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Electronics
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Future Trends and Challenges of Ubiquitous Computing and Smart Systems,...
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Future Trends and Challenges of Ubiquitous Computing and Smart Systems, 2nd Edition

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 15 November 2025 | Viewed by 865

Special Issue Editors

Dr. Namgi Kim

E-Mail Website
Guest Editor
School of Computer Science and Engineering, Kyonggi University, Suwon 16227, Republic of Korea
Interests: mobile networks; networks protocols; intelligent systems
Special Issues, Collections and Topics in MDPI journals
Dr. Ahyoung Lee

E-Mail Website
Guest Editor
Department of Computer Science, Kennesaw State University, Marietta, GA 30060, USA
Interests: SDN; sensor networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The era of ubiquitous computing and smart systems has already begun. With the growing trend in the IoT and sensor devices, many smart applications are already enriching our lives. However, there are still many issues that require their further widespread deployment.

Therefore, this Special Issue focuses on discussing the future trends and challenges of ubiquitous computers and smart systems. Potential topics of interest include, but are not limited to:

  • Ubiquitous computing;
  • Smart intelligent systems;
  • Advanced networks;
  • Big data systems;
  • Computational intelligence;
  • Smart pattern recognition;
  • Sensors, IoT and IioT;
  • Smart image processing;
  • Machine learning;
  • Multimedia systems.

Dr. Namgi Kim
Dr. Ahyoung Lee
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. Electronics 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

  • ubiquitous computing
  • smart intelligent systems
  • advanced networks
  • big data systems
  • computational intelligence
  • smart pattern recognition
  • sensors, IoT and IioT
  • smart image processing
  • machine learning
  • multimedia systems

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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

23 pages, 5983 KiB  
Article
Fuzzy Logic Control for Adaptive Braking Systems in Proximity Sensor Applications
by Adnan Shaout and Luis Castaneda-Trejo
Electronics 2025, 14(14), 2858; https://doi.org/10.3390/electronics14142858 - 17 Jul 2025
Viewed by 173
Abstract
This paper details the design and implementation of a fuzzy logic control system for an advanced driver-assistance system (ADAS) that adjusts brake force based on proximity sensing, vehicle speed, and road conditions. By employing a cost-effective ultrasonic sensor (HC-SR04) and an STM32 microcontroller, [...] Read more.
This paper details the design and implementation of a fuzzy logic control system for an advanced driver-assistance system (ADAS) that adjusts brake force based on proximity sensing, vehicle speed, and road conditions. By employing a cost-effective ultrasonic sensor (HC-SR04) and an STM32 microcontroller, the system facilitates real-time adjustments to braking force, enhancing both vehicle safety and driver comfort. The fuzzy logic controller processes three inputs to deliver a smooth and adaptive brake response, thus addressing the shortcomings of traditional binary systems that can lead to abrupt and unsafe braking actions. The effectiveness of the system is validated through several test cases, demonstrating improved responsiveness and safety across various driving scenarios. This paper presents a cost-effective model for a straightforward braking system using fuzzy logic, laying the groundwork for the development of more advanced systems in emerging technologies. Full article
(This article belongs to the Special Issue Future Trends and Challenges of Ubiquitous Computing and Smart Systems, 2nd Edition)
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24 pages, 9593 KiB  
Article
Deep Learning Approaches for Skin Lesion Detection
by Jonathan Vieira, Fábio Mendonça and Fernando Morgado-Dias
Electronics 2025, 14(14), 2785; https://doi.org/10.3390/electronics14142785 - 10 Jul 2025
Viewed by 196
Abstract
Recently, there has been a rise in skin cancer cases, for which early detection is highly relevant, as it increases the likelihood of a cure. In this context, this work presents a benchmarking study of standard Convolutional Neural Network (CNN) architectures for automated [...] Read more.
Recently, there has been a rise in skin cancer cases, for which early detection is highly relevant, as it increases the likelihood of a cure. In this context, this work presents a benchmarking study of standard Convolutional Neural Network (CNN) architectures for automated skin lesion classification. A total of 38 CNN architectures from ten families (ConvNeXt, DenseNet, EfficientNet, Inception, InceptionResNet, MobileNet, NASNet, ResNet, VGG, and Xception) were evaluated using transfer learning on the HAM10000 dataset for seven-class skin lesion classification, namely, actinic keratoses, basal cell carcinoma, benign keratosis-like lesions, dermatofibroma, melanoma, melanocytic nevi, and vascular lesions. The comparative analysis used standardized training conditions, with all models utilizing frozen pre-trained weights. Cross-database validation was then conducted using the ISIC 2019 dataset to assess generalizability across different data distributions. The ConvNeXtXLarge architecture achieved the best performance, despite having one of the lowest performance-to-number-of-parameters ratios, with 87.62% overall accuracy and 76.15% F1 score on the test set, demonstrating competitive results within the established performance range of existing HAM10000-based studies. A proof-of-concept multiplatform mobile application was also implemented using a client–server architecture with encrypted image transmission, demonstrating the viability of integrating high-performing models into healthcare screening tools. Full article
(This article belongs to the Special Issue Future Trends and Challenges of Ubiquitous Computing and Smart Systems, 2nd Edition)
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