High-Performance Embedded Computing

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

Deadline for manuscript submissions: closed (15 August 2023) | Viewed by 14920

Special Issue Editors


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Guest Editor
Division of Software, Yonsei University, Wonju 26493, Republic of Korea
Interests: fault-tolerant computing

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Guest Editor
Department of Digital Systems, University of Thessaly, 41500 Larissa, Greece
Interests: computer systems design; computer architectures; operating systems; real-time systems; computer-based control; robotics; mechatronics, modelling and simulation
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Guest Editor
Department of Embedded Systems Engineering, College of Information Technology, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon 22012, Republic of Korea
Interests: image processing; particularly image compression; motion estimation; demosaicking and image enhancement; computational intelligence, such as fuzzy and rough sets theories
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the past, there was only one computing machine, a personal computer, in each home. However, now, each individual has at least one computing device such as a smartphone, tablet, and/or wearable device. Moreover, we will face the era of the IoT (Internet of Things) shortly, and the number of computing devices will increase exponentially. In this context, an important challenge for developers in the embedded systems domain is to meet non-functional requirements, such as execution time, memory capacity, and energy consumption based on their characteristics. Therefore, embedded engineers should consider and then evaluate different solutions to optimize the performance of a system. In this Special Issue, we are particularly interested in the latest innovative contributions to the design, development, and applications of novel high-performance embedded systems.

Dr. Yohan Ko
Prof. Dr. George K. Adam
Dr. Gwanggil Jeon
Guest Editors

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Keywords

  • high-performance embedded systems
  • resource management
  • reliability
  • dependability
  • energy-efficient embedded systems
  • real-time embedded systems
  • reconfigurable computing
  • parallel computing
  • multi- and many-core computing

Published Papers (8 papers)

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Research

15 pages, 3336 KiB  
Article
High-Performance Embedded System for Offline Signature Verification Problem Using Machine Learning
by Umair Tariq, Zonghai Hu, Rokham Tariq, Muhammad Shahid Iqbal and Muhammad Sadiq
Electronics 2023, 12(5), 1243; https://doi.org/10.3390/electronics12051243 - 4 Mar 2023
Cited by 1 | Viewed by 2414
Abstract
This paper proposes a high-performance embedded system for offline Urdu handwritten signature verification. Though many signature datasets are publicly available in languages such as English, Latin, Chinese, Persian, Arabic, Hindi, and Bengali, no Urdu handwritten datasets were available in the literature. So, in [...] Read more.
This paper proposes a high-performance embedded system for offline Urdu handwritten signature verification. Though many signature datasets are publicly available in languages such as English, Latin, Chinese, Persian, Arabic, Hindi, and Bengali, no Urdu handwritten datasets were available in the literature. So, in this work, an Urdu handwritten signature dataset is created. The proposed embedded system is then used to distinguish genuine and forged signatures based on various features, such as length, pattern, and edges. The system consists of five steps: data acquisition, pre-processing, feature extraction, signature registration, and signature verification. A majority voting (MV) algorithm is used for improved performance and accuracy of the proposed embedded system. In feature extraction, an improved sinusoidal signal multiplied by a Gaussian function at a specific frequency and orientation is used as a 2D Gabor filter. The proposed framework is tested and compared with existing handwritten signature verification methods. Our test results show accuracies of 66.8% for ensemble, 86.34% for k-nearest neighbor (KNN), 93.31% for support vector machine (SVM), and 95.05% for convolutional neural network (CNN). After applying the majority voting algorithm, the overall accuracy can be improved to 95.13%, with a false acceptance rate (FAR) of 0.2% and a false rejection rate (FRR) of 41.29% on private dataset. To test the generalization ability of the proposed model, we also test it on a public dataset of English handwritten signatures and achieve an overall accuracy of 97.46%. Full article
(This article belongs to the Special Issue High-Performance Embedded Computing)
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17 pages, 6557 KiB  
Article
Integration of Interdomain Flow-Rule in Software-Defined Networks for Embedded Internet of Things Devices
by Sabih Khan Afridi, Saleem Iqbal, Kashif Naseer Qureshi, Saqib Majeed and Gwanggil Jeon
Electronics 2023, 12(5), 1172; https://doi.org/10.3390/electronics12051172 - 28 Feb 2023
Viewed by 1496
Abstract
Software-defined networking (SDN) is an evolving technology providing proper segregation between the control part and data-forwarding domain of network devices. The expansion of the Internet of Things (IoTs) and embedded mobile devices increases the volume of traffic at the network backbone and causes [...] Read more.
Software-defined networking (SDN) is an evolving technology providing proper segregation between the control part and data-forwarding domain of network devices. The expansion of the Internet of Things (IoTs) and embedded mobile devices increases the volume of traffic at the network backbone and causes processing costs in the control plane. This directly affects the Ternary Content Addressable Memory (TCAM) of the switches because insufficient space makes it more challenging to manage the flow-entries. In this situation, providing services to specific users who newly authenticate after the successful handoff from the previous SDN domain is challenging. This paper proposes a method for implanting the users’ primary domain’s flow-rules in the serving SDN domain. As the TCAM is already suffering from a short space, it is hard to handle the flow-tables of multiple SDN domains in limited TCAM storage. The SDN-based Integration of the Interdomain Flow-rule in the SDN (IIF-SDN) scheme maximizes the proficiency of the switches by effectively storing flow-table and flow-entries. The effectiveness of the proposed scheme is benchmarked with proactive and reactive SDN approaches. Full article
(This article belongs to the Special Issue High-Performance Embedded Computing)
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15 pages, 6323 KiB  
Article
Multimodal Mood Consistency and Mood Dependency Neural Network Circuit Based on Memristors
by Yangyang Wang, Junwei Sun, Yanfeng Wang and Peng Liu
Electronics 2023, 12(3), 521; https://doi.org/10.3390/electronics12030521 - 19 Jan 2023
Cited by 1 | Viewed by 1503
Abstract
The factors that affect learning efficiency in different environments have been considered in many studies, but multimode mood-consistent learning has not been considered specifically. In this paper, a neural network circuit based on memristors to determine multimode mood consistency and mood dependency was [...] Read more.
The factors that affect learning efficiency in different environments have been considered in many studies, but multimode mood-consistent learning has not been considered specifically. In this paper, a neural network circuit based on memristors to determine multimode mood consistency and mood dependency was constructed. The circuit is composed of a voltage control module, an emotion module, and a synaptic neuron module. Through the voltage control module and emotion module, learning materials with different properties are input into the synaptic neurons. The learning efficiency of different learning materials under different emotions was analyzed in detail. A dual-channel mood-consistent learning was realized, and the mood dependency was further considered. The influence of different channels on the learning was studied to provide ideas for the future development of intelligent brain-like neural networks. Full article
(This article belongs to the Special Issue High-Performance Embedded Computing)
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15 pages, 16532 KiB  
Article
Memristive Circuit Design of Nonassociative Learning under Different Emotional Stimuli
by Junwei Sun, Linhao Zhao, Shiping Wen and Yanfeng Wang
Electronics 2022, 11(23), 3851; https://doi.org/10.3390/electronics11233851 - 22 Nov 2022
Cited by 1 | Viewed by 1316
Abstract
Most memristor-based circuits only consider the mechanism of nonassociative learning, and the effect of emotion on nonassociative learning is ignored. In this paper, a memristive circuit that can realize nonassociative learning under different emotional stimuli is designed. The designed circuit consists of stimulus [...] Read more.
Most memristor-based circuits only consider the mechanism of nonassociative learning, and the effect of emotion on nonassociative learning is ignored. In this paper, a memristive circuit that can realize nonassociative learning under different emotional stimuli is designed. The designed circuit consists of stimulus judgment module, habituation module, sensitization module, emotion module. When different stimuli are applied, habituation or sensitisation is formed based on the intensity and nature of the stimuli. In addition, the influence of emotion on nonassociative is considered. Different emotional stimuli will affect the speed of habituation formation and strong negative stimuli will lead to sensitization. The simulation results on PSPICE show that the circuit can simulate the above complex biological mechanism. The memristive circuit of nonassociative learning under different emotional stimuli provides some references for brain-like systems. Full article
(This article belongs to the Special Issue High-Performance Embedded Computing)
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14 pages, 3686 KiB  
Article
Gated Multi-Attention Feedback Network for Medical Image Super-Resolution
by Jianrun Shang, Xue Zhang, Guisheng Zhang, Wenhao Song, Jinyong Chen, Qilei Li and Mingliang Gao
Electronics 2022, 11(21), 3554; https://doi.org/10.3390/electronics11213554 - 31 Oct 2022
Cited by 6 | Viewed by 1533
Abstract
Medical imaging technology plays a crucial role in the diagnosis and treatment of diseases. However, the captured medical images are often in a low resolution (LR) due to the limited imaging condition. Super-resolution (SR) technology is a feasible solution to enhance the resolution [...] Read more.
Medical imaging technology plays a crucial role in the diagnosis and treatment of diseases. However, the captured medical images are often in a low resolution (LR) due to the limited imaging condition. Super-resolution (SR) technology is a feasible solution to enhance the resolution of a medical image without increasing the hardware cost. However, the existing SR methods often ignore high-frequency details, which results in blurred edges and an unsatisfying visual perception. In this paper, a gated multi-attention feedback network (GAMA) is proposed for medical image SR. Specifically, a gated multi-feedback network is employed as the backbone to extract hierarchical features. Meanwhile, a layer attention feature extraction (LAFE) module is introduced to refine the feature map. In addition, a channel-space attention reconstruction (CSAR) module is built to enhance the representational ability of the semantic feature map. Furthermore, a gradient variance loss is tailored as the regularization in guiding the model learning to regularize the model in generating a faithful high-resolution image with rich textures and sharp edges. The experiments verify the effectiveness of the proposed GAMA compared with the state-of-the-art approaches. Full article
(This article belongs to the Special Issue High-Performance Embedded Computing)
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12 pages, 1151 KiB  
Article
BTM: Boundary Trimming Module for Temporal Action Detection
by Maher Hamdi, Shiping Wen and Yin Yang
Electronics 2022, 11(21), 3520; https://doi.org/10.3390/electronics11213520 - 29 Oct 2022
Viewed by 1168
Abstract
Temporal action detection (TAD) aims to recognize actions as well as their corresponding time spans from an input video. While techniques exist that accurately recognize actions from manually trimmed videos, current TAD solutions often struggle to identify the precise temporal boundaries of each [...] Read more.
Temporal action detection (TAD) aims to recognize actions as well as their corresponding time spans from an input video. While techniques exist that accurately recognize actions from manually trimmed videos, current TAD solutions often struggle to identify the precise temporal boundaries of each action, which are required in many real applications. This paper addresses this problem with a novel Boundary Trimming Module (BTM), a post-processing method that adjusts the temporal boundaries of the detected actions from existing TAD solutions. Specifically, BTM operates based on the classification of frames in the input video, aiming to detect the action more accurately by adjusting the surrounding frames of the start and end frames of the original detection results. Experimental results on the THUMOS14 benchmark data set demonstrate that the BTM significantly improves the performance of several existing TAD methods. Meanwhile, we establish a new state of the art for temporal action detection through the combination of BTM and the previous best TAD solution. Full article
(This article belongs to the Special Issue High-Performance Embedded Computing)
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13 pages, 2609 KiB  
Article
Energy-Efficient Edge Optimization Embedded System Using Graph Theory with 2-Tiered Security
by Tanzila Saba, Amjad Rehman, Khalid Haseeb, Saeed Ali Bahaj and Gwanggil Jeon
Electronics 2022, 11(18), 2942; https://doi.org/10.3390/electronics11182942 - 16 Sep 2022
Cited by 8 | Viewed by 1945
Abstract
The development of the Internet of Things (IoT) network has greatly benefited from the expansion of sensing technologies. These networks interconnect with wireless systems and collaborate with other devices using multi-hop communication. Besides data sensing, these devices also perform other operations such as [...] Read more.
The development of the Internet of Things (IoT) network has greatly benefited from the expansion of sensing technologies. These networks interconnect with wireless systems and collaborate with other devices using multi-hop communication. Besides data sensing, these devices also perform other operations such as compression, aggregation, and transmission. Recently, many solutions have been proposed to overcome the various research challenges of wireless sensor networks; however, energy efficiency with optimized intelligence is still a burning research problem that needs to be tackled. Thus, this paper presents an energy-efficient enabled edge optimization embedded system using graph theory for increasing performance in terms of network lifetime and scalability. First, minimum spanning trees are extracted using artificial intelligence techniques to improve the embedded system for response time and latency performance. Second, the extracted routes are provided with full protection against anonymous access in a two-tiered system. Third, the IoT systems collaborate with mobile sinks, and they need to be authenticated using lightweight techniques for the involvement in routing sensed information. Moreover, edge networks further provide the timely delivery of data to mobile sinks with less overhead on IoT devices. Finally, the proposed system is verified using simulations, revealing its significance to existing approaches. Full article
(This article belongs to the Special Issue High-Performance Embedded Computing)
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12 pages, 3472 KiB  
Article
A QoS Classifier Based on Machine Learning for Next-Generation Optical Communication
by Somia A. Abd El-Mottaleb, Ahmed Métwalli, Abdellah Chehri, Hassan Yousif Ahmed, Medien Zeghid and Akhtar Nawaz Khan
Electronics 2022, 11(16), 2619; https://doi.org/10.3390/electronics11162619 - 21 Aug 2022
Cited by 11 | Viewed by 1590
Abstract
Code classification is essential nowadays, as determining the transmission code at the receiver side is a challenge. A novel algorithm for fixed right shift (FRS) code may be employed in embedded next-generation optical fiber communication (OFC) systems. The code aims to provide various [...] Read more.
Code classification is essential nowadays, as determining the transmission code at the receiver side is a challenge. A novel algorithm for fixed right shift (FRS) code may be employed in embedded next-generation optical fiber communication (OFC) systems. The code aims to provide various quality of services (QoS): audio, video, and data. The Q-factor, bit error rate (BER), and signal-to-noise ratio (SNR) are studied to be used as predictors for machine learning (ML) and used in the design of an embedded QoS classifier. The hypothesis test is used to prove the ML input data robustness. Pearson’s correlation and variance-inflation factor (VIF) are revealed, as they are typical detectors of a data multicollinearity problem. The hypothesis testing shows that the statistical properties for the samples of Q-factor, BER, and SNR are similar to the population dataset, with p-values of 0.98, 0.99, and 0.97, respectively. Pearson’s correlation matrix shows a highly positive correlation between Q-factor and SNR, with 0.9. The highest VIF value is 4.5, resulting in the Q-factor. In the end, the ML evaluation shows promising results as the decision tree (DT) and the random forest (RF) classifiers achieve 94% and 99% accuracy, respectively. Each case’s receiver operating characteristic (ROC) curves are revealed, showing that the RF outperforms the DT classification performance. Full article
(This article belongs to the Special Issue High-Performance Embedded Computing)
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