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Electronics
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Advances in System Design Automation Using Artificial Intelligence
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Special Issue "Advances in System Design Automation Using Artificial Intelligence"

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

Deadline for manuscript submissions: 30 June 2024 | Viewed by 8985

Special Issue Editors

Dr. Umer Farooq

E-Mail Website
Guest Editor
School of Engineering, University of Sunderland, Sunderland SR6 0AA, UK
Interests: FPGA; CAD; EDA; reconfigurable architectures; routing; machine learning
Dr. Najam Hasan

E-Mail Website
Guest Editor
ECE Department, Dhofar University, Salalah 211, Oman
Interests: communication systems; wireless sensor networks; optimization models; Internet of Things
Dr. Ali Kashif Bashir

E-Mail Website
Guest Editor
Department of Computing and Mathematics, Manchester Metropolitan University, Manchester M15 6BH, UK
Interests: Internet of Things; network security; cloud computing; network function virtualization; wireless networks; 5G
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

System design automation in general, and in electronic design automation in particular, has seen rapid growth over the past few years. It has made the systems ever more capable and powerful. The growth in the popularity of electronic systems is the result of multiple factors, such as shrinking processing technology, improved design processes, better tools, efficient protocols, and ever-improving system optimization models, to name a few. The improved systems have changed human perception about their lives. For example, current handheld devices have more computation power than the supercomputers of a few decades ago. The enormous growth in the popularity and capability of electronic systems has come at the cost of ever-increasing complexity in the design process of these systems. Regarding the state-of-the-art, the complexity of electronic systems is increasing at a rate of 58% per year, whereas the capability of engineers who design those systems is only increasing by 28% per year. Researchers are exploring various avenues where the design of newer and more efficient systems can keep pace with their complexity.

In this regard, we propose a Special Issue on ‘Advances in System Design Automation using Artificial Intelligence’. Lately, artificial intelligence algorithms been applied in almost every aspect of efficient and optimized problem solving. Through this Special Issue, we invite researchers to address the issue of complex system design automation through artificial intelligence algorithms. This Special Issue is particularly interested in research works that provide evidence of optimized system design through artificial intelligence, be it electronic, communication, digital, control systems, etc.

This Special Issue aims to cover the advances in system design automation using various artificial intelligence algorithms and techniques. Topics of interest include, but are not limited to:

  • Efficient embedded system design;
  • Optimized system design automation;
  • Efficient electronic design automation;
  • Artificial intelligence algorithms for system optimization;
  • Machine learning algorithms/techniques for efficient systems;
  • Deep learning techniques/algorithms for optimized systems;
  • Internet of Things and artificial intelligence;
  • Efficient computer-aided design tools;
  • Communication system optimization;
  • Optimized system models;
  • Control system optimization using artificial intelligence/machine learning/deep learning;
  • Cybersecurity;
  • Big data handling through artificial intelligence.

Dr. Umer Farooq
Dr. Najam Hasan
Dr. Ali Kashif Bashir
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 2200 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

  • system design automation
  • electronic design automation
  • artificial intelligence
  • machine learning
  • deep learning
  • Internet of Things

Published Papers (6 papers)

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Research

18 pages, 796 KiB  
Article
Revisiting Homophily Ratio: A Relation-Aware Graph Neural Network for Homophily and Heterophily
by
Wei Huang
,
Xiangshuo Guan
and
Desheng Liu
Electronics 2023, 12(4), 1017; https://doi.org/10.3390/electronics12041017 - 17 Feb 2023
Cited by 1 | Viewed by 1286
Abstract
The graph neural network (GNN) is a type of powerful deep learning model used to process graph data consisting of nodes and edges. Many studies of GNNs have modeled the relationships between the edges and labels of nodes only by homophily/heterophily, where most/few [...] Read more.
The graph neural network (GNN) is a type of powerful deep learning model used to process graph data consisting of nodes and edges. Many studies of GNNs have modeled the relationships between the edges and labels of nodes only by homophily/heterophily, where most/few nodes with the same label tend to have an edge between each other. However, this modeling method cannot describe the multiconnection mode on graphs where homophily can coexist with heterophily. In this work, we propose a transition matrix to describe the relationships between edges and labels at the class level. Through this transition matrix, we constructed a more interpretable GNN in a neighbor-predicting manner, measured the information that the edges can provide for the node classification task, and proposed a method to test whether the labels match the edges. The results show the improvement of the proposed method against state-of-the-art (SOTA) GNNs. We also obtain the following two results: (1) the poor performance of GNNs is highly relevant to the information of edges instead of heterophily, which is always considered the main factor resulting in the decline in performance; and (2) most benchmark heterophilic datasets exhibit the label-edge mismatch problem, leading them to become intractable Full article
(This article belongs to the Special Issue Advances in System Design Automation Using Artificial Intelligence)
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16 pages, 6302 KiB  
Article
Modified Heuristic Computational Techniques for the Resource Optimization in Cognitive Radio Networks (CRNs)
by
Ahmad Bilal
,
Shahzad Latif
,
Sajjad A. Ghauri
,
Oh-Young Song
,
Aaqif Afzaal Abbasi
and
Tehmina Karamat
Electronics 2023, 12(4), 973; https://doi.org/10.3390/electronics12040973 - 15 Feb 2023
Cited by 2 | Viewed by 798
Abstract
With the advancement of internet technologies and multimedia applications, the spectrum scarcity problem is becoming more acute. Thus, spectral-efficient schemes with minimal interference for IoT networks are required. Device-to-device communication (D2D) technology has the potential to solve the issue of spectrum scarcity in [...] Read more.
With the advancement of internet technologies and multimedia applications, the spectrum scarcity problem is becoming more acute. Thus, spectral-efficient schemes with minimal interference for IoT networks are required. Device-to-device communication (D2D) technology has the potential to solve the issue of spectrum scarcity in future wireless networks. Additionally, throughput is considered a non-convex and NP-hard problem, and heuristic approaches are effective in these scenarios. This paper presents two novel heuristic approaches for throughput optimization for D2D users with quality of service (QoS)-aware wireless communication for mobile users (MU): the modified whale colony optimization algorithm (MWOA) and modified non-domination sorted genetic algorithm (MNSGA). The performance of the proposed algorithms is analyzed to show that the proposed mode selection technique efficiently fulfills the QoS requirements. Simulation results show the performance of the proposed heuristic algorithms compared to other understudied approaches. Full article
(This article belongs to the Special Issue Advances in System Design Automation Using Artificial Intelligence)
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23 pages, 741 KiB  
Article
Towards Machine Learning-Based FPGA Backend Flow: Challenges and Opportunities
by
Imran Taj
and
Umer Farooq
Electronics 2023, 12(4), 935; https://doi.org/10.3390/electronics12040935 - 13 Feb 2023
Viewed by 2643
Abstract
Field-Programmable Gate Array (FPGA) is at the core of System on Chip (SoC) design across various Industry 5.0 digital systems—healthcare devices, farming equipment, autonomous vehicles and aerospace gear to name a few. Given that pre-silicon verification using Computer Aided Design (CAD) accounts for [...] Read more.
Field-Programmable Gate Array (FPGA) is at the core of System on Chip (SoC) design across various Industry 5.0 digital systems—healthcare devices, farming equipment, autonomous vehicles and aerospace gear to name a few. Given that pre-silicon verification using Computer Aided Design (CAD) accounts for about 70% of the time and money spent on the design of modern digital systems, this paper summarizes the machine learning (ML)-oriented efforts in different FPGA CAD design steps. With the recent breakthrough of machine learning, FPGA CAD tasks—high-level synthesis (HLS), logic synthesis, placement and routing—are seeing a renewed interest in their respective decision-making steps. We focus on machine learning-based CAD tasks to suggest some pertinent research areas requiring more focus in CAD design. The development of open-source benchmarks optimized for an end-to-end machine learning experience, intra-FPGA optimization, domain-specific accelerators, lack of explainability and federated learning are the issues reviewed to identify important research spots requiring significant focus. The potential of the new cloud-based architectures to understand the application of the right ML algorithms in FPGA CAD decision-making steps is discussed, together with visualizing the scenario of incorporating more intelligence in the cloud platform, with the help of relatively newer technologies such as CAD as Adaptive OpenPlatform Service (CAOS). Altogether, this research explores several research opportunities linked with modern FPGA CAD flow design, which will serve as a single point of reference for modern FPGA CAD flow design. Full article
(This article belongs to the Special Issue Advances in System Design Automation Using Artificial Intelligence)
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13 pages, 1503 KiB  
Article
SNPERS: A Physical Exercise Recommendation System Integrating Statistical Principles and Natural Language Processing
by
Yuer Yang
,
Yifeng Lin
,
Zeguang Chen
,
Yongjia Lei
,
Xingxing Liu
,
Yan Zhang
,
Ying Sun
and
Xiangjie Wang
Electronics 2023, 12(1), 61; https://doi.org/10.3390/electronics12010061 - 23 Dec 2022
Viewed by 885
Abstract
As chronic diseases such as cardiovascular diseases are prevalent and progressively more common in young people, more and more college students are paying attention to exercising, even though they are busy studying. However, some college students are unmindful of their physique and their [...] Read more.
As chronic diseases such as cardiovascular diseases are prevalent and progressively more common in young people, more and more college students are paying attention to exercising, even though they are busy studying. However, some college students are unmindful of their physique and their bodies’ targeted exercise. The exercise they do is either extensive but not refined or too homogeneous. We conducted a statistical analysis of 18,101 college students’ physical examination results. We found that students who exercise regularly but still did not achieve satisfactory results in one or more physical examination items had often exercised in the two unscientific ways mentioned above. This paper presents an intelligent recommendation system that integrates statistical principles and natural language processing, which improves traditional recommendation systems and could provide suitable and targeted exercise suggestions for college students. The R2 increased by about 27.72%. Full article
(This article belongs to the Special Issue Advances in System Design Automation Using Artificial Intelligence)
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19 pages, 6521 KiB  
Article
Scheduling Algorithm for Two-Workshop Production with the Time-Selective Strategy and Backtracking Strategy
by
Xiaohuan Zhang
,
Zhen Wang
,
Dan Zhang
and
Tao Xu
Electronics 2022, 11(23), 4049; https://doi.org/10.3390/electronics11234049 - 06 Dec 2022
Viewed by 812
Abstract
To solve the two-workshop integrated scheduling problem with the same device resources, existing algorithms pay attention to the horizontal parallel processing of the process tree and ignore the tightness between vertical serial processes. A scheduling algorithm for two-workshop production with the time-selective strategy [...] Read more.
To solve the two-workshop integrated scheduling problem with the same device resources, existing algorithms pay attention to the horizontal parallel processing of the process tree and ignore the tightness between vertical serial processes. A scheduling algorithm for two-workshop production with the time-selective strategy and Backtracking Strategy is proposed. The scheduling order of each process in the process tree needs to be determined, which will be completed by the process sequence sequencing strategy. The scheduling plan also needs to be determined, which will be completed using the time-selective scheduling strategy for the two workshops. At the same time, the “reference time” is set for the current scheduling process. To find a better scheduling scheme, the “scheduling reference time” is recorded as T. If the time of the current scheduling process scheme is greater than T, the backtracking adjustment strategy will be used to track the process scheduling scheme. Finally, experiments show that the algorithm not only ensures the parallel processing of parallel processes but also effectively improves the tightness of serial processes and optimizes the results of integrated scheduling. Full article
(This article belongs to the Special Issue Advances in System Design Automation Using Artificial Intelligence)
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22 pages, 5602 KiB  
Article
Zero-Tolerance Security Paradigm for Enterprise-Specific Industrial Internet of Things
by
Usman Tariq
Electronics 2022, 11(23), 3953; https://doi.org/10.3390/electronics11233953 - 29 Nov 2022
Cited by 1 | Viewed by 1162
Abstract
The complex industrial environment of the 21st century is equipped with the Internet of Things platform, with the objective of real-time operational visibility, improved device management and predictive maintenance. To unleash the focused importance of its policy, a secure connectivity must be realized [...] Read more.
The complex industrial environment of the 21st century is equipped with the Internet of Things platform, with the objective of real-time operational visibility, improved device management and predictive maintenance. To unleash the focused importance of its policy, a secure connectivity must be realized through a range of existing and dissimilar devices and data sources. During the conceptualization phase, the authors aimed to compel the following: (a) that restriction of access should be based on the presence of unexpected device actions that may point to a security breach, and (b) ensure the safety of the system by constant tracking of connected devices and data. In this paper, a policy-driven, zero-trust defense model is proposed to address numerous vulnerable entry points, validate device access to legitimate enterprise functions, quarantine unsecure devices, and trigger automated warnings and policy validation for hardware, software, network connectivity and data management. To handle active scanning, bots, passive auditing, outbound threat management, and device interconnections, an experimental environment was put up. This environment provides holistic visibility and a persistent view of all resources, including those that were previously unknown. A steady stream of reliable and authenticated data has helped to develop and adjust a scalable implementation strategy by avoiding recognized anomalous traps. Actual data was aggregated and analyzed to assess the proposed methodology. Comparative analysis of ‘device exposure view, attack path analysis, controlled view of devices, comprehensive vulnerability evaluation, and effective communication of cyber risk’ has proved the effectiveness of the proposed methodology. Full article
(This article belongs to the Special Issue Advances in System Design Automation Using Artificial Intelligence)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Leveraging Swarm Intelligence for Optimal Thermal Camera and Sensor Placement in Industrial Environments
Authors: Hubert Zarzycki; Dawid Ewald; Piotr Prokopowicz
Affiliation: Faculty of Computer Science, Kazimierz Wielki Univesrity in Bydgoszcz, Poland (e-mail: piotrekp@ukw.edu.pl)
Abstract: The strategic placement of thermal cameras and sensors in industrial spaces plays a pivotal role in enhancing process monitoring, safety, and resource optimization. In this research, we harness the potential of a nature-inspired Swarm Intelligence algorithm to address the intricate challenge of sensor placement in industrial settings. Drawing inspiration from collective behaviors in nature, this algorithm has demonstrated its efficacy in solving optimization problems. This study delves into its application for optimizing thermal camera and sensor locations, with a focus on maximizing coverage while minimizing redundancy in the production hull, ultimately leading to enhanced operational efficiency and safety.

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