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Electronics
Special Issues
Machine Learning and Smart Sensing
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Machine Learning and Smart Sensing

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

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 10762

Special Issue Editor

Dr. Bonifacio Martin-del-Brio

E-Mail Website
Guest Editor
Department of Electronics Engineering and Communications, EINA, I3A, University of Zaragoza, 3810-193 Zaragoza, Spain
Interests: artificial neural networks; machine learning; sensors; microprocessor systems

Special Issue Information

Dear Colleagues,

Smart sensing (SS) processes data captured from sensors and other sources in a variety of areas such as ambient intelligence, smart cities, intelligent transportation systems, or electrical grids. In addition, SS contains all the building blocks needed for IoT-based applications for any type of environment (cities, rural zones, etc.).

A network of smart sensors can measure and collect data from the environment for applications related to air and water pollution, meteorology, human activities, health parameters, traffic congestion, and so on. The measured data can be processed locally on the embedded processor (edge computing), and the result can be transmitted for further processing to a central node or the cloud.

Artificial neural networks (ANN) are one of the most relevant technologies for intelligent data processing in SS, with impressive results in image and speech (e.g., deep neural networks running in computers and smartphones), but much more work remains to be done in other applications. The aim of this Special Issue is to bring together academics and industrial practitioners to exchange and discuss the latest innovations and applications of ANN in the domain of smart sensing. Papers addressing, but not limited to, the following topics will be considered for publication:

  • ANN for smart sensing;
  • ANN for the Internet of Things;
  • Hardware implementation of ANN and machine learning algorithms for smart sensors;
  • Real-world applications of smart sensing based on ANN and machine learning algorithms, in areas such as the following:
    • Ambient intelligence;
    • Human activity recognition;
    • Smart health;
    • Environment and weather monitoring, agriculture;
    • Smart homes, home appliances;
    • Smart cities, smart regions;
    • Automation systems and manufacturing;
    • Intelligent transportation systems, connected cars;
    • Energy management systems, smart grids;

Dr. Bonifacio Martin-del-Brio
Guest Editor

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

  • smart sensing
  • smart sensors
  • Internet of Things
  • artificial neural networks
  • machine learning
  • smart cities
  • ambient intelligence
  • home automation
  • environment monitoring
  • intelligent transportation systems

Published Papers (3 papers)

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Research

20 pages, 3501 KiB  
Article
Unsupervised Neural Networks for Identification of Aging Conditions in Li-Ion Batteries
by Pablo Pastor-Flores, Bonifacio Martín-del-Brío, Antonio Bono-Nuez, Iván Sanz-Gorrachategui and Carlos Bernal-Ruiz
Electronics 2021, 10(18), 2294; https://doi.org/10.3390/electronics10182294 - 17 Sep 2021
Cited by 2 | Viewed by 1815
Abstract
This paper explores a new methodology based on data-driven approaches to identify and track degradation processes in Li-ion batteries. Our goal is to study if it is possible to differentiate the state of degradation of cells that present similar aging in terms of [...] Read more.
This paper explores a new methodology based on data-driven approaches to identify and track degradation processes in Li-ion batteries. Our goal is to study if it is possible to differentiate the state of degradation of cells that present similar aging in terms of overall parameters (similar remaining capacity, state of health or internal resistance), but that have had different applications or conditions of use (different discharge currents, depth of discharges, temperatures, etc.). For this purpose, this study proposed to analyze voltage waveforms of cells obtained in cycling tests by using an unsupervised neural network, the Self-Organizing Map (SOM). In this work, a laboratory dataset of real Li-ion cells was used, and the SOM algorithm processed battery cell features, thus carrying out smart sensing of the battery. It was shown that our methodology differentiates the previous conditions of use (history) of a cell, complementing conventional metrics such as the state of health, which could be useful for the growing second-life market because it allows for determining more precisely the state of disease of a battery and assesses its suitability for a specific application. Full article
(This article belongs to the Special Issue Machine Learning and Smart Sensing)
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19 pages, 26781 KiB  
Article
Steel Bar Counting from Images with Machine Learning
by Ana Caren Hernández-Ruiz, Javier Alejandro Martínez-Nieto and Julio David Buldain-Pérez
Electronics 2021, 10(4), 402; https://doi.org/10.3390/electronics10040402 - 07 Feb 2021
Cited by 5 | Viewed by 6318
Abstract
Counting has become a fundamental task for data processing in areas such as microbiology, medicine, agriculture and astrophysics. The proposed SA-CNN-DC (Scale Adaptive—Convolutional Neural Network—Distance Clustering) methodology in this paper is designed for automated counting of steel bars from images. Its design consists [...] Read more.
Counting has become a fundamental task for data processing in areas such as microbiology, medicine, agriculture and astrophysics. The proposed SA-CNN-DC (Scale Adaptive—Convolutional Neural Network—Distance Clustering) methodology in this paper is designed for automated counting of steel bars from images. Its design consists of two Machine Learning techniques: Neural Networks and Clustering. The system has been trained to count round and squared steel bars, obtaining an average detection accuracy of 98.81% and 98.57%, respectively. In the steel industry, counting steel bars is a time consuming task which highly relies on human labour and is prone to errors. Reduction of counting time and resources, safety and productivity of employees and high confidence of the inventory are some of the advantages of the proposed methodology in a steel warehouse. Full article
(This article belongs to the Special Issue Machine Learning and Smart Sensing)
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16 pages, 5122 KiB  
Article
Dimensionality Reduction for Smart IoT Sensors
by Jorge Vizárraga, Roberto Casas, Álvaro Marco and J. David Buldain
Electronics 2020, 9(12), 2035; https://doi.org/10.3390/electronics9122035 - 01 Dec 2020
Cited by 6 | Viewed by 1940
Abstract
Smart IoT sensors are characterized by their ability to sense and process signals, producing high-level information that is usually sent wirelessly while minimising energy consumption and maximising communication efficiency. Systems are getting smarter, meaning that they are providing ever richer information from the [...] Read more.
Smart IoT sensors are characterized by their ability to sense and process signals, producing high-level information that is usually sent wirelessly while minimising energy consumption and maximising communication efficiency. Systems are getting smarter, meaning that they are providing ever richer information from the same raw data. This increasing intelligence can occur at various levels, including in the sensor itself, at the edge, and in the cloud. As sending one byte of data is several orders of magnitude more energy-expensive than processing it, data must be handled as near as possible to its generation. Thus, the intelligence should be located in the sensor; nevertheless, it is not always possible to do so because real data is not always available for designing the algorithms or the hardware capacity is limited. Smart devices detecting data coming from inertial sensors are a good example of this. They generate hundreds of bytes per second (100 Hz, 12-bit sampling of a triaxial accelerometer) but useful information comes out in just a few bytes per minute (number of steps, type of activity, and so forth). We propose a lossy compression method to reduce the dimensionality of raw data from accelerometers, gyroscopes, and magnetometers, while maintaining a high quality of information in the reconstructed signal coming from an embedded device. The implemented method uses an adaptive vector-quantisation algorithm that represents the input data with a limited set of codewords. The adaptive process generates a codebook that evolves to become highly specific for the input data, while providing high compression rates. The codebook’s reconstruction quality is measured with a peak signal-to-noise ratio (PSNR) above 40 dB for a 12-bit representation. Full article
(This article belongs to the Special Issue Machine Learning and Smart Sensing)
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