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Electronics
Volume 11
Issue 12
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Electronics, Volume 11, Issue 12 (June-2 2022) – 120 articles

Cover Story (view full-size image): This paper presents a novel approach of the behavior of the KNOWM memristor. The KNOWM memristor has been shown to act like a static nonlinear resistor under certain conditions. Consequently, the KNOWM memristor is used as a static nonlinear resistor in the chaotic Shinriki oscillator. The circuit’s dynamical behavior is examined by nonlinear methods, such as phase portraits, bifurcation and continuation diagrams, as well as maximal Lyapunov exponent diagrams. Interesting phenomena related to chaos theory, such as the entrance to chaos through the antimonotonicity phenomenon, the hysteresis phenomenon, and the existence of coexisting attractors with regards to the parameters of the system, are investigated. Moreover, the period-doubling route to chaos and crisis phenomena is observed, too. View this paper
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13 pages, 5234 KiB  
Article
Design for the Package-Board Transition and Its Testability Design in the Fan-Out Wafer-Level Package
by Ying Chen, Jun Li and Liqiang Cao
Electronics 2022, 11(12), 1922; https://doi.org/10.3390/electronics11121922 - 20 Jun 2022
Cited by 1 | Viewed by 2542
Abstract
A fan-out wafer level package (FOWLP) with double-sided four redistribution layers (RDLs) and the mega pillars connecting the front and back RDLs has been proposed for millimeter-wave applications. A well-matched package-board transition has been designed in this paper. The simulated insertion loss for [...] Read more.
A fan-out wafer level package (FOWLP) with double-sided four redistribution layers (RDLs) and the mega pillars connecting the front and back RDLs has been proposed for millimeter-wave applications. A well-matched package-board transition has been designed in this paper. The simulated insertion loss for the transition is about 0.82 dB at 79 GHz, and the simulated return loss is better than 10 dB from 72 GHz to 86 GHz. More importantly, two different measurement methods based on the port reduction technique and the Thru-Reflect-Line (TRL) calibration technique have been proposed to get the S parameters of the transition. Moreover, the feasibility of the two methods has been verified by simulation. Full article
(This article belongs to the Section Microwave and Wireless Communications)
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16 pages, 658 KiB  
Article
A Novel Prognostic Model of the Degradation Malfunction Combining a Dynamic Updated-ARIMA and Multivariate Isolation Forest: Application to Radar Transmitter
by Yuting Zhai, Dongli Liu, Zhanxin Cheng and Shaojun Fang
Electronics 2022, 11(12), 1921; https://doi.org/10.3390/electronics11121921 - 20 Jun 2022
Cited by 1 | Viewed by 1534
Abstract
In the prognosis of radar transmitter degradation malfunction, there are some restrictions, such as the fact that it is difficult to obtain fault samples and the monitoring data cannot reach the fault threshold. For these restrictions, a novel data-driven prognostic method is proposed [...] Read more.
In the prognosis of radar transmitter degradation malfunction, there are some restrictions, such as the fact that it is difficult to obtain fault samples and the monitoring data cannot reach the fault threshold. For these restrictions, a novel data-driven prognostic method is proposed to predict the radar transmitter degradation malfunction, in which the dynamic updated-auto-regressive integrated moving average is proposed to be used to predict the subsequent time-step of the microwave measurement historical data, and the multivariate isolation forest established without fault samples is used to detect the degradation malfunction. The validity and portability of the model are verified using two-type of degradation malfunction prognostic experiments. The experimental results show that the degradation malfunction can be predicted at least 10 time-steps (100 min) before the occurrence of a degradation malfunction. Compared with the existing radar degradation malfunction prediction methods, the proposed method needs less historical data, no fault samples, no artificial thresholds, and no extracting features. This method can complete a degradation malfunction prognosis when there are relevant restrictions. Full article
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18 pages, 2219 KiB  
Article
Antimonotonicity, Hysteresis and Coexisting Attractors in a Shinriki Circuit with a Physical Memristor as a Nonlinear Resistor
by Lazaros Laskaridis, Christos Volos and Ioannis Stouboulos
Electronics 2022, 11(12), 1920; https://doi.org/10.3390/electronics11121920 - 20 Jun 2022
Cited by 6 | Viewed by 2326
Abstract
A novel approach to the physical memristor’s behavior of the KNOWM is presented in this work. The KNOWM’s memristor’s intrinsic feature encourages its use as a nonlinear resistor in chaotic circuits. Furthermore, this memristor has been shown to act like a static nonlinear [...] Read more.
A novel approach to the physical memristor’s behavior of the KNOWM is presented in this work. The KNOWM’s memristor’s intrinsic feature encourages its use as a nonlinear resistor in chaotic circuits. Furthermore, this memristor has been shown to act like a static nonlinear resistor under certain situations. Consequently, for the first time, the KNOWM memristor is used as a static nonlinear resistor in the well-known chaotic Shinriki oscillator. In order to examine the circuit’s dynamical behavior, a host of nonlinear simulation tools, such as phase portraits, bifurcation and continuation diagrams, as well as a maximal Lyapunov exponent diagram, are used. Interesting phenomena related to chaos theory are observed. More specifically, the entrance to chaotic behavior through the antimonotonicity phenomenon is observed. Furthermore, the hysteresis phenomenon, as well as the existence of coexisting attractors in regards to the initial conditions and the parameters of the system, are investigated. Moreover, the period-doubling route to chaos and crisis phenomena are observed too. Full article
(This article belongs to the Special Issue Design and Applications of Nonlinear Circuits and Systems)
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50 pages, 2627 KiB  
Review
Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications
by Abdelazim G. Hussien, Laith Abualigah, Raed Abu Zitar, Fatma A. Hashim, Mohamed Amin, Abeer Saber, Khaled H. Almotairi and Amir H. Gandomi
Electronics 2022, 11(12), 1919; https://doi.org/10.3390/electronics11121919 - 20 Jun 2022
Cited by 54 | Viewed by 5925
Abstract
The Harris hawk optimizer is a recent population-based metaheuristics algorithm that simulates the hunting behavior of hawks. This swarm-based optimizer performs the optimization procedure using a novel way of exploration and exploitation and the multiphases of search. In this review research, we focused [...] Read more.
The Harris hawk optimizer is a recent population-based metaheuristics algorithm that simulates the hunting behavior of hawks. This swarm-based optimizer performs the optimization procedure using a novel way of exploration and exploitation and the multiphases of search. In this review research, we focused on the applications and developments of the recent well-established robust optimizer Harris hawk optimizer (HHO) as one of the most popular swarm-based techniques of 2020. Moreover, several experiments were carried out to prove the powerfulness and effectivness of HHO compared with nine other state-of-art algorithms using Congress on Evolutionary Computation (CEC2005) and CEC2017. The literature review paper includes deep insight about possible future directions and possible ideas worth investigations regarding the new variants of the HHO algorithm and its widespread applications. Full article
(This article belongs to the Special Issue Big Data Analytics Using Artificial Intelligence)
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16 pages, 3384 KiB  
Article
Research on Braking Efficiency of Master-Slave Electro-Hydraulic Hybrid Electric Vehicle
by Junyi Wang, Tiezhu Zhang, Hongxin Zhang, Jian Yang, Zhen Zhang and Zewen Meng
Electronics 2022, 11(12), 1918; https://doi.org/10.3390/electronics11121918 - 20 Jun 2022
Cited by 4 | Viewed by 1726
Abstract
To address the problems of short-rangee and poor braking safety of electric vehicles, this paper proposes a master-slave electro-hydraulic hybrid passenger car drive system based on planetary gear. The system couples the electrical energy output from the electric motor with the hydraulic energy [...] Read more.
To address the problems of short-rangee and poor braking safety of electric vehicles, this paper proposes a master-slave electro-hydraulic hybrid passenger car drive system based on planetary gear. The system couples the electrical energy output from the electric motor with the hydraulic energy output from the electro-hydraulic pump/motor through the planetary gear. The hydraulic system is used as the auxiliary power source of the power system giving full play to the advantages of the hydraulic system and the electric system. After theoretical analysis, this paper establishes a master-slave electro-hydraulic hybrid electric vehicle (MSEHH-EV) model based on planetary gear in AMESim software. A braking energy recovery control strategy is designed with the maximum braking energy recovery efficiency as the target. Braking strength determines the switching of braking modes. Finally, comparing the certified pure electric vehicle (EV) model in AMESim, we are able to substantiate the superiority of the strategy proposed in this paper. The simulation results revealed that the battery consumption rate of the new power vehicle is reduced by 17.766%, 11.358%, and 9.427% under UDDS, NEDC, and WLTC conditions, respectively, which supports the range. At the same time, the braking distance is significantly shortened, and the maximum braking distance is shortened by 15.65 m, 21.97 m, and 21.45 m, respectively, under the three operating conditions, which improves the braking safety. Full article
(This article belongs to the Section Electrical and Autonomous Vehicles)
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11 pages, 1705 KiB  
Article
An Effective Deep Learning-Based Architecture for Prediction of N7-Methylguanosine Sites in Health Systems
by Muhammad Tahir, Maqsood Hayat, Rahim Khan and Kil To Chong
Electronics 2022, 11(12), 1917; https://doi.org/10.3390/electronics11121917 - 20 Jun 2022
Cited by 2 | Viewed by 1821
Abstract
N7-methylguanosine (m7G) is one of the most important epigenetic modifications found in rRNA, mRNA, and tRNA, and performs a promising role in gene expression regulation. Owing to its significance, well-equipped traditional laboratory-based techniques have been performed for the identification of N [...] Read more.
N7-methylguanosine (m7G) is one of the most important epigenetic modifications found in rRNA, mRNA, and tRNA, and performs a promising role in gene expression regulation. Owing to its significance, well-equipped traditional laboratory-based techniques have been performed for the identification of N7-methylguanosine (m7G). Consequently, these approaches were found to be time-consuming and cost-ineffective. To move on from these traditional approaches to predict N7-methylguanosine sites with high precision, the concept of artificial intelligence has been adopted. In this study, an intelligent computational model called N7-methylguanosine-Long short-term memory (m7G-LSTM) is introduced for the prediction of N7-methylguanosine sites. One-hot encoding and word2vec feature schemes are used to express the biological sequences while the LSTM and CNN algorithms have been employed for classification. The proposed “m7G-LSTM” model obtained an accuracy value of 95.95%, a specificity value of 95.94%, a sensitivity value of 95.97%, and Matthew’s correlation coefficient (MCC) value of 0.919. The proposed predictive m7G-LSTM model has significantly achieved better outcomes than previous models in terms of all evaluation parameters. The proposed m7G-LSTM computational system aims to support the drug industry and help researchers in the fields of bioinformatics to enhance innovation for the prediction of the behavior of N7-methylguanosine sites. Full article
(This article belongs to the Section Computer Science & Engineering)
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15 pages, 2275 KiB  
Article
Investigation of Near-Field Source Localization Using Uniform Rectangular Array
by Fan Lu, Hengkai Zhao, Xiaorong Zhao, Xiaoyong Wang, Asad Saleem and Guoxin Zheng
Electronics 2022, 11(12), 1916; https://doi.org/10.3390/electronics11121916 - 20 Jun 2022
Cited by 3 | Viewed by 1722
Abstract
In fifth-generation (5G) wireless communications, large-scale arrays pose a challenge to the accuracy of signal models based on the plane wavefront. In this paper, a novel method for 3D near-field direction of arrival (DOA) estimation is proposed based on large-scale uniform rectangular array [...] Read more.
In fifth-generation (5G) wireless communications, large-scale arrays pose a challenge to the accuracy of signal models based on the plane wavefront. In this paper, a novel method for 3D near-field direction of arrival (DOA) estimation is proposed based on large-scale uniform rectangular array (URA). First, the near-field signal model based on the vertical rectangular array and the delay phase shift of the received array is presented. Afterwards, the proposed method divides the complete parameters set into multiple-parameters subsets, and only estimates one of them in each iteration, leaving the others in the fixed subset. As a result, we can obtain the maximum convergence rate of the deterministic maximum likelihood (DML) algorithm. Finally, the simulation results demonstrate that the root mean square errors (RMSEs) of the proposed algorithm are closer to the Cramer-Rao lower bounds and converge faster than those of the DML algorithm, confirming its effectiveness and superiority. Full article
(This article belongs to the Section Microwave and Wireless Communications)
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13 pages, 9132 KiB  
Article
Rapid Extraction of the Fundamental Components for Non-Ideal Three-Phase Grid Based on an Improved Sliding Discrete Fourier Transform
by Kai Li and Wei Nai
Electronics 2022, 11(12), 1915; https://doi.org/10.3390/electronics11121915 - 20 Jun 2022
Cited by 1 | Viewed by 1709
Abstract
In order to make an effective extraction of the fundamental components for a non-ideal three-phase grid, an improved sliding discrete Fourier transform (ISDFT) has been proposed in this paper. Firstly, the non-ideal signal characteristics are studied in detail, which reveals that there are [...] Read more.
In order to make an effective extraction of the fundamental components for a non-ideal three-phase grid, an improved sliding discrete Fourier transform (ISDFT) has been proposed in this paper. Firstly, the non-ideal signal characteristics are studied in detail, which reveals that there are not only typical harmonic components, but also double frequency components, that exist in dq coordinates when the three-phase grid voltages are unbalanced. Then, the structure form of the conventional sliding discrete Fourier transform (SDFT) has been redesigned to form the ISDFT algorithm, in which a special offset link is introduced to reduce the extraction time while the effectiveness is guaranteed. The experimental results show that this proposed ISDFT is suitable for types of non-ideal signals extraction and can keep a nice dynamical and steady performance in cases of grid or load disturbance. For the average extraction time, ISDFT is saving about 44.56% more of the time than SDFT and about 65.32% more than discrete Fourier transform (DFT). Full article
(This article belongs to the Section Power Electronics)
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13 pages, 2746 KiB  
Article
Resource Allocation for TDD Cell-Free Massive MIMO Systems
by Xuanhong Lin, Fangmin Xu, Jingzhao Fu and Yue Wang
Electronics 2022, 11(12), 1914; https://doi.org/10.3390/electronics11121914 - 20 Jun 2022
Cited by 5 | Viewed by 2303
Abstract
In this paper, we investigate a joint resource allocation algorithm in a time-division duplex (TDD)-based cell-free massive MIMO (CFMM) system, which has great potential to improve spectrum efficiency and throughput. Because the throughput of the system is a bottleneck due to the sharing [...] Read more.
In this paper, we investigate a joint resource allocation algorithm in a time-division duplex (TDD)-based cell-free massive MIMO (CFMM) system, which has great potential to improve spectrum efficiency and throughput. Because the throughput of the system is a bottleneck due to the sharing of the pilot, we attempted to alleviate pilot contamination. We propose a pilot assignment approach called user-distance-ordering-based pilot assignment (UDOPA) based on the distance between users and the center, which can be calculated by the K-means method. Then, using an access point (AP) selection algorithm, only the APs having a major impact on the macro diversity gain of a user are selected as the serving APs. In contrast to the existing AP selection algorithms, users with the same pilot are not allowed to share the same serving AP in the proposed AP selection algorithm, which also significantly reduces the complexity of data processing. Finally, a modified max–min power control scheme with teaching–learning-based optimization (TLBO) is proposed to further improve the performance of the systems and guarantee the minimum user rate. Simulation results show that the proposed joint resource allocation scheme can effectively enhance CFMM systems’ performance. Full article
(This article belongs to the Topic Advanced Systems Engineering: Theory and Applications)
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14 pages, 1749 KiB  
Article
A Piecewise Linear Mitchell Algorithm-Based Approximate Multiplier
by Hao Liu, Mingjiang Wang, Longxin Yao and Ming Liu
Electronics 2022, 11(12), 1913; https://doi.org/10.3390/electronics11121913 - 20 Jun 2022
Cited by 2 | Viewed by 2516
Abstract
In the field of integrated circuits, the computational cost has always been a crucial design metric. In recent years, with the continuous development in the field of computing, the requirements for computation have been growing rapidly. Reducing the computational cost and improving computational [...] Read more.
In the field of integrated circuits, the computational cost has always been a crucial design metric. In recent years, with the continuous development in the field of computing, the requirements for computation have been growing rapidly. Reducing the computational cost and improving computational efficiency have become the key issues in the field. There are many error-tolerant applications in the multimedia field where approximate computing techniques can be applied to improve computational efficiency and reduce computational costs at the cost of acceptable computational errors. This paper proposed a piecewise linear Mitchell algorithm based on Mitchell logarithmic approximation multiplication algorithm. Additionally, the Pwl-Mit multiplier is designed according to the improved algorithm combined with the data truncation technique. The proposed approximate multiplier has better statistical performance compared with the state-of-the-art multipliers. The design is simulated and synthesized at the TSMC 65 nm process, and its reliability is verified using discrete cosine transform (DCT) transform. Full article
(This article belongs to the Section Computer Science & Engineering)
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10 pages, 544 KiB  
Article
An Unsafe Behavior Detection Method Based on Improved YOLO Framework
by Binbin Chen, Xiuhui Wang, Qifu Bao, Bo Jia, Xuesheng Li and Yaru Wang
Electronics 2022, 11(12), 1912; https://doi.org/10.3390/electronics11121912 - 20 Jun 2022
Cited by 12 | Viewed by 2499
Abstract
In industrial production, accidents caused by the unsafe behavior of operators often bring serious economic losses. Therefore, how to use artificial intelligence technology to monitor the unsafe behavior of operators in a production area in real time has become a research topic of [...] Read more.
In industrial production, accidents caused by the unsafe behavior of operators often bring serious economic losses. Therefore, how to use artificial intelligence technology to monitor the unsafe behavior of operators in a production area in real time has become a research topic of great concern. Based on the YOLOv5 framework, this paper proposes an improved YOLO network to detect unsafe behaviors such as not wearing safety helmets and smoking in industrial places. First, the proposed network uses a novel adaptive self-attention embedding (ASAE) model to improve the backbone network and reduce the loss of context information in the high-level feature map by reducing the number of feature channels. Second, a new weighted feature pyramid network (WFPN) module is used to replace the original enhanced feature-extraction network PANet to alleviate the loss of feature information caused by too many network layers. Finally, the experimental results on the self-constructed behavior dataset show that the proposed framework has higher detection accuracy than traditional methods. The average detection accuracy of smoking increased by 3.3%, and the average detection accuracy of not wearing a helmet increased by 3.1%. Full article
(This article belongs to the Section Artificial Intelligence)
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14 pages, 2723 KiB  
Article
LGMSU-Net: Local Features, Global Features, and Multi-Scale Features Fused the U-Shaped Network for Brain Tumor Segmentation
by Xuejiao Pang, Zijian Zhao, Yuli Wang, Feng Li and Faliang Chang
Electronics 2022, 11(12), 1911; https://doi.org/10.3390/electronics11121911 - 19 Jun 2022
Cited by 3 | Viewed by 1843
Abstract
Brain tumors are one of the deadliest cancers in the world. Researchers have conducted a lot of research work on brain tumor segmentation with good performance due to the rapid development of deep learning for assisting doctors in diagnosis and treatment. However, most [...] Read more.
Brain tumors are one of the deadliest cancers in the world. Researchers have conducted a lot of research work on brain tumor segmentation with good performance due to the rapid development of deep learning for assisting doctors in diagnosis and treatment. However, most of these methods cannot fully combine multiple feature information and their performances need to be improved. This study developed a novel network fusing local features representing detailed information, global features representing global information, and multi-scale features enhancing the model’s robustness to fully extract the features of brain tumors and proposed a novel axial-deformable attention module for modeling global information to improve the performance of brain tumor segmentation to assist clinicians in the automatic segmentation of brain tumors. Moreover, positional embeddings were used to make the network training faster and improve the method’s performance. Six metrics were used to evaluate the proposed method on the BraTS2018 dataset. Outstanding performance was obtained with Dice score, mean Intersection over Union, precision, recall, params, and inference time of 0.8735, 0.7756, 0.9477, 0.8769, 69.02 M, and 15.66 millisecond, respectively, for the whole tumor. Extensive experiments demonstrated that the proposed network obtained excellent performance and was helpful in providing supplementary advice to the clinicians. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Biomedicine)
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15 pages, 13390 KiB  
Article
Depth Image Denoising Algorithm Based on Fractional Calculus
by Tingsheng Huang, Chunyang Wang and Xuelian Liu
Electronics 2022, 11(12), 1910; https://doi.org/10.3390/electronics11121910 - 19 Jun 2022
Cited by 4 | Viewed by 2469
Abstract
Depth images are often accompanied by unavoidable and unpredictable noise. Depth image denoising algorithms mainly attempt to fill hole data and optimise edges. In this paper, we study in detail the problem of effectively filtering the data of depth images under noise interference. [...] Read more.
Depth images are often accompanied by unavoidable and unpredictable noise. Depth image denoising algorithms mainly attempt to fill hole data and optimise edges. In this paper, we study in detail the problem of effectively filtering the data of depth images under noise interference. The classical filtering algorithm tends to blur edge and texture information, whereas the fractional integral operator can retain more edge and texture information. In this paper, the Grünwald–Letnikov-type fractional integral denoising operator is introduced into the depth image denoising process, and the convolution template of this operator is studied and improved upon to build a fractional integral denoising model and algorithm for depth image denoising. Depth images from the Redwood dataset were used to add noise, and the mask constructed by the fractional integral denoising operator was used to denoise the images by convolution. The experimental results show that the fractional integration order with the best denoising effect was −0.4 ≤ ν ≤ −0.3 and that the peak signal-to-noise ratio was improved by +3 to +6 dB. Under the same environment, median filter denoising had −15 to −30 dB distortion. The filtered depth image was converted to a point cloud image, from which the denoising effect was subjectively evaluated. Overall, the results prove that the fractional integral denoising operator can effectively handle noise in depth images while preserving their edge and texture information and thus has an excellent denoising effect. Full article
(This article belongs to the Special Issue Edge Computing for Urban Internet of Things)
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20 pages, 5254 KiB  
Article
An Endoscope Image Enhancement Algorithm Based on Image Decomposition
by Wei Tan, Chao Xu, Fang Lei, Qianqian Fang, Ziheng An, Dou Wang, Jubao Han, Kai Qian and Bo Feng
Electronics 2022, 11(12), 1909; https://doi.org/10.3390/electronics11121909 - 19 Jun 2022
Cited by 7 | Viewed by 3270
Abstract
The visual quality of endoscopic images is a significant factor in early lesion inspection and surgical procedures. However, due to the interference of light sources, hardware, and other configurations, the endoscopic images collected clinically have uneven illumination, blurred details, and contrast. This paper [...] Read more.
The visual quality of endoscopic images is a significant factor in early lesion inspection and surgical procedures. However, due to the interference of light sources, hardware, and other configurations, the endoscopic images collected clinically have uneven illumination, blurred details, and contrast. This paper proposed a new endoscopic image enhancement algorithm. The image decomposes into a detail layer and a base layer based on noise suppression. The blood vessel information is stretched by channel in the detail layer, and adaptive brightness correction is performed in the base layer. Finally, Fusion obtained a new endoscopic image. This paper compares the algorithm with six other algorithms in the laboratory dataset. The algorithm is in the leading position in all five objective evaluation metrics, further indicating that the algorithm is ahead of other algorithms in contrast, structural similarity, and peak signal-to-noise ratio. It can effectively highlight the blood vessel information in endoscopic images while avoiding the influence of noise and highlight points. The proposed algorithm can well solve the existing problems of endoscopic images. Full article
(This article belongs to the Topic Computer Vision and Image Processing)
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18 pages, 1102 KiB  
Article
Aggregated Boolean Query Processing for Document Retrieval in Edge Computing
by Tao Qiu, Peiliang Xie, Xiufeng Xia, Chuanyu Zong and Xiaoxu Song
Electronics 2022, 11(12), 1908; https://doi.org/10.3390/electronics11121908 - 19 Jun 2022
Cited by 1 | Viewed by 1850
Abstract
Search engines use significant hardware and energy resources to process billions of user queries per day, where Boolean query processing for document retrieval is an essential ingredient. Considering the huge number of users and large scale of the network, traditional query processing mechanisms [...] Read more.
Search engines use significant hardware and energy resources to process billions of user queries per day, where Boolean query processing for document retrieval is an essential ingredient. Considering the huge number of users and large scale of the network, traditional query processing mechanisms may not be applicable since they mostly depend on a centralized retrieval method. To remedy this issue, this paper proposes a processing technique for aggregated Boolean queries in the context of edge computing, where each sub-region of the network corresponds to an edge network regulated by an edge server, and the Boolean queries are evaluated in a distributed fashion on the edge servers. This decentralized query processing technique has demonstrated its efficiency and applicability for the document retrieval problem. Experimental results on two real-world datasets show that this technique achieves high query performance and outperforms the traditional centralized methods by 2–3 times. Full article
(This article belongs to the Special Issue Emerging Ubiquitous Networking and Computing: Techniques, Standards, and Applications)
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18 pages, 1368 KiB  
Article
To Use or Not to Use: Impact of Personality on the Intention of Using Gamified Learning Environments
by Mouna Denden, Ahmed Tlili, Mourad Abed, Aras Bozkurt, Ronghuai Huang and Daniel Burgos
Electronics 2022, 11(12), 1907; https://doi.org/10.3390/electronics11121907 - 18 Jun 2022
Cited by 7 | Viewed by 3355
Abstract
Technology acceptance is essential for technology success. However, individual users are known to differ in their tendency to adopt and interact with new technologies. Among the individual differences, personality has been shown to be a predictor of users’ beliefs about technology acceptance. Gamification, [...] Read more.
Technology acceptance is essential for technology success. However, individual users are known to differ in their tendency to adopt and interact with new technologies. Among the individual differences, personality has been shown to be a predictor of users’ beliefs about technology acceptance. Gamification, on the other hand, has been shown to be a good solution to improve students’ motivation and engagement while learning. Despite the growing interest in gamification, less research attention has been paid to the effect of personality, specifically based on the Five Factor model (FFM), on gamification acceptance in learning environments. Therefore, this study develops a model to elucidate how personality traits affect students’ acceptance of gamified learning environments and their continuance intention to use these environments. In particular, the Technology Acceptance Model (TAM) was used to examine the factors affecting students’ intentions to use a gamified learning environment. To test the research hypotheses, eighty-three students participated in this study, where structural equation modeling via Partial Least Squares (PLS) was performed. The obtained results showed that the research model, based on TAM and FFM, provides a comprehensive understanding of the behaviors related to the acceptance and intention to use gamified learning environments, as follows: (1) usefulness is the most influential factor toward intention to use the gamified learning environment; (2) unexpectedly, perceived ease of use has no significant effect on perceived usefulness and behavioral attitudes toward the gamified learning environment; (3) extraversion affects students’ perceived ease of use of the gamified learning environment; (4) neuroticism affects students’ perceived usefulness of the gamified learning environment; and, (5) Openness affects students’ behavioral attitudes toward using the gamified learning environment. This study can contribute to the Human–Computer Interaction field by providing researchers and practitioners with insights into how to motivate different students’ personality characteristics to continue using gamified learning environments for each personality trait. Full article
(This article belongs to the Special Issue Feature Papers in Computer Science & Engineering)
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15 pages, 11097 KiB  
Article
Document-Level Sentiment Analysis Using Attention-Based Bi-Directional Long Short-Term Memory Network and Two-Dimensional Convolutional Neural Network
by Yanying Mao, Yu Zhang, Liudan Jiao and Heshan Zhang
Electronics 2022, 11(12), 1906; https://doi.org/10.3390/electronics11121906 - 18 Jun 2022
Cited by 13 | Viewed by 3097
Abstract
Due to outstanding feature extraction ability, neural networks have recently achieved great success in sentiment analysis. However, one of the remaining challenges of sentiment analysis is to model long texts to consider the intrinsic relations between two sentences in the semantic meaning of [...] Read more.
Due to outstanding feature extraction ability, neural networks have recently achieved great success in sentiment analysis. However, one of the remaining challenges of sentiment analysis is to model long texts to consider the intrinsic relations between two sentences in the semantic meaning of a document. Moreover, most existing methods are not powerful enough to differentiate the importance of different document features. To address these problems, this paper proposes a new neural network model: AttBiLSTM-2DCNN, which entails two perspectives. First, a two-layer, bidirectional long short-term memory (BiLSTM) network is utilized to obtain the sentiment semantics of a document. The first BiLSTM layer learns the sentiment semantic representation from both directions of a sentence, and the second BiLSTM layer is used to encode the intrinsic relations of sentences into the document matrix representation with a feature dimension and a time-step dimension. Second, a two-dimensional convolutional neural network (2DCNN) is employed to obtain more sentiment dependencies between two sentences. Third, we utilize a two-layer attention mechanism to distinguish the importance of words and sentences in the document. Last, to validate the model, we perform an experiment on two public review datasets that are derived from Yelp2015 and IMDB. Accuracy, F1-Measure, and MSE are used as evaluation metrics. The experimental results show that our model can not only capture sentimental relations but also outperform certain state-of-the-art models. Full article
(This article belongs to the Special Issue Important Features Selection in Deep Neural Networks)
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11 pages, 8661 KiB  
Article
On-Chip Polarization Reconfigurable Microstrip Patch Antennas Using Semiconductor Distributed Doped Areas (ScDDAs)
by Rozenn Allanic, Denis Le Berre, Cédric Quendo, Douglas Silva De Vasconcellos, Virginie Grimal, Damien Valente and Jérôme Billoué
Electronics 2022, 11(12), 1905; https://doi.org/10.3390/electronics11121905 - 17 Jun 2022
Cited by 1 | Viewed by 1810
Abstract
This paper presents two polarization reconfigurable patch antennas using semiconductor distributed doped areas (ScDDAs) as active components. One proposed antenna has a switching polarization between two linear ones, while the other one has a polarization able to commute from a linear to a [...] Read more.
This paper presents two polarization reconfigurable patch antennas using semiconductor distributed doped areas (ScDDAs) as active components. One proposed antenna has a switching polarization between two linear ones, while the other one has a polarization able to commute from a linear to a circular one. The antennas are designed on a silicon substrate in order to have the ScDDAs integrated in the substrate, overcoming the needs of classical PIN diodes. Therefore, the proposed co-design method between the antenna and the ScDDAs permits us to optimize the global reconfigurable function, designing both parts in the same process flow. Both demonstrators have a resonant frequency of around 5 GHz. The simulated results fit well with the measured ones. Full article
(This article belongs to the Topic Antennas)
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19 pages, 2353 KiB  
Review
Reivew of Light Field Image Super-Resolution
by Li Yu, Yunpeng Ma, Song Hong and Ke Chen
Electronics 2022, 11(12), 1904; https://doi.org/10.3390/electronics11121904 - 17 Jun 2022
Cited by 5 | Viewed by 3673
Abstract
Currently, light fields play important roles in industry, including in 3D mapping, virtual reality and other fields. However, as a kind of high-latitude data, light field images are difficult to acquire and store. Thus, the study of light field super-resolution is of great [...] Read more.
Currently, light fields play important roles in industry, including in 3D mapping, virtual reality and other fields. However, as a kind of high-latitude data, light field images are difficult to acquire and store. Thus, the study of light field super-resolution is of great importance. Compared with traditional 2D planar images, 4D light field images contain information from different angles in the scene, and thus the super-resolution of light field images needs to be performed not only in the spatial domain but also in the angular domain. In the early days of light field super-resolution research, many solutions for 2D image super-resolution, such as Gaussian models and sparse representations, were also used in light field super-resolution. With the development of deep learning, light field image super-resolution solutions based on deep-learning techniques are becoming increasingly common and are gradually replacing traditional methods. In this paper, the current research on super-resolution light field images, including traditional methods and deep-learning-based methods, are outlined and discussed separately. This paper also lists publicly available datasets and compares the performance of various methods on these datasets as well as analyses the importance of light field super-resolution research and its future development. Full article
(This article belongs to the Special Issue Analog AI Circuits and Systems)
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17 pages, 599 KiB  
Article
Digital Forensics Classification Based on a Hybrid Neural Network and the Salp Swarm Algorithm
by Moutaz Alazab, Ruba Abu Khurma, Albara Awajan and Mohammad Wedyan
Electronics 2022, 11(12), 1903; https://doi.org/10.3390/electronics11121903 - 17 Jun 2022
Cited by 7 | Viewed by 2410
Abstract
In recent times, cybercrime has increased significantly and dramatically. This made the need for Digital Forensics (DF) urgent. The main objective of DF is to keep proof in its original state by identifying, collecting, analyzing, and evaluating digital data to rebuild past acts. [...] Read more.
In recent times, cybercrime has increased significantly and dramatically. This made the need for Digital Forensics (DF) urgent. The main objective of DF is to keep proof in its original state by identifying, collecting, analyzing, and evaluating digital data to rebuild past acts. The proof of cybercrime can be found inside a computer’s system files. This paper investigates the viability of Multilayer perceptron (MLP) in DF application. The proposed method relies on analyzing the file system in a computer to determine if it is tampered by a specific computer program. A dataset describes a set of features of file system activities in a given period. These data are used to train the MLP and build a training model for classification purposes. Identifying the optimal set of MLP parameters (weights and biases) is a challenging matter in training MLPs. Using traditional training algorithms causes stagnation in local minima and slow convergence. This paper proposes a Salp Swarm Algorithm (SSA) as a trainer for MLP using an optimized set of MLP parameters. SSA has proved its applicability in different applications and obtained promising optimization results. This motivated us to apply SSA in the context of DF to train MLP as it was never used for this purpose before. The results are validated by comparisons with other meta-heuristic algorithms. The SSAMLP-DF is the best algorithm because it achieves the highest accuracy results, minimum error rate, and best convergence scale. Full article
(This article belongs to the Special Issue High Accuracy Detection of Mobile Malware Using Machine Learning)
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9 pages, 5445 KiB  
Article
A Decision-Support Informatics Platform for Minimally Invasive Aortic Valve Replacement
by Katia Capellini, Vincenzo Positano, Michele Murzi, Pier Andrea Farneti, Giovanni Concistrè, Luigi Landini and Simona Celi
Electronics 2022, 11(12), 1902; https://doi.org/10.3390/electronics11121902 - 17 Jun 2022
Cited by 2 | Viewed by 1735
Abstract
Minimally invasive aortic valve replacement is performed by mini-sternotomy (MS) or less invasive right anterior mini-thoracotomy (RT). The possibility of adopting RT is assessed by anatomical criteria derived from manual 2D image analysis. We developed a semi-automatic tool (RT-PLAN) to assess the criteria [...] Read more.
Minimally invasive aortic valve replacement is performed by mini-sternotomy (MS) or less invasive right anterior mini-thoracotomy (RT). The possibility of adopting RT is assessed by anatomical criteria derived from manual 2D image analysis. We developed a semi-automatic tool (RT-PLAN) to assess the criteria of RT, extract other parameters of surgical interest and generate a view of the anatomical region in a 3D space. Twenty-five 3D CT images from a dataset were retrospectively evaluated. The methodology starts with segmentation to reconstruct 3D surface models of the aorta and anterior rib cage. Secondly, the RT criteria and geometric information from these models are automatically and quantitatively evaluated. A comparison is made between the values of the parameters measured by the standard manual 2D procedure and our tool. The RT-PLAN procedure was feasible in all cases. Strong agreement was found between RT-PLAN and the standard manual 2D procedure. There was no difference between the RT-PLAN and the standard procedure when selecting patients for the RT technique. The tool developed is able to effectively perform the assessment of the RT criteria, with the addition of a realistic visualisation of the surgical field through virtual reality technology. Full article
(This article belongs to the Special Issue Digital Twin Technology: New Frontiers for Personalized Healthcare)
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15 pages, 3250 KiB  
Article
Feasible and Optimal Design of an Airborne High-Temperature Superconducting Generator Using Taguchi Method
by Xiaoyi Zhou, Shengnan Zou, Shoujun Song, Wei Chen, Zhanjun Chen, Jiaojiao Xu and Ming Yan
Electronics 2022, 11(12), 1901; https://doi.org/10.3390/electronics11121901 - 17 Jun 2022
Viewed by 1667
Abstract
Aircraft electrification has become a tendency with demands for low carbon emissions and high electrical load capacity nowadays. Aircraft are especially strict with onboard weight; as a result, high-temperature superconducting (HTS) electrical machines are drawing attention for airborne applications due to their potential [...] Read more.
Aircraft electrification has become a tendency with demands for low carbon emissions and high electrical load capacity nowadays. Aircraft are especially strict with onboard weight; as a result, high-temperature superconducting (HTS) electrical machines are drawing attention for airborne applications due to their potential for a significant increase in power density. In this study, a feasible scheme of a hybrid-HTS airborne synchronous generator was proposed to fulfill the requirements of a small aircraft (with fewer than eight seats and a maximum range of about 1000 km). The full design from top to bottom is described. The output characteristics and metallic and superconducting AC losses were calculated based on the finite element method. The power grade of 1 MW was obtained, with a power density of 9.27 kW/kg and an efficiency of 98.73%. Furthermore, the performance of the machine was optimized using the Taguchi method. The preliminary design demonstrated the possibility and benefits of hybrid-HTS machines for airborne applications. Full article
(This article belongs to the Section Semiconductor Devices)
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11 pages, 607 KiB  
Article
Energy Efficient Hybrid Relay-IRS-Aided Wireless IoT Network for 6G Communications
by Shaik Rajak, Inbarasan Muniraj, Karthikeyan Elumalai, A. S. M. Sanwar Hosen, In-Ho Ra and Sunil Chinnadurai
Electronics 2022, 11(12), 1900; https://doi.org/10.3390/electronics11121900 - 16 Jun 2022
Cited by 8 | Viewed by 2876
Abstract
Intelligent Reflecting Surfaces (IRS) have been recognized as presenting a highly energy-efficient and optimal solution for future fast-growing 6G communication systems by reflecting the incident signal towards the receiver. The large number of Internet of Things (IoT) devices are distributed randomly in order [...] Read more.
Intelligent Reflecting Surfaces (IRS) have been recognized as presenting a highly energy-efficient and optimal solution for future fast-growing 6G communication systems by reflecting the incident signal towards the receiver. The large number of Internet of Things (IoT) devices are distributed randomly in order to serve users while providing a high data rate, seamless data transfer, and Quality of Service (QoS). The major challenge in satisfying the above requirements is the energy consumed by IoT network. Hence, in this paper, we examine the energy-efficiency (EE) of a hybrid relay-IRS-aided wireless IoT network for 6G communications. In our analysis, we study the EE performance of IRS-aided and DF relay-aided IoT networks separately, as well as a hybrid relay-IRS-aided IoT network. Our numerical results showed that the EE of the hybrid relay-IRS-aided system has better performance than both the conventional relay and the IRS-aided IoT network. Furthermore, we realized that the multiple IRS blocks can beat the relay in a high SNR regime, which results in lower hardware costs and reduced power consumption. Full article
(This article belongs to the Topic Electronic Communications, IOT and Big Data)
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18 pages, 1035 KiB  
Article
Autonomous Modulation Categorization Algorithm for Amplify and Forward Relaying Diversity Systems
by Mohamed Marey and Hala Mostafa
Electronics 2022, 11(12), 1899; https://doi.org/10.3390/electronics11121899 - 16 Jun 2022
Cited by 1 | Viewed by 1332
Abstract
Modulation categorization, which is a significant duty performed by smart receivers, is critical for applications in both the military and civilian sectors. This research topic has been intensively investigated for single-hop wireless communications. However, there are a few studies that concentrate on multiple [...] Read more.
Modulation categorization, which is a significant duty performed by smart receivers, is critical for applications in both the military and civilian sectors. This research topic has been intensively investigated for single-hop wireless communications. However, there are a few studies that concentrate on multiple hop communications systems. In this paper, we design a novel autonomous modulation categorization technique for amplify-and-forward relaying systems. Analytical formulations of correlation functions used as the foundation of the proposed method are developed. By exploiting the spatial redundancy, we theoretically demonstrate that a set of modulation forms produces peaks for particular correlation functions whereas the other set does not. We design a multiple-level hypothesis assessment for judgment based on this property. The suggested approach has the benefit of not requiring channel coefficients or noise power information. Computer simulations are conducted to test the proposed method’s categorization performance. The findings demonstrate that the suggested method produces appropriate results under various operating situations. The suggested approach reaches an accuracy of about one hundred percent when the SNR is 10 dB or higher. On the other hand, the traditional algorithms fail to provide acceptable performance even at high SNR. Full article
(This article belongs to the Section Microwave and Wireless Communications)
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30 pages, 9373 KiB  
Article
Design and Fabrication of an Isolated Two-Stage AC–DC Power Supply with a 99.50% PF and ZVS for High-Power Density Industrial Applications
by Ahmed H. Okilly and Jeihoon Baek
Electronics 2022, 11(12), 1898; https://doi.org/10.3390/electronics11121898 - 16 Jun 2022
Cited by 4 | Viewed by 4171
Abstract
Power quality in terms of power factor (PF), efficiency, and total harmonic distortions (THDs) is an important consideration in power supplies designed for 5G telecom servers. This paper presents a different magnetic parts design and manufacturing techniques of power supplies, design and selection [...] Read more.
Power quality in terms of power factor (PF), efficiency, and total harmonic distortions (THDs) is an important consideration in power supplies designed for 5G telecom servers. This paper presents a different magnetic parts design and manufacturing techniques of power supplies, design and selection criteria of switching elements as well as the optimal design of control loops based on small-signal stability modeling and an appropriate stability criterion. The designed telecom power supply consists of the power factor correction (PFC) stage to increase the input power factor and the isolated phase-shift pulse width modulation (PWM) zero-voltage switching (ZVS) DC–DC converter stage to regulate the supply voltage to the specified load value while maintaining a high conversion efficiency. A two-stage outdoor telecom power supply with a power rating of 2 kW was designed and fabricated on a printed circuit board (PCB). The distinct two-stage power components of the power supply were subjected to loss analysis. Furthermore, PSIM simulation and experiments were used to demonstrate the total harmonic distortions (THDs), voltage ripples, power efficiency, and PF performance of the supply current for the proposed power supply under various operating situations. This work produces an industrial high power density power supply with a high PF, low THD and high conversion efficiency which is suitable for telecom power server applications. Full article
(This article belongs to the Topic Advanced Systems Engineering: Theory and Applications)
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10 pages, 8663 KiB  
Article
A 3.7-to-10 GHz Low Phase Noise Wideband LC-VCO Array in 55-nm CMOS Technology
by Yan Yao, Zhiqun Li, Zhennan Li, Bofan Chen and Xiaowei Wang
Electronics 2022, 11(12), 1897; https://doi.org/10.3390/electronics11121897 - 16 Jun 2022
Viewed by 2502
Abstract
This paper presents a four-core LC-VCO array in 55 nm CMOS technology. Based on the multi-core VCO array technology and the switched capacitor array technology, the tuning range is expanded, and the phase noise optimization in a wide tuning range is achieved based [...] Read more.
This paper presents a four-core LC-VCO array in 55 nm CMOS technology. Based on the multi-core VCO array technology and the switched capacitor array technology, the tuning range is expanded, and the phase noise optimization in a wide tuning range is achieved based on the second harmonic noise filtering technology and the Q value degeneration technology, as well as the optimization of the capacitor array switching transistors. The proposed VCO array, occupying a chip area of 1.65 × 1.44 mm2, realizes a measured oscillation frequency range of about 3.7−10 GHz with phase noise of −127.5~−116.08 dBc/Hz at 1 MHz frequency offset, and achieves an output power of 2.69 dBm from a total power consumption of 52.8 mW. Full article
(This article belongs to the Special Issue Modeling and Design of Integrated CMOS Circuit)
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18 pages, 9540 KiB  
Article
Deep Learning-Based End-to-End Carrier Signal Detection in Broadband Power Spectrum
by Hao Huang, Peng Wang, Jiao Wang and Jianqing Li
Electronics 2022, 11(12), 1896; https://doi.org/10.3390/electronics11121896 - 16 Jun 2022
Cited by 2 | Viewed by 2854
Abstract
This paper presents an end-to-end deep convolutional neural network (CNN) model for carrier signal detection in the broadband power spectrum, so-called spectrum center net (SCN). By regarding the broadband power spectrum sequence as a one-dimensional (1D) image and each subcarrier on the broadband [...] Read more.
This paper presents an end-to-end deep convolutional neural network (CNN) model for carrier signal detection in the broadband power spectrum, so-called spectrum center net (SCN). By regarding the broadband power spectrum sequence as a one-dimensional (1D) image and each subcarrier on the broadband as the target object, we can transform the carrier signal detection problem into a semantic segmentation problem on a 1D image. Here, the core task of the carrier signal detection problem turns into the frequency center (FC) and bandwidth (BW) regression. We design the SCN to classify the broadband power spectrum as inputs and extract the features of different length scales by the ResNet backbone. Then, the feature pyramid network (FPN) neck fuses the features and outputs the fusion features. Next, the RegNet head regresses the power spectrum distribution (PSD) prediction for FC and the corresponding BW prediction. Finally, we can achieve the subcarrier targets by applying non-maximum suppressions (NMS). Moreover, we train the SCN on a simulation dataset and validate it on a real satellite broadband power spectrum set. As an improvement of the fully convolutional network-based (FCN-based) method, the proposed method directly outputs the detection results without post-processing. Extensive experimental results demonstrate that the proposed method can effectively detect the subcarrier signal in the broadband power spectrum as well as achieve higher and more robust performance than the deep FCN- and threshold-based methods. Full article
(This article belongs to the Topic Artificial Intelligence Models, Tools and Applications)
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25 pages, 12113 KiB  
Article
Secure and Anonymous Voting D-App with IoT Embedded Device Using Blockchain Technology
by Cristian Toma, Marius Popa, Catalin Boja, Cristian Ciurea and Mihai Doinea
Electronics 2022, 11(12), 1895; https://doi.org/10.3390/electronics11121895 - 16 Jun 2022
Cited by 12 | Viewed by 4195
Abstract
The paper presents the construction of a proof-of-concept for a distributed and decentralized e-voting application in an IoT embedded device with the help of blockchain technology. A SoC board was used as an IoT embedded device for testing the PoC. This solution ensures [...] Read more.
The paper presents the construction of a proof-of-concept for a distributed and decentralized e-voting application in an IoT embedded device with the help of blockchain technology. A SoC board was used as an IoT embedded device for testing the PoC. This solution ensures complete voter anonymity and end-to-end security for all entities participating in the electronic voting process. The paper outlines the solution’s two layers. Implementation details are presented for the e-voting application, which was deployed inside of an IoT embedded device. The solution and presented protocols provide two major properties: privacy and verifiability. To ensure privacy, the proposed solution protects the secrecy of each electronic vote. As for implementing verifiability, the solution prevents a corrupt authority from faking in any way the process of counting the votes. Both properties are achieved in the presented solution e-VoteD-App. Full article
(This article belongs to the Special Issue Advances in Machine Learning, IoT and Big Data for Sustainable Communities)
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21 pages, 2092 KiB  
Article
A Robustness Analysis of a Fuzzy Fractional Order PID Controller Based on Genetic Algorithm for a DC-DC Boost Converter
by Luís Felipe da S. C. Pereira, Edson Batista, Moacyr A. G. de Brito and Ruben B. Godoy
Electronics 2022, 11(12), 1894; https://doi.org/10.3390/electronics11121894 - 16 Jun 2022
Cited by 25 | Viewed by 2731
Abstract
In this paper, a new topology of a Fractional Order PID (FOPID) controller is proposed to control a boost DC-DC converter with minimum over/undershoot. The fractional controller parameters are tuned using a genetic algorithm (GA) with a combined cost function composed of the [...] Read more.
In this paper, a new topology of a Fractional Order PID (FOPID) controller is proposed to control a boost DC-DC converter with minimum over/undershoot. The fractional controller parameters are tuned using a genetic algorithm (GA) with a combined cost function composed of the Integral of Time-Weighted Absolute Error (ITAE) and the Integral of Time-Weighted Square Error (ITSE). Despite adding moderate complexity to the control structure, the simulation results reveal that the GA-based FOPID controller tuning provided better performance for the setpoint tracking both under load variations and parameters deviation due to the prolonged use. The proposed FOPID shows a wide operational range concerning load disturbances, and capacitance/inductance deviations of ±30% and ±50% from nominal values, achieving functionality and voltage stability even with output power 50% higher than the converter power specification. The assessment was made considering operation in voltage mode and the performance was compared to conventional Proportional-Integral (PI), Type II and current mode controllers. Finally, a fuzzy fractional-order PID (FFOPID) was designed to outperform the FOPID during disturbances in the control variable. Full article
(This article belongs to the Section Power Electronics)
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43 pages, 5501 KiB  
Review
Towards Secure and Intelligent Internet of Health Things: A Survey of Enabling Technologies and Applications
by Umar Zaman, Imran, Faisal Mehmood, Naeem Iqbal, Jungsuk Kim and Muhammad Ibrahim
Electronics 2022, 11(12), 1893; https://doi.org/10.3390/electronics11121893 - 16 Jun 2022
Cited by 38 | Viewed by 5513
Abstract
With the growth of computing and communication technologies, the information processing paradigm of the healthcare environment is evolving. The patient information is stored electronically, making it convenient to store and retrieve patient information remotely when needed. However, evolving the healthcare systems into smart [...] Read more.
With the growth of computing and communication technologies, the information processing paradigm of the healthcare environment is evolving. The patient information is stored electronically, making it convenient to store and retrieve patient information remotely when needed. However, evolving the healthcare systems into smart healthcare environments comes with challenges and additional pressures. Internet of Things (IoT) connects things, such as computing devices, through wired or wireless mediums to form a network. There are numerous security vulnerabilities and risks in the existing IoT-based systems due to the lack of intrinsic security technologies. For example, patient medical data, data privacy, data sharing, and convenience are considered imperative for collecting and storing electronic health records (EHR). However, the traditional IoT-based EHR systems cannot deal with these paradigms because of inconsistent security policies and data access structures. Blockchain (BC) technology is a decentralized and distributed ledger that comes in handy in storing patient data and encountering data integrity and confidentiality challenges. Therefore, it is a viable solution for addressing existing IoT data security and privacy challenges. BC paves a tremendous path to revolutionize traditional IoT systems by enhancing data security, privacy, and transparency. The scientific community has shown a variety of healthcare applications based on artificial intelligence (AI) that improve health diagnosis and monitoring practices. Moreover, technology companies and startups are revolutionizing healthcare with AI and related technologies. This study illustrates the implication of integrated technologies based on BC, IoT, and AI to meet growing healthcare challenges. This research study examines the integration of BC technology with IoT and analyzes the advancements of these innovative paradigms in the healthcare sector. In addition, our research study presents a detailed survey on enabling technologies for the futuristic, intelligent, and secure internet of health things (IoHT). Furthermore, this study comprehensively studies the peculiarities of the IoHT environment and the security, performance, and progression of the enabling technologies. First, the research gaps are identified by mapping security and performance benefits inferred by the BC technologies. Secondly, practical issues related to the integration process of BC and IoT devices are discussed. Third, the healthcare applications integrating IoT, BC, and ML in healthcare environments are discussed. Finally, the research gaps, future directions, and limitations of the enabling technologies are discussed. Full article
(This article belongs to the Section Computer Science & Engineering)
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