Electronics

11 pages, 5856 KiB

Open AccessArticle

Experimental Comparison of a New 1.2 kV 4H-SiC Split-Gate MOSFET with Conventional SiC MOSFETs in Terms of Reliability Robustness

by Hao Liu, Jiaxing Wei, Zhaoxiang Wei, Siyang Liu and Longxing Shi

Electronics 2023, 12(11), 2551; https://doi.org/10.3390/electronics12112551 - 5 Jun 2023

Cited by 1 | Viewed by 2239

Abstract

In this paper, we compare a new 1.2 kV rated 4H-SiC split-gate (SG) MOSFET with the conventional planar-gate (PG) MOSFETs. Both structures were fabricated with the same design rules and process platform. Therefore, the structures have similar electrical parameters, such as ON-state drain-source [...] Read more.

In this paper, we compare a new 1.2 kV rated 4H-SiC split-gate (SG) MOSFET with the conventional planar-gate (PG) MOSFETs. Both structures were fabricated with the same design rules and process platform. Therefore, the structures have similar electrical parameters, such as ON-state drain-source resistance (R_ON), breakdown voltage (BV), threshold voltage (V_th), and body diode forward voltage (V_SD). It is shown that the C_iss/C_oss/C_rss capacitances of the SG-MOSFET can be reduced by 7%/8%/17%, respectively, compared with PG-MOSFET. It is also shown that the SG-MOSFET has the potential to reduce switching losses without compromising the static performance. Moreover, it maintains the robustness of the device, and an optimized layout design with spaced holes in the gate poly is adopted. Therefore, there is no obvious degradation between the SG-MOSFET and the PG-MOSFET in terms of avalanche and short-circuit endurance capabilities. Full article

► Show Figures

Figure 1

14 pages, 637 KiB

Open AccessArticle

Delay Minimization Using Hybrid RSMA-TDMA for Mobile Edge Computing

by Fengcheng Xiao, Pengxu Chen, Hua Wu, Yuming Mao and Hongwu Liu

Electronics 2023, 12(11), 2550; https://doi.org/10.3390/electronics12112550 - 5 Jun 2023

Viewed by 1438

Abstract

Rate-splitting multiple access (RSMA) has recently received attention due to its benefits in both spectral and energy efficiencies. In this paper, we propose a hybrid RSMA-time-division multiple access (TDMA) scheme for a mobile edge computing (MEC) system, where two edge users need to [...] Read more.

Rate-splitting multiple access (RSMA) has recently received attention due to its benefits in both spectral and energy efficiencies. In this paper, we propose a hybrid RSMA-time-division multiple access (TDMA) scheme for a mobile edge computing (MEC) system, where two edge users need to offload their task data to a MEC server. In the proposed scheme, the offloading time is divided into two time phases. Specifically, we design a cognitive radio (CR)-inspired RSMA scheme, in which two users, namely the primary user and secondary user, offload their task data to the MEC server in the first time phase, while only a single user can offload task data in the second time phase. With the aim of minimizing the overall offloading delay, we formulate the offloading delay minimization problem subject to the transmit power and total energy constraints. We transform the original fractional programming non-convex problem to a convex one by using the Dinkelbach transform and propose Dinkelbach and Newton iterative algorithms to determine the optimal transmit power allocation. Specifically, we establish the optimization criteria for the three offloading schemes and derive the corresponding closed-form expressions for the optimal power allocation. Compared to the existing offloading schemes, the numerical results show that the proposed hybrid RSMA-TDMA scheme in scenarios where having a limited energy budget is superior in offloading delay compared to other offloading schemes and the sum offloading delay tends to a constant with the increase in the energy budget. Full article

(This article belongs to the Section Microwave and Wireless Communications)

► Show Figures

Figure 1

14 pages, 1419 KiB

Open AccessArticle

A Wireless, Battery-Powered Probe Based on a Dual-Tier CMOS SPAD Array for Charged Particle Sensing

by Joana Minga, Paolo Brogi, Gianmaria Collazuol, Gian-Franco Dalla Betta, Pier Simone Marrocchesi, Fabio Morsani, Lucio Pancheri, Lodovico Ratti, Gianmarco Torilla and Carla Vacchi

Electronics 2023, 12(11), 2549; https://doi.org/10.3390/electronics12112549 - 5 Jun 2023

Cited by 3 | Viewed by 1726

Abstract

A compact probe for charged particle imaging, with potential applications in source activity mapping and radio-guided surgery was designed and tested. The development of this technology holds significant implications for medical imaging, offering healthcare professionals accurate and efficient tools for diagnoses and treatments. [...] Read more.

A compact probe for charged particle imaging, with potential applications in source activity mapping and radio-guided surgery was designed and tested. The development of this technology holds significant implications for medical imaging, offering healthcare professionals accurate and efficient tools for diagnoses and treatments. To fulfill the portability requirements of these applications, the probe was designed for battery operation and wireless communication with a PC. The core sensor is a dual-layer CMOS SPAD detector, fabricated using 150 nm technology, which uses overlapping cells to produce a coincidence signal and reduce the dark count rate (DCR). The sensor is managed and interfaced with a microcontroller, and custom firmware was developed to facilitate communication with the sensor. The performance of the probe was evaluated by characterizing the on-board SPAD detector in terms of the DCR, and the results were consistent with the characterization measurements taken on the same chip samples using a purposely developed benchtop setup. Full article

(This article belongs to the Special Issue Feature Papers in Circuit and Signal Processing)

► Show Figures

Figure 1

21 pages, 4263 KiB

Open AccessArticle

Emotion-Recognition Algorithm Based on Weight-Adaptive Thought of Audio and Video

by Yongjian Cheng, Dongmei Zhou, Siqi Wang and Luhan Wen

Electronics 2023, 12(11), 2548; https://doi.org/10.3390/electronics12112548 - 5 Jun 2023

Cited by 2 | Viewed by 1541

Abstract

Emotion recognition commonly relies on single-modal recognition methods, such as voice and video signals, which demonstrate a good practicability and universality in some scenarios. Nevertheless, as emotion-recognition application scenarios continue to expand and the data volume surges, single-modal emotion recognition proves insufficient to [...] Read more.

Emotion recognition commonly relies on single-modal recognition methods, such as voice and video signals, which demonstrate a good practicability and universality in some scenarios. Nevertheless, as emotion-recognition application scenarios continue to expand and the data volume surges, single-modal emotion recognition proves insufficient to meet people’s needs for accuracy and comprehensiveness when the amount of data reaches a certain scale. Thus, this paper proposes the application of multimodal thought to enhance emotion-recognition accuracy and conducts corresponding data preprocessing on the selected dataset. Appropriate models are constructed for both audio and video modalities: for the audio-modality emotion-recognition task, this paper adopts the “time-distributed CNNs + LSTMs” model construction scheme; for the video-modality emotion-recognition task, the “DeepID V3 + Xception architecture” model construction scheme is selected. Furthermore, each model construction scheme undergoes experimental verification and comparison with existing emotion-recognition algorithms. Finally, this paper attempts late fusion and proposes and implements a late-fusion method based on the idea of weight adaptation. The experimental results demonstrate the superiority of the multimodal fusion algorithm proposed in this paper. When compared to the single-modal emotion-recognition algorithm, the accuracy of recognition is increased by almost 4%, reaching 84.33%. Full article

► Show Figures

Graphical abstract

17 pages, 620 KiB

Open AccessArticle

Performance and Capacity Optimization for High Speed Railway Communications Using UAV-IRS Assisted Massive MIMO System

by Ziyue Liu, Mingxi Yang, Jingjing Cui, Yue Xiao and Xuejun Zhang

Electronics 2023, 12(11), 2547; https://doi.org/10.3390/electronics12112547 - 5 Jun 2023

Cited by 3 | Viewed by 1640

Abstract

In this paper, we study the communication performance of applying unmanned aerial vehicles (UAVs) combined with intelligent reflective surfaces (IRS) in a high speed railway (HSR) scenario. This study investigates the design and performance of (multiple-input-multiple-output) MIMO systems with UAV and IRS assistance [...] Read more.

In this paper, we study the communication performance of applying unmanned aerial vehicles (UAVs) combined with intelligent reflective surfaces (IRS) in a high speed railway (HSR) scenario. This study investigates the design and performance of (multiple-input-multiple-output) MIMO systems with UAV and IRS assistance technology in high-mobility scenarios. Direct links between base stations (BS) and trains are often obstructed in suburban environments, especially in mountainous areas. We mount the IRS on the UAVs so that it can assist in the communication between the trains and the BS. With the help of the UAV-IRS, straight-line links can be established effectively, which greatly improves communication for train passengers. This paper considers the employment of large-scale antenna arrays at both the BS and train ends. Train passengers communicate with UAVs via antennas assembled on the roof of the train as gateways, which in turn communicate with the BS. We consider two types of antenna layouts on the train: all antennas are located in the center of the train named Co-located antennas (CA) layout and uniformly distributed along the train called distributed antennas (DA) layout. We can obtain the analytical up-link capacity by averaging over all locations in a cell for the above two layouts by considering the radio frequency consumption. Overall, the CA layout is found to be a better option for trains when attempting to maximize cell mean value of capacity, and DA layout achieves a more uniformly distribution of capacity over the entire cell. Ultimately, the best solution will depend on the specific requirements and constraints of the selected deployment scenario. Full article

(This article belongs to the Special Issue Advances in Reconfigurable Intelligent Surfaces and Wireless Communications)

► Show Figures

Figure 1

13 pages, 2759 KiB

Open AccessArticle

A TEDE Algorithm Studies the Effect of Dataset Grouping on Supervised Learning Accuracy

by Xufei Wang, Penghui Wang, Jeongyoung Song, Taotao Hao and Xinlu Duan

Electronics 2023, 12(11), 2546; https://doi.org/10.3390/electronics12112546 - 5 Jun 2023

Cited by 1 | Viewed by 1467

Abstract

Datasets are the basis for research on deep learning methods in computer vision. The impact of the percentage of training sets in a dataset on the performance of neural network models needs to be further explored. In this paper, a twice equal difference [...] Read more.

Datasets are the basis for research on deep learning methods in computer vision. The impact of the percentage of training sets in a dataset on the performance of neural network models needs to be further explored. In this paper, a twice equal difference enumeration (TEDE) algorithm is proposed to investigate the effect of different training set percentages in the dataset on the performance of the network model, and the optimal training set percentage is determined. By selecting the Pascal VOC dataset and dividing it into six different datasets from largest to smallest, and then dividing each dataset into the datasets to be analyzed according to five different training set percentages, the YOLOv5 convolutional neural network is used to train and test the 30 datasets to determine the optimal neural network model corresponding to the training set percentages. Finally, tests were conducted using the Udacity Self-Driving dataset with a self-made Tire Tread Defects (TTD) dataset. The results show that the network model performance is superior when the training set accounts for between 85% and 90% of the overall dataset. The results of dataset partitioning obtained by the TEDE algorithm can provide a reference for deep learning research. Full article

► Show Figures

Figure 1

17 pages, 2788 KiB

Open AccessArticle

Critic-Only Learning Based Tracking Control for Uncertain Nonlinear Systems with Prescribed Performance

by Yanping Gao and Zuojun Liu

Electronics 2023, 12(11), 2545; https://doi.org/10.3390/electronics12112545 - 5 Jun 2023

Viewed by 1233

Abstract

A critic-only learning-based tracking control with prescribed performance was proposed for a class of uncertain nonlinear systems. Based on an estimator and an optimal controller, a novel controller was designed to make tracking errors uniformly ultimately bounded and limited in a prescribed region. [...] Read more.

A critic-only learning-based tracking control with prescribed performance was proposed for a class of uncertain nonlinear systems. Based on an estimator and an optimal controller, a novel controller was designed to make tracking errors uniformly ultimately bounded and limited in a prescribed region. First, an unknown system dynamic estimator was employed online to approximate the uncertainty with an invariant manifold. Subsequently, by running a novel cost function, an optimal controller was derived by online learning with a critic-only neural network, which ensured that tracking errors can evolve within a prescribed area while minimizing the cost function. Specifically, weight update can be driven by weight estimation error, avoiding introducing an actor-critic architecture with a complicated law. At last, the stability of a closed-loop system was analyzed by Lyapunov theorem, and tracking errors evolved within prescribed performance with the optimal controller. The effectiveness of the proposed control can be demonstrated by two examples. Full article

► Show Figures

Figure 1

25 pages, 1264 KiB

Open AccessReview

Investigation into Perceptual-Aware Optimization for Single-Image Super-Resolution in Embedded Systems

by Khanh Hung Vu, Duc Phuc Nguyen, Duc Dung Nguyen and Hoang-Anh Pham

Electronics 2023, 12(11), 2544; https://doi.org/10.3390/electronics12112544 - 5 Jun 2023

Viewed by 1870

Abstract

Deep learning has been introduced to single-image super-resolution (SISR) in the last decade. These techniques have taken over the benchmarks of SISR tasks. Nevertheless, most architectural designs necessitate substantial computational resources, leading to a prolonged inference time on embedded systems or rendering them [...] Read more.

Deep learning has been introduced to single-image super-resolution (SISR) in the last decade. These techniques have taken over the benchmarks of SISR tasks. Nevertheless, most architectural designs necessitate substantial computational resources, leading to a prolonged inference time on embedded systems or rendering them infeasible for deployment. This paper presents a comprehensive survey of plausible solutions and optimization methods to address this problem. Then, we propose a pipeline that aggregates the latter in order to enhance the inference time without significantly compromising the perceptual quality. We investigate the effectiveness of the proposed method on a lightweight Generative Adversarial Network (GAN)-based perceptual-oriented model as a case study. The experimental results show that our proposed method leads to significant improvement in the inference time on both Desktop and Jetson Xavier NX, especially for higher resolution input sizes on the latter, thereby making it deployable in practice. Full article

(This article belongs to the Special Issue Embedded Systems for Neural Network Applications)

► Show Figures

Figure 1

13 pages, 2824 KiB

Open AccessArticle

Energy Efficient Load-Balancing Mechanism in Integrated IoT–Fog–Cloud Environment

by Meenu Vijarania, Swati Gupta, Akshat Agrawal, Matthew O. Adigun, Sunday Adeola Ajagbe and Joseph Bamidele Awotunde

Electronics 2023, 12(11), 2543; https://doi.org/10.3390/electronics12112543 - 5 Jun 2023

Cited by 17 | Viewed by 2371

Abstract

The Internet of Things (IoT) and cloud computing have revolutionized the technological era unabatedly. These technologies have impacted our lives to a great extent. The traditional cloud model faces a variety of complications with the colossal growth of IoT and cloud applications, such [...] Read more.

The Internet of Things (IoT) and cloud computing have revolutionized the technological era unabatedly. These technologies have impacted our lives to a great extent. The traditional cloud model faces a variety of complications with the colossal growth of IoT and cloud applications, such as network instability, reduced bandwidth, and high latency. Fog computing is utilized to get around these problems, which brings IoT devices and cloud computing closer. Hence, to enhance system, process, and data performance, fog nodes are planted to disperse the load on cloud servers using fog computing, which helps reduce delay time and network traffic. Firstly, in this article, we highlight the various IoT–fog–cloud models for distributing the load uniformly. Secondly, an efficient solution is provided using fog computing for balancing load among fog devices. A performance evaluation of the proposed mechanism with existing techniques shows that the proposed strategy improves performance, energy consumption, throughput, and resource utilization while reducing response time. Full article

(This article belongs to the Special Issue AI in Cybersecurity)

► Show Figures

Figure 1

15 pages, 836 KiB

Open AccessArticle

AV PV-RCNN: Improving 3D Object Detection with Adaptive Deformation and VectorPool Aggregation

by Shijie Guan, Chenyang Wan and Yueqiu Jiang

Electronics 2023, 12(11), 2542; https://doi.org/10.3390/electronics12112542 - 5 Jun 2023

Viewed by 1685

Abstract

Three-dimensional object detection has attracted more and more attention from industry and academia due to its wide application in various fields such as autonomous driving and robotics. Currently, the refinement methods used by advanced two-stage detectors cannot fully adapt to different object scales, [...] Read more.

Three-dimensional object detection has attracted more and more attention from industry and academia due to its wide application in various fields such as autonomous driving and robotics. Currently, the refinement methods used by advanced two-stage detectors cannot fully adapt to different object scales, different point cloud densities, partial deformation and clutter, and excessive resource consumption. We propose a point cloud-based 3D object detection method that can adapt to different object scales and aggregate local features with less resources. The method first passes through an adaptive deformation module based on a 2D deformable convolutional network, which can adaptively collect instance-specific features from where the information content exists. Secondly, through a VectorPool aggregation module, this module can better aggregate local point features with less resource consumption. Finally, through a context fusion module, the key points can filter out relevant context information for the refinement stage. Our proposed detection method not only achieves better accuracy on the KITTI dataset, but also consumes less resources than the original detectors and has faster inference speed. Full article

► Show Figures

Figure 1

20 pages, 3362 KiB

Open AccessArticle

Autonomous Drone Electronics Amplified with Pontryagin-Based Optimization

by Jiahao Xu and Timothy Sands

Electronics 2023, 12(11), 2541; https://doi.org/10.3390/electronics12112541 - 5 Jun 2023

Cited by 7 | Viewed by 1542

Abstract

In the era of electrification and artificial intelligence, direct current motors are widely utilized with numerous innovative adaptive and learning methods. Traditional methods utilize model-based algebraic techniques with system identification, such as recursive least squares, extended least squares, and autoregressive moving averages. The [...] Read more.

In the era of electrification and artificial intelligence, direct current motors are widely utilized with numerous innovative adaptive and learning methods. Traditional methods utilize model-based algebraic techniques with system identification, such as recursive least squares, extended least squares, and autoregressive moving averages. The new method known as deterministic artificial intelligence employs physical-based process dynamics to achieve target trajectory tracking. There are two common autonomous trajectory-generation algorithms: sinusoidal function- and Pontryagin-based generation algorithms. The Pontryagin-based optimal trajectory with deterministic artificial intelligence for DC motors is proposed and its performance compared for the first time in this paper. This paper aims to simulate model following and deterministic artificial intelligence methods using the sinusoidal and Pontryagin methods and to compare the differences in their performance when following the challenging step function slew maneuver. Full article

(This article belongs to the Special Issue Feature Papers in Computer Science & Engineering)

► Show Figures

Figure 1

23 pages, 4738 KiB

Open AccessArticle

A Nonintrusive Load Identification Method Based on Improved Gramian Angular Field and ResNet18

by Jingqin Wang, Yufeng Wu and Liang Shu

Electronics 2023, 12(11), 2540; https://doi.org/10.3390/electronics12112540 - 5 Jun 2023

Cited by 2 | Viewed by 1756

Abstract

Image classification methods based on deep learning have been widely used in the study of nonintrusive load identification. However, in the process of encoding the load electrical signals into images, how to fully retain features of the raw data and thus increase the [...] Read more.

Image classification methods based on deep learning have been widely used in the study of nonintrusive load identification. However, in the process of encoding the load electrical signals into images, how to fully retain features of the raw data and thus increase the recognizability of loads carried with very similar current signals are still challenging, and the loss of load features will cause the overall accuracy of load identification to decrease. To deal with this problem, this paper proposes a nonintrusive load identification method based on the improved Gramian angular field (iGAF) and ResNet18. In the proposed method, fast Fourier transform is used to calculate the amplitude spectrum and the phase spectrum to reconstruct the pixel matrices of the B channel, G channel, and R channel of generated GAF images so that the color image fused by the three channels contains more information. This improvement to the GAF method enables generated images to retain the amplitude feature and phase feature of the raw data that are usually missed in the general GAF image. ResNet18 is trained with iGAF images for nonintrusive load identification. Experiments are conducted on two private datasets, ESEAD and EMCAD, and two public datasets, PLAID and WHITED. Experimental results suggest that the proposed method performs well on both private and public datasets, achieving overall identification accuracies of 99.545%, 99.375%, 98.964%, and 100% on the four datasets, respectively. In particular, the method demonstrates significant identification effects for loads with similar current waveforms in private datasets. Full article

(This article belongs to the Topic Artificial Intelligence Models, Tools and Applications)

► Show Figures

Figure 1

14 pages, 8969 KiB

Open AccessArticle

A Compact Fourth-Order Tunable Bandpass Filter Based on Varactor-Loaded Step-Impedance Resonators

by Shuang Li, Shengxian Li and Jianrong Yuan

Electronics 2023, 12(11), 2539; https://doi.org/10.3390/electronics12112539 - 5 Jun 2023

Cited by 4 | Viewed by 1730

Abstract

In this paper, a compact high-selectivity frequency tunable bandpass filter (BPF) with constant absolute bandwidth (ABW) based on varactor-loaded step-impedance resonators (SIRs) is presented. By introducing cross coupling between resonators, a pair of transmission zeros (TZs) close to the passband are produced and [...] Read more.

In this paper, a compact high-selectivity frequency tunable bandpass filter (BPF) with constant absolute bandwidth (ABW) based on varactor-loaded step-impedance resonators (SIRs) is presented. By introducing cross coupling between resonators, a pair of transmission zeros (TZs) close to the passband are produced and the selectivity of the filter is enhanced significantly. Another pair of TZs are generated to improve the out-of-band rejection by using source-load coupling. The varactor-loaded SIRs are utilized to design the compact fourth-order tunable BPF in order to realize wide tuning range and compact size. In addition, the frequency-dependent coupling feeding structures are employed instead of lumped capacitors used in conventional feeding structures, as a result, the insertion-loss performance is improved. The simulated and measured results are presented and show good agreement. The measured results exhibit a tuning range from 0.8 to 1.14 GHz with a 3 dB constant ABW of about 47 ± 5 MHz, the return loss of the filter is greater than 13.9 dB, and the insertion loss is about 2.7–3.1 dB. Moreover, four TZs are generated, and the proposed tunable filter shows high selectivity with a rectangular coefficient of 2.3–3.1. Full article

(This article belongs to the Special Issue Microwave Devices: Analysis, Design, and Application)

► Show Figures

Figure 1

19 pages, 806 KiB

Open AccessArticle

Lightweight Transmission Behavior Audit Scheme for NDN Industrial Internet Identity Resolution and Transmission Based on Blockchain

by Yunhua He, Yuliang Ma, Qing Hu, Zhihao Zhou, Ke Xiao and Chao Wang

Electronics 2023, 12(11), 2538; https://doi.org/10.3390/electronics12112538 - 4 Jun 2023

Cited by 2 | Viewed by 1383

Abstract

The Named Data Network (NDN) enables efficient content dissemination through interest-based retrieval, name-based routing, and content caching. In the industrial Internet architecture based on NDN, device identity distribution, identification, resolution, and routing rely on identification resolution technology. However, this approach presents challenges such [...] Read more.

The Named Data Network (NDN) enables efficient content dissemination through interest-based retrieval, name-based routing, and content caching. In the industrial Internet architecture based on NDN, device identity distribution, identification, resolution, and routing rely on identification resolution technology. However, this approach presents challenges such as cache poisoning, interest packet flood attacks, and black hole attacks. Existing security schemes primarily focused on routing forwarding and verification fail to address critical concerns, including routing environment credibility and data leakage, while exhibiting poor time and space efficiency. To address these challenges, this paper proposes a lightweight behavior auditing scheme using blockchain technology. The scheme utilizes an improved Bloom filter to compress behavioral information like interest and data packets during the identification transmission process. The compressed data are subsequently uploaded to a blockchain for auditing, achieving efficient space and time utilization while maintaining feasibility. Full article

(This article belongs to the Special Issue Research on the Security Issues of Blockchain)

► Show Figures

Figure 1

14 pages, 5929 KiB

Open AccessArticle

Image Recognition of Group Point Objects under Interference Conditions

by Viliam Ďuriš, Anatoly V. Grigoriev and Sergey G. Chumarov

Electronics 2023, 12(11), 2537; https://doi.org/10.3390/electronics12112537 - 4 Jun 2023

Viewed by 1182

Abstract

The process of forming a vector-field model of flat images of group point objects, with various field-forming functions, is considered in this paper. Algorithms for recognizing group point objects in the presence of false and missing point objects are proposed. The quality of [...] Read more.

The process of forming a vector-field model of flat images of group point objects, with various field-forming functions, is considered in this paper. Algorithms for recognizing group point objects in the presence of false and missing point objects are proposed. The quality of the recognition of group point objects by the proposed algorithms is also evaluated. Full article

(This article belongs to the Special Issue Advances in Image Processing and Detection)

► Show Figures

Figure 1

20 pages, 813 KiB

Open AccessArticle

A Validation Study to Confirm the Accuracy of Wearable Devices Based on Health Data Analysis

by Nikola Hrabovska, Erik Kajati and Iveta Zolotova

Electronics 2023, 12(11), 2536; https://doi.org/10.3390/electronics12112536 - 4 Jun 2023

Cited by 7 | Viewed by 5219

Abstract

This research article presents an analysis of health data collected from wearable devices, aiming to uncover the practical applications and implications of such analyses in personalized healthcare. The study explores insights derived from heart rate, sleep patterns, and specific workouts. The findings demonstrate [...] Read more.

This research article presents an analysis of health data collected from wearable devices, aiming to uncover the practical applications and implications of such analyses in personalized healthcare. The study explores insights derived from heart rate, sleep patterns, and specific workouts. The findings demonstrate potential applications in personalized health monitoring, fitness optimization, and sleep quality assessment. The analysis focused on the heart rate, sleep patterns, and specific workouts of the respondents. Results indicated that heart rate values during functional strength training fell within the target zone, with variations observed between different types of workouts. Sleep patterns were found to be individualized, with variations in sleep interruptions among respondents. The study also highlighted the impact of individual factors, such as demographics and manually defined information, on workout outcomes. The study acknowledges the challenges posed by the emerging nature of wearable devices and technological constraints. However, it emphasizes the significance of the research, highlighting variations in workout intensities based on heart rate data and the individualized nature of sleep patterns and disruptions. Perhaps the future cognitive healthcare platform may harness these insights to empower individuals in monitoring their health and receiving personalized recommendations for improved well-being. This research opens up new horizons in personalized healthcare, transforming how we approach health monitoring and management. Full article

(This article belongs to the Special Issue IoT for Healthcare and Wellbeing: Trends, Challenges, and Applications)

► Show Figures

Figure 1

4 pages, 186 KiB

Open AccessEditorial

Artificial Intelligence Techniques for Electronics

by Gwanggil Jeon

Electronics 2023, 12(11), 2535; https://doi.org/10.3390/electronics12112535 - 4 Jun 2023

Viewed by 1248

Abstract

Artificial intelligence technology has become an indispensable element in the [...] Full article

(This article belongs to the Section Artificial Intelligence)

14 pages, 3121 KiB

Open AccessArticle

A Method for Suppressing False Target Jamming with Non-Uniform Stepped-Frequency Radar

by Yongzhe Zhu, Zhaojian Zhang, Xiaoge Wang, Binbin Li, Weijian Liu and Hao Chen

Electronics 2023, 12(11), 2534; https://doi.org/10.3390/electronics12112534 - 4 Jun 2023

Cited by 3 | Viewed by 1322

Abstract

Stepped-frequency radar can increase the degree of freedom of the range dimension by adding a tiny stepping frequency between neighboring pulse carrier frequencies, which has a clear advantage in countering range false target jamming. However, when the jamming is released by a self-defense [...] Read more.

Stepped-frequency radar can increase the degree of freedom of the range dimension by adding a tiny stepping frequency between neighboring pulse carrier frequencies, which has a clear advantage in countering range false target jamming. However, when the jamming is released by a self-defense jammer carried by the target, the range information is coupled to the Doppler frequency. This makes it impossible for a stepped-frequency radar to extract the range information accurately. In this paper, we derive the correlation between the phase difference of adjacent pulses and range information and the Doppler frequency when the frequency is uniformly stepped, as well as the error caused by the Doppler frequency in range estimation. Then, we propose a decoupling method based on a waveform design and the corresponding suppression method of range false target jamming. Simulation results show that the proposed method can effectively suppress the jamming of self-defense range false targets. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) Based Radar Signal Processing and Radar Imaging)

► Show Figures

Figure 1

16 pages, 1761 KiB

Open AccessArticle

Research on Offloading Strategy for Mobile Edge Computing Based on Improved Grey Wolf Optimization Algorithm

by Wenzhu Zhang and Kaihang Tuo

Electronics 2023, 12(11), 2533; https://doi.org/10.3390/electronics12112533 - 4 Jun 2023

Cited by 6 | Viewed by 1737

Abstract

With the development of intelligent transportation and the rapid growth of application data, the tasks of offloading vehicles in vehicle-to-vehicle communication technology are continuously increasing. To further improve the service efficiency of the computing platform, energy-efficient and low-latency mobile-edge-computing (MEC) offloading methods are [...] Read more.

With the development of intelligent transportation and the rapid growth of application data, the tasks of offloading vehicles in vehicle-to-vehicle communication technology are continuously increasing. To further improve the service efficiency of the computing platform, energy-efficient and low-latency mobile-edge-computing (MEC) offloading methods are urgently needed, which can solve the insufficient computing capacity of vehicle terminals. Based on an improved gray-wolf algorithm designed, an adaptive joint offloading strategy for vehicular edge computing is proposed, which does not require cloud-computing support. This strategy first establishes an offloading computing model, which takes task computing delays, computing energy consumption, and MEC server computing resources as constraints; secondly, a system-utility function is designed to transform the offloading problem into a constrained system-utility optimization problem; finally, the optimal solution to the computation offloading problem is obtained based on an improved gray-wolf optimization algorithm. The simulation results show that the proposed strategy can effectively reduce the system delay and the total energy consumption. Full article

► Show Figures

Figure 1

15 pages, 1845 KiB

Open AccessArticle

Study on the Selection Method of Federated Learning Clients for Smart Manufacturing

by Chi Yang and Xiaoli Zhao

Electronics 2023, 12(11), 2532; https://doi.org/10.3390/electronics12112532 - 4 Jun 2023

Cited by 2 | Viewed by 1533

Abstract

Artificial intelligence technology in the context of smart manufacturing uses manufacturing data to enable the automatic detection, classification, and identification of products in the production process, reducing production costs and human consumption, thereby improving production efficiency and product quality. Federated learning enables the [...] Read more.

Artificial intelligence technology in the context of smart manufacturing uses manufacturing data to enable the automatic detection, classification, and identification of products in the production process, reducing production costs and human consumption, thereby improving production efficiency and product quality. Federated learning enables the distributed implementation of AI technologies, keeping data local to avoid privacy leaks. However, data heterogeneity factors have an impact on federated learning in a manufacturing context, and this paper proposes a customer degree selection method based on model parameter variation. The method relies on transmitting the local model changes in the participants to reflect the data characteristics, calculates the model similarity of the participants using graph theory and similarity, and uses the Top-K mechanism to filter the original participant set through the similarity scores of graph nodes to reduce the influence of heterogeneity factors in the participant set and maximize the training effect and accuracy of federated learning. The effectiveness of this method was verified by using the Dirichlet distribution to perform non-IID data partitioning on the power system attack dataset and the hard disk fault detection dataset. Full article

► Show Figures

Figure 1

15 pages, 4170 KiB

Open AccessArticle

Personalized and Safe Soft Glove for Rehabilitation Training

by Fanye Meng, Chang Liu, Yu Li, Hao Hao, Qishen Li, Chenyi Lyu, Zimo Wang, Gang Ge, Junyi Yin, Xiaoqiang Ji and Xiao Xiao

Electronics 2023, 12(11), 2531; https://doi.org/10.3390/electronics12112531 - 3 Jun 2023

Cited by 6 | Viewed by 2640

Abstract

Traditional hand rehabilitation devices present a challenge in providing personalized training that can lead to finger movements exceeding the safe range, resulting in secondary injuries. To address this issue, we introduce a soft rehabilitation training glove with the function of safety and personalization, [...] Read more.

Traditional hand rehabilitation devices present a challenge in providing personalized training that can lead to finger movements exceeding the safe range, resulting in secondary injuries. To address this issue, we introduce a soft rehabilitation training glove with the function of safety and personalization, which can allow patients to select training modes based on rehabilitation and provide real-time monitoring, as well as feedback on finger movement data. The inner glove is equipped with bending sensors to access the maximum/minimum angle of finger movement and to provide data for the safety of rehabilitation training. The outer glove contains flexible drivers, which can drive fingers for different modes of rehabilitation training. As a result, the rehabilitation glove can drive five fingers to achieve maximum extension/flexion angles of 15.65°/85.97°, 15.34°/89.53°, 16.78°/94.27°, 15.59°/88.82°, and 16.73°/88.65°, from thumb to little finger, respectively, and the rehabilitation training frequency can reach six times per minute. The safety evaluation result indicated an error within ±6.5° of the target-motion threshold. The reliability assessment yielded a high-intra-class correlation coefficient value (0.7763–0.9996). Hence, the rehabilitation glove can achieve targeted improvement in hand function while ensuring safety. Full article

(This article belongs to the Special Issue Advancements in the Application of Artificial Intelligence in Care and Continuity of Care)

► Show Figures

Figure 1

19 pages, 3270 KiB

Open AccessArticle

A Novel Parameter Estimation Method Based on Piecewise Nonlinear Amplitude Transform for the LFM Signal in Impulsive Noise

by Haiying Wang, Qunying Zhang, Wenhai Cheng, Jiaming Dong and Xiaojun Liu

Electronics 2023, 12(11), 2530; https://doi.org/10.3390/electronics12112530 - 3 Jun 2023

Cited by 2 | Viewed by 1275

Abstract

In a complex electromagnetic environment, any noise present generally exhibits strong impulsive characteristics. The performance of existing parameter estimation methods carried out in Gaussian white noise for the linear frequency modulation (LFM) signal degrades or even fails under impulsive noise. This paper proposes [...] Read more.

In a complex electromagnetic environment, any noise present generally exhibits strong impulsive characteristics. The performance of existing parameter estimation methods carried out in Gaussian white noise for the linear frequency modulation (LFM) signal degrades or even fails under impulsive noise. This paper proposes a novel parameter estimation method to address this problem. Firstly, the properties of the piecewise nonlinear amplitude transform (PNAT) are derived. This manuscript verifies that the PNAT can retain phase information of the LFM signal while suppressing the impulsive noise. Subsequently, a new concept known as piecewise nonlinear amplitude transform parametric symmetric instantaneous autocorrelation function (PNAT-PSIAF) is proposed. Based on this concept, a novel method called piecewise nonlinear amplitude transform Lv’s distribution (PNAT-LVD) is proposed to estimate the centroid frequency and chirp rate of the LFM signal. The simulations show that the proposed algorithm can effectively suppress the impulsive noise without prior knowledge of the noise for both the single-component and double-component LFM signal. In addition, two parameters of the LFM signal can be precisely estimated by the proposed method under low generalized signal-to-noise ratios (GSNR). The stronger the impulsive characteristics of the noise, the better the performance of the algorithm. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

► Show Figures

Figure 1

11 pages, 1059 KiB

Open AccessArticle

Threshold Voltage Measurement Protocol “Triple Sense” Applied to GaN HEMTs

by Tamiris Grossl Bade, Hassan Hamad, Adrien Lambert, Hervé Morel and Dominique Planson

Electronics 2023, 12(11), 2529; https://doi.org/10.3390/electronics12112529 - 3 Jun 2023

Cited by 2 | Viewed by 1700

Abstract

The threshold voltage instability in p-GaN gate high electron mobility transistors (HEMTs) has been brought into evidence in recent years. It can lead to reliability issues in switching applications, and it can be followed by other degradation mechanisms. In this paper, a [...] Read more.

The threshold voltage instability in p-GaN gate high electron mobility transistors (HEMTs) has been brought into evidence in recent years. It can lead to reliability issues in switching applications, and it can be followed by other degradation mechanisms. In this paper, a

V_{t h}

measurement protocol established for SiC MOSFETs is applied to GaN HEMTs: the triple sense protocol, which uses voltage bias to precondition the transistor gate. It has been experimentally verified that the proposed protocol increased the stability of the

V_{t h}

measurement, even for measurements following degrading voltage bias stress on both drain and gate. Full article

(This article belongs to the Special Issue GaN Power Devices and Applications)

► Show Figures

Figure 1

21 pages, 2657 KiB

Open AccessArticle

Automated Multimodal Stress Detection in Computer Office Workspace

by Thelma Androutsou, Spyridon Angelopoulos, Evangelos Hristoforou, George K. Matsopoulos and Dimitrios D. Koutsouris

Electronics 2023, 12(11), 2528; https://doi.org/10.3390/electronics12112528 - 3 Jun 2023

Cited by 4 | Viewed by 1725

Abstract

Nowadays, changes in the conditions and nature of the workplace make it imperative to create unobtrusive systems for the automatic detection of occupational stress, which can be feasibly addressed through the adoption of Internet of Things (IoT) technologies and advances in data analysis. [...] Read more.

Nowadays, changes in the conditions and nature of the workplace make it imperative to create unobtrusive systems for the automatic detection of occupational stress, which can be feasibly addressed through the adoption of Internet of Things (IoT) technologies and advances in data analysis. This paper presents the development of a multimodal automated stress detection system in an office environment that utilizes measurements derived from individuals’ interactions with the computer and its peripheral units. In our analysis, behavioral parameters of computer keyboard and mouse dynamics are combined with physiological parameters recorded by sensors embedded in a custom-made smart computer mouse device. To validate the system, we designed and implemented an experimental protocol simulating an office environment and included the most known work stressors. We applied known classifiers and different data labeling methods to the physiological and behavioral parameters extracted from the collected data, resulting in high-performance metrics. The feature-level fusion analysis of physiological and behavioral parameters successfully detected stress with an accuracy of 90.06% and F1 score of 0.90. The decision-level fusion analysis, combining the features extracted from both the computer mouse and keyboard, showed an average accuracy of 66% and an average F1 score of 0.56. Full article

(This article belongs to the Special Issue Emerging E-health Applications and Medical Information Systems)

► Show Figures

Figure 1

17 pages, 6571 KiB

Open AccessArticle

A Multi-View Face Expression Recognition Method Based on DenseNet and GAN

by Jingwei Dong, Yushun Zhang and Lingye Fan

Electronics 2023, 12(11), 2527; https://doi.org/10.3390/electronics12112527 - 3 Jun 2023

Cited by 3 | Viewed by 2005

Abstract

Facial expression recognition (FER) techniques can be widely used in human-computer interaction, intelligent robots, intelligent monitoring, and other domains. Currently, FER methods based on deep learning have become the mainstream schemes. However, these methods have some problems, such as a large number of [...] Read more.

Facial expression recognition (FER) techniques can be widely used in human-computer interaction, intelligent robots, intelligent monitoring, and other domains. Currently, FER methods based on deep learning have become the mainstream schemes. However, these methods have some problems, such as a large number of parameters, difficulty in being applied to embedded processors, and the fact that recognition accuracy is affected by facial deflection. To solve the problem of a large number of parameters, we propose a DSC-DenseNet model, which improves the standard convolution in DenseNet to depthwise separable convolution (DSC). To solve the problem wherein face deflection affects the recognition effect, we propose a posture normalization model based on GAN: a GAN with two local discriminators (LD-GAN) that strengthen the discriminatory abilities of the expression-related local parts, such as the parts related to the eyes, eyebrows, mouth, and nose. These discriminators improve the model’s ability to retain facial expressions and evidently benefits FER. Quantitative and qualitative experimental results on the Fer2013 and KDEF datasets have consistently shown the superiority of our FER method when working with multi-pose face images. Full article

(This article belongs to the Special Issue Deep Learning in Image Processing and Pattern Recognition)

► Show Figures

Figure 1

18 pages, 4512 KiB

Open AccessArticle

A Visual Enhancement Network with Feature Fusion for Image Aesthetic Assessment

by Xin Zhang, Xinyu Jiang, Qing Song and Pengzhou Zhang

Electronics 2023, 12(11), 2526; https://doi.org/10.3390/electronics12112526 - 3 Jun 2023

Cited by 1 | Viewed by 1526

Abstract

Image aesthetic assessment (IAA) with neural attention has made significant progress due to its effectiveness in object recognition. Current studies have shown that the features learned by convolutional neural networks (CNN) at different learning stages indicate meaningful information. The shallow feature contains the [...] Read more.

Image aesthetic assessment (IAA) with neural attention has made significant progress due to its effectiveness in object recognition. Current studies have shown that the features learned by convolutional neural networks (CNN) at different learning stages indicate meaningful information. The shallow feature contains the low-level information of images, and the deep feature perceives the image semantics and themes. Inspired by this, we propose a visual enhancement network with feature fusion (FF-VEN). It consists of two sub-modules, the visual enhancement module (VE module) and the shallow and deep feature fusion module (SDFF module). The former uses an adaptive filter in the spatial domain to simulate human eyes according to the region of interest (ROI) extracted by neural feedback. The latter not only extracts the shallow feature and the deep feature via transverse connection, but also uses a feature fusion unit (FFU) to fuse the pooled features together with the aim of information contribution maximization. Experiments on standard AVA dataset and Photo.net dataset show the effectiveness of FF-VEN. Full article

(This article belongs to the Special Issue On the Use of Deep Learning for Image/Video Coding and Visual Quality Assessment)

► Show Figures

Figure 1

15 pages, 3759 KiB

Open AccessArticle

Hardware Design and Implementation of a Lightweight Saber Algorithm Based on DRC Method

by Weifang Zheng, Huihong Zhang, Yuejun Zhang, Yongzhong Wen, Jie Lv, Lei Ni and Zhiyi Li

Electronics 2023, 12(11), 2525; https://doi.org/10.3390/electronics12112525 - 3 Jun 2023

Cited by 1 | Viewed by 1765

Abstract

With the development of quantum computers, the security of classical cryptosystems is seriously threatened, and the Saber algorithm has become one of the potential candidates for post-quantum cryptosystems (PQCs). To address the problems of long delay and the high power consumption of Saber [...] Read more.

With the development of quantum computers, the security of classical cryptosystems is seriously threatened, and the Saber algorithm has become one of the potential candidates for post-quantum cryptosystems (PQCs). To address the problems of long delay and the high power consumption of Saber algorithm hardware implementation, a lightweight Saber algorithm hardware design scheme based on the joint optimization of data readout and clock (DRC) was proposed. Firstly, an analysis was carried out on the hardware architecture, timing overhead and power consumption distribution of the Saber algorithm, and the key circuits that limit the performance of the algorithm were identified; secondly, a dual-port SRAM parallel reading method was adopted to improve the data reading efficiency and reduce the timing overhead of double data reading in the multiplier module. Then, a clock gating technology was used to reduce the dynamic flipping probability of internal registers and reduce the hardware power consumption of the Saber algorithm; finally, data reading and clock gating were jointly optimized to design a high-speed and low-power Saber algorithm hardware IP core. Lightweight IP cores were integrated into RISC-V SoC systems via APB bus in a TSMC 65 nm process to complete the digital back-end design. The experimental results show an IP core area of 0.99 mm² and power consumption of 8.49 mW, which is 33% lower than that reported in the related literature. Under 72 MHz & 1 V operating conditions, the number of clock cycles for the Saber algorithm’s key generation, encryption and decryption are 3315, 9204 and 1420, respectively. Full article

(This article belongs to the Special Issue Computer-Aided Design for Hardware Security and Trust)

► Show Figures

Figure 1

19 pages, 2039 KiB

Open AccessArticle

Neural Network-Based Robust Bipartite Consensus Tracking Control of Multi-Agent System with Compound Uncertainties and Actuator Faults

by Tong Li, Kaiyu Qin, Bing Jiang, Qian Huang, Hui Liu, Boxian Lin and Mengji Shi

Electronics 2023, 12(11), 2524; https://doi.org/10.3390/electronics12112524 - 2 Jun 2023

Cited by 3 | Viewed by 1326

Abstract

This paper addresses the challenging problem of bipartite consensus tracking of multi-agent systems that are subject to compound uncertainties and actuator faults. Specifically, the study considers a leader agent with fractional-order nonlinear dynamics unknown to the followers. In addition, both cooperative and competitive [...] Read more.

This paper addresses the challenging problem of bipartite consensus tracking of multi-agent systems that are subject to compound uncertainties and actuator faults. Specifically, the study considers a leader agent with fractional-order nonlinear dynamics unknown to the followers. In addition, both cooperative and competitive interactions among agents are taken into account. To tackle these issues, the proposed approach employs a fully distributed robust bipartite consensus tracking controller, which integrates a neural network approximator to estimate the uncertainties of the leader and the followers. The adaptive laws of neural network parameters are continuously updated online based on the bipartite consensus tracking error. Furthermore, an adaptive control technique is utilized to generate the fault-tolerant component to mitigate the partial loss caused by actuator effectiveness faults. Compared with the existing works on nonlinear multi-agent systems, we consider the compound uncertainties, actuator faults and cooperative–competition interactions simultaneously. By implementing the proposed control scheme, the robustness of the closed-loop system can be significantly improved. Finally, numerical simulations are performed to validate the effectiveness of the control scheme. Full article

(This article belongs to the Special Issue Cooperative Control of Multi-Agent Systems with Communication Constraints)

► Show Figures

Figure 1

16 pages, 6141 KiB

Open AccessArticle

Inspection Algorithm of Welding Bead Based on Image Projection

by Jaeeun Lee, Hongseok Choi and Jongnam Kim

Electronics 2023, 12(11), 2523; https://doi.org/10.3390/electronics12112523 - 2 Jun 2023

Cited by 1 | Viewed by 1388

Abstract

The shear reinforcement of dual-anchorage (SRD) is used to enhance the safety of reinforced concrete structures in construction sites. In SRD, welding is used to create shear reinforcement, and after production, a quality inspection of the welding bead is required. Since the welding [...] Read more.

The shear reinforcement of dual-anchorage (SRD) is used to enhance the safety of reinforced concrete structures in construction sites. In SRD, welding is used to create shear reinforcement, and after production, a quality inspection of the welding bead is required. Since the welding bead of SRD is inspected for quality by measuring both horizontal and vertical lengths, it is necessary to obtain this information for quality inspection. However, it is difficult to inspect the quality of welding beads using existing methods based on segmentation, due to the similarity in texture between the welding bead and the base material, as well as discoloration around the welded area after welding. In this paper, we propose an algorithm that detects the welding bead using an image projection algorithm for pixels and classifies the quality of the welding bead. This algorithm detects the position of welding beads using the brightness values of an image. The proposed algorithm reduces the amount of computation time by first specifying the region of interest and then performing the analysis. Results from experiments reveal that the algorithm accurately classifies welding beads into good or bad classes by obtaining all brightness values in the vertical and horizontal directions in the SRD image. Furthermore, comparison tests with conventional algorithms demonstrate that the classification accuracy of the proposed algorithm is the highest. The proposed algorithm will be helpful in the real-time welding bead inspection field where fast and accurate inspection is crucial. Full article

► Show Figures

Figure 1

17 pages, 505 KiB

Open AccessArticle

Managing Digital Transformation: A Case Study in a Higher Education Institution

by Vicente Díaz-Garcia, Antonio Montero-Navarro, José-Luis Rodríguez-Sánchez and Rocío Gallego-Losada

Electronics 2023, 12(11), 2522; https://doi.org/10.3390/electronics12112522 - 2 Jun 2023

Cited by 9 | Viewed by 4677

Abstract

The new paradigms derived from technological innovations lead to the digital transformation of organisations. Higher Education Institutions cannot ignore these changes, which affect them like any other organisation, but especially because of their activity: training professionals who need to learn to manage and [...] Read more.

The new paradigms derived from technological innovations lead to the digital transformation of organisations. Higher Education Institutions cannot ignore these changes, which affect them like any other organisation, but especially because of their activity: training professionals who need to learn to manage and lead organisations in this new information society. This article aims to identify the main factors that can drive and facilitate the digital transformation of Higher Education Institutions from the point of view of internal stakeholders. In terms of methodology and due to the complexity of this phenomenon, the case method was considered the most appropriate for this study. As the results show, it is necessary to implement technological innovations according to the needs, establish adequate channels to communicate the process and transform the current traditional culture into a digital one. Data-driven decision-making and the development of a participative leadership style will allow the organisation to adapt to changes over time. It will also enable the retention of digital talent, which is critical to the success of the organisation’s transformation. This will ensure the development and survival of the institution. Full article

(This article belongs to the Special Issue Innovations and Challenges of Higher Education Institutions in the Post-COVID-19 Era)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Electronics, Volume 12, Issue 11 (June-1 2023) – 208 articles

Further Information

Guidelines

MDPI Initiatives

Follow MDPI