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Electronics
Journal Prestige (SJR): 0.548
Citation Impact (citeScore): 3
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  This is an Open Access Journal Open Access journal
ISSN (Print) 2079-9292
Published by MDPI Homepage  [246 journals]
  • Electronics, Vol. 11, Pages 3892: A Survey of DDOS Attack Detection
           Techniques for IoT Systems Using BlockChain Technology

    • Authors: Zulfiqar Ali Khan, Akbar Siami Namin
      First page: 3892
      Abstract: The Internet of Things (IoT) is a network of sensors that helps collect data 24/7 without human intervention. However, the network may suffer from problems such as the low battery, heterogeneity, and connectivity issues due to the lack of standards. Even though these problems can cause several performance hiccups, security issues need immediate attention because hackers access vital personal and financial information and then misuse it. These security issues can allow hackers to hijack IoT devices and then use them to establish a Botnet to launch a Distributed Denial of Service (DDoS) attack. Blockchain technology can provide security to IoT devices by providing secure authentication using public keys. Similarly, Smart Contracts (SCs) can improve the performance of the IoT–blockchain network through automation. However, surveyed work shows that the blockchain and SCs do not provide foolproof security; sometimes, attackers defeat these security mechanisms and initiate DDoS attacks. Thus, developers and security software engineers must be aware of different techniques to detect DDoS attacks. In this survey paper, we highlight different techniques to detect DDoS attacks. The novelty of our work is to classify the DDoS detection techniques according to blockchain technology. As a result, researchers can enhance their systems by using blockchain-based support for detecting threats. In addition, we provide general information about the studied systems and their workings. However, we cannot neglect the recent surveys. To that end, we compare the state-of-the-art DDoS surveys based on their data collection techniques and the discussed DDoS attacks on the IoT subsystems. The study of different IoT subsystems tells us that DDoS attacks also impact other computing systems, such as SCs, networking devices, and power grids. Hence, our work briefly describes DDoS attacks and their impacts on the above subsystems and IoT. For instance, due to DDoS attacks, the targeted computing systems suffer delays which cause tremendous financial and utility losses to the subscribers. Hence, we discuss the impacts of DDoS attacks in the context of associated systems. Finally, we discuss Machine-Learning algorithms, performance metrics, and the underlying technology of IoT systems so that the readers can grasp the detection techniques and the attack vectors. Moreover, associated systems such as Software-Defined Networking (SDN) and Field-Programmable Gate Arrays (FPGA) are a source of good security enhancement for IoT Networks. Thus, we include a detailed discussion of future development encompassing all major IoT subsystems.
      Citation: Electronics
      PubDate: 2022-11-24
      DOI: 10.3390/electronics11233892
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3894: Linear-Matrix-Inequality-Based
           Controller and Observer Design for Induction Machine

    • Authors: Zoltán Németh, Miklós Kuczmann
      First page: 3894
      Abstract: The modeling and drive control of electric machines are still actively researched scientific topics. Most of the existing models contain parameters that have no physical content or cannot be measured at all. For this reason, the use of observers in modern drive control algorithms is necessary. The main goal of this paper is to present the mathematical formalism of a linear matrix inequality (LMI)-based controller-observer design for a tensor product (TP) transformation-based model, including its implementation in a simulation environment. Based solely upon simulation results, the designed observer can provide a stable and accurate state space variable, regardless of the highly nonlinear induction machine model.
      Citation: Electronics
      PubDate: 2022-11-24
      DOI: 10.3390/electronics11233894
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3895: License Plate Detection with
           Attention-Guided Dual Feature Pyramid Networks in Complex Environments

    • Authors: Yu-Jie Xiong, Yong-Bin Gao, Jun-Qing Zhang, Jian-Xin Ren
      First page: 3895
      Abstract: License plate detection plays a significant role in intelligent transportation systems. Convolutional neural networks have shown a remarkable performance and made significant progress for the detection task. Despite these outstanding achievements, license plate detection in complex environments is still a challenging task, due to the noisy background, unpredictable environments and varying shapes and sizes of the license plates. In order to improve the performance of license plate detection in complex environments, we propose a novel approach using an attention-guided dual feature pyramid and a cascaded positioning strategy. At first, the original features of images are extracted by the residual network. In order to make sure that each feature map contains higher- and lower-level semantic information, we utilize a bottom-up and a top-down pathway, respectively. Meanwhile, the proposed attention-guided dual feature pyramid network is used to receive the extracted features for a multilevel feature fusion. Our proposed attention-guided modules contain both spatial and channel attention. Attention-guided modules deduce the attention weights according to channel and spatial dimensions and multiply the calculated result with the input to obtain the refined feature maps. Then, a region proposal network is used to generate the candidate regions for the license plates. Finally, a cascaded positioning network is utilized to obtain the final locations of the license plates. To validate the effectiveness of the proposed method, we conducted a series of experiments on different public datasets. Experiments on AOLP and CCPD validated the effectiveness of our proposed method.
      Citation: Electronics
      PubDate: 2022-11-25
      DOI: 10.3390/electronics11233895
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3896: Detent Force Reduction in Linear
           Interior Permanent Magnet Generator for Direct-Drive Wave Power Conversion
           

    • Authors: Tao Xia, Hang Li, Yongming Xia, Yangfei Zhang, Pengfei Hu
      First page: 3896
      Abstract: The permanent magnet linear generator is widely applied in the direct-drive wave energy converter (DD-WEC) because of its high power density. In this paper, a novel tubular permanent magnet linear generator, which consists of multilayer and interior permanent magnets (MI-TLPMGs), is presented for DD-WEC, which improves the output power and back electromotive force (back EMF) through the flux concentrating effect. However, MI-TLPMGs with multilayer embedded permanent magnets have severe problems regarding force ripples and detent force, which affect the DD-WEC’s dynamics. Therefore, a DD-WEC system with MI-TLPMGs is proposed, and the effect of the detent force on the dynamic performance of the DD-WEC is analyzed theoretically. Then, the L-type auxiliary teeth and magnetic barriers, which are optimized by the Taguchi method, are introduced to minimize the detent force of the MI-TLPMGs. After optimization using the Taguchi method, the amplitude of the detent force is reduced from the initial 21.7 N to 5.2 N, which means it has weakened by nearly 76.1%. Finally, a prototype has been manufactured and measured in the wave tank to verify the optimization results.
      Citation: Electronics
      PubDate: 2022-11-25
      DOI: 10.3390/electronics11233896
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3897: A Novel Knowledge Base Question
           Answering Method Based on Graph Convolutional Network and Optimized Search
           Space

    • Authors: Xia Hou, Jintao Luo, Junzhe Li, Liangguo Wang, Hongbo Yang
      First page: 3897
      Abstract: Knowledge base question answering (KBQA) aims to provide answers to natural language questions from information in the knowledge base. Although many methods perform well when dealing with simple questions, there are still two challenges for complex questions: huge search space and information missing from the query graphs’ structure. To solve these problems, we propose a novel KBQA method based on a graph convolutional network and optimized search space. When generating the query graph, we rank the query graphs by both their semantic and structural similarities with the question. Then, we just use the top k for the next step. In this process, we specifically extract the structure information of the query graphs by a graph convolutional network while extracting semantic information by a pre-trained model. Thus, we can enhance the method’s ability to understand complex questions. We also introduce a constraint function to optimize the search space. Furthermore, we use the beam search algorithm to reduce the search space further. Experiments on the WebQuestionsSP dataset demonstrate that our method outperforms some baseline methods, showing that the structural information of the query graph has a significant impact on the KBQA task.
      Citation: Electronics
      PubDate: 2022-11-25
      DOI: 10.3390/electronics11233897
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3898: Time–Frequency Analysis of
           Experimental Measurements for the Determination of EMI Noise Generators in
           Power Converters

    • Authors: Javier Oyarzun, Iosu Aizpuru, Igor Baraia-Etxaburu
      First page: 3898
      Abstract: In the context of recent decades in which there has been great development in power electronics systems with increasingly better operating characteristics, the study of the factors that affect the behaviour of these systems in terms of electromagnetic interference (EMI) is mandatory. Within this general perspective, it is essential to know the time and frequency characteristics of the switching signals, as they are the main sources of EMI noise. This work analyses the suitability of different spectral analysis techniques, specifically short-time Fourier transform (STFT) and continuous wavelet transform (CWT), to extract the frequency characteristics of the switching signals of a converter. Thus, a test bench based on a half-bridge was designed as a preliminary step in the development of a model of noise generators. After the analysis of the main parameters of these techniques, a comparison of the results obtained was carried out. It was concluded that both techniques are considered valid and complementary; not only that, but they overcome some limitations of the fast Fourier transform (FFT) as they offer the possibility of determining which are the frequency components associated with different types of events occurring at different times.
      Citation: Electronics
      PubDate: 2022-11-25
      DOI: 10.3390/electronics11233898
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3899: A Novel MQTT 5.0-Based Over-the-Air
           Updating Architecture Facilitating Stronger Security

    • Authors: Hung-Yu Chien, Nian-Zu Wang
      First page: 3899
      Abstract: Over-the-air (OTA) updating is a critical mechanism for secure internet of things (IoT) systems for remotely updating the firmware (or keys) of IoT devices. Message queue telemetry transport (MQTT) is a very popular internet of things (IoT) communication protocol globally. Therefore, MQTT also becomes popular in facilitating the OTA mechanism in many IoT platforms, such as the Amazon IoT platform. In these IoT platforms, the MQTT broker acts as the message broker and as an OTA server simultaneously; in these broker-based OTA architectures, it is quite common that an IoT application manager not only uploads the new firmware/software to the broker but also delegates his signing authority on the firmware/software to the same broker. If the broker is secure and trusted, this OTA model works well; however, it incurs lots of security concerns if the broker is not fully trusted or if it is curious. Many MQTT deployments do not own their own brokers, but rely on a third-party broker, which sometimes is a freeware program or is maintained by a curious third party. Therefore, a secure OTA process should protect privacy against these brokers. This paper designs a novel MQTT-based OTA model in which an IoT application manager can fully control the OTA process through an end-to-end (E2E) channel. We design the model using MQTT 5.0’s new features and functions. The analysis shows that the new model greatly enhances security and privacy properties while maintaining high efficiency.
      Citation: Electronics
      PubDate: 2022-11-25
      DOI: 10.3390/electronics11233899
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3900: A Fast and Accurate Approximation of
           IEEE 802.11 Physical Layer Models for Network Simulators

    • Authors: Diego Javier Reinoso-Chisaguano, Xavier Alejandro Flores Cabezas, Juan Pablo Astudillo León, Martha Cecilia Paredes Paredes, Pablo Anibal Lupera Morillo, Luis F. Urquiza-Aguiar
      First page: 3900
      Abstract: Network simulators are used for the research and development of several types of networks. However, one of the limitations of these simulators is the usage of simplified theoretical models of the Packet Error Rate (PER) at the Physical Layer (PHY) of the IEEE 802.11 family of wireless standards. Although the simplified PHY model can significantly reduce the simulation time, the resulting PER can differ considerably from other more realistic results. In this work, we first study several PER theoretical models. Then, we propose a curve fitting algorithm, which is able to obtain a fast and accurate approximation of other PER models. The curve fitting algorithm uses simulated data as input and outputs the coefficients of a simple model that offers a very accurate approximation of the original PER. Finally, we implemented this approximation in the ns-3 network simulator, thus obtaining high realism since now we can select several theoretical PER models or even a more realistic scenario with the effect of the high Peak-to-Average Power Ratio (PAPR) in the signal. The ns-3 results show how the selection of the PER model at the PHY can significantly impact the Packet Loss Rate (PLR) of a scenario composed of a linear chain of several nodes, one of the simplest multi-hop scenarios.
      Citation: Electronics
      PubDate: 2022-11-25
      DOI: 10.3390/electronics11233900
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3901: Certificateless Data Integrity Auditing
           

    • Authors: Genqing Bian, Xusen Guo, Rong Li, Wenjing Qu, Yu Zhao
      First page: 3901
      Abstract: With the rapid development of science and technology, enterprises will provide their customers with cloud data storage services. These massive amounts of data bring huge management costs to enterprises. Therefore, enterprises choose to store their data in professional cloud service providers and have third-party auditors check the integrity of cloud data to ensure security. Although the appearance of auditors reduces the enormous calculation pressure on enterprises, if the number of auditors is not limited, it will also bring an expensive management burden to enterprises. At the same time, in the process of performing data integrity auditing on behalf of the enterprise, auditors may be interested in some sensitive information of the enterprise’s customers (such as customer’s identity and specific content of customer data). Therefore, this paper proposes a remote data integrity auditing scheme based on designated verifiers. An essential feature of the scheme is that the auditor cannot obtain any customer’s identity information and data in the process of auditing; data integrity, the anonymity of the user’s identity, and data privacy are maintained in the process of auditing. Both theoretical analysis and experimental results show that our scheme is efficient and feasible.
      Citation: Electronics
      PubDate: 2022-11-25
      DOI: 10.3390/electronics11233901
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3902: Development of an Enhanced Selective
           Harmonic Elimination for a Single-Phase Multilevel Inverter with Staircase
           Modulation

    • Authors: Govind S., Anilkumar Chappa, K. Dhananjay Rao, Subhojit Dawn, Taha Selim Ustun
      First page: 3902
      Abstract: A low device switching frequency is recommended for the operation of multilevel inverters (MLIs) to achieve reduced switching losses. Selective harmonic elimination (SHE) and total harmonic distortion (THD) minimization are the two primary switching angle estimation methodologies for low-frequency modulation control. In this regard, a new generalized condition has been developed in this paper for the SHE technique. This original condition will give an output voltage with improved THD in comparison to the conventional SHE technique, while achieving its primary objective of eliminating the specific harmonic content from the output voltage. The proposed condition has been formulated by estimating the error associated with the staircase waveform and the desired sinusoidal output at the fundamental frequency. An infinite harmonic count has been considered for the evaluation of the quality of output, to obtain an accurate THD value without any underestimation. The proposed technique is analyzed, and its critical features are studied in Simulink. The effectiveness of the present work has been also validated by the experimental results.
      Citation: Electronics
      PubDate: 2022-11-25
      DOI: 10.3390/electronics11233902
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3903: Research on Topology Recognition
           Technology Based on Intelligent Measurement Switches

    • Authors: Xiong, Du
      First page: 3903
      Abstract: Distribution network topology identification provides information on low-voltage station areas in a power system. However, it requires either heavy computation or additional measuring equipment. This paper proposes topology identification technology based on intelligent measurement switches. The topology is identified by using the characteristic current measured by the designed intelligent measurement switch. The modulation/demodulation method and information encoding method for the topology identification are presented. The communication protocol stack structure and message encapsulation format of the topology identification unit are designed. The experimental verification and analysis show that the topology identification technology proposed in this paper has a short identification time and an identification accuracy of 100%, and it can be widely promoted and applied in low-voltage distribution networks.
      Citation: Electronics
      PubDate: 2022-11-25
      DOI: 10.3390/electronics11233903
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3904: Advanced Data Systems for Energy
           Consumption Optimization and Air Quality Control in Smart Public Buildings
           Using a Versatile Open Source Approach

    • Authors: Giuseppe Starace, Amber Tiwari, Gianpiero Colangelo, Alessandro Massaro
      First page: 3904
      Abstract: This work discusses smart building applications involving the Internet of Things (IoT) which are focused on energy consumption monitoring and forecasting systems, as well as indoor air quality (IAQ) control. Low-cost hardware integrating sensors and open source platforms are implemented for cloud data transmission, data storage and data processing. Advanced data analytics is performed by the seasonal autoregressive integrated moving average (SARIMA) method and a long short-term memory (LSTM) neural network with an accurate calculation performance about energy predictions. The proposed results are developed within the framework of the R&D project Data System Platform for Smart Communities (D-SySCOM), which is oriented to a smart public building application. The main goal of the work was to define a guideline-matching energy efficiency with wellness in public indoor environments, by providing modular low-cost solutions which are easily implementable for advanced data processing. The implemented technologies are suitable to define an efficient organizational user protocol based on energy efficiency and worker wellness. The estimated performance of mean square error (MSE) of 0.01 of the adopted algorithms proves the efficiency of the implemented building monitoring system in terms of energy consumption forecasting. In addition, the possibility of designing and implementing a modular low-cost hardware–software system was demonstrated utilizing open source tools in a way that was oriented to smart buildings approaches.
      Citation: Electronics
      PubDate: 2022-11-25
      DOI: 10.3390/electronics11233904
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3905: An Adaptive Beetle Swarm Optimization
           Algorithm with Novel Opposition-Based Learning

    • Authors: Qifa Wang, Guanhua Cheng, Peng Shao
      First page: 3905
      Abstract: The Beetle Swarm Optimization (BSO) algorithm is a high-performance swarm intelligent algorithm based on beetle behaviors. However, it suffers from poor search speeds and is prone to local optimization due to the size of the step length. To address this further, a novel improved opposition-based learning mechanism is utilized, and an adaptive beetle swarm optimization algorithm with novel opposition-based learning (NOBBSO) is proposed. In the proposed NOBBSO algorithm, the novel opposition-based learning is designed as follows. Firstly, according to the characteristics of the swarm intelligence algorithms, a new opposite solution is obtained to generate the current optimal solution by iterations in the current population. The novel opposition-based learning strategy is easy to converge quickly. Secondly, an adaptive strategy is used to make NOBBSO parameters self-adaptive, which makes the results tend to converge more easily. Finally, 27 CEC2017 benchmark functions are tested to verify its effectiveness. Comprehensive numerical experiment outcomes demonstrate that the NOBBSO algorithm has obtained faster convergent speed and higher convergent accuracy in comparison with other outstanding competitors.
      Citation: Electronics
      PubDate: 2022-11-25
      DOI: 10.3390/electronics11233905
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3906: SKG-Lock: A Provably Secure Logic
           Locking SchemeCreating Significant Output Corruption

    • Authors: Quang-Linh Nguyen, Sophie Dupuis, Marie-Lise Flottes, Bruno Rouzeyre
      First page: 3906
      Abstract: The current trend to globalize the supply chain in the Integrated Circuits (ICs) industry has raised several security concerns including, among others, IC overproduction. Over the past years, logic locking has grown into a prominent countermeasure to tackle this threat in particular. Logic locking consists of “locking” an IC with an added primary input, the so-called key, which, unless fed with the correct secret value, renders the ICs unusable. One of the first criteria ensuring the quality of a logic locking technique was the output corruption, i.e., the corruption at the outputs of a locked circuit, for any wrong key value. However, since the introduction of SAT-based attacks, resulting countermeasures have compromised this criterion in favor of a better resilience against such attacks. In this work, we propose SKG-Lock+, a Provably Secure Logic Locking scheme that can thwart SAT-based attacks while maintaining significant output corruption. We perform a comprehensive security analysis of SKG-Lock+ and show its resilience against SAT-based attacks, as well as various other state-of-the-art attacks. Compared with related works, SKG-Lock+ provides higher output corruption and incurs acceptable overhead.
      Citation: Electronics
      PubDate: 2022-11-25
      DOI: 10.3390/electronics11233906
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3907: Blockchain Application Analysis Based on
           IoT Data Flow

    • Authors: Juxia Li, Xing Zhang, Wei Shi
      First page: 3907
      Abstract: In the Internet of Things (IoT) system, data leakage can easily occur due to the differing security of edge devices and the different processing methods of data in the transmission process. Blockchain technology has the advantages of good non-tamperability, decentralization, de-trust, openness, and transparency, and it can protect data security on the Internet of Things. This research integrates the means by which data flow can be combined with blockchain technology to prevent privacy leakage throughout the entire transportation process from sender to receiver. Through a keyword search of the last five years, 94 related papers in Web of Science and IEEE Xplore were extracted and the complex papers and frameworks explained using a reconstruction graph. The data processing process is divided into five modules: data encryption, data access control, data expansion, data storage, and data visualization. A total of 11 methods combining blockchain technology to process IoT data were summarized. The blockchain application technology in the IoT field was summarized objectively and comprehensively, and a new perspective for studying IoT data flow was given.
      Citation: Electronics
      PubDate: 2022-11-26
      DOI: 10.3390/electronics11233907
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3908: Design of PAM-8 VLC Transceiver System
           Employing Neural Network-Based FFE and Post-Equalization

    • Authors: Bo Xu, Tianxin Min, Chik Patrick Yue
      First page: 3908
      Abstract: Wireless communication technology adopting electromagnetic waves in an unlicensed spectrum, such as visible light, for communication has attracted wide research efforts. Visible light communication (VLC) utilizes visible light as a communication medium to transmit signals but faces a limited communication bandwidth and low data rate, which is caused by the intrinsic characteristics of LEDs. This paper first studies a mathematical model of limited bandwidth and its effect on transmitted signals and then analyzes the free space and underwater channel loss. With the theoretical analysis, a VLC transceiver system is presented for solving bandwidth limitation by utilizing a pulse-amplitude modulation-8 (PAM-8) scheme and a hybrid equalization method. The proposed hybrid equalization combined a passive equalizer, a neural network (NN)-based feed-forward equalization (FFE), and a radial basis function neural network (RBF-NN). The feasibility of this VLC system was verified through a co-simulation platform with both free-space and underwater channels. Compared with a VLC system adopting a deep neural network (DNN)-based post-equalization method, the proposed VLC system could achieve a data rate of 3.6 Gbps with a bit error rate (BER) of 3.8 × 10−3 over a 3 m free-space channel. The RBF-NN achieved a reduced training time of 10 min, which was 86.7% lower than the conventional DNN-based post-equalization method.
      Citation: Electronics
      PubDate: 2022-11-26
      DOI: 10.3390/electronics11233908
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3909: UFace: An Unsupervised Deep Learning
           Face Verification System

    • Authors: Enoch Solomon, Abraham Woubie, Krzysztof J. Cios
      First page: 3909
      Abstract: Deep convolutional neural networks are often used for image verification but require large amounts of labeled training data, which are not always available. To address this problem, an unsupervised deep learning face verification system, called UFace, is proposed here. It starts by selecting from large unlabeled data the k most similar and k most dissimilar images to a given face image and uses them for training. UFace is implemented using methods of the autoencoder and Siamese network; the latter is used in all comparisons as its performance is better. Unlike in typical deep neural network training, UFace computes the loss function k times for similar images and k times for dissimilar images for each input image. UFace’s performance is evaluated using four benchmark face verification datasets: Labeled Faces in the Wild (LFW), YouTube Faces (YTF), Cross-age LFW (CALFW) and Celebrities in Frontal Profile in the Wild (CFP-FP). UFace with the Siamese network achieved accuracies of 99.40%, 96.04%, 95.12% and 97.89%, respectively, on the four datasets. These results are comparable with the state-of-the-art methods, such as ArcFace, GroupFace and MegaFace. The biggest advantage of UFace is that it uses much less training data and does not require labeled data.
      Citation: Electronics
      PubDate: 2022-11-26
      DOI: 10.3390/electronics11233909
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3910: Emergency Mechanical Ventilator Design:
           Low-Cost and Accessible Components

    • Authors: Omar Flor, Mauricio Fuentes, Henry Carvajal, Josué Quiroga, Verónica Luzuriaga, Jeysson Tapia, Patricia Acosta-Vargas
      First page: 3910
      Abstract: This paper presents the fundamentals; criteria; and mechanical, electrical, and electronic aspects required to properly operate and control emerging mechanical ventilators. We present the basis for their design and manufacture as a contribution to implementing this type of equipment at low cost for intensive care units. In particular, we describe the materials and the mechanical, electrical, and electronic aspects used to implement the SURKAN mechanical ventilator, which was developed in Ecuador during the COVID-19 pandemic for some health centers in the country. The proposed mechanical ventilator provides a functional and reliable design that can be considered a reference for future developments and new implementations.
      Citation: Electronics
      PubDate: 2022-11-26
      DOI: 10.3390/electronics11233910
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3911: k-Level Extended Sparse Array Design for
           Direction-of-Arrival Estimation

    • Authors: Pinjiao Zhao, Qisong Wu, Na Wu, Guobing Hu, Liwei Wang
      First page: 3911
      Abstract: Sparse arrays based on the concept of a sum-difference coarray (SDCA) have increased degrees of freedom and enlarged effective array aperture compared to those only considering a difference coarray. Nevertheless, there still exist a number of overlapping virtual sensors between the difference coarray and the sum coarray, yielding high coarray redundancy. In this paper, we propose a k-level extended sparse array configuration consisting of one sparse subarray with k-level expansion and one uniform linear subarray. By systematically analyzing the inherent structure of the k-level extended sparse array, the closed-form expressions for sensor locations, uniform DOF and coarray redundancy ratio (CARR) are derived. Moreover, with the utilization of a k-level extended strategy, the proposed array remains a hole-free property and achieves low coarray redundancy. According to the proposed sparse array, the spatial and temporal information of the incident sources are jointly exploited for underdetermined direction-of-arrival estimation. The theoretical propositions are proven and numerical simulations are performed to demonstrate the superior performance of the proposed array.
      Citation: Electronics
      PubDate: 2022-11-26
      DOI: 10.3390/electronics11233911
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3912: Numerical Modelling of Dynamic
           Electromagnetic Problems Based on the Time-Domain Finite Integration
           Technique

    • Authors: Zhuochen Lou, Xiongwei Wu, Junming Hou, Jianan Zhang, Jianwei You, Tiejun Cui
      First page: 3912
      Abstract: Developing numerical methods to solve dynamic electromagnetic problems has broad application prospects. In computational electromagnetics, traditional numerical methods are commonly used to deal with static electromagnetic problems. However, they can hardly be applied in the modeling of time-varying materials and moving objects. So far, the studies on numerical methods that can efficiently solve dynamic electromagnetic problems are still very limited. In this paper, a numerical method called the time-domain finite integration technique (TDFIT) is extended to tackle this problem via the introduction of time-varying iterative coefficients. In order to validate the effectiveness of the proposed algorithm, three numerical examples are demonstrated, including two microstrip structures with a time-varying medium and a rapidly rotating structure. The numerical results reveal that the time-varying medium can induce a nonlinear spectrum shift, and the radar cross section (RCS) of a rapidly rotating structure is highly dependent on the rotating speed. The proposed algorithm opens a new avenue for the exploration of many intriguing phenomena in fundamental physics, including frequency conversion and magnetless nonreciprocity. Meanwhile, it can also lead to a wide range of promising practical applications, such as active electron devices, space-time metamaterials, and hypersonic vehicles.
      Citation: Electronics
      PubDate: 2022-11-26
      DOI: 10.3390/electronics11233912
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3913: Application of Knowledge Graph in Water
           Conservancy Education Resource Organization under the Background of Big
           Data

    • Authors: Yangrui Yang, Yaping Zhu, Pengpeng Jian
      First page: 3913
      Abstract: The key to improving the readability and usage of educational resources is their orderly arrangement and integration. Knowledge graphs, which are a large-scale form of knowledge engineering, are an effective tool for managing and organizing educational resources. The water conservancy’s educational big data is separated into three tiers of objectives–courses–knowledge units based on the connotation level of self-directed learning. Combined with the idea of Outcome-based Education(OBE), the goal-oriented knowledge graph structure of water conservancy disciplines and graph creation method is proposed. The focus is the error accumulation problem brought about by the traditional relational extraction method of Named Entity Recognition based on rules or sequence labeling. We first complete this objective, and then the relationship classification is performed according to the water conservancy disciplines entity and relations joint extraction (WDERJE) model, on which the prompt mechanism design is based. Think of the entity-relationship extraction task as a sequence-to-sequence generation task, and take the structured extraction language to unify the coding entity extraction and relationship extraction structures. The evaluation results of the WDERJE model show that the F_0.5 value of each entity extraction is above 0.76, and the cumulative extraction relationship triple is nearly 180,000. The graph fully optimizes the organization and management of water conservancy education resources and effectively improves the readability and utilization rate of water conservancy teaching resources.
      Citation: Electronics
      PubDate: 2022-11-26
      DOI: 10.3390/electronics11233913
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3914: An Enhancement for IEEE 802.11 STA Power
           Saving and Access Point Memory Management Mechanism

    • Authors: Vishal Bhargava, Nallanthighal Raghava
      First page: 3914
      Abstract: Wi-Fi researchers are trying hard to extend battery life by optimizing 802.11 power save. The rising number of Wi-Fi devices and IoT devices and daily demands have reduced Station (STA) device power consumption. Better memory management at the Access Point (AP) side is also needed, so that AP can store maximum data to deliver sleepy STA devices. There are three main contributions of this study. The first one focuses on a power-saving mechanism scheme with an adaptive change to Listen Interval (LI) based on the battery status of station devices. The second contribution aims to examine better memory management for the AP buffer to store packets that will in the future deliver power-saving STA when awake. The third contribution, under the implementation of the proposed method, includes Wi-Fi corner cases covered as Beacon frames missed via STA, the keep-alive factor, and the upper-layer time taken to care for and ensure the delivery of unicast/multicast/broadcast data,. The proposed approach introduced 802.11 protocols to share battery status, a protocol to announce proposed features via AP, and a protocol to change LI at runtime. Simulation results show that the proposed scheme performs better than 802.11 power saving in terms of power usage at the STA and access point memory management.
      Citation: Electronics
      PubDate: 2022-11-26
      DOI: 10.3390/electronics11233914
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3915: A Hand Gesture Recognition Circuit
           Utilizing an Analog Voting Classifier

    • Authors: Vassilis Alimisis, Vassilis Mouzakis, Georgios Gennis, Errikos Tsouvalas, Christos Dimas, Paul P. Sotiriadis
      First page: 3915
      Abstract: Electromyography is a diagnostic medical procedure used to assess the state of a muscle and its related nerves. Electromyography signals are monitored to detect neuromuscular abnormalities and diseases but can also prove useful in decoding movement-related signals. This information is vital to controlling prosthetics in a more natural way. To this end, a novel analog integrated voting classifier is proposed as a hand gesture recognition system. The voting classifiers utilize 3 separate centroid-based classifiers, each one attached to a different electromyographic electrode and a voting circuit. The main building blocks of the architecture are bump and winner-take-all circuits. To confirm the proper operation of the proposed classifier, its post-layout classification results (91.2% accuracy) are compared to a software-based implementation (93.8% accuracy) of the same voting classifier. A TSMC 90 nm CMOS process in the Cadence IC Suite was used to design and simulate the following circuits and architectures.
      Citation: Electronics
      PubDate: 2022-11-26
      DOI: 10.3390/electronics11233915
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3916: Application of FCM Clustering Algorithm
           in Digital Library Management System

    • Authors: Yuqing Shi
      First page: 3916
      Abstract: Traditional library systems are gradually being replaced by digital libraries. Digital libraries are developing from simple database-based storage and retrieval to knowledge-based implementation. The fuzzy C-means (FCM) clustering algorithm is an example of data collection and data processing technology. It evaluates and draws conclusions based on mathematics, large data, and other technologies. In order to better improve the digital library management system, this paper applied FCM clustering algorithm to the digital library management system. Based on the in-depth study of the FCM clustering algorithm, this paper built a digital library management system. The clustering algorithm was used to cluster library borrowing records and reader information. It provided technical support and suggestions on library collection construction and book purchase and promoted book management to form a good spitting cycle. The experimental results extracted during the evaluation phase demonstrated that the overall error rate of the suggested FCM clustering algorithm for information clustering is 3.66%, which is better than the existing comparative models. This shows that applying the FCM clustering algorithm to a digital library management system has some practical significance.
      Citation: Electronics
      PubDate: 2022-11-26
      DOI: 10.3390/electronics11233916
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3917: A New Decentralized PQ Control for
           Parallel Inverters in Grid-Tied Microgrids Propelled by SMC-Based
           Buck–Boost Converters

    • Authors: Ali M. Jasim, Basil H. Jasim, Bogdan-Constantin Neagu
      First page: 3917
      Abstract: Nowadays, the microgrid (MG) concept is regarded as an efficient approach to incorporating renewable generation resources into distribution networks. However, managing power flows to distribute load power among distribution generators (DGs) remains a critical focus, particularly during peak demand. The purpose of this paper is to control the adopted grid-tied MG performance and manage the power flow from/to the parallel DGs and the main grid using discrete-time active/reactive power (PQ) control based on digital proportional resonant (PR) controllers. The PR controller is used to eliminate harmonics by acting as a digital infinite-impulse response (IIR) filter with a high gain at the resonant frequency. Additionally, the applied PR controller has fast reference signal tracking, responsiveness to grid frequency drift, and no steady-state error. Moreover, this paper describes the application of robust nonlinear sliding mode control (SMC)-technique-based buck–boost (BB) converters. The sliding adaptive control scheme is applied to prevent the output voltage error that occurs during DG failure, load variations, or system parameter changes. This paper deals with two distinct case studies. The first one focuses on applying the proposed control for two parallel DGs with and without load-changing conditions. In the latter case, the MG is expanded to include five DGs (with and without DG failure). The proposed control technique has been compared with the droop control and model predictive control (MPC) techniques. As demonstrated by the simulation results in MATLAB software, the proposed method outperformed the others in terms of both performance analysis and the ability to properly share power between parallel DGs and the utility grid.
      Citation: Electronics
      PubDate: 2022-11-27
      DOI: 10.3390/electronics11233917
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3918: A Quasi-Wireless Intraoperatory
           Neurophysiological Monitoring System

    • Authors: Eduardo Alonso Rivas, Romano Giannetti, Carlos Rodríguez-Morcillo García, Javier Matanza Domingo, José Daniel Muñoz Frías, Graziella Scandurra, Carmine Ciofi, Lorena Vega-Zelaya, Jesús Pastor
      First page: 3918
      Abstract: Intraoperative Neurophysiological Monitoring is a set of monitoring techniques that reads electrical activity generated by the nervous system structures during surgeries. In non-trivial surgeries, neurophysiologists require a significant number of electrical signals to be picked up to check the effects of the surgeon’s actions in real time or to confirm that the correct nerves are selected. As a result, cabling the patient in the operating room can become cumbersome. The proposed WIONM module solves part of the problem by converting a good part of those cables into a wireless connection that is substantially transparent to the human operator and the existing medical instrumentation.
      Citation: Electronics
      PubDate: 2022-11-27
      DOI: 10.3390/electronics11233918
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3919: Construction of a Character Dataset for
           Historical Uchen Tibetan Documents under Low-Resource Conditions

    • Authors: Ce Zhang, Weilan Wang, Guowei Zhang
      First page: 3919
      Abstract: The construction of a character dataset is an important part of the research on document analysis and recognition of historical Tibetan documents. The results of character segmentation research in the previous stage are presented by coloring the characters with different color values. On this basis, the characters are annotated, and the character images corresponding to the annotation are extracted to construct a character dataset. The construction of a character dataset is carried out as follows: (1) text annotation of segmented characters is performed; (2) the character image is extracted from the character block based on the real position information; (3) according to the class of annotated text, the extracted character images are classified to construct a preliminary character dataset; (4) data augmentation is used to solve the imbalance of classes and samples in the preliminary dataset; (5) research on character recognition based on the constructed dataset is performed. The experimental results show that under low-resource conditions, this paper solves the challenges in the construction of a historical Uchen Tibetan document character dataset and constructs a 610-class character dataset. This dataset lays the foundation for the character recognition of historical Tibetan documents and provides a reference for the construction of relevant document datasets.
      Citation: Electronics
      PubDate: 2022-11-27
      DOI: 10.3390/electronics11233919
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3920: PEASE: A PUF-Based Efficient
           Authentication and Session Establishment Protocol for Machine-to-Machine
           Communication in Industrial IoT

    • Authors: Xiang Gong, Tao Feng, Maher Albettar
      First page: 3920
      Abstract: Machine-to-machine (M2M) communication is one of the critical technologies of the industrial Internet of Things (IoT), which consists of sensors, actuators at the edge, and servers. In order to solve the security and availability problems regarding communication between edge devices with constrained resources and servers in M2M communication, in this study we proposed an authentication and session establishment protocol based on physical unclonable functions (PUFs). The scheme does not require clock synchronization among the devices, and it circumvents the situation where the authentication phase has to use a high computational overhead fuzzy extractor due to PUF noise. The protocol contains two message interactions, which provide strong security and availability while being lightweight. The security modelling is based on CPN Tools, which verifies security attributes and attack resistance in the authentication phase. After considering the design of the fuzzy extractor and scalability, the proposed scheme significantly reduces the computational overhead by more than 93.83% in the authentication phase compared with other schemes using PUFs. Meanwhile, under the guarantee of availability, the communication overhead is maintained at a balanced and reasonable level, at least 19.67% lower than the solution using XOR, hashing, or an elliptic curve.
      Citation: Electronics
      PubDate: 2022-11-27
      DOI: 10.3390/electronics11233920
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3921: Joint Analysis of Front-Door and
           Back-Door Couplings of PIN Limiter Based on Improved Equivalent Circuit
           Model

    • Authors: Tao Liu, Le Xu, Qiwei Li, Bin Yao, Xiaowei Shi
      First page: 3921
      Abstract: Based on the previous research on electromagnetic pulse coupling, which pays more attention to the front-door coupling or the back-door coupling alone, this paper analyzes the influence of an electromagnetic pulse on electronic devices and systems through the joint analysis of front-door and back-door couplings using the finite-difference time-domain method (FDTD). This specific measure is used to simplify the front-door coupling to the voltage source injection, which occurs simultaneously with plane wave irradiation. This coupling scheme of the front door and back door with the voltage source and plane wave acting simultaneously is rarely seen in previous analyses, which also gives consideration to the working state of the circuit. Although the equivalent circuit model is widely used, it cannot effectively reflect the working state of the diode circuit under the conditions of large injection and high frequency. In view of the limited application scenarios of the traditional equivalent circuit model, which cannot accurately describe the internal response characteristics of the diode under different electromagnetic pulse coupling, this paper introduces an improved equivalent circuit model based on the physical model. Taking the Positive Intrinsic-Negative (PIN) limiter as the target, this paper analyzes the influence of the front-door and back-door joint coupling on its performance under different electromagnetic pulses and then gives protection suggestions.
      Citation: Electronics
      PubDate: 2022-11-28
      DOI: 10.3390/electronics11233921
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3922: Efficient Clustering Based Routing for
           Energy Management in Wireless Sensor Network-Assisted Internet of Things

    • Authors: Sadia Firdous, Nargis Bibi, Madiha Wahid, Samah Alhazmi
      First page: 3922
      Abstract: Wireless sensor networks (WSNs) play a huge part in arising innovations like smart applications, the Internet of Things, and numerous self-designed, independent applications. Energy exhaustion and efficient energy consumption are principal issues in wireless sensor networks. Energy is a significant and valuable asset of sensor nodes; early energy depletion ultimately leads to a shorter network lifetime and the replacement of sensor nodes. This research proposes a novel Power-Efficient Cluster-based Routing (PECR) algorithm. It takes in clustering using K-Means, the arrangement of Cluster Heads (CHs) and a Main Cluster Head (MCH), the optimal route choice, communication in light of the energy utilization model, cluster heads, and main cluster head alternation based on residual energy and relative location. PECR decreases traffic overburden, restricts energy usage, and at last, expands the network lifetime. Sensor nodes sense the information and transmit traffic to a Base Station (BS) through a legitimate channel. The results confirm it decreases the traffic overhead and effectively utilizes the energy assets. The simulation results show that PECR’s performance is 44% more improved than LEACH, EC, EECRP, and EECA algorithms. It is suitable for networks that require a stretched life.
      Citation: Electronics
      PubDate: 2022-11-27
      DOI: 10.3390/electronics11233922
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3923: Customized Imperialist Competitive
           Algorithm Methodology to Optimize Robust Miller CMOS OTAs

    • Authors: Egon Henrique Salerno Galembeck, Salvador Pinillos Gimenez, Rodrigo Alves de Lima Moreto
      First page: 3923
      Abstract: The design and optimization of the analog complementary metal-oxide-semiconductor (CMOS) integrated circuits (ICs) are intrinsically complicated and depend heavily on the designer’s experience, and are associated with very long design and optimization-cycle times. In addition, in order to the analog and radiofrequency (RF) CMOS IC work suitably in practice, it is necessary to perform robustness analyses (RAs) through Simulation Program with Integrated Circuit Emphasis (SPICE) simulations, which result in still-higher design and optimization cycle times and therefore represent the biggest bottleneck to the launching of new electronic products. In this context, this manuscript aims to present, for the first time, the use of a custom imperialist competitive algorithm (ICA) in order to reduce the design and optimization-cycle times of analog CMOS ICs. In this study, we implement some Miller CMOS operational transconductance amplifiers (OTAs) using the computational tool named iMTGSPICE, considering two different bulk CMOS IC manufacturing processes from Taiwan Semiconductor Company (TSMC) (180 nm and 65 nm nodes) and two evolutionary optimization methodologies of artificial intelligence, i.e., ICA and a genetic algorithm (GA). The main result obtained by this work shows that, by using an ICA-customized evolutionary algorithm to perform the design and optimization processes of Miller CMOS OTAs, it is possible to reduce the design and optimization-cycle times by up to 83% in relation to those implemented with the GA-customized evolutionary algorithm, achieving practically the same electrical performance.
      Citation: Electronics
      PubDate: 2022-11-27
      DOI: 10.3390/electronics11233923
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3924: Thermal Biometric Features for Drunk
           Person Identification Using Multi-Frame Imagery

    • Authors: Georgia Koukiou
      First page: 3924
      Abstract: In this work, multi-frame thermal imagery of the face of a person is employed for drunk identification. Regions with almost constant temperature on the face of sober and drunk persons are thoroughly examined for their capability to discriminate intoxication. Novel image processing approaches as well as feature extraction techniques are developed to support the drunk identification procedure. These techniques constitute novel ideas in the theory of image analysis and algorithm development. Nonlinear anisotropic diffusion is employed for a light smoothing on the images before feature extraction. Feature vector extraction is based on morphological operations performed on the isothermal regions on the face. The classifier chosen to verify the drunk person discrimination capabilities of the procedure is a Support Vector Machine (SVM). Obviously, the isothermal regions on the face change their shape and size with alcohol consumption. Consequently, intoxication identification can be carried out based only on the thermal signatures of the drunk person, while the signature of the corresponding sober person is not needed. A sample of 41 participants who drank in a controlled alcohol consumption procedure was employed for creating the database, which contains 4100 thermal images. The proposed method for intoxication identification achieves a success rate of over 86% and constitutes a fast non-invasive test that can replace existing breathalyzer check up.
      Citation: Electronics
      PubDate: 2022-11-27
      DOI: 10.3390/electronics11233924
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3925: Control Synchronization Design of a
           Multiple Electrohydraulic Actuator System with Linearization Dynamics and
           an External Disturbance Observer

    • Authors: Jun Qi, Qing Guo, Hualong Ren, Zhenlei Chen, Yao Yan, Dan Jiang
      First page: 3925
      Abstract: The control synchronization of multiple electrohydraulic actuators (MEHAs) is initially discussed to ensure the consensus of every electrohydraulic actuator (EHA) with three-order isomorphic dynamics. First, the EHA model is linearized using the Lie derivative method to obtain the state-space model of MEHAs. Then, the disturbance observer is used to estimate and compensate for the unknown external load caused by the driving force of a motion plant. Via the Lyapunov technique, this protocol asymptotically achieves consensus to a zero neighborhood with the ultimate boundaries of the MEHAs’ state errors. The effectiveness of the synchronous control protocol is verified by both simulation and experimental benches with two-node EHAs.
      Citation: Electronics
      PubDate: 2022-11-27
      DOI: 10.3390/electronics11233925
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3926: Investigating the Effect of Perceived
           Security, Perceived Trust, and Information Quality on Mobile Payment Usage
           through Near-Field Communication (NFC) in Saudi Arabia

    • Authors: Mohammed Amin Almaiah, Ali Al-Rahmi, Fahad Alturise, Lamia Hassan, Abdalwali Lutfi, Mahmaod Alrawad, Salem Alkhalaf, Waleed Mugahed Al-Rahmi, Saleh Alsharaieh, Theyazn H. H. Aldhyani
      First page: 3926
      Abstract: This study aims to investigate the perceptions of near-field communication (NFC) usage for mobile payments in Saudi Arabia. In order to develop a mathematical framework for the acceptance of NFC quality of information for mobile payments, researchers have combined the technological acceptance model (TAM) and the idea of perceived risk. An online and physical study of 1217 NFC portable credit card holders in Saudi Arabia was conducted. Exploratory and confirmatory analyses were utilized to analyze the factor structure of the measurement items, and Smart PLS 2.0 from structural equation modeling (SEM) was used to assess the theories and hypotheses that had been put forth. The results show that (1) social influence, perceived element of risk, and subjective norms each have a negative influence on preconceptions of trust in online payment methods using NFC; (2) social influence, perceived element of risk, and social norms all have a positive effect on satisfaction with the security of electronic payment using NFC; (3) perceived ease of use has a negative effect on perceived confidence in digital payment using NFC; and (4) perceived ease of use has a negative effect on perceived trust in online payment using NFC. As a consequence of these findings, users’ attitudes regarding the use of NFC and behavioral intentions to utilize NFC mobile payment can be revealed. This study created a unique approach for assessing perceptions, perceived trust, and NFC information quality in mobile payment uptake in Saudi Arabia. As a consequence, banks may find this research useful as they implement new strategies to attract more customers, such as perceived security, brand trust, and NFC information quality in mobile payment adaption.
      Citation: Electronics
      PubDate: 2022-11-28
      DOI: 10.3390/electronics11233926
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3927: Limitations of Nature-Inspired
           Algorithms for Pricing on Digital Platforms

    • Authors: J. Manuel Sanchez-Cartas, Ines P. Sancristobal
      First page: 3927
      Abstract: Digital platforms have begun to rely more on algorithms to perform basic tasks such as pricing. These platforms must set prices that coordinate two or more sides that need each other in some way (e.g., developers and users or buyers and sellers). Therefore, it is essential to form correct expectations about how both sides behave. The purpose of this paper was to study the effect of different levels of information on two biology-inspired metaheuristics (differential evolution and particle swarm optimization algorithms) that were programmed to set prices on multisided platforms. We assumed that one platform always formed correct expectations (human platform) while the competitor always used a generic version of particle swarm optimization or differential evolution algorithms. We tested different levels of information that modified how expectations were formed. We found that both algorithms might end up in suboptimal solutions, showing that algorithms needed to account for expectation formation explicitly or risk setting nonoptimal prices. In addition, we found regularity in the way algorithms set prices when they formed incorrect expectations that can help practitioners detect cases in need of intervention.
      Citation: Electronics
      PubDate: 2022-11-28
      DOI: 10.3390/electronics11233927
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3928: A Real-Time Maximum Efficiency Tracker
           for Wireless Power Transfer Systems with Cross-Coupling

    • Authors: Arpan Laha, Abirami Kalathy, Majid Pahlevani, Praveen Jain
      First page: 3928
      Abstract: This article proposes a real-time dynamic maximum efficiency tracking algorithm for wireless power transfer (WPT) systems with multiple receivers. The algorithm sequentially varies the net reactance of each of the receivers using switched capacitor circuits (SCCs) to reach the maximum efficiency point (MEP). The MEP in multiple-receiver systems varies in the presence of cross-coupling. This article provides an in-depth analysis of the effects of cross-coupling and proves that cross-coupling could be beneficial or detrimental to efficiency, depending on circuit conditions such as the load resistances and coupling factors among the coils. Hence, unlike previous research, this article emphasizes the improvement of the link efficiency in the presence of cross-coupling rather than the complete elimination of its effects. Experimental results have been included for a single-transmitter and two-receiver system to validate the feasibility of the proposed algorithm.
      Citation: Electronics
      PubDate: 2022-11-28
      DOI: 10.3390/electronics11233928
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3929: Handling Missing Values Based on
           Similarity Classifiers and Fuzzy Entropy Measures

    • Authors: Faten Khalid Karim, Hela Elmannai, Abdelrahman Seleem, Safwat Hamad, Samih M. Mostafa
      First page: 3929
      Abstract: Handling missing values (MVs) and feature selection (FS) are vital preprocessing tasks for many pattern recognition, data mining, and machine learning (ML) applications, involving classification and regression problems. The existence of MVs in data badly affects making decisions. Hence, MVs have to be taken into consideration during preprocessing tasks as a critical problem. To this end, the authors proposed a new algorithm for manipulating MVs using FS. Bayesian ridge regression (BRR) is the most beneficial type of Bayesian regression. BRR estimates a probabilistic model of the regression problem. The proposed algorithm is dubbed as cumulative Bayesian ridge with similarity and Luca’s fuzzy entropy measure (CBRSL). CBRSL reveals how the fuzzy entropy FS used for selecting the candidate feature holding MVs aids in the prediction of the MVs within the selected feature using the Bayesian Ridge technique. CBRSL can be utilized to manipulate MVs within other features in a cumulative order; the filled features are incorporated within the BRR equation in order to predict the MVs for the next selected incomplete feature. An experimental analysis was conducted on four datasets holding MVs generated from three missingness mechanisms to compare CBRSL with state-of-the-art practical imputation methods. The performance was measured in terms of R2 score (determination coefficient), RMSE (root mean square error), and MAE (mean absolute error). Experimental results indicate that the accuracy and execution times differ depending on the amount of MVs, the dataset’s size, and the mechanism type of missingness. In addition, the results show that CBRSL can manipulate MVs generated from any missingness mechanism with a competitive accuracy against the compared methods.
      Citation: Electronics
      PubDate: 2022-11-28
      DOI: 10.3390/electronics11233929
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3930: Design Research Insights on Text Mining
           Analysis: Establishing the Most Used and Trends in Keywords of Design
           Research Journals

    • Authors: Muneer Nusir, Ali Louati, Hassen Louati, Usman Tariq, Raed Abu Zitar, Laith Abualigah, Amir H. Gandomi
      First page: 3930
      Abstract: Design research topics attract exponentially more attention and consideration among researchers. This study is the first research article that endeavors to analyze selected design research publications using an advanced approach called “text mining”. This approach speculates its results depending on the existence of a research term (i.e., keywords), which can be more robust than other methods/approaches that rely on contextual data or authors’ perspectives. The main aim of this research paper is to expand knowledge and familiarity with design research and explore future research directions by addressing the gaps in the literature; relying on the literature review, it can be stated that the research area in the design domain still not built-up a theory, which can unify the field. In general, text mining with these features allows increased validity and generalization as compared to other approaches in the literature. We used a text mining technique to collect data and analyzed 3553 articles collected in 10 journals using 17,487 keywords. New topics were investigated in the domain of design concepts, which included attracting researchers, practitioners, and journal editorial boards. Such issues as co-innovation, ethical design, social practice design, conceptual thinking, collaborative design, creativity, and generative methods and tools were subject to additional research. On the other hand, researchers pursued topics such as collaborative design, human-centered design, interdisciplinary design, design education, participatory design, design practice, collaborative design, design development, collaboration, design theories, design administration, and service/product design areas. The key categories investigated and reported in this paper helped in determining what fields are flourishing and what fields are eroding.
      Citation: Electronics
      PubDate: 2022-11-28
      DOI: 10.3390/electronics11233930
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3931: Investigations on the Modulation
           

    • Authors: Rajababu Durgam, Ramsha Karampuri, Shriram S. Rangarajan, Umashankar Subramaniam, E. Randolph Collins, Tomonobu Senjyu
      First page: 3931
      Abstract: The challenges faced in an isolated wind energy conversion system (WECS) are larger transient times, high steady-state error, and larger harmonic content. To overcome these issues, an adaptive voltage controller (AVC) along with the load current observer (LCO) could be the better proposition. However, the AVC and LCO, in conjunction with the conventional space vector pulse width modulation (SVPWM) technique to operate the three-phase inverter of WECS, would not be able to further improve these parameters. This paper proposes the use of the unified voltage SVPWM (UVSVPWM) technique along with the AVC and LCO, which could improve the transient behavior by about 30% as well as reduce the harmonic content of the load voltage and current by about 70% and 2%, respectively. This paper considers an isolated WECS connected to the linear load, which is operated under balanced as well as unbalanced load conditions. The proposed control technique is verified for both the balanced and unbalanced cases using MATLAB/Simulink.
      Citation: Electronics
      PubDate: 2022-11-28
      DOI: 10.3390/electronics11233931
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3932: Energy-Aware Live VM Migration Using
           Ballooning in Cloud Data Center

    • Authors: Neha Gupta, Kamali Gupta, Abdulrahman M. Qahtani, Deepali Gupta, Fahd S. Alharithi, Aman Singh, Nitin Goyal
      First page: 3932
      Abstract: The demand for digitization has inspired organizations to move towards cloud computing, which has increased the challenge for cloud service providers to provide quality service. One of the challenges is energy consumption, which can shoot up the cost of using computing resources and has raised the carbon footprint in the atmosphere; therefore, it is an issue that it is imperative to address. Virtualization, bin-packing, and live VM migration techniques are the key resolvers that have been found to be efficacious in presenting sound solutions. Thus, in this paper, a new live VM migration algorithm, live migration with efficient ballooning (LMEB), is proposed; LMEB focuses on decreasing the size of the data that need to be shifted from the source to the destination server so that the total energy consumption of migration can be reduced. A simulation was performed with a specific configuration of virtual machines and servers, and the results proved that the proposed algorithm could trim down energy usage by 18%, migration time by 20%, and downtime by 20% in comparison with the existing approach of live migration with ballooning (LMB).
      Citation: Electronics
      PubDate: 2022-11-28
      DOI: 10.3390/electronics11233932
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3933: Implanting Intelligence in 5G Mobile
           Networks—A Practical Approach

    • Authors: Malik, Khan, Aadam, El-Sayed, Khan, Ullah
      First page: 3933
      Abstract: With the advancement in various technological fronts, we are expecting the design goals of smart cities to be realized earlier than expected. Undoubtedly, communication networks play the crucial role of backbone to all the verticals of smart cities, which is why we are surrounded by terminologies such as the Internet of Things, the Internet of Vehicles, the Internet of Medical Things, etc. In this paper, we focus on implanting intelligence in 5G and beyond mobile networks. In this connection, we design and develop a novel data-driven predictive model which may serve as an intelligent slicing framework for different verticals of smart cities. The proposed model is trained on different machine learning algorithms to predict the optimal network slice for a requested service resultantly assisting in allocating enough resources to the slice based on the traffic prediction.
      Citation: Electronics
      PubDate: 2022-11-28
      DOI: 10.3390/electronics11233933
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3934: Comparative Evaluation of AI-Based
           Techniques for Zero-Day Attacks Detection

    • Authors: Shamshair Ali, Saif Ur Rehman, Azhar Imran, Ghazif Adeem, Zafar Iqbal, Ki-Il Kim
      First page: 3934
      Abstract: Many intrusion detection and prevention systems (IDPS) have been introduced to identify suspicious activities. However, since attackers are exploiting new vulnerabilities in systems and are employing more sophisticated advanced cyber-attacks, these zero-day attacks remain hidden from IDPS in most cases. These features have incentivized many researchers to propose different artificial intelligence-based techniques to prevent, detect, and respond to such advanced attacks. This has also created a new requirement for a comprehensive comparison of the existing schemes in several aspects ; after a thorough study we found that there currently exists no detailed comparative analysis of artificial intelligence-based techniques published in the last five years. Therefore, there is a need for this kind of work to be published, as there are many comparative analyses in other fields of cyber security that are available for readers to review.In this paper, we provide a comprehensive review of the latest and most recent literature, which introduces well-known machine learning and deep learning algorithms and the challenges they face in detecting zero-day attacks. Following these qualitative analyses, we present the comparative evaluation results regarding the highest accuracy, precision, recall, and F1 score compared to different datasets.
      Citation: Electronics
      PubDate: 2022-11-28
      DOI: 10.3390/electronics11233934
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3935: Speech Emotion Recognition Based on
           Parallel CNN-Attention Networks with Multi-Fold Data Augmentation

    • Authors: Bautista, Lee, Shin
      First page: 3935
      Abstract: : In this paper, an automatic speech emotion recognition (SER) task of classifying eight different emotions was experimented using parallel based networks trained using the Ryeson Audio-Visual Dataset of Speech and Song (RAVDESS) dataset. A combination of a CNN-based network and attention-based networks, running in parallel, was used to model both spatial features and temporal feature representations. Multiple Augmentation techniques using Additive White Gaussian Noise (AWGN), SpecAugment, Room Impulse Response (RIR), and Tanh Distortion techniques were used to augment the training data to further generalize the model representation. Raw audio data were transformed into Mel-Spectrograms as the model’s input. Using CNN’s proven capability in image classification and spatial feature representations, the spectrograms were treated as an image with the height and width represented by the spectrogram’s time and frequency scales. Temporal feature representations were represented by attention-based models Transformer, and BLSTM-Attention modules. Proposed architectures of the parallel CNN-based networks running along with Transformer and BLSTM-Attention modules were compared with standalone CNN architectures and attention-based networks, as well as with hybrid architectures with CNN layers wrapped in time-distributed wrappers stacked on attention-based networks. In these experiments, the highest accuracy of 89.33% for a Parallel CNN-Transformer network and 85.67% for a Parallel CNN-BLSTM-Attention Network were achieved on a 10% hold-out test set from the dataset. These networks showed promising results based on their accuracies, while keeping significantly less training parameters compared with non-parallel hybrid models.
      Citation: Electronics
      PubDate: 2022-11-28
      DOI: 10.3390/electronics11233935
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3936: A Method of Transparent Graceful
           Failover in Low Latency Stateful Microservices

    • Authors: Kęstutis Pakrijauskas, Dalius Mažeika
      First page: 3936
      Abstract: Microservice architecture is a preferred way to build applications. Being flexible and loosely coupled, it allows to deploy code at a high pace. State, or, in other words, data is not only a commodity but crucial to any business. The high availability and accessibility of data enables companies to remain competitive. However, maintaining low latency stateful microservices, for example, performing updates, is difficult compared to stateless microservices. Making changes to a stateful microservice requires a graceful failover, which has an impact on the availability budget. The method of graceful failover is proposed to improve availability of a low latency stateful microservice when performing maintenance. By observing database connection activity and forcefully terminating idle client connections, the method allows to redirect database requests from one node to another with negligible impact on the client. Thus, the proposed method allows to keep the precious availability budget untouched while performing maintenance operations on low latency stateful microservices. A set of experiments was performed to evaluate stateful microservice availability during failover and to validate the method. The results have shown that near-zero downtime was achieved during a graceful failover.
      Citation: Electronics
      PubDate: 2022-11-28
      DOI: 10.3390/electronics11233936
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3937: DNN Beamforming for LEO Satellite
           Communication at sub-THz Bands

    • Authors: Rajnish Kumar, Shlomi Arnon
      First page: 3937
      Abstract: The 6G communication system will be designed at sub-THz frequencies due to increasing demand in data rates, emerging new applications and advanced communication technologies. These high-performing systems will heavily rely on artificial intelligence (AI) for efficient and robust design of transceivers. In this work, we propose a deep neural network (DNN) beamformer that will replace the use of phase shifters for a massive array of antenna elements employed at the ground station for wideband LEO satellite communication at sub-THz bands. We show that the signal processing algorithm employed using DNN is capable to match the performance of a true-time delay beamformer as the angle of arrival of the received wideband signal at the ground station is changing due to rapid movement of the LEO satellite. The implementation of DNN beamformer will be able to reduce the cost of receiver and provide a way for the efficient and compact design of the massive array beamforming for wideband LEO satellite applications.
      Citation: Electronics
      PubDate: 2022-11-28
      DOI: 10.3390/electronics11233937
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3938: Security Access Control Method for
           Wind-Power-Monitoring System Based on Agile Authentication Mechanism

    • Authors: Yingli Shu, Quande Yuan, Wende Ke, Lei Kou
      First page: 3938
      Abstract: With the continuous increase in the proportion of wind power construction and grid connection, the deployment scale of state sensors in wind-power-monitoring systems has grown rapidly with an aim on the problems that the communication authentication process between the wind turbine status sensor and the monitoring gateway is complex and the adaptability of the massive sensors is insufficient. A security access control method for a wind-power-monitoring system based on agile authentication mechanism is proposed in this paper. First, a lightweight key generation algorithm based on one-way hash function is designed. The algorithm realizes fixed-length compression and encryption of measurement data of any length. Under the condition of ensuring security, the calculation and communication cost in the later stage of authentication are effectively reduced. Second, to reduce the redundant process of wind turbine status sensor authentication, an agile authentication model of wind turbine status sensor based on a lightweight key is constructed. Constrained by the reverse order extraction of key information in the lightweight keychain, the model can realize lightweight communication between massive wind turbine status sensors and regional gateways. Finally, the proposed method is compared and verified using the wind turbine detection data set provided by the National New Energy Laboratory of the United States. The experimental results show that this method can effectively reduce the certification cost of a wind-power-monitoring system. Additionally, it can improve the efficiency of status sensor identity authentication and realize the agility and efficiency of the authentication process.
      Citation: Electronics
      PubDate: 2022-11-28
      DOI: 10.3390/electronics11233938
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3939: Adaptive Fault-Tolerant Control for
           Second-Order Multiagent Systems with Unknown Control Directions via a
           Self-Tuning Distributed Observer

    • Authors: Rongrong Gu, Xudong Sun, Dongyi Pu
      First page: 3939
      Abstract: In this paper, we first design a self-tuning distributed observer for second-order multi-agent systems which is capable of providing the estimation of the leader’s signal to various followers. We then further develop an adaptive sliding-mode controller to solve the cooperative tracking problem between leader and followers for second-order multi-agent systems subject to time-varying actuator faults and unknown external disturbances, which can ensure that the leader-following cooperative tracking errors converge to zero asymptotically. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed controller. This control law offers three advantages: first, the problem of communication barriers among the leader and followers can be solved by the self-tuning distributed observer, which can calculate the observer gain online; second, a new type of adaptive sliding-mode controller is proposed by introducing a Nussbaum function; and lastly, the bounds of unknown actuator faults and unknown external disturbances can be adaptively estimated.
      Citation: Electronics
      PubDate: 2022-11-28
      DOI: 10.3390/electronics11233939
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3940: An Adaptive Rational Fitting Technique
           of Sommerfeld Integrals for the Efficient MoM Analysis of Planar
           Structures

    • Authors: Zi-Hao Zhao, Bi-Yi Wu, Ze-Lin Li, Ming-Lin Yang, Xin-Qing Sheng
      First page: 3940
      Abstract: The integral equation method is one of the most successful computational models for microwave devices or integrated circuits in planar layered media. However, the efficient and accurate evaluation of the associated Green’s function consisting of Sommerfeld integrals (SIs) is still a remaining challenge. To mitigate this difficulty, this work proposes a spatial domain rational function fitting technique (RFFT) for SIs so that the approximation accuracy is controllable. In conjunction with an adaptive sampling strategy, the proposed RFFT minimizes the orders of rational functions, and the resultant SI evaluation efficiency is optimized. In addition, we investigate the semi-analytical singularity treatment for the rational expression of SIs in method of moment (MoM) implementation. Extensive simulation of representative planar devices validates the correctness of the proposed method and demonstrates its superior performance over conventional SI approximation methods.
      Citation: Electronics
      PubDate: 2022-11-28
      DOI: 10.3390/electronics11233940
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3941: Buck-Boost DC-DC Converters for Fuel
           Cell Applications in DC Microgrids—State-of-the-Art

    • Authors: Pedro Andrade, Adérito Neto Alcaso, Fernando Bento, Antonio J. Marques Cardoso
      First page: 3941
      Abstract: The use of fuel cells in DC microgrids has been receiving a lot of attention from researchers and industry since both technologies can deliver clean energy with little to no environmental impact. To effectively integrate fuel cells in DC microgrids, a power converter that can equate the fuel cell’s voltage with the DC microgrid’s reference voltage is required. Based on the typical output voltages of fuel cells, buck-boost topologies are commonly used in this type of application. A variety of DC-DC buck-boost topologies, showing distinctive merits and drawbacks, are available in the literature. Therefore, this paper compiles, compares and describes different DC-DC buck-boost topologies that have been introduced in the literature over the past few years. Additionally, some design considerations are addressed, and future work is proposed.
      Citation: Electronics
      PubDate: 2022-11-28
      DOI: 10.3390/electronics11233941
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3942: Evaluation of the Flourish Dashboard for
           Context-Aware Fault Diagnosis in Industry 4.0 Smart Factories

    • Authors: Lukas Kaupp, Kawa Nazemi, Bernhard Humm
      First page: 3942
      Abstract: Cyber-physical systems become more complex, therewith production lines become more complex in the smart factory. Every employed system produces high amounts of data with unknown dependencies and relationships, making incident reasoning difficult. Context-aware fault diagnosis can unveil such relationships on different levels. A fault diagnosis application becomes context-aware when the current production situation is used in the reasoning process. We have already published TAOISM, a visual analytics model defining the context-aware fault diagnosis process for the Industry 4.0 domain. In this article, we propose the Flourish dashboard for context-aware fault diagnosis. The eponymous visualization Flourish is a first implementation of a context-displaying visualization for context-aware fault diagnosis in an Industry 4.0 setting. We conducted a questionnaire and interview-based bilingual evaluation with two user groups based on contextual faults recorded in a production-equal smart factory. Both groups provided qualitative feedback after using the Flourish dashboard. We positively evaluate the Flourish dashboard as an essential part of the context-aware fault diagnosis and discuss our findings, open gaps, and future research directions.
      Citation: Electronics
      PubDate: 2022-11-28
      DOI: 10.3390/electronics11233942
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3943: Speech Emotion Recognition Using Audio
           Matching

    • Authors: Iti Chaturvedi, Tim Noel, Ranjan Satapathy
      First page: 3943
      Abstract: It has become popular for people to share their opinions about products on TikTok and YouTube. Automatic sentiment extraction on a particular product can assist users in making buying decisions. For videos in languages such as Spanish, the tone of voice can be used to determine sentiments, since the translation is often unknown. In this paper, we propose a novel algorithm to classify sentiments in speech in the presence of environmental noise. Traditional models rely on pretrained audio feature extractors for humans that do not generalize well across different accents. In this paper, we leverage the vector space of emotional concepts where words with similar meanings often have the same prefix. For example, words starting with ‘con’ or ‘ab’ signify absence and hence negative sentiments. Augmentations are a popular way to amplify the training data during audio classification. However, some augmentations may result in a loss of accuracy. Hence, we propose a new metric based on eigenvalues to select the best augmentations. We evaluate the proposed approach on emotions in YouTube videos and outperform baselines in the range of 10–20%. Each neuron learns words with similar pronunciations and emotions. We also use the model to determine the presence of birds from audio recordings in the city.
      Citation: Electronics
      PubDate: 2022-11-29
      DOI: 10.3390/electronics11233943
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3944: A Mach–Zehnder Interferometer
           Refractive Index Sensor on a Spoof Surface Plasmon Polariton Waveguide

    • Authors: Yawei Zhang, Yuzhu Liu, Haoyan Xi, Tianhua Meng, Guozhong Zhao
      First page: 3944
      Abstract: In this paper, we experimentally and numerically confirm a planar Mach–Zehnder interferometer (MZI) device for sensing dielectric samples based on a spoof surface plasmon polariton (SSPP) waveguide. The MZI system is constructed using two different ultrathin transmission lines with distinct dispersion units supporting SSPPs. After SSPPs propagate a certain propagation distance, a resonant dip is formed at a specific frequency due to destructive interference, whose displacement enables the SSPP to be modulated by one of the MZI arms loaded with dielectric samples. We investigate how the variations in the permittivity and thickness of dielectric samples affect the sensibility. Through an error analysis between the experimental measurements and numerical calculations, it is demonstrated that the plasmonic sensor based on the MZI has a high precision. The proposed technique is compact and robust and paves a versatile route toward the chip-scale functional devices in microwave circuits.
      Citation: Electronics
      PubDate: 2022-11-29
      DOI: 10.3390/electronics11233944
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3945: Extended Simplified Electro-Mechanical
           Model of a Variable-Speed Wind Turbine for Grid Integration Studies:
           Emulation and Validation on a Microgrid Lab

    • Authors: Danny Ochoa, Sergio Martinez, Paul Arévalo
      First page: 3945
      Abstract: The energy transition towards renewable energies is crucial for the sustainable development of a society based on hydrocarbons. The current level of penetration and growth of wind energy in electric power systems is evident and many researchers have presented new methods for simulating and representing the electrical and mechanical characteristics of variable-speed wind turbines. However, complete mathematical models developed and implemented, for example, in MATLAB/Simulink® software, require significant computational efforts that could make grid studies impractical when its scale tends to increase. To contribute to facing this issue, this paper proposes an extended simplified model for a variable-speed wind turbine that considers the dynamic behavior of its mechanical system and includes an approximate representation of the power electronic converter. This approach broadens the scope of studies related to grid frequency control and power quality (fast-frequency response, primary frequency control, and voltage control, among others), considerably reducing the computational burden. Several validations of the proposed simplified model are presented, including comparisons with a doubly fed induction generator-based wind turbine model (phasor type) from the MATLAB/Simulink® library, and laboratory experiments under controlled conditions. The results show a good fit of the proposed simplified model to the MATLAB/Simulink® model, with minimal delays about 3% of the wind turbine inertia constant. Moreover, with the proposal, the computational time is reduced by up to 80% compared to a detailed model. This time reduction is achieved without penalizing the numerical accuracy and the estimation quality of the real behavior of the variable-speed wind turbine.
      Citation: Electronics
      PubDate: 2022-11-29
      DOI: 10.3390/electronics11233945
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3946: Orientation Detection System Based on
           Edge-Orientation Selective Neurons

    • Authors: Tianqi Chen, Bin Li, Yuki Todo
      First page: 3946
      Abstract: In this paper, we propose a mechanism of orientation detection system based on edge-orientation selective neurons. We assume that there are neurons in the V1 that can generate response to object’s edge, and each neuron has the optimal response to specific orientation in a local receptive field. The global orientation is inferred from the aggregation of local orientation information. An orientation detection system is further developed based on the proposed mechanism. We design four types of neurons for four local orientations and used these neurons to extract local orientation information. The global orientation is obtained according to the neuron with the most activation. The performance of this orientation detection system is evaluated on orientation detection tasks. From the experiment results, we can conclude that our proposed global orientation mechanism is feasible and explainable. The mechanism-based orientation detection system shows better recognition accuracy and noise immunity than the traditional convolution neural network-based orientation detection systems and EfficientNet-based orientation detection system, which have the most accuracy for now. In addition, our edge-orientation selective cell based artificial visual system can greatly save time and learning cost compared to the traditional convolution neural network and EfficientNet.
      Citation: Electronics
      PubDate: 2022-11-29
      DOI: 10.3390/electronics11233946
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3947: Lightweight Multi-Scale Dilated U-Net
           for Crop Disease Leaf Image Segmentation

    • Authors: Cong Xu, Changqing Yu, Shanwen Zhang
      First page: 3947
      Abstract: Crop disease leaf image segmentation (CDLIS) is the premise of disease detection, disease type recognition and disease degree evaluation. Various convolutional neural networks (CNN) and their modified models have been provided for CDLIS, but their training time is very long. Aiming at the low segmentation accuracy of various diseased leaf images caused by different sizes, colors, shapes, blurred speckle edges and complex backgrounds of traditional U-Net, a lightweight multi-scale extended U-Net (LWMSDU-Net) is constructed for CDLIS. It is composed of encoding and decoding sub-networks. Encoding the sub-network adopts multi-scale extended convolution, the decoding sub-network adopts a deconvolution model, and the residual connection between the encoding module and the corresponding decoding module is employed to fuse the shallow features and deep features of the input image. Compared with the classical U-Net and multi-scale U-Net, the number of layers of LWMSDU-Net is decreased by 1 with a small number of the trainable parameters and less computational complexity, and the skip connection of U-Net is replaced by the residual path (Respath) to connect the encoder and decoder before concatenating. Experimental results on a crop disease leaf image dataset demonstrate that the proposed method can effectively segment crop disease leaf images with an accuracy of 92.17%.
      Citation: Electronics
      PubDate: 2022-11-29
      DOI: 10.3390/electronics11233947
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3948: Energy Scheduling and Performance
           Evaluation of an e-Vehicle Charging Station

    • Authors: Ana Cabrera-Tobar, Nicola Blasuttigh, Alessandro Massi Pavan, Vanni Lughi, Giovanni Petrone, Giovanni Spagnuolo
      First page: 3948
      Abstract: This paper proposes an energy management system (EMS) for a photovoltaic (PV) grid-connected charging station with a battery energy storage system (BESS). The main objective of this EMS is to manage the energy delivered to the electric vehicle (EV), considering the price and CO2 emissions due to the grid’s connection. Thus, we present a multi-objective two-stage optimization to reduce the impact of the charging station on the environment, as well as the costs. The first stage of the optimization provides an energy schedule, taking into account the PV forecast, the hourly grid’s CO2 emissions factor, the electricity price, and the initial state of charge of the BESS. The output from this first stage corresponds to the maximum power permitted to be delivered to the EV by the grid. Then, the second stage of the optimization is based on model predictive control that looks to manage the energy flow from the grid, the PV, and the BESS. The proposed EMS is validated using an actual PV/BESS charging station located at the University of Trieste, Italy. Then, this paper presents an analysis of the performance of the charging station under the new EMS considering three main aspects, economic, environmental, and energy, for one month of data. The results show that due to the proposed optimization, the new energy profile guarantees a reduction of 32% in emissions and 29% in energy costs.
      Citation: Electronics
      PubDate: 2022-11-29
      DOI: 10.3390/electronics11233948
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3949: Design of a Quadruple Band-Notched
           Ultra-Wideband (UWB) Antenna Using Curled C-Shaped Structures and
           Interdigital Inductance Slots

    • Authors: Zhongliang Deng, Chengqi Lai, Yucheng Wang, Kun Deng
      First page: 3949
      Abstract: In this paper, a novel ultra-wideband (UWB) printed antenna with quadruple band-notched characteristics is proposed and investigated. The quadruple band rejections are achieved by etching two interdigital capacitance slots on each side of the ground plane, embedding a curled C-shaped slot on the circle patch, and adding a curled split-ring resonator on the backside of the antenna. Interdigital inductance slots can obtain a narrower notched band than general structures due to their high inductance, thereby preserving some valuable frequencies. Adjusting the tail branch’s length and distance of the curled C-shaped slot and the curled split-ring resonator can control the notch frequency and width. Finally, the proposed antenna operates from 2.9–11 GHz (VSWR < 2) with four band stops (VSWR > 2) for rejecting WiMAX, WLAN, and downlink of X-band satellite communication. Furthermore, the difference between the experimental results and the expected value is less than 3%. The proposed antenna can accurately filter out narrow-band signals.
      Citation: Electronics
      PubDate: 2022-11-29
      DOI: 10.3390/electronics11233949
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3950: Resolving Security Issues in the IoT
           Using Blockchain

    • Authors: Hafiz Abid Mahmood Malik, Asghar Ali Shah, Abdul Hafeez Muhammad, Ahmad Kananah, Ayesha Aslam
      First page: 3950
      Abstract: The Internet of Things is a system of interconnected smart devices that can communicate with each other and with other devices over the Internet, with or without human-to-human or human-to-computer interaction. Although IoT devices, which have IPs, make living easier, they are also a threat to the security and privacy of people. This research work presents a solution to the problem of security in the network of IoT, based on the idea of implementing the blockchain in IoT. Blockchain is a decentralized technology that adds blocks at the end of the chain. It saves the hash value for every block, and corresponds to the previous block. The decentralized behavior of blockchain is best for IoT as an extensive network, because IoT must not have a single point of failure, and one entity must not decide what to do. All the capable storage devices will save the same data entered from any device, removing the risk of receiving altered data.
      Citation: Electronics
      PubDate: 2022-11-29
      DOI: 10.3390/electronics11233950
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3951: Stereoscopic Projection Policy
           Optimization Method Based on Deep Reinforcement Learning

    • Authors: Jing An, Guang-Ya Si, Lei Zhang, Wei Liu, Xue-Chao Zhang
      First page: 3951
      Abstract: Based on the good performance of deep reinforcement learning (DRL) in policy optimization, a stereoscopic projection policy optimization method is proposed, which combines the simulation experiment method with the DRL method. On the basis of policy optimization research, a deep learning framework is selected according to the research problems, and a DRL stereoscopic project policy model based on the asynchronous advantage actor–critic (A3C) algorithm, which uses two groups of neural networks, is constructed. The optimized stereoscopic projection policy is obtained by the interactive learning between the DRL model and the simulation. The effectiveness of the cooperative optimization policy between the DRL and the simulation experiment is verified.
      Citation: Electronics
      PubDate: 2022-11-29
      DOI: 10.3390/electronics11233951
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3952: A 180 nm CMOS Integrated Optoelectronic
           Sensing System for Biomedical Applications

    • Authors: Guido Di Patrizio Stanchieri, Andrea De Marcellis, Marco Faccio, Elia Palange, Graziano Battisti, Ulkuhan Guler
      First page: 3952
      Abstract: This paper reports on a CMOS fully integrated optoelectronic sensing system composed of a Si photodiode and a transimpedance amplifier acting as the electronic analog front-end for the conditioning of the photocurrent generated by the photodiode. The proposed device has been specifically designed and fabricated for wearable/portable/implantable biomedical applications. The massive employment of sensor systems in different industrial and medical fields requires the development of small sensing devices that, together with suitable electronic analog front ends, must be designed to be integrated into proper standard CMOS technologies. Concerning biomedical applications, these devices must be as small as possible, making them non-invasive, comfortable tools for patients and operating with a reduced supply voltage and power consumption. In this sense, optoelectronic solutions composed of a semiconductor light source and a photodiode fulfill these requirements while also ensuring high compatibility with biological tissues. The reported optoelectronic sensing system is implemented and fabricated in TSMC 180 nm integrated CMOS technology and combines a Si photodiode based on a PNP junction with a Si area of 0.01 mm2 and a transimpedance amplifier designed at a transistor level requiring a Si area of 0.002 mm2 capable to manage up to nanoampere input currents generated by the photodiode. The transimpedance amplifier is powered at a 1.8 V single supply showing a maximum power consumption of about 54 μW, providing a high transimpedance gain that is tunable up to 123 dBΩ with an associated bandwidth of about 500 kHz. The paper reports on both the working principle of the developed ASIC and the experimental measurements for its full electrical and optoelectronic characterizations. Moreover, as case-examples of biomedical applications, the proposed integrated sensing system has also been validated through the optical detection of emulated standard electrocardiography and photoplethysmography signal patterns.
      Citation: Electronics
      PubDate: 2022-11-29
      DOI: 10.3390/electronics11233952
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3953: Zero-Tolerance Security Paradigm for
           Enterprise-Specific Industrial Internet of Things

    • Authors: Usman Tariq
      First page: 3953
      Abstract: The complex industrial environment of the 21st century is equipped with the Internet of Things platform, with the objective of real-time operational visibility, improved device management and predictive maintenance. To unleash the focused importance of its policy, a secure connectivity must be realized through a range of existing and dissimilar devices and data sources. During the conceptualization phase, the authors aimed to compel the following: (a) that restriction of access should be based on the presence of unexpected device actions that may point to a security breach, and (b) ensure the safety of the system by constant tracking of connected devices and data. In this paper, a policy-driven, zero-trust defense model is proposed to address numerous vulnerable entry points, validate device access to legitimate enterprise functions, quarantine unsecure devices, and trigger automated warnings and policy validation for hardware, software, network connectivity and data management. To handle active scanning, bots, passive auditing, outbound threat management, and device interconnections, an experimental environment was put up. This environment provides holistic visibility and a persistent view of all resources, including those that were previously unknown. A steady stream of reliable and authenticated data has helped to develop and adjust a scalable implementation strategy by avoiding recognized anomalous traps. Actual data was aggregated and analyzed to assess the proposed methodology. Comparative analysis of ‘device exposure view, attack path analysis, controlled view of devices, comprehensive vulnerability evaluation, and effective communication of cyber risk’ has proved the effectiveness of the proposed methodology.
      Citation: Electronics
      PubDate: 2022-11-29
      DOI: 10.3390/electronics11233953
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3954: Development of Quantum Protocol
           Modification CSLOE–2022, Increasing the Cryptographic Strength of
           Classical Quantum Protocol BB84

    • Authors: Larissa V. Cherckesova, Olga A. Safaryan, Alexey N. Beskopylny, Elena Revyakina
      First page: 3954
      Abstract: Quantum cryptography protocols make it possible not only to ensure the protection of data transmitted in a communication channel from unauthorized access by intruders, but also to detect the existence of any attempted interception. This scientific direction is currently relevant, since it is related to the problem of security and data protection in current information and communication networks. The article is devoted to quantum cryptography; it describes the development of quantum protocols as quantum key distribution systems. Grounded on the laws of quantum mechanics, the elaboration of modifications of secure data transfer protocols is shown. The authors considered the best-known protocol to be BB84 of quantum key distribution; a more modern modification of this protocol is BB84 Info-Z. Comparative analysis of these has also been carried out. It has been established that the BB84-Info-Z quantum protocol works more efficiently than BB84 since its lower error threshold allows the interceptor to obtain much less information about the secret key. The authors put forward a new idea to improve the BB84 protocol (which has been quite outdated for almost 40 years), due to the increase in modern requirements for quantum cryptography protocols. The modification is called CSLOE-2022. It enables significant intensification of cryptographic strength and the entanglement degree of the interceptor (cryptanalyst), which greatly complicates the very possibility of intercepting information. The ultimate goal of the CSLOE-2022 modification is to complicate the eavesdropping process so much that it can be considered completely useless for an attacker in terms of wasting time and resources. The modification allows exceeding the known speed limit of key generation without repeaters since it uses two sources, the phases of which, in addition to the hundreds of kilometers of fiber between them, are very difficult to stabilize. Comparison of the protocols by working distance showed that for BB84, this distance does not exceed 70 km; for BB84-Info-Z it is similar, at no more than 70 km, and the modification of CSLOE-2022 proposed by the authors theoretically allows increasing the working distance of the quantum protocol to 511 km (7.3 times).
      Citation: Electronics
      PubDate: 2022-11-29
      DOI: 10.3390/electronics11233954
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3955: Anomaly-PTG: A Time Series
           Data-Anomaly-Detection Transformer Framework in Multiple Scenarios

    • Authors: Gang Li, Zeyu Yang, Honglin Wan, Min Li
      First page: 3955
      Abstract: In actual scenarios, industrial and cloud computing platforms usually need to monitor equipment and traffic anomalies through multivariable time series data. However, the existing anomaly detection methods can not capture the long-distance temporal correlations of data and the potential relationships between features simultaneously, and only have high detection accuracy for specific time sequence anomaly detection scenarios without good generalization ability. This paper proposes a time-series anomaly-detection framework for multiple scenarios, Anomaly-PTG (anomaly parallel transformer GRU), given the above limitations. The model uses the parallel transformer GRU as the information extraction module of the model to learn the long-distance correlation between timestamps and the global feature relationship of multivariate time series, which enhances the ability to extract hidden information from time series data. After extracting the information, the model learns the sequential representation of the data, conducts the sequential modeling, and transmits the data to the full connection layer for prediction. At the same time, it also uses the autoencoder to learn the potential representation of the data and reconstruct the data. The two are optimally combined to form an anomaly detection module of the model. The module combines timestamp prediction with time series data reconstruction, improving the detection rate of rare anomalies and detection accuracy. By using three public datasets of physical devices and one dataset of network traffic intrusion detection, the model’s effectiveness was verified, and the model’s generalization ability and strong robustness were demonstrated. Compared with the most advanced method, the average F1 value of the Anomaly-PTG model on four datasets was increased by 2.2%, and the F1 value on each dataset was over 94%.
      Citation: Electronics
      PubDate: 2022-11-29
      DOI: 10.3390/electronics11233955
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3956: Study on Frequency Stability of an
           Independent System Based on Wind-Photovoltaic-Energy Storage-Diesel
           Generator

    • Authors: Yonghu Wu, Cun Huang, Fen Dong, Guoxiang Li, Gaowei Wang, Sai Zhang
      First page: 3956
      Abstract: Wind and photovoltaic power generation connected to the independent power system can save fuel, reduce carbon emissions, and provide significant economic and environmental benefits. Influenced by the characteristics of light resources and wind resources, the wind and photovoltaic output active power is characterized by volatility and randomness, which affects the frequency stability of the independent power system. In order to evaluate the frequency stability, in this paper, the simulation model of an independent power system is established, and the simulation model of a diesel generator, wind and photovoltaic are connected. Through droop calculation and Simulink simulation, the frequency characteristics of an independent power system under different working conditions are analyzed, and the maximum absorption capacity of wind and photovoltaic is studied. In an independent power system, when the new energy output is 25% of the total output, all the new energy output is cut off, the frequency drops by 0.5 Hz, and the frequency fluctuation is within the specified range.
      Citation: Electronics
      PubDate: 2022-11-29
      DOI: 10.3390/electronics11233956
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3957: A Universal Electronically Controllable
           Memelement Emulator Based on VDCC with Variable Configuration

    • Authors: Predrag B. Petrović
      First page: 3957
      Abstract: In this paper, a universal fractional order memelement (FOME) emulator is proposed based on the use of a voltage differentiating current conveyor (VDCC) as active block. The emulation circuit was implemented without an analog voltage multiplier and with only one type of grounded passive element—capacitors. Specially designed switching networks allow controlling the type of memelement and the emulator mode—floating or/and grounded, electronically controlled (by changing the bias voltage of the VDCC) FOMEs. The proposed emulator was theoretically analyzed, and the influence of possible non-idealities and parasitic effects was also been analyzed to reduce the undesirable effects by selecting the passive circuit elements. The proposed designs are very simple compared to most of the designs available in the literature and can operate in a wide frequency range (up to 50 MHz) and also satisfy the non-volatility test. All realized memelements can be used in incremental and decremental modes as well as in inverse configuration. The performance of the circuit was verified by HSPICE simulations using 0.18 μm TSMC process parameters and ±0.9 V power supply. The proposal is also supported by experimental results with off-the-shelf components (LM13700 and one AD844) in order to confirm the proposed solution’s workability.
      Citation: Electronics
      PubDate: 2022-11-29
      DOI: 10.3390/electronics11233957
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3958: Vertically Federated Learning with
           Correlated Differential Privacy

    • Authors: Jianzhe Zhao, Jiayi Wang, Zhaocheng Li, Weiting Yuan, Stan Matwin
      First page: 3958
      Abstract: Federated learning (FL) aims to address the challenges of data silos and privacy protection in artificial intelligence. Vertically federated learning (VFL) with independent feature spaces and overlapping ID spaces can capture more knowledge and facilitate model learning. However, VFL has both privacy and utility problems in framework construction. On the one hand, sharing gradients may cause privacy leakage. On the other hand, the increase in participants brings a surge in the feature dimension of the global model, which results in higher computation costs and lower model accuracy. To address these issues, we propose a vertically federated learning algorithm with correlated differential privacy (CRDP-FL) to meet FL systems’ privacy and utility requirements. A privacy-preserved VFL framework is designed based on differential privacy (DP) between organizations with many network edge devices. Meanwhile, feature selection is performed to improve the algorithm’s efficiency and model performance to solve the problem of dimensionality explosion. We also propose a quantitative correlation analysis technique for VFL to reduce the correlated sensitivity and noise injection, balancing the utility decline due to DP protection. We theoretically analyze the privacy level and utility of CRDP-FL. A real vertically federated learning scenario is simulated with personalized settings based on the ISOLET and Breast Cancer datasets to verify the method’s effectiveness in model accuracy, privacy budget, and data correlation.
      Citation: Electronics
      PubDate: 2022-11-29
      DOI: 10.3390/electronics11233958
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3959: Newly Added Construction Land
           Information Extraction Method for High-Resolution Remote Sensing Images
           Based on Weakening of the Negative Sample Weight

    • Authors: Guiling Zhao, Weidong Liang, Zhe Liang, Quanrong Guo
      First page: 3959
      Abstract: Information regarding newly added construction land can be extracted from high-resolution remote sensing images. The retrieval accuracy of land cover changes across the country has improved, and the illegal use of land is actively monitored. To address the imbalance between positive and negative training samples in extracting information regarding newly added construction land, a method for identifying newly added construction land by weakening the weight of negative samples was proposed. A focal loss function was used to weaken the negative samples’ weights and improve the overfitting U-net. Since the two parameters of the focal loss function are not independent of each other, they need to be selected at the same time. Therefore, this paper developed a formula for selecting the balance factor α based on a large number of experimental results. First, the GF-2 image was combined with the historical land change survey data and monitoring vector results to construct a dataset, and then the training dataset was input into a fully convolutional neural network (CNN) integrated with feature fusion and a focal loss function. Finally, the accuracy of the trained network model was verified. To demonstrate the applicability of the method of determining the parameters of the focal loss function, the validation set was divided into four subsets for accuracy verification. The experimental results showed that the F1-score of newly added construction land information extracted by this method reached 0.913, which is 0.078 and 0.033 higher than those of the U-net and the improved U-net. The parameters obtained by the method proposed in this study achieved the best results on the four verification sets, which shows that the method for extracting newly added construction land information and that for selecting parameters have strong applicability.
      Citation: Electronics
      PubDate: 2022-11-29
      DOI: 10.3390/electronics11233959
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3960: The Role of ML, AI and 5G Technology in
           Smart Energy and Smart Building Management

    • Authors: Tehseen Mazhar, Muhammad Amir Malik, Inayatul Haq, Iram Rozeela, Inam Ullah, Muhammad Abbas Khan, Deepak Adhikari, Mohamed Tahar Ben Othman, Habib Hamam
      First page: 3960
      Abstract: With the help of machine learning, many tasks can be automated. The use of computers and mobile devices in “intelligent” buildings may make tasks such as controlling the indoor climate, monitoring security, and performing routine maintenance much easier. Intelligent buildings employ the Internet of Things to establish connections among the many components that make up the structure. As the notion of the Internet of Things (IoT) gains attraction, smart grids are being integrated into larger networks. The IoT is an integral part of smart grids since it enables beneficial services that improve the experience for everyone inside and individuals are protected because of tried-and-true life support systems. The reason for installing Internet of Things gadgets in smart structures is the primary focus of this investigation. In this context, the infrastructure behind IoT devices and their component units is of the highest concern.
      Citation: Electronics
      PubDate: 2022-11-29
      DOI: 10.3390/electronics11233960
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3961: Preliminary Study of a G-Band Extended
           Interaction Oscillator Operating in the TM31-3π Mode Driven by
           Pseudospark-Sourced Multiple Electron Beams

    • Authors: Ruibin Peng, Bin Wang, Yong Yin, Hailong Li, Xuesong Yuan, Xiaotao Xu, Liangjie Bi, Yu Qin, Lin Meng
      First page: 3961
      Abstract: This paper presents the first design that combines pseudospark-sourced (PS) electron beams with a multiple-beam extended interaction oscillator (EIO). The PS electron beam is an excellent choice for driving EIOs because it has high current density and does not require a focusing magnetic field. The EIO with coaxial structure adopts the method of multiple electron beams, which plays a crucial role in improving the average output power. At the same frequency, the EIO operating in the high-order TM31-3π mode has a larger cavity size than the EIO operating in the traditional TM01-2π mode. The high-order TM31-3π mode solves the problem of the EIO’s manufacture at high frequency. In order to verify the above points, a G-band PS multiple-beam EIO operating in TM31-3π mode has been designed. The beam–wave interaction particle-in-cell simulation results show that the EIO’s peak output power is 39.2 kW at 217 GHz, and that its efficiency is around 6.1%. The EIO with six pencil beams operates at a voltage of 43 kV. The total current of the six electron beams is 15 A (equally distributed among the six beams), and the corresponding current density is about 5000 A/cm2. Considering the ohmic loss and the effect of skin depth, the conductivity used in these simulations is 2 × 107 S/m. The design is an excellent way to improve the output power of EIO operating at high frequency.
      Citation: Electronics
      PubDate: 2022-11-29
      DOI: 10.3390/electronics11233961
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3962: An Incremental Learning Framework for
           Photovoltaic Production and Load Forecasting in Energy Microgrids

    • Authors: Elissaios Sarmas, Sofoklis Strompolas, Vangelis Marinakis, Francesca Santori, Marco Antonio Bucarelli, Haris Doukas
      First page: 3962
      Abstract: Energy management is crucial for various activities in the energy sector, such as effective exploitation of energy resources, reliability in supply, energy conservation, and integrated energy systems. In this context, several machine learning and deep learning models have been developed during the last decades focusing on energy demand and renewable energy source (RES) production forecasting. However, most forecasting models are trained using batch learning, ingesting all data to build a model in a static fashion. The main drawback of models trained offline is that they tend to mis-calibrate after launch. In this study, we propose a novel, integrated online (or incremental) learning framework that recognizes the dynamic nature of learning environments in energy-related time-series forecasting problems. The proposed paradigm is applied to the problem of energy forecasting, resulting in the construction of models that dynamically adapt to new patterns of streaming data. The evaluation process is realized using a real use case consisting of an energy demand and a RES production forecasting problem. Experimental results indicate that online learning models outperform offline learning models by 8.6% in the case of energy demand and by 11.9% in the case of RES forecasting in terms of mean absolute error (MAE), highlighting the benefits of incremental learning.
      Citation: Electronics
      PubDate: 2022-11-29
      DOI: 10.3390/electronics11233962
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3963: Experience with Using BBC Micro:Bit and
           Perceived Professional Efficacy of Informatics Teachers

    • Authors: Nika Kvaššayová, Martin Cápay, Štefan Petrík, Magdaléna Bellayová, Eva Klimeková
      First page: 3963
      Abstract: Our study is focused on the perceived professional efficacy of informatics in-service teachers with the experience of using micro-controller BBC micro:bit. In Slovakia, teaching using hardware is not typical. In addition, many teachers do not teach programming. BBC micro:bit is designed to be a tool for computer science (CS) teachers that should make a significant contribution to the innovation of CS teaching and enable CS teachers to implement CS lessons. The following research questions were asked. Q1: Is there a difference in the perceived efficacy to use teaching strategies based on experience with the micro:bit' Q2: Is there a relationship between the perceived efficacy of using teaching strategies and experience using the micro:bit' The research sample comprised N = 388 CS teachers employed in Slovak schools from the available selection. The research sample included CS teachers who participated in the project called ENTER. All participants have a grant, weekly online practices, supporting materials, and also consultant for implementation of a new teaching strategy. This study’s findings indicate that the use of a microcontroller such as the micro:bit has a positive impact on self-efficacy for instructional strategies, but not for classroom management.
      Citation: Electronics
      PubDate: 2022-11-29
      DOI: 10.3390/electronics11233963
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3964: Exploiting a Deep Learning Toolbox for
           Human-Machine Feedback towards Analog Integrated Circuit Placement
           Automation

    • Authors: António Gusmão, Rafael Vieira, Nuno Horta, Nuno Lourenço, Ricardo Martins
      First page: 3964
      Abstract: The layout design of analog integrated circuits has been defying all automation attempts, and it is still primarily a handcrafting process carried by circuit designers on traditional layout editing frameworks. This paper presents a toolbox based on deep learning techniques and a sturdy graphical user interface to assist designers during that process. The underlying mechanism of this toolbox relies on a simple pairwise device interaction circuit description, i.e., the circuits’ topological constraints, to propose valid floorplan solutions for block-level structures, including topologies and deep nanometer technology nodes not used for its training, at push-button speed. Despite its automatic functionalities, the toolbox is focused on explainable artificial intelligence, involving the designer in the synthesis flow via filtering and editing options over the candidate floorplan solutions. This constant state of human-machine feedback environment turns the designer aware of the impact of each device’s position change and inherent tradeoffs while suggesting subsequent moves, ultimately increasing the designers’ productivity in this time-consuming and iterative task. Finally, the toolbox is shown to instantly generate floorplans with similar or better constraint fulfilment than human designed solutions for state-of-the-art analog circuit blocks.
      Citation: Electronics
      PubDate: 2022-11-29
      DOI: 10.3390/electronics11233964
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3965: Driving Assistance System for Ambulances
           to Minimise the Vibrations in Patient Cabin

    • Authors: Abdulaziz Aldegheishem, Nabil Alrajeh, Lorena Parra, Oscar Romero, Jaime Lloret
      First page: 3965
      Abstract: The ambulance service is the main transport for diseased or injured people which suffers the same acceleration forces as regular vehicles. These accelerations, caused by the movement of the vehicle, impact the performance of tasks executed by sanitary personnel, which can affect patient survival or recovery time. In this paper, we have trained, validated, and tested a system to assess driving in ambulance services. The proposed system is composed of a sensor node which measures the vehicle vibrations using an accelerometer. It also includes a GPS sensor, a battery, a display, and a speaker. When two possible routes reach the same destination point, the system compares the two routes based on previously classified data and calculates an index and a score. Thus, the index balances the possible routes in terms of time to reach the destination and the vibrations suffered in the patient cabin to recommend the route that minimises those vibrations. Three datasets are used to train, validate, and test the system. Based on an Artificial Neural network (ANN), the classification model is trained with tagged data classified as low, medium, and high vibrations, and 97% accuracy is achieved. Then, the obtained model is validated using data from three routes of another region. Finally, the system is tested in two new scenarios with two possible routes to reach the destination. The results indicate that the route with less vibration is preferred when there are low time differences (less than 6%) between the two possible routes. Nonetheless, with the current weighting factors, the shortest route is preferred when time differences between routes are higher than 20%, regardless of the higher vibrations in the shortest route.
      Citation: Electronics
      PubDate: 2022-11-30
      DOI: 10.3390/electronics11233965
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3966: Multi-Model Inference Accelerator for
           Binary Convolutional Neural Networks

    • Authors: André L. de Sousa, Mário P. Véstias, Horácio C. Neto
      First page: 3966
      Abstract: Binary convolutional neural networks (BCNN) have shown good accuracy for small to medium neural network models. Their extreme quantization of weights and activations reduces off-chip data transfer and greatly reduces the computational complexity of convolutions. Further reduction in the complexity of a BCNN model for fast execution can be achieved with model size reduction at the cost of network accuracy. In this paper, a multi-model inference technique is proposed to reduce the execution time of the binarized inference process without accuracy reduction. The technique considers a cascade of neural network models with different computation/accuracy ratios. A parameterizable binarized neural network with different trade-offs between complexity and accuracy is used to obtain multiple network models. We also propose a hardware accelerator to run multi-model inference throughput in embedded systems. The multi-model inference accelerator is demonstrated on low-density Zynq-7010 and Zynq-7020 FPGA devices, classifying images from the CIFAR-10 dataset. The proposed accelerator improves the frame rate per number of LUTs by 7.2× those of previous solutions on a ZYNQ7020 FPGA with similar accuracy. This shows the effectiveness of the multi-model inference technique and the efficiency of the proposed hardware accelerator.
      Citation: Electronics
      PubDate: 2022-11-30
      DOI: 10.3390/electronics11233966
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3967: Hybrid Encryption Scheme for Medical
           Imaging Using AutoEncoder and Advanced Encryption Standard

    • Authors: Yasmeen Alslman, Eman Alnagi, Ashraf Ahmad, Yousef AbuHour, Remah Younisse, Qasem Abu Al-haija
      First page: 3967
      Abstract: Recently, medical image encryption has gained special attention due to the nature and sensitivity of medical data and the lack of effective image encryption using innovative encryption techniques. Several encryption schemes have been recommended and developed in an attempt to improve medical image encryption. The majority of these studies rely on conventional encryption techniques. However, such improvements have come with increased computational complexity and slower processing for encryption and decryption processes. Alternatively, the engagement of intelligent models such as deep learning along with encryption schemes exhibited more effective outcomes, especially when used with digital images. This paper aims to reduce and change the transferred data between interested parties and overcome the problem of building negative conclusions from encrypted medical images. In order to do so, the target was to transfer from the domain of encrypting an image to encrypting features of an image, which are extracted as float number values. Therefore, we propose a deep learning-based image encryption scheme using the autoencoder (AE) technique and the advanced encryption standard (AES). Specifically, the proposed encryption scheme is supposed to encrypt the digest of the medical image prepared by the encoder from the autoencoder model on the encryption side. On the decryption side, the analogous decoder from the auto-decoder is used after decrypting the carried data. The autoencoder was used to enhance the quality of corrupted medical images with different types of noise. In addition, we investigated the scores of structure similarity (SSIM) and mean square error (MSE) for the proposed model by applying four different types of noise: salt and pepper, speckle, Poisson, and Gaussian. It has been noticed that for all types of noise added, the decoder reduced this noise in the resulting images. Finally, the performance evaluation demonstrated that our proposed system improved the encryption/decryption overhead by 50–75% over other existing models.
      Citation: Electronics
      PubDate: 2022-11-30
      DOI: 10.3390/electronics11233967
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3968: Federated Deep Reinforcement
           Learning-Based Caching and Bitrate Adaptation for VR Panoramic Video in
           Clustered MEC Networks

    • Authors: Yan Li
      First page: 3968
      Abstract: Virtual reality (VR) panoramic video is more expressive and experiential than traditional video. With the accelerated deployment of 5G networks, VR panoramic video has experienced explosive development. The large data volume and multi-viewport characteristics of VR panoramic videos make it more difficult to cache and transcode them in advance. Therefore, VR panoramic video services urgently need to provide powerful caching and computing power over the edge network. To address this problem, this paper establishes a hierarchical clustered mobile edge computing (MEC) network and develops a data perception-driven clustered-edge transmission model to meet the edge computing and caching capabilities required for VR panoramic video services. The joint optimization problem of caching and bitrate adaptation can be formulated as a Markov Decision Process (MDP). The federated deep reinforcement learning (FDRL) algorithm is proposed to solve the problem of caching and bitrate adaptation (called FDRL-CBA) for VR panoramic video services. The simulation results show that FDRL-CBA can outperform existing DRL-based methods in the same scenarios in terms of cache hit rate and quality of experience (QoE). In conclusion, this work developed a FDRL-CBA algorithm based on a data perception-driven clustered-edge transmission model, called Hierarchical Clustered MEC Networks. The proposed method can improve the performance of VR panoramic video services.
      Citation: Electronics
      PubDate: 2022-11-30
      DOI: 10.3390/electronics11233968
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3969: Quantum Dynamic Optimization Algorithm
           for Neural Architecture Search on Image Classification

    • Authors: Jin Jin, Qian Zhang, Jia He, Hongnian Yu
      First page: 3969
      Abstract: Deep neural networks have proven to be effective in solving computer vision and natural language processing problems. To fully leverage its power, manually designed network templates, i.e., Residual Networks, are introduced to deal with various vision and natural language tasks. These hand-crafted neural networks rely on a large number of parameters, which are both data-dependent and laborious. On the other hand, architectures suitable for specific tasks have also grown exponentially with their size and topology, which prohibits brute force search. To address these challenges, this paper proposes a quantum dynamic optimization algorithm to find the optimal structure for a candidate network using Quantum Dynamic Neural Architecture Search (QDNAS). Specifically, the proposed quantum dynamics optimization algorithm is used to search for meaningful architectures for vision tasks and dedicated rules to express and explore the search space. The proposed quantum dynamics optimization algorithm treats the iterative evolution process of the optimization over time as a quantum dynamic process. The tunneling effect and potential barrier estimation in quantum mechanics can effectively promote the evolution of the optimization algorithm to the global optimum. Extensive experiments on four benchmarks demonstrate the effectiveness of QDNAS, which is consistently better than all baseline methods in image classification tasks. Furthermore, an in-depth analysis is conducted on the searchable networks that provide inspiration for the design of other image classification networks.
      Citation: Electronics
      PubDate: 2022-11-30
      DOI: 10.3390/electronics11233969
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3970: Empirical Evidence on the Development
           and Digitalization of the Accounting and Finance Profession in Europe

    • Authors: Liliana Ionescu-Feleagă, Voicu D. Dragomir, Ștefan Bunea, Oana Cristina Stoica, Laura-Eugenia-Lavinia Barna
      First page: 3970
      Abstract: The objective of this research was to evaluate the development and digitalization of professional services in the field of accounting and finance, as well as to calculate and compare several indicators of the development of the profession in each European country. We also sought to identify the factors that drive the development of the accounting and finance profession at the international level. We collected rich information on 337 professional associations in 40 countries in Europe. Using this dataset, 20 accounting and finance services and 14 membership services and benefits provided by professional associations were identified. Digitalization of the profession is a prominent membership service, but also a characteristic of country competitiveness. The results of the intergroup analysis showed that high-income countries have a significantly larger number of professional associations and services compared to middle-income countries. Furthermore, the accounting and finance profession in high-income countries covers a larger number of accounting and membership services. The size of the population and the competitiveness of the national economy are the main predictors of the development and digitalization of the accounting and finance profession in a country. This research has implications for professional associations and national regulators in reducing disparities between European countries on the matter of accounting education and service quality. The scale of this research can provide institutional actors with a holistic perspective on the accounting and finance profession at the national and international level.
      Citation: Electronics
      PubDate: 2022-11-30
      DOI: 10.3390/electronics11233970
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3971: Location-Aware IoT-Enabled Wireless
           Sensor Networks for Landslide Early Warning

    • Authors: Dhouha El Houssaini, Sabrine Khriji, Christian Viehweger, Thomas Keutel, Olfa Kanoun
      First page: 3971
      Abstract: Wireless Sensor Networks (WSNs) represent an interesting technology for designing early warning systems for landslides as they can ensure real-time and continuous monitoring. Through accurate localization techniques, changes in the position of installed nodes can be detected even during the early stage of field instability. This is through an accurate detection of nodes position changes independently from systematic deviations resulting from outdoor environmental conditions. In this study, we propose an accurate measurement system for distance measurement between wireless sensor nodes based on an ultra-wideband (UWB) localization method. In particular, distance measurements at different real weather conditions were performed to identify the impact of weather changes on distance measurement deviations. A proptotype for a landslide warning system has been developed realizing a localization accuracy of 98%.
      Citation: Electronics
      PubDate: 2022-11-30
      DOI: 10.3390/electronics11233971
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3972: Indoor Propagation Analysis of IQRF
           Technology for Smart Building Applications

    • Authors: Mohammed Bouzidi, Nishu Gupta, Yaser Dalveren, Marshed Mohamed, Faouzi Alaya Cheikh, Mohammad Derawi
      First page: 3972
      Abstract: Owing to its efficiency in the Internet of Things (IoT) applications in terms of low-power connectivity, IQRF (Intelligent Connectivity using Radio Frequency) technology appears to be one of the most reasonable IoT technologies in the commercial market. To realize emerging smart building applications using IQRF, it is necessary to study the propagation characteristics of IQRF technology in indoor environments. In this study, preliminary propagation measurements are conducted using IQRF transceivers that operate on the 868 MHz band in a peer-to-peer (P2P) configured system. The measurements are conducted both in a single corridor of a building in a Line-of-Sight (LoS) link and two perpendicular corridors in a Non-Line-of-Sight (NLoS) with one single knife-edge link. Moreover, the measured path loss values are compared with the predicted path loss values in order to comparatively assess the prediction accuracy of the well-known empirical models, such as log-distance, ITU, and WINNER II. According to the results, it is concluded that the ITU-1 path loss model agrees well with the measurements and could be used in the planning of an IQRF network deployment in a typical LoS corridor environment. For NLoS corridors, both ITU-3 and WINNERII-2 models could be used due to their higher prediction accuracy. We expect that the initial results achieved in this study could open new perspectives for future research on the development of smart building applications.
      Citation: Electronics
      PubDate: 2022-11-30
      DOI: 10.3390/electronics11233972
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3973: The Characteristics of the Second and
           Third Virtual Cathodes in an Axial Vircator for the Generation of
           High-Power Microwaves

    • Authors: Sohail Mumtaz, Eun-Ha Choi
      First page: 3973
      Abstract: A virtual cathode oscillator or vircator is a vacuum tube for producing high-power microwaves (HPM). The efficiency of the vircator has been a difficult task for decades. The main reasons for low efficiency are intense relativistic electron beam (IREB) loss and few or no interactions between IREB and HPM. In this case, forming multiple virtual cathodes may be beneficial in overcoming these constraints. By reusing the axially propagating leaked electrons (LE), we could confine them and form multiple virtual cathodes (VCs). This article discussed the characteristics of newly formed VCs based on simulation results. The formation time of new VCs was discovered to be highly dependent on the reflector position and the density of LE approaching their surfaces. Furthermore, multiple VC formation in the waveguide region does not affect conventional VCs’ position or forming time. The emission mode of the generated HPM was TM01 with single and multiple VCs and remained unaffected. The formation of multiple VCs positively influenced the axial and radial electric fields. When compared to a single VC, the axial and radial electric field increased 25.5 and 18 times with multiple VCs. The findings suggested that forming multiple VCs could be a future hope for achieving high vircator efficiency.
      Citation: Electronics
      PubDate: 2022-11-30
      DOI: 10.3390/electronics11233973
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3974: Detection of Diseases in Pandemic: A
           Predictive Approach Using Stack Ensembling on Multi-Modal Imaging Data

    • Authors: Rabeea Mansoor, Munam Ali Shah, Hasan Ali Khattak, Shafaq Mussadiq, Hafiz Tayyab Rauf, Zoobia Ameer
      First page: 3974
      Abstract: Deep Learning (DL) in Medical Imaging is an emerging technology for diagnosing various diseases, i.e., pneumonia, lung cancer, brain stroke, breast cancer, etc. In Machine Learning (ML) and traditional data mining approaches, feature extraction is performed before building a predictive model, which is a cumbersome task. In the case of complex data, there are a lot of challenges, such as insufficient domain knowledge while performing feature engineering. With the advancement in the application of Artificial Neural Networks (ANNs) and DL, ensemble learning is an essential foundation for developing an automated diagnostic system. Medical Imaging with different modalities is effective for the detailed analysis of various chronic diseases, in which the healthy and infected scans of multiple organs are compared and analyzed. In this study, the transfer learning approach is applied to train 15 state-of-the-art DL models on three datasets (X-ray, CT-scan and Ultrasound) for predicting diseases. The performance of these models is evaluated and compared. Furthermore, a two-level stack ensembling of fine-tuned DL models is proposed. The DL models having the best performances among the 15 will be used for stacking in the first layer. Support Vector Machine (SVM) is used in Level 2 as a meta-classifier to predict the result as one of the following: pandemic positive (1) or negative (0). The proposed architecture has achieved 98.3%, 98.2% and 99% accuracy for D1, D2 and D3, respectively, which outperforms the performance of existing research. These experimental results and findings can be considered helpful tools for pandemic screening on chest X-rays, CT scan images and ultrasound images of infected patients. This architecture aims to provide clinicians with more accurate results.
      Citation: Electronics
      PubDate: 2022-11-30
      DOI: 10.3390/electronics11233974
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3975: A Convolutional Neural Network for
           COVID-19 Diagnosis: An Analysis of Coronavirus Infections through Chest
           X-rays

    • Authors: Avani Kirit Mehta, R. Swarnalatha, M. Subramoniam, Sachin Salunkhe
      First page: 3975
      Abstract: Coronavirus (COVID-19) disease has not only become a pandemic but also an overwhelming strain on the healthcare industry. The conventional diagnostic methods include Antigen Rapid Kits and Reverse Transcription–Polymerase Chain Reaction (RT-PCR) tests. However, they entail several drawbacks such as low precision in diagnosis, increased time in obtaining test results, increased human–patient interaction, and high inaccuracy in the diagnosis of asymptomatic individuals, thus posing a significant challenge in today’s medical practice in curbing an extremely infectious disease such as COVID-19. To overcome these shortcomings, a machine learning (ML) approach was proposed to aid clinicians in more accurate and precise infection diagnoses. A Convolutional Neural Network was built using a sample size of 1920 chest X-rays (CXR) of healthy individuals and COVID-19-infected patients. The developed CNN’s performance was further cross-checked using the clinical results of the validation dataset comprising 300 CXRs. By converting the final output to binary, an intuitive classification of whether a specific CXR is of a healthy or a COVID-infected patient was accomplished. The statistical analysis of the CNN was: Accuracy: 95%; Precision: 96%; Specificity: 95%; Recall: 95%, and F1 score: 95%, thus, proving it to be a promising diagnostic tool in comparison to the other existing ML-based models. The datasets were obtained from Kaggle, GitHub, and European Institute for Biomedical Imaging Research repositories. The prospects of the proposed CNN lie in its flexibility to be altered and extrapolated in diagnosing other lung infections, such as pneumonia and bacterial infections, with relevant training algorithms and inputs. Additionally, the usage of other bio-imaging modalities as input datasets such as CT scans, Lung Ultrasounds and Heat Maps gives the CNN immense potential to assess for better insights on the severity of infection in both infected and asymptomatic patients as well as other related medical diagnoses.
      Citation: Electronics
      PubDate: 2022-11-30
      DOI: 10.3390/electronics11233975
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3976: Diagnosis Myocardial Infarction Based on
           Stacking Ensemble of Convolutional Neural Network

    • Authors: Hela Elmannai, Hager Saleh, Abeer D. Algarni, Ibrahim Mashal, Kyung Sup Kwak, Shaker El-Sappagh, Sherif Mostafa
      First page: 3976
      Abstract: Artificial Intelligence (AI) technologies are vital in identifying patients at risk of serious illness by providing an early hazards risk. Myocardial infarction (MI) is a silent disease that has been harvested and is still threatening many lives. The aim of this work is to propose a stacking ensemble based on Convolutional Neural Network model (CNN). The proposed model consists of two primary levels, Level-1 and Level-2. In Level-1, the pre-trained CNN models (i.e., CNN-Model1, CNN-Model2, and CNN-Model3) produce the output probabilities and collect them in stacking for the training and testing sets. In Level-2, four meta-leaner classifiers (i.e., SVM, LR, RF, or KNN) are trained by stacking the output probabilities of the training set and are evaluated using the stacking of the output probabilities of the testing set to make the final prediction results. The proposed work was evaluated based on two ECG heartbeat signals datasets for MI: Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) and Physikalisch-Technische Bundesanstalt (PTB) datasets. The proposed model was compared with a diverse set of classical machine learning algorithms such as decision tree, K-nearest neighbor, and support vector machine, and the three base CNN classifiers of CNN-Model1, CNN-Model2, and CNN-Model3. The proposed model based on the RF meta-learner classifier obtained the highest scores, achieving remarkable results on both databases used. For the MIT-BIH dataset it achieved an accuracy of 99.8%, precision of 97%, recall of 96%, and F1-score of 94.4%, outperforming all other methods. while with PTB dataset achieved an accuracy of 99.7%, precision of 99%, recall of 99%, and F1-score of 99%, exceeding the other methods.
      Citation: Electronics
      PubDate: 2022-11-30
      DOI: 10.3390/electronics11233976
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3977: A Context Awareness Hierarchical
           Attention Network for Next POI Recommendation in IoT Environment

    • Authors: Xuebo Liu, Jingjing Guo, Peng Qiao
      First page: 3977
      Abstract: The rapid increase in the number of sensors in the Internet of things (IoT) environment has resulted in the continuous generation of massive and rich data in Location-Based Social Networks (LBSN). In LBSN, the next point-of-interest (POI) recommendation has become an important task, which provides the best POI recommendation according to the user’s recent check-in sequences. However, all existing methods for the next POI recommendation only focus on modeling the correlation between POIs based on users’ check-in sequences but ignore the significant fact that the next POI recommendation is a time-subtle recommendation task. In view of the fact that the attention mechanism does not comprehensively consider the influence of the user’s trajectory sequences, time information, social relations and geographic information of Point-of-Interest (POI) in the next POI recommendation field, a Context Geographical-Temporal-Social Awareness Hierarchical Attention Network (CGTS-HAN) model is proposed. The model extracts context information from the user’s trajectory sequences and designs a Geographical-Temporal-Social attention network and a common attention network for learning dynamic user preferences. In particular, a bidirectional LSTM model is used to capture the temporal influence between POIs in a user’s check-in trajectory. Moreover, In the context interaction layer, a feedforward neural network is introduced to capture the interaction between users and context information, which can connect multiple context factors with users. Then an embedded layer is added after the interaction layer, and three types of vectors are established for each POI to represent its sign-in trend so as to solve the heterogeneity problem between context factors. Finally reconstructs the objective function and learns model parameters through a negative sampling algorithm. The experimental results on Foursquare and Yelp real datasets show that the AUC, precision and recall of CGTS-HAN are better than the comparison models, which proves the effectiveness and superiority of CGTS-HAN.
      Citation: Electronics
      PubDate: 2022-11-30
      DOI: 10.3390/electronics11233977
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3978: SOC Balanced Power Distribution Control
           Strategy of a DC–DC Converter with Virtual Synchronous Generator

    • Authors: Haodong Zhao, Xiangyong Chen, Chunmei Wang, Xueqiang Liu, Jianlong Qiu
      First page: 3978
      Abstract: The DC microgrid does not need to consider frequency when accessing distributed energy, but the distributed energy access port does not have inertia and damping characteristics, so there are problems of voltage instability and power fluctuation. In this paper, the bidirectional DC–DC converter is the main object; based on the virtual synchronous generator (VSG) control strategy, the inertia regulation is added to adjust the bus voltage dynamically. In addition, a balancing strategy is proposed to ensure the balanced distribution of the state of charge (SOC) and power for multiple batteries. Finally, a simulink is built to prove the viability and availability of the VSG control strategy.
      Citation: Electronics
      PubDate: 2022-11-30
      DOI: 10.3390/electronics11233978
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3979: A 16-Bit 120 MS/s Pipelined ADC Using a
           Multi-Level Dither Technique

    • Authors: Wu, Xu, Cao, Liu
      First page: 3979
      Abstract: In wireless applications, such as radars, tens of MHz signals need to be quantized using an analog-to-digital converter (ADC) with a large dynamic range. The detected signal amplitude can be random, with a small or large amplitude. In addition, the dynamic performance is degraded by capacitor mismatches. A 16-bit 120 MS/s pipelined ADC implemented in a 180 nm complementary metal–oxide–semiconductor (CMOS) process is presented in this work. We propose a multi-level dither technique that can significantly enhance the ADC linearity. The injected dither also helps improve the linearity when the ADC handles an input signal with a small amplitude. Traditional dither injection leads to an increase in the amplifier output swing. A counteracting dither injection scheme, both in sub-flash ADC and the multiplying digital-to-analog converter (MDAC), is proposed to remedy this issue. Moreover, capacitor mismatches in the first three pipeline stages are calibrated in a foreground way. The inter-stage residue gain accuracy is guaranteed by a gain-boosting amplifier. To demonstrate the effectiveness of the dither scheme, we obtained the dynamic performance of the ADC with a small input signal (−12 dBFS). The proposed calibration and dither injection technique improved the spurious-free dynamic range (SFDR) from 77 dBc to 85 dBc with −12 dBFS input. With −1 dBFS input, the SFDR remained at over 85 dBc, reaching up to the Nyquist input frequency. Therefore, the dither scheme enhances the dynamic performance when the ADC handles a signal with small amplitude.
      Citation: Electronics
      PubDate: 2022-11-30
      DOI: 10.3390/electronics11233979
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3980: A PSO-Based Approach for Optimal
           Allocation and Sizing of Resistive-Type SFCLs to Enhance the Transient
           Stability of Power Systems

    • Authors: Khatibi, Jalilzadeh, Hussain, Haider
      First page: 3980
      Abstract: Transient stability improvement of power systems in the event of short-circuit faults has always been an important issue in power systems analysis and studies. Resistive-type superconducting fault current limiters (RSFCL), owing to their capability in restricting fault currents, have been often taken into account as an efficient method to improve the transient stability of a power system. Regarding technical constraints as well as economic concerns, optimal allocation and sizing of RSFCLs in a power system play a crucial role in their efficient utilization. This paper aims to continue the authors’ previous work and enhance the transient stability of power systems by proposing an optimization approach for optimal sizing and the allocation of various candidate numbers of RSFCLs, as the most employed type of SFCL and the most efficient one in transient stability improvement. To solve the optimization problem, a PSO-based algorithm is solved in MATLAB through an objective function and related constraints. The efficacy of the proposed algorithm is evaluated by numerical studies on the IEEE 39-Bus New England system in various scenarios through the assessment of critical fault clearing time (CCT) as well as the generators rotor angle deviations as two crucial criteria for the transient stability of power systems. Simulating the optimization results in DIgSILENT Power Factory indicates an evident enhancement of the power system transient stability via employing optimized RSFCLs resulted from the proposed optimization algorithm. Moreover, the level of transient stability enhancement highly depends on the number of optimized RSFCLs employed in the power system. The results of this paper present a helpful guideline for power system planners to select an appropriate stability scheme based on RSFCLs besides other related technical and economic issues.
      Citation: Electronics
      PubDate: 2022-11-30
      DOI: 10.3390/electronics11233980
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3981: A Learning Based Framework for Enhancing
           Physical Layer Security in Cooperative D2D Network

    • Authors: Noman Ahmad, Guftaar Ahmad Sardar Sidhu, Wali Ullah Khan
      First page: 3981
      Abstract: Next-generation wireless communication networks demand high spectrum efficiency to serve the requirements of an enormous number of devices over a limited available frequency spectrum. Device-to-device (D2D) communication with spectrum reuse offers a potential solution to spectrum scarcity. On the other hand, non-orthogonal multiple access (NOMA) as a multiple-access approach has emerged as a key technology to re-use a spectrum among multiple users. A cellular users (CUs) can share their spectrum with D2D users (DUs) and in response, the D2D network can help relay the CU signal to achieve better secrecy from an eavesdropper. Power optimization is known to be a promising technique to enhance system performance in challenging communication environments. This work aimed to enhance the secrecy rate of the CUs where the D2D transmitter (DT) helps in relaying the CU’s message under the amplify and forward (AF) protocol. A power optimization problem is considered under the quality of service constraints in terms of minimum rate requirements at the receivers and maximum power budgets at the transmitters. The problem is a non-convex complex optimization. A deep learning-based solution is proposed and promising results are obtained in terms of the secrecy rate of CU and the rate of D2D users.
      Citation: Electronics
      PubDate: 2022-11-30
      DOI: 10.3390/electronics11233981
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3982: Decentralized Blockchain Network for
           Resisting Side-Channel Attacks in Mobility-Based IoT

    • Authors: Rashidah Funke Olanrewaju, Burhan Ul Islam Khan, Miss Laiha Binti Mat Kiah, Nor Aniza Abdullah, Khang Wen Goh
      First page: 3982
      Abstract: The inclusion of mobility-based Internet-of-Things (IoT) devices accelerates the data transmission process, thereby catering to IoT users’ demands; however, securing the data transmission in mobility-based IoT is one complex and challenging concern. The adoption of unified security architecture has been identified to prevent side-channel attacks in the IoT, which has been discussed extensively in developing security solutions. Despite blockchain’s apparent superiority in withstanding a wide range of security threats, a careful examination of the relevant literature reveals that some common pitfalls are associated with these methods. Therefore, the proposed scheme introduces a novel computational security framework wherein a branched and decentralized blockchain network is formulated to facilitate coverage from different variants of side-channel IoT attacks that are yet to be adequately reported. A unique blockchain-based authentication approach is designed to secure communication among mobile IoT devices using multiple stages of security implementation with Smart Agreement and physically unclonable functions. Analytical modeling with lightweight finite field encryption is used to create this framework in Python. The study’s benchmark results show that the proposed scheme offers 4% less processing time, 5% less computational overhead, 1% more throughput, 12% less latency, and 30% less energy consumption compared to existing blockchain methods.
      Citation: Electronics
      PubDate: 2022-12-01
      DOI: 10.3390/electronics11233982
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3983: Comparison between Piezoelectric Filter
           and Passive LC Filter in a Class L−Piezo Inverter

    • Authors: Vincent Massavie, Ghislain Despesse, Sebastien Carcouet, Xavier Maynard
      First page: 3983
      Abstract: This paper presents a comparison between piezoelectric filtering and passive LC filtering integrated into an HF class L−Piezo inverter. This L−Piezo inverter is a variant of class φ2 where the filtering of the second harmonic is carried out by a piezoelectric resonator. Piezoelectric filters are well known in the signal domain (RF filtering), but their use in the field of power electronics, as a temporary energy storage element, is rather recent. In power electronics, piezoelectricity has mainly been used as a transformer, in particular, to greatly increase voltages (backlight applications). A class L−Piezo inverter with Lithium Niobate (LNO) piezoelectric resonator is designed for a switching frequency of 10.4 MHz, an input voltage of 30 V, and an output power of 15 W. To compare these two filtering methods, two prototypes are built, one with piezoelectric filtering and one with passive LC filtering. Measurements show a reduction of 60% of the losses in the filter, while the volume of the filter is reduced by a factor of 50.
      Citation: Electronics
      PubDate: 2022-12-01
      DOI: 10.3390/electronics11233983
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3984: Design Optimization of an Efficient
           Bicolor LED Driving System

    • Authors: Fouzia Ferdous, A.B.M. Harun-ur Rashid
      First page: 3984
      Abstract: There are some challenges involved in the design of a multicolor LED driver, such as the precise control of color consistency, i.e., maintaining the correlated color temperature (CCT) and luminous intensity. CCT deviation causes a color shift of composite light. This paper approaches the method of nonlinear optimization of the LED currents of two LED sources to achieve the desired CCT. A bicolor blended-shade white LED system is formed by using a warm color LED source of 1000 K CCT and a cool color LED source of 6500 K CCT. By using a nonlinear optimization methodology, the reduced deviation of the blended CCT and optimum LED currents are obtained. The optimized currents in the two LED strings are maintained by the control circuit of the single-ended primary inductor converter (SEPIC). The obtained reduced deviation of the CCT is 43 K, and the precision is 99.15%. Again, harmonics in the input current hamper power quality, i.e., reduce the power factor and increase power loss. This paper proposes the harmonic reduction technique to achieve the lowest value of total harmonic distortion (THD) through the nonlinear parametric optimization of the SEPIC. Measured THD = 4.37%; PF = 0.96; and efficiency = 92.8%. The system stability was determined and found to be satisfactory.
      Citation: Electronics
      PubDate: 2022-12-01
      DOI: 10.3390/electronics11233984
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3985: A Survey on Citizens Broadband Radio
           Service (CBRS)

    • Authors: Pranay Agarwal, Mohammedhusen Manekiya, Tahir Ahmad, Ashish Yadav, Abhinav Kumar, Massimo Donelli, Saurabh Tarun Mishra
      First page: 3985
      Abstract: To leverage the existing spectrum and mitigate the global spectrum dearth, the Federal Communications Commission of the United States has recently opened the Citizens Broadband Radio Service (CBRS) spectrum, spanning 3550–3700 MHz, for commercial cognitive operations. The CBRS has a three-tier hierarchical architecture, wherein the incumbents, including military radars, occupy the topmost tier. The priority access licenses (PAL) and general authorized access (GAA) are second and third tier, respectively, facilitating licensed and unlicensed access to the spectrum. This combination of licensed and unlicensed access to the spectrum in a three-tier model has opened novel research directions in optimal spectrum sharing as well as privacy preservation, and hence, several schemes have been proposed for the same. This article provides a detailed survey of the existing literature on the CBRS. We provide an overview of the CBRS ecosystem and discuss the regulation and standardization process and industrial developments on the CBRS. The existing schemes for optimal spectrum sharing and resource allocation in CBRS are discussed in detail. Further, an in-depth study of the existing literature on the privacy of incumbents, PAL devices, and GAA devices in CBRS is presented. Finally, we discuss the open issues in CBRS, which demand more attention and effort.
      Citation: Electronics
      PubDate: 2022-12-01
      DOI: 10.3390/electronics11233985
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3986: SARIMA: A Seasonal Autoregressive
           Integrated Moving Average Model for Crime Analysis in Saudi Arabia

    • Authors: Talal H. Noor, Abdulqader M. Almars, Majed Alwateer, Malik Almaliki, Ibrahim Gad, El-Sayed Atlam
      First page: 3986
      Abstract: Crimes have clearly had a detrimental impact on a nation’s development, prosperity, reputation, and economy. The issue of crime has become one of the most pressing concerns in societies, thus reducing the crime rate has become an increasingly critical task. Recently, several studies have been proposed to identify the causes and occurrences of crime in order to identify ways to reduce crime rates. However, few studies have been conducted in Saudi Arabia technological solutions based on crime analysis. The analysis of crime can help governments identify hotspots of crime and monitor crime distribution. This study aims to investigate which Saudi Arabian areas will experience increased crime rates in the coming years. This research helps law enforcement agencies to effectively utilize available resources in order to reduce crime rates. This paper proposes SARIMA model which focuses on identifying factors that affect crimes in Saudi Arabia, estimating a reasonable crime rate, and identifying the likelihood of crime distribution based on various locations. The dataset used in this study is obtained from Saudi Arabian official government channels. There is detailed information related to time and place along with crime statistics pertaining to different types of crimes. Furthermore, the new proposed method performs better than other traditional classifiers such as Linear Regression, XGB, and Random Forest. Finally, SARIMA model has an MAE score of 0.066559, which is higher than the other models.
      Citation: Electronics
      PubDate: 2022-12-01
      DOI: 10.3390/electronics11233986
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3987: Automatic Knee Injury Identification
           through Thermal Image Processing and Convolutional Neural Networks

    • Authors: Omar Trejo-Chavez, Juan P. Amezquita-Sanchez, Jose R. Huerta-Rosales, Luis A. Morales-Hernandez, Irving A. Cruz-Albarran, Martin Valtierra-Rodriguez
      First page: 3987
      Abstract: Knee injury is a common health problem that affects both people who practice sports and those who do not do it. The high prevalence of knee injuries produces a considerable impact on the health-related life quality of patients. For this reason, it is essential to develop procedures for an early diagnosis, allowing patients to receive timely treatment for preventing and correcting knee injuries. In this regard, this paper presents, as main contribution, a methodology based on infrared thermography (IT) and convolutional neural networks (CNNs) to automatically differentiate between a healthy knee and an injured knee, being an alternative tool to help medical specialists. In general, the methodology consists of three steps: (1) database generation, (2) image processing, and (3) design and validation of a CNN for automatically identifying a patient with an injured knee. In the image-processing stage, grayscale images, equalized images, and thermal images are obtained as inputs for the CNN, where 98.72% of accuracy is obtained by the proposed method. To test its robustness, different infrared images with changes in rotation angle and different brightness levels (i.e., possible conditions at the time of imaging) are used, obtaining 97.44% accuracy. These results demonstrate the effectiveness and robustness of the proposal for differentiating between a patient with a healthy knee and an injured knee, having the advantages of using a fast, low-cost, innocuous, and non-invasive technology.
      Citation: Electronics
      PubDate: 2022-12-01
      DOI: 10.3390/electronics11233987
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3988: Implementation and Experimental
           Verification of Resistorless Fractional-Order Basic Filters

    • Authors: Dimitrios Patrinos, Georgios Tsirmpas, Panagiotis Bertsias, Costas Psychalinos, Ahmed S. Elwakil
      First page: 3988
      Abstract: Novel structures of fractional-order differentiation and integration stages are presented in this work, where passive resistors are not required for their implementation. This has been achieved by considering the inherent resistive behavior of fractional-order capacitors. The implementation of the presented stages is performed using a current feedback operational amplifier as active element and fractional-order capacitors based on multi-walled carbon nano-tubes. Basic filter and controller stages are realized using the introduced fundamental blocks, and their behavior is evaluated through experimental results.
      Citation: Electronics
      PubDate: 2022-12-01
      DOI: 10.3390/electronics11233988
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3989: A Novel Expert System for the Diagnosis
           and Treatment of Heart Disease

    • Authors: Tehseen Mazhar, Qandeel Nasir, Inayatul Haq, Mian Muhammad Kamal, Inam Ullah, Taejoon Kim, Heba G. Mohamed, Norah Alwadai
      First page: 3989
      Abstract: The diagnosis of diseases in their early stages can assist us in preventing life-threatening infections and caring for them better than in the last phase because prevention is better than cure. The death rate can be very high due to the unapproachability of diagnosed patients at an early point. Expert systems help us to defeat the problem mentioned above and enable us to automatically diagnose diseases in their early phases. Expert systems use a fuzzy, rule-based inference engine to provide forward-chain methods for diagnosing the patient. In this research, data have been gathered from different sources, such as a hospital, by performing the test on the patients’ age, gender, blood sugar, heart rate, and ECG to calculate the values. The proposed expert system for medical diagnosis can be used to find minimum disease levels and demonstrate the predominant method for curing different medical diseases, such as heart diseases. In the next step, the diagnostic test at the hospital with the novel expert system, the crisp, fuzzy value is generated for input into the expert system. After taking the crisp input, the expert system starts working on fuzzification and compares it with the knowledge base processed by the inference engine. After the fuzzification, the next step starts with the expert system in the defuzzification process converting the fuzzy sets’ value into a crisp value that is efficient for human readability. Later, the expert physician system’s diagnosis calculates the value by using fuzzy sets, and gives an output to determine the patient’s heart disease. In one case, the diagnosis step was accomplished, and the expert system provided the yield with the heart disease risk level as “low”, “high”, or “risky”. After the expert system’s responsibilities have been completed, the physician decides on the treatment and recommends a proper dose of medicine according to the level the expert system provided after the diagnosis step. The findings indicate that this research achieves better performance in finding appropriate heart disease risk levels, while also fulfilling heart disease patient treatment due to the physicians shortfalls.
      Citation: Electronics
      PubDate: 2022-12-01
      DOI: 10.3390/electronics11233989
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3990: Semi-Supervised Group Emotion
           Recognition Based on Contrastive Learning

    • Authors: Jiayi Zhang, Xingzhi Wang, Dong Zhang, Dah-Jye Lee
      First page: 3990
      Abstract: The performance of all learning-based group emotion recognition (GER) methods depends on the number of labeled samples. Although there are lots of group emotion images available on the Internet, labeling them manually is a labor-intensive and cost-expensive process. For this reason, datasets for GER are usually small in size, which limits the performance of GER. Considering labeling manually is challenging, using limited labeled images and a large number of unlabeled images in the network training is a potential way to improve the performance of GER. In this work, we propose a semi-supervised group emotion recognition framework based on contrastive learning to learn efficient features from both labeled and unlabeled images. In the proposed method, the unlabeled images are used to pretrain the backbone by a contrastive learning method, and the labeled images are used to fine-tune the network. The unlabeled images are then given pseudo-labels by the fine-tuned network and used for further training. In order to alleviate the uncertainty of the given pseudo-labels, we propose a Weight Cross-Entropy Loss (WCE-Loss) to suppress the influence of the samples with unreliable pseudo-labels in the training process. Experiment results on three prominent benchmark datasets for GER show the effectiveness of the proposed framework and its superiority compared with other competitive state-of-the-art methods.
      Citation: Electronics
      PubDate: 2022-12-01
      DOI: 10.3390/electronics11233990
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3991: A Platform for Analysing Huge Amounts of
           Data from Households, Photovoltaics, and Electrical Vehicles: From Data to
           Information

    • Authors: Antonio Cano-Ortega, Miguel A. García-Cumbreras, Francisco Sánchez-Sutil, Jesús C. Hernández
      First page: 3991
      Abstract: Analytics is an essential procedure to acquire knowledge and support applications for determining electricity consumption in smart homes. Electricity variables measured by the smart meter (SM) produce a significant amount of data on consumers, making the data sets very sizable and the analytics complex. Data mining and emerging cloud computing technologies make collecting, processing, and analysing the so-called big data possible. The monitoring and visualization of information aid in personalizing applications that benefit both homeowners and researchers in analysing consumer profiles. This paper presents a smart meter for household (SMH) to obtain load profiles and a new platform that allows the innovative analysis of captured Internet of Things data from smart homes, photovoltaics, and electrical vehicles. We propose the use of cloud systems to enable data-based services and address the challenges of complexities and resource demands for online and offline data processing, storage, and classification analysis. The requirements and system design components are discussed.
      Citation: Electronics
      PubDate: 2022-12-01
      DOI: 10.3390/electronics11233991
      Issue No: Vol. 11, No. 23 (2022)
       
  • Electronics, Vol. 11, Pages 3992: Hybrid Convolutional Network Combining
           3D Depthwise Separable Convolution and Receptive Field Control for
           Hyperspectral Image Classification

    • Authors: Chengle Lin, Tingyu Wang, Shuyan Dong, Qizhong Zhang, Zhangyi Yang, Farong Gao
      First page: 3992
      Abstract: Deep-learning-based methods have been widely used in hyperspectral image classification. In order to solve the problems of the excessive parameters and computational cost of 3D convolution, and loss of detailed information due to the excessive increase in the receptive field in pursuit of multi-scale features, this paper proposes a lightweight hybrid convolutional network called the 3D lightweight receptive control network (LRCNet). The proposed network consists of a 3D depthwise separable convolutional network and a receptive field control network. The 3D depthwise separable convolutional network uses the depthwise separable technique to capture the joint features of spatial and spectral dimensions while reducing the number of computational parameters. The receptive field control network ensures the extraction of hyperspectral image (HSI) details by controlling the convolution kernel. In order to verify the validity of the proposed method, we test the classification accuracy of the LRCNet based on three public datasets, which exceeds 99.50% The results show that compare with state-of-the-art methods, the proposed network has competitive classification performance.
      Citation: Electronics
      PubDate: 2022-12-01
      DOI: 10.3390/electronics11233992
      Issue No: Vol. 11, No. 23 (2022)
       
 
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