Editor’s Choice Articles

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

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20 pages, 2136 KiB  
Article
Radar-Based Hand Gesture Recognition Using Spiking Neural Networks
by Ing Jyh Tsang, Federico Corradi, Manolis Sifalakis, Werner Van Leekwijck and Steven Latré
Electronics 2021, 10(12), 1405; https://doi.org/10.3390/electronics10121405 - 11 Jun 2021
Cited by 32 | Viewed by 7090
Abstract
We propose a spiking neural network (SNN) approach for radar-based hand gesture recognition (HGR), using frequency modulated continuous wave (FMCW) millimeter-wave radar. After pre-processing the range-Doppler or micro-Doppler radar signal, we use a signal-to-spike conversion scheme that encodes radar Doppler maps into spike [...] Read more.
We propose a spiking neural network (SNN) approach for radar-based hand gesture recognition (HGR), using frequency modulated continuous wave (FMCW) millimeter-wave radar. After pre-processing the range-Doppler or micro-Doppler radar signal, we use a signal-to-spike conversion scheme that encodes radar Doppler maps into spike trains. The spike trains are fed into a spiking recurrent neural network, a liquid state machine (LSM). The readout spike signal from the SNN is then used as input for different classifiers for comparison, including logistic regression, random forest, and support vector machine (SVM). Using liquid state machines of less than 1000 neurons, we achieve better than state-of-the-art results on two publicly available reference datasets, reaching over 98% accuracy on 10-fold cross-validation for both data sets. Full article
(This article belongs to the Special Issue Neuromorphic Sensing and Computing Systems)
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19 pages, 662 KiB  
Article
Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach
by Sk Mahmudul Hassan, Arnab Kumar Maji, Michał Jasiński, Zbigniew Leonowicz and Elżbieta Jasińska
Electronics 2021, 10(12), 1388; https://doi.org/10.3390/electronics10121388 - 9 Jun 2021
Cited by 343 | Viewed by 33173
Abstract
The timely identification and early prevention of crop diseases are essential for improving production. In this paper, deep convolutional-neural-network (CNN) models are implemented to identify and diagnose diseases in plants from their leaves, since CNNs have achieved impressive results in the field of [...] Read more.
The timely identification and early prevention of crop diseases are essential for improving production. In this paper, deep convolutional-neural-network (CNN) models are implemented to identify and diagnose diseases in plants from their leaves, since CNNs have achieved impressive results in the field of machine vision. Standard CNN models require a large number of parameters and higher computation cost. In this paper, we replaced standard convolution with depth=separable convolution, which reduces the parameter number and computation cost. The implemented models were trained with an open dataset consisting of 14 different plant species, and 38 different categorical disease classes and healthy plant leaves. To evaluate the performance of the models, different parameters such as batch size, dropout, and different numbers of epochs were incorporated. The implemented models achieved a disease-classification accuracy rates of 98.42%, 99.11%, 97.02%, and 99.56% using InceptionV3, InceptionResNetV2, MobileNetV2, and EfficientNetB0, respectively, which were greater than that of traditional handcrafted-feature-based approaches. In comparison with other deep-learning models, the implemented model achieved better performance in terms of accuracy and it required less training time. Moreover, the MobileNetV2 architecture is compatible with mobile devices using the optimized parameter. The accuracy results in the identification of diseases showed that the deep CNN model is promising and can greatly impact the efficient identification of the diseases, and may have potential in the detection of diseases in real-time agricultural systems. Full article
(This article belongs to the Special Issue New Technological Advancements and Applications of Deep Learning)
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17 pages, 1450 KiB  
Review
Machine Learning-Based Data-Driven Fault Detection/Diagnosis of Lithium-Ion Battery: A Critical Review
by Akash Samanta, Sumana Chowdhuri and Sheldon S. Williamson
Electronics 2021, 10(11), 1309; https://doi.org/10.3390/electronics10111309 - 30 May 2021
Cited by 150 | Viewed by 16064
Abstract
Fault detection/diagnosis has become a crucial function of the battery management system (BMS) due to the increasing application of lithium-ion batteries (LIBs) in highly sophisticated and high-power applications to ensure the safe and reliable operation of the system. The application of Machine Learning [...] Read more.
Fault detection/diagnosis has become a crucial function of the battery management system (BMS) due to the increasing application of lithium-ion batteries (LIBs) in highly sophisticated and high-power applications to ensure the safe and reliable operation of the system. The application of Machine Learning (ML) in the BMS of LIB has long been adopted for efficient, reliable, accurate prediction of several important states of LIB such as state of charge, state of health and remaining useful life. Inspired by some of the promising features of ML-based techniques over the conventional LIB fault detection/diagnosis methods such as model-based, knowledge-based and signal processing-based techniques, ML-based data-driven methods have been a prime research focus in the last few years. This paper provides a comprehensive review exclusively on the state-of-the-art ML-based data-driven fault detection/diagnosis techniques to provide a ready reference and direction to the research community aiming towards developing an accurate, reliable, adaptive and easy to implement fault diagnosis strategy for the LIB system. Current issues of existing strategies and future challenges of LIB fault diagnosis are also explained for better understanding and guidance. Full article
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20 pages, 5508 KiB  
Article
Bearing Fault Classification Using Ensemble Empirical Mode Decomposition and Convolutional Neural Network
by Rafia Nishat Toma, Cheol-Hong Kim and Jong-Myon Kim
Electronics 2021, 10(11), 1248; https://doi.org/10.3390/electronics10111248 - 24 May 2021
Cited by 55 | Viewed by 5042
Abstract
Condition monitoring is used to track the unavoidable phases of rolling element bearings in an induction motor (IM) to ensure reliable operation in domestic and industrial machinery. The convolutional neural network (CNN) has been used as an effective tool to recognize and classify [...] Read more.
Condition monitoring is used to track the unavoidable phases of rolling element bearings in an induction motor (IM) to ensure reliable operation in domestic and industrial machinery. The convolutional neural network (CNN) has been used as an effective tool to recognize and classify multiple rolling bearing faults in recent times. Due to the nonlinear and nonstationary nature of vibration signals, it is quite difficult to achieve high classification accuracy when directly using the original signal as the input of a convolution neural network. To evaluate the fault characteristics, ensemble empirical mode decomposition (EEMD) is implemented to decompose the signal into multiple intrinsic mode functions (IMFs) in this work. Then, based on the kurtosis value, insignificant IMFs are filtered out and the original signal is reconstructed with the rest of the IMFs so that the reconstructed signal contains the fault characteristics. After that, the 1-D reconstructed vibration signal is converted into a 2-D image using a continuous wavelet transform with information from the damage frequency band. This also transfers the signal into a time-frequency domain and reduces the nonstationary effects of the vibration signal. Finally, the generated images of various fault conditions, which possess a discriminative pattern relative to the types of faults, are used to train an appropriate CNN model. Additionally, with the reconstructed signal, two different methods are used to create an image to compare with our proposed image creation approach. The vibration signal is collected from a self-designed testbed containing multiple bearings of different fault conditions. Two other conventional CNN architectures are compared with our proposed model. Based on the results obtained, it can be concluded that the image generated with fault signatures not only accurately classifies multiple faults with CNN but can also be considered as a reliable and stable method for the diagnosis of fault bearings. Full article
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21 pages, 2453 KiB  
Article
A Novel Energy-Efficiency Optimization Approach Based on Driving Patterns Styles and Experimental Tests for Electric Vehicles
by Juan Diego Valladolid, Diego Patino, Giambattista Gruosso, Carlos Adrián Correa-Flórez, José Vuelvas and Fabricio Espinoza
Electronics 2021, 10(10), 1199; https://doi.org/10.3390/electronics10101199 - 18 May 2021
Cited by 27 | Viewed by 5511
Abstract
This article proposes an energy-efficiency strategy based on the optimization of driving patterns for an electric vehicle (EV). The EV studied in this paper is a commercial vehicle only driven by a traction motor. The motor drives the front wheels indirectly through the [...] Read more.
This article proposes an energy-efficiency strategy based on the optimization of driving patterns for an electric vehicle (EV). The EV studied in this paper is a commercial vehicle only driven by a traction motor. The motor drives the front wheels indirectly through the differential drive. The electrical inverter model and the power-train efficiency are established by lookup tables determined by power tests in a dynamometric bank. The optimization problem is focused on maximizing energy-efficiency between the wheel power and battery pack, not only to maintain but also to improve its value by modifying the state of charge (SOC). The solution is found by means of a Particle Swarm Optimization (PSO) algorithm. The optimizer simulation results validate the increasing efficiency with the speed setpoint variations, and also show that the battery SOC is improved. The best results are obtained when the speed variation is between 5% and 6%. Full article
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52 pages, 2321 KiB  
Article
Towards Secure Fog Computing: A Survey on Trust Management, Privacy, Authentication, Threats and Access Control
by Abdullah Al-Noman Patwary, Ranesh Kumar Naha, Saurabh Garg, Sudheer Kumar Battula, Md Anwarul Kaium Patwary, Erfan Aghasian, Muhammad Bilal Amin, Aniket Mahanti and Mingwei Gong
Electronics 2021, 10(10), 1171; https://doi.org/10.3390/electronics10101171 - 14 May 2021
Cited by 48 | Viewed by 8103
Abstract
Fog computing is an emerging computing paradigm that has come into consideration for the deployment of Internet of Things (IoT) applications amongst researchers and technology industries over the last few years. Fog is highly distributed and consists of a wide number of autonomous [...] Read more.
Fog computing is an emerging computing paradigm that has come into consideration for the deployment of Internet of Things (IoT) applications amongst researchers and technology industries over the last few years. Fog is highly distributed and consists of a wide number of autonomous end devices, which contribute to the processing. However, the variety of devices offered across different users are not audited. Hence, the security of Fog devices is a major concern that should come into consideration. Therefore, to provide the necessary security for Fog devices, there is a need to understand what the security concerns are with regards to Fog. All aspects of Fog security, which have not been covered by other literature works, need to be identified and aggregated. On the other hand, privacy preservation for user’s data in Fog devices and application data processed in Fog devices is another concern. To provide the appropriate level of trust and privacy, there is a need to focus on authentication, threats and access control mechanisms as well as privacy protection techniques in Fog computing. In this paper, a survey along with a taxonomy is proposed, which presents an overview of existing security concerns in the context of the Fog computing paradigm. Moreover, the Blockchain-based solutions towards a secure Fog computing environment is presented and various research challenges and directions for future research are discussed. Full article
(This article belongs to the Special Issue Embedded IoT: System Design and Applications)
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15 pages, 4552 KiB  
Article
Optimization of Multi-Level Operation in RRAM Arrays for In-Memory Computing
by Eduardo Pérez, Antonio Javier Pérez-Ávila, Rocío Romero-Zaliz, Mamathamba Kalishettyhalli Mahadevaiah, Emilio Pérez-Bosch Quesada, Juan Bautista Roldán, Francisco Jiménez-Molinos and Christian Wenger
Electronics 2021, 10(9), 1084; https://doi.org/10.3390/electronics10091084 - 3 May 2021
Cited by 18 | Viewed by 4851
Abstract
Accomplishing multi-level programming in resistive random access memory (RRAM) arrays with truly discrete and linearly spaced conductive levels is crucial in order to implement synaptic weights in hardware-based neuromorphic systems. In this paper, we implemented this feature on 4-kbit 1T1R RRAM arrays by [...] Read more.
Accomplishing multi-level programming in resistive random access memory (RRAM) arrays with truly discrete and linearly spaced conductive levels is crucial in order to implement synaptic weights in hardware-based neuromorphic systems. In this paper, we implemented this feature on 4-kbit 1T1R RRAM arrays by tuning the programming parameters of the multi-level incremental step pulse with verify algorithm (M-ISPVA). The optimized set of parameters was assessed by comparing its results with a non-optimized one. The optimized set of parameters proved to be an effective way to define non-overlapped conductive levels due to the strong reduction of the device-to-device variability as well as of the cycle-to-cycle variability, assessed by inter-levels switching tests and during 1 k reset-set cycles. In order to evaluate this improvement in real scenarios, the experimental characteristics of the RRAM devices were captured by means of a behavioral model, which was used to simulate two different neuromorphic systems: an 8 × 8 vector-matrix-multiplication (VMM) accelerator and a 4-layer feedforward neural network for MNIST database recognition. The results clearly showed that the optimization of the programming parameters improved both the precision of VMM results as well as the recognition accuracy of the neural network in about 6% compared with the use of non-optimized parameters. Full article
(This article belongs to the Special Issue Resistive Memory Characterization, Simulation, and Compact Modeling)
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22 pages, 5068 KiB  
Article
An Autonomous Grape-Harvester Robot: Integrated System Architecture
by Eleni Vrochidou, Konstantinos Tziridis, Alexandros Nikolaou, Theofanis Kalampokas, George A. Papakostas, Theodore P. Pachidis, Spyridon Mamalis, Stefanos Koundouras and Vassilis G. Kaburlasos
Electronics 2021, 10(9), 1056; https://doi.org/10.3390/electronics10091056 - 29 Apr 2021
Cited by 44 | Viewed by 7526
Abstract
This work pursues the potential of extending “Industry 4.0” practices to farming toward achieving “Agriculture 4.0”. Our interest is in fruit harvesting, motivated by the problem of addressing the shortage of seasonal labor. In particular, here we present an integrated system architecture of [...] Read more.
This work pursues the potential of extending “Industry 4.0” practices to farming toward achieving “Agriculture 4.0”. Our interest is in fruit harvesting, motivated by the problem of addressing the shortage of seasonal labor. In particular, here we present an integrated system architecture of an Autonomous Robot for Grape harvesting (ARG). The overall system consists of three interdependent units: (1) an aerial unit, (2) a remote-control unit and (3) the ARG ground unit. Special attention is paid to the ARG; the latter is designed and built to carry out three viticultural operations, namely harvest, green harvest and defoliation. We present an overview of the multi-purpose overall system, the specific design of each unit of the system and the integration of all subsystems. In addition, the fully sensory-based sensing system architecture and the underlying vision system are analyzed. Due to its modular design, the proposed system can be extended to a variety of different crops and/or orchards. Full article
(This article belongs to the Special Issue Control of Mobile Robots)
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19 pages, 6227 KiB  
Article
An Active/Reactive Power Control Strategy for Renewable Generation Systems
by Iván Andrade, Rubén Pena, Ramón Blasco-Gimenez, Javier Riedemann, Werner Jara and Cristián Pesce
Electronics 2021, 10(9), 1061; https://doi.org/10.3390/electronics10091061 - 29 Apr 2021
Cited by 14 | Viewed by 5446
Abstract
The development of distributed generation, mainly based on renewable energies, requires the design of control strategies to allow the regulation of electrical variables, such as power, voltage (V), and frequency (f), and the coordination of multiple generation units in microgrids or islanded systems. [...] Read more.
The development of distributed generation, mainly based on renewable energies, requires the design of control strategies to allow the regulation of electrical variables, such as power, voltage (V), and frequency (f), and the coordination of multiple generation units in microgrids or islanded systems. This paper presents a strategy to control the active and reactive power flow in the Point of Common Connection (PCC) of a renewable generation system operating in islanded mode. Voltage Source Converters (VSCs) are connected between individual generation units and the PCC to control the voltage and frequency. The voltage and frequency reference values are obtained from the P–V and Q–f droop characteristics curves, where P and Q are the active and reactive power supplied to the load, respectively. Proportional–Integral (PI) controllers process the voltage and frequency errors and set the reference currents (in the dq frame) to be imposed by each VSC. Simulation results considering high-power solar and wind generation systems are presented to validate the proposed control strategy. Full article
(This article belongs to the Section Systems & Control Engineering)
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19 pages, 6036 KiB  
Article
Facial Emotion Recognition Using Transfer Learning in the Deep CNN
by M. A. H. Akhand, Shuvendu Roy, Nazmul Siddique, Md Abdus Samad Kamal and Tetsuya Shimamura
Electronics 2021, 10(9), 1036; https://doi.org/10.3390/electronics10091036 - 27 Apr 2021
Cited by 239 | Viewed by 27473
Abstract
Human facial emotion recognition (FER) has attracted the attention of the research community for its promising applications. Mapping different facial expressions to the respective emotional states are the main task in FER. The classical FER consists of two major steps: feature extraction and [...] Read more.
Human facial emotion recognition (FER) has attracted the attention of the research community for its promising applications. Mapping different facial expressions to the respective emotional states are the main task in FER. The classical FER consists of two major steps: feature extraction and emotion recognition. Currently, the Deep Neural Networks, especially the Convolutional Neural Network (CNN), is widely used in FER by virtue of its inherent feature extraction mechanism from images. Several works have been reported on CNN with only a few layers to resolve FER problems. However, standard shallow CNNs with straightforward learning schemes have limited feature extraction capability to capture emotion information from high-resolution images. A notable drawback of the most existing methods is that they consider only the frontal images (i.e., ignore profile views for convenience), although the profile views taken from different angles are important for a practical FER system. For developing a highly accurate FER system, this study proposes a very Deep CNN (DCNN) modeling through Transfer Learning (TL) technique where a pre-trained DCNN model is adopted by replacing its dense upper layer(s) compatible with FER, and the model is fine-tuned with facial emotion data. A novel pipeline strategy is introduced, where the training of the dense layer(s) is followed by tuning each of the pre-trained DCNN blocks successively that has led to gradual improvement of the accuracy of FER to a higher level. The proposed FER system is verified on eight different pre-trained DCNN models (VGG-16, VGG-19, ResNet-18, ResNet-34, ResNet-50, ResNet-152, Inception-v3 and DenseNet-161) and well-known KDEF and JAFFE facial image datasets. FER is very challenging even for frontal views alone. FER on the KDEF dataset poses further challenges due to the diversity of images with different profile views together with frontal views. The proposed method achieved remarkable accuracy on both datasets with pre-trained models. On a 10-fold cross-validation way, the best achieved FER accuracies with DenseNet-161 on test sets of KDEF and JAFFE are 96.51% and 99.52%, respectively. The evaluation results reveal the superiority of the proposed FER system over the existing ones regarding emotion detection accuracy. Moreover, the achieved performance on the KDEF dataset with profile views is promising as it clearly demonstrates the required proficiency for real-life applications. Full article
(This article belongs to the Special Issue Deep Learning Technologies for Machine Vision and Audition)
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25 pages, 815 KiB  
Article
Accelerating Neural Network Inference on FPGA-Based Platforms—A Survey
by Ran Wu, Xinmin Guo, Jian Du and Junbao Li
Electronics 2021, 10(9), 1025; https://doi.org/10.3390/electronics10091025 - 25 Apr 2021
Cited by 66 | Viewed by 14210
Abstract
The breakthrough of deep learning has started a technological revolution in various areas such as object identification, image/video recognition and semantic segmentation. Neural network, which is one of representative applications of deep learning, has been widely used and developed many efficient models. However, [...] Read more.
The breakthrough of deep learning has started a technological revolution in various areas such as object identification, image/video recognition and semantic segmentation. Neural network, which is one of representative applications of deep learning, has been widely used and developed many efficient models. However, the edge implementation of neural network inference is restricted because of conflicts between the high computation and storage complexity and resource-limited hardware platforms in applications scenarios. In this paper, we research neural networks which are involved in the acceleration on FPGA-based platforms. The architecture of networks and characteristics of FPGA are analyzed, compared and summarized, as well as their influence on acceleration tasks. Based on the analysis, we generalize the acceleration strategies into five aspects—computing complexity, computing parallelism, data reuse, pruning and quantization. Then previous works on neural network acceleration are introduced following these topics. We summarize how to design a technical route for practical applications based on these strategies. Challenges in the path are discussed to provide guidance for future work. Full article
(This article belongs to the Section Artificial Intelligence Circuits and Systems (AICAS))
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30 pages, 1401 KiB  
Review
Drone Deep Reinforcement Learning: A Review
by Ahmad Taher Azar, Anis Koubaa, Nada Ali Mohamed, Habiba A. Ibrahim, Zahra Fathy Ibrahim, Muhammad Kazim, Adel Ammar, Bilel Benjdira, Alaa M. Khamis, Ibrahim A. Hameed and Gabriella Casalino
Electronics 2021, 10(9), 999; https://doi.org/10.3390/electronics10090999 - 22 Apr 2021
Cited by 229 | Viewed by 29061
Abstract
Unmanned Aerial Vehicles (UAVs) are increasingly being used in many challenging and diversified applications. These applications belong to the civilian and the military fields. To name a few; infrastructure inspection, traffic patrolling, remote sensing, mapping, surveillance, rescuing humans and animals, environment monitoring, and [...] Read more.
Unmanned Aerial Vehicles (UAVs) are increasingly being used in many challenging and diversified applications. These applications belong to the civilian and the military fields. To name a few; infrastructure inspection, traffic patrolling, remote sensing, mapping, surveillance, rescuing humans and animals, environment monitoring, and Intelligence, Surveillance, Target Acquisition, and Reconnaissance (ISTAR) operations. However, the use of UAVs in these applications needs a substantial level of autonomy. In other words, UAVs should have the ability to accomplish planned missions in unexpected situations without requiring human intervention. To ensure this level of autonomy, many artificial intelligence algorithms were designed. These algorithms targeted the guidance, navigation, and control (GNC) of UAVs. In this paper, we described the state of the art of one subset of these algorithms: the deep reinforcement learning (DRL) techniques. We made a detailed description of them, and we deduced the current limitations in this area. We noted that most of these DRL methods were designed to ensure stable and smooth UAV navigation by training computer-simulated environments. We realized that further research efforts are needed to address the challenges that restrain their deployment in real-life scenarios. Full article
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18 pages, 1006 KiB  
Article
Automated Quantum Hardware Selection for Quantum Workflows
by Benjamin Weder, Johanna Barzen, Frank Leymann and Marie Salm
Electronics 2021, 10(8), 984; https://doi.org/10.3390/electronics10080984 - 20 Apr 2021
Cited by 16 | Viewed by 4905
Abstract
The execution of a quantum algorithm typically requires various classical pre- and post-processing tasks. Hence, workflows are a promising means to orchestrate these tasks, benefiting from their reliability, robustness, and features, such as transactional processing. However, the implementations of the tasks may be [...] Read more.
The execution of a quantum algorithm typically requires various classical pre- and post-processing tasks. Hence, workflows are a promising means to orchestrate these tasks, benefiting from their reliability, robustness, and features, such as transactional processing. However, the implementations of the tasks may be very heterogeneous and they depend on the quantum hardware used to execute the quantum circuits of the algorithm. Additionally, today’s quantum computers are still restricted, which limits the size of the quantum circuits that can be executed. As the circuit size often depends on the input data of the algorithm, the selection of quantum hardware to execute a quantum circuit must be done at workflow runtime. However, modeling all possible alternative tasks would clutter the workflow model and require its adaptation whenever a new quantum computer or software tool is released. To overcome this problem, we introduce an approach to automatically select suitable quantum hardware for the execution of quantum circuits in workflows. Furthermore, it enables the dynamic adaptation of the workflows, depending on the selection at runtime based on reusable workflow fragments. We validate our approach with a prototypical implementation and a case study demonstrating the hardware selection for Simon’s algorithm. Full article
(This article belongs to the Special Issue Quantum Computing System Design and Architecture)
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17 pages, 1603 KiB  
Article
Self-Biased and Supply-Voltage Scalable Inverter-Based Operational Transconductance Amplifier with Improved Composite Transistors
by Luis Henrique Rodovalho, Cesar Ramos Rodrigues and Orazio Aiello
Electronics 2021, 10(8), 935; https://doi.org/10.3390/electronics10080935 - 14 Apr 2021
Cited by 28 | Viewed by 5320
Abstract
This paper deals with a single-stage single-ended inverter-based Operational Transconductance Amplifiers (OTA) with improved composite transistors for ultra-low-voltage supplies, while maintaining a small-area, high power-efficiency and low output signal distortion. The improved composite transistor is a combination of the conventional composite transistor and [...] Read more.
This paper deals with a single-stage single-ended inverter-based Operational Transconductance Amplifiers (OTA) with improved composite transistors for ultra-low-voltage supplies, while maintaining a small-area, high power-efficiency and low output signal distortion. The improved composite transistor is a combination of the conventional composite transistor and forward-body-biasing to further increase voltage gain. The impact of the proposed technique on performance is demonstrated through post-layout simulations referring to the TSMC 180 nm technology process. The proposed OTA achieves 54 dB differential voltage gain, 210 Hz gain–bandwidth product for a 10 pF capacitive load, with a power consumption of 273 pW with a 0.3 V power supply, and occupies an area of 1026 μm2. For a 0.6 V voltage supply, the proposed OTA improves its voltage gain to 73 dB, and achieves a 15 kHz gain–bandwidth product with a power consumption of 41 nW. Full article
(This article belongs to the Special Issue Analog Microelectronic Circuit Design and Applications)
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33 pages, 8507 KiB  
Review
Embedded Intelligence on FPGA: Survey, Applications and Challenges
by Kah Phooi Seng, Paik Jen Lee and Li Minn Ang
Electronics 2021, 10(8), 895; https://doi.org/10.3390/electronics10080895 - 8 Apr 2021
Cited by 74 | Viewed by 12162
Abstract
Embedded intelligence (EI) is an emerging research field and has the objective to incorporate machine learning algorithms and intelligent decision-making capabilities into mobile and embedded devices or systems. There are several challenges to be addressed to realize efficient EI implementations in hardware such [...] Read more.
Embedded intelligence (EI) is an emerging research field and has the objective to incorporate machine learning algorithms and intelligent decision-making capabilities into mobile and embedded devices or systems. There are several challenges to be addressed to realize efficient EI implementations in hardware such as the need for: (1) high computational processing; (2) low power consumption (or high energy efficiency); and (3) scalability to accommodate different network sizes and topologies. In recent years, an emerging hardware technology which has demonstrated strong potential and capabilities for EI implementations is the FPGA (field programmable gate array) technology. This paper presents an overview and review of embedded intelligence on FPGA with a focus on applications, platforms and challenges. There are four main classification and thematic descriptors which are reviewed and discussed in this paper for EI: (1) EI techniques including machine learning and neural networks, deep learning, expert systems, fuzzy intelligence, swarm intelligence, self-organizing map (SOM) and extreme learning; (2) applications for EI including object detection and recognition, indoor localization and surveillance monitoring, and other EI applications; (3) hardware and platforms for EI; and (4) challenges for EI. The paper aims to introduce interested researchers to this area and motivate the development of practical FPGA solutions for EI deployment. Full article
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23 pages, 2721 KiB  
Article
An Interpretable Deep Learning Model for Automatic Sound Classification
by Pablo Zinemanas, Martín Rocamora, Marius Miron, Frederic Font and Xavier Serra
Electronics 2021, 10(7), 850; https://doi.org/10.3390/electronics10070850 - 2 Apr 2021
Cited by 47 | Viewed by 9567
Abstract
Deep learning models have improved cutting-edge technologies in many research areas, but their black-box structure makes it difficult to understand their inner workings and the rationale behind their predictions. This may lead to unintended effects, such as being susceptible to adversarial attacks or [...] Read more.
Deep learning models have improved cutting-edge technologies in many research areas, but their black-box structure makes it difficult to understand their inner workings and the rationale behind their predictions. This may lead to unintended effects, such as being susceptible to adversarial attacks or the reinforcement of biases. There is still a lack of research in the audio domain, despite the increasing interest in developing deep learning models that provide explanations of their decisions. To reduce this gap, we propose a novel interpretable deep learning model for automatic sound classification, which explains its predictions based on the similarity of the input to a set of learned prototypes in a latent space. We leverage domain knowledge by designing a frequency-dependent similarity measure and by considering different time-frequency resolutions in the feature space. The proposed model achieves results that are comparable to that of the state-of-the-art methods in three different sound classification tasks involving speech, music, and environmental audio. In addition, we present two automatic methods to prune the proposed model that exploit its interpretability. Our system is open source and it is accompanied by a web application for the manual editing of the model, which allows for a human-in-the-loop debugging approach. Full article
(This article belongs to the Special Issue Machine Learning Applied to Music/Audio Signal Processing)
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16 pages, 5585 KiB  
Article
A Simulated Annealing Algorithm and Grid Map-Based UAV Coverage Path Planning Method for 3D Reconstruction
by Sichen Xiao, Xiaojun Tan and Jinping Wang
Electronics 2021, 10(7), 853; https://doi.org/10.3390/electronics10070853 - 2 Apr 2021
Cited by 81 | Viewed by 6351
Abstract
With the extensive application of 3D maps, acquiring high-quality images with unmanned aerial vehicles (UAVs) for precise 3D reconstruction has become a prominent topic of study. In this research, we proposed a coverage path planning method for UAVs to achieve full coverage of [...] Read more.
With the extensive application of 3D maps, acquiring high-quality images with unmanned aerial vehicles (UAVs) for precise 3D reconstruction has become a prominent topic of study. In this research, we proposed a coverage path planning method for UAVs to achieve full coverage of a target area and to collect high-resolution images while considering the overlap ratio of the collected images and energy consumption of clustered UAVs. The overlap ratio of the collected image set is guaranteed through a map decomposition method, which can ensure that the reconstruction results will not get affected by model breaking. In consideration of the small battery capacity of common commercial quadrotor UAVs, ray-scan-based area division was adopted to segment the target area, and near-optimized paths in subareas were calculated by a simulated annealing algorithm to find near-optimized paths, which can achieve balanced task assignment for UAV formations and minimum energy consumption for each UAV. The proposed system was validated through a site experiment and achieved a reduction in path length of approximately 12.6% compared to the traditional zigzag path. Full article
(This article belongs to the Special Issue Advances in SLAM and Data Fusion for UAVs/Drones)
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17 pages, 7946 KiB  
Article
Real-Time Face Mask Detection Method Based on YOLOv3
by Xinbei Jiang, Tianhan Gao, Zichen Zhu and Yukang Zhao
Electronics 2021, 10(7), 837; https://doi.org/10.3390/electronics10070837 - 1 Apr 2021
Cited by 135 | Viewed by 13990
Abstract
The rapid outbreak of COVID-19 has caused serious harm and infected tens of millions of people worldwide. Since there is no specific treatment, wearing masks has become an effective method to prevent the transmission of COVID-19 and is required in most public areas, [...] Read more.
The rapid outbreak of COVID-19 has caused serious harm and infected tens of millions of people worldwide. Since there is no specific treatment, wearing masks has become an effective method to prevent the transmission of COVID-19 and is required in most public areas, which has also led to a growing demand for automatic real-time mask detection services to replace manual reminding. However, few studies on face mask detection are being conducted. It is urgent to improve the performance of mask detectors. In this paper, we proposed the Properly Wearing Masked Face Detection Dataset (PWMFD), which included 9205 images of mask wearing samples with three categories. Moreover, we proposed Squeeze and Excitation (SE)-YOLOv3, a mask detector with relatively balanced effectiveness and efficiency. We integrated the attention mechanism by introducing the SE block into Darknet53 to obtain the relationships among channels so that the network can focus more on the important feature. We adopted GIoUloss, which can better describe the spatial difference between predicted and ground truth boxes to improve the stability of bounding box regression. Focal loss was utilized for solving the extreme foreground-background class imbalance. Besides, we performed corresponding image augmentation techniques to further improve the robustness of the model on the specific task. Experimental results showed that SE-YOLOv3 outperformed YOLOv3 and other state-of-the-art detectors on PWMFD and achieved a higher 8.6% mAP compared to YOLOv3 while having a comparable detection speed. Full article
(This article belongs to the Section Computer Science & Engineering)
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13 pages, 2243 KiB  
Article
Semi-Automatic Guidance vs. Manual Guidance in Agriculture: A Comparison of Work Performance in Wheat Sowing
by Antonio Scarfone, Rodolfo Picchio, Angelo del Giudice, Francesco Latterini, Paolo Mattei, Enrico Santangelo and Alberto Assirelli
Electronics 2021, 10(7), 825; https://doi.org/10.3390/electronics10070825 - 31 Mar 2021
Cited by 19 | Viewed by 5141
Abstract
The use of digital systems in precision agriculture is becoming more and more attractive for farmers at every level. A few years ago, the use of these technologies was limited to large farms, due to the considerable income needed to amortize the large [...] Read more.
The use of digital systems in precision agriculture is becoming more and more attractive for farmers at every level. A few years ago, the use of these technologies was limited to large farms, due to the considerable income needed to amortize the large investment required. Although this technology has now become more affordable, there is a lack of scientific data directed to demonstrate how these systems are able to determine quantifiable advantages for farmers. Thus, the transition towards precision agriculture is still very slow. This issue is not just negatively affecting the agriculture economy, but it is also slowing down potential environmental benefits that may result from it. The starting point of precision agriculture can be considered as the introduction of satellite tractor guidance. For instance, with semi-automatic and automatic tractor guidance, farmers can profit from more accuracy and higher machine performance during several farm operations such as plowing, harrowing, sowing, and fertilising. The goal of this study is to compare semi-automatic guidance with manual guidance in wheat sowing, evaluating parameters such as machine performance, seed supply and operational costs of both the configurations. Full article
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15 pages, 1729 KiB  
Article
On the Sampling of the Fresnel Field Intensity over a Full Angular Sector
by Rocco Pierri and Raffaele Moretta
Electronics 2021, 10(7), 832; https://doi.org/10.3390/electronics10070832 - 31 Mar 2021
Cited by 7 | Viewed by 2679
Abstract
In this article, the question of how to sample the square amplitude of the radiated field in the framework of phaseless antenna diagnostics is addressed. In particular, the goal of the article is to find a discretization scheme that exploits a non-redundant number [...] Read more.
In this article, the question of how to sample the square amplitude of the radiated field in the framework of phaseless antenna diagnostics is addressed. In particular, the goal of the article is to find a discretization scheme that exploits a non-redundant number of samples and returns a discrete model whose mathematical properties are similar to those of the continuous one. To this end, at first, the lifting technique is used to obtain a linear representation of the square amplitude of the radiated field. Later, a discretization scheme based on the Shannon sampling theorem is exploited to discretize the continuous model. More in detail, the kernel of the related eigenvalue problem is first recast as the Fourier transform of a window function, and after, it is evaluated. Finally, the sampling theory approach is applied to obtain a discrete model whose singular values approximate all the relevant singular values of the continuous linear model. The study refers to a strip source whose square magnitude of the radiated field is observed in the Fresnel zone over a 2D observation domain. Full article
(This article belongs to the Special Issue Photonic and Microwave Sensing Developments and Applications)
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16 pages, 6257 KiB  
Article
A Gated Oscillator Clock and Data Recovery Circuit for Nanowatt Wake-Up and Data Receivers
by Matteo D’Addato, Alessia M. Elgani, Luca Perilli, Eleonora Franchi Scarselli, Antonio Gnudi, Roberto Canegallo and Giulio Ricotti
Electronics 2021, 10(7), 780; https://doi.org/10.3390/electronics10070780 - 25 Mar 2021
Cited by 3 | Viewed by 4005
Abstract
This article presents a data-startable baseband logic featuring a gated oscillator clock and data recovery (GO-CDR) circuit for nanowatt wake-up and data receivers (WuRxs). At each data transition, the phase misalignment between the data coming from the analog front-end (AFE) and the clock [...] Read more.
This article presents a data-startable baseband logic featuring a gated oscillator clock and data recovery (GO-CDR) circuit for nanowatt wake-up and data receivers (WuRxs). At each data transition, the phase misalignment between the data coming from the analog front-end (AFE) and the clock is cleared by the GO-CDR circuit, thus allowing the reception of long data streams. Any free-running frequency mismatch between the GO and the bitrate does not limit the number of receivable bits, but only the maximum number of equal consecutive bits (Nm). To overcome this limitation, the proposed system includes a frequency calibration circuit, which reduces the frequency mismatch to ±0.5%, thus enabling the WuRx to be used with different encoding techniques up to Nm = 100. A full WuRx prototype, including an always-on clockless AFE operating in subthreshold, was fabricated with STMicroelectronics 90 nm BCD technology. The WuRx is supplied with 0.6 V, and the power consumption, excluding the calibration circuit, is 12.8 nW during the rest state and 17 nW at a 1 kbps data rate. With a 1 kbps On-Off Keying (OOK) modulated input and −35 dBm of input RF power after the input matching network (IMN), a 10−3 missed detection rate with a 0 bit error tolerance is measured, transmitting 63 bit packets with the Nm ranging from 1 to 63. The total sensitivity, including the estimated IMN gain at 100 MHz and 433 MHz, is −59.8 dBm and −52.3 dBm, respectively. In comparison with an ideal CDR, the degradation of the sensitivity due to the GO-CDR is 1.25 dBm. False alarm rate measurements lasting 24 h revealed zero overall false wake-ups. Full article
(This article belongs to the Special Issue Energy Efficient Circuit Design Techniques for Low Power Systems)
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16 pages, 7162 KiB  
Article
Modeling Small UAV Micro-Doppler Signature Using Millimeter-Wave FMCW Radar
by Marco Passafiume, Neda Rojhani, Giovanni Collodi and Alessandro Cidronali
Electronics 2021, 10(6), 747; https://doi.org/10.3390/electronics10060747 - 22 Mar 2021
Cited by 28 | Viewed by 6938
Abstract
With the increase in small unmanned aerial vehicle (UAV) applications in several technology areas, detection and small UAVs classification have become of interest. To cope with small radar cross-sections (RCSs), slow-flying speeds, and low flying altitudes, the micro-Doppler signature provides some of the [...] Read more.
With the increase in small unmanned aerial vehicle (UAV) applications in several technology areas, detection and small UAVs classification have become of interest. To cope with small radar cross-sections (RCSs), slow-flying speeds, and low flying altitudes, the micro-Doppler signature provides some of the most distinctive information to identify and classify targets in many radar systems. In this paper, we introduce an effective model for the micro-Doppler effect that is suitable for frequency-modulated continuous-wave (FMCW) radar applications, and exploit it to investigate UAV signatures. The latter depends on the number of UAV motors, which are considered vibrational sources, and their rotation speed. To demonstrate the reliability of the proposed model, it is used to build simulated FMCW radar images, which are compared with experimental data acquired by a 77 GHz FMCW multiple-input multiple-output (MIMO) cost-effective automotive radar platform. The experimental results confirm the model’s ability to estimate the class of the UAV, namely its number of motors, in different operative scenarios. In addition, the experimental results show that the motors rotation speed does not imprint a significant signature on the classification of the UAV; thus, the estimation of the number of motors represents the only viable parameter for small UAV classification using the micro-Doppler effect. Full article
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13 pages, 4575 KiB  
Article
Objective Assessment of Walking Impairments in Myotonic Dystrophy by Means of a Wearable Technology and a Novel Severity Index
by Giovanni Saggio, Alessandro Manoni, Vito Errico, Erica Frezza, Ivan Mazzetta, Rosario Rota, Roberto Massa and Fernanda Irrera
Electronics 2021, 10(6), 708; https://doi.org/10.3390/electronics10060708 - 17 Mar 2021
Cited by 1 | Viewed by 2495
Abstract
Myotonic dystrophy type 1 (DM1) is a genetic inherited autosomal dominant disease characterized by multisystem involvement, including muscle, heart, brain, eye, and endocrine system. Although several methods are available to evaluate muscle strength, endurance, and dexterity, there are no validated outcome measures aimed [...] Read more.
Myotonic dystrophy type 1 (DM1) is a genetic inherited autosomal dominant disease characterized by multisystem involvement, including muscle, heart, brain, eye, and endocrine system. Although several methods are available to evaluate muscle strength, endurance, and dexterity, there are no validated outcome measures aimed at objectively evaluating qualitative and quantitative gait alterations. Advantageously, wearable sensing technology has been successfully adopted in objectifying the assessment of motor disabilities in different medical occurrences, so that here we consider the adoption of such technology specifically for DM1. In particular, we measured motor tasks through inertial measurement units on a cohort of 13 DM1 patients and 11 healthy control counterparts. The motor tasks consisted of 16 meters of walking both at a comfortable speed and fast pace. Measured data consisted of plantar-flexion and dorsi-flexion angles assumed by both ankles, so to objectively evidence the footdrop behavior of the DM1 disease, and to define a novel severity index, termed SI-Norm2, to rate the grade of walking impairments. According to the obtained results, our approach could be useful for a more precise stratification of DM1 patients, providing a new tool for a personalized rehabilitation approach. Full article
(This article belongs to the Special Issue Wearable Electronics for Assessing Human Motor (dis)Abilities)
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23 pages, 92419 KiB  
Article
Virtual Scenario Simulation and Modeling Framework in Autonomous Driving Simulators
by Mingyun Wen, Jisun Park, Yunsick Sung, Yong Woon Park and Kyungeun Cho
Electronics 2021, 10(6), 694; https://doi.org/10.3390/electronics10060694 - 16 Mar 2021
Cited by 12 | Viewed by 5187
Abstract
Recently, virtual environment-based techniques to train sensor-based autonomous driving models have been widely employed due to their efficiency. However, a simulated virtual environment is required to be highly similar to its real-world counterpart to ensure the applicability of such models to actual autonomous [...] Read more.
Recently, virtual environment-based techniques to train sensor-based autonomous driving models have been widely employed due to their efficiency. However, a simulated virtual environment is required to be highly similar to its real-world counterpart to ensure the applicability of such models to actual autonomous vehicles. Though advances in hardware and three-dimensional graphics engine technology have enabled the creation of realistic virtual driving environments, the myriad of scenarios occurring in the real world can only be simulated up to a limited extent. In this study, a scenario simulation and modeling framework that simulates the behavior of objects that may be encountered while driving is proposed to address this problem. This framework maximizes the number of scenarios, their types, and the driving experience in a virtual environment. Furthermore, a simulator was implemented and employed to evaluate the performance of the proposed framework. Full article
(This article belongs to the Special Issue AI-Based Autonomous Driving System)
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20 pages, 9218 KiB  
Article
Toward an Advanced Human Monitoring System Based on a Smart Body Area Network for Industry Use
by Kento Takabayashi, Hirokazu Tanaka and Katsumi Sakakibara
Electronics 2021, 10(6), 688; https://doi.org/10.3390/electronics10060688 - 15 Mar 2021
Cited by 9 | Viewed by 2788
Abstract
This research provides a study on a smart body area network (SmartBAN) physical layer (PHY), as an of the Internet of medical things (IoMT) technology, for an advanced human monitoring system in industrial use. The SmartBAN provides a new PHY and a medium [...] Read more.
This research provides a study on a smart body area network (SmartBAN) physical layer (PHY), as an of the Internet of medical things (IoMT) technology, for an advanced human monitoring system in industrial use. The SmartBAN provides a new PHY and a medium access control (MAC) layer, improving its performance and providing very low-latency emergency information transmission with low energy consumption compared with other wireless body area network (WBAN) standards. On the other hand, IoMT applications are expected to become more advanced with smarter wearable devices, such as augmented reality-based human monitoring and work support in a factory. Therefore, it is possible to develop more advanced human monitoring systems for industrial use by combining the SmartBAN with multimedia devices. However, the SmartBAN PHY is not designed to transmit multimedia information such as audio and video. To address this issue, multilevel phase shift keying (PSK) modulation is applied to the SmartBAN PHY, and the symbol rate is improved by setting the roll-off rate appropriately to realize the system. The numerical results show that a sufficient link budget, receiver sensitivity and fade margin were obtained even when those approaches were applied to the SmartBAN PHY. The results indicate that these techniques are required for high-quality audio or video transmission, as well as vital sign data transmission, in a SmartBAN. Full article
(This article belongs to the Special Issue Smart Bioelectronics and Wearable Systems)
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26 pages, 818 KiB  
Article
Role of Wide Bandgap Materials in Power Electronics for Smart Grids Applications
by Javier Ballestín-Fuertes, Jesús Muñoz-Cruzado-Alba, José F. Sanz-Osorio and Erika Laporta-Puyal
Electronics 2021, 10(6), 677; https://doi.org/10.3390/electronics10060677 - 13 Mar 2021
Cited by 81 | Viewed by 9649
Abstract
At present, the energy transition is leading to the replacement of large thermal power plants by distributed renewable generation and the introduction of different assets. Consequently, a massive deployment of power electronics is expected. A particular case will be the devices destined for [...] Read more.
At present, the energy transition is leading to the replacement of large thermal power plants by distributed renewable generation and the introduction of different assets. Consequently, a massive deployment of power electronics is expected. A particular case will be the devices destined for urban environments and smart grids. Indeed, such applications have some features that make wide bandgap (WBG) materials particularly relevant. This paper analyzes the most important features expected by future smart applications from which the characteristics that their power semiconductors must perform can be deduced. Following, not only the characteristics and theoretical limits of wide bandgap materials already available on the market (SiC and GaN) have been analyzed, but also those currently being researched as promising future alternatives (Ga2O3, AlN, etc.). Finally, wide bandgap materials are compared under the needs determined by the smart applications, determining the best suited to them. We conclude that, although SiC and GaN are currently the only WBG materials available on the semiconductor portfolio, they may be displaced by others such as Ga2O3 in the near future. Full article
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13 pages, 4936 KiB  
Article
A 40-nm CMOS Piezoelectric Energy Harvesting IC for Wearable Biomedical Applications
by Chua-Chin Wang, Lean Karlo S. Tolentino, Pin-Chuan Chen, John Richard E. Hizon, Chung-Kun Yen, Cheng-Tang Pan and Ya-Hsin Hsueh
Electronics 2021, 10(6), 649; https://doi.org/10.3390/electronics10060649 - 11 Mar 2021
Cited by 6 | Viewed by 4275
Abstract
This investigation presents an energy harvesting IC (integrated circuit) for piezoelectric materials as a substitute for battery of a wearable biomedical device. It employs a voltage multiplier as first stage which uses water bucket fountain approach to boost the very low voltage generated [...] Read more.
This investigation presents an energy harvesting IC (integrated circuit) for piezoelectric materials as a substitute for battery of a wearable biomedical device. It employs a voltage multiplier as first stage which uses water bucket fountain approach to boost the very low voltage generated by the piezoelectric. The boosted voltage was further improved by the boost DC/DC converter which follows a predefined timing control directed by the digital logic for the said converter to be operated efficiently. TSMC 40-nm CMOS process was used for implementation and fabrication of the energy harvesting IC. The chip’s core has an area of 0.013 mm2. With an output of 1 V which is enough to supply the wearable biomedical devices, it exhibited the highest pump gain and accommodated the lowest piezoelectric generated voltage among recent related works. Full article
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28 pages, 2015 KiB  
Article
State Estimation for Cooperative Lateral Vehicle Following Using Vehicle-to-Vehicle Communication
by Wouter Schinkel, Tom van der Sande and Henk Nijmeijer
Electronics 2021, 10(6), 651; https://doi.org/10.3390/electronics10060651 - 11 Mar 2021
Cited by 14 | Viewed by 3143
Abstract
A cooperative state estimation framework for automated vehicle applications is presented and demonstrated via simulations, the estimation framework is used to estimate the state of a lead and following vehicle simultaneously. Recent developments in the field of cooperative driving require novel techniques to [...] Read more.
A cooperative state estimation framework for automated vehicle applications is presented and demonstrated via simulations, the estimation framework is used to estimate the state of a lead and following vehicle simultaneously. Recent developments in the field of cooperative driving require novel techniques to ensure accurate and stable vehicle following behavior. Control schemes for the cooperative control of longitudinal and lateral vehicle dynamics generally require vehicle state information about the lead vehicle, which in some cases cannot be accurately measured. Including vehicle-to-vehicle communication in the state estimation process can provide the required input signals for the practical implementation of cooperative control schemes. This study is focused on demonstrating the benefits of using vehicle-to-vehicle communication in the state estimation of a lead and following vehicle via simulations. The state estimator, which uses a cascaded Kalman filtering process, takes the operating frequencies of different sensors into account in the estimation process. Simulation results of three different driving scenarios demonstrate the benefits of using vehicle-to-vehicle communication as well as the attenuation of measurement noise. Furthermore, in contrast to relying on low frequency measurement data for the input signals of cooperative control schemes, the state estimator provides a state estimate at every sample. Full article
(This article belongs to the Special Issue Autonomous Vehicles Technology)
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22 pages, 1515 KiB  
Article
When Data Fly: An Open Data Trading System in Vehicular Ad Hoc Networks
by Markus Lücking, Felix Kretzer, Niclas Kannengießer, Michael Beigl, Ali Sunyaev and Wilhelm Stork
Electronics 2021, 10(6), 654; https://doi.org/10.3390/electronics10060654 - 11 Mar 2021
Cited by 7 | Viewed by 5052
Abstract
Communication between vehicles and their environment (i.e., vehicle-to-everything or V2X communication) in vehicular ad hoc networks (VANETs) has become of particular importance for smart cities. However, economic challenges, such as the cost incurred by data sharing (e.g., due to power consumption), hinder the [...] Read more.
Communication between vehicles and their environment (i.e., vehicle-to-everything or V2X communication) in vehicular ad hoc networks (VANETs) has become of particular importance for smart cities. However, economic challenges, such as the cost incurred by data sharing (e.g., due to power consumption), hinder the integration of data sharing in open systems into smart city applications, such as dynamic environmental zones. Moving from open data sharing to open data trading can address the economic challenges and incentivize vehicle drivers to share their data. In this context, integrating distributed ledger technology (DLT) into open systems for data trading is promising for reducing the transaction cost of payments in data trading, avoiding dependencies on third parties, and guaranteeing openness. However, because the integration of DLT conflicts with the short available communication time between fast moving objects in VANETs, it remains unclear how open data trading in VANETs using DLT should be designed to be viable. In this work, we present a system design for data trading in VANETs using DLT. We measure the required communication time for data trading between a vehicle and a roadside unit in a real scenario and estimate the associated cost. Our results show that the proposed system design is technically feasible and economically viable. Full article
(This article belongs to the Special Issue Blockchain-Based Technology for Mobile Application)
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9 pages, 3800 KiB  
Article
AlGaN Channel High Electron Mobility Transistors with Regrown Ohmic Contacts
by Idriss Abid, Jash Mehta, Yvon Cordier, Joff Derluyn, Stefan Degroote, Hideto Miyake and Farid Medjdoub
Electronics 2021, 10(6), 635; https://doi.org/10.3390/electronics10060635 - 10 Mar 2021
Cited by 40 | Viewed by 6968
Abstract
High power electronics using wide bandgap materials are maturing rapidly, and significant market growth is expected in a near future. Ultra wide bandgap materials, which have an even larger bandgap than GaN (3.4 eV), represent an attractive choice of materials to further push [...] Read more.
High power electronics using wide bandgap materials are maturing rapidly, and significant market growth is expected in a near future. Ultra wide bandgap materials, which have an even larger bandgap than GaN (3.4 eV), represent an attractive choice of materials to further push the performance limits of power devices. In this work, we report on the fabrication of AlN/AlGaN/AlN high-electron mobility transistors (HEMTs) using 50% Al-content on the AlGaN channel, which has a much wider bandgap than the commonly used GaN channel. The structure was grown by metalorganic chemical vapor deposition (MOCVD) on AlN/sapphire templates. A buffer breakdown field as high as 5.5 MV/cm was reported for short contact distances. Furthermore, transistors have been successfully fabricated on this heterostructure, with low leakage current and low on-resistance. A remarkable three-terminal breakdown voltage above 4 kV with an off-state leakage current below 1 μA/mm was achieved. A regrown ohmic contact was used to reduce the source/drain ohmic contact resistance, yielding a drain current density of about 0.1 A/mm. Full article
(This article belongs to the Special Issue Advances in Ultra-Wide Bandgap Devices)
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19 pages, 577 KiB  
Article
Efficient Design Strategy for Optimizing the Settling Time in Three-Stage Amplifiers Including Small- and Large-Signal Behavior
by Gianluca Giustolisi and Gaetano Palumbo
Electronics 2021, 10(5), 612; https://doi.org/10.3390/electronics10050612 - 6 Mar 2021
Cited by 7 | Viewed by 3035
Abstract
An analytical criterion for the optimization of the small-signal settling time in three-stage amplifiers is carried out. The criterion is based on making equal the two exponential decays of the step response. Including slew-rate effects, a useful design strategy for the design of [...] Read more.
An analytical criterion for the optimization of the small-signal settling time in three-stage amplifiers is carried out. The criterion is based on making equal the two exponential decays of the step response. Including slew-rate effects, a useful design strategy for the design of three-stage operational transconductance amplifier is provided. Extensive time-domain simulations on a transistor-level design in a 65-nm CMOS process confirm the validity of the proposed approach. Full article
(This article belongs to the Section Microelectronics)
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15 pages, 5589 KiB  
Article
A 2.53 NEF 8-bit 10 kS/s 0.5 μm CMOS Neural Recording Read-Out Circuit with High Linearity for Neuromodulation Implants
by Nishat Tarannum Tasneem and Ifana Mahbub
Electronics 2021, 10(5), 590; https://doi.org/10.3390/electronics10050590 - 3 Mar 2021
Cited by 11 | Viewed by 3722
Abstract
This paper presents a power-efficient complementary metal-oxide-semiconductor (CMOS) neural signal-recording read-out circuit for multichannel neuromodulation implants. The system includes a neural amplifier and a successive approximation register analog-to-digital converter (SAR-ADC) for recording and digitizing neural signal data to transmit to a remote receiver. [...] Read more.
This paper presents a power-efficient complementary metal-oxide-semiconductor (CMOS) neural signal-recording read-out circuit for multichannel neuromodulation implants. The system includes a neural amplifier and a successive approximation register analog-to-digital converter (SAR-ADC) for recording and digitizing neural signal data to transmit to a remote receiver. The synthetic neural signal is generated using a LabVIEW myDAQ device and processed through a LabVIEW GUI. The read-out circuit is designed and fabricated in the standard 0.5 μμm CMOS process. The proposed amplifier uses a fully differential two-stage topology with a reconfigurable capacitive-resistive feedback network. The amplifier achieves 49.26 dB and 60.53 dB gain within the frequency bandwidth of 0.57–301 Hz and 0.27–12.9 kHz to record the local field potentials (LFPs) and the action potentials (APs), respectively. The amplifier maintains a noise–power tradeoff by reducing the noise efficiency factor (NEF) to 2.53. The capacitors are manually laid out using the common-centroid placement technique, which increases the linearity of the ADC. The SAR-ADC achieves a signal-to-noise ratio (SNR) of 45.8 dB, with a resolution of 8 bits. The ADC exhibits an effective number of bits of 7.32 at a low sampling rate of 10 ksamples/s. The total power consumption of the chip is 26.02 μμW, which makes it highly suitable for a multi-channel neural signal recording system. Full article
(This article belongs to the Section Circuit and Signal Processing)
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13 pages, 700 KiB  
Article
Analytical Study of Periodic Restricted Access Window Mechanism for Short Slots
by Elizaveta Zazhigina, Ruslan Yusupov, Evgeny Khorov and Andrey Lyakhov
Electronics 2021, 10(5), 549; https://doi.org/10.3390/electronics10050549 - 26 Feb 2021
Cited by 8 | Viewed by 2359
Abstract
The tremendous number of devices involved in the Internet of Things is bringing new challenges to wireless networking. The more devices that transmit in a wireless network, the higher the contention for the channel. The novel Wi-Fi HaLow standard introduces a new channel [...] Read more.
The tremendous number of devices involved in the Internet of Things is bringing new challenges to wireless networking. The more devices that transmit in a wireless network, the higher the contention for the channel. The novel Wi-Fi HaLow standard introduces a new channel access mechanism called the Periodic Restricted Access Window (PRAW), which aims to reduce this contention. With this mechanism, an access point can define a series of time intervals during which only a predefined group of stations can transmit data while the other stations are forbidden to access the channel. Unfortunately, the standard does not suggest how to configure the PRAW mechanism according to scenario-specific requirements and restrictions. Many Internet of Things scenarios require the fast and low energy consumption delivery of measurement data from wireless sensors while saving channel resources for other stations that transmit, for example, multimedia traffic. Therefore, this paper studies the problem of the minimization of the channel timeshare consumed by the PRAW with restrictions on the average delay and power consumption. To solve the problem and configure the PRAW optimally, a novel analytical model is developed. The key feature of the model is the consideration of the case of short PRAW slots that allow the computational complexity to be reduced and high accuracy to be achieved. These properties make the model suitable for implementation in real devices. Full article
(This article belongs to the Special Issue Wireless Network Protocols and Performance Evaluation)
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16 pages, 2662 KiB  
Review
Relations between Electronics, Artificial Intelligence and Information Society through Information Society Rules
by Matjaž Gams and Tine Kolenik
Electronics 2021, 10(4), 514; https://doi.org/10.3390/electronics10040514 - 22 Feb 2021
Cited by 49 | Viewed by 6946
Abstract
This paper presents relations between information society (IS), electronics and artificial intelligence (AI) mainly through twenty-four IS laws. The laws not only make up a novel collection, currently non-existing in the literature, but they also highlight the core boosting mechanism for the progress [...] Read more.
This paper presents relations between information society (IS), electronics and artificial intelligence (AI) mainly through twenty-four IS laws. The laws not only make up a novel collection, currently non-existing in the literature, but they also highlight the core boosting mechanism for the progress of what is called the information society and AI. The laws mainly describe the exponential growth in a particular field, be it the processing, storage or transmission capabilities of electronic devices. Other rules describe the relations to production prices and human interaction. Overall, the IS laws illustrate the most recent and most vibrant part of human history based on the unprecedented growth of device capabilities spurred by human innovation and ingenuity. Although there are signs of stalling, at the same time there are still many ways to prolong the fascinating progress of electronics that stimulates the field of artificial intelligence. There are constant leaps in new areas, such as the perception of real-world signals, where AI is already occasionally exceeding human capabilities and will do so even more in the future. In some areas where AI is presumed to be incapable of performing even at a modest level, such as the production of art or programming software, AI is making progress that can sometimes reflect true human skills. Maybe it is time for AI to boost the progress of electronics in return. Full article
(This article belongs to the Special Issue Artificial Intelligence and Ambient Intelligence)
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20 pages, 11932 KiB  
Article
An Efficient FPGA Implementation of Richardson-Lucy Deconvolution Algorithm for Hyperspectral Images
by Karine Avagian and Milica Orlandić
Electronics 2021, 10(4), 504; https://doi.org/10.3390/electronics10040504 - 21 Feb 2021
Cited by 5 | Viewed by 4119
Abstract
This paper proposes an implementation of a Richardson-Lucy (RL) deconvolution method to reduce the spatial degradation in hyperspectral images during the image acquisition process. The degradation, modeled by convolution with a point spread function (PSF), is reduced by applying both standard and accelerated [...] Read more.
This paper proposes an implementation of a Richardson-Lucy (RL) deconvolution method to reduce the spatial degradation in hyperspectral images during the image acquisition process. The degradation, modeled by convolution with a point spread function (PSF), is reduced by applying both standard and accelerated RLdeconvolution algorithms on the individual images in spectral bands. Boundary conditions are introduced to maintain a constant image size without distorting the estimated image boundaries. The RL deconvolution algorithm is implemented on a field-programmable gate array (FPGA)-based Xilinx Zynq-7020 System-on-Chip (SoC). The proposed architecture is parameterized with respect to the image size and configurable with respect to the algorithm variant, the number of iterations, and the kernel size by setting the dedicated configuration registers. A speed-up by factors of 61 and 21 are reported compared to software-only and FPGA-based state-of-the-art implementations, respectively. Full article
(This article belongs to the Special Issue Hardware Architectures for Real Time Image Processing)
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16 pages, 7001 KiB  
Article
Optimization of a 3D-Printed Permanent Magnet Coupling Using Genetic Algorithm and Taguchi Method
by Ekaterina Andriushchenko, Ants Kallaste, Anouar Belahcen, Toomas Vaimann, Anton Rassõlkin, Hamidreza Heidari and Hans Tiismus
Electronics 2021, 10(4), 494; https://doi.org/10.3390/electronics10040494 - 20 Feb 2021
Cited by 16 | Viewed by 3537
Abstract
In recent decades, the genetic algorithm (GA) has been extensively used in the design optimization of electromagnetic devices. Despite the great merits possessed by the GA, its processing procedure is highly time-consuming. On the contrary, the widely applied Taguchi optimization method is faster [...] Read more.
In recent decades, the genetic algorithm (GA) has been extensively used in the design optimization of electromagnetic devices. Despite the great merits possessed by the GA, its processing procedure is highly time-consuming. On the contrary, the widely applied Taguchi optimization method is faster with comparable effectiveness in certain optimization problems. This study explores the abilities of both methods within the optimization of a permanent magnet coupling, where the optimization objectives are the minimization of coupling volume and maximization of transmitted torque. The optimal geometry of the coupling and the obtained characteristics achieved by both methods are nearly identical. The magnetic torque density is enhanced by more than 20%, while the volume is reduced by 17%. Yet, the Taguchi method is found to be more time-efficient and effective within the considered optimization problem. Thanks to the additive manufacturing techniques, the initial design and the sophisticated geometry of the Taguchi optimal designs are precisely fabricated. The performances of the coupling designs are validated using an experimental setup. Full article
(This article belongs to the Special Issue Robust Design Optimization of Electrical Machines and Devices)
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20 pages, 4541 KiB  
Article
Deep Learning Methods for Classification of Certain Abnormalities in Echocardiography
by Imayanmosha Wahlang, Arnab Kumar Maji, Goutam Saha, Prasun Chakrabarti, Michal Jasinski, Zbigniew Leonowicz and Elzbieta Jasinska
Electronics 2021, 10(4), 495; https://doi.org/10.3390/electronics10040495 - 20 Feb 2021
Cited by 33 | Viewed by 5706
Abstract
This article experiments with deep learning methodologies in echocardiogram (echo), a promising and vigorously researched technique in the preponderance field. This paper involves two different kinds of classification in the echo. Firstly, classification into normal (absence of abnormalities) or abnormal (presence of abnormalities) [...] Read more.
This article experiments with deep learning methodologies in echocardiogram (echo), a promising and vigorously researched technique in the preponderance field. This paper involves two different kinds of classification in the echo. Firstly, classification into normal (absence of abnormalities) or abnormal (presence of abnormalities) has been done, using 2D echo images, 3D Doppler images, and videographic images. Secondly, based on different types of regurgitation, namely, Mitral Regurgitation (MR), Aortic Regurgitation (AR), Tricuspid Regurgitation (TR), and a combination of the three types of regurgitation are classified using videographic echo images. Two deep-learning methodologies are used for these purposes, a Recurrent Neural Network (RNN) based methodology (Long Short Term Memory (LSTM)) and an Autoencoder based methodology (Variational AutoEncoder (VAE)). The use of videographic images distinguished this work from the existing work using SVM (Support Vector Machine) and also application of deep-learning methodologies is the first of many in this particular field. It was found that deep-learning methodologies perform better than SVM methodology in normal or abnormal classification. Overall, VAE performs better in 2D and 3D Doppler images (static images) while LSTM performs better in the case of videographic images. Full article
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12 pages, 5423 KiB  
Article
Performance Evaluation of LoRa 920 MHz Frequency Band in a Hilly Forested Area
by Bilguunmaa Myagmardulam, Ryu Miura, Fumie Ono, Toshinori Kagawa, Lin Shan, Tadachika Nakayama, Fumihide Kojima and Baasandash Choijil
Electronics 2021, 10(4), 502; https://doi.org/10.3390/electronics10040502 - 20 Feb 2021
Cited by 18 | Viewed by 5347
Abstract
Long-range (LoRa) wireless communication technology has been widely used in many Internet-of-Things (IoT) applications in industry and academia. Radio wave propagation characteristics in forested areas are important to ensure communication quality in forest IoT applications. In this study, 920 MHz band propagation characteristics [...] Read more.
Long-range (LoRa) wireless communication technology has been widely used in many Internet-of-Things (IoT) applications in industry and academia. Radio wave propagation characteristics in forested areas are important to ensure communication quality in forest IoT applications. In this study, 920 MHz band propagation characteristics in forested areas and tree canopy openness were investigated in the Takakuma experimental forest in Kagoshima, Japan. The aim was to evaluate the performance of the LoRa 920 MHz band with spreading factor (SF12) in a forested hilly area. The received signal strength indicator (RSSI) was measured as a function of the distance between the transmitter antenna and ground station (GS). To illustrate the effect of canopy openness on radio wave propagation, sky view factor (SVF) and a forest canopy height model were considered at each location of a successfully received RSSI. A positive correlation was found between the RSSI and SVF. It was found that between the GS and transmitter antenna, if the canopy height is above 23 m, the signal diffracted and RSSI fell to −120 to −127 dBm, so the presence of the obstacle height should be considered. Further research is needed to clarify the detailed tree density between the transmitter and ground station to propose an optimal propagation model for a forested environment. Full article
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23 pages, 8107 KiB  
Article
Ensemble-Based Classification Using Neural Networks and Machine Learning Models for Windows PE Malware Detection
by Robertas Damaševičius, Algimantas Venčkauskas, Jevgenijus Toldinas and Šarūnas Grigaliūnas
Electronics 2021, 10(4), 485; https://doi.org/10.3390/electronics10040485 - 18 Feb 2021
Cited by 87 | Viewed by 9913
Abstract
The security of information is among the greatest challenges facing organizations and institutions. Cybercrime has risen in frequency and magnitude in recent years, with new ways to steal, change and destroy information or disable information systems appearing every day. Among the types of [...] Read more.
The security of information is among the greatest challenges facing organizations and institutions. Cybercrime has risen in frequency and magnitude in recent years, with new ways to steal, change and destroy information or disable information systems appearing every day. Among the types of penetration into the information systems where confidential information is processed is malware. An attacker injects malware into a computer system, after which he has full or partial access to critical information in the information system. This paper proposes an ensemble classification-based methodology for malware detection. The first-stage classification is performed by a stacked ensemble of dense (fully connected) and convolutional neural networks (CNN), while the final stage classification is performed by a meta-learner. For a meta-learner, we explore and compare 14 classifiers. For a baseline comparison, 13 machine learning methods are used: K-Nearest Neighbors, Linear Support Vector Machine (SVM), Radial basis function (RBF) SVM, Random Forest, AdaBoost, Decision Tree, ExtraTrees, Linear Discriminant Analysis, Logistic, Neural Net, Passive Classifier, Ridge Classifier and Stochastic Gradient Descent classifier. We present the results of experiments performed on the Classification of Malware with PE headers (ClaMP) dataset. The best performance is achieved by an ensemble of five dense and CNN neural networks, and the ExtraTrees classifier as a meta-learner. Full article
(This article belongs to the Special Issue High Accuracy Detection of Mobile Malware Using Machine Learning)
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11 pages, 2607 KiB  
Article
Dynamic Analysis of the Switched-Inductor Buck-Boost Converter Based on the Memristor
by Yan Yang, Dongdong Li and Dongqing Wang
Electronics 2021, 10(4), 452; https://doi.org/10.3390/electronics10040452 - 11 Feb 2021
Cited by 20 | Viewed by 4082
Abstract
The direct current (DC)–DC converter presents abundant nonlinear phenomena, such as periodic bifurcation and chaotic motion, under certain conditions. For a switched-inductor buck-boost (SIBB) converter with the memristive load, this paper constructs its state equation model under two operating statuses, investigates its chaotic [...] Read more.
The direct current (DC)–DC converter presents abundant nonlinear phenomena, such as periodic bifurcation and chaotic motion, under certain conditions. For a switched-inductor buck-boost (SIBB) converter with the memristive load, this paper constructs its state equation model under two operating statuses, investigates its chaotic dynamic characteristics, and draws and analyzes the bifurcation diagrams of the inductive current and phase portraits, under some parameter changing by the MATLAB simulation based on the state equation. Then, by applying certain minor perturbations to parameters, the chaotic phenomenon suppression method is explored by controlling peak current in continuous current mode (CCM) to keep the converter run normally. Finally, the power simulation (PSIM) verifies that the waveforms and the phase portraits controlling the corresponding parameters are consistent with those of the MATLAB simulation. Full article
(This article belongs to the Special Issue Recent Advances in Chaotic Systems and Their Security Applications)
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26 pages, 1484 KiB  
Article
Time Efficient Unmanned Aircraft Systems Deployment in Disaster Scenarios Using Clustering Methods and a Set Cover Approach
by Donald Mahoro Ntwari, Daniel Gutierrez-Reina, Sergio Luis Toral Marín and Hissam Tawfik
Electronics 2021, 10(4), 422; https://doi.org/10.3390/electronics10040422 - 9 Feb 2021
Cited by 3 | Viewed by 2538
Abstract
Unmanned aircraft, which are more commonly known as drones, are nowadays extensively used in an ever increasing set of applications. In a wider system, the aircraft are usually associated to additional elements such as ground-based controllers. Furthermore, when these components form a network [...] Read more.
Unmanned aircraft, which are more commonly known as drones, are nowadays extensively used in an ever increasing set of applications. In a wider system, the aircraft are usually associated to additional elements such as ground-based controllers. Furthermore, when these components form a network of elements that can communicate, the system is said to form an Unmanned Aircraft System (UAS). This system is particularly effective when the aircraft within are organized into swarms with sets of objectives to accomplish. The extensive use of swarms into UASs is more and more exploited nowadays due to the decreasing cost of those aircraft. In the present work we are interested in a particular application of UASs, namely their deployment in disaster scenarios for communications services provision to targets on the ground. These ground targets, however, are not part of the UASs and should not be confused with ground-based controllers. The present work does not only focus on coverage for ground targets but also on a guaranteed minimum number of covers for each target, which is called the redundancy requirement. The research work also ensures that the deployed UAS forms a unique connected component so that a steady stream of communication is kept with the targets to cover. Research work similar to the present perform the initial deployment of their aircraft in a different manner, either randomly, based on a predetermined grid formation, or using other elaborated methods. This work proposes a new solution based on the use of clustering algorithms, combined to a design of the problem formulated as a set cover optimization model. The clustering phase is used to discretize the search space and ease the optimization phase by locating regions of interest, and then a further procedure is applied, only when needed, to reconnect scattered connected components and guarantee connectivity in the networks. This way of doing it has achieved a deployment of UASs with maximum coverage for all targets, a guaranteed minimum number of covers for each of them, and results in a competitive computation time. The latter also allowed for more scalability by extending the tests to very large input instances. Full article
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13 pages, 800 KiB  
Article
A Machine Learning Approach for Anomaly Detection in Industrial Control Systems Based on Measurement Data
by Sohrab Mokhtari, Alireza Abbaspour, Kang K. Yen and Arman Sargolzaei
Electronics 2021, 10(4), 407; https://doi.org/10.3390/electronics10040407 - 8 Feb 2021
Cited by 138 | Viewed by 15130
Abstract
Attack detection problems in industrial control systems (ICSs) are commonly known as a network traffic monitoring scheme for detecting abnormal activities. However, a network-based intrusion detection system can be deceived by attackers that imitate the system’s normal activity. In this work, we proposed [...] Read more.
Attack detection problems in industrial control systems (ICSs) are commonly known as a network traffic monitoring scheme for detecting abnormal activities. However, a network-based intrusion detection system can be deceived by attackers that imitate the system’s normal activity. In this work, we proposed a novel solution to this problem based on measurement data in the supervisory control and data acquisition (SCADA) system. The proposed approach is called measurement intrusion detection system (MIDS), which enables the system to detect any abnormal activity in the system even if the attacker tries to conceal it in the system’s control layer. A supervised machine learning model is generated to classify normal and abnormal activities in an ICS to evaluate the MIDS performance. A hardware-in-the-loop (HIL) testbed is developed to simulate the power generation units and exploit the attack dataset. In the proposed approach, we applied several machine learning models on the dataset, which show remarkable performances in detecting the dataset’s anomalies, especially stealthy attacks. The results show that the random forest is performing better than other classifier algorithms in detecting anomalies based on measured data in the testbed. Full article
(This article belongs to the Special Issue Security of Cyber-Physical Systems)
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28 pages, 11195 KiB  
Article
Deadlock-Free Planner for Occluded Intersections Using Estimated Visibility of Hidden Vehicles
by Patiphon Narksri, Eijiro Takeuchi, Yoshiki Ninomiya and Kazuya Takeda
Electronics 2021, 10(4), 411; https://doi.org/10.3390/electronics10040411 - 8 Feb 2021
Cited by 8 | Viewed by 3461
Abstract
A common approach used for planning blind intersection crossings is to assume that hypothetical vehicles are approaching the intersection at a constant speed from the occluded areas. Such an assumption can result in a deadlock problem, causing the ego vehicle to remain stopped [...] Read more.
A common approach used for planning blind intersection crossings is to assume that hypothetical vehicles are approaching the intersection at a constant speed from the occluded areas. Such an assumption can result in a deadlock problem, causing the ego vehicle to remain stopped at an intersection indefinitely due to insufficient visibility. To solve this problem and facilitate safe, deadlock-free intersection crossing, we propose a blind intersection planner that utilizes both the ego vehicle and the approaching vehicle’s visibility. The planner uses a particle filter and our proposed visibility-dependent behavior model of approaching vehicles for predicting hidden vehicles. The behavior model is designed based on an analysis of actual driving data from multiple drivers crossing blind intersections. The proposed planner was tested in a simulation and found to be effective for allowing deadlock-free crossings at intersections where a baseline planner became stuck in a deadlock. The effects of perception accuracy and sensor position on output motion were also investigated. It was found that the proposed planner delayed crossing motion when the perception was imperfect. Furthermore, our results showed that the planner decelerated less while crossing the intersection with the front-mounted sensor configuration compared to the roof-mounted configuration due to the improved visibility. The minimum speed difference between the two sensor configurations was 1.82 m/s at an intersection with relatively poor visibility and 1.50 m/s at an intersection with good visibility. Full article
(This article belongs to the Special Issue Autonomous Vehicles Technology)
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12 pages, 700 KiB  
Article
Transparent Control Flow Transfer between CPU and Accelerators for HPC
by Daniel Granhão and João Canas Ferreira
Electronics 2021, 10(4), 406; https://doi.org/10.3390/electronics10040406 - 7 Feb 2021
Cited by 1 | Viewed by 2411
Abstract
Heterogeneous platforms with FPGAs have started to be employed in the High-Performance Computing (HPC) field to improve performance and overall efficiency. These platforms allow the use of specialized hardware to accelerate software applications, but require the software to be adapted in what can [...] Read more.
Heterogeneous platforms with FPGAs have started to be employed in the High-Performance Computing (HPC) field to improve performance and overall efficiency. These platforms allow the use of specialized hardware to accelerate software applications, but require the software to be adapted in what can be a prolonged and complex process. The main goal of this work is to describe and evaluate mechanisms that can transparently transfer the control flow between CPU and FPGA within the scope of HPC. Combining such a mechanism with transparent software profiling and accelerator configuration could lead to an automatic way of accelerating regular applications. In this work, a mechanism based on the ptrace system call is proposed, and its performance on the Intel Xeon+FPGA platform is evaluated. The feasibility of the proposed approach is demonstrated by a working prototype that performs the transparent control flow transfer of any function call to a matching hardware accelerator. This approach is more general than shared library interposition at the cost of a small time overhead in each accelerator use (about 1.3 ms in the prototype implementation). Full article
(This article belongs to the Special Issue Recent Advances in Field-Programmable Logic and Applications)
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26 pages, 1016 KiB  
Article
Identity and Access Management Resilience against Intentional Risk for Blockchain-Based IOT Platforms
by Alberto Partida, Regino Criado and Miguel Romance
Electronics 2021, 10(4), 378; https://doi.org/10.3390/electronics10040378 - 4 Feb 2021
Cited by 17 | Viewed by 7390
Abstract
Some Internet of Things (IoT) platforms use blockchain to transport data. The value proposition of IoT is the connection to the Internet of a myriad of devices that provide and exchange data to improve people’s lives and add value to industries. The blockchain [...] Read more.
Some Internet of Things (IoT) platforms use blockchain to transport data. The value proposition of IoT is the connection to the Internet of a myriad of devices that provide and exchange data to improve people’s lives and add value to industries. The blockchain technology transfers data and value in an immutable and decentralised fashion. Security, composed of both non-intentional and intentional risk management, is a fundamental design requirement for both IoT and blockchain. We study how blockchain answers some of the IoT security requirements with a focus on intentional risk. The review of a sample of security incidents impacting public blockchains confirm that identity and access management (IAM) is a key security requirement to build resilience against intentional risk. This fact is also applicable to IoT solutions built on a blockchain. We compare the two IoT platforms based on public permissionless distributed ledgers with the highest market capitalisation: IOTA, run on an alternative to a blockchain, which is a directed acyclic graph (DAG); and IoTeX, its contender, built on a blockchain. Our objective is to discover how we can create IAM resilience against intentional risk in these IoT platforms. For that, we turn to complex network theory: a tool to describe and compare systems with many participants. We conclude that IoTeX and possibly IOTA transaction networks are scale-free. As both platforms are vulnerable to attacks, they require resilience against intentional risk. In the case of IoTeX, DIoTA provides a resilient IAM solution. Furthermore, we suggest that resilience against intentional risk requires an IAM concept that transcends a single blockchain. Only with the interplay of edge and global ledgers can we obtain data integrity in a multi-vendor and multi-purpose IoT network. Full article
(This article belongs to the Special Issue IoT Security and Privacy through the Blockchain)
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16 pages, 12094 KiB  
Article
Development and Realization of an Experimental Bench Test for Synchronized Small Angle Light Scattering and Biaxial Traction Analysis of Tissues
by Emanuele Vignali, Emanuele Gasparotti, Luigi Landini and Simona Celi
Electronics 2021, 10(4), 386; https://doi.org/10.3390/electronics10040386 - 4 Feb 2021
Cited by 11 | Viewed by 2972
Abstract
Insights into the mechanical and microstructural status of biological soft tissues are fundamental in analyzing diseases. Biaxial traction is the gold standard approach for mechanical characterization. The state of the art methods for microstructural assessment have different advantages and drawbacks. Small angle light [...] Read more.
Insights into the mechanical and microstructural status of biological soft tissues are fundamental in analyzing diseases. Biaxial traction is the gold standard approach for mechanical characterization. The state of the art methods for microstructural assessment have different advantages and drawbacks. Small angle light scattering (SALS) represents a valuable low energy technique for soft tissue assessment. The objective of the current work was to develop a bench test integrating mechanical and microstructural characterization capabilities for tissue specimens. The setup’s principle is based on the integration of biaxial traction and SALS analysis. A dedicated control application was developed with the objective of managing the test procedure. The different components of the setup are described and discussed, both in terms of hardware and software. The realization of the system and the corresponding performances are then presented. Full article
(This article belongs to the Special Issue Digital Twin Technology: New Frontiers for Personalized Healthcare)
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31 pages, 57911 KiB  
Article
Comparative Study of Optimal Multivariable LQR and MPC Controllers for Unmanned Combat Air Systems in Trajectory Tracking
by Alvaro Ortiz, Sergio Garcia-Nieto and Raul Simarro
Electronics 2021, 10(3), 331; https://doi.org/10.3390/electronics10030331 - 1 Feb 2021
Cited by 11 | Viewed by 4601
Abstract
Guidance, navigation, and control system design is, undoubtedly, one of the most relevant issues in any type of unmanned aerial vehicle, especially in the case of military missions. This task needs to be performed in the most efficient way possible, which involves trying [...] Read more.
Guidance, navigation, and control system design is, undoubtedly, one of the most relevant issues in any type of unmanned aerial vehicle, especially in the case of military missions. This task needs to be performed in the most efficient way possible, which involves trying to satisfy a set of requirements that are sometimes in opposition. The purpose of this article was to compare two different control strategies in conjunction with a path-planning and guidance system with the objective of completing military missions in the most satisfactory way. For this purpose, a novel dynamic trajectory-planning algorithm is employed, which can obtain an appropriate trajectory by analyzing the environment as a discrete 3D adaptive mesh and performs a softening process a posteriori. Moreover, two multivariable control techniques are proposed, i.e., the linear quadratic regulator and the model predictive control, which were designed to offer optimal responses in terms of stability and robustness. Full article
(This article belongs to the Special Issue Autonomous Navigation Systems: Design, Control and Applications)
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16 pages, 7646 KiB  
Article
An Advanced CNN-LSTM Model for Cryptocurrency Forecasting
by Ioannis E. Livieris, Niki Kiriakidou, Stavros Stavroyiannis and Panagiotis Pintelas
Electronics 2021, 10(3), 287; https://doi.org/10.3390/electronics10030287 - 26 Jan 2021
Cited by 107 | Viewed by 18628
Abstract
Nowadays, cryptocurrencies are established and widely recognized as an alternative exchange currency method. They have infiltrated most financial transactions and as a result cryptocurrency trade is generally considered one of the most popular and promising types of profitable investments. Nevertheless, this constantly increasing [...] Read more.
Nowadays, cryptocurrencies are established and widely recognized as an alternative exchange currency method. They have infiltrated most financial transactions and as a result cryptocurrency trade is generally considered one of the most popular and promising types of profitable investments. Nevertheless, this constantly increasing financial market is characterized by significant volatility and strong price fluctuations over a short-time period therefore, the development of an accurate and reliable forecasting model is considered essential for portfolio management and optimization. In this research, we propose a multiple-input deep neural network model for the prediction of cryptocurrency price and movement. The proposed forecasting model utilizes as inputs different cryptocurrency data and handles them independently in order to exploit useful information from each cryptocurrency separately. An extensive empirical study was performed using three consecutive years of cryptocurrency data from three cryptocurrencies with the highest market capitalization i.e., Bitcoin (BTC), Etherium (ETH), and Ripple (XRP). The detailed experimental analysis revealed that the proposed model has the ability to efficiently exploit mixed cryptocurrency data, reduces overfitting and decreases the computational cost in comparison with traditional fully-connected deep neural networks. Full article
(This article belongs to the Special Issue Regularization Techniques for Machine Learning and Their Applications)
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20 pages, 11766 KiB  
Article
An Enhanced Multicell-to-Multicell Battery Equalizer Based on Bipolar-Resonant LC Converter
by Xuan Luo, Longyun Kang, Chusheng Lu, Jinqing Linghu, Hongye Lin and Bihua Hu
Electronics 2021, 10(3), 293; https://doi.org/10.3390/electronics10030293 - 26 Jan 2021
Cited by 30 | Viewed by 4009
Abstract
In a battery management system (BMS), battery equalizer is used to achieve voltage consistency between series connected battery cells. Recently, serious inconsistency has been founded to exist in retired batteries, and traditional equalizers are slow or inefficient to handle the situation. The multicell-to-multicell [...] Read more.
In a battery management system (BMS), battery equalizer is used to achieve voltage consistency between series connected battery cells. Recently, serious inconsistency has been founded to exist in retired batteries, and traditional equalizers are slow or inefficient to handle the situation. The multicell-to-multicell (MC2MC) topology, which can directly transfer energy from consecutive strong cells to consecutive weak cells, is promising to solve the problem, but its performance is limited by the existing converter. Therefore, this paper proposes an enhanced MC2MC equalizer based on a novel bipolar-resonant LC converter (BRLCC), which supports flexible and efficient operation modes with stable balancing power, can greatly improve the balancing speed without much sacrificing the efficiency. Mathematical analysis and comparison with typical equalizers are provided to illustrate its high balancing speed and good efficiency. An experimental prototype for 8 cells is built, and the balancing powers under different operation modes are from 1.426 W to 12.559 W with balancing efficiencies from 84.84% to 91.68%. Full article
(This article belongs to the Special Issue Challenges of Battery Management System)
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24 pages, 8233 KiB  
Article
FPGA Implementation for CNN-Based Optical Remote Sensing Object Detection
by Ning Zhang, Xin Wei, He Chen and Wenchao Liu
Electronics 2021, 10(3), 282; https://doi.org/10.3390/electronics10030282 - 25 Jan 2021
Cited by 65 | Viewed by 8857
Abstract
In recent years, convolutional neural network (CNN)-based methods have been widely used for optical remote sensing object detection and have shown excellent performance. Some aerospace systems, such as satellites or aircrafts, need to adopt these methods to observe objects on the ground. Due [...] Read more.
In recent years, convolutional neural network (CNN)-based methods have been widely used for optical remote sensing object detection and have shown excellent performance. Some aerospace systems, such as satellites or aircrafts, need to adopt these methods to observe objects on the ground. Due to the limited budget of the logical resources and power consumption in these systems, an embedded device is a good choice to implement the CNN-based methods. However, it is still a challenge to strike a balance between performance and power consumption. In this paper, we propose an efficient hardware-implementation method for optical remote sensing object detection. Firstly, we optimize the CNN-based model for hardware implementation, which establishes a foundation for efficiently mapping the network on a field-programmable gate array (FPGA). In addition, we propose a hardware architecture for the CNN-based remote sensing object detection model. In this architecture, a general processing engine (PE) is proposed to implement multiple types of convolutions in the network using the uniform module. An efficient data storage and access scheme is also proposed, and it achieves low-latency calculations and a high memory bandwidth utilization rate. Finally, we deployed the improved YOLOv2 network on a Xilinx ZYNQ xc7z035 FPGA to evaluate the performance of our design. The experimental results show that the performance of our implementation on an FPGA is only 0.18% lower than that on a graphics processing unit (GPU) in mean average precision (mAP). Under a 200 MHz working frequency, our design achieves a throughput of 111.5 giga-operations per second (GOP/s) with a 5.96 W on-chip power consumption. Comparison with the related works demonstrates that the proposed design has obvious advantages in terms of energy efficiency and that it is suitable for deployment on embedded devices. Full article
(This article belongs to the Section Artificial Intelligence Circuits and Systems (AICAS))
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