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Micromachines
Special Issues
Embedded Artificial Intelligence for Energy and Sustainability Issues
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Embedded Artificial Intelligence for Energy and Sustainability Issues

A special issue of Micromachines (ISSN 2072-666X). This special issue belongs to the section "E：Engineering and Technology".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 2886

Special Issue Editors

Prof. Dr. Juvenal Rodriguez-Resendiz

E-Mail Website
Guest Editor
Facultad de Ingeniería, Universidad Autónoma de Querétaro, Querétaro 76010, Mexico
Interests: robotics; biomedical applications; instrumentation and measurement; signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. José Manuel Álvarez-Alvarado

E-Mail Website
Guest Editor
Facultad de Ingeniería, Universidad Autónoma de Queretaro, Queretaro 76010, Mexico
Interests: solar energy; power generation; waste heat recovery; control techniques; renewable energy technologies; solar radiation; energy engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence is a technology with the most significant presence in energy and sustainability issues. Integrating this technology into embedded systems can help utilize available resources and develop new technologies focused on improving the environment. This has promoted the development of cutting-edge technologies focused on smart grid projects based on new microsystem architectures, making tasks more efficient for a sustainable society. These systems have been implemented in different areas of industry and research. This Special Issue aims to show innovative findings using artificial intelligence as the main solution in the energy transition of developing technologies with embedded systems for energy and sustainability.

This Special Issue aims to highlight findings on research and developments of embedded artificial intelligence applied to sustainability technologies.

We invite contributions to this Special Issue on topics including but not limited to the following:

Artificial Intelligence techniques focused on sustainability engineering issues:

  • Machine learning;
  • Deep learning;

Optimization of autonomous systems using artificial intelligence;

  • Metaheuristic algorithms;
  • Fuzzy or neural technics;
  • Mixed techniques;

Embedded artificial intelligence;

  • FPGA;
  • DSP;
  • Microcontrollers;

Applications with embedded artificial intelligence;

  • Energy management;
  • Monitoring;
  • Mechatronics;
  • Alternative energy systems;
  • Renewable energy;
  • Information and communication technologies.

Dr. Juvenal Rodriguez-Resendiz
Dr. José Manuel Álvarez-Alvarado
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. Micromachines is an international peer-reviewed open access monthly 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 2600 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.

Dr. Juvenal Rodriguez-Resendiz
Dr. José Manuel Álvarez-Alvarado
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. Micromachines is an international peer-reviewed open access monthly 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 2600 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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14 pages, 3639 KiB  
Article
HedgeRank: Heterogeneity-Aware, Energy-Efficient Partitioning of Personalized PageRank at the Edge
by Young-Ho Gong
Micromachines 2023, 14(9), 1714; https://doi.org/10.3390/mi14091714 - 31 Aug 2023
Viewed by 668
Abstract
Personalized PageRank (PPR) is a widely used graph processing algorithm used to calculate the importance of source nodes in a graph. Generally, PPR is executed by using a high-performance microprocessor of a server, but it needs to be executed on edge devices to [...] Read more.
Personalized PageRank (PPR) is a widely used graph processing algorithm used to calculate the importance of source nodes in a graph. Generally, PPR is executed by using a high-performance microprocessor of a server, but it needs to be executed on edge devices to guarantee data privacy and network latency. However, since PPR has a variety of computation/memory characteristics that vary depending on the graph datasets, it causes performance/energy inefficiency when it is executed on edge devices with limited hardware resources. In this paper, we propose HedgeRank, a heterogeneity-aware, energy-efficient, partitioning technique of personalized PageRank at the edge. HedgeRank partitions the PPR subprocesses and allocates them to appropriate edge devices by considering their computation capability and energy efficiency. When combining low-power and high-performance edge devices, HedgeRank improves the execution time and energy consumption of PPR execution by up to 26.7% and 15.2% compared to the state-of-the-art PPR technique. Full article
(This article belongs to the Special Issue Embedded Artificial Intelligence for Energy and Sustainability Issues)
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12 pages, 486 KiB  
Article
A Deep Learning Approach for Predicting Multiple Sclerosis
by Edgar Rafael Ponce de Leon-Sanchez, Omar Arturo Dominguez-Ramirez, Ana Marcela Herrera-Navarro, Juvenal Rodriguez-Resendiz, Carlos Paredes-Orta and Jorge Domingo Mendiola-Santibañez
Micromachines 2023, 14(4), 749; https://doi.org/10.3390/mi14040749 - 29 Mar 2023
Cited by 2 | Viewed by 1645
Abstract
This paper proposes a deep learning model based on an artificial neural network with a single hidden layer for predicting the diagnosis of multiple sclerosis. The hidden layer includes a regularization term that prevents overfitting and reduces the model complexity. The purposed learning [...] Read more.
This paper proposes a deep learning model based on an artificial neural network with a single hidden layer for predicting the diagnosis of multiple sclerosis. The hidden layer includes a regularization term that prevents overfitting and reduces the model complexity. The purposed learning model achieved higher prediction accuracy and lower loss than four conventional machine learning techniques. A dimensionality reduction method was used to select the most relevant features from 74 gene expression profiles for training the learning models. The analysis of variance test was performed to identify the statistical difference between the mean of the proposed model and the compared classifiers. The experimental results show the effectiveness of the proposed artificial neural network. Full article
(This article belongs to the Special Issue Embedded Artificial Intelligence for Energy and Sustainability Issues)
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