Deep learning (AI) architecture
  • Deep learning applications
  • Intelligent bioinformatics
  • Intelligent robots
  • Intelligent systems
  • Machine learning
  • Memristors
  • Neural networks
  • Neural rehabilitation engineering
  • Neuromorphics
  • Parallel processing
  • Web intelligence applications and search

Dr. Shyam Prasad Adhikari
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Artificial Intelligence
  • Brain–computer interface (BCI)
  • Brain signal processing for BCI
  • Deep learning (AI) algorithm
  • Deep learning (AI) architecture
  • Deep learning applications
  • Intelligent bioinformatics
  • Intelligent robots
  • Intelligent systems
  • Machine learning
  • Memristors
  • Neural networks
  • Neural rehabilitation engineering
  • Neuromorphics
  • Parallel processing
  • Web intelligence applications and search

Published Papers (1 paper)

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Research

Open AccessArticle
MDEAN: Multi-View Disparity Estimation with an Asymmetric Network
Electronics 2020, 9(6), 924; https://doi.org/10.3390/electronics9060924 - 02 Jun 2020
Abstract
In recent years, disparity estimation of a scene based on deep learning methods has been extensively studied and significant progress has been made. In contrast, a traditional image disparity estimation method requires considerable resources and consumes much time in processes such as stereo [...] Read more.
In recent years, disparity estimation of a scene based on deep learning methods has been extensively studied and significant progress has been made. In contrast, a traditional image disparity estimation method requires considerable resources and consumes much time in processes such as stereo matching and 3D reconstruction. At present, most deep learning based disparity estimation methods focus on estimating disparity based on monocular images. Motivated by the results of traditional methods that multi-view methods are more accurate than monocular methods, especially for scenes that are textureless and have thin structures, in this paper, we present MDEAN, a new deep convolutional neural network to estimate disparity using multi-view images with an asymmetric encoder–decoder network structure. First, our method takes an arbitrary number of multi-view images as input. Next, we use these images to produce a set of plane-sweep cost volumes, which are combined to compute a high quality disparity map using an end-to-end asymmetric network. The results show that our method performs better than state-of-the-art methods, in particular, for outdoor scenes with the sky, flat surfaces and buildings. Full article
(This article belongs to the Special Issue Deep Neural Networks and Their Applications, Volume II)
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