Selected Papers from the "South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM)"

A special issue of Signals (ISSN 2624-6120).

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 3790

Special Issue Editors

Department of Electrical and Computer Engineering, University of Western Macedonia, GR50100 Kozani, Greece
Interests: biomedical engineering; mathematical modelling; HRV analysis; EEG signal processing; biostatistics; signal processing; image processing
Electrical and Computer Engineering, University of Western Macedonia, 5010 Kozani, Greece
Interests: PHSHealth; information capturing; transmission and reception
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM) gives an insight into the unique world stemming from the interaction between the fields of computer engineering, networks and design automation. The SSEDA-CECNSM technical program includes all aspects of Computer Engineering from Networks to Signal Analysis. This Special Issue aims at publishing extended versions of papers in Signal Processing from the conference. Potential topics include but are not limited to the following:

  • Biomedical signals and image processing;
  • Human–computer interaction;
  • Image processing and visualization;
  • Mobile, ad hoc, and sensor network optimization and management;
  • Wireless, cellular, and mobile communications;
  • E-health technologies and applications.

Prof. Dr. Dimitrios G. Tsalikakis
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 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. Signals is an international peer-reviewed open access quarterly 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 1000 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

  • Biomedical signal processing 
  • Biomedical image processing 
  • Brain–computer interface 
  • Human–computer interaction 
  • Wearable devices 
  • Sensor networks 
  • Electrocardiography 
  • Electroencephalography 
  • Electromyography 
  • Evoked potentials
  • Sleep spindles/classification

Published Papers (2 papers)

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Research

13 pages, 1877 KiB  
Article
Auditing Accessibility of Pavements and Points of Interest in Urban Areas: The ‘Seek & Go’ Tool
Signals 2023, 4(3), 604-616; https://doi.org/10.3390/signals4030032 - 23 Aug 2023
Viewed by 1143
Abstract
In recent years, accessibility has become a topic of great interest on a global scale across the scientific, business, and policy sectors. There are two primary reasons for this growing trend. Firstly, accessibility serves as a vital indicator reflecting the social performance of [...] Read more.
In recent years, accessibility has become a topic of great interest on a global scale across the scientific, business, and policy sectors. There are two primary reasons for this growing trend. Firstly, accessibility serves as a vital indicator reflecting the social performance of communities, and the public is increasingly aware of critical social issues such as accessibility. Secondly, accessibility is essential for the sustainable development of regions and civil settings, facilitating inclusion and business growth. In this regard, information and communications technologies can play a crucial role in facilitating the accessibility of spaces by disabled people. Numerous digital tools and smart applications are already available to serve this purpose. This study presents a novel digital tool called ‘Seek & Go’, a comprehensive aid application designed specifically for disabled individuals. The app features a GPS navigation system that caters to pedestrians with disabilities and unique accessibility requirements. The present study documents the models underlying the development of ‘Seek & Go’, discusses technical aspects of the application, and presents user experience insights. Full article
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16 pages, 2940 KiB  
Article
Performance Evaluation of Classification Algorithms to Detect Bee Swarming Events Using Sound
Signals 2022, 3(4), 807-822; https://doi.org/10.3390/signals3040048 - 03 Nov 2022
Cited by 2 | Viewed by 1753
Abstract
This paper presents a machine-learning approach for detecting swarming events. Three different classification algorithms are tested: The k-Nearest Neighbors algorithm (k-NN) and Support Vector Machine (SVM), and a newly proposed by the authors, U-Net Convolutional Neural Network (CNN), developed for biomedical image segmentation. [...] Read more.
This paper presents a machine-learning approach for detecting swarming events. Three different classification algorithms are tested: The k-Nearest Neighbors algorithm (k-NN) and Support Vector Machine (SVM), and a newly proposed by the authors, U-Net Convolutional Neural Network (CNN), developed for biomedical image segmentation. Next, the authors present their experimental scenario of collecting audio data of swarming and non-swarming events and evaluating the results from the k-NN and SVM classifiers and their proposed CNN algorithm. Finally, the authors compare these three methods and present the cross-comparison results of the optimal method for early and late/close-to-the-event detection of swarming. Full article
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