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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

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

Deadline for manuscript submissions: 15 October 2024 | Viewed by 4026

Special Issue Editor

Dr. Namgi Kim

E-Mail Website
Guest Editor
School of Computer Science and Engineering, Kyonggi University, Gyeonggi-do 16227, Republic of Korea
Interests: mobile networks; networks protocols; intelligent systems
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 of 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 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
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. 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.

Published Papers (7 papers)

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Research

19 pages, 16050 KiB  
Article
A Comparative Analysis of Computational Intelligence Methods for Autonomous Navigation of Smart Ships
by Agnieszka Lazarowska
Electronics 2024, 13(7), 1370; https://doi.org/10.3390/electronics13071370 - 04 Apr 2024
Viewed by 339
Abstract
This paper presents the author’s approaches based on computational intelligence methods for application in the Autonomous Navigation System (ANS) of a smart ship. The considered task is collision avoidance, which is one of the vital functions of the ANS. The proposed methods, applying [...] Read more.
This paper presents the author’s approaches based on computational intelligence methods for application in the Autonomous Navigation System (ANS) of a smart ship. The considered task is collision avoidance, which is one of the vital functions of the ANS. The proposed methods, applying the Ant Colony Optimization and the Firefly Algorithm, were compared with other artificial intelligence approaches introduced in the recent literature, e.g., evolutionary algorithms and machine learning. The advantages and disadvantages of different algorithms are formulated. Results of simulation experiments carried out with the use of the developed algorithms are presented and discussed. Future trends and challenges of presented smart technologies are also stated. Full article
(This article belongs to the Special Issue Future Trends and Challenges of Ubiquitous Computing and Smart Systems)
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19 pages, 17320 KiB  
Article
Three-Dimensional Double Random-Phase Encryption for Simultaneous Two-Primary Data
by Jae-Young Jang and Myungjin Cho
Electronics 2024, 13(5), 823; https://doi.org/10.3390/electronics13050823 - 20 Feb 2024
Viewed by 458
Abstract
In this paper, we propose a three-dimensional (3D) optical encryption technique for simultaneous two-primary data using double random-phase encryption (DRPE). In conventional DRPE, the primary data can be encrypted through two different random phase masks optically. Thus, its speed is the same as [...] Read more.
In this paper, we propose a three-dimensional (3D) optical encryption technique for simultaneous two-primary data using double random-phase encryption (DRPE). In conventional DRPE, the primary data can be encrypted through two different random phase masks optically. Thus, its speed is the same as the speed of light. However, in this method, each primary dataset can be decrypted by the individual key data. For simultaneous two primary dataset such as stereo images or multi-view images, a new encryption technique is required. Therefore, in this paper, we encrypt the simultaneous two different primary datasets by DRPE. In our method, the first and second primary data are regarded as the amplitude and phase with single key data for encryption. To verify the feasibility of our method, we implement the simulation and measure the performance metrics such as thw peak signal to noise ratio (PSNR) and the peak sidelobe ratio (PSR). As a result, PSNR values of two-dimensional decryption results for the first (“LENA” text) and second (lena image) primary data by our proposed method with the correct and incorrect key data are 311.0139, 41.9609, 12.0166, and 7.4626, respectively, since the first primary data are lossless, and the second primary data are lossy. For 3D reconstruction, PSR values of the first and second primary data are 914.2644 and 774.1400, respectively. Full article
(This article belongs to the Special Issue Future Trends and Challenges of Ubiquitous Computing and Smart Systems)
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21 pages, 13794 KiB  
Article
Computational Intelligence Supporting the Safe Control of Autonomous Multi-Objects
by Józef Lisowski
Electronics 2024, 13(4), 780; https://doi.org/10.3390/electronics13040780 - 16 Feb 2024
Viewed by 386
Abstract
The essence of this work, which is an extension of the author’s previous research, is an analysis of computational intelligence algorithms that the support safe control of an autonomous object moving in a large group of other autonomous objects. Linear and dynamic programming [...] Read more.
The essence of this work, which is an extension of the author’s previous research, is an analysis of computational intelligence algorithms that the support safe control of an autonomous object moving in a large group of other autonomous objects. Linear and dynamic programming methods with neural constraints on the process state, as well as positional and matrix game methods, were used to synthesize computational algorithms for the safe trajectory of one’s own object. The aim of the comparative analysis of intelligent computational methods for the safe trajectory of an object was to show, through their use, the possibility of taking into account the risk of collision resulting from both the degree of cooperation of objects while observing traffic laws and the impact of the environment in the form of visibility and the complexity of the situation. Simulation tests of the algorithms were carried out on the example of a real navigation situation of several dozen objects passing each other at sea. Full article
(This article belongs to the Special Issue Future Trends and Challenges of Ubiquitous Computing and Smart Systems)
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23 pages, 729 KiB  
Article
Semi-Supervised Feature Selection of Educational Data Mining for Student Performance Analysis
by Shanshan Yu, Yiran Cai, Baicheng Pan and Man-Fai Leung
Electronics 2024, 13(3), 659; https://doi.org/10.3390/electronics13030659 - 05 Feb 2024
Viewed by 600
Abstract
In recent years, the informatization of the educational system has caused a substantial increase in educational data. Educational data mining can assist in identifying the factors influencing students’ performance. However, two challenges have arisen in the field of educational data mining: (1) How [...] Read more.
In recent years, the informatization of the educational system has caused a substantial increase in educational data. Educational data mining can assist in identifying the factors influencing students’ performance. However, two challenges have arisen in the field of educational data mining: (1) How to handle the abundance of unlabeled data? (2) How to identify the most crucial characteristics that impact student performance? In this paper, a semi-supervised feature selection framework is proposed to analyze the factors influencing student performance. The proposed method is semi-supervised, enabling the processing of a considerable amount of unlabeled data with only a few labeled instances. Additionally, by solving a feature selection matrix, the weights of each feature can be determined, to rank their importance. Furthermore, various commonly used classifiers are employed to assess the performance of the proposed feature selection method. Extensive experiments demonstrate the superiority of the proposed semi-supervised feature selection approach. The experiments indicate that behavioral characteristics are significant for student performance, and the proposed method outperforms the state-of-the-art feature selection methods by approximately 3.9% when extracting the most important feature. Full article
(This article belongs to the Special Issue Future Trends and Challenges of Ubiquitous Computing and Smart Systems)
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14 pages, 1578 KiB  
Article
Separable ConvNet Spatiotemporal Mixer for Action Recognition
by Hsu-Yung Cheng, Chih-Chang Yu and Chenyu Li
Electronics 2024, 13(3), 496; https://doi.org/10.3390/electronics13030496 - 24 Jan 2024
Viewed by 516
Abstract
Video action recognition is vital in the research area of computer vision. In this paper, we develop a novel model, named Separable ConvNet Spatiotemporal Mixer (SCSM). Our goal is to develop an efficient and lightweight action recognition backbone that can be applied to [...] Read more.
Video action recognition is vital in the research area of computer vision. In this paper, we develop a novel model, named Separable ConvNet Spatiotemporal Mixer (SCSM). Our goal is to develop an efficient and lightweight action recognition backbone that can be applied to multi-task models to increase the accuracy and processing speed. The SCSM model uses a new hierarchical spatial compression, employing the spatiotemporal fusion method, consisting of a spatial domain and a temporal domain. The SCSM model maintains the independence of each frame in the spatial domain for feature extraction and fuses the spatiotemporal features in the temporal domain. The architecture can be adapted to different frame rate requirements due to its high scalability. It is suitable to serve as a backbone for multi-task video feature extraction or industrial applications with its low prediction and training costs. According to the experimental results, SCSM has a low number of parameters and low computational complexity, making it highly scalable with strong transfer learning capabilities. The model achieves video action recognition accuracy comparable to state-of-the-art models with a smaller parameter size and fewer computational requirements. Full article
(This article belongs to the Special Issue Future Trends and Challenges of Ubiquitous Computing and Smart Systems)
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18 pages, 1465 KiB  
Article
Recognizing Complex Activities by Combining Sequences of Basic Motions
by Chenghong Lu, Wu-Chun Hsu and Lei Jing
Electronics 2024, 13(2), 372; https://doi.org/10.3390/electronics13020372 - 16 Jan 2024
Viewed by 506
Abstract
For daily motion recognition, each researcher builds their own method to recognize their own specific target actions. However, for other types of target motions, they cannot use their method to recognize other kinds of motions because the features of their target motions that [...] Read more.
For daily motion recognition, each researcher builds their own method to recognize their own specific target actions. However, for other types of target motions, they cannot use their method to recognize other kinds of motions because the features of their target motions that they extracted cannot be extracted from other kinds of motions. Therefore, we wanted to develop a general method that can be used in most kinds of motions. From our observations, we found that a meaningful motion is combined with some basic motions. Therefore, we could recognize basic motions and then combine them to recognize a target motion. First, we simply defined the basic motions according to the sensor’s basic sensing directions. Second, we used k-nearest neighbors (KNN) and dynamic time warping (DTW) to recognize different categories of basic motions. Then, we gave each basic motion a specific number to represent it, and finally, used continuous dynamic programming (CDP) to recognize a target motion by the sequence of basic motions we collected. In our experiment on our basic motions, the accuracy of all of the basic motions is higher than 80%, so the recognition of basic motions is reliable. Then, we performed an experiment for recognizing the target motions. The results of recognizing the target motions were not good, the average accuracy being only 65.9%, and we still have to improve our system. However, we also compared our system with recognizing motions by using another general recognition method, KNN. And the average accuracy of using KNN to recognize motions was 53.4%. As this result shows, our method still obtains better results in recognizing different kinds of motions than using KNN. Full article
(This article belongs to the Special Issue Future Trends and Challenges of Ubiquitous Computing and Smart Systems)
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16 pages, 3067 KiB  
Article
Sleep Analysis by Evaluating the Cyclic Alternating Pattern A Phases
by Arturo Alves, Fábio Mendonça, Sheikh Shanawaz Mostafa and Fernando Morgado-Dias
Electronics 2024, 13(2), 333; https://doi.org/10.3390/electronics13020333 - 12 Jan 2024
Viewed by 481
Abstract
Sleep is a complex process divided into different stages, and a decrease in sleep quality can lead to adverse health-related effects. Therefore, diagnosing and treating sleep-related conditions is crucial. The Cyclic Alternating Pattern (CAP) is an indicator of sleep instability and can assist [...] Read more.
Sleep is a complex process divided into different stages, and a decrease in sleep quality can lead to adverse health-related effects. Therefore, diagnosing and treating sleep-related conditions is crucial. The Cyclic Alternating Pattern (CAP) is an indicator of sleep instability and can assist in assessing sleep-related disorders such as sleep apnea. However, manually detecting CAP-related events is time-consuming and challenging. Therefore, automatic detection is needed. Despite their usually higher performance, the utilization of deep learning solutions may result in models that lack interpretability. Addressing this issue can be achieved through the implementation of feature-based analysis. Nevertheless, it becomes necessary to identify which features can better highlight the patterns associated with CAP. Such is the purpose of this work, where 98 features were computed from the patient’s electroencephalographic signals and used to train a neural network to identify the CAP activation phases. Feature selection and model tuning with a genetic algorithm were also employed to improve the classification results. The proposed method’s performance was found to be among the best state-of-the-art works that use more complex models. Full article
(This article belongs to the Special Issue Future Trends and Challenges of Ubiquitous Computing and Smart Systems)
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