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Applied Sciences
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Multimedia Signal Processing: Theory, Methods, and Applications
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Multimedia Signal Processing: Theory, Methods, and Applications

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 (20 August 2024) | Viewed by 4697

Special Issue Editors

Dr. Iman Ardekani

E-Mail Website
Guest Editor
School of Computing, Electrical and Applied Technology, UNITEC Institute of Technology, Auckland, New Zealand
Interests: signal processing; acoustics
Dr. Waleed Abdulla

E-Mail Website
Guest Editor
Faculty of Engineering, Department of Electrical, Computer and Software Engineering, The University of Auckland, Auckland 1010, New Zealand
Interests: speech signal processing; human biometrics; audio watermarking; active noise control; hyperspectral imaging for food quality

Special Issue Information

Dear Colleagues,

This Special Issue focuses on exploring the diverse realm of multimedia signal processing, covering the various theories, methods, and applications in this dynamic field. With a primary emphasis on addressing existing challenges, this collection of research papers spans multiple topics, including acoustic signal processing, image processing, IoT, and machine learning.

In the domain of acoustic signal processing, the Special Issue showcases advancements in areas such as audio coding, speech recognition, and music analysis. It presents innovative techniques for audio quality enhancement, feature extraction from sound, and efficient audio compression algorithms.

Within image processing, the Special Issue highlights breakthroughs in image and video coding, content analysis, and computer vision. Researchers contribute novel approaches for tasks such as image denoising, object detection and recognition, and image segmentation, pushing the boundaries of visual data processing.

Furthermore, this Special Issue acknowledges the growing influence of the Internet of Things (IoT) and machine learning in multimedia signal processing. It accepts studies on IoT-based multimedia systems, sensor data fusion, and intelligent multimedia analytics. Contributions related to machine learning algorithms for multimedia analysis, classification, and retrieval are also featured, illustrating the impact of AI techniques on multimedia signal processing.

By gathering state-of-the-art research in these areas, this Special Issue aims to foster collaboration, inspire innovation, and pave the way for future advancements in multimedia signal processing.

Dr. Iman Ardekani
Dr. Waleed Abdulla
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 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. 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

  • audio coding
  • speech recognition
  • music analysis
  • acoustic signal processing
  • image and video coding
  • content analysis
  • computer vision
  • Internet of Things (IoT)
  • sensor data fusion
  • intelligent multimedia analytics

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

24 pages, 4151 KiB  
Article
Optimizing Plant Disease Classification with Hybrid Convolutional Neural Network–Recurrent Neural Network and Liquid Time-Constant Network
by An Thanh Le, Masoud Shakiba, Iman Ardekani and Waleed H. Abdulla
Appl. Sci. 2024, 14(19), 9118; https://doi.org/10.3390/app14199118 - 9 Oct 2024
Cited by 2 | Viewed by 2303
Abstract
This paper addresses the practical challenge of detecting tomato plant diseases using a hybrid lightweight model that combines a Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). Traditional image classification models demand substantial computational resources, limiting their practicality. This study aimed to [...] Read more.
This paper addresses the practical challenge of detecting tomato plant diseases using a hybrid lightweight model that combines a Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). Traditional image classification models demand substantial computational resources, limiting their practicality. This study aimed to develop a model that can be easily implemented on low-cost IoT devices while maintaining high accuracy with real-world images. The methodology leverages a CNN for extracting high-level image features and an RNN for capturing temporal relationships, thereby enhancing model performance. The proposed model incorporates a Closed-form Continuous-time Neural Network, a lightweight variant of liquid time-constant networks, and integrates Neural Circuit Policy to capture long-term dependencies in image patterns, reducing overfitting. Augmentation techniques such as random rotation and brightness adjustments were applied to the training data to improve generalization. The results demonstrate that the hybrid models outperform their single pre-trained CNN counterparts in both accuracy and computational cost, achieving a 97.15% accuracy on the test set with the proposed model, compared to around 94% for state-of-the-art pre-trained models. This study provides evidence of the effectiveness of hybrid CNN-RNN models in improving accuracy without increasing computational cost and highlights the potential of liquid neural networks in such applications. Full article
(This article belongs to the Special Issue Multimedia Signal Processing: Theory, Methods, and Applications)
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11 pages, 445 KiB  
Article
Classification of Japanese Handwritten Characters Using Biometrics Approach
by Piotr Szymkowski, Khalid Saeed, Łukasz Szymkowski and Nobuyuki Nishiuchi
Appl. Sci. 2024, 14(1), 225; https://doi.org/10.3390/app14010225 - 26 Dec 2023
Cited by 1 | Viewed by 1666
Abstract
The following paper presents a solution to the problem of offline recognition of Japanese characters. Minutiae and other features extractable from handwriting images have been used to recognize individual characters. The solution presented by the authors uses minutiae to recognise single Japanese characters. [...] Read more.
The following paper presents a solution to the problem of offline recognition of Japanese characters. Minutiae and other features extractable from handwriting images have been used to recognize individual characters. The solution presented by the authors uses minutiae to recognise single Japanese characters. Due to the complexity of this typeface, the solution presented can be used to recognise archaic characters, from old documents or also works of art. Neural Networks and hybrid classifiers based on five basic types of classifiers, i.e., k-nearest neighbour method, decision trees, support vector machine, logistic regression and Gaussian Naive Bayes classifier have been developed for classification. The study was conducted on Hiragana, Katakana and Kanji characters (ETL9G Database). The accuracy value obtained was 99.934%. The authors present what is probably the first algorithm using minutiae to recognize Japanese handwriting. Full article
(This article belongs to the Special Issue Multimedia Signal Processing: Theory, Methods, and Applications)
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