Special Issue "Advances in Embedded Artificial Intelligence and Internet-of-Things"

A special issue of Journal of Low Power Electronics and Applications (ISSN 2079-9268).

Deadline for manuscript submissions: 28 February 2023 | Viewed by 963

Special Issue Editors

Prof. Dr. Lan-Da Van
E-Mail Website
Guest Editor
Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
Interests: design of low-power/high-performance/cost-effective adaptive filter; computer arithmetic; independent component analysis (ICA); multi-dimensional filter; transform; 3-D graphics system; intelligent elevator system; UAV; wearable data fusion system
Dr. Khanh N. Dang
E-Mail Website
Guest Editor
Adaptive Systems Laboratory, University of Aizu, Aizuwakamatsu 965-8580, Fukushima, Japan
Interests: 3-D integrated circuits; networks-on-chip; reliability; neuromorphic computing; computer architecture
Special Issues, Collections and Topics in MDPI journals
Dr. Kun-Chih Chen
E-Mail Website
Guest Editor
Department of Computer Science and Engineering, National Sun Yat-Sen University, Kaohsiung 80421, Taiwan
Interests: multiprocessor SoC (MPSoC) design; neural network learning algorithm design; reliable system design; VLSI/CAD design
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Since edge computing plays an important role in the computing communicating world, embedded artificial intelligence (AI) and embedded Internet of Things (IoT) are cutting-edge research topics. Embedded AI and IoT require lightweight computation and communication complexity and green power/energy with satisfactory accuracy and quality in terms of algorithm, architecture, integrated circuit, system, standard, and application levels. The embedded AI research topics can cover issues of lightweight machine learning, especially for state-of-the-art deep learning. Embedded IoT can include issues of green cyber-physical communications and network systems.

This Special Issue collaborates with the IEEE 15th International Symposium on Embedded Multicore/Manycore Systems-on-Chip (MCSoC), Malaysia, 19–22 December 2022 (https://www.mcsoc-forum.org/). Selected papers from IEEE MCSoC 2022 and the external submissions (not limited to IEEE MCSoC2022) related to the above research topics are welcome in this Special Issue.

Prof. Dr. Lan-Da Van
Dr. Khanh N. Dang
Dr. Kun-Chih Chen
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. Journal of Low Power Electronics and Applications 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 1600 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 (2 papers)

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Research

Article
BIoU: An Improved Bounding Box Regression for Object Detection
J. Low Power Electron. Appl. 2022, 12(4), 51; https://doi.org/10.3390/jlpea12040051 - 28 Sep 2022
Viewed by 133
Abstract
Object detection is a predominant challenge in computer vision and image processing to detect instances of objects of various classes within an image or video. Recently, a new domain of vehicular platforms, e-scooters, has been widely used across domestic and urban environments. The [...] Read more.
Object detection is a predominant challenge in computer vision and image processing to detect instances of objects of various classes within an image or video. Recently, a new domain of vehicular platforms, e-scooters, has been widely used across domestic and urban environments. The driving behavior of e-scooter users significantly differs from other vehicles on the road, and their interactions with pedestrians are also increasing. To ensure pedestrian safety and develop an efficient traffic monitoring system, a reliable object detection system for e-scooters is required. However, existing object detectors based on IoU loss functions suffer various drawbacks when dealing with densely packed objects or inaccurate predictions. To address this problem, a new loss function, balanced-IoU (BIoU), is proposed in this article. This loss function considers the parameterized distance between the centers and the minimum and maximum edges of the bounding boxes to address the localization problem. With the help of synthetic data, a simulation experiment was carried out to analyze the bounding box regression of various losses. Extensive experiments have been carried out on a two-stage object detector, MASK_RCNN, and single-stage object detectors such as YOLOv5n6, YOLOv5x on Microsoft Common Objects in Context, SKU110k, and our custom e-scooter dataset. The proposed loss function demonstrated an increment of 3.70% at APS on the COCO dataset, 6.20% at AP55 on SKU110k, and 9.03% at AP80 of the custom e-scooter dataset. Full article
(This article belongs to the Special Issue Advances in Embedded Artificial Intelligence and Internet-of-Things)
Article
LoRa-Based Wireless Sensors Network for Rockfall and Landslide Monitoring: A Case Study in Pantelleria Island with Portable LoRaWAN Access
J. Low Power Electron. Appl. 2022, 12(3), 47; https://doi.org/10.3390/jlpea12030047 - 07 Sep 2022
Viewed by 515
Abstract
Rockfalls and landslides are hazards triggered from geomorphological and climatic factors other than human interaction. The economic and social impacts are not negligible, therefore the topic has become an important field in the application of remote monitoring. Wireless sensor networks (WSNs) are particularly [...] Read more.
Rockfalls and landslides are hazards triggered from geomorphological and climatic factors other than human interaction. The economic and social impacts are not negligible, therefore the topic has become an important field in the application of remote monitoring. Wireless sensor networks (WSNs) are particularly suited for the deployment of such systems, thanks to the different technologies and topologies that are evolving nowadays. Among these, LoRa modulation technique represents a fitting technical solution for nodes communication in a WSN. In this paper, a smart autonomous LoRa-based rockfall and landslide monitoring system is presented. The structure has been operating in Pantelleria Island, Sicily, Italy. The sensing elements are disposed in sensor nodes arranged in a star topology. Network access to the LoRaWAN and the Internet is provided through gateways using a portable, solar powered device assembly. A system overview concerning both hardware and functionality of the nodes and gateways devices, then a power analysis is reported, and a monthly recorded result is presented, with related discussion. Full article
(This article belongs to the Special Issue Advances in Embedded Artificial Intelligence and Internet-of-Things)
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