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Volume 10
Issue 23
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Electronics, Volume 10, Issue 23 (December-1 2021) – 163 articles

Cover Story (view full-size image): Advanced wireless sensors are essential for smart factories by enabling comprehensive data collection about machines, processes, and human–machine interaction with reduced installation and maintenance costs. Novel energy harvesting and wireless energy transmission technologies reveal exciting possibilities to realize a sustainable energy supply and enhance flexibility and reliability of wireless sensors. These involve reducing energy consumption of wireless nodes and include high-performance converters with advanced design, high bandwidth, hybridization, and flexible nanogenerators. Also, fascinating technologies of wireless power transfer via RF and inductive link are today able to couple energy to movable elements. View this paper.
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23 pages, 12255 KiB  
Article
An Effective Decoupling Control with Simple Structure for Induction Motor Drive System Considering Digital Delay
by Cheng Wang, Asem Jaidaa, Ze Wang and Lei Lu
Electronics 2021, 10(23), 3048; https://doi.org/10.3390/electronics10233048 - 6 Dec 2021
Cited by 5 | Viewed by 5144
Abstract
Digital processing poses a considerable time delay on controllers of induction motor (IM) driving system, which degrades the effects of torque/flux decoupling, slows the motor torque response down, or even makes the entire system unstable, especially when operating at a low switching frequency. [...] Read more.
Digital processing poses a considerable time delay on controllers of induction motor (IM) driving system, which degrades the effects of torque/flux decoupling, slows the motor torque response down, or even makes the entire system unstable, especially when operating at a low switching frequency. The existing methods, such as feed-forward and feed-back decoupling methods based on the proportional integral controller (PI), have an intrinsic disadvantage in the compromise between high performance and low switching frequency. Besides, the digital delay cannot be well compensated, which may affect the system loop and bring instability. Conventional complex vector decoupling control based on an accurate IM model employs complicated decoupling loops that may be degraded by digital delay leading to discrete error. This article aims to give an alternative complex vector decoupling solution with a simple structure, intended for optimized decoupling and improving the system dynamic performance throughout the entire operating range. The digital delay-caused impacts, including secondary coupling effect and voltage vector amplitude/phase inaccuracy, are specified. Given this, the digital delay impact is canceled accurately in advance, simplifying the entire decoupling process greatly while achieving uncompromised decoupling performance. The simulation and experimental results prove the effectiveness and feasibility of the proposed decoupling technique. Full article
(This article belongs to the Special Issue Advancements in Electric Motors, Drives, Power Converters and Related Systems)
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19 pages, 3867 KiB  
Article
Automatic Failure Recovery for Container-Based IoT Edge Applications
by Kolade Olorunnife, Kevin Lee and Jonathan Kua
Electronics 2021, 10(23), 3047; https://doi.org/10.3390/electronics10233047 - 6 Dec 2021
Cited by 7 | Viewed by 3742
Abstract
Recent years have seen the rapid adoption of Internet of Things (IoT) technologies, where billions of physical devices are interconnected to provide data sensing, computing and actuating capabilities. IoT-based systems have been extensively deployed across various sectors, such as smart homes, smart cities, [...] Read more.
Recent years have seen the rapid adoption of Internet of Things (IoT) technologies, where billions of physical devices are interconnected to provide data sensing, computing and actuating capabilities. IoT-based systems have been extensively deployed across various sectors, such as smart homes, smart cities, smart transport, smart logistics and so forth. Newer paradigms such as edge computing are developed to facilitate computation and data intelligence to be performed closer to IoT devices, hence reducing latency for time-sensitive tasks. However, IoT applications are increasingly being deployed in remote and difficult to reach areas for edge computing scenarios. These deployment locations make upgrading application and dealing with software failures difficult. IoT applications are also increasingly being deployed as containers which offer increased remote management ability but are more complex to configure. This paper proposes an approach for effectively managing, updating and re-configuring container-based IoT software as efficiently, scalably and reliably as possible with minimal downtime upon the detection of software failures. The approach is evaluated using docker container-based IoT application deployments in an edge computing scenario. Full article
(This article belongs to the Special Issue Edge Computing for Internet of Things)
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29 pages, 12482 KiB  
Article
Modeling and Validation of the Switching Techniques Applied to Back-to-Back Power Converter Connected to a DFIG-Based Wind Turbine for Harmonic Analysis
by Emmanuel Hernández-Mayoral, Efraín Dueñas-Reyes, Reynaldo Iracheta-Cortez, Eduardo Campos-Mercado, Vicente Torres-García and Rafael Uriza-Gosebruch
Electronics 2021, 10(23), 3046; https://doi.org/10.3390/electronics10233046 - 6 Dec 2021
Cited by 7 | Viewed by 3015
Abstract
Most power quality problems for electrical grids connected to Doubly-Fed Induction Generators (DFIGs) include flicker, variations of the RMS voltage profile, and injected harmonics because of switching in power converters. These converters have different topologies with the back-to-back (B2B) topology being the most [...] Read more.
Most power quality problems for electrical grids connected to Doubly-Fed Induction Generators (DFIGs) include flicker, variations of the RMS voltage profile, and injected harmonics because of switching in power converters. These converters have different topologies with the back-to-back (B2B) topology being the most exploited in high-powered three-phase systems. Therefore, in this article a model of a DFIG connected to the B2B power converter is proposed to which different switching techniques are implemented for interharmonic propagation studies. The switching techniques that are implemented include the Sinusoidal PWM (SPWM), the third harmonic injection PWM (THIPWM), and the space vector PWM (SVPWM), to reduce the Total Harmonic Distortion (THD) index of voltage and current in both windings of the machine. MATLAB-Simulink® software is used for modeling and simulating the B2B power converter and the switching techniques. The proposed model is validated with an experimental prototype that includes a 3-kW DFIG, a 10 HP motor, a gear-box with a transmission ratio of 4.5: 1, a B2B power converter, and a three-phase transformer connecting the system to the electrical grid. Finally, it is shown that the results obtained from the experimental tests corroborate the correct operation of the proposed model. Full article
(This article belongs to the Section Power Electronics)
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33 pages, 4326 KiB  
Article
Estimation and Interpretation of Machine Learning Models with Customized Surrogate Model
by Mudabbir Ali, Asad Masood Khattak, Zain Ali, Bashir Hayat, Muhammad Idrees, Zeeshan Pervez, Kashif Rizwan, Tae-Eung Sung and Ki-Il Kim
Electronics 2021, 10(23), 3045; https://doi.org/10.3390/electronics10233045 - 6 Dec 2021
Cited by 3 | Viewed by 3400
Abstract
Machine learning has the potential to predict unseen data and thus improve the productivity and processes of daily life activities. Notwithstanding its adaptiveness, several sensitive applications based on such technology cannot compromise our trust in them; thus, highly accurate machine learning models require [...] Read more.
Machine learning has the potential to predict unseen data and thus improve the productivity and processes of daily life activities. Notwithstanding its adaptiveness, several sensitive applications based on such technology cannot compromise our trust in them; thus, highly accurate machine learning models require reason. Such models are black boxes for end-users. Therefore, the concept of interpretability plays the role if assisting users in a couple of ways. Interpretable models are models that possess the quality of explaining predictions. Different strategies have been proposed for the aforementioned concept but some of these require an excessive amount of effort, lack generalization, are not agnostic and are computationally expensive. Thus, in this work, we propose a strategy that can tackle the aforementioned issues. A surrogate model assisted us in building interpretable models. Moreover, it helped us achieve results with accuracy close to that of the black box model but with less processing time. Thus, the proposed technique is computationally cheaper than traditional methods. The significance of such a novel technique is that data science developers will not have to perform strenuous hands-on activities to undertake feature engineering tasks and end-users will have the graphical-based explanation of complex models in a comprehensive way—consequently building trust in a machine. Full article
(This article belongs to the Special Issue Machine Learning with Applications: Dealing with Interpretability and Imbalanced Datasets)
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17 pages, 3835 KiB  
Article
An Active Power Coordination Control Strategy for AC/DC Transmission Systems to Mitigate Subsequent Commutation Failures in HVDC Systems
by Xia Zhou, Cangbi Ding, Jianfeng Dai, Zhaowei Li, Yang Hu, Zhaohui Qie and Feng Xue
Electronics 2021, 10(23), 3044; https://doi.org/10.3390/electronics10233044 - 6 Dec 2021
Cited by 5 | Viewed by 2254
Abstract
Subsequent commutation failures (CFs) in HVDC systems will cause large-scale power flow transfer in AC/DC transmission systems and lead to overload risk in HVAC systems. In order to cope with these effects, a power coordination control strategy for the AC/DC transmission system with [...] Read more.
Subsequent commutation failures (CFs) in HVDC systems will cause large-scale power flow transfer in AC/DC transmission systems and lead to overload risk in HVAC systems. In order to cope with these effects, a power coordination control strategy for the AC/DC transmission system with high-proportion wind power is proposed. Firstly, a model of the AC/DC transmission system considering the large-scale wind farms access is established by analyzing the power transmission characteristics of the AC/DC transmission system with high-proportion wind power, and the power transmission characteristics are analyzed after subsequent CFs. Secondly, the HVDC subsequent CFs can be mitigated by adjusting DC power transmission, while the active power output of the sending-end AC system is reduced by active control of wind turbine generators (WTGs) to reduce the overload risk of the HVAC system. Finally, the proposed power coordination control strategy is simulated and verified based on the established simulation model and actual power grid, and the results show that this strategy can effectively mitigate HVDC’s subsequent CFs and reduce the overload risk in HVAC systems. Full article
(This article belongs to the Section Power Electronics)
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11 pages, 994 KiB  
Article
Mutual Inductance Calculation of Circular Coils Sandwiched between 3-Layer Magnetic Mediums for Wireless Power Transfer Systems
by Minsheng Yang, Zhongqi Li, Min Zhang and Jingying Wan
Electronics 2021, 10(23), 3043; https://doi.org/10.3390/electronics10233043 - 6 Dec 2021
Cited by 6 | Viewed by 3174
Abstract
The mutual inductance between coils directly affects many aspects of performance in wireless power transmission systems. Therefore, a reliable calculation method for the mutual inductance between coils is of great significance to the optimal design of transmission coil structures. In this paper, a [...] Read more.
The mutual inductance between coils directly affects many aspects of performance in wireless power transmission systems. Therefore, a reliable calculation method for the mutual inductance between coils is of great significance to the optimal design of transmission coil structures. In this paper, a mutual inductance calculation for circular coils sandwiched between 3-layer magnetic mediums in a wireless power transmission system is proposed. First, the structure of circular coils sandwiched between 3-layer magnetic mediums is presented, and then a mutual inductance model of the circular coils is established. Accordingly, a corresponding magnetic vector potential analysis method is proposed based on Maxwell equations and the Bessel transform. Finally, the mutual inductance calculation method for circular coils between 3-layer magnetic mediums is obtained. The correctness of the proposed mutual inductance calculation method is verified by comparing the calculated, simulated, and measured mutual inductance data. Full article
(This article belongs to the Special Issue IoT Applications for Renewable Energy Management and Control)
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12 pages, 8434 KiB  
Article
A 4 GHz Single-to-Differential Cross-Coupled Variable-Gain Transimpedance Amplifier for Optical Communication
by Samuel B. S. Lee and Kiat Seng Yeo
Electronics 2021, 10(23), 3042; https://doi.org/10.3390/electronics10233042 - 5 Dec 2021
Cited by 3 | Viewed by 3331
Abstract
This letter presents an inductorless transimpedance amplifier (TIA) for visible light communication, using the UMC 40 nm CMOS process. It consists of a single-to-differential input stage with a modified cross-coupled regulated cascode design, followed by a modified fT-doubler mid-stage with a [...] Read more.
This letter presents an inductorless transimpedance amplifier (TIA) for visible light communication, using the UMC 40 nm CMOS process. It consists of a single-to-differential input stage with a modified cross-coupled regulated cascode design, followed by a modified fT-doubler mid-stage with a combined active inductor and capacitive degeneration design for bandwidth-enhancement and differential output. The mid-stage also has an attached common-mode feedback (CMFB) circuit. Both the input and mid-stages have gain-varying and peaking-varying functions. It has a measured gain range of 37.5–58.7 dBΩ and 4.15 GHz bandwidth using a 0.5 pF capacitive load. The gain range results in an input dynamic range of 33.2 µA–1.46 mA. Its input referred noise current is 10.7 pA/Hz, core DC power consumption is 7.84 mW from a VDDTIA of 1.6 V and core area is 39 µm × 26 µm. Full article
(This article belongs to the Special Issue Feature Papers in Circuit and Signal Processing)
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17 pages, 10627 KiB  
Article
A Carrier-Based Discontinuous PWM Strategy of NPC Three-Level Inverter for Common-Mode Voltage and Switching Loss Reduction
by Guozheng Zhang, Yingjie Su, Zhanqing Zhou and Qiang Geng
Electronics 2021, 10(23), 3041; https://doi.org/10.3390/electronics10233041 - 5 Dec 2021
Cited by 10 | Viewed by 4199
Abstract
For the conventional carrier-based pulse width modulation (CBPWM) strategies of neutral point clamped (NPC) three-level inverters, the higher common-mode voltage (CMV) is a major drawback. However, with CMV suppression strategies, the switching loss is relatively high. In order to solve the above issue, [...] Read more.
For the conventional carrier-based pulse width modulation (CBPWM) strategies of neutral point clamped (NPC) three-level inverters, the higher common-mode voltage (CMV) is a major drawback. However, with CMV suppression strategies, the switching loss is relatively high. In order to solve the above issue, a carrier-based discontinuous PWM (DPWM) strategy for NPC three-level inverter is proposed in this paper. Firstly, the reference voltage is modified by the twice injection of zero-sequence voltage. Switching states of the three-phase are clamped alternatively to reduce both the CMV and the switching loss. Secondly, the carriers are also modified by the phase opposite disposition of the upper and lower carriers. The extra switching at the border of two adjacent regions in the space vector diagram is reduced. Meanwhile, a neutral-point voltage (NPV) control method is also presented. The duty cycle of the switching state that affects the NPV is adjusted to obtain the balance control of the NPV. Still, the switching sequence in each carrier period remains the same. Finally, the feasibility and effectiveness of the proposed DPWM strategy are tested on a rapid control prototype platform based on RT-Lab. Full article
(This article belongs to the Special Issue Smart Energy Control & Conversion Systems)
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20 pages, 10361 KiB  
Article
Rotation Estimation and Segmentation for Patterned Image Vision Inspection
by Cheonin Oh, Hyungwoo Kim and Hyeonjoong Cho
Electronics 2021, 10(23), 3040; https://doi.org/10.3390/electronics10233040 - 5 Dec 2021
Cited by 4 | Viewed by 3187
Abstract
Pattern images can be segmented in a template unit for efficient fabric vision inspection; however, segmentation criteria critically affect the segmentation and defect detection performance. To get the undistorted criteria for rotated images, rotation estimation of absolute angle needs to be proceeded. Given [...] Read more.
Pattern images can be segmented in a template unit for efficient fabric vision inspection; however, segmentation criteria critically affect the segmentation and defect detection performance. To get the undistorted criteria for rotated images, rotation estimation of absolute angle needs to be proceeded. Given that conventional rotation estimations do not satisfy both rotation errors and computation times, patterned fabric defects are detected using manual visual methods. To solve these problems, this study proposes the application of segmentation reference point candidate (SRPC), generated based on a Euclidean distance map (EDM). SRPC is used to not only extract criteria points but also estimate rotation angle. The rotation angle is predicted using the orientation vector of SRPC instead of all pixels to reduce estimation times. SRPC-based image segmentation increases the robustness against the rotation angle and defects. The separation distance value for SRPC area distinction is calculated automatically. The performance of the proposed method is similar to state-of-the-art rotation estimation methods, with a suitable inspection time in actual operations for patterned fabric. The similarity between the segmented images is better than conventional methods. The proposed method extends the target of vision inspection on plane fabric to checked or striped pattern. Full article
(This article belongs to the Section Computer Science & Engineering)
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21 pages, 1605 KiB  
Article
RPPUF: An Ultra-Lightweight Reconfigurable Pico-Physically Unclonable Function for Resource-Constrained IoT Devices
by Zhao Huang, Liang Li, Yin Chen, Zeyu Li, Quan Wang and Xiaohong Jiang
Electronics 2021, 10(23), 3039; https://doi.org/10.3390/electronics10233039 - 5 Dec 2021
Cited by 8 | Viewed by 2837
Abstract
With the advancement of the Internet of Things (IoTs) technology, security issues have received an increasing amount of attention. Since IoT devices are typically resource-limited, conventional security solutions, such as classical cryptography, are no longer applicable. A physically unclonable function (PUF) is a [...] Read more.
With the advancement of the Internet of Things (IoTs) technology, security issues have received an increasing amount of attention. Since IoT devices are typically resource-limited, conventional security solutions, such as classical cryptography, are no longer applicable. A physically unclonable function (PUF) is a hardware-based, low-cost alternative solution to provide security for IoT devices. It utilizes the inherent nature of hardware to generate a random and unpredictable fingerprint to uniquely identify an IoT device. However, despite existing PUFs having exhibited a good performance, they are not suitable for effective application on resource-constrained IoT devices due to the limited number of challenge-response pairs (CRPs) generated per unit area and the large hardware resources overhead. To solve these problems, this article presents an ultra-lightweight reconfigurable PUF solution, which is named RPPUF. Our method is built on pico-PUF (PPUF). By incorporating configurable logics, one single RPPUF can be instantiated into multiple samples through configurable information K. We implement and verify our design on the Xilinx Spartan-6 field programmable gate array (FPGA) microboards. The experimental results demonstrate that, compared to previous work, our method increases the uniqueness, reliability and uniformity by up to 4.13%, 16.98% and 10.5%, respectively, while dramatically reducing the hardware resource overhead by 98.16% when a 128-bit PUF response is generated. Moreover, the bit per cost (BPC) metric of our proposed RPPUF increased by up to 28.5 and 53.37 times than that of PPUF and the improved butterfly PUF, respectively. This confirms that the proposed RPPUF is ultra-lightweight with a good performance, making it more appropriate and efficient to apply in FPGA-based IoT devices with constrained resources. Full article
(This article belongs to the Special Issue Advanced Security, Trust and Privacy Solutions for Wireless Networks)
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14 pages, 1029 KiB  
Article
Task Offloading Strategy and Simulation Platform Construction in Multi-User Edge Computing Scenario
by Guilu Wu and Zhongliang Li
Electronics 2021, 10(23), 3038; https://doi.org/10.3390/electronics10233038 - 5 Dec 2021
Cited by 6 | Viewed by 2725
Abstract
Various types of service applications increase the amount of computing in vehicular networks. The lack of computing resources of the vehicle itself will hinder the improvement of network performance. Mobile edge computing (MEC) technology is an effective computing method that is used to [...] Read more.
Various types of service applications increase the amount of computing in vehicular networks. The lack of computing resources of the vehicle itself will hinder the improvement of network performance. Mobile edge computing (MEC) technology is an effective computing method that is used to solve this problem at the edge of network for multiple mobile users. In this paper, we propose the multi-user task offloading strategy based on game theory to reduce the computational complexity and improve system performance. The task offloading decision making as a multi-user task offloading game is formulated to demonstrate how to achieve the Nash equilibrium (NE). Additionally, a task offloading algorithm is designed to achieve a NE, which represents an optimal or sub-optimal system overhead. In addition, the vehicular communication simulation frameworks Veins, SUMO model and OMNeT++ are adopted to run the proposed task offloading strategy. Numerical results show that the system overhead of the proposed task offloading strategy can degrade about 24.19% and 33.76%, respectively, in different scenarios. Full article
(This article belongs to the Section Computer Science & Engineering)
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19 pages, 2588 KiB  
Review
A Survey on EEG Signal Processing Techniques and Machine Learning: Applications to the Neurofeedback of Autobiographical Memory Deficits in Schizophrenia
by Miguel Ángel Luján, María Verónica Jimeno, Jorge Mateo Sotos, Jorge Javier Ricarte and Alejandro L. Borja
Electronics 2021, 10(23), 3037; https://doi.org/10.3390/electronics10233037 - 5 Dec 2021
Cited by 35 | Viewed by 7591
Abstract
In this paper, a general overview regarding neural recording, classical signal processing techniques and machine learning classification algorithms applied to monitor brain activity is presented. Currently, several approaches classified as electrical, magnetic, neuroimaging recordings and brain stimulations are available to obtain neural activity [...] Read more.
In this paper, a general overview regarding neural recording, classical signal processing techniques and machine learning classification algorithms applied to monitor brain activity is presented. Currently, several approaches classified as electrical, magnetic, neuroimaging recordings and brain stimulations are available to obtain neural activity of the human brain. Among them, non-invasive methods like electroencephalography (EEG) are commonly employed, as they can provide a high degree of temporal resolution (on the order of milliseconds) and acceptable space resolution. In addition, it is simple, quick, and does not create any physical harm or stress to patients. Concerning signal processing, once the neural signals are acquired, different procedures can be applied for feature extraction. In particular, brain signals are normally processed in time, frequency, and/or space domains. The features extracted are then used for signal classification depending on its characteristics such us the mean, variance or band power. The role of machine learning in this regard has become of key importance during the last years due to its high capacity to analyze complex amounts of data. The algorithms employed are generally classified in supervised, unsupervised and reinforcement techniques. A deep review of the most used machine learning algorithms and the advantages/drawbacks of most used methods is presented. Finally, a study of these procedures utilized in a very specific and novel research field of electroencephalography, i.e., autobiographical memory deficits in schizophrenia, is outlined. Full article
(This article belongs to the Special Issue Recent Advances in Neurocognitive Analysis Based on EEG Signal Processing)
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22 pages, 487 KiB  
Article
Embedded LUKS (E-LUKS): A Hardware Solution to IoT Security
by German Cano-Quiveu, Paulino Ruiz-de-clavijo-Vazquez, Manuel J. Bellido, Jorge Juan-Chico, Julian Viejo-Cortes, David Guerrero-Martos and Enrique Ostua-Aranguena
Electronics 2021, 10(23), 3036; https://doi.org/10.3390/electronics10233036 - 5 Dec 2021
Cited by 6 | Viewed by 4194
Abstract
The Internet of Things (IoT) security is one of the most important issues developers have to face. Data tampering must be prevented in IoT devices and some or all of the confidentiality, integrity, and authenticity of sensible data files must be assured in [...] Read more.
The Internet of Things (IoT) security is one of the most important issues developers have to face. Data tampering must be prevented in IoT devices and some or all of the confidentiality, integrity, and authenticity of sensible data files must be assured in most practical IoT applications, especially when data are stored in removable devices such as microSD cards, which is very common. Software solutions are usually applied, but their effectiveness is limited due to the reduced resources available in IoT systems. This paper introduces a hardware-based security framework for IoT devices (Embedded LUKS) similar to the Linux Unified Key Setup (LUKS) solution used in Linux systems to encrypt data partitions. Embedded LUKS (E-LUKS) extends the LUKS capabilities by adding integrity and authentication methods, in addition to the confidentiality already provided by LUKS. E-LUKS uses state-of-the-art encryption and hash algorithms such as PRESENT and SPONGENT. Both are recognized as adequate solutions for IoT devices being PRESENT incorporated in the ISO/IEC 29192-2:2019 for lightweight block ciphers. E-LUKS has been implemented in modern XC7Z020 FPGA chips, resulting in a smaller hardware footprint compared to previous LUKS hardware implementations, a footprint of about a 10% of these LUKS implementations, making E-LUKS a great alternative to provide Full Disk Encryption (FDE) alongside authentication to a wide range of IoT devices. Full article
(This article belongs to the Special Issue Security in Embedded Systems and IoT: Challenges and New Directions)
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24 pages, 6817 KiB  
Article
Technoeconomic and Environmental Study of Multi-Objective Integration of PV/Wind-Based DGs Considering Uncertainty of System
by Ashraf Ramadan, Mohamed Ebeed, Salah Kamel, Mohamed I. Mosaad and Ahmed Abu-Siada
Electronics 2021, 10(23), 3035; https://doi.org/10.3390/electronics10233035 - 5 Dec 2021
Cited by 16 | Viewed by 2684
Abstract
For technological, economic, and environmental reasons, renewable distributed generators (RDGs) have been extensively used in distribution networks. This paper presents an effective approach for technoeconomic analysis of optimal allocation of REDGs considering the uncertainties of the system. The primary issue with renewable-based distributed [...] Read more.
For technological, economic, and environmental reasons, renewable distributed generators (RDGs) have been extensively used in distribution networks. This paper presents an effective approach for technoeconomic analysis of optimal allocation of REDGs considering the uncertainties of the system. The primary issue with renewable-based distributed generators, especially wind and photovoltaic systems, is their intermittent characteristic that results in fluctuating output power and, hence, increasing power system uncertainty. Thus, it is essential to consider the uncertainty of such resources while selecting their optimal allocation within the grid. The main contribution of this study is to figure out the optimal size and location for RDGs in radial distribution systems while considering the uncertainty of load demand and RDG output power. A Monte Carlo simulation approach and a backward reduction algorithm were used to generate a reasonable number of scenarios to reflect the uncertainties of loading and RDG output power. Manta ray foraging optimization (MRFO), an efficient technique, was used to estimate the ratings and placements of the RDGs for a multi-objective function that includes the minimization of the expected total cost, total emissions, and total system voltage deviation, in addition to enhancing predicted total voltage stability. An IEEE 118-bus network was used as a large interconnected network, along with a rural 51-bus distribution grid and the IEEE 15-bus model as a small distribution network to test the developed technique. Simulations demonstrate that the proposed optimization technique effectively addresses the optimal DG allocation problem. Furthermore, the results indicate that using the proposed method to optimally integrate wind turbines with solar-based DG decreases the expected costs, emissions, and voltage deviations while improving voltage stability by 40.27%, 62.6%, 29.33%, and 4.76%, respectively, for the IEEE 118-bus system and enhances the same parameters by 35.57%, 59.92%, 68.95%, and 11.88%, respectively, for the rural 51-bus system and by 37.74%, 61.46%, 58.39%, and 8.86%, respectively, for the 15-bus system. Full article
(This article belongs to the Special Issue 10th Anniversary of Electronics: Recent Advances in Power Electronics)
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13 pages, 2343 KiB  
Article
Multipopulation Particle Swarm Optimization for Evolutionary Multitasking Sparse Unmixing
by Dan Feng, Mingyang Zhang and Shanfeng Wang
Electronics 2021, 10(23), 3034; https://doi.org/10.3390/electronics10233034 - 5 Dec 2021
Cited by 4 | Viewed by 2432
Abstract
Recently, the multiobjective evolutionary algorithms (MOEAs) have been designed to cope with the sparse unmixing problem. Due to the excellent performance of MOEAs in solving the NP hard optimization problems, they have also achieved good results for the sparse unmixing problems. However, most [...] Read more.
Recently, the multiobjective evolutionary algorithms (MOEAs) have been designed to cope with the sparse unmixing problem. Due to the excellent performance of MOEAs in solving the NP hard optimization problems, they have also achieved good results for the sparse unmixing problems. However, most of these MOEA-based methods only deal with a single pixel for unmixing and are subjected to low efficiency and are time-consuming. In fact, sparse unmixing can naturally be seen as a multitasking problem when the hyperspectral imagery is clustered into several homogeneous regions, so that evolutionary multitasking can be employed to take advantage of the implicit parallelism from different regions. In this paper, a novel evolutionary multitasking multipopulation particle swarm optimization framework is proposed to solve the hyperspectral sparse unmixing problem. First, we resort to evolutionary multitasking optimization to cluster the hyperspectral image into multiple homogeneous regions, and directly process the entire spectral matrix in multiple regions to avoid dimensional disasters. In addition, we design a novel multipopulation particle swarm optimization method for major evolutionary exploration. Furthermore, an intra-task and inter-task transfer and a local exploration strategy are designed for balancing the exchange of useful information in the multitasking evolutionary process. Experimental results on two benchmark hyperspectral datasets demonstrate the effectiveness of the proposed method compared with the state-of-the-art sparse unmixing algorithms. Full article
(This article belongs to the Special Issue Applications of Computational Intelligence)
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18 pages, 6938 KiB  
Article
Research on Target Localization Method of CRTS-III Slab Ballastless Track Plate Based on Machine Vision
by Xinjun Liu, Wenjiang Wu, Liaomo Zheng, Shiyu Wang, Qiang Zhang and Qi Wang
Electronics 2021, 10(23), 3033; https://doi.org/10.3390/electronics10233033 - 4 Dec 2021
Cited by 4 | Viewed by 2446
Abstract
In the construction of high-speed railway infrastructure, a CRTS-III slab ballastless track plate has been widely used. Anchor sealing is an essential step in the production of track plates. We design a novel automated platform based on industrial robots with vision guidance to [...] Read more.
In the construction of high-speed railway infrastructure, a CRTS-III slab ballastless track plate has been widely used. Anchor sealing is an essential step in the production of track plates. We design a novel automated platform based on industrial robots with vision guidance to improve the automation of a predominantly human-powered anchor sealing station. This paper proposes a precise and efficient target localization method for large and high-resolution images to obtain accurate target position information. To accurately update the robot’s work path and reduce idle waiting time, this paper proposes a low-cost and easily configurable visual localization system based on dual monocular cameras, which realizes the acquisition of track plate position information and the correction of position deviation in the robot coordinate system. We evaluate the repeatable positioning accuracy and the temporal performance of the visual localization system in a real production environment. The results show that the repeatable positioning accuracy of this localization system in the robot coordinate system can reach ±0.150 mm in the x- and y-directions and ±0.120° in the rotation angle. Moreover, this system completes two 18-megapixel image acquisitions, and the whole process takes around 570 ms to meet real production needs. Full article
(This article belongs to the Section Computer Science & Engineering)
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14 pages, 1918 KiB  
Article
Unbalanced-Tests to the Improvement of Yield and Quality
by Chung-Huang Yeh and Jwu-E Chen
Electronics 2021, 10(23), 3032; https://doi.org/10.3390/electronics10233032 - 4 Dec 2021
Cited by 5 | Viewed by 2605
Abstract
An integrated-circuit testing model (DITM) is used to describe various factors that affect test yield during a test process. We used a probability distribution model to evaluate test yield and quality and introduced a threshold test and a guardband test. As a result [...] Read more.
An integrated-circuit testing model (DITM) is used to describe various factors that affect test yield during a test process. We used a probability distribution model to evaluate test yield and quality and introduced a threshold test and a guardband test. As a result of the development speed of the semiconductor manufacturing industry in the future being unpredictable, we use electrical properties of existing products and the current manufacturing technology to estimate future product-distribution trends. In the development of very-large-scale integration (VLSI) testing, the progress of testing technology is very slow. To improve product testing yield and quality, we change the test method and propose an unbalanced-test method, leading to improvements in test results. The calculation using our proposed model and data estimated by the product published by the IEEE International Roadmap for Devices and Systems (IRDS, 2017) proves that the proposed unbalanced-test method can greatly improve test yield and quality and achieve the goal of high-quality, near-zero-defect products. Full article
(This article belongs to the Section Circuit and Signal Processing)
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21 pages, 8990 KiB  
Article
Design and Analysis of Wideband Flexible Self-Isolating MIMO Antennas for Sub-6 GHz 5G and WLAN Smartphone Terminals
by Jayshri Kulkarni, Abdullah G. Alharbi, Arpan Desai, Chow-Yen-Desmond Sim and Ajay Poddar
Electronics 2021, 10(23), 3031; https://doi.org/10.3390/electronics10233031 - 4 Dec 2021
Cited by 34 | Viewed by 3440
Abstract
A single radiator that is a part of four-port diversity Multiple-Input Multiple-Output (MIMO) antenna design is composed of four octagonal rings embedded between the two opposite sides of a T-shaped conductive layer surrounded by inverted angular edge cut L-shaped and E-shaped structures. The [...] Read more.
A single radiator that is a part of four-port diversity Multiple-Input Multiple-Output (MIMO) antenna design is composed of four octagonal rings embedded between the two opposite sides of a T-shaped conductive layer surrounded by inverted angular edge cut L-shaped and E-shaped structures. The radiators are placed at the four corners with common ground at the center of a smartphone to form a four-element mobile MIMO antenna. The printing of the antenna is carried out on the flexible polyamide substrate (dielectric constant = 3.5 and loss tangent = 0.0027) with dimensions of 70 × 145 × 0.2 mm3. A wide impedance bandwidth of (84.12%) 2.39 to 5.86 GHz is achieved for all four radiators. The compact size of the radiators along with their placement enables the proposed MIMO antenna to occupy much less area while preserving the space for 2G/3G/4G antennas. The placement of the antennas results in self-isolation between antenna elements by achieving isolation greater than 17.5 dB in the desired operating bands. Furthermore, besides showing a high efficiency of 85% and adequate gain above 4 dBi, good diversity performances such as Envelope Correlation Coefficient (ECC) of less than 0.05, Diversity Gain (DG) of above 9.8 dB, Mean Effective Gain (MEG) of −3.1 dB, Channel Capacity of 21.50 bps/Hz, and Total Active Reflection Coefficient (TARC) of below −10 dB are achieved by the flexible MIMO smartphone antenna. The effect of bending along the X and Y-axis on the performance of the proposed MIMO antenna is also analyzed where decent performance is observed. This makes the proposed flexible four-element MIMO antenna a potential candidate to be deployed in future smartphones. Full article
(This article belongs to the Special Issue Antenna Designs for 5G/IoT and Space Applications)
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15 pages, 1854 KiB  
Article
Evaluation of Multi-Objective Optimization Techniques for Resilience Enhancement of Electric Vehicles
by Akhtar Hussain and Hak-Man Kim
Electronics 2021, 10(23), 3030; https://doi.org/10.3390/electronics10233030 - 4 Dec 2021
Cited by 10 | Viewed by 2924
Abstract
The pervasiveness of electric vehicles (EVs) has increased recently, which results in the interdependence of power and transport networks. Power outages may adversely impact the transportation sector, and the available energy may not be sufficient to meet the needs of all EVs during [...] Read more.
The pervasiveness of electric vehicles (EVs) has increased recently, which results in the interdependence of power and transport networks. Power outages may adversely impact the transportation sector, and the available energy may not be sufficient to meet the needs of all EVs during such events. In addition, EVs will be used for diverse purposes in the future, ranging from personal usage to emergency response. Therefore, the allocation of energy to different EVs may have different degrees of societal-, community-, and individual-level benefits. To capture these diverse aspects, the energy allocation problem to EVs during outages is modeled as a multiobjective optimization (MOO) problem in this study. Three indices are formulated to quantify the value of different EVs for societies, communities, and individuals during outages, and, correspondingly, three objective functions are formulated. The formulated MOO problem is solved using the five most widely used MOO solution methods, and their performance is evaluated. These methods include the weighted-sum method, lexicographic method, normal boundary intersection method, min–max method, and nondominated sorting genetic algorithm II. To compare the performance of these methods, two indices are proposed in this study, which include the demand fulfillment index and total demand fulfillment index. The former is for analyzing the demand fulfillment ratio of different priority EVs, while the latter is for the demand fulfillment analysis of the whole EV fleet requiring a recharge. In addition, the computational complexity, variance, and additional constraints required by each method are also analyzed. The simulation results have shown that the lexicographic method has the best performance when the relative priorities are known, while the min–max method is the most suitable method if the priorities are not known. Full article
(This article belongs to the Special Issue 10th Anniversary of Electronics: Related Researches in Electrical and Autonomous Vehicles)
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16 pages, 338 KiB  
Review
GaAs-Based Serial-Input-Parallel-Output Interfaces for Microwave Core-Chips
by Chiara Ramella, Motahhareh Estebsari, Abbas Nasri and Marco Pirola
Electronics 2021, 10(23), 3029; https://doi.org/10.3390/electronics10233029 - 4 Dec 2021
Cited by 1 | Viewed by 2347
Abstract
Microwave core-chips are highly integrated MMICs that are in charge of all the beam-shaping functions of a transmit-receive module within a phased array system. Such chips include switches, amplifiers and attenuators, phase shifters, and possibly other elements, each to be controlled by external [...] Read more.
Microwave core-chips are highly integrated MMICs that are in charge of all the beam-shaping functions of a transmit-receive module within a phased array system. Such chips include switches, amplifiers and attenuators, phase shifters, and possibly other elements, each to be controlled by external digital signals. Given the large number of control lines to be integrated in a core-chip, the embedding of a serial to parallel interface is indispensable. Digital design in compound semiconductor technology is still rather challenging due to the absence of complementary devices and the availability of a limited number of metallization layers. Moreover, in large arrays, high chip yield and repeatability are required. This paper discusses and compares challenges and solutions for the key sub-circuits of GaAs serial to parallel converters for core-chip applications, reviewing the pros and cons of the different implementations proposed in the literature. Full article
(This article belongs to the Special Issue Microwave Integrated Core-Chips)
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19 pages, 843 KiB  
Article
Revisiting Symptom-Based Fault Tolerant Techniques against Soft Errors
by Hwisoo So, Moslem Didehban, Yohan Ko, Reiley Jeyapaul, Jongho Kim, Youngbin Kim, Kyoungwoo Lee and Aviral Shrivastava
Electronics 2021, 10(23), 3028; https://doi.org/10.3390/electronics10233028 - 4 Dec 2021
Cited by 2 | Viewed by 1990
Abstract
Aggressive technology scaling and near-threshold computing have made soft error reliability one of the leading design considerations in modern embedded microprocessors. Although traditional hardware/software redundancy-based schemes can provide a high level of protection, they incur significant overheads in terms of performance and hardware [...] Read more.
Aggressive technology scaling and near-threshold computing have made soft error reliability one of the leading design considerations in modern embedded microprocessors. Although traditional hardware/software redundancy-based schemes can provide a high level of protection, they incur significant overheads in terms of performance and hardware resources. The considerable overheads from such full redundancy-based techniques has motivated researchers to propose low-cost soft error protection schemes, such as symptom-based error protection schemes. The main idea behind a symptom-based error protection scheme is that soft errors in the system will quickly generate some symptoms, such as exceptions, branch mispredictions, cache or TLB misses, or unpredictable variable values. Therefore, monitoring such infrequent symptoms makes it possible to cover the manifestation of failures caused by soft errors. Symptom-based protection schemes have been suggested as shortcuts to achieve acceptable reliability with comparable overheads. Since the symptom-based protection schemes seem attractive due to their generality and simplicity, even state-of-the-art protection schemes exploit them as the baseline protections. However, our detailed analysis of the fault coverage and performance overheads of such schemes reveals that the user-visible failure coverage, particularly of ReStore, is limited (29% on average). By contrast, the runtime overheads are significant (40% on average) because the majority of the fault injection experiments, which were considered as detected/recovered failures by low-level symptoms, are actually benign faults by program-level masking effects. Full article
(This article belongs to the Special Issue Advancement of Fault Detection/Diagnosis and Fault-Tolerant Control with Applications)
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18 pages, 907 KiB  
Article
An Adaptive Routing Framework for Efficient Power Consumption in Software-Defined Datacenter Networks
by Mohammed Nsaif, Gergely Kovásznai, Anett Rácz, Ali Malik and Ruairí de Fréin
Electronics 2021, 10(23), 3027; https://doi.org/10.3390/electronics10233027 - 4 Dec 2021
Cited by 9 | Viewed by 2852
Abstract
Data Center Networks (DCNs) form the backbone of many Internet applications and services that have become necessary in daily life. Energy consumption causes both economic and environmental issues. It is reported that 10% of global energy consumption is due to ICT and network [...] Read more.
Data Center Networks (DCNs) form the backbone of many Internet applications and services that have become necessary in daily life. Energy consumption causes both economic and environmental issues. It is reported that 10% of global energy consumption is due to ICT and network usage. Computer networking equipment is designed to accommodate network traffic; however, the level of use of the equipment is not necessarily proportional to the power consumed by it. For example, DCNs do not always run at full capacity yet the fact that they are supporting a lighter load is not mirrored by a reduction in energy consumption. DCNs have been shown to unnecessarily over-consume energy when they are not fully loaded. In this paper, we propose a new framework that reduces power consumption in software-defined DCNs. The proposed approach is composed of a new Integer Programming model and a heuristic link utility-based algorithm that strikes a balance between energy consumption and performance. We evaluate the proposed framework using an experimental platform, which consists of an optimization tool called LinGo for solving convex and non-convex optimization problems, the POX controller and the Mininet network emulator. Compared with the state-of-the-art approach, the equal cost multi-path algorithm, the results show that the proposed method reduces the power consumption by up to 10% when the network is experiencing a high traffic load and 63.3% when the traffic load is low. Based on these results, we outline how machine learning approaches could be used to further improve our approach in future work. Full article
(This article belongs to the Special Issue 10th Anniversary of Electronics: Advances in Networks)
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23 pages, 2405 KiB  
Article
Effective Voting Ensemble of Homogenous Ensembling with Multiple Attribute-Selection Approaches for Improved Identification of Thyroid Disorder
by Tehseen Akhtar, Syed Omer Gilani, Zohaib Mushtaq, Saad Arif, Mohsin Jamil, Yasar Ayaz, Shahid Ikramullah Butt and Asim Waris
Electronics 2021, 10(23), 3026; https://doi.org/10.3390/electronics10233026 - 3 Dec 2021
Cited by 20 | Viewed by 3006
Abstract
Thyroid disease is characterized by abnormal development of glandular tissue on the periphery of the thyroid gland. Thyroid disease occurs when this gland produces an abnormally high or low level of hormones, with hyperthyroidism (active thyroid gland) and hypothyroidism (inactive thyroid gland) being [...] Read more.
Thyroid disease is characterized by abnormal development of glandular tissue on the periphery of the thyroid gland. Thyroid disease occurs when this gland produces an abnormally high or low level of hormones, with hyperthyroidism (active thyroid gland) and hypothyroidism (inactive thyroid gland) being the two most common types. The purpose of this work was to create an efficient homogeneous ensemble of ensembles in conjunction with numerous feature-selection methodologies for the improved detection of thyroid disorder. The dataset employed is based on real-time thyroid information obtained from the District Head Quarter (DHQ) teaching hospital, Dera Ghazi (DG) Khan, Pakistan. Following the necessary preprocessing steps, three types of attribute-selection strategies; Select From Model (SFM), Select K-Best (SKB), and Recursive Feature Elimination (RFE) were used. Decision Tree (DT), Gradient Boosting (GB), Logistic Regression (LR), and Random Forest (RF) classifiers were used as promising feature estimators. The homogeneous ensembling activated the bagging- and boosting-based classifiers, which were then classified by the Voting ensemble using both soft and hard voting. Accuracy, sensitivity, mean square error, hamming loss, and other performance assessment metrics have been adopted. The experimental results indicate the optimum applicability of the proposed strategy for improved thyroid ailment identification. All of the employed approaches achieved 100% accuracy with a small feature set. In terms of accuracy and computational cost, the presented findings outperformed similar benchmark models in its domain. Full article
(This article belongs to the Special Issue Advanced Sensing Techniques for Intelligent Human Activity Recognition Using Machine Learning)
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12 pages, 2128 KiB  
Article
The Human—Unmanned Aerial Vehicle System Based on SSVEP—Brain Computer Interface
by Ming-An Chung, Chia-Wei Lin and Chih-Tsung Chang
Electronics 2021, 10(23), 3025; https://doi.org/10.3390/electronics10233025 - 3 Dec 2021
Cited by 8 | Viewed by 2971
Abstract
The brain–computer interface (BCI) is a mechanism for extracting information from the brain, with this information used for various applications. This study proposes a method to control an unmanned aerial vehicle (UAV) flying through a BCI system using the steady-state visual evoked potential [...] Read more.
The brain–computer interface (BCI) is a mechanism for extracting information from the brain, with this information used for various applications. This study proposes a method to control an unmanned aerial vehicle (UAV) flying through a BCI system using the steady-state visual evoked potential (SSVEP) approach. The UAV’s screen emits three frequencies for visual stimulation: 15, 23, and 31 Hz for the UAV’s left-turn, forward-flight, and right-turn functions. Due to the requirement of immediate response to the UAV flight, this paper proposes a method to improve the accuracy rate and reduce the time required to correct instruction errors in the resolution of brainwave signals received by UAVs. This study tested ten subjects and verified that the proposed method has a 10% improvement inaccuracy. While the traditional method can take 8 s to correct an error, the proposed method requires only 1 s, making it more suitable for practical applications in UAVs. Furthermore, such a BCI application for UAV systems can achieve the same experience of using the remote control for physically challenged patients. Full article
(This article belongs to the Special Issue Advanced Technologies and Challenges in Brain Machine Interface)
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13 pages, 5850 KiB  
Article
Environmental Perception Q-Learning to Prolong the Lifetime of Poultry Farm Monitoring Networks
by Zike Wu, Pan Pan, Jieqiang Liu, Beibei Shi, Ming Yan and Hongguang Zhang
Electronics 2021, 10(23), 3024; https://doi.org/10.3390/electronics10233024 - 3 Dec 2021
Cited by 2 | Viewed by 2277
Abstract
The reduction of the effects of heat-stress phenomena on poultry health and energy conservation of poultry farm monitoring networks are highly related problems. To address these problems, we propose environmental perception Q-learning (EPQL) to prolong the lifetime of poultry farm monitoring networks. EPQL [...] Read more.
The reduction of the effects of heat-stress phenomena on poultry health and energy conservation of poultry farm monitoring networks are highly related problems. To address these problems, we propose environmental perception Q-learning (EPQL) to prolong the lifetime of poultry farm monitoring networks. EPQL consists of an environmental-perception module and Q-learning. According to the temperature and humidity model of heat stress, an environmental-perception module determines the transmission rate, while Q-learning adjusts the transmission rate according to the success rate of packet transmission and the remaining energy. In real-world tests, our poultry farm monitoring networks used only about 8% of energy in a month. The real-time information of these monitoring networks was available on smartphones. In laboratory tests, compared with CSMA/CA (23.67 days), S-MAC (109.37 days), and T-MAC (252.79 days) under real systems with 2000 mAh battery, the battery-life performance of EPQL (436.48 days) was better. Moreover, EPQL reduces the packet loss rate by about 60% while simultaneously decreasing the average delay by about 20%. Generally, based on the framework of EPQL, the implemented temperature and humidity model of heat stress for poultry could be replaced by other models to extend its applicability range. Full article
(This article belongs to the Collection Electronics for Agriculture)
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15 pages, 2513 KiB  
Article
Vehicular Visible Light Positioning Using Receiver Diversity with Machine Learning
by Abdulrahman A. Mahmoud, Zahir Ahmad, Uche Onyekpe, Yousef Almadani, Muhammad Ijaz, Olivier C. L. Haas and Sujan Rajbhandari
Electronics 2021, 10(23), 3023; https://doi.org/10.3390/electronics10233023 - 3 Dec 2021
Cited by 6 | Viewed by 2709
Abstract
This paper proposes a 2-D vehicular visible light positioning (VLP) system using existing streetlights and diversity receivers. Due to the linear arrangement of streetlights, traditional positioning techniques based on triangulation or similar algorithms fail. Thus, in this work, we propose a spatial and [...] Read more.
This paper proposes a 2-D vehicular visible light positioning (VLP) system using existing streetlights and diversity receivers. Due to the linear arrangement of streetlights, traditional positioning techniques based on triangulation or similar algorithms fail. Thus, in this work, we propose a spatial and angular diversity receiver with machine learning (ML) techniques for VLP. It is shown that a multi-layer neural network (NN) with the proposed receiver scheme outperforms other ML algorithms and can offer high accuracy with root mean square (RMS) error of 0.22 m and 0.14 m during the day and night time, respectively. Furthermore, the NN shows robustness in VLP across different weather conditions and road scenarios. The results show that only dense fog deteriorates the performance of the system due to reduced visibility across the road. Full article
(This article belongs to the Special Issue 10th Anniversary of Electronics: Related Researches in Electrical and Autonomous Vehicles)
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12 pages, 7014 KiB  
Article
An Eight Element Dual Band Antenna for Future 5G Smartphones
by Haider Ali, Xin-Cheng Ren, Anas M. Hashmi, Muhammad Rizwan Anjum, Inam Bari, Saad Ijaz Majid, Naveed Jan, Wajahat Ullah Khan Tareen, Amjad Iqbal and Muhammad Abbas Khan
Electronics 2021, 10(23), 3022; https://doi.org/10.3390/electronics10233022 - 3 Dec 2021
Cited by 15 | Viewed by 2707
Abstract
The demand of 5G in modern communication era due to its high data rate, reliable connectivity and low latency is enormous. This paper presents a novel dual band antenna resonating at two distinct bands allotted for 5G services. The proposed antenna is composed [...] Read more.
The demand of 5G in modern communication era due to its high data rate, reliable connectivity and low latency is enormous. This paper presents a novel dual band antenna resonating at two distinct bands allotted for 5G services. The proposed antenna is composed of inverted L shape probes comprising a rectangular defected ground structure. The propose antenna covers 3.4–3.6 GHz and 5.4–5.6 GHz spectrum. In propose MIMO system, the efficiency ranges from 52 to 69% with peak gain of 3.1 dBi. The proposed antenna system is sufficiently isolated with minimum value of 13 dB and ECC less than 0.05 among any two radiating elements. Similarly, the channel capacity is found to be 38 and 39.5 at both resonating bands at 20 dB SNR and diversity and mean effective gains lies in acceptable range. The radiation characteristics of the proposed design shows that the proposed antenna is providing good diversity characteristics and SAR values have demonstrated to be safe for user vicinity. The proposed dual band antenna prototype is developed tested. With the measured results obtained, the MIMO system proposed can be seen as vital candidate for 5G LTE band 42 and 46 services. Full article
(This article belongs to the Special Issue Prospective Multiple Antenna Technologies for 5G and Beyond)
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19 pages, 2688 KiB  
Article
IPGM: Inertial Proximal Gradient Method for Convolutional Dictionary Learning
by Jing Li, Xiao Wei, Fengpin Wang and Jinjia Wang
Electronics 2021, 10(23), 3021; https://doi.org/10.3390/electronics10233021 - 3 Dec 2021
Cited by 3 | Viewed by 1877
Abstract
Inspired by the recent success of the proximal gradient method (PGM) and recent efforts to develop an inertial algorithm, we propose an inertial PGM (IPGM) for convolutional dictionary learning (CDL) by jointly optimizing both an ℓ2-norm data fidelity term and a [...] Read more.
Inspired by the recent success of the proximal gradient method (PGM) and recent efforts to develop an inertial algorithm, we propose an inertial PGM (IPGM) for convolutional dictionary learning (CDL) by jointly optimizing both an ℓ2-norm data fidelity term and a sparsity term that enforces an ℓ1 penalty. Contrary to other CDL methods, in the proposed approach, the dictionary and needles are updated with an inertial force by the PGM. We obtain a novel derivative formula for the needles and dictionary with respect to the data fidelity term. At the same time, a gradient descent step is designed to add an inertial term. The proximal operation uses the thresholding operation for needles and projects the dictionary to a unit-norm sphere. We prove the convergence property of the proposed IPGM algorithm in a backtracking case. Simulation results show that the proposed IPGM achieves better performance than the PGM and slice-based methods that possess the same structure and are optimized using the alternating-direction method of multipliers (ADMM). Full article
(This article belongs to the Special Issue Novel Technologies on Image and Signal Processing)
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20 pages, 3914 KiB  
Article
Explainable Convolutional Neural Network to Investigate Age-Related Changes in Multi-Order Functional Connectivity
by Sunghee Dong, Yan Jin, SuJin Bak, Bumchul Yoon and Jichai Jeong
Electronics 2021, 10(23), 3020; https://doi.org/10.3390/electronics10233020 - 3 Dec 2021
Cited by 4 | Viewed by 2789
Abstract
Functional connectivity (FC) is a potential candidate that can increase the performance of brain-computer interfaces (BCIs) in the elderly because of its compensatory role in neural circuits. However, it is difficult to decode FC by the current machine learning techniques because of a [...] Read more.
Functional connectivity (FC) is a potential candidate that can increase the performance of brain-computer interfaces (BCIs) in the elderly because of its compensatory role in neural circuits. However, it is difficult to decode FC by the current machine learning techniques because of a lack of physiological understanding. To investigate the suitability of FC in BCIs for the elderly, we propose the decoding of lower- and higher-order FC using a convolutional neural network (CNN) in six cognitive-motor tasks. The layer-wise relevance propagation (LRP) method describes how age-related changes in FCs impact BCI applications for the elderly compared to younger adults. A total of 17 young adults 24.5±2.7 years and 12 older 72.5±3.2 years adults were recruited to perform tasks related to hand-force control with or without mental calculation. The CNN yielded a six-class classification accuracy of 75.3% in the elderly, exceeding the 70.7% accuracy for the younger adults. In the elderly, the proposed method increased the classification accuracy by 88.3% compared to the filter-bank common spatial pattern. The LRP results revealed that both lower- and higher-order FCs were dominantly overactivated in the prefrontal lobe, depending on the task type. These findings suggest a promising application of multi-order FC with deep learning on BCI systems for the elderly. Full article
(This article belongs to the Special Issue Advanced Technologies and Challenges in Brain Machine Interface)
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14 pages, 1206 KiB  
Article
Blockchain-Based Privacy-Preserving System for Genomic Data Management Using Local Differential Privacy
by Young-Hoon Park, Yejin Kim and Junho Shim
Electronics 2021, 10(23), 3019; https://doi.org/10.3390/electronics10233019 - 3 Dec 2021
Cited by 10 | Viewed by 3723
Abstract
The advances made in genome technology have resulted in significant amounts of genomic data being generated at an increasing speed. As genomic data contain various privacy-sensitive information, security schemes that protect confidentiality and control access are essential. Many security techniques have been proposed [...] Read more.
The advances made in genome technology have resulted in significant amounts of genomic data being generated at an increasing speed. As genomic data contain various privacy-sensitive information, security schemes that protect confidentiality and control access are essential. Many security techniques have been proposed to safeguard healthcare data. However, these techniques are inadequate for genomic data management because of their large size. Additionally, privacy problems due to the sharing of gene data are yet to be addressed. In this study, we propose a secure genomic data management system using blockchain and local differential privacy (LDP). The proposed system employs two types of storage: private storage for internal staff and semi-private storage for external users. In private storage, because encrypted gene data are stored, only internal employees can access the data. Meanwhile, in semi-private storage, gene data are irreversibly modified by LDP. Through LDP, different noises are added to each section of the genomic data. Therefore, even though the third party uses or exposes the shared data, the owner’s privacy is guaranteed. Furthermore, the access control for each storage is ensured by the blockchain, and the gene owner can trace the usage and sharing status using a decentralized application in a mobile device. Full article
(This article belongs to the Special Issue Blockchain-Based Technology for Mobile Application)
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