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Artificial Intelligence in Signal, Image and Video Processing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 April 2026) | Viewed by 331

Editors


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Guest Editor
Instituto Politécnico Nacional, ESIME Culhuacan, Av. Santa Ana No. 1000, Ciudad de México 04440, Mexico
Interests: compressive sensing; speech recognition; speech processing; digital image processing; active noise canceling; interference cancelation; ADF structures
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Escuela Superior de Ingeniería Mecánica y Eléctrica, Unidad Culhuacan, Instituto Politécnico Nacional, Av. Santa Ana No. 1000, Col. San Francisco Culhucan, Mexico City 04430, Mexico
Interests: image processing; signal processing; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The development of artificial intelligence systems (AI) that allow for solving problems in signal, image and video processing is a fast growing field, and it has become a topic of active research in the last few years. This research has produced many algorithms that provide solutions to remaining problems in fields such as information security, biometrics, medicine applications, information restoration, access control, pattern recognition, image and audio synthesis, image understanding, etc.

We invite the academic and industrial research community to submit original research, as well as review articles, to this Special Issue. Potential topics include the following:

  • Audio synthesis and recogntion;
  • Moquito syntehesis classification;
  • Biometric pattern recognition;
  • Deep neural networks structures and algorithms;
  • Mood recognition using voice signals;
  • Face expression recognition;
  • Image classification;
  • Image clustering;
  • Abnormal movement recogntion;
  • Semisupervised machine learning;
  • Age and gender recognition;
  • Image encryption;
  • Face expression recognition;
  • Deep learning-based pattern recognition;
  • Three-dimensional image processing;
  • Medical image analysis.

Prof. Dr. Héctor Manuel Pérez-Meana
Prof. Dr. Mariko Nakano-Miyatake
Guest Editors

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 submissions that pass pre-check are 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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • biometrics
  • pattern recognition
  • enhancement
  • deep learning algorithms
  • audio classification
  • encryption
  • audios and images authentication
  • restoration

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Published Papers (1 paper)

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Research

20 pages, 7699 KB  
Article
A UDL-Driven Framework for Designing Digital Tactile Graphics in Cultural Heritage Learning
by Tae-Eun Lee
Appl. Sci. 2026, 16(13), 6467; https://doi.org/10.3390/app16136467 (registering DOI) - 29 Jun 2026
Abstract
This study develops a digital tactile graphic learning framework based on Korean cultural heritage images to support potential concept learning of students with visual impairments and examines its educational appropriateness through expert validation. The lack of standardized tactile graphic guidelines in visual-centric educational [...] Read more.
This study develops a digital tactile graphic learning framework based on Korean cultural heritage images to support potential concept learning of students with visual impairments and examines its educational appropriateness through expert validation. The lack of standardized tactile graphic guidelines in visual-centric educational environments imposes considerable burden on teachers, who must restructure content individually. Using a design-based research (DBR) methodology grounded in the Universal Design for Learning (UDL) framework, this study constructed a dataset of 200 cultural heritage images from elementary textbooks across four categories—architecture, artifacts, cultural symbols, and traditional objects—and restructured them through illustration simplification, initial tactile graphic conversion (informed by braille production principles), and two expert revision cycles. Ninety educationally applicable items were finalized for second-stage validation, and five tactile graphic design guidelines were derived. A panel of 15 experts evaluated the materials using a 5-point Likert scale and Content Validity Index (CVI) analysis. The overall mean was M = 4.69 (SD = 0.51), with the final 15-item instrument yielding an overall S-CVI/Ave of 0.99 (initial 0.98 across the original 16 items, refined after removal of one underperforming item per standard CVI practice); the practical usability domain reached S-CVI/Ave = 1.00, indicating full expert agreement. The study contributes a cultural heritage image dataset, a systematic image restructuring procedure, UDL-based design guidelines, iteratively refined and expert-validated CVI evaluation criteria, and a prototype TUI-based tactile learning environment configuration. Full article
(This article belongs to the Special Issue Artificial Intelligence in Signal, Image and Video Processing)
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