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Electronics
Volume 10
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Electronics, Volume 10, Issue 1 (January-1 2021) – 97 articles

Cover Story (view full-size image): Based on the characteristics of the state presented by the EW conflict system over time, this paper first analyzes the dynamic relationship between the state of the target unit and the task unit of the EW system by studying the conflicts in the EW system. Secondly, based on studying the measurability of condition state attributes of the EW conflict system, the measurable state space of EW effectiveness is established by referring to the multidimensional holistic analysis strategy in the spatial analysis method. Then, randomness is introduced into the state space. Finally, a dynamic conflict analysis model for EW effectiveness is constructed. The static and dynamic correlation characteristics are analyzed during the conflict states of EW, and the availability of the method is verified by an example. View this paper
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15 pages, 7254 KiB  
Article
SmartFit: Smartphone Application for Garment Fit Detection
by Kamrul H. Foysal, Hyo Jung Chang, Francine Bruess and Jo Woon Chong
Electronics 2021, 10(1), 97; https://doi.org/10.3390/electronics10010097 - 05 Jan 2021
Cited by 14 | Viewed by 5246
Abstract
The apparel e-commerce industry is growing day by day. In recent times, consumers are particularly interested in an easy and time-saving way of online apparel shopping. In addition, the COVID-19 pandemic has generated more need for an effective and convenient online shopping solution [...] Read more.
The apparel e-commerce industry is growing day by day. In recent times, consumers are particularly interested in an easy and time-saving way of online apparel shopping. In addition, the COVID-19 pandemic has generated more need for an effective and convenient online shopping solution for consumers. However, online shopping, particularly online apparel shopping, has several challenges for consumers. These issues include sizing, fit, return, and cost concerns. Especially, the fit issue is one of the cardinal factors causing hesitance and drawback in online apparel purchases. The conventional method of clothing fit detection based on body shapes relies upon manual body measurements. Since no convenient and easy-to-use method has been proposed for body shape detection, we propose an interactive smartphone application, “SmartFit”, that will provide the optimal fitting clothing recommendation to the consumer by detecting their body shape. This optimal recommendation is provided by using image processing and machine learning that are solely dependent on smartphone images. Our preliminary assessment of the developed model shows an accuracy of 87.50% for body shape detection, producing a promising solution to the fit detection problem persisting in the digital apparel market. Full article
(This article belongs to the Special Issue Smart Bioelectronics and Wearable Systems)
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15 pages, 3615 KiB  
Article
Gaining a Sense of Touch Object Stiffness Estimation Using a Soft Gripper and Neural Networks
by Michal Bednarek, Piotr Kicki, Jakub Bednarek and Krzysztof Walas
Electronics 2021, 10(1), 96; https://doi.org/10.3390/electronics10010096 - 05 Jan 2021
Cited by 11 | Viewed by 3705
Abstract
Soft grippers are gaining significant attention in the manipulation of elastic objects, where it is required to handle soft and unstructured objects, which are vulnerable to deformations. The crucial problem is to estimate the physical parameters of a squeezed object to adjust the [...] Read more.
Soft grippers are gaining significant attention in the manipulation of elastic objects, where it is required to handle soft and unstructured objects, which are vulnerable to deformations. The crucial problem is to estimate the physical parameters of a squeezed object to adjust the manipulation procedure, which poses a significant challenge. The research on physical parameters estimation using deep learning algorithms on measurements from direct interaction with objects using robotic grippers is scarce. In our work, we proposed a trainable system which performs the regression of an object stiffness coefficient from the signals registered during the interaction of the gripper with the object. First, using the physics simulation environment, we performed extensive experiments to validate our approach. Afterwards, we prepared a system that works in a real-world scenario with real data. Our learned system can reliably estimate the stiffness of an object, using the Yale OpenHand soft gripper, based on readings from Inertial Measurement Units (IMUs) attached to the fingers of the gripper. Additionally, during the experiments, we prepared three datasets of IMU readings gathered while squeezing the objects—two created in the simulation environment and one composed of real data. The dataset is the contribution to the community providing the way for developing and validating new approaches in the growing field of soft manipulation. Full article
(This article belongs to the Special Issue Artificial Intelligence and Ambient Intelligence)
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17 pages, 3091 KiB  
Article
Multi-Step Learning-by-Examples Strategy for Real-Time Brain Stroke Microwave Scattering Data Inversion
by Marco Salucci, Alessandro Polo and Jan Vrba
Electronics 2021, 10(1), 95; https://doi.org/10.3390/electronics10010095 - 05 Jan 2021
Cited by 24 | Viewed by 3223
Abstract
This work deals with the computationally-efficient inversion of microwave scattering data for brain stroke detection and monitoring. The proposed multi-step approach is based on the Learning-by-Examples (LBE) paradigm and naturally matches the stages and time constraints of an effective clinical diagnosis. Stroke detection, [...] Read more.
This work deals with the computationally-efficient inversion of microwave scattering data for brain stroke detection and monitoring. The proposed multi-step approach is based on the Learning-by-Examples (LBE) paradigm and naturally matches the stages and time constraints of an effective clinical diagnosis. Stroke detection, identification, and localization are solved with real-time performance through support vector machines (SVMs) operating both in binary/multi-class classification and in regression modalities. Experimental results dealing with the inversion of laboratory-controlled data are shown to verify the effectiveness of the proposed multi-step LBE methodology and prove its suitability as a viable alternative/support to standard medical diagnostic methods. Full article
(This article belongs to the Special Issue New Trends and Future Challenges in Computational Microwave Imaging)
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10 pages, 353 KiB  
Article
Efficient Memory Organization for DNN Hardware Accelerator Implementation on PSoC
by Antonio Rios-Navarro, Daniel Gutierrez-Galan, Juan Pedro Dominguez-Morales, Enrique Piñero-Fuentes, Lourdes Duran-Lopez, Ricardo Tapiador-Morales and Manuel Jesús Dominguez-Morales
Electronics 2021, 10(1), 94; https://doi.org/10.3390/electronics10010094 - 05 Jan 2021
Cited by 4 | Viewed by 2719
Abstract
The use of deep learning solutions in different disciplines is increasing and their algorithms are computationally expensive in most cases. For this reason, numerous hardware accelerators have appeared to compute their operations efficiently in parallel, achieving higher performance and lower latency. These algorithms [...] Read more.
The use of deep learning solutions in different disciplines is increasing and their algorithms are computationally expensive in most cases. For this reason, numerous hardware accelerators have appeared to compute their operations efficiently in parallel, achieving higher performance and lower latency. These algorithms need large amounts of data to feed each of their computing layers, which makes it necessary to efficiently handle the data transfers that feed and collect the information to and from the accelerators. For the implementation of these accelerators, hybrid devices are widely used, which have an embedded computer, where an operating system can be run, and a field-programmable gate array (FPGA), where the accelerator can be deployed. In this work, we present a software API that efficiently organizes the memory, preventing reallocating data from one memory area to another, which improves the native Linux driver with a 85% speed-up and reduces the frame computing time by 28% in a real application. Full article
(This article belongs to the Special Issue Advanced AI Hardware Designs Based on FPGAs)
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11 pages, 4520 KiB  
Letter
Determining Ultrasound Arrival Time by HHT and Kurtosis in Wind Speed Measurement
by Shiyuan Liu, Zhipeng Li, Tong Wu and Wei Zhang
Electronics 2021, 10(1), 93; https://doi.org/10.3390/electronics10010093 - 05 Jan 2021
Cited by 2 | Viewed by 2334
Abstract
The determination of ultrasonic echo signal onset time is the core of performing the time difference method to calculate wind speed. However, in practical cases, background noise makes precise determination extremely difficult. This paper carries out research on the accurate determination of onset [...] Read more.
The determination of ultrasonic echo signal onset time is the core of performing the time difference method to calculate wind speed. However, in practical cases, background noise makes precise determination extremely difficult. This paper carries out research on the accurate determination of onset time, exploring the advantages of an improved method based on the combination of Hilbert-Huang Transform (HHT) and high-order statistics (kurtosis). Performing Hilbert-Huang Transform to the received wave is aimed at determining a rough arrival time, around which a fixed size of data is extracted as initial sample to avoid a false pick. Then the fourth-order kurtosis of a smaller sample, extracted successively by a moving window from the initial sample, is calculated. The minimum point corresponds to the initial onset time. This approach was tested on a real ultrasonic echo signal dataset, acquired in a wind tunnel with an ultrasonic anemometer. The proposed method showed satisfying results in both ideal cases and low signal-to-noise ratio (SNR) environment, compared with traditional onset time determination approaches, including Akaike Information Criterion (AIC-picker), Short-term Average over Long-term Average (STA/LTA), and Teager-Kaiser energy operator (TKEO). The experimental results acquired by the HHT-kurtosis method demonstrated that the proposed method possesses a high accuracy. Full article
(This article belongs to the Section Circuit and Signal Processing)
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26 pages, 11215 KiB  
Article
SPD-Safe: Secure Administration of Railway Intelligent Transportation Systems
by George Hatzivasilis, Konstantinos Fysarakis, Sotiris Ioannidis, Ilias Hatzakis, George Vardakis, Nikos Papadakis and George Spanoudakis
Electronics 2021, 10(1), 92; https://doi.org/10.3390/electronics10010092 - 05 Jan 2021
Cited by 8 | Viewed by 3938
Abstract
The railway transport system is critical infrastructure that is exposed to numerous man-made and natural threats, thus protecting this physical asset is imperative. Cyber security, privacy, and dependability (SPD) are also important, as the railway operation relies on cyber-physical systems (CPS) systems. This [...] Read more.
The railway transport system is critical infrastructure that is exposed to numerous man-made and natural threats, thus protecting this physical asset is imperative. Cyber security, privacy, and dependability (SPD) are also important, as the railway operation relies on cyber-physical systems (CPS) systems. This work presents SPD-Safe—an administration framework for railway CPS, leveraging artificial intelligence for monitoring and managing the system in real-time. The network layer protections integrated provide the core security properties of confidentiality, integrity, and authentication, along with energy-aware secure routing and authorization. The effectiveness in mitigating attacks and the efficiency under normal operation are assessed through simulations with the average delay in real equipment being 0.2–0.6 s. SPD metrics are incorporated together with safety semantics for the application environment. Considering an intelligent transportation scenario, SPD-Safe is deployed on railway critical infrastructure, safeguarding one outdoor setting on the railway’s tracks and one in-carriage setting on a freight train that contains dangerous cargo. As demonstrated, SPD-Safe provides higher security and scalability, while enhancing safety response procedures. Nonetheless, emergence response operations require a seamless interoperation of the railway system with emergency authorities’ equipment (e.g., drones). Therefore, a secure integration with external systems is considered as future work. Full article
(This article belongs to the Special Issue Advances in Public Transport Platform for the Development of Sustainability Cities)
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28 pages, 6033 KiB  
Article
Implementation of a Fast Link Rate Adaptation Algorithm for WLAN Systems
by Chester Sungchung Park and Sungkyung Park
Electronics 2021, 10(1), 91; https://doi.org/10.3390/electronics10010091 - 05 Jan 2021
Cited by 5 | Viewed by 2563
Abstract
With a target to maximize the throughput, a fast link rate adaptation algorithm for IEEE 802.11a/b/g/n/ac is proposed, which is basically preamble based and can adaptively compensate for the discrepancy between transmitter and receiver radio frequency performances by exploiting the acknowledgment signal. The [...] Read more.
With a target to maximize the throughput, a fast link rate adaptation algorithm for IEEE 802.11a/b/g/n/ac is proposed, which is basically preamble based and can adaptively compensate for the discrepancy between transmitter and receiver radio frequency performances by exploiting the acknowledgment signal. The target system is a 1 × 1 wireless local area network chip with no null data packet or sounding. The algorithm can be supplemented by automatic rate fallback at the initial phase to further expedite rate adaptation. The target system receives wireless channel coefficients and previous packet information, translates them to amended signal-to-noise ratios, and then, via the mean mutual information, selects the modulation and coding scheme with the maximum throughput. Extensive simulation and wireless tests are carried out to demonstrate the validity of the proposed adaptive preamble-based link adaptation in comparison with both the popular automatic rate fallback and ideal link adaptation. The throughput gain of the proposed link adaptation over automatic rate fallback is demonstrated over various packet transmission intervals and Doppler frequencies. The throughput gain of the proposed algorithm over ARF is 46% (15%) for a 1-tap (3-tap) channel over 10 m–250 m (16 m–160 m) normalized Doppler frequencies. Assuming a 3-tap channel and 30 m–50 m normalized Doppler frequencies, the throughput of the proposed algorithm is about 31 Mbps, nearly the same as that of ideal link adaptation, whereas the throughput of ARF is about 24 Mbps, leading to a 30% throughput gain of the proposed algorithm over ARF. The firmware is implemented in C and on Xilinx Zynq 7020 (Xilinx, San Jose, CA, USA) for wireless tests. Full article
(This article belongs to the Section Microwave and Wireless Communications)
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15 pages, 17341 KiB  
Article
Improving Object Detection Quality by Incorporating Global Contexts via Self-Attention
by Donghyeon Lee, Joonyoung Kim and Kyomin Jung
Electronics 2021, 10(1), 90; https://doi.org/10.3390/electronics10010090 - 05 Jan 2021
Cited by 6 | Viewed by 2589
Abstract
Fully convolutional structures provide feature maps acquiring local contexts of an image by only stacking numerous convolutional layers. These structures are known to be effective in modern state-of-the-art object detectors such as Faster R-CNN and SSD to find objects from local contexts. However, [...] Read more.
Fully convolutional structures provide feature maps acquiring local contexts of an image by only stacking numerous convolutional layers. These structures are known to be effective in modern state-of-the-art object detectors such as Faster R-CNN and SSD to find objects from local contexts. However, the quality of object detectors can be further improved by incorporating global contexts when some ambiguous objects should be identified by surrounding objects or background. In this paper, we introduce a self-attention module for object detectors to incorporate global contexts. More specifically, our self-attention module allows the feature extractor to compute feature maps with global contexts by the self-attention mechanism. Our self-attention module computes relationships among all elements in the feature maps, and then blends the feature maps considering the computed relationships. Therefore, this module can capture long-range relationships among objects or backgrounds, which is difficult for fully convolutional structures. Furthermore, our proposed module is not limited to any specific object detectors, and it can be applied to any CNN-based model for any computer vision task. In the experimental results on the object detection task, our method shows remarkable gains in average precision (AP) compared to popular models that have fully convolutional structures. In particular, compared to Faster R-CNN with the ResNet-50 backbone, our module applied to the same backbone achieved +4.0 AP gains without the bells and whistles. In image semantic segmentation and panoptic segmentation tasks, our module improved the performance in all metrics used for each task. Full article
(This article belongs to the Section Artificial Intelligence)
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3 pages, 156 KiB  
Editorial
Low-Voltage Integrated Circuits Design and Application
by Anna Richelli
Electronics 2021, 10(1), 89; https://doi.org/10.3390/electronics10010089 - 05 Jan 2021
Cited by 3 | Viewed by 2382
Abstract
One of the most challenging tasks for analog and digital designers is to maintain the circuit performances by developing novel circuit structures, robust, reliable, and capable of operating with low supply voltage [...] Full article
(This article belongs to the Special Issue Low-Voltage Integrated Circuits Design and Application)
17 pages, 4884 KiB  
Article
Extending the Input Voltage Range of Solar PV Inverters with Supercapacitor Energy Circulation
by Kosala Gunawardane, Nalin Bandara, Kasun Subasinghage and Nihal Kularatna
Electronics 2021, 10(1), 88; https://doi.org/10.3390/electronics10010088 - 04 Jan 2021
Cited by 8 | Viewed by 2602
Abstract
Cleaner and greener energy sources have proliferated on a worldwide basis, creating distributed energy systems. Given the unreliable nature of the renewable sources such as solar and wind, they are traditionally based on inverters interfaced with legacy AC grid systems. While efficiency, output [...] Read more.
Cleaner and greener energy sources have proliferated on a worldwide basis, creating distributed energy systems. Given the unreliable nature of the renewable sources such as solar and wind, they are traditionally based on inverters interfaced with legacy AC grid systems. While efficiency, output waveform quality and other technical specifications of inverters keep improving gradually, only limited attention is given to widening the input range of inverters. This paper presents a new supercapacitor assisted (SCA) technique to widen the input range of an inverter without modifying the inverter itself. Developing a prototype version of a 24 V DC input capable supercapacitor-assisted wide input (SCASWI) inverter using a supercapacitor circulation front end and a commercial 12 V DC line frequency inverter is detailed in the article, explaining how the SCASWI inverter technique doubles the input voltage while maintaining the useful characteristics of the commercial inverter. The new technique has the added advantage of DC-UPS capability based on a long-life supercapacitor module. Full article
(This article belongs to the Section Power Electronics)
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22 pages, 5334 KiB  
Article
Reliability and Economic Efficiency Analysis of 4-Leg Inverter Compared with 3-Leg Inverters
by Yun-gi Kwak, Dae-ho Heo, Sun-Pil Kim, Sung-Geun Song, Sung-Jun Park and Feel-soon Kang
Electronics 2021, 10(1), 87; https://doi.org/10.3390/electronics10010087 - 04 Jan 2021
Cited by 10 | Viewed by 2763
Abstract
The 4-leg inverter can adjust the load current or output voltage even under unbalanced load conditions, but it is known that the additional switch arm to the 3-leg inverter can increase the overall cost and the failure rate. This paper aims to analyze [...] Read more.
The 4-leg inverter can adjust the load current or output voltage even under unbalanced load conditions, but it is known that the additional switch arm to the 3-leg inverter can increase the overall cost and the failure rate. This paper aims to analyze the failure rate and mean time between failures (MTBF) of 3-leg inverters and 4-leg inverters using part count failure analysis (PCA) and fault-tree analysis (FTA), and to compare the price of the inverters. The FTA can analyze the failure rate, including the type, number and connection status of the circuit components, and moreover the redundancy effect of the 4-leg inverter. For more accurate failure-rate prediction, the failure rate and MTBF of the 4-leg inverter according to the lifecycle of the controller are analyzed. Finally, by comparing the price of 3-leg inverters and 4-leg inverters using the cost model of major parts, the degree of reliability improvement against price increase is quantitatively analyzed. Full article
(This article belongs to the Section Power Electronics)
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20 pages, 876 KiB  
Article
A Real-Time Scheduling Approach to Mitigation of Li-Ion Battery Aging in Low Earth Orbit Satellite Systems
by Seongik Jang and Hoeseok Yang
Electronics 2021, 10(1), 86; https://doi.org/10.3390/electronics10010086 - 04 Jan 2021
Cited by 4 | Viewed by 2035
Abstract
Thanks to their higher performance compared to conventional batteries, lithium-ion (Li-ion) batteries have recently become popular as a power source in many electronic systems. However, Li-ion batteries are known to suffer from an aging issue: the available capacity is gradually degraded as the [...] Read more.
Thanks to their higher performance compared to conventional batteries, lithium-ion (Li-ion) batteries have recently become popular as a power source in many electronic systems. However, Li-ion batteries are known to suffer from an aging issue: the available capacity is gradually degraded as the operation goes by. The impact of aging is particularly critical to satellite systems where no maintenance is available after the initial deployment. Recently, a real-time scheduling framework was proposed to decelerate the aging of Li-ion batteries. However, this framework simply relies on the fact that the elevated temperature results in a worse lifespan of the battery. In contrast to this, in this paper, we argue that the reduced temperature may actually cause an adverse effect in the battery lifetime when considering satellite environments. To evidently demonstrate this anomaly, we extend an open-source Li-ion battery aging simulator to consider the temperature-dependent aging characteristics of the Li-ion batteries. Then, a couple of alternative scheduling policies that better suit the target satellite systems are evaluated in the simulator in comparison with the existing scheduling policies. Our simulation results show that the existing scheduling method, which does not consider the satellite temperature environments, rather deteriorates the lifespan of battery and the proposed scheduling technique can extend the lifespan by up to 65.51%. Full article
(This article belongs to the Section Computer Science & Engineering)
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18 pages, 5065 KiB  
Article
Performance Evaluation of Power-Line Communication Systems for LIN-Bus Based Data Transmission
by Martin Brandl and Karlheinz Kellner
Electronics 2021, 10(1), 85; https://doi.org/10.3390/electronics10010085 - 04 Jan 2021
Cited by 8 | Viewed by 3233
Abstract
Powerline communication (PLC) is a versatile method that uses existing infrastructure such as power cables for data transmission. This makes PLC an alternative and cost-effective technology for the transmission of sensor and actuator data by making dual use of the power line and [...] Read more.
Powerline communication (PLC) is a versatile method that uses existing infrastructure such as power cables for data transmission. This makes PLC an alternative and cost-effective technology for the transmission of sensor and actuator data by making dual use of the power line and avoiding the need for other communication solutions; such as wireless radio frequency communication. A PLC modem using DSSS (direct sequence spread spectrum) for reliable LIN-bus based data transmission has been developed for automotive applications. Due to the almost complete system implementation in a low power microcontroller; the component cost could be radically reduced which is a necessary requirement for automotive applications. For performance evaluation the DSSS modem was compared to two commercial PLC systems. The DSSS and one of the commercial PLC systems were designed as a direct conversion receiver; the other commercial module uses a superheterodyne architecture. The performance of the systems was tested under the influence of narrowband interference and additive Gaussian noise added to the transmission channel. It was found that the performance of the DSSS modem against singleton interference is better than that of commercial PLC transceivers by at least the processing gain. The performance of the DSSS modem was at least 6 dB better than the other modules tested under the influence of the additive white Gaussian noise on the transmission channel at data rates of 19.2 kB/s. Full article
(This article belongs to the Section Electrical and Autonomous Vehicles)
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14 pages, 3542 KiB  
Article
An Efficient Clustering Protocol for Cognitive Radio Sensor Networks
by Vladimir Shakhov and Insoo Koo
Electronics 2021, 10(1), 84; https://doi.org/10.3390/electronics10010084 - 04 Jan 2021
Cited by 10 | Viewed by 1941
Abstract
Wireless sensor networks are considered an integral part of the Internet of Things, which is the focus of research centers and governments around the world. Clustering mechanisms and cognitive radio, in turn, are considered promising wireless network technologies for network management and spectral [...] Read more.
Wireless sensor networks are considered an integral part of the Internet of Things, which is the focus of research centers and governments around the world. Clustering mechanisms and cognitive radio, in turn, are considered promising wireless network technologies for network management and spectral efficiency, respectively. In this paper, we consider the flaws in the previously proposed network stability-aware clustering technique. In particular, we demonstrate that existing solutions do not operate properly based on the remaining energy and the quality of available common channels, even if their fusion is declared. In addition, security issues have not been sufficiently developed. We offer an approach to address these flaws. To improve protocol efficiency, the problem of parameter tuning is discussed, and a performance analysis of the proposed solution is provided as well. Full article
(This article belongs to the Special Issue Wireless Network Protocols and Performance Evaluation)
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12 pages, 5747 KiB  
Article
Miniaturized Antipodal Vivaldi Antenna with Improved Bandwidth Using Exponential Strip Arms
by Mohammad Mahdi Honari, Mohammad Saeid Ghaffarian and Rashid Mirzavand
Electronics 2021, 10(1), 83; https://doi.org/10.3390/electronics10010083 - 04 Jan 2021
Cited by 17 | Viewed by 4142
Abstract
In this paper, a miniaturized ultra-wideband antipodal tapered slot antenna with exponential strip arms is presented. Two exponential arms with designed equations are optimized to reduce the lower edge cut-off frequency of the impedance bandwidth from 1480 MHz to 720 MHz, resulting in [...] Read more.
In this paper, a miniaturized ultra-wideband antipodal tapered slot antenna with exponential strip arms is presented. Two exponential arms with designed equations are optimized to reduce the lower edge cut-off frequency of the impedance bandwidth from 1480 MHz to 720 MHz, resulting in antenna miniaturization by 51%. This approach also improves antenna bandwidth without compromising the radiation characteristics. The dimension of the proposed antenna structure including the feeding line and transition is 158 × 125 × 1 mm3. The results show that a peak gain more than 1 dBi is achieved all over the impedance bandwidth (0.72–17 GHz), which is an improvement to what have been reported for antipodal tapered slot and Vivaldi antennas with similar size. Full article
(This article belongs to the Special Issue Antennas for Next-Generation Communication Systems)
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22 pages, 17886 KiB  
Article
Adaptive View Sampling for Efficient Synthesis of 3D View Using Calibrated Array Cameras
by Geonwoo Kim and Deokwoo Lee
Electronics 2021, 10(1), 82; https://doi.org/10.3390/electronics10010082 - 04 Jan 2021
Cited by 2 | Viewed by 2058
Abstract
Recovery of three-dimensional (3D) coordinates using a set of images with texture mapping to generate a 3D mesh has been of great interest in computer graphics and 3D imaging applications. This work aims to propose an approach to adaptive view selection (AVS) that [...] Read more.
Recovery of three-dimensional (3D) coordinates using a set of images with texture mapping to generate a 3D mesh has been of great interest in computer graphics and 3D imaging applications. This work aims to propose an approach to adaptive view selection (AVS) that determines the optimal number of images to generate the synthesis result using the 3D mesh and textures in terms of computational complexity and image quality (peak signal-to-noise ratio (PSNR)). All 25 images were acquired by a set of cameras in a 5×5 array structure, and rectification had already been performed. To generate the mesh, depth map extraction was carried out by calculating the disparity between the matched feature points. Synthesis was performed by fully exploiting the content included in the images followed by texture mapping. Both the 2D colored images and grey-scale depth images were synthesized based on the geometric relationship between the images, and to this end, three-dimensional synthesis was performed with a smaller number of images, which was less than 25. This work determines the optimal number of images that sufficiently provides a reliable 3D extended view by generating a mesh and image textures. The optimal number of images contributes to an efficient system for 3D view generation that reduces the computational complexity while preserving the quality of the result in terms of the PSNR. To substantiate the proposed approach, experimental results are provided. Full article
(This article belongs to the Special Issue Applications of Computer Vision)
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19 pages, 972 KiB  
Review
A Review of Plant Phenotypic Image Recognition Technology Based on Deep Learning
by Jianbin Xiong, Dezheng Yu, Shuangyin Liu, Lei Shu, Xiaochan Wang and Zhaoke Liu
Electronics 2021, 10(1), 81; https://doi.org/10.3390/electronics10010081 - 04 Jan 2021
Cited by 69 | Viewed by 7199
Abstract
Plant phenotypic image recognition (PPIR) is an important branch of smart agriculture. In recent years, deep learning has achieved significant breakthroughs in image recognition. Consequently, PPIR technology that is based on deep learning is becoming increasingly popular. First, this paper introduces the development [...] Read more.
Plant phenotypic image recognition (PPIR) is an important branch of smart agriculture. In recent years, deep learning has achieved significant breakthroughs in image recognition. Consequently, PPIR technology that is based on deep learning is becoming increasingly popular. First, this paper introduces the development and application of PPIR technology, followed by its classification and analysis. Second, it presents the theory of four types of deep learning methods and their applications in PPIR. These methods include the convolutional neural network, deep belief network, recurrent neural network, and stacked autoencoder, and they are applied to identify plant species, diagnose plant diseases, etc. Finally, the difficulties and challenges of deep learning in PPIR are discussed. Full article
(This article belongs to the Collection Electronics for Agriculture)
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12 pages, 6596 KiB  
Article
An Accuracy Improvement Method Based on Multi-Source Information Fusion and Deep Learning for TSSC and Water Content Nondestructive Detection in “Luogang” Orange
by Sai Xu, Huazhong Lu, Christopher Ference and Qianqian Zhang
Electronics 2021, 10(1), 80; https://doi.org/10.3390/electronics10010080 - 04 Jan 2021
Cited by 7 | Viewed by 2427
Abstract
The objective of this study was to find an efficient method for measuring the total soluble solid content (TSSC) and water content of “Luogang” orange. Quick, accurate, and nondestructive detection tools (VIS/NIR spectroscopy, NIR spectroscopy, machine vision, and electronic nose), four data processing [...] Read more.
The objective of this study was to find an efficient method for measuring the total soluble solid content (TSSC) and water content of “Luogang” orange. Quick, accurate, and nondestructive detection tools (VIS/NIR spectroscopy, NIR spectroscopy, machine vision, and electronic nose), four data processing methods (Savitzky–Golay (SG), genetic algorithm (GA), multi-source information fusion (MIF), convolutional neural network (CNN) as the deep learning method, and a partial least squares regression (PLSR) modeling method) were compared and investigated. The results showed that the optimal TSSC detection method was based on VIS/NIR and machine vision data fusion and processing and modeling by SG + GA + CNN + PLSR. The R2 and RMSE of the TSSC detection results were 0.8580 and 0.4276, respectively. The optimal water content detection result was based on VIS/NIR data and processing and modeling by SG + GA + CNN + PLSR. The R2 and RMSE of the water content detection results were 0.7013 and 0.0063, respectively. This optimized method largely improved the internal quality detection accuracy of “Luogang” orange when compared to the data from a single detection tool with traditional data processing method, and provides a reference for the accuracy improvement of internal quality detection of other fruits. Full article
(This article belongs to the Collection Electronics for Agriculture)
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20 pages, 3923 KiB  
Article
Carbon Nanotube Field Effect Transistor (CNTFET) and Resistive Random Access Memory (RRAM) Based Ternary Combinational Logic Circuits
by Furqan Zahoor, Fawnizu Azmadi Hussin, Farooq Ahmad Khanday, Mohamad Radzi Ahmad, Illani Mohd Nawi, Chia Yee Ooi and Fakhrul Zaman Rokhani
Electronics 2021, 10(1), 79; https://doi.org/10.3390/electronics10010079 - 04 Jan 2021
Cited by 45 | Viewed by 7628
Abstract
The capability of multiple valued logic (MVL) circuits to achieve higher storage density when compared to that of existing binary circuits is highly impressive. Recently, MVL circuits have attracted significant attention for the design of digital systems. Carbon nanotube field effect transistors (CNTFETs) [...] Read more.
The capability of multiple valued logic (MVL) circuits to achieve higher storage density when compared to that of existing binary circuits is highly impressive. Recently, MVL circuits have attracted significant attention for the design of digital systems. Carbon nanotube field effect transistors (CNTFETs) have shown great promise for design of MVL based circuits, due to the fact that the scalable threshold voltage of CNTFETs can be utilized easily for the multiple voltage designs. In addition, resistive random access memory (RRAM) is also a feasible option for the design of MVL circuits, owing to its multilevel cell capability that enables the storage of multiple resistance states within a single cell. In this manuscript, a design approach for ternary combinational logic circuits while using CNTFETs and RRAM is presented. The designs of ternary half adder, ternary half subtractor, ternary full adder, and ternary full subtractor are evaluated while using Synopsis HSPICE simulation software with standard 32 nm CNTFET technology under different operating conditions, including different supply voltages, output load variation, and different operating temperatures. Finally, the proposed designs are compared with the state-of-the-art ternary designs. Based on the obtained simulation results, the proposed designs show a significant reduction in the transistor count, decreased cell area, and lower power consumption. In addition, due to the participation of RRAM, the proposed designs have advantages in terms of non-volatility. Full article
(This article belongs to the Special Issue RRAM Devices: Materials, Designs, and Properties)
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16 pages, 905 KiB  
Article
LSTM Networks for Overcoming the Challenges Associated with Photovoltaic Module Maintenance in Smart Cities
by Jorge Vicente-Gabriel, Ana-Belén Gil-González, Ana Luis-Reboredo, Pablo Chamoso and Juan M. Corchado
Electronics 2021, 10(1), 78; https://doi.org/10.3390/electronics10010078 - 04 Jan 2021
Cited by 9 | Viewed by 2813
Abstract
Predictive maintenance is a field of research that has emerged from the need to improve the systems in place. This research focuses on controlling the degradation of photovoltaic (PV) modules in outdoor solar panels, which are exposed to a variety of climatic loads. [...] Read more.
Predictive maintenance is a field of research that has emerged from the need to improve the systems in place. This research focuses on controlling the degradation of photovoltaic (PV) modules in outdoor solar panels, which are exposed to a variety of climatic loads. Improved reliability, operation, and performance can be achieved through monitoring. In this study, a system capable of predicting the output power of a solar module was implemented. It monitors different parameters and uses automatic learning techniques for prediction. Its use improved reliability, operation, and performance. On the other hand, automatic learning algorithms were evaluated with different metrics in order to optimize and find the best configuration that provides an optimal solution to the problem. With the aim of increasing the share of renewable energy penetration, an architectural proposal based on Edge Computing was included to implement the proposed model into a system. The proposed model is designated for outdoor predictions and offers many advantages, such as monitoring of individual panels, optimization of system response, and speed of communication with the Cloud. The final objective of the work was to contribute to the smart Energy system concept, providing solutions for planning the entire energy system together with the identification of suitable energy infrastructure designs and operational strategies. Full article
(This article belongs to the Special Issue Data Analytics Challenges in Smart Cities Applications)
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12 pages, 3210 KiB  
Letter
Design and Implementation of LoRa Based IoT Scheme for Indonesian Rural Area
by Setya Widyawan Prakosa, Muhamad Faisal, Yudhi Adhitya, Jenq-Shiou Leu, Mario Köppen and Cries Avian
Electronics 2021, 10(1), 77; https://doi.org/10.3390/electronics10010077 - 04 Jan 2021
Cited by 21 | Viewed by 5068
Abstract
The development of the Internet of Things (IoT) in electronics, computer, robotics, and internet technology is inevitable and has rapidly accelerated more than before as the IoT paradigm is a promising solution in terms of solving real world problems, especially for digitizing and [...] Read more.
The development of the Internet of Things (IoT) in electronics, computer, robotics, and internet technology is inevitable and has rapidly accelerated more than before as the IoT paradigm is a promising solution in terms of solving real world problems, especially for digitizing and monitoring in real time. Various IoT schemes have successfully been applied to some areas such as smart health and smart agriculture. Since the agriculture areas are getting narrow, the development of IoT in agriculture should be prioritized to enhance crop production. This paper proposes the IoT scheme for long range communication based on Long Range (LoRa) modules applied to smart agriculture. The scheme utilizes the low power modules and long-distance communication for monitoring temperature, humidity, soil moisture, and pH soil. Our IoT design has successfully been applied to agriculture areas which have unstable network connections. The design is analyzed to obtain the maximum coverage using different spreading factors and bandwidths. We show that as the spreading factor increases to 12, the maximum coverage can be transmitted to 1000 m. However, the large coverage also comes with the disadvantages of the increased delays. Full article
(This article belongs to the Section Industrial Electronics)
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32 pages, 6873 KiB  
Article
AMC2N: Automatic Modulation Classification Using Feature Clustering-Based Two-Lane Capsule Networks
by Dhamyaa H. Al-Nuaimi, Muhammad F. Akbar, Laith B. Salman, Intan S. Zainal Abidin and Nor Ashidi Mat Isa
Electronics 2021, 10(1), 76; https://doi.org/10.3390/electronics10010076 - 04 Jan 2021
Cited by 12 | Viewed by 3113
Abstract
The automatic modulation classification (AMC) of a detected signal has gained considerable prominence in recent years owing to its numerous facilities. Numerous studies have focused on feature-based AMC. However, improving accuracy under low signal-to-noise ratio (SNR) rates is a serious issue in AMC. [...] Read more.
The automatic modulation classification (AMC) of a detected signal has gained considerable prominence in recent years owing to its numerous facilities. Numerous studies have focused on feature-based AMC. However, improving accuracy under low signal-to-noise ratio (SNR) rates is a serious issue in AMC. Moreover, research on the enhancement of AMC performance under low and high SNR rates is limited. Motivated by these issues, this study proposes AMC using a feature clustering-based two-lane capsule network (AMC2N). In the AMC2N, accuracy of the MC process is improved by designing a new two-layer capsule network (TL-CapsNet), and classification time is reduced by introducing a new feature clustering approach in the TL-CapsNet. Firstly, the AMC2N executes blind equalization, sampling, and quantization in trilevel preprocessing. Blind equalization is executed using a binary constant modulus algorithm to avoid intersymbol interference. To extract features from the preprocessed signal and classify signals accurately, the AMC2N employs the TL-CapsNet, in which individual lanes are incorporated to process the real and imaginary parts of the signal. In addition, it is robust to SNR variations, that is, low and high SNR rates. The TL-CapsNet extracts features from the real and imaginary parts of the given signal, which are then clustered based on feature similarity. For feature extraction and clustering, the dynamic routing procedure of the TL-CapsNet is adopted. Finally, classification is performed in the SoftMax layer of the TL-CapsNet. This study proves that the AMC2N outperforms existing methods, particularly, convolutional neural network(CNN), Robust-CNN (R-CNN), curriculum learning(CL), and Local Binary Pattern (LBP), in terms of accuracy, precision, recall, F-score, and computation time. All metrics are validated in two scenarios, and the proposed method shows promising results in both. Full article
(This article belongs to the Section Computer Science & Engineering)
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25 pages, 11048 KiB  
Article
A Hybrid Optimization Approach for the Enhancement of Efficiency of a Piezoelectric Energy Harvesting System
by Mahidur R. Sarker, Ramizi Mohamed, Mohamad Hanif Md Saad, Muhammad Tahir, Aini Hussain and Azah Mohamed
Electronics 2021, 10(1), 75; https://doi.org/10.3390/electronics10010075 - 04 Jan 2021
Cited by 9 | Viewed by 2906
Abstract
This paper presents a hybrid optimization approach for the enhancement of performance of a piezoelectric energy harvesting system (PEHS). The existing PEHS shows substantial power loss during hardware implementation. To overcome the problem, this study proposes a hybrid optimization technique to improve the [...] Read more.
This paper presents a hybrid optimization approach for the enhancement of performance of a piezoelectric energy harvesting system (PEHS). The existing PEHS shows substantial power loss during hardware implementation. To overcome the problem, this study proposes a hybrid optimization technique to improve the PEHS efficiency. In addition, the converter design as well as controller technique are enhanced and simulated in a MATLAB/Simulink platform. The controller technique of the proposed structure is connected to the converter prototype through the dSPACE DS1104 board (dSPACE, Paderborn, Germany). To enhance the proportional-integral voltage controller (PIVC) based on hybrid optimization method, a massive enhancement in reducing the output error is done in terms of power efficiency, power loss, rising time and settling time. The results show that the overall PEHS converter efficiency is about 85% based on the simulation and experimental implementations. Full article
(This article belongs to the Section Power Electronics)
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11 pages, 3912 KiB  
Article
Analysis of Terahertz Wave on Increasing Radar Cross Section of 3D Conductive Model
by Hongyao Liu, Panpan Wang, Jiali Wu, Xin Yan, Yangan Zhang and Xia Zhang
Electronics 2021, 10(1), 74; https://doi.org/10.3390/electronics10010074 - 03 Jan 2021
Cited by 7 | Viewed by 2220
Abstract
Enhancing the frequency band of the electromagnetic wave is regarded as an efficient way to solve the communication blackout problem. In this paper, frequency of incident wave is raised to Terahertz (THz) band and the radar cross section (RCS) of the three-dimensional conductive [...] Read more.
Enhancing the frequency band of the electromagnetic wave is regarded as an efficient way to solve the communication blackout problem. In this paper, frequency of incident wave is raised to Terahertz (THz) band and the radar cross section (RCS) of the three-dimensional conductive model is calculated and simulated based on the Runge–Kutta Exponential Time Differencing–Finite Difference Time Domain method (RKETD-FDTD). Interaction of THz wave and magnetized plasma sheath is discussed. Attenuations in incident wave frequencies of 0.34 THz and 3 GHz and different plasma densities are analyzed. The monostatic RCS is used to compare the penetration in different incident wave frequencies while the bistatic RCS is fixed on 0.34 THz to study its characteristics. The simulation result has almost the same RCS as that of the model without coating plasma when the frequency of incident wave reaches 0.34 THz. The advantages of THz wave at 0.34 THz on increasing RCS and reducing the attenuation are demonstrated from different aspects including polarizations, incident angles, magnetization and anisotropy of plasma, thickness of plasma, scan planes and inhomogeneous distribution of plasma. It can be concluded that 0.34 THz has unique advantages in increasing the radar cross section and can be applied to solve the problem of communication interruption. Full article
(This article belongs to the Special Issue Applications of Terahertz Wave)
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20 pages, 1396 KiB  
Article
Mutual Impact between Clock Gating and High Level Synthesis in Reconfigurable Hardware Accelerators
by Francesco Ratto, Tiziana Fanni, Luigi Raffo and Carlo Sau
Electronics 2021, 10(1), 73; https://doi.org/10.3390/electronics10010073 - 03 Jan 2021
Cited by 3 | Viewed by 2109
Abstract
With the diffusion of cyber-physical systems and internet of things, adaptivity and low power consumption became of primary importance in digital systems design. Reconfigurable heterogeneous platforms seem to be one of the most suitable choices to cope with such challenging context. However, their [...] Read more.
With the diffusion of cyber-physical systems and internet of things, adaptivity and low power consumption became of primary importance in digital systems design. Reconfigurable heterogeneous platforms seem to be one of the most suitable choices to cope with such challenging context. However, their development and power optimization are not trivial, especially considering hardware acceleration components. On the one hand high level synthesis could simplify the design of such kind of systems, but on the other hand it can limit the positive effects of the adopted power saving techniques. In this work, the mutual impact of different high level synthesis tools and the application of the well known clock gating strategy in the development of reconfigurable accelerators is studied. The aim is to optimize a clock gating application according to the chosen high level synthesis engine and target technology (Application Specific Integrated Circuit (ASIC) or Field Programmable Gate Array (FPGA)). Different levels of application of clock gating are evaluated, including a novel multi level solution. Besides assessing the benefits and drawbacks of the clock gating application at different levels, hints for future design automation of low power reconfigurable accelerators through high level synthesis are also derived. Full article
(This article belongs to the Section Circuit and Signal Processing)
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16 pages, 745 KiB  
Article
Layer-Wise Network Compression Using Gaussian Mixture Model
by Eunho Lee and Youngbae Hwang
Electronics 2021, 10(1), 72; https://doi.org/10.3390/electronics10010072 - 03 Jan 2021
Cited by 9 | Viewed by 2715
Abstract
Due to the large number of parameters and heavy computation, the real-time operation of deep learning in low-performance embedded board is still difficult. Network Pruning is one of effective methods to reduce the number of parameters without additional network structure modification. However, the [...] Read more.
Due to the large number of parameters and heavy computation, the real-time operation of deep learning in low-performance embedded board is still difficult. Network Pruning is one of effective methods to reduce the number of parameters without additional network structure modification. However, the conventional method prunes redundant parameters up to the same rate for all layers. It may cause a bottleneck problem, which leads to the performance degradation, because the minimum number of optimal parameters is different according to the each layer. We propose a layer adaptive pruning method based on the modeling of weight distribution. We can measure the amount of weights close to zero accurately by applying Gaussian Mixture Model (GMM). Until the target compression rate is reached, the layer selection and pruning are iteratively performed. The layer selection in each iteration considers the timing to reach the target compression rate and the degree of weight pruning. We apply the proposed network compression method for image classification and semantic segmentation to show the effectiveness of the proposed method. In the experiments, the proposed method shows higher compression rate during maintaining the accuracy compared with previous methods. Full article
(This article belongs to the Special Issue Deep Learning Based Object Detection)
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10 pages, 5295 KiB  
Article
Design and Implementation of Fast Locking All-Digital Duty Cycle Corrector Circuit with Wide Range Input Frequency
by Shao-Ku Kao
Electronics 2021, 10(1), 71; https://doi.org/10.3390/electronics10010071 - 03 Jan 2021
Cited by 2 | Viewed by 2841
Abstract
This paper presents a fast locking and wide range input frequency all-digital duty cycle corrector (ADDCC). The proposed ADDCC circuit comprises a pulse generator and a clock generator. The pulse generator is edge-triggered by an input signal to produce a 0 degree and [...] Read more.
This paper presents a fast locking and wide range input frequency all-digital duty cycle corrector (ADDCC). The proposed ADDCC circuit comprises a pulse generator and a clock generator. The pulse generator is edge-triggered by an input signal to produce a 0 degree and 180 degree phase. The clock generator uses a 0 degree and 180 degree phase to produce the 50% duty cycle output signal. It corrects the duty cycle of the input signal in six clock cycles. The proposed ADDCC is implemented in a 0.35 µm CMOS process. The circuit can operate from 10 MHz to 100 MHz, and accommodates a wide range of input duty cycles ranging from 30% to 70%. The duty-cycle error of the output signal is less than ±1%. Full article
(This article belongs to the Section Microelectronics)
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20 pages, 1731 KiB  
Article
Implementation of Autoencoders with Systolic Arrays through OpenCL
by Rafael Gadea-Gironés, Vicente Herrero-Bosch, Jose Monzó-Ferrer and Ricardo Colom-Palero
Electronics 2021, 10(1), 70; https://doi.org/10.3390/electronics10010070 - 03 Jan 2021
Cited by 4 | Viewed by 2296
Abstract
In the world of algorithm acceleration and the implementation of deep neural networks’ recall phase, OpenCL based solutions have a clear tendency to produce perfectly adapted kernels in graphic processor unit (GPU) architectures. However, they fail to obtain the same results when applied [...] Read more.
In the world of algorithm acceleration and the implementation of deep neural networks’ recall phase, OpenCL based solutions have a clear tendency to produce perfectly adapted kernels in graphic processor unit (GPU) architectures. However, they fail to obtain the same results when applied to field-programmable gate array (FPGA) based architectures. This situation, along with an enormous advance in new GPU architectures, makes it unfeasible to defend an acceleration solution based on FPGA, even in terms of energy efficiency. Our goal in this paper is to demonstrate that multikernel structures can be written based on classic systolic arrays in OpenCL, trying to extract the most advanced features of FPGAs without having to resort to traditional FPGA development using lower level hardware description languages (HDLs) such as Verilog or VHDL. This OpenCL methodology is based on the intensive use of channels (IntelFPGA extension of OpenCL) for the communication of both data and control and on the refinement of the OpenCL libraries using register transfer logic (RTL) code to improve the performance of the implementation of the base and activation functions of the neurons and, above all, to reflect the importance of adequate communication between the layers when implementing neuronal networks. Full article
(This article belongs to the Special Issue Advanced AI Hardware Designs Based on FPGAs)
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15 pages, 730 KiB  
Article
OQPSK Synchronization Parameter Estimation Based on Burst Signal Detection
by Zilong Liu, Kexian Gong, Peng Sun, Jicai Deng, Kunheng Zou and Linlin Duan
Electronics 2021, 10(1), 69; https://doi.org/10.3390/electronics10010069 - 02 Jan 2021
Cited by 1 | Viewed by 2329
Abstract
The fast estimation of synchronization parameters plays an extremely important role in the demodulation of burst signals. In order to solve the problem of high computational complexity in the implementation of traditional algorithms, a synchronization parameter (frequency offset, phase offset, and timing error) [...] Read more.
The fast estimation of synchronization parameters plays an extremely important role in the demodulation of burst signals. In order to solve the problem of high computational complexity in the implementation of traditional algorithms, a synchronization parameter (frequency offset, phase offset, and timing error) estimation algorithm based on Offset Quadrature Phase Shift Keying (OQPSK) burst signal detection is proposed in this article. We first use the Data-Aided (DA) method to detect where the burst signal begins by taking the segment correlation between the receiving signals and the known local Unique Word (UW). In the sequel, the above results are adopted directly to estimate the synchronization parameters, which is obviously different from the conventional algorithms. In this way, the complexity of the proposed algorithm is greatly reduced, and it is more convenient for hardware implementation. The simulation results show that the proposed algorithm has high accuracy and can track the Modified Cramer–Rao Bound (MCRB) closely. Full article
(This article belongs to the Special Issue Theory and Applications in Digital Signal Processing)
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10 pages, 5712 KiB  
Article
A 1.93-pJ/Bit PCI Express Gen4 PHY Transmitter with On-Chip Supply Regulators in 28 nm CMOS
by Woorham Bae, Sung-Yong Cho and Deog-Kyoon Jeong
Electronics 2021, 10(1), 68; https://doi.org/10.3390/electronics10010068 - 02 Jan 2021
Cited by 2 | Viewed by 3049
Abstract
This paper presents a fully integrated Peripheral Component Interconnect (PCI) Express (PCIe) Gen4 physical layer (PHY) transmitter. The prototype chip is fabricated in a 28 nm low-power CMOS process, and the active area of the proposed transmitter is 0.23 mm2. To [...] Read more.
This paper presents a fully integrated Peripheral Component Interconnect (PCI) Express (PCIe) Gen4 physical layer (PHY) transmitter. The prototype chip is fabricated in a 28 nm low-power CMOS process, and the active area of the proposed transmitter is 0.23 mm2. To enable voltage scaling across wide operating rates from 2.5 Gb/s to 16 Gb/s, two on-chip supply regulators are included in the transmitter. At the same time, the regulators maintain the output impedance of the transmitter to meet the return loss specification of the PCIe, by including replica segments of the output driver and reference resistance in the regulator loop. A three-tap finite-impulse-response (FIR) equalization is implemented and, therefore, the transmitter provides more than 9.5 dB equalization which is required in the PCIe specification. At 16 Gb/s, the prototype chip achieves energy efficiency of 1.93 pJ/bit including all the interface, bias, and built-in self-test circuits. Full article
(This article belongs to the Special Issue Mixed-Signal VLSI Design)
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