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Selected Papers from the International Symposium on Electronics and Telecommunications ISETC 2020

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (30 April 2021) | Viewed by 95866

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Special Issue Editors

Prof. Dr. Daniel-Ioan Curiac

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Department of Automation and Applied Informatics, Politehnica University of Timisoara, V. Parvan 2, 300223 Timisoara, Romania
Interests: wireless sensor networks; artificial intelligence; wireless sensor and actuator networks; information security; chaotic systems; robot path planning; Internet of Things
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Florin Alexa

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Guest Editor

Department of Communications, Politehnica University Timisoara, 300223 Timisoara, Romania
Interests: audio and image processing; signal processing; antennas; radiocommunications; wireless sensor and actuator networks; audio and video compression
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Prof. Dr. Marius Otesteanu

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Guest Editor

Department of Communications, Politehnica University Timisoara, 300223 Timisoara, Romania
Interests: communications; coding and compression for audio and video signals; pattern recognition and artificial vision; 2D and 3D image sensors
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The 2020 International Symposium on Electronics and Telecommunications (ISETC 20), 14th Edition will be held on 5–6 November, Timisoara, Romania (http://conference.etc.upt.ro/isetc2020/).

The International Symposium on Electronics and Telecommunications (ISETC) will bring together members from academia and industry to present their achievements in electronics and telecommunications. The conference is organized once every two years for the exchange of information on the progress of research and development in electronics and telecommunications.

Authors of selected high-quality papers fits sensors scope from the conference will be invited to submit extended versions of their original papers (50% extensions of contents of the conference paper). In addition to the ISETC 2020 papers, other independent submissions are also welcome. The subject of these contributions should be in the same research topics as the ones in the conference:

Advances on intelligent electronic systems;
Microprocessor and microcontroller applications;
Artificial intelligence and computer visions;
Neural networks, fuzzy systems, evolutionary computing, reinforcement learning;
Instrumentation, sensors, measurement techniques, and devices;
Robotic systems in enhanced perception, autonomous and intelligent integrations;
Automation solutions in dynamically reconfigurable manufacturing processes;
Modern control in power electronics;
Renewable energy power systems;
Modelling and simulation in power electronics;
Network and service management;
Internet of things and sensor networks;
Antenna and RCS measurements.

Prof. Dr. Daniel-Ioan Curiac
Prof. Dr. Florin Alexa
Prof. Dr. Marius Otesteanu
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. Sensors 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 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.

Keywords

Advances on intelligent electronic systems
Artificial intelligence and computer vision
Instrumentation and measurement
Open education and emerging technologies
Power electronics
Signal processing
Telecommunications.

Published Papers (11 papers)

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Research

20 pages, 2858 KiB

Open AccessArticle

Automated Specification-Based Testing of REST APIs

by Ovidiu Baniaș, Diana Florea, Robert Gyalai and Daniel-Ioan Curiac

Sensors 2021, 21(16), 5375; https://doi.org/10.3390/s21165375 - 9 Aug 2021

Cited by 8 | Viewed by 3464

Abstract

Nowadays, REpresentational State Transfer Application Programming Interfaces (REST APIs) are widely used in web applications, hence a plethora of test cases are developed to validate the APIs calls. We propose a solution that automates the generation of test cases for REST APIs based on their specifications. In our approach, apart from the automatic generation of test cases, we provide an option for the user to influence the test case generation process. By adding user interaction, we aim to augment the automatic generation of APIs test cases with human testing expertise and specific context. We use the latest version of OpenAPI 3.x and a wide range of coverage metrics to analyze the functionality and performance of the generated test cases, and non-functional metrics to analyze the performance of the APIs. The experiments proved the effectiveness and practicability of our method. Full article

(This article belongs to the Special Issue Selected Papers from the International Symposium on Electronics and Telecommunications ISETC 2020)

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27 pages, 7848 KiB

Open AccessArticle

Optimization of AUTOSAR Communication Stack in the Context of Advanced Driver Assistance Systems

by Răzvan Bogdan, Mihaela Crișan-Vida, Darius Barmayoun, Loredana Lavinia Staicu, Robert Valentin Puiu, Mădălina Lup and Marius Marcu

Sensors 2021, 21(13), 4561; https://doi.org/10.3390/s21134561 - 2 Jul 2021

Cited by 1 | Viewed by 7160

Abstract

New trends in the automotive industry such as autonomous driving and Car2X require a large amount of data to be exchanged between different devices. Radar sensors are key components in developing vehicles of the future, therefore these devices are used in a large spectrum of applications, where data traffic is of paramount importance. As a result, communication traffic volumes have become more complex, leading to the research of optimization approaches to be applied at the AUTOSAR level. Our paper offers such an optimization solution at the AUTOSAR communication level. The radar sensor is accessed in a remote manner, and the experiments aimed at performance measurements revealed that our solution is superior to the Full AUTOSAR implementation in terms of memory usage and runtime measurements. Full article

(This article belongs to the Special Issue Selected Papers from the International Symposium on Electronics and Telecommunications ISETC 2020)

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15 pages, 5173 KiB

Open AccessCommunication

Dynamic Spectrum Sharing for Future LTE-NR Networks

by Gordana Barb, Florin Alexa and Marius Otesteanu

Sensors 2021, 21(12), 4215; https://doi.org/10.3390/s21124215 - 19 Jun 2021

Cited by 7 | Viewed by 3723

Abstract

5G is the next mobile generation, already being deployed in some countries. It is expected to revolutionize our society, having extremely high target requirements. The use of spectrum is, therefore, tremendously important, as it is a limited and expensive resource. A solution for the spectrum efficiency consists of the use of dynamic spectrum sharing, where an operator can share the spectrum between two different technologies. In this paper, we studied the concept of dynamic spectrum sharing between LTE and 5G New Radio. We presented a solution that allows operators to offer both LTE and New Radio services using the same frequency bands, although in an interleaved mode. We evaluated the performance, in terms of throughput, of a communication system using the dynamic spectrum sharing feature. The results obtained led to the conclusion that using the dynamic spectrum sharing comes with a compromise of a maximum 25% loss on throughput. Nevertheless, the decrease is not that substantial, as the mobile network operator does not need to buy an additional 15 MHz of bandwidth, using the already existing bandwidth of LTE to offer 5G services, leading to cost reduction and an increase in spectrum efficiency. Full article

(This article belongs to the Special Issue Selected Papers from the International Symposium on Electronics and Telecommunications ISETC 2020)

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27 pages, 10587 KiB

Open AccessArticle

Monopulse Secondary Surveillance Radar Coverage—Determinant Factors

by Gheorghe Minteuan, Tudor Palade, Emanuel Puschita, Paul Dolea and Andra Pastrav

Sensors 2021, 21(12), 4198; https://doi.org/10.3390/s21124198 - 18 Jun 2021

Cited by 5 | Viewed by 5837

Abstract

This paper presents a comprehensive study on monopulse secondary surveillance radar (MSSR) coverage. The design and radiation pattern of an improved MSSR antenna is presented herein, highlighting the horizontal and vertical factors of the SUM beam. Moreover, the impact of other determinant factors, such as signal reflection and atmospheric refraction, on the radar coverage were assessed in this work. Real positioning measurement data and coverage simulations were used to support and exemplify theoretical findings. Full article

(This article belongs to the Special Issue Selected Papers from the International Symposium on Electronics and Telecommunications ISETC 2020)

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19 pages, 5012 KiB

Open AccessArticle

Deep Neural Architectures for Contrast Enhanced Ultrasound (CEUS) Focal Liver Lesions Automated Diagnosis

by Cătălin Daniel Căleanu, Cristina Laura Sîrbu and Georgiana Simion

Sensors 2021, 21(12), 4126; https://doi.org/10.3390/s21124126 - 16 Jun 2021

Cited by 25 | Viewed by 3068

Abstract

Computer vision, biomedical image processing and deep learning are related fields with a tremendous impact on the interpretation of medical images today. Among biomedical image sensing modalities, ultrasound (US) is one of the most widely used in practice, since it is noninvasive, accessible, and cheap. Its main drawback, compared to other imaging modalities, like computed tomography (CT) or magnetic resonance imaging (MRI), consists of the increased dependence on the human operator. One important step toward reducing this dependence is the implementation of a computer-aided diagnosis (CAD) system for US imaging. The aim of the paper is to examine the application of contrast enhanced ultrasound imaging (CEUS) to the problem of automated focal liver lesion (FLL) diagnosis using deep neural networks (DNN). Custom DNN designs are compared with state-of-the-art architectures, either pre-trained or trained from scratch. Our work improves on and broadens previous work in the field in several aspects, e.g., a novel leave-one-patient-out evaluation procedure, which further enabled us to formulate a hard-voting classification scheme. We show the effectiveness of our models, i.e., 88% accuracy reported against a higher number of liver lesion types: hepatocellular carcinomas (HCC), hypervascular metastases (HYPERM), hypovascular metastases (HYPOM), hemangiomas (HEM), and focal nodular hyperplasia (FNH). Full article

(This article belongs to the Special Issue Selected Papers from the International Symposium on Electronics and Telecommunications ISETC 2020)

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23 pages, 5108 KiB

Open AccessArticle

An IoT-Based Smart Home Automation System

by Cristina Stolojescu-Crisan, Calin Crisan and Bogdan-Petru Butunoi

Sensors 2021, 21(11), 3784; https://doi.org/10.3390/s21113784 - 30 May 2021

Cited by 111 | Viewed by 51815

Abstract

Home automation has achieved a lot of popularity in recent years, as day-to-day life is getting simpler due to the rapid growth of technology. Almost everything has become digitalized and automatic. In this paper, a system for interconnecting sensors, actuators, and other data sources with the purpose of multiple home automations is proposed. The system is called qToggle and works by leveraging the power of a flexible and powerful Application Programming Interface (API), which represents the foundation of a simple and common communication scheme. The devices used by qToggle are usually sensors or actuators with an upstream network connection implementing the qToggle API. Most devices used by qToggle are based on ESP8266/ESP8285 chips and/or on Raspberry Pi boards. A smartphone application has been developed that allows users to control a series of home appliances and sensors. The qToggle system is user friendly, flexible, and can be further developed by using different devices and add-ons. Full article

(This article belongs to the Special Issue Selected Papers from the International Symposium on Electronics and Telecommunications ISETC 2020)

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26 pages, 33893 KiB

Open AccessArticle

SDN-Based Network Slicing Mechanism for a Scalable 4G/5G Core Network: A Kubernetes Approach

by Robert Botez, Jose Costa-Requena, Iustin-Alexandru Ivanciu, Vlad Strautiu and Virgil Dobrota

Sensors 2021, 21(11), 3773; https://doi.org/10.3390/s21113773 - 29 May 2021

Cited by 20 | Viewed by 6265

Abstract

Managing the large volumes of IoT and M2M traffic requires the evaluation of the scalability and reliability for all the components in the end-to-end system. This includes connectivity, mobile network functions, and application or services receiving and processing the data from end devices. Firstly, this paper discusses the design of a containerized IoT and M2M application and the mechanisms for delivering automated scalability and high availability when deploying it in: (1) the edge using balenaCloud; (2) the Amazon Web Services cloud with EC2 instances; and (3) the dedicated Amazon Web Services IoT service. The experiments showed that there are no significant differences between edge and cloud deployments regarding resource consumption. Secondly, the solutions for scaling the 4G/5G network functions and mobile backhaul that provide the connectivity between devices and IoT/M2M applications are analyzed. In this case, the scalability and high availability of the 4G/5G components are provided by Kubernetes. The experiments showed that our proposed scaling algorithm for network slicing managed with SDN guarantees the necessary radio and network resources for end-to-end high availability. Full article

(This article belongs to the Special Issue Selected Papers from the International Symposium on Electronics and Telecommunications ISETC 2020)

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26 pages, 20500 KiB

Open AccessArticle

End-To-End Computer Vision Framework: An Open-Source Platform for Research and Education

by Ciprian Orhei, Silviu Vert, Muguras Mocofan and Radu Vasiu

Sensors 2021, 21(11), 3691; https://doi.org/10.3390/s21113691 - 26 May 2021

Cited by 15 | Viewed by 4298

Abstract

Computer Vision is a cross-research field with the main purpose of understanding the surrounding environment as closely as possible to human perception. The image processing systems is continuously growing and expanding into more complex systems, usually tailored to the certain needs or applications it may serve. To better serve this purpose, research on the architecture and design of such systems is also important. We present the End-to-End Computer Vision Framework, an open-source solution that aims to support researchers and teachers within the image processing vast field. The framework has incorporated Computer Vision features and Machine Learning models that researchers can use. In the continuous need to add new Computer Vision algorithms for a day-to-day research activity, our proposed framework has an advantage given by the configurable and scalar architecture. Even if the main focus of the framework is on the Computer Vision processing pipeline, the framework offers solutions to incorporate even more complex activities, such as training Machine Learning models. EECVF aims to become a useful tool for learning activities in the Computer Vision field, as it allows the learner and the teacher to handle only the topics at hand, and not the interconnection necessary for visual processing flow. Full article

(This article belongs to the Special Issue Selected Papers from the International Symposium on Electronics and Telecommunications ISETC 2020)

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21 pages, 3147 KiB

Open AccessArticle

A Kalman Filter for Multilinear Forms and Its Connection with Tensorial Adaptive Filters

by Laura-Maria Dogariu, Constantin Paleologu, Jacob Benesty, Cristian-Lucian Stanciu, Claudia-Cristina Oprea and Silviu Ciochină

Sensors 2021, 21(10), 3555; https://doi.org/10.3390/s21103555 - 20 May 2021

Cited by 5 | Viewed by 2175

Abstract

The Kalman filter represents a very popular signal processing tool, with a wide range of applications within many fields. Following a Bayesian framework, the Kalman filter recursively provides an optimal estimate of a set of unknown variables based on a set of noisy observations. Therefore, it fits system identification problems very well. Nevertheless, such scenarios become more challenging (in terms of the convergence and accuracy of the solution) when the parameter space becomes larger. In this context, the identification of linearly separable systems can be efficiently addressed by exploiting tensor-based decomposition techniques. Such multilinear forms can be modeled as rank-1 tensors, while the final solution is obtained by solving and combining low-dimension system identification problems related to the individual components of the tensor. Recently, the identification of multilinear forms was addressed based on the Wiener filter and most well-known adaptive algorithms. In this work, we propose a tensorial Kalman filter tailored to the identification of multilinear forms. Furthermore, we also show the connection between the proposed algorithm and other tensor-based adaptive filters. Simulation results support the theoretical findings and show the appealing performance features of the proposed Kalman filter for multilinear forms. Full article

(This article belongs to the Special Issue Selected Papers from the International Symposium on Electronics and Telecommunications ISETC 2020)

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17 pages, 5454 KiB

Open AccessArticle

Nonparametric Frequency Response Identification for Dc-Dc Converters Based on Spectral Analysis with Automatic Determination of the Perturbation Amplitude

by Marlon Granda, Cristina Fernandez, Andres Barrado and Pablo Zumel

Sensors 2021, 21(9), 3234; https://doi.org/10.3390/s21093234 - 7 May 2021

Cited by 1 | Viewed by 2184

Abstract

Digital control for high switching frequency converter enables new features on DC-DC power conversion for a minimum cost. Frequency response identification is one such enabled functionality used in auto tunning, measurement of components to assess the converter’s state of health, or system stability monitoring. High accuracy, flexibility to operate in open or closed loop, and minimum impact in the converter’s regular operation are the frequency response identification system’s goals. We propose in this paper a nonparametric identification system addressing these main goals. First, it can autoadjust the perturbation size to reduce the perturbation’s impact on the converter’s output quantities. Second, as it is based on spectral analysis, it is suitable for open and closed-loop operation. Third, we demonstrate the identification system’s high accuracy, achieving a very low difference between the experimental measurements and the discrete model used as reference. Full article

(This article belongs to the Special Issue Selected Papers from the International Symposium on Electronics and Telecommunications ISETC 2020)

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21 pages, 2557 KiB

Open AccessArticle

Cluster Analysis and Model Comparison Using Smart Meter Data

by Muhammad Arslan Shaukat, Haafizah Rameeza Shaukat, Zakria Qadir, Hafiz Suliman Munawar, Abbas Z. Kouzani and M. A. Parvez Mahmud

Sensors 2021, 21(9), 3157; https://doi.org/10.3390/s21093157 - 2 May 2021

Cited by 25 | Viewed by 3752

Abstract

Load forecasting plays a crucial role in the world of smart grids. It governs many aspects of the smart grid and smart meter, such as demand response, asset management, investment, and future direction. This paper proposes time-series forecasting for short-term load prediction to unveil the load forecast benefits through different statistical and mathematical models, such as artificial neural networks, auto-regression, and ARIMA. It targets the problem of excessive computational load when dealing with time-series data. It also presents a business case that is used to analyze different clusters to find underlying factors of load consumption and predict the behavior of customers based on different parameters. On evaluating the accuracy of the prediction models, it is observed that ARIMA models with the (P, D, Q) values as (1, 1, 1) were most accurate compared to other values. Full article

(This article belongs to the Special Issue Selected Papers from the International Symposium on Electronics and Telecommunications ISETC 2020)

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