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 5502

Special Issue Editors


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Guest Editor
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

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Co-Guest Editor
Electrical and Computer Engineering, University of Western Macedonia, 5010 Kozani, Greece
Interests: PHSHealth; information capturing; transmission and reception
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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

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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

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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
by Charisios Achillas, Dimitrios Aidonis, Naoum Tsolakis, Ioannis Tsampoulatidis, Alexandros Mourouzis, Christos Bialas and Kyriakos Koritsoglou
Signals 2023, 4(3), 604-616; https://doi.org/10.3390/signals4030032 - 23 Aug 2023
Viewed by 1679
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
by Kiromitis I. Dimitrios, Christos V. Bellos, Konstantinos A. Stefanou, Georgios S. Stergios, Ioannis Andrikos, Thomas Katsantas and Sotirios Kontogiannis
Signals 2022, 3(4), 807-822; https://doi.org/10.3390/signals3040048 - 3 Nov 2022
Cited by 7 | Viewed by 2745
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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