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Electronics
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AI for Embedded Systems
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AI for Embedded Systems

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

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 15889

Special Issue Editors

Dr. József Sütő

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Guest Editor
1. Department of IT Systems and Networks, University of Debrecen, 4028 Debrecen, Hungary
2. Department of IT, Eszterházy Károly Catholic University, 3300 Eger, Hungary
Interests: AI in embedded systems; AI for computer vision
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Stefan Oniga

E-Mail
Guest Editor
Department of Electric, Electronic and Computer Engineering, Technical University of Cluj-Napoca, Baia Mare 430122, Romania
Interests: field-programmable gate arrays; digital design; ambient assisted living
Special Issues, Collections and Topics in MDPI journals
Dr. Alin-Sasa Tisan

E-Mail Website
Guest Editor
Department of Electronic Engineering, Royal Holloway, University of London, Egham TW20 0EX, UK
Interests: adaptive compute acceleration platform designs for pervasive healthcare; ML for neuronal data; data processing in olfaction; signal processing in gas sensors; smart sensors; digital controllers
Prof. Dr. Fernando Morgado-Dias

E-Mail Website
Guest Editor
Madeira Interactive Technologies Institute and ITI/Larsys, Universidade da Madeira, 9000-390 Funchal, Portugal
Interests: artificial neural networks; artificial intelligence; sleep monitoring; FPGA; digital hardware; modeling; renewable and energy policy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The emergence of deep learning has caused a significant breakthrough in the area of machine learning. The idea of using deep networks with new types of layers has been very interesting to researchers as these techniques can automatically build high-level representations of the raw data. Thus, deep learning offers a more generic solution because the feature construction process can be fully automated. The deeper techniques have been successfully applied to many different research fields and have already outperformed many of the state-of-the-art shallow machine learning solutions, mainly in object recognition. However, the usage of deep methods is not always optimal.

The recent developments in hardware technology have enabled both shallow and deep machine learning algorithms to be hardware implementable on various embedded system frameworks. However, due to their constraints, such as computational capacity or power consumption, to mention a few, their implementation suffers significant drawbacks.

The aim of this Special Issue is to encourage researchers to present original and recent developments on shallow and deep machine learning methods that are primarily designed for embedded systems implementation. Topics of interest include, but are not limited to:

  • Bio-inspired systems;
  • Autonomous vehicles;
  • Autonomous robots;
  • Precision agriculture;
  • Health monitoring;
  • Ambient-assisted living;
  • Human activity recognition;
  • Human–machine interaction;
  • AI in computer networks.

Dr. József Sütő
Prof. Dr. Stefan Oniga
Dr. Alin-Sasa Tisan
Prof. Dr. Fernando Morgado-Dias
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

  • Bio-inspired systems
  • Autonomous vehicles
  • Autonomous robots
  • Precision agriculture
  • Health monitoring
  • Ambient-assisted living
  • Human activity recognition
  • Human–machine interaction
  • AI in computer networks

Published Papers (6 papers)

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Research

14 pages, 695 KiB  
Article
Overview of the EEG-Based Classification of Motor Imagery Activities Using Machine Learning Methods and Inference Acceleration with FPGA-Based Cards
by Tamás Majoros and Stefan Oniga
Electronics 2022, 11(15), 2293; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics11152293 - 22 Jul 2022
Cited by 4 | Viewed by 1949
Abstract
In this article, we provide a brief overview of the EEG-based classification of motor imagery activities using machine learning methods. We examined the effect of data segmentation and different neural network structures. By applying proper window size and using a purely convolutional neural [...] Read more.
In this article, we provide a brief overview of the EEG-based classification of motor imagery activities using machine learning methods. We examined the effect of data segmentation and different neural network structures. By applying proper window size and using a purely convolutional neural network, we achieved 97.7% recognition accuracy on data from twenty subjects in three classes. The proposed architecture outperforms several networks used in previous research and makes the motor imagery-based BCI more efficient in some applications. In addition, we examined the performance of the neural network on a FPGA-based card and compared it with the inference speed and accuracy provided by a general-purpose processor. Full article
(This article belongs to the Special Issue AI for Embedded Systems)
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19 pages, 8574 KiB  
Article
An Integrated Analysis Framework of Convolutional Neural Network for Embedded Edge Devices
by Seung-Ho Lim, Shin-Hyeok Kang, Byeong-Hyun Ko, Jaewon Roh, Chaemin Lim and Sang-Young Cho
Electronics 2022, 11(7), 1041; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics11071041 - 26 Mar 2022
Cited by 2 | Viewed by 1662
Abstract
Recently, IoT applications using Deep Neural Network (DNN) to embedded edge devices are increasing. Generally, in the case of DNN applications in the IoT system, training is mainly performed in the server and inference operation is performed on the edge device. The embedded [...] Read more.
Recently, IoT applications using Deep Neural Network (DNN) to embedded edge devices are increasing. Generally, in the case of DNN applications in the IoT system, training is mainly performed in the server and inference operation is performed on the edge device. The embedded edge devices still take a lot of loads in inference operations due to low computing resources, so proper customization of DNN with architectural exploration is required. However, there are few integrated frameworks to facilitate exploration and customization of various DNN models and their operations in embedded edge devices. In this paper, we propose an integrated framework that can explore and customize DNN inference operations of DNN models on embedded edge devices. The framework consists of the GUI interface part, the inference engine part, and the hardware Deep Learning Accelerator (DLA) Virtual Platform (VP) part. Specifically it focuses on Convolutional Neural Network (CNN), and provides integrated interoperability for convolutional neural network models and neural network customization techniques such as quantization and cross-inference functions. In addition, performance estimation is possible by providing hardware DLA VP for embedded edge devices. Those features are provided as web-based GUI interfaces, so users can easily utilize them. Full article
(This article belongs to the Special Issue AI for Embedded Systems)
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13 pages, 1789 KiB  
Article
An Improved Image Enhancement Method for Traffic Sign Detection
by József Sütő
Electronics 2022, 11(6), 871; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics11060871 - 10 Mar 2022
Cited by 7 | Viewed by 2440
Abstract
Traffic sign detection (TRD) is an essential component of advanced driver-assistance systems and an important part of autonomous vehicles, where the goal is to localize image regions that contain traffic signs. Over the last decade, the amount of research on traffic sign detection [...] Read more.
Traffic sign detection (TRD) is an essential component of advanced driver-assistance systems and an important part of autonomous vehicles, where the goal is to localize image regions that contain traffic signs. Over the last decade, the amount of research on traffic sign detection and recognition has significantly increased. Although TRD is a built-in feature in modern cars and several methods have been proposed, it is a challenging problem due to the high computational demand, the large number of traffic signs, complex traffic scenes, and occlusions. In addition, it is not clear how can we perform real-time traffic sign detection in embedded systems. In this paper, we focus on image enhancement, which is the first step of many object detection methods. We propose an improved probability-model-based image enhancement method for traffic sign detection. To demonstrate the efficiency of the proposed method, we compared it with other widely used image enhancement approaches in traffic sign detection. The experimental results show that our method increases the performance of object detection. In combination with the Selective Search object proposal algorithm, the average detection accuracies were 98.64% and 99.1% on the GTSDB and Swedish Traffic Signs datasets. In addition, its relatively low computational cost allows for its usage in embedded systems. Full article
(This article belongs to the Special Issue AI for Embedded Systems)
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16 pages, 4954 KiB  
Article
Using a Flexible IoT Architecture and Sequential AI Model to Recognize and Predict the Production Activities in the Labor-Intensive Manufacturing Site
by Cadmus Yuan, Chic-Chang Wang, Ming-Lun Chang, Wen-Ting Lin, Po-An Lin, Chang-Chi Lee and Zhe-Luen Tsui
Electronics 2021, 10(20), 2540; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10202540 - 18 Oct 2021
Cited by 1 | Viewed by 1801
Abstract
Under the pressures of global market uncertainty and rapid production changes, the labor-intensive industries demand instant manufacturing site information and accurate production forecasting. This research applies sensor modules with noise reduction, information abstracting, and wireless transmission functions to form a flexible internet of [...] Read more.
Under the pressures of global market uncertainty and rapid production changes, the labor-intensive industries demand instant manufacturing site information and accurate production forecasting. This research applies sensor modules with noise reduction, information abstracting, and wireless transmission functions to form a flexible internet of things (IoT) architecture for acquiring field information. Moreover, AI models are used to reveal human activities and predict the output of a group of workstations. The IoT architecture has been implemented in the actual shoe making site. Although there is a 5% missing data issue due to network transmission, neural network models can successfully convert the IoT data to machine utilization. By analyzing the field data, the actual collaboration among the worker team can be revealed. Furthermore, a sequential AI model is applied to learn to capture the characteristics of the team working. This AI model only requires training by 15 min of IoT data, then it can predict the current and next few days’ productions within 10% error. This research confirms that implementing the IoT architecture and applying the AI model enables instant manufacturing monitoring of labor-intensive manufacturing sites and accurate production forecasting. Full article
(This article belongs to the Special Issue AI for Embedded Systems)
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13 pages, 62834 KiB  
Article
Embedded System-Based Sticky Paper Trap with Deep Learning-Based Insect-Counting Algorithm
by József Sütő
Electronics 2021, 10(15), 1754; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10151754 - 21 Jul 2021
Cited by 18 | Viewed by 3496
Abstract
Flying insect detection, identification, and counting are the key components of agricultural pest management. Insect identification is also one of the most challenging tasks in agricultural image processing. With the aid of machine vision and machine learning, traditional (manual) identification and counting can [...] Read more.
Flying insect detection, identification, and counting are the key components of agricultural pest management. Insect identification is also one of the most challenging tasks in agricultural image processing. With the aid of machine vision and machine learning, traditional (manual) identification and counting can be automated. To achieve this goal, a particular data acquisition device and an accurate insect recognition algorithm (model) is necessary. In this work, we propose a new embedded system-based insect trap with an OpenMV Cam H7 microcontroller board, which can be used anywhere in the field without any restrictions (AC power supply, WIFI coverage, human interaction, etc.). In addition, we also propose a deep learning-based insect-counting method where we offer solutions for problems such as the “lack of data” and “false insect detection”. By means of the proposed trap and insect-counting method, spraying (pest swarming) could then be accurately scheduled. Full article
(This article belongs to the Special Issue AI for Embedded Systems)
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20 pages, 4624 KiB  
Article
Smart-Object-Based Reasoning System for Indoor Acoustic Profiling of Elderly Inhabitants
by Jeannette Chin, Alin Tisan, Victor Callaghan and David Chik
Electronics 2021, 10(12), 1433; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10121433 - 15 Jun 2021
Cited by 2 | Viewed by 1936
Abstract
Many countries are facing significant challenges in relation to providing adequate care for their elderly citizens. The roots of these issues are manifold, but include changing demographics, changing behaviours, and a shortage of resources. As has been witnessed in the health sector and [...] Read more.
Many countries are facing significant challenges in relation to providing adequate care for their elderly citizens. The roots of these issues are manifold, but include changing demographics, changing behaviours, and a shortage of resources. As has been witnessed in the health sector and many others in society, technology has much to offer in terms of supporting people’s needs. This paper explores the potential for ambient intelligence to address this challenge by creating a system that is able to passively monitor the home environment, detecting abnormal situations which may indicate that the inhabitant needs help. There are many ways that this might be achieved, but in this paper, we will describe our investigation into an approach involving unobtrusively ’listening’ to sound patterns within the home, which classifies these as either normal daily activities, or abnormal situations. The experimental system we built was composed of an innovative combination of acoustic sensing, artificial intelligence (AI), and the Internet-of-Things (IoT), which we argue in the paper that it provides a cost-effective approach to alerting care providers when an elderly person in their charge needs help. The majority of the innovation in our work concerns the AI in which we employ Machine Learning to classify the sound profiles, analyse the data for abnormal events, and to make decisions for raising alerts with carers. A Neural Network classifier was used to train and identify the sound profiles associated with normal daily routines within a given person’s home, signalling departures from the daily routines that were then used as templates to measure deviations from normality, which were used to make weighted decisions regarding calling for assistance. A practical experimental system was then designed and deployed to evaluate the methods advocated by this research. The methodology involved gathering pre-design and post-design data from both a professionally run residential home and a domestic home. The pre-design data gathered the views on the system design from 11 members of the residential home, using survey questionnaires and focus groups. These data were used to inform the design of the experimental system, which was then deployed in a domestic home setting to gather post-design experimental data. The experimental results revealed that the system was able to detect 84% of abnormal events, and advocated several refinements which would improve the performance of the system. Thus, the research concludes that the system represents an important advancement to the state-of-the-art and, when taken together with the refinements, represents a line of research which has the potential to deliver significant improvements to care provision for the elderly. Full article
(This article belongs to the Special Issue AI for Embedded Systems)
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