Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.5
2.7
Journals
Applied Sciences
Special Issues
Application of Artificial Intelligence in Signal Processing
share announcement

Application of Artificial Intelligence in Signal 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 (20 October 2022) | Viewed by 2717

Special Issue Editors

Dr. Domenico Vitulano

E-Mail Website
Guest Editor
Department of Basic and Applied Sciences for Engineering (SBAI), Sapienza University of Rome, Via A. Scarpa 14, 00161 Rome, Italy
Interests: signal processing; image processing; pattern recognition; image forensics; data coding
Dr. Vittoria Bruni

E-Mail Website
Guest Editor
Department of Basic and Applied Sciences for Engineering (SBAI), Sapienza University of Rome, Via A. Scarpa 14, 00161 Rome, Italy
Interests: multispectral image processing; instantaneous frequency estimation; multiscale analysis

Special Issue Information

Dear Colleagues,

The enormous amount of available multimedia information raises important questions in the scientific community as the availability of many data is useless without the ability to properly access, classify, and store them. Artificial intelligence is proving to be a feasible and promising answer to that problem as it is offering new methods, solutions, and different points of view in different fields as well as achieving outstanding results and successful solutions for different specific problems. Even signal processing approaches and methods have benefited from recent advances in artificial intelligence by yielding novel and improved tools for signal classification, prediction, estimation, manipulation, as well as making feasible frameworks available at an exceptionally large scale in both dimensionality and data size. On the other hand, the mathematical foundations of signal processing are contributing to the analysis of the strength and weakness of AI-based methods as well as to their theoretical characterization in order to overcome their inherent weaknesses.

This Special Issue aims to collect contributions related to current trends and open questions in AI and signal processing. Contributions focusing on both longstanding and emergent signal processing applications, advanced computational methods for AI in signal processing, new theoretical results and perspectives, and review papers are welcome.

Dr. Domenico Vitulano
Dr. Vittoria Bruni
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

  • artificial intelligence
  • signal processing
  • pattern recognition
  • image forensics
  • data coding
  • multispectral image processing
  • instantaneous frequency estimation
  • multiscale analysis

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

14 pages, 1547 KiB  
Article
3D Convolution Recurrent Neural Networks for Multi-Label Earthquake Magnitude Classification
by Muhammad Shakeel, Kenji Nishida, Katsutoshi Itoyama and Kazuhiro Nakadai
Appl. Sci. 2022, 12(4), 2195; https://doi.org/10.3390/app12042195 - 20 Feb 2022
Cited by 4 | Viewed by 1851
Abstract
We examine a classification task in which signals of naturally occurring earthquakes are categorized ranging from minor to major, based on their magnitude. Generalized to a single-label classification task, most prior investigations have focused on assessing whether an earthquake’s magnitude falls into the [...] Read more.
We examine a classification task in which signals of naturally occurring earthquakes are categorized ranging from minor to major, based on their magnitude. Generalized to a single-label classification task, most prior investigations have focused on assessing whether an earthquake’s magnitude falls into the minor or large categories. This procedure is often not practical since the tremor it generates has a wide range of variation in the neighboring regions based on the distance, depth, type of surface, and several other factors. We present an integrated 3-dimensional convolutional recurrent neural network (3D-CNN-RNN) trained to classify the seismic waveforms into multiple categories based on the problem formulation. Recent studies demonstrate using artificial intelligence-based techniques in earthquake detection and location estimation tasks with progress in collecting seismic data. However, less work has been performed in classifying the seismic signals into single or multiple categories. We leverage the use of a benchmark dataset comprising of earthquake waveforms having different magnitude and present 3D-CNN-RNN, a highly scalable neural network for multi-label classification problems. End-to-end learning has become a conventional approach in audio and image-related classification studies. However, for seismic signals classification, it has yet to be established. In this study, we propose to deploy the trained model on personal seismometers to effectively categorize earthquakes and increase the response time by leveraging the data-centric approaches. For this purpose, firstly, we transform the existing benchmark dataset into a series of multi-label examples. Secondly, we develop a novel 3D-CNN-RNN model for multi-label seismic event classification. Finally, we validate and evaluate the learned model with unseen seismic waveforms instances and report whether a specific event is associated with a particular class or not. Experimental results demonstrate the superiority and effectiveness of the proposed approach on unseen data using the multi-label classifier. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Signal Processing)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop