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Special Issue "Applications of Statistics and Machine Learning in Electronics"

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A special issue of Computation (ISSN 2079-3197). This special issue belongs to the section "Computational Engineering".

Deadline for manuscript submissions: 31 August 2023 | Viewed by 10664

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Special Issue Editors

Prof. Dr. Stefan Hensel
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Guest Editor

Institute for Machine Learning and Analysis, Department for Electrical Engineering, University of Applied Sciences Offenburg, Offenburg, Germany
Interests: autonomous mobile systems; image processing and machine learning

Prof. Dr. Marin B. Marinov

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Guest Editor

Faculty of Electronic Engineering and Technology, Technical University of Sofia, Sofia, Bulgaria
Interests: measurement; sensors; actuators; smart sensors; neural networks

Dr. Malinka Ivanova

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Faculty of Applied Mathematics and Informatics, Technical University of Sofia, Sofia, Bulgaria
Interests: design and analysis of electronic circuit through machine learning; security and privacy

Dr. Maya Dimitrova

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The Institute of Robotics, Bulgarian Academy of Science, PO Box 79, 1113 Sofia, Bulgaria
Interests: human-robot interaction; brain-like intelligent agents; pedagogical rehabilitation; socially competent robotic systems
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Hiroaki Wagatsuma

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Guest Editor

Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Kitakyushu, Japan
Interests: emergent intelligence; episodic memory and emotion; societal robot; computational neuroscience; neuroinformatics; sport biomechanics; rehabilitation support
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It is our great pleasure to invite you to participate in this Special Issue of Computation, named “Applications of Statistics and Machine Learning in Electronics”. It is devoted to better understanding the role of statistics and machine learning in supporting and facilitating a wide variety of engineering tasks in electronics. The Special Issue will publish extended variants of the accepted papers presented at the International Conference on Statistics and Machine Learning in Electronics, but colleagues from all over the world who cannot be a part of the conference are also invited. These papers will follow a rigorous peer-review process to satisfy a high standard of publication.

Statistical methods are utilized in different areas of electronics to model, analyse, and evaluate events, processes, and phenomena. Complex interactions between electronic components, as well as the influence of external or accidental internal factors, can impair the performance of an electronic circuit or device, cause unexpected behaviour and output response, or lead to irreversible damage. Statistical analysis allows malfunctioning components and devices to be thoroughly investigated and corrected. In addition, statistical methods are applied to assess the quality of the manufacturing process, ensuring the production of electronic components and devices that possess characteristics according to technical specifications.

The applications of machine and deep learning in electronics contribute to the study, prediction, and better understanding of the behaviour of electronic circuits and devices. Machine learning algorithms and artificial neural networks can model electronic circuits and solve complex problems. They are also applied in the field of measurement, testing, and diagnostics. Methodologies and models for processing "big data" and building forecasting and analytical models to support decision making and solve engineering problems could be created to implement intelligent electronic systems.

Fuzzy logic implementation in control systems and systems, regulating one or several variables, is also in the scope of the discussion.

The topics of the Special Issue include but are not limited to the following research fields: statistics; machine and deep learning; and fuzzy logic methods, algorithms, techniques, methodologies, and models in:

Electronic circuit design;
Electronic circuit analysis;
Electronic circuit and device testing and diagnosis;
Electronics circuit and device measurement;
Manufacturing of electronic components and devices;
Electronic process management;
Quality management in electronics;
Digital twins.

Prof. Dr. Stefan Hensel
Prof. Dr. Marin B. Marinov
Dr. Malinka Ivanova
Dr. Maya Dimitrova
Dr. Hiroaki Wagatsuma
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. Computation 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 1600 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

machine learning
artificial neural networks
electronics
intelligent systems
manufacturing process
measurement and control
smart sensors
testing and diagnosis

Published Papers (7 papers)

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Research

Open AccessArticle

Stochastic Modeling with Applications in Supply Chain Management and ICT Systems

Meglena Lazarova

and

Fatima Sapundzhi

Computation 2023, 11(2), 21; https://doi.org/10.3390/computation11020021 - 31 Jan 2023

Viewed by 767

Abstract

Fast-growing technology and the development of IT services have yielded the idea of founding a new application of stochastic processes and their properties. We give a new connection between electronic process management and a relatively new stochastic process named the Non-central Polya-Aeppli process. This process is applied as a counting process in the mathematical construction of the given model, and it has been introduced as a counting process in electronic process management. Full article

(This article belongs to the Special Issue Applications of Statistics and Machine Learning in Electronics)

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Open AccessArticle

Precision Calibration of Omnidirectional Camera Using a Statistical Approach

and

Computation 2022, 10(12), 209; https://doi.org/10.3390/computation10120209 - 30 Nov 2022

Viewed by 1118

Abstract

Omnidirectional optoelectronic systems (OOES) find applications in many areas where a wide viewing angle is crucial. The disadvantage of these systems is the large distortion of the images, which makes it difficult to make wide use of them. The purpose of this study is the development an algorithm for the precision calibration of an omnidirectional camera using a statistical approach. The calibration approach comprises three basic stages. The first stage is the formation of a cloud of points characterizing the view field of the virtual perspective camera. In the second stage, a calibration procedure that provides the projection function for the camera calibration is performed. The projection functions of traditional perspective lenses and omnidirectional wide-angle fisheye lenses with a viewing angle of no less than 180° are compared. The construction of the corrected image is performed in the third stage. The developed algorithm makes it possible to obtain an image for part of the field of view of an OOES by correcting the distortion from the original omnidirectional image.Using the developed algorithm, a non-mechanical pivoting camera based on an omnidirectional camera is implemented. The achieved mean squared error of the reproducing points from the original omnidirectional image onto the image with corrected distortion is less than the size of a very few pixels. Full article

(This article belongs to the Special Issue Applications of Statistics and Machine Learning in Electronics)

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Open AccessArticle

Reviewing and Discussing Graph Reduction in Edge Computing Context

Asier Garmendia-Orbegozo

José David Núñez-Gonzalez

and

Miguel Ángel Antón

Computation 2022, 10(9), 161; https://doi.org/10.3390/computation10090161 - 16 Sep 2022

Viewed by 877

Abstract

Much effort has been devoted to transferring efficiently different machine-learning algorithms, and especially deep neural networks, to edge devices in order to fulfill, among others, real-time, storage and energy-consumption issues. The limited resources of edge devices and the necessity for energy saving to lengthen the durability of their batteries, has encouraged an interesting trend in reducing neural networks and graphs, while keeping their predictability almost untouched. In this work, an alternative to the latest techniques for finding these reductions in networks size is proposed, seeking to figure out a simplistic way to shrink networks while maintaining, as far as possible, their predictability testing on well-known datasets. Full article

(This article belongs to the Special Issue Applications of Statistics and Machine Learning in Electronics)

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Open AccessArticle

Comparison and Evaluation of Machine Learning-Based Classification of Hand Gestures Captured by Inertial Sensors

and

Computation 2022, 10(9), 159; https://doi.org/10.3390/computation10090159 - 14 Sep 2022

Cited by 1 | Viewed by 1127

Abstract

Gesture recognition is a topic in computer science and language technology that aims to interpret human gestures with computer programs and many different algorithms. It can be seen as the way computers can understand human body language. Today, the main interaction tools between computers and humans are still the keyboard and mouse. Gesture recognition can be used as a tool for communication with the machine and interaction without any mechanical device such as a keyboard or mouse. In this paper, we present the results of a comparison of eight different machine learning (ML) classifiers in the task of human hand gesture recognition and classification to explore how to efficiently implement one or more tested ML algorithms on an 8-bit AVR microcontroller for on-line human gesture recognition with the intention to gesturally control the mobile robot. The 8-bit AVR microcontrollers are still widely used in the industry, but due to their lack of computational power and limited memory, it is a challenging task to efficiently implement ML algorithms on them for on-line classification. Gestures were recorded by using inertial sensors, gyroscopes, and accelerometers placed at the wrist and index finger. One thousand and eight hundred (1800) hand gestures were recorded and labelled. Six important features were defined for the identification of nine different hand gestures using eight different machine learning classifiers: Decision Tree (DT), Random Forests (RF), Logistic Regression (LR), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) with linear kernel, Naïve Bayes classifier (NB), K-Nearest Neighbours (KNN), and Stochastic Gradient Descent (SGD). All tested algorithms were ranged according to Precision, Recall, and F1-score (abb.: P-R-F1). The best algorithms were SVM (P-R-F1: 0.9865, 0.9861, and 0.0863), and RF (P-R-F1: 0.9863, 0.9861, and 0.0862), but their main disadvantage is their unusability for on-line implementations in 8-bit AVR microcontrollers, as proven in the paper. The next best algorithms have had only slightly poorer performance than SVM and RF: KNN (P-R-F1: 0.9835, 0.9833, and 0.9834) and LR (P-R-F1: 0.9810, 0.9810, and 0.9810). Regarding the implementation on 8-bit microcontrollers, KNN has proven to be inadequate, like SVM and RF. However, the analysis for LR has proved that this classifier could be efficiently implemented on targeted microcontrollers. Having in mind its high F1-score (comparable to SVM, RF, and KNN), this leads to the conclusion that the LR is the most suitable classifier among tested for on-line applications in resource-constrained environments, such as embedded devices based on 8-bit AVR microcontrollers, due to its lower computational complexity in comparison with other tested algorithms. Full article

(This article belongs to the Special Issue Applications of Statistics and Machine Learning in Electronics)

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Open AccessArticle

3D LiDAR Based SLAM System Evaluation with Low-Cost Real-Time Kinematics GPS Solution

Stefan Hensel

Marin B. Marinov

and

Markus Obert

Computation 2022, 10(9), 154; https://doi.org/10.3390/computation10090154 - 04 Sep 2022

Viewed by 3214

Abstract

Positioning mobile systems with high accuracy is a prerequisite for intelligent autonomous behavior, both in industrial environments and in field robotics. This paper describes the setup of a robotic platform and its use for the evaluation of simultaneous localization and mapping (SLAM) algorithms. A configuration using a mobile robot Husky A200, and a LiDAR (light detection and ranging) sensor was used to implement the setup. For verification of the proposed setup, different scan matching methods for odometry determination in indoor and outdoor environments are tested. An assessment of the accuracy of the baseline 3D-SLAM system and the selected evaluation system is presented by comparing different scenarios and test situations. It was shown that the hdl_graph_slam in combination with the LiDAR OS1 and the scan matching algorithms FAST_GICP and FAST_VGICP achieves good mapping results with accuracies up to 2 cm. Full article

(This article belongs to the Special Issue Applications of Statistics and Machine Learning in Electronics)

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Open AccessArticle

Machine Learning and Rules Induction in Support of Analog Amplifier Design

Malinka Ivanova

and

Miona Andrejević Stošović

Computation 2022, 10(9), 145; https://doi.org/10.3390/computation10090145 - 25 Aug 2022

Viewed by 946

Abstract

The aim of the paper is to present a two-step method for facilitating the design of analog amplifiers taking into account the bottom–top approach and utilizing machine learning techniques. The X-chart and a framework describing the specificity of analog circuit design using machine learning are introduced. The possibility of libraries with open machine learning models to support the designer is also discussed. The proposed method is verified for a three-stage amplifier design. In the first step, the stage type is predicted with 89.74% accuracy as the applied learner is a Decision Tree machine learning algorithm. Moreover, two induction rule algorithms are used for predictive logic generation. In the second step, some typical parameters for a given stage are predicted considering four learners: Decision Tree, Random Forest, Gradient Boosted Trees, and Support Vector Machine. The most suitable is found to be Support Vector Machine, which is characterized with the smallest obtained errors. Full article

(This article belongs to the Special Issue Applications of Statistics and Machine Learning in Electronics)

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Open AccessArticle

Calibration Methods of a Portable Polarizing System for Monitoring Optically Inhomogeneous Media

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Computation 2022, 10(8), 131; https://doi.org/10.3390/computation10080131 - 28 Jul 2022

Cited by 1 | Viewed by 1221

Abstract

Theoretical aspects of methods for calibrating Stokes polarimeters are considered. The prospects and opportunities for implementing the presented methods for calibrating portable polarization systems used in biology and medicine are determined. Based on a comparative analysis, a method for calibrating a portable Stokes polarimeter for medical applications is proposed. The chosen method provides the smallest error in measuring the parameters of the Stokes vector for calculating the parameters of optical anisotropy and researching the polarization properties of biological tissues. A series of experimental research and statistical analysis of the spatial distributions of the polarization parameters of the calibration sample was carried out to use the results for forming the instrument matrix of the developed Stokes polarimeter during calibration. Full article

(This article belongs to the Special Issue Applications of Statistics and Machine Learning in Electronics)

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Special Issue "Applications of Statistics and Machine Learning in Electronics"

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