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Electronics, Volume 10, Issue 24 (December-2 2021) – 139 articles

Cover Story (view full-size image): Internet of Vehicles and artificial intelligence enables fine-grained vehicle motion control at unsignalized intersections. This research study describes a Traffic-Aware Federated Imitation Learning framework for Motion Control (TAFI-MC) and discusses motion control and privacy protection, which consists of vehicle interactors, edge trainers, and a cloud aggregator. The TAFI-MC framework integrates an imitation learning algorithm in order to obtain collision avoidance ability. TAFI-MC can also save communication overhead between vehicle interactors and edge trainers with model training loss. View this paper.
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Article
Sentiment-Target Word Pair Extraction Model Using Statistical Analysis of Sentence Structures
Electronics 2021, 10(24), 3187; https://doi.org/10.3390/electronics10243187 - 20 Dec 2021
Viewed by 422
Abstract
Product information has been propagated online via forums and social media. Lots of merchandise are recommended via an expert system method and is considered for purchase by online comments or product reviews. For predicting people’s opinions on products, studying people’s thoughts via extracting [...] Read more.
Product information has been propagated online via forums and social media. Lots of merchandise are recommended via an expert system method and is considered for purchase by online comments or product reviews. For predicting people’s opinions on products, studying people’s thoughts via extracting information in documents is referred to as sentiment analysis. Finding sentiment-target word pairs is an important sentiment mining research issue. With the Korean language, as the predicate appears at the very end, it is not easy to find the exact word pairs without first identifying the syntactic structure of the sentence. In this study, we propose a model that parses sentence structures and extracts sentiment-target word pairs from the parse tree. The proposed model extracts the sentiment-target word pairs that appear in the sentence by using parsing and statistical methods. For extracting sentiment-target word pairs, this model uses a sentiment word extractor and a target word extractor. After testing data from 4000 movie reviews, the applicable model showed high performance in both accuracy 93.25 (+14.45) and F1-score 82.29 (+3.31) compared with others. However, improvements in the recall rate (−0.35) are needed and computational costs must be reduced. Full article
(This article belongs to the Special Issue Applied AI-Based Platform Technology and Application)
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Article
Multi-Household Energy Management in a Smart Neighborhood in the Presence of Uncertainties and Electric Vehicles
Electronics 2021, 10(24), 3186; https://doi.org/10.3390/electronics10243186 - 20 Dec 2021
Viewed by 340
Abstract
The pathway toward the reduction of greenhouse gas emissions is dependent upon increasing Renewable Energy Sources (RESs), demand response, and electrification of public and private transportation. Energy management techniques are necessary to coordinate the operation in this complex scenario, and in recent years [...] Read more.
The pathway toward the reduction of greenhouse gas emissions is dependent upon increasing Renewable Energy Sources (RESs), demand response, and electrification of public and private transportation. Energy management techniques are necessary to coordinate the operation in this complex scenario, and in recent years several works have appeared in the literature on this topic. This paper presents a study on multi-household energy management for Smart Neighborhoods integrating RESs and electric vehicles participating in Vehicle-to-Home (V2H) and Vehicle-to-Neighborhood (V2N) programs. The Smart Neighborhood comprises multiple households, a parking lot with public charging stations, and an aggregator that coordinates energy transactions using a Multi-Household Energy Manager (MH-EM). The MH-EM jointly maximizes the profits of the aggregator and the households by using the augmented ϵ-constraint approach. The generated Pareto optimal solutions allow for different decision policies to balance the aggregator’s and households’ profits, prioritizing one of them or the RES energy usage within the Smart Neighborhood. The experiments have been conducted over an entire year considering uncertainties related to the energy price, electric vehicles usage, energy production of RESs, and energy demand of the households. The results show that the MH-EM optimizes the Smart Neighborhood operation and that the solution that maximizes the RES energy usage provides the greatest benefits also in terms of peak-shaving and valley-filling capability of the energy demand. Full article
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Article
Research on the Cross-Platform Co-Simulation Strategy of Power Systems Based on the Model-Segmentation Algorithm
Electronics 2021, 10(24), 3185; https://doi.org/10.3390/electronics10243185 - 20 Dec 2021
Viewed by 281
Abstract
In recent years, cross-platform co-simulation has become an important development direction of the real-time simulation of power systems. Model segmentation is at the core of the realization of cross-platform joint simulation and parallel real-time simulation of these systems. In essence, it is based [...] Read more.
In recent years, cross-platform co-simulation has become an important development direction of the real-time simulation of power systems. Model segmentation is at the core of the realization of cross-platform joint simulation and parallel real-time simulation of these systems. In essence, it is based on the deep application of a system-decoupling algorithm. In order to solve problems that a single interface cannot, it considers the data interaction of large- and small-step systems at the same time This paper proposes an improved joint-simulation strategy based on the model-segmentation method for the cross-platform joint-simulation technology of a large-scale, flexible direct-power grid sent by the wind farms of RT-lab and Hypersim. Firstly, by studying several common interface algorithms in the current project, the adaptability of different interface algorithms is analyzed. Secondly, the problem of high-frequency oscillation caused by the inductance-decoupling algorithm is improved, and an improved segmentation-model algorithm is proposed. Finally, according to the adaptability, each interface algorithm is applied to the wind-power-based, flexible direct-transmission, dual-platform simulation model that was built for this study. The simulation results verify the feasibility of the improved interface in system decoupling and platform interfacing, and indicate the significantly improved accuracy and stability of the system. Full article
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Article
Design and Development of Smart Parking System Based on Fog Computing and Internet of Things
Electronics 2021, 10(24), 3184; https://doi.org/10.3390/electronics10243184 - 20 Dec 2021
Viewed by 354
Abstract
Current parking systems employ a single gateway-centered solution (i.e., cloud) for data processing which leads to the possibility of a single point of failure, data loss, and high delays. Moreover, the parking-spot selection process considers criteria that do not maximize parking utilization and [...] Read more.
Current parking systems employ a single gateway-centered solution (i.e., cloud) for data processing which leads to the possibility of a single point of failure, data loss, and high delays. Moreover, the parking-spot selection process considers criteria that do not maximize parking utilization and revenue. The pricing strategy does not achieve high revenue because a fixed pricing rate is utilized. To address these issues, this paper proposes a smart parking system based on the Internet of Things (IoT) that provides useful information to drivers and parking administrators about available parking spots and related services such as parking navigation, reservation, and availability estimation. A multi-layer architecture is developed that consists of multiple sensor nodes, and fog and cloud computing layers. The acquired parking data are processed through fog computing nodes to facilitate obtaining the required real-time parking data. A novel algorithm to obtain the optimal parking spot with the minimum arrival time is also presented. Proof-of-concept implementation and simulation evaluations are conducted to validate the system performance. The findings show that the system reduces the parking arrival time by 16–46% compared to current parking systems. In addition, the revenue is increased for the parking authority by 10–15%. Full article
(This article belongs to the Special Issue V2X Communications and Applications for NET-2030)
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Article
A Novel Progressive Image Classification Method Based on Hierarchical Convolutional Neural Networks
Electronics 2021, 10(24), 3183; https://doi.org/10.3390/electronics10243183 - 20 Dec 2021
Viewed by 374
Abstract
Deep Neural Networks (DNNs) are commonly used methods in computational intelligence. Most prevalent DNN-based image classification methods are dedicated to promoting the performance by designing complicated network architectures and requiring large amounts of model parameters. These large-scale DNN-based models are performed on all [...] Read more.
Deep Neural Networks (DNNs) are commonly used methods in computational intelligence. Most prevalent DNN-based image classification methods are dedicated to promoting the performance by designing complicated network architectures and requiring large amounts of model parameters. These large-scale DNN-based models are performed on all images consistently. However, since there are meaningful differences between images, it is difficult to accurately classify all images by a consistent network architecture. For example, a deeper network is fit for the images that are difficult to be distinguished, but may lead to model overfitting for simple images. Therefore, we should selectively use different models to deal with different images, which is similar to the human cognition mechanism, in which different levels of neurons are activated according to the difficulty of object recognition. To this end, we propose a Hierarchical Convolutional Neural Network (HCNN) for image classification in this paper. HCNNs comprise multiple sub-networks, which can be viewed as different levels of neurons in humans, and these sub-networks are used to classify the images progressively. Specifically, we first initialize the weight of each image and each image category, and these images and initial weights are used for training the first sub-network. Then, according to the predicted results of the first sub-network, the weights of misclassified images are increased, while the weights of correctly classified images are decreased. Furthermore, the images with the updated weights are used for training the next sub-networks. Similar operations are performed on all sub-networks. In the test stage, each image passes through the sub-networks in turn. If the prediction confidences in a sub-network are higher than a given threshold, then the results are output directly. Otherwise, deeper visual features need to be learned successively by the subsequent sub-networks until a reliable image classification result is obtained or the last sub-network is reached. Experimental results show that HCNNs can obtain better results than classical CNNs and the existing models based on ensemble learning. HCNNs have 2.68% higher accuracy than Residual Network 50 (Resnet50) on the ultrasonic image dataset, 1.19% than Resnet50 on the chimpanzee facial image dataset, and 10.86% than Adaboost-CNN on the CIFAR-10 dataset. Furthermore, the HCNN is extensible, since the types of sub-networks and their combinations can be dynamically adjusted. Full article
(This article belongs to the Special Issue Applications of Computational Intelligence)
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Article
Optimal Placement of Reclosers in a Radial Distribution System for Reliability Improvement
Electronics 2021, 10(24), 3182; https://doi.org/10.3390/electronics10243182 - 20 Dec 2021
Viewed by 500
Abstract
There is a need for the optimal positioning of protective devices to maximize customers satisfaction per their demands. Such arrangement advances the distribution system reliability to maximum achievable. Thus, radial distribution system (RDS) reliability can be improved by placing reclosers at suitable feeder [...] Read more.
There is a need for the optimal positioning of protective devices to maximize customers satisfaction per their demands. Such arrangement advances the distribution system reliability to maximum achievable. Thus, radial distribution system (RDS) reliability can be improved by placing reclosers at suitable feeder sections. This article presents comprehensive details of an attempt to determine the reclosers’ optimal location in an RDS to maximize the utility profit by reliability improvement. Assessment of different reliability indices such as SAIDI, SAIFI, CAIFI, CAIDI, etc., with recloser placement, exhibits a considerable improvement in these indices in contrast with the absence of recloser. Consequently, a new bidirectional formulation has been proposed for the optimized arrangement of reclosers’. This formulation efficiently handles the bidirectional power flow, resulting from distributed generation (DG) unit (s) in the system. The proposed model has been solved for a test system by utilizing the Genetic algorithm (GA) optimization method. Later, test results conclude that reclosers’ optimal placement contributes significantly towards utility profit with minimum investment and outage costs. Full article
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Article
Net-Shape-Based Automated Detection of Integrated-Circuit Layout Plagiarism
Electronics 2021, 10(24), 3181; https://doi.org/10.3390/electronics10243181 - 20 Dec 2021
Viewed by 362
Abstract
Plagiarism of integrated-circuit (IC) layout is a problem encountered both in academia and in industry. A procedure was proposed that compares IC layouts based on the physical representation of particular electrical nets, i.e., on the shape of the features drawn on conducting layers [...] Read more.
Plagiarism of integrated-circuit (IC) layout is a problem encountered both in academia and in industry. A procedure was proposed that compares IC layouts based on the physical representation of particular electrical nets, i.e., on the shape of the features drawn on conducting layers (metals and polysilicon). At the heart of this method is the Needleman–Wunsch algorithm, used for decades in tools aligning sequences of amino acids or nucleotides. Here, it is used to quantify the visual similarity of nets within the pair of layouts being compared. The method was implemented in Python and successfully used to identify clusters of similar layouts within two pools of designs: one composed of logic gates and one containing operational transconductance amplifiers. Full article
(This article belongs to the Section Microelectronics)
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Article
Experimental Evaluation of Malware Family Classification Methods from Sequential Information of TLS-Encrypted Traffic
Electronics 2021, 10(24), 3180; https://doi.org/10.3390/electronics10243180 - 20 Dec 2021
Viewed by 348
Abstract
In parallel with the rapid adoption of transport layer security (TLS), malware has utilized the encrypted communication channel provided by TLS to hinder detection from network traffic. To this end, recent research efforts are directed toward malware detection and malware family classification for [...] Read more.
In parallel with the rapid adoption of transport layer security (TLS), malware has utilized the encrypted communication channel provided by TLS to hinder detection from network traffic. To this end, recent research efforts are directed toward malware detection and malware family classification for TLS-encrypted traffic. However, amongst their feature sets, the proposals to utilize the sequential information of each TLS session has not been properly evaluated, especially in the context of malware family classification. In this context, we propose a systematic framework to evaluate the state-of-the-art malware family classification methods for TLS-encrypted traffic in a controlled environment and discuss the advantages and limitations of the methods comprehensively. In particular, our experimental results for the 10 representations and classifier combinations show that the graph-based representation for the sequential information achieves better performance regardless of the evaluated classification algorithms. With our framework and findings, researchers can design better machine learning based classifiers. Full article
(This article belongs to the Special Issue Advances on Networks and Cyber Security)
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Article
A Real-Time Matrix Iterative Optimization Algorithm of Booking Elevator Group and Numerical Simulation Formed by Multi-Sensor Combination
Electronics 2021, 10(24), 3179; https://doi.org/10.3390/electronics10243179 - 20 Dec 2021
Viewed by 327
Abstract
Elevators are an essential indoor transportation tool in high-rise buildings. The world is advocating the design concept of safety, energy-saving, and intelligence. We focus on improving operation speed and utilization efficiency of the elevator group. This paper proposed a real-time reservation elevator groups [...] Read more.
Elevators are an essential indoor transportation tool in high-rise buildings. The world is advocating the design concept of safety, energy-saving, and intelligence. We focus on improving operation speed and utilization efficiency of the elevator group. This paper proposed a real-time reservation elevator groups optimization algorithm, and a dynamic matrix iterative model has been established. The indoor navigation technology UWB is applied, which can help users to quickly find elevators. The manned equilibrium efficiency and running time equilibrium efficiency of elevator group are given. Moreover, the data filtering criterion formulas for user waiting time and elevator remaining space are defined. In this paper, three numerical examples are given. Example 1 is a single elevator in n-storey building. Example 2 is compared with different scheduling algorithms, such as FCFS, SSTF, LOOK, and SCAN algorithms, and the results show that our method has the advantages of short total running time and less round-trip frequency. At last, the matrix of numerical iteration results are visualized, and the data movement status of people on each floor can be observed. Example 3 introduced elevator group algorithms. For high-rise buildings, this paper adopts a high, medium, and low hierarchical management model; this model has high coordination, as well as fast response, batch process, and adaptive function. Finally, we also discussed and compared the complexity of single elevator and elevator group algorithms. Therefore, this method has great development potential and practical application value, which deserves further study. Full article
(This article belongs to the Section Systems & Control Engineering)
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Article
An Improved GWO Algorithm Optimized RVFL Model for Oil Layer Prediction
Electronics 2021, 10(24), 3178; https://doi.org/10.3390/electronics10243178 - 20 Dec 2021
Viewed by 376
Abstract
In this study, a model based on the improved grey wolf optimizer (GWO) for optimizing RVFL is proposed to enable the problem of poor accuracy of Oil layer prediction due to the randomness of the parameters present in the random vector function link [...] Read more.
In this study, a model based on the improved grey wolf optimizer (GWO) for optimizing RVFL is proposed to enable the problem of poor accuracy of Oil layer prediction due to the randomness of the parameters present in the random vector function link (RVFL) model to be addressed. Firstly, GWO is improved based on the advantages of chaos theory and the marine predator algorithm (MPA) to overcome the problem of low convergence accuracy in the optimization process of the GWO optimization algorithm. The improved GWO algorithm was then used to optimize the input weights and implicit layer biases of the RVFL network model so that the problem of inaccurate and unstable classification of RVFL due to the randomness of the parameters was avoided. MPA-GWO was used for comparison with algorithms of the same type under a function of 15 standard tests. From the results, it was concluded that it outperformed the algorithms of its type in terms of search accuracy and search speed. At the same time, the MPA-GWO-RVFL model was applied to the field of Oil layer prediction. From the comparison tests, it is concluded that the prediction accuracy of the MPA-GWO-RVFL model is on average 2.9%, 3.04%, 2.27%, 8.74%, 1.47% and 10.41% better than that of the MPA-RVFL, GWO-RVFL, PSO-RVFL, WOA-RVFL, GWFOA-RVFL and RVFL algorithms, respectively, and its practical applications are significant. Full article
(This article belongs to the Section Systems & Control Engineering)
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Article
Unsupervised Anomaly Detection in Printed Circuit Boards through Student–Teacher Feature Pyramid Matching
Electronics 2021, 10(24), 3177; https://doi.org/10.3390/electronics10243177 - 20 Dec 2021
Viewed by 402
Abstract
Deep learning methods are currently used in industries to improve the efficiency and quality of the product. Detecting defects on printed circuit boards (PCBs) is a challenging task and is usually solved by automated visual inspection, automated optical inspection, manual inspection, and supervised [...] Read more.
Deep learning methods are currently used in industries to improve the efficiency and quality of the product. Detecting defects on printed circuit boards (PCBs) is a challenging task and is usually solved by automated visual inspection, automated optical inspection, manual inspection, and supervised learning methods, such as you only look once (YOLO) of tiny YOLO, YOLOv2, YOLOv3, YOLOv4, and YOLOv5. Previously described methods for defect detection in PCBs require large numbers of labeled images, which is computationally expensive in training and requires a great deal of human effort to label the data. This paper introduces a new unsupervised learning method for the detection of defects in PCB using student–teacher feature pyramid matching as a pre-trained image classification model used to learn the distribution of images without anomalies. Hence, we extracted the knowledge into a student network which had same architecture as the teacher network. This one-step transfer retains key clues as much as possible. In addition, we incorporated a multi-scale feature matching strategy into the framework. A mixture of multi-level knowledge from the features pyramid passes through a better supervision, known as hierarchical feature alignment, which allows the student network to receive it, thereby allowing for the detection of various sizes of anomalies. A scoring function reflects the probability of the occurrence of anomalies. This framework helped us to achieve accurate anomaly detection. Apart from accuracy, its inference speed also reached around 100 frames per second. Full article
(This article belongs to the Section Artificial Intelligence)
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Review
Memristive System Based Image Processing Technology: A Review and Perspective
Electronics 2021, 10(24), 3176; https://doi.org/10.3390/electronics10243176 - 20 Dec 2021
Viewed by 394
Abstract
As the acquisition, transmission, storage and conversion of images become more efficient, image data are increasing explosively. At the same time, the limitations of conventional computational processing systems based on the Von Neumann architecture continue to emerge, and thus, improving the efficiency of [...] Read more.
As the acquisition, transmission, storage and conversion of images become more efficient, image data are increasing explosively. At the same time, the limitations of conventional computational processing systems based on the Von Neumann architecture continue to emerge, and thus, improving the efficiency of image processing has become a key issue that has bothered scholars working on images for a long time. Memristors with non-volatile, synapse-like, as well as integrated storage-and-computation properties can be used to build intelligent processing systems that are closer to the structure and function of biological brains. They are also of great significance when constructing new intelligent image processing systems with non-Von Neumann architecture and for achieving the integrated storage and computation of image data. Based on this, this paper analyses the mathematical models of memristors and discusses their applications in conventional image processing based on memristive systems as well as image processing based on memristive neural networks, to investigate the potential of memristive systems in image processing. In addition, recent advances and implications of memristive system-based image processing are presented comprehensively, and its development opportunities and challenges in different major areas are explored as well. By establishing a complete spectrum of image processing technologies based on memristive systems, this review attempts to provide a reference for future studies in the field, and it is hoped that scholars can promote its development through interdisciplinary academic exchanges and cooperation. Full article
(This article belongs to the Special Issue Memristive Devices and Systems: Modelling, Properties & Applications)
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Review
Smart Manufacturing and Tactile Internet Based on 5G in Industry 4.0: Challenges, Applications and New Trends
Electronics 2021, 10(24), 3175; https://doi.org/10.3390/electronics10243175 - 20 Dec 2021
Viewed by 401
Abstract
For many applications deployed in manufacturing networks, communication latency has been a significant barrier. Despite the constant development of improved communication protocols and standards during Industry 4.0, the latency problem persists, lowering quality of services (QoS) and quality of experience (QoE). Tactile internet [...] Read more.
For many applications deployed in manufacturing networks, communication latency has been a significant barrier. Despite the constant development of improved communication protocols and standards during Industry 4.0, the latency problem persists, lowering quality of services (QoS) and quality of experience (QoE). Tactile internet (TI), with its high availability, security, and ultra-low latency, will add a new dimension to human-machine interaction (HMI) by enabling haptic and tactile sensations. The tactile internet (TI) is a cutting-edge technology that uses 5G and beyond (B5G) communications to enable real-time interaction of haptic data over the internet between tactile ends. This emerging TI technology is regarded as the next evolutionary step for the Internet of Things (IoT) and is expected to bring about massive changes towards Society 5.0 and to address complex issues in current society. To that end, the 5G mobile communication systems will support the TI at the wireless edge. As a result, TI can be used as a backbone for delay mitigation in conjunction with 5G networks, allowing for ultra-reliable low latency applications like Smart Manufacturing, virtual reality, and augmented reality. Consequently, the purpose of this paper is to present the current state of 5G and TI, as well as the challenges and future trends for 5G networks beyond 2021, as well as a conceptual framework for integrating 5G and TI into existing industrial case studies, with a focus on the design aspects and layers of TI, such as the master, network, and slave layers. Finally, the key publications focused on the key enabling technologies of TI are summarized and the beyond 5G era towards Society 5.0 based on cyber-physical systems is discussed. Full article
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Article
Generation of Beam Tilt through Three-Dimensional Printed Surface
Electronics 2021, 10(24), 3174; https://doi.org/10.3390/electronics10243174 - 20 Dec 2021
Viewed by 287
Abstract
In this paper, 3D printed surfaces are presented to study this technology’s application in generating beam tilt for the electromagnetic waves in the Ku-band. Additionally, the input source is maintained by a feed horn that is additively manufactured and is coated with copper [...] Read more.
In this paper, 3D printed surfaces are presented to study this technology’s application in generating beam tilt for the electromagnetic waves in the Ku-band. Additionally, the input source is maintained by a feed horn that is additively manufactured and is coated with copper spray paint to add conductivity, which is fed by a WR-75 waveguide. The proposed beam tilt generating surface is also referred to as a Beam Deviating Surface (BDS). There is no relative gap between the BDS and the aperture of the horn, which eventually decreased the overall antenna height. The BDS layer is able to deviate the beam for a fixed elevation angle of 22.5 and could be consequently rotated along with the rotation of the BDS prototype. The voltage standing wave ratio value is less than two over the operating frequency range, which depicts the wideband behavior. The measured and simulated radiation patterns show that we can tilt the electromagnetic waves in ranges of up to +/−22.5 with a minimum side lobe level of −5 dB at frequencies from 10 to 15 GHz. This signifies the wideband characteristic of the proposed prototype, which is achieved by Vero material from Multijet Printing that is a low-cost and rapid manufacturing 3D printing technology. Full article
(This article belongs to the Special Issue The New Era of Satellite Communications, Challenges and Promises)
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Article
A 2.5-GS/s Four-Way-Interleaved Ringamp-Based Pipelined-SAR ADC with Digital Background Calibration in 28-nm CMOS
Electronics 2021, 10(24), 3173; https://doi.org/10.3390/electronics10243173 - 20 Dec 2021
Viewed by 330
Abstract
A 2.5-GS/s 12-bit four-way time-interleaved pipelined-SAR ADC is presented in 28-nm CMOS. A bias-enhanced ring amplifier is utilized as the residue amplifier to achieve high bandwidth and excellent power efficiency compared with a traditional operational amplifier. A high linearity front-end is proposed to [...] Read more.
A 2.5-GS/s 12-bit four-way time-interleaved pipelined-SAR ADC is presented in 28-nm CMOS. A bias-enhanced ring amplifier is utilized as the residue amplifier to achieve high bandwidth and excellent power efficiency compared with a traditional operational amplifier. A high linearity front-end is proposed to alleviate the non-linearity of the diode for ESD protection in the input PAD. The embedded input buffer can suppress the kickback noise at high input frequencies. A blind background calibration based on digital-mixing is used to correct the mismatches between channels. Additionally, an optional neural network calibration is also provided. The prototype ADC achieves a low-frequency SNDR/SFDR of 51.0/68.0 dB, translating a competitive FoMw of 0.48 pJ/conv.-step at 250 MHz input running at 2.5 GS/s. Full article
(This article belongs to the Section Circuit and Signal Processing)
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Article
Domain-Adversarial Based Model with Phonological Knowledge for Cross-Lingual Speech Recognition
Electronics 2021, 10(24), 3172; https://doi.org/10.3390/electronics10243172 - 20 Dec 2021
Viewed by 371
Abstract
Phonological-based features (articulatory features, AFs) describe the movements of the vocal organ which are shared across languages. This paper investigates a domain-adversarial neural network (DANN) to extract reliable AFs, and different multi-stream techniques are used for cross-lingual speech recognition. First, a novel universal [...] Read more.
Phonological-based features (articulatory features, AFs) describe the movements of the vocal organ which are shared across languages. This paper investigates a domain-adversarial neural network (DANN) to extract reliable AFs, and different multi-stream techniques are used for cross-lingual speech recognition. First, a novel universal phonological attributes definition is proposed for Mandarin, English, German and French. Then a DANN-based AFs detector is trained using source languages (English, German and French). When doing the cross-lingual speech recognition, the AFs detectors are used to transfer the phonological knowledge from source languages (English, German and French) to the target language (Mandarin). Two multi-stream approaches are introduced to fuse the acoustic features and cross-lingual AFs. In addition, the monolingual AFs system (i.e., the AFs are directly extracted from the target language) is also investigated. Experiments show that the performance of the AFs detector can be improved by using convolutional neural networks (CNN) with a domain-adversarial learning method. The multi-head attention (MHA) based multi-stream can reach the best performance compared to the baseline, cross-lingual adaptation approach, and other approaches. More specifically, the MHA-mode with cross-lingual AFs yields significant improvements over monolingual AFs with the restriction of training data size and, which can be easily extended to other low-resource languages. Full article
(This article belongs to the Special Issue Applications of Neural Networks for Speech and Language Processing)
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Article
Factors Influencing Students’ Acceptance of M-Learning in Higher Education: An Application and Extension of the UTAUT Model
Electronics 2021, 10(24), 3171; https://doi.org/10.3390/electronics10243171 - 20 Dec 2021
Viewed by 370
Abstract
The goal of this study was to develop a new model and conduct confirmatory factor analysis to learn more about how students use M-learning in higher education. The study is theoretically based on the unified theory of acceptance and use of technology (UTAUT) [...] Read more.
The goal of this study was to develop a new model and conduct confirmatory factor analysis to learn more about how students use M-learning in higher education. The study is theoretically based on the unified theory of acceptance and use of technology (UTAUT) theory and the technology acceptance model (TAM). Theoretically, the factors related to the adoption of M-learning in higher education, identified as contributory to perceived ease of use, perceived usefulness, and attitudes towards M-learning and actual use of M-learning, were analyzed. A questionnaire survey was distributed to 362 university students who were randomly selected. Structural Equation Modeling (SEM)-AMOS was used for data analysis. Based on the findings, M-learning appears to be one of the most promising educational technologies for development in educational environments. Perceived facilitating conditions, performance expectancy, effort expectancy, social influence, and perceived enjoyment have a significant positive effect on the perceived ease of use and perceived usefulness, while performance expectancy has a negative effect on the perceived ease of use. Perceived ease of use and perceived usefulness have a positive and significant effect on attitudes towards using M-learning and actual use of M-learning. Therefore, we recommend lecturers encourage students to utilize M-learning for educational purposes in higher education. Full article
(This article belongs to the Special Issue Mobile Learning and Technology Enhanced Learning during COVID-19)
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Editorial
Multi-Sensory Interaction for Blind and Visually Impaired People
Electronics 2021, 10(24), 3170; https://doi.org/10.3390/electronics10243170 - 20 Dec 2021
Viewed by 282
Abstract
Multi-sensory interaction aids learning, inclusion, and collaboration because it accommodates the diverse cognitive and perceptual needs [...] Full article
(This article belongs to the Special Issue Multi-Sensory Interaction for Blind and Visually Impaired People)
Article
Memory-Based LT Codes for Efficient 5G Networks and Beyond
Electronics 2021, 10(24), 3169; https://doi.org/10.3390/electronics10243169 - 20 Dec 2021
Viewed by 272
Abstract
The next-generation networks (5G and beyond) require robust channel codes to support their high specifications, such as low latency, low complexity, significant coding gain, and flexibility. In this paper, we propose using a fountain code as a promising solution to 5G and 6G [...] Read more.
The next-generation networks (5G and beyond) require robust channel codes to support their high specifications, such as low latency, low complexity, significant coding gain, and flexibility. In this paper, we propose using a fountain code as a promising solution to 5G and 6G networks, and then we propose using a modified version of the fountain codes (Luby transform codes) over a network topology (Y-network) that is relevant in the context of the 5G networks. In such a network, the user can be connected to two different cells at the same time. In addition, the paper presents the necessary techniques for analyzing the system and shows that the proposed scheme enhances the system performance in terms of decoding success probability, error probability, and code rate (or overhead). Furthermore, the analyses in this paper allow us to quantify the trade-off between overhead, on the one hand, and the decoding success probability and error probability, on the other hand. Finally, based on the analytical approach and numerical results, our simulation results demonstrate that the proposed scheme achieves better performance than the regular LT codes and the other schemes in the literature. Full article
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Article
Adaptive Noise-Resistant Low-Power ASK Demodulator Design in UHF RFID Chips
Electronics 2021, 10(24), 3168; https://doi.org/10.3390/electronics10243168 - 20 Dec 2021
Viewed by 394
Abstract
This paper presents a new signal demodulator for ultra-high frequency (UHF) radio frequency identification (RFID) tag chips. The demodulator is used to demodulate amplitude shift keying (ASK) modulated signals with the advantages of high noise immunity, large input range and low power consumption. [...] Read more.
This paper presents a new signal demodulator for ultra-high frequency (UHF) radio frequency identification (RFID) tag chips. The demodulator is used to demodulate amplitude shift keying (ASK) modulated signals with the advantages of high noise immunity, large input range and low power consumption. The demodulator consists of a charge pump, an envelope detector, and a comparator. In particular, the demodulator provides a hysteresis input signal to the comparator through two envelope detectors, resulting in better noise immunity. The demodulator is based on a standard 0.13 µm CMOS process. The demodulator is suitable for demodulating high frequency signals at 900 MHz with a data rate of 128 Kbps and can operate up to 78 °C. The input signal has a peak of 1.2 V and consumes as little as 113.6 nW. The demodulator also has a noise immunity threshold of approximately 3.729 V. Full article
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Article
A Hybrid Imputation Method for Multi-Pattern Missing Data: A Case Study on Type II Diabetes Diagnosis
Electronics 2021, 10(24), 3167; https://doi.org/10.3390/electronics10243167 - 19 Dec 2021
Viewed by 454
Abstract
Real medical datasets usually consist of missing data with different patterns which decrease the performance of classifiers used in intelligent healthcare and disease diagnosis systems. Many methods have been proposed to impute missing data, however, they do not fulfill the need for data [...] Read more.
Real medical datasets usually consist of missing data with different patterns which decrease the performance of classifiers used in intelligent healthcare and disease diagnosis systems. Many methods have been proposed to impute missing data, however, they do not fulfill the need for data quality especially in real datasets with different missing data patterns. In this paper, a four-layer model is introduced, and then a hybrid imputation (HIMP) method using this model is proposed to impute multi-pattern missing data including non-random, random, and completely random patterns. In HIMP, first, non-random missing data patterns are imputed, and then the obtained dataset is decomposed into two datasets containing random and completely random missing data patterns. Then, concerning the missing data patterns in each dataset, different single or multiple imputation methods are used. Finally, the best-imputed datasets gained from random and completely random patterns are merged to form the final dataset. The experimental evaluation was conducted by a real dataset named IRDia including all three missing data patterns. The proposed method and comparative methods were compared using different classifiers in terms of accuracy, precision, recall, and F1-score. The classifiers’ performances show that the HIMP can impute multi-pattern missing values more effectively than other comparative methods. Full article
(This article belongs to the Special Issue Big Data Analytics using Artificial Intelligence)
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Article
Soft Robotic Sensing, Proprioception via Cable and Microfluidic Transmission
Electronics 2021, 10(24), 3166; https://doi.org/10.3390/electronics10243166 - 19 Dec 2021
Viewed by 485
Abstract
Current challenges in soft robotics include sensing and state awareness. Modern soft robotic systems require many more sensors than traditional robots to estimate pose and contact forces. Existing soft sensors include resistive, conductive, optical, and capacitive sensing, with each sensor requiring electronic circuitry [...] Read more.
Current challenges in soft robotics include sensing and state awareness. Modern soft robotic systems require many more sensors than traditional robots to estimate pose and contact forces. Existing soft sensors include resistive, conductive, optical, and capacitive sensing, with each sensor requiring electronic circuitry and connection to a dedicated line to a data acquisition system, creating a rapidly increasing burden as the number of sensors increases. We demonstrate a network of fiber-based displacement sensors to measure robot state (bend, twist, elongation) and two microfluidic pressure sensors to measure overall and local pressures. These passive sensors transmit information from a soft robot to a nearby display assembly, where a digital camera records displacement and pressure data. We present a configuration in which one camera tracks 11 sensors consisting of nine fiber-based displacement sensors and two microfluidic pressure sensors, eliminating the need for an array of electronic sensors throughout the robot. Finally, we present a Cephalopod-chromatophore-inspired color cell pressure sensor. While these techniques can be used in a variety of soft robot devices, we present fiber and fluid sensing on an elastomeric finger. These techniques are widely suitable for state estimation in the soft robotics field and will allow future progress toward robust, low-cost, real-time control of soft robots. This increased state awareness is necessary for robots to interact with humans, potentially the greatest benefit of the emerging soft robotics field. Full article
(This article belongs to the Special Issue Human Computer Interaction and Its Future)
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Review
Remote Eye Gaze Tracking Research: A Comparative Evaluation on Past and Recent Progress
Electronics 2021, 10(24), 3165; https://doi.org/10.3390/electronics10243165 - 19 Dec 2021
Viewed by 514
Abstract
Several decades of eye related research has shown how valuable eye gaze data are for applications that are essential to human daily life. Eye gaze data in a broad sense has been used in research and systems for eye movements, eye tracking, and [...] Read more.
Several decades of eye related research has shown how valuable eye gaze data are for applications that are essential to human daily life. Eye gaze data in a broad sense has been used in research and systems for eye movements, eye tracking, and eye gaze tracking. Since early 2000, eye gaze tracking systems have emerged as interactive gaze-based systems that could be remotely deployed and operated, known as remote eye gaze tracking (REGT) systems. The drop point of visual attention known as point of gaze (PoG), and the direction of visual attention known as line of sight (LoS), are important tasks of REGT systems. In this paper, we present a comparative evaluation of REGT systems intended for the PoG and LoS estimation tasks regarding past to recent progress. Our literature evaluation presents promising insights on key concepts and changes recorded over time in hardware setup, software process, application, and deployment of REGT systems. In addition, we present current issues in REGT research for future attempts. Full article
(This article belongs to the Special Issue Human Computer Interaction and Its Future)
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Article
A Novel Approach for Securing Nodes Using Two-Ray Model and Shadow Effects in Flying Ad-Hoc Network
Electronics 2021, 10(24), 3164; https://doi.org/10.3390/electronics10243164 - 19 Dec 2021
Cited by 1 | Viewed by 403
Abstract
In the last decades, flying ad-hoc networks (FANET) have provided unique features in the field of unmanned aerial vehicles (UAVs). This work intends to propose an efficient algorithm for secure load balancing in FANET. It is performed with the combination of the firefly [...] Read more.
In the last decades, flying ad-hoc networks (FANET) have provided unique features in the field of unmanned aerial vehicles (UAVs). This work intends to propose an efficient algorithm for secure load balancing in FANET. It is performed with the combination of the firefly algorithm and radio propagation model. To provide the optimal path and to improve the data communication of different nodes, two-ray and shadow fading models are used, which secured the multiple UAVs in some high-level applications. The performance analysis of the proposed efficient optimization technique is compared in terms of packet loss, throughput, end-to-end delay, and routing overhead. Simulation results showed that the secure firefly algorithm and radio propagation models demonstrated the least packet loss, maximum throughput, least delay, and least overhead compared with other existing techniques and models. Full article
(This article belongs to the Special Issue Wireless Sensor Networks in Intelligent Transportation Systems)
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Article
Input-Series-Output-Parallel DC Transformer Impedance Modeling and Phase Reshaping for Rapid Stabilization of MVDC Distribution Systems
Electronics 2021, 10(24), 3163; https://doi.org/10.3390/electronics10243163 - 18 Dec 2021
Viewed by 455
Abstract
This paper focuses on the instability problem of the medium-voltage DC (MVDC) distribution system and proposes an impedance phase reshaping (IPR) method. To obtain the load impedance model of the MVDC distribution system, the input impedance of the input-series-output-parallel (ISOP) DC transformer (DCT) [...] Read more.
This paper focuses on the instability problem of the medium-voltage DC (MVDC) distribution system and proposes an impedance phase reshaping (IPR) method. To obtain the load impedance model of the MVDC distribution system, the input impedance of the input-series-output-parallel (ISOP) DC transformer (DCT) is derived by the generalized average modeling (GAM). Based on the obtained model, the traditional ISOP DCT controller optimization (IDCO) approach is discussed and the IPR method is developed. An impedance phase controller is introduced based on the original control method. According to the optimized impedance stability criterion, the parameters of the impedance phase controller are determined. Compared with the IDCO approach, the proposed method weakens the negative resistance characteristic of the load impedance at the resonant frequency. Therefore, the MV bus voltage oscillation is rapidly mitigated. Besides, the dynamic performance of the system using the IPR method can be classified as good. The simulation results show that the mathematical model is correct, and the proposed method is effective for the rapid stabilization of MVDC distribution systems. Full article
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Article
A Multi-Objective Design Optimization for a Permanent Magnet Synchronous Machine with Hairpin Winding Intended for Transport Applications
Electronics 2021, 10(24), 3162; https://doi.org/10.3390/electronics10243162 - 18 Dec 2021
Viewed by 432
Abstract
Nowadays, interest in electric propulsion is increasing due to the need to decarbonize society. Electric drives and their components play a key role in this electrification trend. The electrical machine, in particular, is seeing an ever-increasing development and extensive research is currently being [...] Read more.
Nowadays, interest in electric propulsion is increasing due to the need to decarbonize society. Electric drives and their components play a key role in this electrification trend. The electrical machine, in particular, is seeing an ever-increasing development and extensive research is currently being dedicated to the improvement of its efficiency and torque/power density. Among the winding methods, hairpin technologies are gaining extensive attention due to their inherently high slot fill factor, good heat dissipation, strong rigidity, and short end-winding length. These features make hairpin windings a potential candidate for some traction applications which require high power and/or torque densities. However, they also have some drawbacks, such as high losses at high frequency operations due to skin and proximity effects. In this paper, a multi-objective design optimization is proposed aiming to provide a fast and useful tool to enhance the exploitation of the hairpin technology in electrical machines. Efficiency and volume power density are considered as main design objectives. Analytical and finite element evaluations are performed to support the proposed methodology. Full article
(This article belongs to the Special Issue Robust Design Optimization of Electrical Machines and Devices)
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Article
Privacy-Preserving Tampering Detection in Automotive Systems
Electronics 2021, 10(24), 3161; https://doi.org/10.3390/electronics10243161 - 18 Dec 2021
Viewed by 355
Abstract
Modern auto-vehicles are built upon a vast collection of sensors that provide large amounts of data processed by dozens of Electronic Control Units (ECUs). These, in turn, monitor and control advanced technological systems providing a large palette of features to the vehicle’s end-users [...] Read more.
Modern auto-vehicles are built upon a vast collection of sensors that provide large amounts of data processed by dozens of Electronic Control Units (ECUs). These, in turn, monitor and control advanced technological systems providing a large palette of features to the vehicle’s end-users (e.g., automated parking, autonomous vehicles). As modern cars become more and more interconnected with external systems (e.g., cloud-based services), enforcing privacy on data originating from vehicle sensors is becoming a challenging research topic. In contrast, deliberate manipulations of vehicle components, known as tampering, require careful (and remote) monitoring of the vehicle via data transmissions and processing. In this context, this paper documents an efficient methodology for data privacy protection, which can be integrated into modern vehicles. The approach leverages the Fast Fourier Transform (FFT) as a core data transformation algorithm, accompanied by filters and additional transformations. The methodology is seconded by a Random Forest-based regression technique enriched with further statistical analysis for tampering detection in the case of anonymized data. Experimental results, conducted on a data set collected from the On-Board Diagnostics (OBD II) port of a 2015 EUR6 Skoda Rapid 1.2 L TSI passenger vehicle, demonstrate that the restored time-domain data preserves the characteristics required by additional processing algorithms (e.g., tampering detection), showing at the same time an adjustable level of privacy. Moreover, tampering detection is shown to be 100% effective in certain scenarios, even in the context of anonymized data. Full article
(This article belongs to the Special Issue Security & Privacy in Intelligent Transportation Systems)
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Article
Analysis of Single Event Effects on Embedded Processor
Electronics 2021, 10(24), 3160; https://doi.org/10.3390/electronics10243160 - 18 Dec 2021
Viewed by 316
Abstract
The continuous scaling of electronic components has led to the development of high-performance microprocessors which are even suitable for safety-critical applications where radiation-induced errors, such as single event effects (SEEs), are one of the most important reliability issues. This work focuses on the [...] Read more.
The continuous scaling of electronic components has led to the development of high-performance microprocessors which are even suitable for safety-critical applications where radiation-induced errors, such as single event effects (SEEs), are one of the most important reliability issues. This work focuses on the development of a fault injection environment capable of analyzing the impact of errors on the functionality of an ARM Cortex-A9 microprocessor embedded within a Zynq-7000 AP-SoC, considering different fault models affecting both the system memory and register resources of the embedded processor. We developed a novel Python-based fault injection platform for the emulation of radiation-induced faults within the AP-SoC hardware resources during the execution of software applications. The fault injection approach is not intrusive, and it does not require modifying the software application under evaluation. The experimental analyses have been performed on a subset of the MiBench benchmark software suite. Fault injection results demonstrate the capability of the developed method and the possibility of evaluating various sets of fault models. Full article
(This article belongs to the Special Issue Reliability and Fault Tolerance Techniques in Emerging Technologies)
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Article
Deep and Transfer Learning Approaches for Pedestrian Identification and Classification in Autonomous Vehicles
Electronics 2021, 10(24), 3159; https://doi.org/10.3390/electronics10243159 - 18 Dec 2021
Viewed by 360
Abstract
Pedestrian detection is at the core of autonomous road vehicle navigation systems as they allow a vehicle to understand where potential hazards lie in the surrounding area and enable it to act in such a way that avoids traffic-accidents, which may result in [...] Read more.
Pedestrian detection is at the core of autonomous road vehicle navigation systems as they allow a vehicle to understand where potential hazards lie in the surrounding area and enable it to act in such a way that avoids traffic-accidents, which may result in individuals being harmed. In this work, a review of the convolutional neural networks (CNN) to tackle pedestrian detection is presented. We further present models based on CNN and transfer learning. The CNN model with the VGG-16 architecture is further optimised using the transfer learning approach. This paper demonstrates that the use of image augmentation on training data can yield varying results. In addition, a pre-processing system that can be used to prepare 3D spatial data obtained via LiDAR sensors is proposed. This pre-processing system is able to identify candidate regions that can be put forward for classification, whether that be 3D classification or a combination of 2D and 3D classifications via sensor fusion. We proposed a number of models based on transfer learning and convolutional neural networks and achieved over 98% accuracy with the adaptive transfer learning model. Full article
(This article belongs to the Special Issue Autonomous Vehicles Technological Trends)
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Article
Deep Learning and Machine Learning Techniques of Diagnosis Dermoscopy Images for Early Detection of Skin Diseases
Electronics 2021, 10(24), 3158; https://doi.org/10.3390/electronics10243158 - 18 Dec 2021
Viewed by 375
Abstract
With the increasing incidence of severe skin diseases, such as skin cancer, endoscopic medical imaging has become urgent for revealing the internal and hidden tissues under the skin. Diagnostic information to help doctors make an accurate diagnosis is provided by endoscopy devices. Nonetheless, [...] Read more.
With the increasing incidence of severe skin diseases, such as skin cancer, endoscopic medical imaging has become urgent for revealing the internal and hidden tissues under the skin. Diagnostic information to help doctors make an accurate diagnosis is provided by endoscopy devices. Nonetheless, most skin diseases have similar features, which make it challenging for dermatologists to diagnose patients accurately. Therefore, machine and deep learning techniques can have a critical role in diagnosing dermatoscopy images and in the accurate early detection of skin diseases. In this study, systems for the early detection of skin lesions were developed. The performance of the machine learning and deep learning was evaluated on two datasets (e.g., the International Skin Imaging Collaboration (ISIC 2018) and Pedro Hispano (PH2)). First, the proposed system was based on hybrid features that were extracted by three algorithms: local binary pattern (LBP), gray level co-occurrence matrix (GLCM), and wavelet transform (DWT). Such features were then integrated into a feature vector and classified using artificial neural network (ANN) and feedforward neural network (FFNN) classifiers. The FFNN and ANN classifiers achieved superior results compared to the other methods. Accuracy rates of 95.24% for diagnosing the ISIC 2018 dataset and 97.91% for diagnosing the PH2 dataset were achieved using the FFNN algorithm. Second, convolutional neural networks (CNNs) (e.g., ResNet-50 and AlexNet models) were applied to diagnose skin diseases using the transfer learning method. It was found that the ResNet-50 model fared better than AlexNet. Accuracy rates of 90% for diagnosing the ISIC 2018 dataset and 95.8% for the PH2 dataset were reached using the ResNet-50 model. Full article
(This article belongs to the Section Computer Science & Engineering)
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