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Electronics
Volume 10
Issue 4
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Electronics, Volume 10, Issue 4 (February-2 2021) – 160 articles

Cover Story (view full-size image): A method for automatic analysis of dynamic handwritten signatures for biometric verification is presented in this article. Signature features are extracted using a neural network and transformed into a high-dimensional representation constrained to a unit hypersphere. The neural network was trained with the triplet loss algorithm, known from previous successful applications in face image biometrics. Signature feature vectors of different people are located far apart from one another in the latent space, whereas those belonging to the same person lie close together in a tight group. The proposed method’s effectiveness was verified on a new dataset of signatures acquired with a described biometric pen designed for biometric authentication applications. View this paper
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14 pages, 4165 KiB  
Article
Provision of Data to Use in Artificial Intelligence Algorithms for Single Room Heating
by Kristin Majetta, Christoph Clauß and Christoph Nytsch-Geusen
Electronics 2021, 10(4), 523; https://doi.org/10.3390/electronics10040523 - 23 Feb 2021
Cited by 1 | Viewed by 1840
Abstract
This paper describes a way to generate a great amount of data and to use it to find a relation between a room controller and a certain room. Therefore, simulation scenarios are defined and developed that contain different room, location, usage and controller [...] Read more.
This paper describes a way to generate a great amount of data and to use it to find a relation between a room controller and a certain room. Therefore, simulation scenarios are defined and developed that contain different room, location, usage and controller models. With parameter variation and optimization of the corresponding controller parameters a data basis is created with about 5300 entries. On the basis of this data, machine learning algorithms like artificial neural networks can be used to investigate the relation between rooms and their best suited controllers. Full article
(This article belongs to the Special Issue Tools and Languages for Object-Oriented Modeling and Simulation)
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18 pages, 6688 KiB  
Article
Co-Simulation and Data-Driven Based Procedure for Estimation of Nodal Voltage Phasors in Power Distribution Networks Using a Limited Number of Measured Data
by Marinko Barukčić, Toni Varga, Vedrana Jerković Štil and Tin Benšić
Electronics 2021, 10(4), 522; https://doi.org/10.3390/electronics10040522 - 23 Feb 2021
Cited by 1 | Viewed by 2369
Abstract
The paper studies the framework for the application of computational intelligence methods used for estimations in the distribution power system when a decreased number of measured data is present. Due to the lack of all measured data, the estimation of the distribution power [...] Read more.
The paper studies the framework for the application of computational intelligence methods used for estimations in the distribution power system when a decreased number of measured data is present. Due to the lack of all measured data, the estimation of the distribution power system state is very challenging. The paper studies the application of the artificial neural network and metaheuristic optimization in synergy to solve the voltage phasors estimation problem. The proposed method uses a metaheuristic optimization technique to find virtual input data for the physical model of the network. The presented framework is based on the usage of different computational tools in co-simulation configuration. The research output is the proposed co-simulation setup for the estimation in the distribution power system using a decreased and limited number of available measured data. The estimation procedure was applied on four test distribution networks to validate the presented approach. The maximal estimation errors in voltage magnitudes and angles, using the proposed setup, are below 1.75% and 1∘, respectively, without considering the measurement errors. When the measurement errors are taken into account, the proposed procedure estimates voltage magnitudes and angles with errors below 2.5% and 1.4∘, respectively. In the scenario considering the consumers’ load shape, including the uncertainty range of 20%, the maximal estimation errors are below 1% for magnitude and 0.45∘ for the angle taking the measurement errors in the range of 2% into account. Full article
(This article belongs to the Special Issue Optimization and Modeling of Complex Energy Systems)
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18 pages, 4190 KiB  
Article
An Efficient Routing Protocol with Overload Control for Group Mobility in Delay-Tolerant Networking
by Min Wook Kang and Yun Won Chung
Electronics 2021, 10(4), 521; https://doi.org/10.3390/electronics10040521 - 23 Feb 2021
Cited by 3 | Viewed by 2039
Abstract
In delay-tolerant networking (DTN), messages are delivered to destination nodes by using opportunistic contacts between contact nodes, even if stable routing paths from source nodes to destination nodes do not exist. In some DTN network environments, such as military networks, nodes movement follows [...] Read more.
In delay-tolerant networking (DTN), messages are delivered to destination nodes by using opportunistic contacts between contact nodes, even if stable routing paths from source nodes to destination nodes do not exist. In some DTN network environments, such as military networks, nodes movement follows a group movement model, and an efficient DTN routing protocol is required to use the characteristics of group mobility. In this paper, we consider a network environment, where both intra- and intergroup routing are carried out by using DTN protocols. Then, we propose an efficient routing protocol with overload control for group mobility, where delivery predictability for group mobility is defined and proactive overload control is applied. Performance evaluation results show that the proposed protocol had better delivery ratios and overhead ratios than compared protocols, although the delivery latency was increased. Full article
(This article belongs to the Special Issue Delay Tolerant Networks and Applications)
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14 pages, 470 KiB  
Article
Improving Performance Estimation for Design Space Exploration for Convolutional Neural Network Accelerators
by Martin Ferianc, Hongxiang Fan, Divyansh Manocha, Hongyu Zhou, Shuanglong Liu, Xinyu Niu and Wayne Luk
Electronics 2021, 10(4), 520; https://doi.org/10.3390/electronics10040520 - 23 Feb 2021
Cited by 7 | Viewed by 2931
Abstract
Contemporary advances in neural networks (NNs) have demonstrated their potential in different applications such as in image classification, object detection or natural language processing. In particular, reconfigurable accelerators have been widely used for the acceleration of NNs due to their reconfigurability and efficiency [...] Read more.
Contemporary advances in neural networks (NNs) have demonstrated their potential in different applications such as in image classification, object detection or natural language processing. In particular, reconfigurable accelerators have been widely used for the acceleration of NNs due to their reconfigurability and efficiency in specific application instances. To determine the configuration of the accelerator, it is necessary to conduct design space exploration to optimize the performance. However, the process of design space exploration is time consuming because of the slow performance evaluation for different configurations. Therefore, there is a demand for an accurate and fast performance prediction method to speed up design space exploration. This work introduces a novel method for fast and accurate estimation of different metrics that are of importance when performing design space exploration. The method is based on a Gaussian process regression model parametrised by the features of the accelerator and the target NN to be accelerated. We evaluate the proposed method together with other popular machine learning based methods in estimating the latency and energy consumption of our implemented accelerator on two different hardware platforms targeting convolutional neural networks. We demonstrate improvements in estimation accuracy, without the need for significant implementation effort or tuning. Full article
(This article belongs to the Special Issue Recent Advances in Embedded Computing, Intelligence and Applications)
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17 pages, 1689 KiB  
Article
Deep Learning Techniques for Android Botnet Detection
by Suleiman Y. Yerima, Mohammed K. Alzaylaee, Annette Shajan and Vinod P
Electronics 2021, 10(4), 519; https://doi.org/10.3390/electronics10040519 - 23 Feb 2021
Cited by 37 | Viewed by 4741
Abstract
Android is increasingly being targeted by malware since it has become the most popular mobile operating system worldwide. Evasive malware families, such as Chamois, designed to turn Android devices into bots that form part of a larger botnet are becoming prevalent. This calls [...] Read more.
Android is increasingly being targeted by malware since it has become the most popular mobile operating system worldwide. Evasive malware families, such as Chamois, designed to turn Android devices into bots that form part of a larger botnet are becoming prevalent. This calls for more effective methods for detection of Android botnets. Recently, deep learning has gained attention as a machine learning based approach to enhance Android botnet detection. However, studies that extensively investigate the efficacy of various deep learning models for Android botnet detection are currently lacking. Hence, in this paper we present a comparative study of deep learning techniques for Android botnet detection using 6802 Android applications consisting of 1929 botnet applications from the ISCX botnet dataset. We evaluate the performance of several deep learning techniques including: CNN, DNN, LSTM, GRU, CNN-LSTM, and CNN-GRU models using 342 static features derived from the applications. In our experiments, the deep learning models achieved state-of-the-art results based on the ISCX botnet dataset and also outperformed the classical machine learning classifiers. Full article
(This article belongs to the Special Issue High Accuracy Detection of Mobile Malware Using Machine Learning)
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21 pages, 4612 KiB  
Article
Customizable Vector Acceleration in Extreme-Edge Computing: A RISC-V Software/Hardware Architecture Study on VGG-16 Implementation
by Stefano Sordillo, Abdallah Cheikh, Antonio Mastrandrea, Francesco Menichelli and Mauro Olivieri
Electronics 2021, 10(4), 518; https://doi.org/10.3390/electronics10040518 - 23 Feb 2021
Cited by 7 | Viewed by 3681
Abstract
Computing in the cloud-edge continuum, as opposed to cloud computing, relies on high performance processing on the extreme edge of the Internet of Things (IoT) hierarchy. Hardware acceleration is a mandatory solution to achieve the performance requirements, yet it can be tightly tied [...] Read more.
Computing in the cloud-edge continuum, as opposed to cloud computing, relies on high performance processing on the extreme edge of the Internet of Things (IoT) hierarchy. Hardware acceleration is a mandatory solution to achieve the performance requirements, yet it can be tightly tied to particular computation kernels, even within the same application. Vector-oriented hardware acceleration has gained renewed interest to support artificial intelligence (AI) applications like convolutional networks or classification algorithms. We present a comprehensive investigation of the performance and power efficiency achievable by configurable vector acceleration subsystems, obtaining evidence of both the high potential of the proposed microarchitecture and the advantage of hardware customization in total transparency to the software program. Full article
(This article belongs to the Special Issue Advanced Embedded HW/SW Development)
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22 pages, 4782 KiB  
Review
A Survey on Deep Learning Based Methods and Datasets for Monocular 3D Object Detection
by Seong-heum Kim and Youngbae Hwang
Electronics 2021, 10(4), 517; https://doi.org/10.3390/electronics10040517 - 22 Feb 2021
Cited by 29 | Viewed by 6260
Abstract
Owing to recent advancements in deep learning methods and relevant databases, it is becoming increasingly easier to recognize 3D objects using only RGB images from single viewpoints. This study investigates the major breakthroughs and current progress in deep learning-based monocular 3D object detection. [...] Read more.
Owing to recent advancements in deep learning methods and relevant databases, it is becoming increasingly easier to recognize 3D objects using only RGB images from single viewpoints. This study investigates the major breakthroughs and current progress in deep learning-based monocular 3D object detection. For relatively low-cost data acquisition systems without depth sensors or cameras at multiple viewpoints, we first consider existing databases with 2D RGB photos and their relevant attributes. Based on this simple sensor modality for practical applications, deep learning-based monocular 3D object detection methods that overcome significant research challenges are categorized and summarized. We present the key concepts and detailed descriptions of representative single-stage and multiple-stage detection solutions. In addition, we discuss the effectiveness of the detection models on their baseline benchmarks. Finally, we explore several directions for future research on monocular 3D object detection. Full article
(This article belongs to the Special Issue Deep Learning Based Object Detection)
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18 pages, 3275 KiB  
Article
Low-Complexity High-Throughput QC-LDPC Decoder for 5G New Radio Wireless Communication
by Tram Thi Bao Nguyen, Tuy Nguyen Tan and Hanho Lee
Electronics 2021, 10(4), 516; https://doi.org/10.3390/electronics10040516 - 22 Feb 2021
Cited by 26 | Viewed by 6378
Abstract
This paper presents a pipelined layered quasi-cyclic low-density parity-check (QC-LDPC) decoder architecture targeting low-complexity, high-throughput, and efficient use of hardware resources compliant with the specifications of 5G new radio (NR) wireless communication standard. First, a combined min-sum (CMS) decoding algorithm, which is a [...] Read more.
This paper presents a pipelined layered quasi-cyclic low-density parity-check (QC-LDPC) decoder architecture targeting low-complexity, high-throughput, and efficient use of hardware resources compliant with the specifications of 5G new radio (NR) wireless communication standard. First, a combined min-sum (CMS) decoding algorithm, which is a combination of the offset min-sum and the original min-sum algorithm, is proposed. Then, a low-complexity and high-throughput pipelined layered QC-LDPC decoder architecture for enhanced mobile broadband specifications in 5G NR wireless standards based on CMS algorithm with pipeline layered scheduling is presented. Enhanced versions of check node-based processor architectures are proposed to improve the complexity of the LDPC decoders. An efficient minimum-finder for the check node unit architecture that reduces the hardware required for the computation of the first two minima is introduced. Moreover, a low complexity a posteriori information update unit architecture, which only requires one adder array for their operations, is presented. The proposed architecture shows significant improvements in terms of area and throughput compared to other QC-LDPC decoder architectures available in the literature. Full article
(This article belongs to the Special Issue System-on-Chip (SoC) Design and Its Applications)
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11 pages, 2601 KiB  
Article
Research on Evaluation Index of Automotive Antenna Testing
by Meijun Qu, Siyang Sun, Huanhuan Jing and Long Pei
Electronics 2021, 10(4), 515; https://doi.org/10.3390/electronics10040515 - 22 Feb 2021
Cited by 2 | Viewed by 2352
Abstract
Mounted locations and the ground plane structure have remarkable influences on the performance of roof-mounted automotive antennas. To distinguish this influence in radiation, figure of merits (FoMs), including total radiated power (TRP), near-horizontal part radiated power (NHPRP), and cumulative [...] Read more.
Mounted locations and the ground plane structure have remarkable influences on the performance of roof-mounted automotive antennas. To distinguish this influence in radiation, figure of merits (FoMs), including total radiated power (TRP), near-horizontal part radiated power (NHPRP), and cumulative distribution function (CDF), are studied in this paper. It is proved that TRPs are almost the same with different mounting configurations. Because the radiation toward the horizon is a critical performance metric for automotive antennas, NHPRP is analyzed within certain degrees near the horizon. Even though a bigger deviation has been observed in NHPRP, the discrimination between different mounted scenarios is still not enough. Different from TPR and NHPRP, which are efficiency-based FoMs, CDF combines the gain values and the pattern shape together, achieving a comprehensive and intuitive insight into the antenna performance. It is more predictive and distinguishable in terms of the radiation pattern than NHPRP and TRP. Therefore, CDF can be utilized as a good supplement to existing metrics and can better distinguish the radiation performance of different antenna mounting configurations. Full article
(This article belongs to the Section Microwave and Wireless Communications)
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16 pages, 2662 KiB  
Review
Relations between Electronics, Artificial Intelligence and Information Society through Information Society Rules
by Matjaž Gams and Tine Kolenik
Electronics 2021, 10(4), 514; https://doi.org/10.3390/electronics10040514 - 22 Feb 2021
Cited by 16 | Viewed by 5858
Abstract
This paper presents relations between information society (IS), electronics and artificial intelligence (AI) mainly through twenty-four IS laws. The laws not only make up a novel collection, currently non-existing in the literature, but they also highlight the core boosting mechanism for the progress [...] Read more.
This paper presents relations between information society (IS), electronics and artificial intelligence (AI) mainly through twenty-four IS laws. The laws not only make up a novel collection, currently non-existing in the literature, but they also highlight the core boosting mechanism for the progress of what is called the information society and AI. The laws mainly describe the exponential growth in a particular field, be it the processing, storage or transmission capabilities of electronic devices. Other rules describe the relations to production prices and human interaction. Overall, the IS laws illustrate the most recent and most vibrant part of human history based on the unprecedented growth of device capabilities spurred by human innovation and ingenuity. Although there are signs of stalling, at the same time there are still many ways to prolong the fascinating progress of electronics that stimulates the field of artificial intelligence. There are constant leaps in new areas, such as the perception of real-world signals, where AI is already occasionally exceeding human capabilities and will do so even more in the future. In some areas where AI is presumed to be incapable of performing even at a modest level, such as the production of art or programming software, AI is making progress that can sometimes reflect true human skills. Maybe it is time for AI to boost the progress of electronics in return. Full article
(This article belongs to the Special Issue Artificial Intelligence and Ambient Intelligence)
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24 pages, 11933 KiB  
Article
The Design and Development of the Destiny-Class CyberCANOE Hybrid Reality Environment
by Dylan Kobayashi, Ryan Theriot, Noel Kawano, Jack Lam, Eric Wu, Tyson Seto-Mook, Alberto Gonzalez, Ken Uchida, Andrew Guagliardo, Kari Noe and Jason Leigh
Electronics 2021, 10(4), 513; https://doi.org/10.3390/electronics10040513 - 22 Feb 2021
Viewed by 2662
Abstract
The Destiny-class CyberCANOE (Destiny) is a Hybrid Reality environment that provides 20/20 visual acuity in a 13-foot-wide, 320-degree cylindrical structure comprised of tiled passive stereo-capable organic light emitting diode (OLED) displays. Hybrid Reality systems combine surround-screen virtual reality environments with ultra-high-resolution digital project-rooms. [...] Read more.
The Destiny-class CyberCANOE (Destiny) is a Hybrid Reality environment that provides 20/20 visual acuity in a 13-foot-wide, 320-degree cylindrical structure comprised of tiled passive stereo-capable organic light emitting diode (OLED) displays. Hybrid Reality systems combine surround-screen virtual reality environments with ultra-high-resolution digital project-rooms. They are intended as collaborative environments that enable multiple users to work minimally encumbered for long periods of time in rooms surrounded by data in the form of visualizations that benefit from being displayed at resolutions matching visual acuity and/or in stereoscopic 3D. Destiny is unique in that it is the first Hybrid Reality system to use OLED displays and it uses a real-time GPU-based approach for minimizing stereoscopic crosstalk. This paper chronicles the non-trivial engineering research and attention-to-detail that is required to develop a production quality hybrid-reality environment by providing details about Destiny’s design and construction process. This detailed account of how a Hybrid Reality system is designed and constructed from the ground up will help VR researchers and developers understand the engineering complexity of developing such systems. This paper also discusses a GPU-based crosstalk mitigation technique and evaluation, and the use of Microsoft’s augmented reality headset, the HoloLens, as a design and training aid during construction. Full article
(This article belongs to the Special Issue Recent Advances in Virtual Reality and Augmented Reality)
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20 pages, 4679 KiB  
Article
Deep Learning-Based Content Caching in the Fog Access Points
by Sovit Bhandari, Navin Ranjan, Pervez Khan, Hoon Kim and Youn-Sik Hong
Electronics 2021, 10(4), 512; https://doi.org/10.3390/electronics10040512 - 22 Feb 2021
Cited by 13 | Viewed by 3747
Abstract
Proactive caching of the most popular contents in the cache memory of fog-access points (F-APs) is regarded as a promising solution for the 5G and beyond cellular communication to address latency-related issues caused by the unprecedented demand of multimedia data traffic. However, it [...] Read more.
Proactive caching of the most popular contents in the cache memory of fog-access points (F-APs) is regarded as a promising solution for the 5G and beyond cellular communication to address latency-related issues caused by the unprecedented demand of multimedia data traffic. However, it is still challenging to correctly predict the user’s content and store it in the cache memory of the F-APs efficiently as the user preference is dynamic. In this article, to solve this issue to some extent, the deep learning-based content caching (DLCC) method is proposed due to recent advances in deep learning. In DLCC, a 2D CNN-based method is exploited to formulate the caching model. The simulation results in terms of deep learning (DL) accuracy, mean square error (MSE), the cache hit ratio, and the overall system delay is displayed to show that the proposed method outperforms the performance of known DL-based caching strategies, as well as transfer learning-based cooperative caching (LECC) strategy, randomized replacement (RR), and the Zipf’s probability distribution. Full article
(This article belongs to the Special Issue Edge Computing for Internet of Things)
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21 pages, 7736 KiB  
Article
High-Level Renewable Energy Integrated System Frequency Control with SMES-Based Optimized Fractional Order Controller
by Md. Shafiul Alam, Majed A. Alotaibi, Md Ahsanul Alam, Md. Alamgir Hossain, Md Shafiullah, Fahad Saleh Al-Ismail, Md. Mamun Ur Rashid and Mohammad A. Abido
Electronics 2021, 10(4), 511; https://doi.org/10.3390/electronics10040511 - 22 Feb 2021
Cited by 20 | Viewed by 3241
Abstract
The high-level penetration of renewable energy sources (RESs) is the main reason for shifting the conventional centralized power system control paradigm into distributed power system control. This massive integration of RESs faces two main problems: complex controller structure and reduced inertia. Since the [...] Read more.
The high-level penetration of renewable energy sources (RESs) is the main reason for shifting the conventional centralized power system control paradigm into distributed power system control. This massive integration of RESs faces two main problems: complex controller structure and reduced inertia. Since the system frequency stability is directly linked to the system’s total inertia, the renewable integrated system frequency control is badly affected. Thus, a fractional order controller (FOC)-based superconducting magnetic energy storage (SMES) is proposed in this work. The detailed modeling of SMES, FOC, wind, and solar systems, along with the power network, is introduced to facilitate analysis. The FOC-based SMES virtually augments the inertia to stabilize the system frequency in generation and load mismatches. Since the tuning of FOC and SMES controller parameters is challenging due to nonlinearities, the whale optimization algorithm (WOA) is used to optimize the parameters. The optimized FOC-based SMES is tested under fluctuating wind and solar powers. The extensive simulations are carried out using MATLAB Simulink environment considering different scenarios, such as light and high load profile variations, multiple load profile variations, and reduced system inertia. It is observed that the proposed FOC-based SMES improves several performance indices, such as settling time, overshoot, undershoot compared to the conventional technique. Full article
(This article belongs to the Special Issue Advanced Optimization Algorithms for High Penetration of Renewable Energy Sources)
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18 pages, 1924 KiB  
Article
Neural Network Based Robust Lateral Control for an Autonomous Vehicle
by Subrat Kumar Swain, Jagat J. Rath and Kalyana C. Veluvolu
Electronics 2021, 10(4), 510; https://doi.org/10.3390/electronics10040510 - 22 Feb 2021
Cited by 24 | Viewed by 3972
Abstract
The lateral motion of an Automated Vehicle (AV) is highly affected by the model’s uncertainties and unknown external disturbances during its navigation in adverse environmental conditions. Among the variety of controllers, the sliding mode controller (SMC), known for its robustness towards disturbances, is [...] Read more.
The lateral motion of an Automated Vehicle (AV) is highly affected by the model’s uncertainties and unknown external disturbances during its navigation in adverse environmental conditions. Among the variety of controllers, the sliding mode controller (SMC), known for its robustness towards disturbances, is considered to generate a robust control signal under uncertainties. However, conventional SMC suffers from the issue of high frequency oscillations, called chattering. To address the issue of chattering and reduce the effect of unknown external disturbances in the absence of precise model information, a radial basis function neural network (RBFNN) is employed to estimate the equivalent control. Further, a higher order sliding mode (HOSM) based switching control is proposed in this paper to compensate for the effect of external disturbances. The effectiveness of the proposed controller in terms of lane-keeping and lateral stability is demonstrated through simulation in a high-fidelity Carsim-Matlab Simulink environment under a variety of road and environmental conditions. Full article
(This article belongs to the Special Issue AI-Based Autonomous Driving System)
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16 pages, 460 KiB  
Article
Optimal Energy Beamforming to Minimize Transmit Power in a Multi-Antenna Wireless Powered Communication Network
by Iqra Hameed, Pham-Viet Tuan, Mario R. Camana and Insoo Koo
Electronics 2021, 10(4), 509; https://doi.org/10.3390/electronics10040509 - 22 Feb 2021
Cited by 9 | Viewed by 3040
Abstract
In this paper, we study the transmit power minimization problem with optimal energy beamforming in a multi-antenna wireless powered communication network (WPCN). The considered network consists of one hybrid access point (H-AP) with multiple antennae and multiple users with a single antenna each. [...] Read more.
In this paper, we study the transmit power minimization problem with optimal energy beamforming in a multi-antenna wireless powered communication network (WPCN). The considered network consists of one hybrid access point (H-AP) with multiple antennae and multiple users with a single antenna each. The H-AP broadcasts an energy signal on the downlink, using energy beamforming to enhance the efficiency of the transmit energy. In this paper, we jointly optimize the downlink time allocation for wireless energy transfer (WET), the uplink time allocation for each user to send a wireless information signal to the H-AP, the power allocation to each user on the uplink, and the downlink energy beamforming vectors while controlling the transmit power at the H-AP. It is challenging to solve this non-convex complex optimization problem because it is numerically intractable and involves high computational complexity. We exploit a sequential parametric convex approximation (SPCA)-based iterative method, and propose optimal and sub-optimal solutions for the transmit power minimization problem. All the proposed schemes are verified by numerical simulations. Through the simulation results, we present the performance of the proposed schemes based on the effect of the number of transmit antennae and the number of users in the proposed WPCN. Through the performance evaluation, we show that the SPCA-based joint optimization solution performance is superior to other solutions. Full article
(This article belongs to the Special Issue Wireless Network Protocols and Performance Evaluation)
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24 pages, 1825 KiB  
Article
On Increasing the Accuracy of Modeling Multi-Service Overflow Systems with Erlang-Engset-Pascal Streams
by Mariusz Głąbowski, Damian Kmiecik and Maciej Stasiak
Electronics 2021, 10(4), 508; https://doi.org/10.3390/electronics10040508 - 22 Feb 2021
Cited by 7 | Viewed by 2140
Abstract
In this article, we present an analysis of the accuracy level of methods for modeling the multi-service overflow systems that service Erlang, Engset, and Pascal traffic. In systems with traffic overflow, new calls that cannot be serviced by the primary resources are overflown [...] Read more.
In this article, we present an analysis of the accuracy level of methods for modeling the multi-service overflow systems that service Erlang, Engset, and Pascal traffic. In systems with traffic overflow, new calls that cannot be serviced by the primary resources are overflown (directed) to other available resources that can service a given call, that is, to the secondary resources (alternative resources). In the article, we focus on studying the influence of methods for determining the parameters of traffic that overflows to the secondary resources on the accuracy of determining the traffic characteristics of overflow systems. Our analysis revealed that the main source of the inaccuracy of the existing methods is their approach to determining both the average value and the variance of multi-service Pascal traffic streams offered to the secondary resources. Therefore, we proposed a new method for determining the parameters of Pascal overflow traffic. The method is based on the decomposition of multi-service primary resources into single-service resources and the subsequent conversion of Engset and Pascal streams into equivalents of Erlang traffic. The results of the analytical calculations obtained on the basis of the new method are then compared with the results of simulation experiments for a number of selected structures of overflow systems that service Erlang, Engset, and Pascal traffic. The results of the study indicate that the proposed theoretical model has a significantly higher accuracy than the models proposed in the literature. The method can be used in the analysis, dimensioning, and optimization of multi-service telecommunication systems composed of separated resources, for example, mobile cellular systems. Full article
(This article belongs to the Special Issue Telecommunication Networks)
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3 pages, 178 KiB  
Editorial
Micro- and Nanotechnology of Wide-Bandgap Semiconductors
by Anna B. Piotrowska, Eliana Kamińska and Wojciech Wojtasiak
Electronics 2021, 10(4), 507; https://doi.org/10.3390/electronics10040507 - 22 Feb 2021
Cited by 2 | Viewed by 2188
Abstract
Gallium Nitride and Related Wide-Bandgap Semiconductors (WBS) have constantly received a great amount of attention in recent years [...] Full article
(This article belongs to the Special Issue Micro- and Nanotechnology of Wide Bandgap Semiconductors)
18 pages, 916 KiB  
Article
Discrete Time Domain Modeling and Control of a Grid-Connected Four-Wire Split-Link Converter
by Jeroen D. M. De Kooning, Dimitar Bozalakov and Lieven Vandevelde
Electronics 2021, 10(4), 506; https://doi.org/10.3390/electronics10040506 - 21 Feb 2021
Cited by 1 | Viewed by 2345
Abstract
Distributed generation (DG) allows the production of renewable energy where it is consumed, avoiding transport losses. It is envisioned that future DG units will become more intelligent, not just injecting power into the grid but also actively improving the power quality by means [...] Read more.
Distributed generation (DG) allows the production of renewable energy where it is consumed, avoiding transport losses. It is envisioned that future DG units will become more intelligent, not just injecting power into the grid but also actively improving the power quality by means of active power filtering techniques. In this manner, voltage and current harmonics, voltage unbalance or over-voltages can be mitigated. To achieve such a smart DG unit, an appropriate multi-functional converter topology is required, with full control over the currents exchanged with the grid, including the neutral-wire current. For this purpose, this article studies the three-phase four-wire split-link converter. A known problem of the split-link converter is voltage unbalance of the bus capacitors. This mid-point can be balanced either by injecting additional zero-sequence currents into the grid, which return through the neutral wire, or by injecting a compensating current into the mid-point with an additional half-bridge chopper. For both methods, this article presents a discrete time domain model to allow controller design and implementation in digital control. Both techniques are validated and compared by means of simulation results and experiments on a test setup. Full article
(This article belongs to the Special Issue Modeling and Control of Power Electronic Converters in Renewable Energy and Smart Grid Systems)
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18 pages, 2653 KiB  
Article
An Effective Joint Soft-Sensing Strategy for Multi-Information under Diverse Vehicle Driving Scenarios
by Jianfeng Chen, Jiantian Sun, Shulin Hu, Yicai Ye, Haoqian Huang and Chuanye Tang
Electronics 2021, 10(4), 505; https://doi.org/10.3390/electronics10040505 - 21 Feb 2021
Viewed by 1888
Abstract
A variety of accurate information inputs are of great importance for automotive control. In this paper, a novel joint soft-sensing strategy is proposed to obtain multi-information under diverse vehicle driving scenarios. This strategy is realized by an information interaction including three modules: vehicle [...] Read more.
A variety of accurate information inputs are of great importance for automotive control. In this paper, a novel joint soft-sensing strategy is proposed to obtain multi-information under diverse vehicle driving scenarios. This strategy is realized by an information interaction including three modules: vehicle state estimation, road slope observer and vehicle mass determination. In the first module, a variational Bayesian-based adaptive cubature Kalman filter is employed to estimate the vehicle states with the time-variant noise interference. Under the assumption of road continuity, a slope prediction model is proposed to reduce the time delay of the road slope observation. Meanwhile, a fast response nonlinear cubic observer is introduced to design the road slope module. On the basis of the vehicle states and road slope information, the vehicle mass is determined by a forgetting-factor recursive least square algorithm. In the experiments, a contrasted strategy is introduced to analyse and evaluate performance. Results declare that the proposed strategy is effective and has the advantages of low time delay, high accuracy and good stability. Full article
(This article belongs to the Section Circuit and Signal Processing)
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20 pages, 11932 KiB  
Article
An Efficient FPGA Implementation of Richardson-Lucy Deconvolution Algorithm for Hyperspectral Images
by Karine Avagian and Milica Orlandić
Electronics 2021, 10(4), 504; https://doi.org/10.3390/electronics10040504 - 21 Feb 2021
Cited by 4 | Viewed by 3410
Abstract
This paper proposes an implementation of a Richardson-Lucy (RL) deconvolution method to reduce the spatial degradation in hyperspectral images during the image acquisition process. The degradation, modeled by convolution with a point spread function (PSF), is reduced by applying both standard and accelerated [...] Read more.
This paper proposes an implementation of a Richardson-Lucy (RL) deconvolution method to reduce the spatial degradation in hyperspectral images during the image acquisition process. The degradation, modeled by convolution with a point spread function (PSF), is reduced by applying both standard and accelerated RLdeconvolution algorithms on the individual images in spectral bands. Boundary conditions are introduced to maintain a constant image size without distorting the estimated image boundaries. The RL deconvolution algorithm is implemented on a field-programmable gate array (FPGA)-based Xilinx Zynq-7020 System-on-Chip (SoC). The proposed architecture is parameterized with respect to the image size and configurable with respect to the algorithm variant, the number of iterations, and the kernel size by setting the dedicated configuration registers. A speed-up by factors of 61 and 21 are reported compared to software-only and FPGA-based state-of-the-art implementations, respectively. Full article
(This article belongs to the Special Issue Hardware Architectures for Real Time Image Processing)
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18 pages, 3887 KiB  
Article
Analysis of ATO System Operation Scenarios Based on UPPAAL and the Operational Design Domain
by Zicong Meng, Tao Tang, Guodong Wei and Lei Yuan
Electronics 2021, 10(4), 503; https://doi.org/10.3390/electronics10040503 - 21 Feb 2021
Cited by 13 | Viewed by 3944
Abstract
With the gradual maturity of the automatic train operation (ATO) system in subways, its application scope has also expanded to the high-speed railway field. Considering that the ATO system is still in the early stages of operation, it will take time to fully [...] Read more.
With the gradual maturity of the automatic train operation (ATO) system in subways, its application scope has also expanded to the high-speed railway field. Considering that the ATO system is still in the early stages of operation, it will take time to fully mature, and definite specifications of the requirements for system operation have not yet been formed. This paper presents the operational design domain (ODD) of the high-speed railway ATO system and proposes a scenario analysis method based on the operational design domain to obtain the input conditions of the system requirements. The article models and verifies the scenario of the linkage control of the door and platform door based on the UPPAAL tools and extracts the input and expected output of the system requirements of the vehicle ATO system. Combined with the input conditions of the system requirements, the system requirements of the vehicle ATO in this scenario are finally obtained, which provides a reference for future functional specification generation and test case generation. Full article
(This article belongs to the Section Systems & Control Engineering)
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12 pages, 5423 KiB  
Article
Performance Evaluation of LoRa 920 MHz Frequency Band in a Hilly Forested Area
by Bilguunmaa Myagmardulam, Ryu Miura, Fumie Ono, Toshinori Kagawa, Lin Shan, Tadachika Nakayama, Fumihide Kojima and Baasandash Choijil
Electronics 2021, 10(4), 502; https://doi.org/10.3390/electronics10040502 - 20 Feb 2021
Cited by 16 | Viewed by 4508
Abstract
Long-range (LoRa) wireless communication technology has been widely used in many Internet-of-Things (IoT) applications in industry and academia. Radio wave propagation characteristics in forested areas are important to ensure communication quality in forest IoT applications. In this study, 920 MHz band propagation characteristics [...] Read more.
Long-range (LoRa) wireless communication technology has been widely used in many Internet-of-Things (IoT) applications in industry and academia. Radio wave propagation characteristics in forested areas are important to ensure communication quality in forest IoT applications. In this study, 920 MHz band propagation characteristics in forested areas and tree canopy openness were investigated in the Takakuma experimental forest in Kagoshima, Japan. The aim was to evaluate the performance of the LoRa 920 MHz band with spreading factor (SF12) in a forested hilly area. The received signal strength indicator (RSSI) was measured as a function of the distance between the transmitter antenna and ground station (GS). To illustrate the effect of canopy openness on radio wave propagation, sky view factor (SVF) and a forest canopy height model were considered at each location of a successfully received RSSI. A positive correlation was found between the RSSI and SVF. It was found that between the GS and transmitter antenna, if the canopy height is above 23 m, the signal diffracted and RSSI fell to −120 to −127 dBm, so the presence of the obstacle height should be considered. Further research is needed to clarify the detailed tree density between the transmitter and ground station to propose an optimal propagation model for a forested environment. Full article
(This article belongs to the Special Issue IoT Anywhere—A Low Power Sensors and Long-Range communication for IoT)
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16 pages, 6402 KiB  
Article
Bridge Dynamic Cable-Tension Estimation with Interferometric Radar and APES-Based Time-Frequency Analysis
by Jian Wang, Xiang Wang, Chongyi Fan, Yueli Li and Xiaotao Huang
Electronics 2021, 10(4), 501; https://doi.org/10.3390/electronics10040501 - 20 Feb 2021
Cited by 6 | Viewed by 2013
Abstract
Dynamic cable-tension is an important bridge-health indicator. However, it is difficult to be measured precisely and efficiently. A remote bridge dynamic cable-tension measurement method is proposed. It uses an interferometric radar sensor, a time-frequency analysis technique, and a tension estimation approach based on [...] Read more.
Dynamic cable-tension is an important bridge-health indicator. However, it is difficult to be measured precisely and efficiently. A remote bridge dynamic cable-tension measurement method is proposed. It uses an interferometric radar sensor, a time-frequency analysis technique, and a tension estimation approach based on a string-vibration-equation. One radar can measure the displacements of multiple cables aligned on one side of a bridge, at the same time. By solving the string vibration equation, each cable-tension is calculated from its fundamental frequency, which is obtained by time-frequency analyzing a short section of the cable’s whole displacement vector in an overlapped-piecewise manner. An adaptive amplitude and phase estimation (APES) algorithm is used to solve the frequency resolution deterioration problem due to the short duration. Simulations and field experiments with a K band interferometric radar validate that the proposed method is superior to traditional cable-tension measurements in terms of precision, robustness, and efficiency. The proposed method is of great application value in measuring and monitoring large cable-stayed bridges and cable-suspended bridges. Full article
(This article belongs to the Section Microwave and Wireless Communications)
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25 pages, 3979 KiB  
Article
Modeling and Performance Evaluation of Multi-Class Queuing System with QoS and Priority Constraints
by Fahad Mazaed Alotaibi, Israr Ullah and Shakeel Ahmad
Electronics 2021, 10(4), 500; https://doi.org/10.3390/electronics10040500 - 20 Feb 2021
Cited by 4 | Viewed by 2770
Abstract
Many service providers often categorize their users into multi-classes, depending on their service requirements. Each class has strict quality of service (QoS) demands (e.g., minimum required service rate or transfer time) that must be ensured throughout its service. In some cases, priorities are [...] Read more.
Many service providers often categorize their users into multi-classes, depending on their service requirements. Each class has strict quality of service (QoS) demands (e.g., minimum required service rate or transfer time) that must be ensured throughout its service. In some cases, priorities are also assigned in a multi-class user’s environment to ensure that the important class user shall be serviced first. In this paper, we have developed a novel Markov chain based analytical model to investigate and evaluate a multi-class queuing system with a strict QoS requirement and priority constraints. Experimental analysis is conducted for two users classes, i.e., class-1 (may be free/student users) and class-2 (may be paid/research users). Each class requests have strict QoS requirements in terms of the minimum required rate (MRR) that must be ensured throughout its lifetime once the request is admitted into the system. Secondly, class-2 requests have preemption priority over class-1, i.e., if there is no room for newly arriving class-2 requests, then one or more active flows of class-1 can be ejected in order to accommodate high-class requests. Model results are validated through simulation results and performance measures of our interest include blocking probability (BP) of individual classes and the overall system, effect of higher-class jobs on lower-class jobs, and link capacity utilization. The proposed model can be instrumental in developing advanced connection admission control (CAC), efficient resource dimensioning, and capacity planning of the queuing system. Full article
(This article belongs to the Section Systems & Control Engineering)
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12 pages, 5168 KiB  
Article
A Gaussian Beam Mode Analysis Method for 3-D Multi-Reflector Quasi-Optical Systems
by Tianyang Chen, Xiaodong Chen, Yuan Yao, Xiaoming Liu and Junsheng Yu
Electronics 2021, 10(4), 499; https://doi.org/10.3390/electronics10040499 - 20 Feb 2021
Cited by 2 | Viewed by 2080
Abstract
3-D quasi-optical systems have a more comprehensive range of application scenarios, and their analysis and design are more complicated than those of 2-D systems. In this work, we improve Gaussian beam mode analysis (GBMA) to analyze 3-D multi-reflector systems. The expressions of co- [...] Read more.
3-D quasi-optical systems have a more comprehensive range of application scenarios, and their analysis and design are more complicated than those of 2-D systems. In this work, we improve Gaussian beam mode analysis (GBMA) to analyze 3-D multi-reflector systems. The expressions of co- and cross-polarization and their derivations are given and discussed in detail. Furthermore, several 3-D dual-reflector systems with different rotation angles are chosen as simulation examples to assess the validity and precision of 3-D GBMA compared with physical optics (PO) in the commercial software GRASP10. Furthermore, a 3-D double ellipsoidal reflector system with a π/2 rotation angle operating at 183 GHz is designed, manufactured, and tested. Measured results of the system demonstrate that it is in good agreement with the simulated results of 3-D GBMA and PO for both the co- and cross-polarization. By comparing the computing time performance of 3-D GBMA and PO in GRASP10, the high efficiency of 3-D GBMA is clarified. With 3-D GBMA, the field in 3-D quasi-optical systems can be calculated preciously and rapidly. Full article
(This article belongs to the Section Microwave and Wireless Communications)
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13 pages, 5064 KiB  
Article
Demonstration of Digital Retrodirective Method for Solar Power Satellite
by Mudassir Raza and Koji Tanaka
Electronics 2021, 10(4), 498; https://doi.org/10.3390/electronics10040498 - 20 Feb 2021
Cited by 2 | Viewed by 2627
Abstract
This work presents digital retrodirective method to do microwave power transfer (MPT) for solar power satellite (SPS). Due to space environment, there is concern of antenna deformation, which will affect beam forming. Size of SPS is large and synchronization among antenna modules is [...] Read more.
This work presents digital retrodirective method to do microwave power transfer (MPT) for solar power satellite (SPS). Due to space environment, there is concern of antenna deformation, which will affect beam forming. Size of SPS is large and synchronization among antenna modules is difficult. Flexibility regarding frequency selection for MPT is also a requirement for SPS. Digital Retrodirective method determines phase of pilot signal and power signal is transmitted with conjugate phase. Digital Signal Processing (DSP) circuit is used for digital retrodirective method. Experiment is performed without antenna deformation and with antenna deformation cases. Digital retrodirective method performs beam forming without synchronization among antenna modules and corrects effect of antenna deformation successfully. Flexibility for frequency selection is also achieved by the DSP circuit. The presented results confirm that digital retrodirective method is a good candidate for power transfer from SPS. Full article
(This article belongs to the Special Issue Antenna Array Processing for Wireless Power Transfer)
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15 pages, 26018 KiB  
Article
Colorization of Logo Sketch Based on Conditional Generative Adversarial Networks
by Nannan Tian, Yuan Liu, Bo Wu and Xiaofeng Li
Electronics 2021, 10(4), 497; https://doi.org/10.3390/electronics10040497 - 20 Feb 2021
Cited by 6 | Viewed by 3257
Abstract
Logo design is a complex process for designers and color plays a very important role in logo design. The automatic colorization of logo sketch is of great value and full of challenges. In this paper, we propose a new logo design method based [...] Read more.
Logo design is a complex process for designers and color plays a very important role in logo design. The automatic colorization of logo sketch is of great value and full of challenges. In this paper, we propose a new logo design method based on Conditional Generative Adversarial Networks, which can output multiple colorful logos only by providing one logo sketch. We improve the traditional U-Net structure, adding channel attention and spatial attention in the process of skip-connection. In addition, the generator consists of parallel attention-based U-Net blocks, which can output multiple logo images. During the model optimization process, a style loss function is proposed to improve the color diversity of the logos. We evaluate our method on the self-built edges2logos dataset and the public edges2shoes dataset. Experimental results show that our method can generate more colorful and realistic logo images based on simple sketches. Compared to the classic networks, the logos generated by our network are also superior in visual effects. Full article
(This article belongs to the Special Issue Deep Learning for Computer Vision and Pattern Recognition)
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35 pages, 8480 KiB  
Article
Open Information Architecture for Seamless Integration of Renewable Energy Sources
by Armin Veichtlbauer, Oliver Langthaler, Filip Pröstl Andrén, Christian Kasberger and Thomas I. Strasser
Electronics 2021, 10(4), 496; https://doi.org/10.3390/electronics10040496 - 20 Feb 2021
Cited by 4 | Viewed by 2835
Abstract
Electric power systems are currently confronted with a fundamental paradigm change related to its planning and operation, mainly caused by the massive integration of renewables. To allow higher penetration of them within existing grid infrastructures, the “smart grid” makes more efficient use of [...] Read more.
Electric power systems are currently confronted with a fundamental paradigm change related to its planning and operation, mainly caused by the massive integration of renewables. To allow higher penetration of them within existing grid infrastructures, the “smart grid” makes more efficient use of existing resources by integrating appropriate information technologies. Utilising the benefits of such smart grids, it is necessary to develop new automation architectures and control strategies, as well as corresponding information and communication solutions. This makes it possible to effectively use and manage a large amount of dispersed generators and to utilise their “smart” capabilities. The scalability and openness of automation systems currently used by energy utilities have to be improved significantly for handling a high amount of distributed generators. This will be needed to meet the challenges of missing common and open interfaces, as well as the large number of different protocols. In the work at hand, these shortcomings have been tackled by a conceptual solution for open and interoperable information exchange and engineering of automation applications. The approach is characterised by remote controllable services, a generic communication concept, and a formal application modelling method for distributed energy resource components. Additionally, the specification of an access management scheme for distributed energy resources, taking into account different user roles in the smart grid, allowed for a fine-grained distinction of access rights for use cases and actors. As a concrete result of this work, a generic and open communication underlay for smart grid components was developed, providing a flexible and adaptable infrastructure and supporting future smart grid requirements and roll-out. A proof-of-concept validation of the remote controllable service concept based on this infrastructure has been conducted in appropriate laboratory environments to confirm the main benefits of this approach. Full article
(This article belongs to the Special Issue Automation and Electrical Grids)
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20 pages, 4541 KiB  
Article
Deep Learning Methods for Classification of Certain Abnormalities in Echocardiography
by Imayanmosha Wahlang, Arnab Kumar Maji, Goutam Saha, Prasun Chakrabarti, Michal Jasinski, Zbigniew Leonowicz and Elzbieta Jasinska
Electronics 2021, 10(4), 495; https://doi.org/10.3390/electronics10040495 - 20 Feb 2021
Cited by 26 | Viewed by 4940
Abstract
This article experiments with deep learning methodologies in echocardiogram (echo), a promising and vigorously researched technique in the preponderance field. This paper involves two different kinds of classification in the echo. Firstly, classification into normal (absence of abnormalities) or abnormal (presence of abnormalities) [...] Read more.
This article experiments with deep learning methodologies in echocardiogram (echo), a promising and vigorously researched technique in the preponderance field. This paper involves two different kinds of classification in the echo. Firstly, classification into normal (absence of abnormalities) or abnormal (presence of abnormalities) has been done, using 2D echo images, 3D Doppler images, and videographic images. Secondly, based on different types of regurgitation, namely, Mitral Regurgitation (MR), Aortic Regurgitation (AR), Tricuspid Regurgitation (TR), and a combination of the three types of regurgitation are classified using videographic echo images. Two deep-learning methodologies are used for these purposes, a Recurrent Neural Network (RNN) based methodology (Long Short Term Memory (LSTM)) and an Autoencoder based methodology (Variational AutoEncoder (VAE)). The use of videographic images distinguished this work from the existing work using SVM (Support Vector Machine) and also application of deep-learning methodologies is the first of many in this particular field. It was found that deep-learning methodologies perform better than SVM methodology in normal or abnormal classification. Overall, VAE performs better in 2D and 3D Doppler images (static images) while LSTM performs better in the case of videographic images. Full article
(This article belongs to the Special Issue Bioelectronic Technologies and Artificial Intelligence for Medical Diagnosis and Healthcare)
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16 pages, 7001 KiB  
Article
Optimization of a 3D-Printed Permanent Magnet Coupling Using Genetic Algorithm and Taguchi Method
by Ekaterina Andriushchenko, Ants Kallaste, Anouar Belahcen, Toomas Vaimann, Anton Rassõlkin, Hamidreza Heidari and Hans Tiismus
Electronics 2021, 10(4), 494; https://doi.org/10.3390/electronics10040494 - 20 Feb 2021
Cited by 14 | Viewed by 3135
Abstract
In recent decades, the genetic algorithm (GA) has been extensively used in the design optimization of electromagnetic devices. Despite the great merits possessed by the GA, its processing procedure is highly time-consuming. On the contrary, the widely applied Taguchi optimization method is faster [...] Read more.
In recent decades, the genetic algorithm (GA) has been extensively used in the design optimization of electromagnetic devices. Despite the great merits possessed by the GA, its processing procedure is highly time-consuming. On the contrary, the widely applied Taguchi optimization method is faster with comparable effectiveness in certain optimization problems. This study explores the abilities of both methods within the optimization of a permanent magnet coupling, where the optimization objectives are the minimization of coupling volume and maximization of transmitted torque. The optimal geometry of the coupling and the obtained characteristics achieved by both methods are nearly identical. The magnetic torque density is enhanced by more than 20%, while the volume is reduced by 17%. Yet, the Taguchi method is found to be more time-efficient and effective within the considered optimization problem. Thanks to the additive manufacturing techniques, the initial design and the sophisticated geometry of the Taguchi optimal designs are precisely fabricated. The performances of the coupling designs are validated using an experimental setup. Full article
(This article belongs to the Special Issue Robust Design Optimization of Electrical Machines and Devices)
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