Next Issue
Volume 10, October-2
Previous Issue
Volume 10, September-2
 
 

Electronics, Volume 10, Issue 19 (October-1 2021) – 129 articles

Cover Story (view full-size image): In this paper, we introduce TETRAPAC (TElematic data of TRucks for Advanced Predictive Analysis of their Component), a methodology able to analyze data collected from heavy trucks during their use, offering a generalizable approach to estimating vehicle health conditions based on monitored features enriched by innovative key performance indicators. The methodology has been evaluated using two different use cases: (1) identifying vehicles with potential DTCs (diagnostic trouble codes) and (2) the estimation of the battery life of the trucks. In both use cases, TETRAPAC has been proven to bring significant benefits to the company, in terms of cost savings and increasing customer satisfaction. View this paper
  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.
Order results
Result details
Section
Select all
Export citation of selected articles as:
12 pages, 3791 KiB  
Article
Single-Sensor EMI Source Localization Using Time Reversal: An Experimental Validation
by Hamidreza Karami, Mohammad Azadifar, Zhaoyang Wang, Marcos Rubinstein and Farhad Rachidi
Electronics 2021, 10(19), 2448; https://doi.org/10.3390/electronics10192448 - 8 Oct 2021
Cited by 5 | Viewed by 1621
Abstract
The localization of electromagnetic interference (EMI) sources is of high importance in electromagnetic compatibility applications. Recently, a novel localization technique based on the time-reversal cavity (TRC) concept was proposed using only one sensor, and its application to localize EMI sources was validated numerically. [...] Read more.
The localization of electromagnetic interference (EMI) sources is of high importance in electromagnetic compatibility applications. Recently, a novel localization technique based on the time-reversal cavity (TRC) concept was proposed using only one sensor, and its application to localize EMI sources was validated numerically. In this paper, we present a validation of the proposed time-reversal process in which the forward step of the time-reversal process is performed experimentally and the backward step is carried out via numerical simulations, a realistic scenario which is applicable to practical source localization problems. To the best of the authors’ knowledge, this is the first implementation of a three-dimensional electromagnetic time-reversal process in which the forward signal is provided experimentally while the backward propagation step is carried out numerically. The considered experimental setup is formed by a partially open cavity and two monopole antennas to emulate the EMI source and the sensor (receiving antenna), respectively. Assuming that the location of the source is the feed point of the monopole antenna, the resulting three-dimensional location error in the experimental validation was only 1.49 cm, which is about one-third the length of the monopole antenna, corresponding to about λmin/2 (diffraction limit). Full article
(This article belongs to the Section Circuit and Signal Processing)
Show Figures

Figure 1

18 pages, 8582 KiB  
Article
Hardware-in-the-Loop Simulation of Self-Driving Electric Vehicles by Dynamic Path Planning and Model Predictive Control
by Yi Chung and Yee-Pien Yang
Electronics 2021, 10(19), 2447; https://doi.org/10.3390/electronics10192447 - 8 Oct 2021
Cited by 4 | Viewed by 2973
Abstract
This paper applies a dynamic path planning and model predictive control (MPC) to simulate self-driving and parking for an electric van on a hardware-in-the-loop (HiL) platform. The hardware platform is a simulator which consists of an electric power steering system, accelerator and brake [...] Read more.
This paper applies a dynamic path planning and model predictive control (MPC) to simulate self-driving and parking for an electric van on a hardware-in-the-loop (HiL) platform. The hardware platform is a simulator which consists of an electric power steering system, accelerator and brake pedals, and an Nvidia drive PX2 with a robot operating system (ROS). The vehicle dynamics model, sensors, controller, and test field map are virtually built with the PreScan simulation platform. Both manual and autonomous driving modes can be simulated, and a graphic user interface allows a test driver to select a target parking space on a display screen. Three scenarios are demonstrated: forward parking, reverse parking, and obstacle avoidance. When the vehicle perceives an obstacle, the map is updated and the route is adaptively planned. The effectiveness of the proposed MPC is verified in experiments and proved to be superior to a traditional proportional–integral–derivative controller with regards to safety, energy-saving, comfort, and agility. Full article
(This article belongs to the Special Issue Unmanned Vehicles and Intelligent Robotic Alike Systems)
Show Figures

Figure 1

7 pages, 4197 KiB  
Article
RF Pogo-Pin Probe Card Design Aimed at Automated Millimeter-Wave Multi-Port Integrated-Circuit Testing
by K. M. Lee, J. H. Oh, M. S. Kim, T. S. Kim and M. Kim
Electronics 2021, 10(19), 2446; https://doi.org/10.3390/electronics10192446 - 8 Oct 2021
Cited by 4 | Viewed by 4203
Abstract
A prototype RF probe card is assembled to test the feasibility of Pogo-pins as robust probe tips for the automized testing of multiple-port millimeter-wave circuits. A custom-made ceramic housing machined from a low-loss dielectric holds an array of 157 Pogo-pins, each with 2.9 [...] Read more.
A prototype RF probe card is assembled to test the feasibility of Pogo-pins as robust probe tips for the automized testing of multiple-port millimeter-wave circuits. A custom-made ceramic housing machined from a low-loss dielectric holds an array of 157 Pogo-pins, each with 2.9 mm-length in fixed positions. The ceramic housing is then mounted onto a probe-card PCB for power-loss measurements on two signal-ground Pogo-pin connections arbitrarily selected from the array. The probing results on a test circuit with a simple thru-line indicate a successful power transfer with a small insertion loss of less than 0.5 dB per single Pogo-pin connection up to 25 GHz. A new probe card design using shorter Pogo-pins is being prepared to extend the operation frequency to beyond 40 GHz. Full article
(This article belongs to the Section Microwave and Wireless Communications)
Show Figures

Figure 1

14 pages, 5971 KiB  
Article
Formulation and Analysis of Single Switch High Gain Hybrid DC to DC Converter for High Power Applications
by Sathiya Ranganathan and Arun Noyal Doss Mohan
Electronics 2021, 10(19), 2445; https://doi.org/10.3390/electronics10192445 - 8 Oct 2021
Cited by 9 | Viewed by 2357
Abstract
The necessity for DC−DC converters has been rapidly increasing due to the emergence of RES-based electrification. However, the converter designed so far exhibits the drawbacks of lower efficiency and non-compactness in size. Hence, to rectify this problem, the new topology of a flyback [...] Read more.
The necessity for DC−DC converters has been rapidly increasing due to the emergence of RES-based electrification. However, the converter designed so far exhibits the drawbacks of lower efficiency and non-compactness in size. Hence, to rectify this problem, the new topology of a flyback converter for PV application is proposed in this work. The proposed converter exhibits reduced ripple in input current and enhances the conversion efficiency. Finally, the efficiency of this proposed converter is verified using MATLAB. The results indicate that this projected topology can be suitable for high voltage DC applications. Full article
Show Figures

Figure 1

19 pages, 6321 KiB  
Article
Image-Based Malware Classification Using VGG19 Network and Spatial Convolutional Attention
by Mazhar Javed Awan, Osama Ahmed Masood, Mazin Abed Mohammed, Awais Yasin, Azlan Mohd Zain, Robertas Damaševičius and Karrar Hameed Abdulkareem
Electronics 2021, 10(19), 2444; https://doi.org/10.3390/electronics10192444 - 8 Oct 2021
Cited by 76 | Viewed by 6375
Abstract
In recent years the amount of malware spreading through the internet and infecting computers and other communication devices has tremendously increased. To date, countless techniques and methodologies have been proposed to detect and neutralize these malicious agents. However, as new and automated malware [...] Read more.
In recent years the amount of malware spreading through the internet and infecting computers and other communication devices has tremendously increased. To date, countless techniques and methodologies have been proposed to detect and neutralize these malicious agents. However, as new and automated malware generation techniques emerge, a lot of malware continues to be produced, which can bypass some state-of-the-art malware detection methods. Therefore, there is a need for the classification and detection of these adversarial agents that can compromise the security of people, organizations, and countless other forms of digital assets. In this paper, we propose a spatial attention and convolutional neural network (SACNN) based on deep learning framework for image-based classification of 25 well-known malware families with and without class balancing. Performance was evaluated on the Malimg benchmark dataset using precision, recall, specificity, precision, and F1 score on which our proposed model with class balancing reached 97.42%, 97.95%, 97.33%, 97.11%, and 97.32%. We also conducted experiments on SACNN with class balancing on benign class, also produced above 97%. The results indicate that our proposed model can be used for image-based malware detection with high performance, despite being simpler as compared to other available solutions. Full article
(This article belongs to the Special Issue Security and Privacy for IoT and Multimedia Services)
Show Figures

Figure 1

16 pages, 1396 KiB  
Article
DWSA: An Intelligent Document Structural Analysis Model for Information Extraction and Data Mining
by Tan Yue, Yong Li and Zonghai Hu
Electronics 2021, 10(19), 2443; https://doi.org/10.3390/electronics10192443 - 8 Oct 2021
Cited by 6 | Viewed by 1968
Abstract
The structure of a document contains rich information such as logical relations in context, hierarchy, affiliation, dependence, and applicability. It will greatly affect the accuracy of document information processing, particularly of legal documents and business contracts. Therefore, intelligent document structural analysis is important [...] Read more.
The structure of a document contains rich information such as logical relations in context, hierarchy, affiliation, dependence, and applicability. It will greatly affect the accuracy of document information processing, particularly of legal documents and business contracts. Therefore, intelligent document structural analysis is important to information extraction and data mining. However, unlike the well-studied field of text semantic analysis, current work in document structural analysis is still scarce. In this paper, we propose an intelligent document structural analysis framework through data pre-processing, feature engineering, and structural classification with a dynamic sample weighting algorithm. As a typical application, we collect more than 11,000 insurance document content samples and carry out the machine learning experiments to check the efficiency of our framework. Meanwhile, to address the sample imbalance problem in the hierarchy classification task, a dynamic sample weighting algorithm is incorporated into our Dynamic Weighting Structural Analysis (DWSA) framework, in which the weights of different category tags according to the structural levels are iterated dynamically in training. Our results show that the DWSA has significantly improved the comprehensive accuracy and the classification F1-score of each category. The comprehensive accuracy is as high as 94.68% (3.36% absolute improvement) and the Macro F1-score is 88.29% (5.1% absolute improvement). Full article
(This article belongs to the Special Issue Advances in Swarm Intelligence, Data Science and Their Applications)
Show Figures

Figure 1

19 pages, 2207 KiB  
Article
CA-CRE: Classification Algorithm-Based Controller Area Network Payload Format Reverse-Engineering Method
by Cheongmin Ji, Taehyoung Ko and Manpyo Hong
Electronics 2021, 10(19), 2442; https://doi.org/10.3390/electronics10192442 - 8 Oct 2021
Cited by 1 | Viewed by 1673
Abstract
In vehicles, dozens of electronic control units are connected to one or more controller area network (CAN) buses to exchange information and send commands related to the physical system of the vehicles. Furthermore, modern vehicles are connected to the Internet via telematics control [...] Read more.
In vehicles, dozens of electronic control units are connected to one or more controller area network (CAN) buses to exchange information and send commands related to the physical system of the vehicles. Furthermore, modern vehicles are connected to the Internet via telematics control units (TCUs). This leads to an attack vector in which attackers can control vehicles remotely once they gain access to in-vehicle networks (IVNs) and can discover the formats of important messages. Although the format information is kept secret by car manufacturers, CAN is vulnerable, since payloads are transmitted in plain text. In contrast, the secrecy of message formats inhibits IVN security research by third-party researchers. It also hinders effective security tests for in-vehicle networks as performed by evaluation authorities. To mitigate this problem, a method of reverse-engineering CAN payload formats is proposed. The method utilizes classification algorithms to predict signal boundaries from CAN payloads. Several features were uniquely chosen and devised to quantify the type-specific characteristics of signals. The method is evaluated on real-world and synthetic CAN traces, and the results show that our method can predict at least 10% more signal boundaries than the existing methods. Full article
(This article belongs to the Special Issue Data-Driven Security)
Show Figures

Figure 1

16 pages, 6868 KiB  
Article
A Low-Cost Hardware-Friendly Spiking Neural Network Based on Binary MRAM Synapses, Accelerated Using In-Memory Computing
by Yihao Wang, Danqing Wu, Yu Wang, Xianwu Hu, Zizhao Ma, Jiayun Feng and Yufeng Xie
Electronics 2021, 10(19), 2441; https://doi.org/10.3390/electronics10192441 - 8 Oct 2021
Cited by 5 | Viewed by 2318
Abstract
In recent years, the scaling down that Moore’s Law relies on has been gradually slowing down, and the traditional von Neumann architecture has been limiting the improvement of computing power. Thus, neuromorphic in-memory computing hardware has been proposed and is becoming a promising [...] Read more.
In recent years, the scaling down that Moore’s Law relies on has been gradually slowing down, and the traditional von Neumann architecture has been limiting the improvement of computing power. Thus, neuromorphic in-memory computing hardware has been proposed and is becoming a promising alternative. However, there is still a long way to make it possible, and one of the problems is to provide an efficient, reliable, and achievable neural network for hardware implementation. In this paper, we proposed a two-layer fully connected spiking neural network based on binary MRAM (Magneto-resistive Random Access Memory) synapses with low hardware cost. First, the network used an array of multiple binary MRAM cells to store multi-bit fixed-point weight values. This helps to simplify the read/write circuit. Second, we used different kinds of spike encoders that ensure the sparsity of input spikes, to reduce the complexity of peripheral circuits, such as sense amplifiers. Third, we designed a single-step learning rule, which fit well with the fixed-point binary weights. Fourth, we replaced the traditional exponential Leak-Integrate-Fire (LIF) neuron model to avoid the massive cost of exponential circuits. The simulation results showed that, compared to other similar works, our SNN with 1184 neurons and 313,600 synapses achieved an accuracy of up to 90.6% in the MNIST recognition task with full-resolution (28 × 28) and full-bit-depth (8-bit) images. In the case of low-resolution (16 × 16) and black-white (1-bit) images, the smaller version of our network with 384 neurons and 32,768 synapses still maintained an accuracy of about 77%, extending its application to ultra-low-cost situations. Both versions need less than 30,000 samples to reach convergence, which is a >50% reduction compared to other similar networks. As for robustness, it is immune to the fluctuation of MRAM cell resistance. Full article
(This article belongs to the Special Issue Neuromorphic Sensing and Computing Systems)
Show Figures

Figure 1

18 pages, 2816 KiB  
Article
k-NDDP: An Efficient Anonymization Model for Social Network Data Release
by Shafaq Shakeel, Adeel Anjum, Alia Asheralieva and Masoom Alam
Electronics 2021, 10(19), 2440; https://doi.org/10.3390/electronics10192440 - 8 Oct 2021
Cited by 6 | Viewed by 1946
Abstract
With the evolution of Internet technology, social networking sites have gained a lot of popularity. People make new friends, share their interests, experiences in life, etc. With these activities on social sites, people generate a vast amount of data that is analyzed by [...] Read more.
With the evolution of Internet technology, social networking sites have gained a lot of popularity. People make new friends, share their interests, experiences in life, etc. With these activities on social sites, people generate a vast amount of data that is analyzed by third parties for various purposes. As such, publishing social data without protecting an individual’s private or confidential information can be dangerous. To provide privacy protection, this paper proposes a new degree anonymization approach k-NDDP, which extends the concept of k-anonymity and differential privacy based on Node DP for vertex degrees. In particular, this paper considers identity disclosures on social data. If the adversary efficiently obtains background knowledge about the victim’s degree and neighbor connections, it can re-identify its victim from the social data even if the user’s identity is removed. The contribution of this paper is twofold. First, a simple and, at the same time, effective method k–NDDP is proposed. The method is the extension of k-NMF, i.e., the state-of-the-art method to protect against mutual friend attack, to defend against identity disclosures by adding noise to the social data. Second, the achieved privacy using the concept of differential privacy is evaluated. An extensive empirical study shows that for different values of k, the divergence produced by k-NDDP for CC, BW and APL is not more than 0.8%, also added dummy links are 60% less, as compared to k-NMF approach, thereby it validates that the proposed k-NDDP approach provides strong privacy while maintaining the usefulness of data. Full article
(This article belongs to the Special Issue Big Data Privacy-Preservation)
Show Figures

Figure 1

10 pages, 1870 KiB  
Communication
Radiation Beam Pattern Control of UHF RFID Tag Antenna Design for Automotive License Plates
by Youchung Chung and Teklebrhan H. Berhe
Electronics 2021, 10(19), 2439; https://doi.org/10.3390/electronics10192439 - 8 Oct 2021
Cited by 1 | Viewed by 2009
Abstract
This paper presents a design of a radio frequency identification (RFID) tag antenna in the ultra-high-frequency (UHF) range, which is applicable to a vehicular license plate attached to a vehicle bumper. The main goals are to first improve the identification ratio by controlling [...] Read more.
This paper presents a design of a radio frequency identification (RFID) tag antenna in the ultra-high-frequency (UHF) range, which is applicable to a vehicular license plate attached to a vehicle bumper. The main goals are to first improve the identification ratio by controlling the radiation beam pattern and, second, to control the beam direction. Since every vehicle has a license plate, the available plate structure is used to design the antenna. The shape of the tag is rectangular and has a dimension of 525 mm × 116 mm, which is smaller than the typical size of standard plates, 540 mm × 120 mm, used in Europe and Korea. The fabricated tag antenna, the license plate, and the vehicular bumper are fixed by volt and nut. For vehicle tracking and identification, RFID readers are deployed on the road side. For efficient identification, a long distance passive UHF RFID license plate with a patch antenna is proposed to provide not only line-of-sight identification but also left and right beams. Unlike the general UHF tag antennas, in this paper, the patch antenna is designed to attach to the metal part of the car, the license plate holder. The beam patterns of the RFID tag antenna can be controlled by the patch antenna parameter values. The simulation result demonstrates that the proposed UHF RFID tag antenna has a beam radiation pattern as required at 920 MHz. In addition, the estimated read range of the proposed plate meets the requirement of RFID systems. Full article
(This article belongs to the Collection Smart Sensing RFID Tags)
Show Figures

Figure 1

12 pages, 723 KiB  
Article
Reliability of Recurrence Quantification Analysis Measures for Sit-to-Stand and Stand-to-Sit Activities in Healthy Older Adults Using Wearable Sensors
by Amnah Nasim, David C. Nchekwube and Yoon Sang Kim
Electronics 2021, 10(19), 2438; https://doi.org/10.3390/electronics10192438 - 8 Oct 2021
Cited by 2 | Viewed by 1773
Abstract
Standing up and sitting down are prerequisite motions in most activities of daily living scenarios. The ability to sit down in and stand up from a chair or a bed depreciates and becomes a complex task with increasing age. Hence, research on the [...] Read more.
Standing up and sitting down are prerequisite motions in most activities of daily living scenarios. The ability to sit down in and stand up from a chair or a bed depreciates and becomes a complex task with increasing age. Hence, research on the analysis and recognition of these two activities can help in the design of algorithms for assistive devices. In this work, we propose a reliability analysis for testing the internal consistency of nonlinear recurrence features for sit-to-stand (Si2St) and stand-to-sit (St2Si) activities for motion acceleration data collected by a wearable sensing device for 14 healthy older subjects in the age range of 78 ± 4.9 years. Four recurrence features—%recurrence rate, %determinism, entropy, and average diagonal length—were calculated by using recurrence plots for both activities. A detailed relative and absolute reliability statistical analysis based on Cronbach’s correlation coefficient (α) and standard error of measurement was performed for all recurrence measures. Correlation values as high as α = 0.68 (%determinism) and α = 0.72 (entropy) in the case of Si2St and α = 0.64 (%determinism) and α = 0.69 (entropy) in the case of St2Si—with low standard error in the measurements—show the reliability of %determinism and entropy for repeated acceleration measurements for the characterization of both the St2Si and Si2St activities in the case of healthy older adults. Full article
(This article belongs to the Special Issue Wearable Electronics for Assessing Human Motor (dis)Abilities)
Show Figures

Figure 1

14 pages, 4153 KiB  
Article
Transient Stability Enhancement of a Grid-Connected Large-Scale PV System Using Fuzzy Logic Controller
by Md. Rifat Hazari, Effat Jahan, Mohammad Abdul Mannan and Narottam Das
Electronics 2021, 10(19), 2437; https://doi.org/10.3390/electronics10192437 - 8 Oct 2021
Cited by 12 | Viewed by 2851
Abstract
This paper presents a new intelligent control strategy to augment the low-voltage ride-through (LVRT) potential of photovoltaic (PV) plants, and the transient stability of a complete grid system. Modern grid codes demand that a PV plant should be connected to the main power [...] Read more.
This paper presents a new intelligent control strategy to augment the low-voltage ride-through (LVRT) potential of photovoltaic (PV) plants, and the transient stability of a complete grid system. Modern grid codes demand that a PV plant should be connected to the main power system during network disturbance, providing voltage support. Therefore, in this paper, a novel fuzzy logic controller (FLC) using the controlled cascaded strategy is proposed for the grid side converter (GSC) of a PV plant to guarantee voltage recovery. The proposed FLC offers variable gains based upon the system requirements, which can inject a useful amount of reactive power after a severe network disturbance. Therefore, the terminal voltage dip will be low, restoring its pre-fault value and resuming its operation quickly. To make it realistic, the PV system is linked to the well-known IEEE nine bus system. Comparative analysis is shown—using power system computer-aided design/electromagnetic transients including DC (PSCAD/EMTDC) software—between the conventional proportional–integral (PI) controller-based cascaded strategy and the proposed control strategy to authenticate the usefulness of the proposed strategy. The comparative simulation results indicate that the transient stability and the LVRT capability of a grid-tied PV system can be augmented against severe fault using the proposed FLC-based cascaded GSC controller. Full article
Show Figures

Figure 1

2 pages, 133 KiB  
Editorial
High-Power Vacuum Electronic Devices from Microwave to THz Band: Way Forward
by Glyavin Mikhail
Electronics 2021, 10(19), 2436; https://doi.org/10.3390/electronics10192436 - 8 Oct 2021
Cited by 4 | Viewed by 1518
Abstract
It is generally accepted that the 20th century was the age of electronics [...] Full article
15 pages, 3893 KiB  
Article
Social Distance Monitoring Approach Using Wearable Smart Tags
by Tareq Alhmiedat and Majed Aborokbah
Electronics 2021, 10(19), 2435; https://doi.org/10.3390/electronics10192435 - 8 Oct 2021
Cited by 20 | Viewed by 11527
Abstract
Coronavirus has affected millions of people worldwide, with the rate of infected people still increasing. The virus is transmitted between people through direct, indirect, or close contact with infected people. To help prevent the social transmission of COVID-19, this paper presents a new [...] Read more.
Coronavirus has affected millions of people worldwide, with the rate of infected people still increasing. The virus is transmitted between people through direct, indirect, or close contact with infected people. To help prevent the social transmission of COVID-19, this paper presents a new smart social distance system that allows individuals to keep social distances between others in indoor and outdoor environments, avoiding exposure to COVID-19 and slowing its spread locally and across the country. The proposed smart monitoring system consists of a new smart wearable prototype of a compact and low-cost electronic device, based on human detection and proximity distance functions, to estimate the social distance between people and issue a notification when the social distance is less than a predefined threshold value. The developed social system has been validated through several experiments, and achieved a high acceptance rate (96.1%) and low localization error (<6 m). Full article
Show Figures

Figure 1

12 pages, 6244 KiB  
Article
An Effective Multi-Task Two-Stage Network with the Cross-Scale Training Strategy for Multi-Scale Image Super Resolution
by Jucheng Yang, Feng Wei, Yaxin Bai, Meiran Zuo, Xiao Sun and Yarui Chen
Electronics 2021, 10(19), 2434; https://doi.org/10.3390/electronics10192434 - 7 Oct 2021
Cited by 3 | Viewed by 1720
Abstract
Convolutional neural networks and the per-pixel loss function have shown their potential to be the best combination for super-resolving severely degraded images. However, there are still challenges, such as the massive number of parameters requiring prohibitive memory and vast computing and storage resources [...] Read more.
Convolutional neural networks and the per-pixel loss function have shown their potential to be the best combination for super-resolving severely degraded images. However, there are still challenges, such as the massive number of parameters requiring prohibitive memory and vast computing and storage resources as well as time-consuming training and testing. What is more, the per-pixel loss measured by L2 and the Peak Signal-to-Noise Ratio do not correlate well with human perception of image quality, since L2 simply does not capture the intricate characteristics of human visual systems. To address these issues, we propose an effective two-stage hourglass network with multi-task co-optimization, which enables the entire network to focus on training and testing time and inherent image patterns such as local luminance, contrast, structure and data distribution. Moreover, to avoid overwhelming memory overheads, our model is capable of performing real-time single image multi-scale super-resolution, so it is memory-friendly, meaning that memory space is utilized efficiently. In addition, in order to best use the underlying structure and perception of image quality and the intermediate estimates during the inference process, we introduce a cross-scale training strategy with 2×, 3× and 4× image super-resolution. This effective multi-task two-stage network with the cross-scale strategy for multi-scale image super-resolution is named EMTCM. Quantitative and qualitative experiment results show that the proposed EMTCM network outperforms state-of-the-art methods in recovering high-quality images. Full article
(This article belongs to the Special Issue New Techniques for Image and Video Coding)
Show Figures

Figure 1

17 pages, 4224 KiB  
Article
QiBAM: Approximate Sub-String Index Search on Quantum Accelerators Applied to DNA Read Alignment
by Aritra Sarkar, Zaid Al-Ars, Carmen G. Almudever and Koen L. M. Bertels
Electronics 2021, 10(19), 2433; https://doi.org/10.3390/electronics10192433 - 7 Oct 2021
Cited by 7 | Viewed by 2837
Abstract
With small-scale quantum processors transitioning from experimental physics labs to industrial products, these processors in a few years are expected to scale up and be more robust for efficiently computing important algorithms in various fields. In this paper, we propose a quantum algorithm [...] Read more.
With small-scale quantum processors transitioning from experimental physics labs to industrial products, these processors in a few years are expected to scale up and be more robust for efficiently computing important algorithms in various fields. In this paper, we propose a quantum algorithm to address the challenging field of data processing for genome sequence reconstruction. This research describes an architecture-aware implementation of a quantum algorithm for sub-sequence alignment. A new algorithm named QiBAM (quantum indexed bidirectional associative memory) is proposed, which uses approximate pattern-matching based on Hamming distances. QiBAM extends the Grover’s search algorithm in two ways, allowing: (1) approximate matches needed for read errors in genomics, and (2) a distributed search for multiple solutions over the quantum encoding of DNA sequences. This approach gives a quadratic speedup over the classical algorithm. A full implementation of the algorithm is provided and verified using the OpenQL compiler and QX Simulator framework. Our implementation represents a first exploration towards a full-stack quantum accelerated genome sequencing pipeline design. Full article
(This article belongs to the Special Issue Quantum Computing System Design and Architecture)
Show Figures

Figure 1

23 pages, 9017 KiB  
Article
A Three-Stage Data-Driven Approach for Determining Reaction Wheels’ Remaining Useful Life Using Long Short-Term Memory
by Md Sirajul Islam and Afshin Rahimi
Electronics 2021, 10(19), 2432; https://doi.org/10.3390/electronics10192432 - 7 Oct 2021
Cited by 7 | Viewed by 2333
Abstract
Reaction wheels are widely used in the attitude control system of small satellites. Unfortunately, reaction wheels failure restricts the efficacy of a satellite, and it is one of the many reasons leading to premature abandonment of the satellites. This study observes the measurable [...] Read more.
Reaction wheels are widely used in the attitude control system of small satellites. Unfortunately, reaction wheels failure restricts the efficacy of a satellite, and it is one of the many reasons leading to premature abandonment of the satellites. This study observes the measurable system parameter of a faulty reaction wheel induced with incipient fault to estimate the remaining useful life of the reaction wheels. We achieve this goal in three stages, as none of the observable system parameters are directly related to the health of a reaction wheel. In the first stage, we identify the necessary observable system parameter and predict the future of these parameters using sensor acquired data and a long short-term memory recurrent neural network. In the second stage, we estimate the health index parameter using a multivariate long short-term memory network. In the third stage, we predict the remaining useful life of reaction wheels based on historical data of the health index parameter. Normalized root mean squared error is used to evaluate the performance of the various models in each stage. Additionally, three different timespans (short, moderate, and extended in the scale of small satellite orbit times) are simulated and tested for the performance of the proposed methodology regarding the malfunction of reaction wheels. Furthermore, the robustness of the proposed method to missing values, input frequency, and noise is studied. The results show promising performance for the proposed scheme with accuracy in predicting health index parameter around 0.01–0.02 normalized root mean squared error, the accuracy in prediction of RUL of 1%–2.5%, and robustness to various uncertainty factors, as discussed above. Full article
(This article belongs to the Special Issue Advances in Machine Condition Monitoring and Fault Diagnosis)
Show Figures

Figure 1

15 pages, 4524 KiB  
Article
Development of C-Shaped Parasitic MIMO Antennas for Mutual Coupling Reduction
by Hamizan Yon, Nurul Huda Abd Rahman, Mohd Aziz Aris, Mohd Haizal Jamaluddin, Irene Kong Cheh Lin, Hadi Jumaat, Fatimah Nur Mohd Redzwan and Yoshihide Yamada
Electronics 2021, 10(19), 2431; https://doi.org/10.3390/electronics10192431 - 7 Oct 2021
Cited by 18 | Viewed by 2357
Abstract
In the 5G system, multiple-input multiple-output (MIMO) antennas for both transmitting and receiving ends are required. However, the design of MIMO antennas at the 5G upper band is challenging due to the mutual coupling issues. Many techniques have been proposed to improve antenna [...] Read more.
In the 5G system, multiple-input multiple-output (MIMO) antennas for both transmitting and receiving ends are required. However, the design of MIMO antennas at the 5G upper band is challenging due to the mutual coupling issues. Many techniques have been proposed to improve antenna isolation; however, some of the designs have impacts on the antenna performance, especially on the gain and bandwidth reduction, or an increase in the overall size. Thus, a design with a detailed trade-off study must be implemented. This article proposes a new C-shaped parasitic structure around a main circular radiating patch of a MIMO antenna at 16 GHz with enhanced isolation features. The proposed antenna comprises two elements with a separation of 0.32λ edge to edge between radiation parts placed in a linear configuration with an overall dimension of 15 mm × 26 mm. The C-shaped parasitic element was introduced around the main radiating antenna for better isolation. Based on the measurement results, the proposed structure significantly improved the isolation from −23.86 dB to −32.32 dB and increased the bandwidth from 1150 MHz to 1400 MHz. For validation, the envelope correlation coefficient (ECC) and the diversity gain (DG) were also measuredas 0.148 dB and 9.89 dB, respectively. Other parameters, such as the radiation pattern, the total average reflection coefficient and the mean effective gain, were also calculated to ensure the validity of the proposed structure. Based on the design work and analysis, the proposed structure was proven to improve the antenna isolation and increase the bandwidth, while maintaining the small overall dimension. Full article
(This article belongs to the Special Issue Antennas in the 5G System)
Show Figures

Figure 1

9 pages, 10159 KiB  
Article
Inverse Design of a Microstrip Meander Line Slow Wave Structure with XGBoost and Neural Network
by Yijun Zhu, Yang Xie, Ningfeng Bai and Xiaohan Sun
Electronics 2021, 10(19), 2430; https://doi.org/10.3390/electronics10192430 - 7 Oct 2021
Cited by 4 | Viewed by 1774
Abstract
We present a new machine learning (ML) deep learning (DL) synthesis algorithm for the design of a microstrip meander line (MML) slow wave structure (SWS). Exact numerical simulation data are used in the training of our network as a form of supervised learning. [...] Read more.
We present a new machine learning (ML) deep learning (DL) synthesis algorithm for the design of a microstrip meander line (MML) slow wave structure (SWS). Exact numerical simulation data are used in the training of our network as a form of supervised learning. The learning results show that the training mean squared error is as low as 5.23 × 10−2 when using 900 sets of data. When the desired performance is reached, workable geometry parameters can be obtained by this algorithm. A D-band MML SWS with 20 GHz bandwidth at 160 GHz center frequency is then designed using the auto-design neural network (ADNN). A cold test shows that its phase velocity varies by 0.005 c, and the transmission rate of a 50-period SWS is greater than −5 dB with the reflectivity below −15 dB when the frequency is from 150 to 170 GHz. Particle-in-cell (PIC) simulation also illustrates that a maximum power of 3.2 W is reached at 160 GHz with 34.66 dB gain and output power greater than 1 W from 152 to 168 GHz. Full article
(This article belongs to the Special Issue High-Frequency Vacuum Electron Devices)
Show Figures

Figure 1

18 pages, 36122 KiB  
Article
Reconfigurable Morphological Processor for Grayscale Image Processing
by Bin Zhang
Electronics 2021, 10(19), 2429; https://doi.org/10.3390/electronics10192429 - 7 Oct 2021
Cited by 4 | Viewed by 2085
Abstract
Grayscale morphology is a powerful tool in image, video, and visual applications. A reconfigurable processor is proposed for grayscale image morphological processing. The architecture of the processor is a combination of a reconfigurable grayscale processing module (RGPM) and peripheral circuits. The RGPM, which [...] Read more.
Grayscale morphology is a powerful tool in image, video, and visual applications. A reconfigurable processor is proposed for grayscale image morphological processing. The architecture of the processor is a combination of a reconfigurable grayscale processing module (RGPM) and peripheral circuits. The RGPM, which consists of four grayscale computing units, conducts grayscale morphological operations and implements related algorithms of more than 100 f/s for a 1024 × 1024 image. The periphery circuits control the entire image processing and dynamic reconfiguration process. Synthesis results show that the proposed processor can provide 43.12 GOPS and achieve 8.87 GOPS/mm2 at a 220-MHz system clock. The simulation and experimental results show that the processor is suitable for high-performance embedded systems. Full article
Show Figures

Figure 1

30 pages, 40138 KiB  
Article
Per-Core Power Modeling for Heterogenous SoCs
by Ganapati Bhat, Sumit K. Mandal, Sai T. Manchukonda, Sai V. Vadlamudi, Ayushi Agarwal, Jun Wang and Umit Y. Ogras
Electronics 2021, 10(19), 2428; https://doi.org/10.3390/electronics10192428 - 7 Oct 2021
Cited by 1 | Viewed by 2556
Abstract
State-of-the-art mobile platforms, such as smartphones and tablets, are powered by heterogeneous system-on-chips (SoCs). These SoCs are composed of many processing elements, including multiple CPU core clusters (e.g., big.LITTLE cores), graphics processing units (GPUs), memory controllers and other on-chip resources. On the one [...] Read more.
State-of-the-art mobile platforms, such as smartphones and tablets, are powered by heterogeneous system-on-chips (SoCs). These SoCs are composed of many processing elements, including multiple CPU core clusters (e.g., big.LITTLE cores), graphics processing units (GPUs), memory controllers and other on-chip resources. On the one hand, mobile platforms need to provide a swift response time for interactive apps and high throughput for graphics-oriented workloads; on the other hand, the power consumption must be under tight control to prevent high skin temperatures and energy consumption. Therefore, commercial systems feature a range of mechanisms for dynamic power and temperature control. However, these techniques rely on simple indicators, such as core utilization and total power consumption. System architects are typically limited to the total power consumption, since multiple resources share the same power rail. More importantly, most of the power rails are not exposed to the input/output pins. To address this challenge, this paper presents a thorough methodology to model the power consumption of major resources in heterogeneous SoCs. The proposed models utilize a wide range of performance counters to capture the workload dynamics accurately. Experimental validation on a Nexus 6P phone, powered by an octa-core Snapdragon 810 SoC, showed that the proposed models can estimate the power consumption within a 10% error margin. Full article
(This article belongs to the Section Microelectronics)
Show Figures

Figure 1

24 pages, 21801 KiB  
Article
Assessment and Improvement of the Pattern Recognition Performance of Memdiode-Based Cross-Point Arrays with Randomly Distributed Stuck-at-Faults
by Fernando L. Aguirre, Sebastián M. Pazos, Félix Palumbo, Antoni Morell, Jordi Suñé and Enrique Miranda
Electronics 2021, 10(19), 2427; https://doi.org/10.3390/electronics10192427 - 6 Oct 2021
Cited by 3 | Viewed by 2057
Abstract
In this work, the effect of randomly distributed stuck-at faults (SAFs) in memristive cross-point array (CPA)-based single and multi-layer perceptrons (SLPs and MLPs, respectively) intended for pattern recognition tasks is investigated by means of realistic SPICE simulations. The quasi-static memdiode model (QMM) is [...] Read more.
In this work, the effect of randomly distributed stuck-at faults (SAFs) in memristive cross-point array (CPA)-based single and multi-layer perceptrons (SLPs and MLPs, respectively) intended for pattern recognition tasks is investigated by means of realistic SPICE simulations. The quasi-static memdiode model (QMM) is considered here for the modelling of the synaptic weights implemented with memristors. Following the standard memristive approach, the QMM comprises two coupled equations, one for the electron transport based on the double-diode equation with a single series resistance and a second equation for the internal memory state of the device based on the so-called logistic hysteron. By modifying the state parameter in the current-voltage characteristic, SAFs of different severeness are simulated and the final outcome is analysed. Supervised ex-situ training and two well-known image datasets involving hand-written digits and human faces are employed to assess the inference accuracy of the SLP as a function of the faulty device ratio. The roles played by the memristor’s electrical parameters, line resistance, mapping strategy, image pixelation, and fault type (stuck-at-ON or stuck-at-OFF) on the CPA performance are statistically analysed following a Monte-Carlo approach. Three different re-mapping schemes to help mitigate the effect of the SAFs in the SLP inference phase are thoroughly investigated. Full article
(This article belongs to the Special Issue RRAM Devices: Multilevel State Control and Applications)
Show Figures

Graphical abstract

19 pages, 3099 KiB  
Article
Nonlinear Model Predictive Control of Single-Link Flexible-Joint Robot Using Recurrent Neural Network and Differential Evolution Optimization
by Anlong Zhang, Zhiyun Lin, Bo Wang and Zhimin Han
Electronics 2021, 10(19), 2426; https://doi.org/10.3390/electronics10192426 - 6 Oct 2021
Cited by 14 | Viewed by 2999
Abstract
A recurrent neural network (RNN) and differential evolution optimization (DEO) based nonlinear model predictive control (NMPC) technique is proposed for position control of a single-link flexible-joint (FJ) robot. First, a simple three-layer recurrent neural network with rectified linear units as an activation function [...] Read more.
A recurrent neural network (RNN) and differential evolution optimization (DEO) based nonlinear model predictive control (NMPC) technique is proposed for position control of a single-link flexible-joint (FJ) robot. First, a simple three-layer recurrent neural network with rectified linear units as an activation function (ReLU-RNN) is employed for approximating the system dynamic model. Then, using the RNN predictive model and model predictive control (MPC) scheme, an RNN and DEO based NMPC controller is designed, and the DEO algorithm is used to solve the controller. Finally, comparing numerical simulation findings demonstrates the efficiency and performance of the proposed approach. The merit of this method is that not only is the control precision satisfied, but also the overshoots and the residual vibration are well suppressed. Full article
(This article belongs to the Collection Predictive and Learning Control in Engineering Applications)
Show Figures

Graphical abstract

14 pages, 4980 KiB  
Article
The Prediction of Capacity Trajectory for Lead–Acid Battery Based on Steep Drop Curve of Discharge Voltage and Gaussian Process Regression
by Qian Li, Guangzhen Liu, Ji’ang Zhang, Zhan Su, Chunyan Hao, Ju He and Ze Cheng
Electronics 2021, 10(19), 2425; https://doi.org/10.3390/electronics10192425 - 6 Oct 2021
Cited by 4 | Viewed by 1852
Abstract
In this paper, a method of capacity trajectory prediction for lead-acid battery, based on the steep drop curve of discharge voltage and improved Gaussian process regression model, is proposed by analyzing the relationship between the current available capacity and the voltage curve of [...] Read more.
In this paper, a method of capacity trajectory prediction for lead-acid battery, based on the steep drop curve of discharge voltage and improved Gaussian process regression model, is proposed by analyzing the relationship between the current available capacity and the voltage curve of short-time discharging. The battery under average charging is discharged for a short time, and the voltage drop of short-time discharging during equal time intervals, which has the highest relevance with presently available capacity, is extracted as the health feature (HF), and the ergodic method is used to search the optimal time interval. Then, a Gaussian process regression (GPR) model, which reflects the capacity degradation of lead–acid battery, is established (with the HF series as input and current available capacity series as output). Considering the complex trend of capacity trajectory, the rational quadratic covariance function is used as the kernel function of GPR model, and the conjugate gradient algorithm is used for optimization, in order to improve the nonlinear mapping ability of GPR. Finally, the experimental results of lead-acid batteries under different charging cut-off voltages and operating temperatures show that the proposed method can effectively predict the capacity change trajectory of lead–acid batteries with a small training sample, showing high prediction accuracy and wide applicability. Full article
(This article belongs to the Section Power Electronics)
Show Figures

Figure 1

20 pages, 2669 KiB  
Article
Fractional Order Adaptive Fast Super-Twisting Sliding Mode Control for Steer-by-Wire Vehicles with Time-Delay Estimation
by Yong Yang, Yunbing Yan and Xiaowei Xu
Electronics 2021, 10(19), 2424; https://doi.org/10.3390/electronics10192424 - 5 Oct 2021
Cited by 5 | Viewed by 1720
Abstract
It is difficult to model and determine the parameters of the steer-by-wire (SBW) system accurately, and the perturbation is variable with complex and changeable tire–road conditions. In order to improve the control performance of the vehicle SBW system, an adaptive fast super-twisting sliding [...] Read more.
It is difficult to model and determine the parameters of the steer-by-wire (SBW) system accurately, and the perturbation is variable with complex and changeable tire–road conditions. In order to improve the control performance of the vehicle SBW system, an adaptive fast super-twisting sliding mode control (AFST-SMC) scheme with time-delay estimation (TDE) is proposed. The proposed scheme uses TDE to acquire the lumped dynamics in a simple way and establishes a practical model-free structure. Then, a fractional order (FO) sliding mode surface and a fast super-twisting sliding mode control structure were designed on the basic super-twisting sliding mode to ensure fast convergence and high control accuracy. Since the uncertain boundary information of the actual system is unknown, a novel adaptive algorithm is proposed to regulate the control gain based on the control errors. Theoretical analysis concerning system stability is given based on the Lyapunov theory. Finally, the effectiveness of the method is verified through comparative experiments. The results show that the proposed TDE-AFST-FOSMC control scheme has the advantages of model-free, fast response and high accuracy. Full article
(This article belongs to the Section Systems & Control Engineering)
Show Figures

Figure 1

16 pages, 6178 KiB  
Article
A Low-Power GPIO-Based Size Sensor to Monitor the Imbibition of Corn Seeds Beneath Soil
by Ehab A. Hamed, Jordan Athas, Xincheng Zhang, Noah Ashenden and Inhee Lee
Electronics 2021, 10(19), 2423; https://doi.org/10.3390/electronics10192423 - 4 Oct 2021
Viewed by 1865
Abstract
Seed imbibition, absorption of water by a dry seed, is an essential process in which embryo hydration and root establishment occur. In natural environments, this process occurs beneath the soil, making it difficult to observe preliminary growth of seeds. This paper presents a [...] Read more.
Seed imbibition, absorption of water by a dry seed, is an essential process in which embryo hydration and root establishment occur. In natural environments, this process occurs beneath the soil, making it difficult to observe preliminary growth of seeds. This paper presents a new technique for tracking the imbibition of corn seeds. The proposed system is designed to measure imbibition through seed expansion and wirelessly transmit data, permitting the system to remain beneath the soil with the subject seed. The system consists of low-cost commercial off-the-shelf components and 3D-printed probes. The proposed system is optimized to measure the size of multiple seeds with a single Analog-to-Digital Converter (ADC) pin by utilizing the General-Purpose Input Output (GPIO) pins of the microcontroller, to reconfigure connections to supply voltage or ground. The circuit design of the system shows low power consumption compared to other conventional circuits and utilizes fewer components by taking advantage of the microcontroller GPIOs. Additionally, the proposed circuit design shows less error and insensitivity to the supply voltage variations. Full article
(This article belongs to the Special Issue Application of Wireless Sensor Networks in Accredited Monitoring)
Show Figures

Figure 1

17 pages, 6901 KiB  
Article
Supercapacitor Assisted Hybrid PV System for Efficient Solar Energy Harnessing
by Kasun Piyumal, Aruna Ranaweera, Sudath Kalingamudali and Nihal Kularatna
Electronics 2021, 10(19), 2422; https://doi.org/10.3390/electronics10192422 - 4 Oct 2021
Cited by 2 | Viewed by 3295
Abstract
In photovoltaic (PV) systems, maximum power point (MPP) is tracked by matching the load impedance to the internal impedance of the PV array by adjusting the duty cycle of the associated DC-DC converter. Scientists are trying to improve the efficiency of these converters [...] Read more.
In photovoltaic (PV) systems, maximum power point (MPP) is tracked by matching the load impedance to the internal impedance of the PV array by adjusting the duty cycle of the associated DC-DC converter. Scientists are trying to improve the efficiency of these converters by improving the performance of the power stage, while limited attention is given to finding alternative methods. This article describes a novel supercapacitor (SC) assisted technique to enhance the efficiency of a PV system without modifying the power stage of the charge controller. The proposed system is an SC—battery hybrid PV system where an SC bank is coupled in series with a PV array to enhance the overall system efficiency. Developed prototype of the proposed system with SC assisted loss circumvention embedded with a DC microgrid application detailed in the article showed that the average efficiency of the PV system is increased by 8%. This article further describes the theoretical and experimental investigation of the impedance matching technique for the proposed PV system, explaining how to adapt typical impedance matching for maximum power transfer. Full article
(This article belongs to the Section Power Electronics)
Show Figures

Figure 1

4 pages, 161 KiB  
Editorial
Electronic Solutions for Artificial Intelligence Healthcare
by Hyeyoung Ko and Jun-Ho Huh
Electronics 2021, 10(19), 2421; https://doi.org/10.3390/electronics10192421 - 4 Oct 2021
Cited by 4 | Viewed by 2289
Abstract
At present, diverse, innovative technology is used in electronics and ubiquitous computing environments [...] Full article
(This article belongs to the Special Issue Electronic Solutions for Artificial Intelligence Healthcare)
Show Figures

Graphical abstract

20 pages, 5425 KiB  
Article
Edge Container for Speech Recognition
by Lukáš Beňo, Rudolf Pribiš and Peter Drahoš
Electronics 2021, 10(19), 2420; https://doi.org/10.3390/electronics10192420 - 4 Oct 2021
Cited by 4 | Viewed by 2900
Abstract
Containerization has been mainly used in pure software solutions, but it is gradually finding its way into the industrial systems. This paper introduces the edge container with artificial intelligence for speech recognition, which performs the voice control function of the actuator as a [...] Read more.
Containerization has been mainly used in pure software solutions, but it is gradually finding its way into the industrial systems. This paper introduces the edge container with artificial intelligence for speech recognition, which performs the voice control function of the actuator as a part of the Human Machine Interface (HMI). This work proposes a procedure for creating voice-controlled applications with modern hardware and software resources. The created architecture integrates well-known digital technologies such as containerization, cloud, edge computing and a commercial voice processing tool. This methodology and architecture enable the actual speech recognition and the voice control on the edge device in the local network, rather than in the cloud, like the majority of recent solutions. The Linux containers are designed to run without any additional configuration and setup by the end user. A simple adaptation of voice commands via configuration file may be considered as an additional contribution of the work. The architecture was verified by experiments with running containers on different devices, such as PC, Tinker Board 2, Raspberry Pi 3 and 4. The proposed solution and the practical experiment show how a voice-controlled system can be created, easily managed and distributed to many devices around the world in a few seconds. All this can be achieved by simple downloading and running two types of ready-made containers without any complex installations. The result of this work is a proven stable (network-independent) solution with data protection and low latency. Full article
Show Figures

Figure 1

59 pages, 9802 KiB  
Article
Meta-Heuristic Optimization Techniques Used for Maximum Power Point Tracking in Solar PV System
by Preeti Verma, Afroz Alam, Adil Sarwar, Mohd Tariq, Hani Vahedi, Deeksha Gupta, Shafiq Ahmad and Adamali Shah Noor Mohamed
Electronics 2021, 10(19), 2419; https://doi.org/10.3390/electronics10192419 - 3 Oct 2021
Cited by 33 | Viewed by 4101
Abstract
A critical advancement in solar photovoltaic (PV) establishment has led to robust acceleration towards the evolution of new MPPT techniques. The sun-oriented PV framework has a non-linear characteristic in varying climatic conditions, which considerably impact the PV framework yield. Furthermore, the partial shading [...] Read more.
A critical advancement in solar photovoltaic (PV) establishment has led to robust acceleration towards the evolution of new MPPT techniques. The sun-oriented PV framework has a non-linear characteristic in varying climatic conditions, which considerably impact the PV framework yield. Furthermore, the partial shading condition (PSC) causes major problems, such as a drop in the output power yield and multiple peaks in the P–V attribute. Hence, following the global maximum power point (GMPP) under PSC is a demanding problem. Subsequently, different maximum power point tracking (MPPT) strategies have been utilized to improve the yield of a PV framework. However, the disarray lies in choosing the best MPPT technique from the wide algorithms for a particular purpose. Each algorithm has its benefits and drawbacks. Hence, there is a fundamental need for an appropriate audit of the MPPT strategies from time to time. This article presents new works done in the global power point tracking (GMPPT) algorithm field under the PSCs. It sums up different MPPT strategies alongside their working principle, mathematical representation, and flow charts. Moreover, tables depicted in this study briefly organize the significant attributes of algorithms. This work will serve as a reference for sorting an MPPT technique while designing PV systems. Full article
(This article belongs to the Special Issue Power Electronics in Automotive Industry Applications)
Show Figures

Figure 1

Previous Issue
Back to TopTop