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Electronics
Special Issues
Emerging Technologies for Computer, Electrical and Systems Engineering
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Emerging Technologies for Computer, Electrical and Systems Engineering

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 January 2021) | Viewed by 25947

Special Issue Editors

Prof. Dr. Li-Minn Ang

E-Mail Website
Guest Editor
School of Science and Engineering, University of the Sunshine Coast, Sippy Downs, QLD 4556, Australia
Interests: electrical and computer engineering; intelligent and smart sensor systems; IoT and data analytics for environmental sensing; innovative technologies for real world systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Kah Phooi Seng

E-Mail Website
Guest Editor
School of Computer Science, The Queensland University of Technology (QUT), Brisbane, QLD 4000, Australia
Interests: AI and intelligent systems; distributed AI; edge AI; on-device machine learning; embedded intelligence; machine learning and data analytics; Internet of Things (IoT); mobile AI software development; multimodal information processing and innovative technologies for urban and regional environments
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent developments in emerging technologies such as the Internet of Things (IoT), machine learning, machine vision, reconfigurable engineering, and blockchain technologies have enabled new applications and services to be developed and deployed in the areas of computer, electrical, and systems engineering. These emerging technologies offer unprecedented opportunities for solving problems in diverse applications for industry, health, agriculture, defence, and the environment. As examples, bridges and automation systems have the capacity to build smart structures augmented with intelligent sensors and smart decision making to reduce costs and maintenance. Transportation systems can be designed not only to carry people but adapted to serve the needs of smart cities and provide mobile sensing systems for environmental monitoring and waste management.

This Special Issue invites topics broadly across the various emerging technologies for computer, electrical, and systems engineering. Some specific topics include, but are not limited to: 

- Emerging technologies in IoT for computer, electrical, and systems engineering;

- Emerging technologies in machine learning for computer, electrical, and systems engineering;

- Emerging technologies in machine vision for computer, electrical, and systems engineering;

- Emerging technologies in embedded systems for computer, electrical, and systems engineering;

- Emerging technologies in wireless sensor systems for computer, electrical, and systems engineering;

- Emerging technologies in reconfigurable engineering for computer, electrical, and systems engineering;

- Emerging technologies in blockchain technologies for computer, electrical, and systems engineering; and

- Other novel applications for emerging technologies in computer, electrical, and systems engineering.  

Prof. Dr. Li-minn Ang
Prof. Dr. Kah Phooi Seng
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

  • Emerging technologies 
  • IoT/wireless sensor-based systems 
  • Intelligent systems/machine learning 
  • Machine vision 
  • Reconfigurable architectures 
  • Trust and blockchain technologies

Published Papers (5 papers)

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Research

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22 pages, 2109 KiB  
Article
Mobile Collectors for Opportunistic Internet of Things in Smart City Environment with Wireless Power Transfer
by Gerald K. Ijemaru, Kenneth L.-M. Ang and Jasmine K. P. Seng
Electronics 2021, 10(6), 697; https://doi.org/10.3390/electronics10060697 - 16 Mar 2021
Cited by 15 | Viewed by 2235
Abstract
In the context of Internet of Things (IoT) for Smart City (SC) applications, Mobile Data Collectors (MDCs) can be opportunistically exploited as wireless energy transmitters to recharge the energy-constrained IoT sensor-nodes placed within their charging vicinity or coverage area. The use of MDCs [...] Read more.
In the context of Internet of Things (IoT) for Smart City (SC) applications, Mobile Data Collectors (MDCs) can be opportunistically exploited as wireless energy transmitters to recharge the energy-constrained IoT sensor-nodes placed within their charging vicinity or coverage area. The use of MDCs has been well studied and presents several advantages compared to the traditional methods that employ static sinks. However, data collection and transmission from the hundreds of thousands of sensors sparsely distributed across virtually every smart city has raised some new challenges. One of these concerns lies in how these sensors are being powered as majority of the IoT sensors are extremely energy-constrained owing to their smallness and mode of deployments. It is also evident that sensor-nodes closer to the sinks dissipate their energy faster than their counterparts. Moreover, battery recharging or replacement is impractical and incurs very large operational costs. Recent breakthrough in wireless power transfer (WPT) technologies allows the transfer of energy to the energy-hungry IoT sensor-nodes wirelessly. WPT finds applications in medical implants, electric vehicles, wireless sensor networks (WSNs), unmanned aerial vehicles (UAVs), mobile phones, and so on. The present study highlights the use of mobile collectors (data mules) as wireless power transmitters for opportunistic IoT-SC operations. Specifically, mobile vehicles used for data collection are further exploited as wireless power transmitters (wireless battery chargers) to wirelessly recharge the energy-constrained IoT nodes placed within their coverage vicinity. This paper first gives a comprehensive survey of the different aspects of wireless energy transmission technologies—architecture, energy sources, IoT energy harvesting modes, WPT techniques and applications that can be exploited for SC scenarios. A comparative analysis of the WPT technologies is also highlighted to determine the most energy-efficient technique for IoT scenarios. We then propose a WPT scheme that exploits vehicular networks for opportunistic IoT-SC operations. Experiments are conducted using simulations to evaluate the performance of the proposed model and to investigate WPT efficiency of a power-hungry opportunistic IoT network for different trade-off factors. Full article
(This article belongs to the Special Issue Emerging Technologies for Computer, Electrical and Systems Engineering)
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16 pages, 3356 KiB  
Article
Blockchain Use in IoT for Privacy-Preserving Anti-Pandemic Home Quarantine
by Jinxin Zhang and Meng Wu
Electronics 2020, 9(10), 1746; https://doi.org/10.3390/electronics9101746 - 21 Oct 2020
Cited by 27 | Viewed by 3722
Abstract
The outbreak of the respiratory disease caused by the new coronavirus (COVID-19) has caused the world to face an existential health crisis. To contain the infectious disease, many countries have quarantined their citizens for several weeks to months and even suspended most economic [...] Read more.
The outbreak of the respiratory disease caused by the new coronavirus (COVID-19) has caused the world to face an existential health crisis. To contain the infectious disease, many countries have quarantined their citizens for several weeks to months and even suspended most economic activities. To track the movements of residents, the governments of many states have adopted various novel technologies. Connecting billions of sensors and devices over the Internet, the so-called Internet of Things (IoT), has been used for outbreak control. However, these technologies also pose serious privacy risks and security concerns with regards to data transmission and storage. In this paper, we propose a blockchain-based system to provide the secure management of home quarantine. The privacy and security attributes for various events are based on advanced cryptographic primitives. To demonstrate the application of the system, we provide a case study in an IoT system with a desktop computer, laptop, Raspberry Pi single-board computer, and the Ethereum smart contract platform. The obtained results prove its ability to satisfy security, efficiency, and low-cost requirements. Full article
(This article belongs to the Special Issue Emerging Technologies for Computer, Electrical and Systems Engineering)
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Review

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20 pages, 5440 KiB  
Review
GPU-Based Embedded Intelligence Architectures and Applications
by Li Minn Ang and Kah Phooi Seng
Electronics 2021, 10(8), 952; https://doi.org/10.3390/electronics10080952 - 16 Apr 2021
Cited by 10 | Viewed by 3373
Abstract
This paper present contributions to the state-of-the art for graphics processing unit (GPU-based) embedded intelligence (EI) research for architectures and applications. This paper gives a comprehensive review and representative studies of the emerging and current paradigms for GPU-based EI with the focus on [...] Read more.
This paper present contributions to the state-of-the art for graphics processing unit (GPU-based) embedded intelligence (EI) research for architectures and applications. This paper gives a comprehensive review and representative studies of the emerging and current paradigms for GPU-based EI with the focus on the architecture, technologies and applications: (1) First, the overview and classifications of GPU-based EI research are presented to give the full spectrum in this area that also serves as a concise summary of the scope of the paper; (2) Second, various architecture technologies for GPU-based deep learning techniques and applications are discussed in detail; and (3) Third, various architecture technologies for machine learning techniques and applications are discussed. This paper aims to give useful insights for the research area and motivate researchers towards the development of GPU-based EI for practical deployment and applications. Full article
(This article belongs to the Special Issue Emerging Technologies for Computer, Electrical and Systems Engineering)
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33 pages, 8507 KiB  
Review
Embedded Intelligence on FPGA: Survey, Applications and Challenges
by Kah Phooi Seng, Paik Jen Lee and Li Minn Ang
Electronics 2021, 10(8), 895; https://doi.org/10.3390/electronics10080895 - 08 Apr 2021
Cited by 49 | Viewed by 8469
Abstract
Embedded intelligence (EI) is an emerging research field and has the objective to incorporate machine learning algorithms and intelligent decision-making capabilities into mobile and embedded devices or systems. There are several challenges to be addressed to realize efficient EI implementations in hardware such [...] Read more.
Embedded intelligence (EI) is an emerging research field and has the objective to incorporate machine learning algorithms and intelligent decision-making capabilities into mobile and embedded devices or systems. There are several challenges to be addressed to realize efficient EI implementations in hardware such as the need for: (1) high computational processing; (2) low power consumption (or high energy efficiency); and (3) scalability to accommodate different network sizes and topologies. In recent years, an emerging hardware technology which has demonstrated strong potential and capabilities for EI implementations is the FPGA (field programmable gate array) technology. This paper presents an overview and review of embedded intelligence on FPGA with a focus on applications, platforms and challenges. There are four main classification and thematic descriptors which are reviewed and discussed in this paper for EI: (1) EI techniques including machine learning and neural networks, deep learning, expert systems, fuzzy intelligence, swarm intelligence, self-organizing map (SOM) and extreme learning; (2) applications for EI including object detection and recognition, indoor localization and surveillance monitoring, and other EI applications; (3) hardware and platforms for EI; and (4) challenges for EI. The paper aims to introduce interested researchers to this area and motivate the development of practical FPGA solutions for EI deployment. Full article
(This article belongs to the Special Issue Emerging Technologies for Computer, Electrical and Systems Engineering)
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53 pages, 4029 KiB  
Review
Energy Harvesting Strategies for Wireless Sensor Networks and Mobile Devices: A Review
by Marco Grossi
Electronics 2021, 10(6), 661; https://doi.org/10.3390/electronics10060661 - 12 Mar 2021
Cited by 29 | Viewed by 7030
Abstract
Wireless sensor network nodes and mobile devices are normally powered by batteries that, when depleted, must be recharged or replaced. This poses important problems, in particular for sensor nodes that are placed in inaccessible areas or biomedical sensors implanted in the human body [...] Read more.
Wireless sensor network nodes and mobile devices are normally powered by batteries that, when depleted, must be recharged or replaced. This poses important problems, in particular for sensor nodes that are placed in inaccessible areas or biomedical sensors implanted in the human body where the battery replacement is very impractical. Moreover, the depleted battery must be properly disposed of in accordance with national and international regulations to prevent environmental pollution. A very interesting alternative to power mobile devices is energy harvesting where energy sources naturally present in the environment (such as sunlight, thermal gradients and vibrations) are scavenged to provide the power supply for sensor nodes and mobile systems. Since the presence of these energy sources is discontinuous in nature, electronic systems powered by energy harvesting must include a power management system and a storage device to store the scavenged energy. In this paper, the main strategies to design a wireless mobile sensor system powered by energy harvesting are reviewed and different sensor systems powered by such energy sources are presented. Full article
(This article belongs to the Special Issue Emerging Technologies for Computer, Electrical and Systems Engineering)
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