Electronics

14 pages, 2625 KiB

Open AccessArticle

Device Status Evaluation Method Based on Deep Learning for PHM Scenarios

by Pengjun Wang, Jiahao Qin, Jiucheng Li, Meng Wu, Shan Zhou and Le Feng

Electronics 2023, 12(3), 779; https://doi.org/10.3390/electronics12030779 - 03 Feb 2023

Cited by 3 | Viewed by 1470

The emergence of fault prediction and health management (PHM) technology has proposed a new solution and is suitable for implementing the functions of improving the intelligent management and control system. However, the research and application of the PHM model in the intelligent management [...] Read more.

The emergence of fault prediction and health management (PHM) technology has proposed a new solution and is suitable for implementing the functions of improving the intelligent management and control system. However, the research and application of the PHM model in the intelligent management and control system of electronic equipment are few at present, and there are many problems that need to be solved urgently in PHM technology itself. In order to solve such problems, this paper studies the application of the equipment-status-assessment method based on deep learning in PHM scenarios, in order to conduct in-depth research on the intelligent control system of electronic equipment. The experimental results in this paper show that the change in unimproved deep learning is very subtle before the performance change point, while improvements in deep learning increase the health value by about 10 times. Thus, improved deep learning amplifies subtle changes in health early in degradation and slows down mutations in health late at performance failure points. At the same time, comparing health-index-evaluation indicators, it can be concluded that although the monotonicity of the health index is low, its robustness and correlation are significantly improved. Additionally, it is very close to 1, making the health index curve more in line with traditional cognition and convenient for application. Therefore, an in-depth study of methods for health assessment by improving deep learning is of practical significance. Full article

(This article belongs to the Section Artificial Intelligence)

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14 pages, 13274 KiB

Open AccessArticle

Asymmetric 5.5 GHz Three-Stage Voltage-Controlled Ring-Oscillator in 65 nm CMOS Technology

by Gabriele Ciarpi, Danilo Monda, Marco Mestice, Daniele Rossi and Sergio Saponara

Electronics 2023, 12(3), 778; https://doi.org/10.3390/electronics12030778 - 03 Feb 2023

Cited by 1 | Viewed by 1958

Abstract

The current trend of increasing the complexity of hardware accelerators to improve their functionality is highlighting the problem of sharing a high-frequency clock signal for all integrated modules. As the clock itself is becoming the main limitation to the performance of accelerators, in [...] Read more.

The current trend of increasing the complexity of hardware accelerators to improve their functionality is highlighting the problem of sharing a high-frequency clock signal for all integrated modules. As the clock itself is becoming the main limitation to the performance of accelerators, in this manuscript, we present the design of an asymmetric Ring Oscillator-Voltage-Controlled Oscillator (RO-VCO) based on the Current Mode Logic architecture. The RO-VCO was designed on commercial-grade 65 nm CMOS technology, and it is capable of driving large capacitance loads, avoiding the need for additional buffers for clock-trees, reducing the silicon area and power consumption. The proposed RO-VCO is composed of three closed-loop differential and asymmetrical stages, and it is able to tune the working frequency in the range from 4.72 GHz to 6.12 GHz. The phase noise and a figure of merit of −103.2 dBc/Hz and −186 dBc/Hz were obtained at 1 MHz offset from the 5.5 GHz carrier. In this article, the analytical model, full custom schematic, and layout of the proposed RO-VCO are presented and discussed in detail together with the experimental electrical and thermal characterization of the fabricated device. Full article

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

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11 pages, 1859 KiB

Open AccessArticle

Solution pH Effect on Drain-Gate Characteristics of SOI FET Biosensor

by Anastasia Bulgakova, Anton Berdyugin, Olga Naumova, Boris Fomin, Dmitrii Pyshnyi, Alexey Chubarov, Elena Dmitrienko and Alexander Lomzov

Electronics 2023, 12(3), 777; https://doi.org/10.3390/electronics12030777 - 03 Feb 2023

Cited by 4 | Viewed by 1545

Abstract

Nanowire or nanobelt sensors based on silicon-on-insulator field-effect transistors (SOI-FETs) are one of the leading directions of label-free biosensors. An essential issue in this device construction type is obtaining reproducible results from electrochemical measurements. It is affected by many factors, including the measuring [...] Read more.

Nanowire or nanobelt sensors based on silicon-on-insulator field-effect transistors (SOI-FETs) are one of the leading directions of label-free biosensors. An essential issue in this device construction type is obtaining reproducible results from electrochemical measurements. It is affected by many factors, including the measuring solution and the design parameters of the sensor. The biosensor surface should be charged minimally for the highest sensitivity and maximum effect from interaction with other charged molecules. Therefore, the pH value should be chosen so that the surface has a minimum charge. Here, we studied the SOI-FET sensor containing 12 nanobelt elements concatenated on a single substrate. Two types of sensing elements of similar design and different widths (0.2 or 3 μm) were located in the chips. The drain-gate measurements of wires with a width of 3 µm are sufficiently reproducible for the entire chip to obtain measurement statistics in air and deionized water. For the pH values from 3 to 12, we found significant changes in source-drain characteristics of nanobelts, which reach the plateau at pH values of 7 and higher. High pH sensitivity (ca. 1500 and 970 mV/pH) was observed in sensors of 3 μm and 0.2 μm in width in the range of pH values from 3 to 7. We found a higher “on” current to “off” current ratio for wide wires. At all studied pH values, I_on/I_off was up to 4600 and 30,800 for 0.2 and 3 μm wires, respectively. In the scheme on the source-drain current measurements at fixed gate voltages, the highest sensitivity to the pH changes reaches a gate voltage of 13 and 19 V for 0.2 μm and 3 μm sensors, respectively. In summary, the most suitable is 3 μm nanobelt sensing elements for the reliable analysis of biomolecules and measurements at pH over 7. Full article

(This article belongs to the Special Issue Bioelectronics and Biosensors in Diagnostics, Biotechnology and Clinical Research: Challenges and Opportunities)

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21 pages, 3492 KiB

Open AccessArticle

An Interoperable Blockchain Security Frameworks Based on Microservices and Smart Contract in IoT Environment

by Khulud Salem Alshudukhi, Maher Ali Khemakhem, Fathy Elbouraey Eassa and Kamal Mansur Jambi

Electronics 2023, 12(3), 776; https://doi.org/10.3390/electronics12030776 - 03 Feb 2023

Cited by 10 | Viewed by 2145

Abstract

In the Internet of Things (IoT), technological developments have increased the significance of federated cloud systems with integrated cloud providers for exchange transactions. Monolithic IoT systems implement service-oriented architecture (SOA), which is complex for supporting scalability and communicating transactions in a federated cloud [...] Read more.

In the Internet of Things (IoT), technological developments have increased the significance of federated cloud systems with integrated cloud providers for exchange transactions. Monolithic IoT systems implement service-oriented architecture (SOA), which is complex for supporting scalability and communicating transactions in a federated cloud system. One weakness of conventional security methods is that they depend on a centralized party, which means there is a single point of failure for the system. In contrast, blockchain (BC) and microservice (MS) technologies allow services to split for independent tasks. In this research paper, we introduce BC security managers based on MS technology for federated cloud systems in an IoT environment. In addition, we present the design of the Federation Security System Manager (FSSM) MS with interoperability features. This enables the exchange of transactions between permissioned BC managers at different cloud providers, with some constraints. Furthermore, a security framework based on MSs and BCs is implemented to ensure security and protect access control. The security functions are deployed based on a smart contract between the permissioned BC managers to achieve interoperability. Finally, we introduce the development process of the proposed framework, which allows for interoperability and ensures the security and privacy of the participating data for a distributed IoT based on the federated cloud system. Full article

(This article belongs to the Special Issue Advances in Security and Blockchain Technologies)

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19 pages, 3197 KiB

Open AccessArticle

Design and Multi-Objective Optimization of a Composite Cage Rotor Bearingless Induction Motor

by Chengling Lu, Zebin Yang, Xiaodong Sun and Qifeng Ding

Electronics 2023, 12(3), 775; https://doi.org/10.3390/electronics12030775 - 03 Feb 2023

Cited by 1 | Viewed by 1240

Abstract

To improve the quality of starting torque and suspension force, a composite cage rotor bearingless induction motor (CCR-BIM) is designed, which adopts a composite cage rotor structure that combines an inner rotor and an outer rotor. First, the overall structure of the CCR-BIM [...] Read more.

To improve the quality of starting torque and suspension force, a composite cage rotor bearingless induction motor (CCR-BIM) is designed, which adopts a composite cage rotor structure that combines an inner rotor and an outer rotor. First, the overall structure of the CCR-BIM is designed, the composite cage rotor of the CCR-BIM is specially designed and analyzed for the induction principle, and the mathematical model of suspension force and torque is deduced. Second, the initial structural parameters are determined, and motor qualities, such as starting torque quality and suspension force quality, are compared and analyzed between the proposed motor and BIM using a finite element model (FEM). Third, based on the response surface model (RSM), a multi-objective improved NSGA-II is constructed, and the three optimization objectives of starting torque, suspension force, and suspension force pulsation are optimized. Finally, the results of the experimental setup prove that the starting torque increases by 6.98%, the suspension force increases by 5.45%, and the suspension force pulsation decreases by 18.54%. The effectiveness of the proposed motor and the correctness of the multi-objective optimization strategy are verified. Full article

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26 pages, 4347 KiB

Open AccessArticle

Facial Image Encryption for Secure Face Recognition System

by Eimad Abusham, Basil Ibrahim, Kashif Zia and Muhammad Rehman

Electronics 2023, 12(3), 774; https://doi.org/10.3390/electronics12030774 - 03 Feb 2023

Cited by 6 | Viewed by 3889

Abstract

A biometric authentication system is more convenient and secure than graphical or textual passwords when accessing information systems. Unfortunately, biometric authentication systems have the disadvantage of being susceptible to spoofing attacks. Authentication schemes based on biometrics, including face recognition, are susceptible to spoofing. [...] Read more.

A biometric authentication system is more convenient and secure than graphical or textual passwords when accessing information systems. Unfortunately, biometric authentication systems have the disadvantage of being susceptible to spoofing attacks. Authentication schemes based on biometrics, including face recognition, are susceptible to spoofing. This paper proposes an image encryption scheme to counter spoofing attacks by integrating it into the pipeline of Linear Discriminant Analysis (LDA) based face recognition. The encryption scheme uses XOR pixels substitution and cellular automata for scrambling. A single key is used to encrypt the training and testing datasets in LDA face recognition system. For added security, the encryption step requires input images of faces to be encrypted with the correct key before the system can recognize the images. An LDA face recognition scheme based on random forest classifiers has achieved 96.25% accuracy on ORL dataset in classifying encrypted test face images. In a test where original test face images were not encrypted with keys used for encrypted feature databases, the system achieved 8.75% accuracy only showing it is capable of resisting spoofing attacks. Full article

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21 pages, 730 KiB

Open AccessArticle

Reinforcement-Learning-Based Software-Defined Edge Task Allocation Algorithm

by Tianhao Zhang, Xiaojuan Zhu and Cai Wu

Electronics 2023, 12(3), 773; https://doi.org/10.3390/electronics12030773 - 03 Feb 2023

Cited by 2 | Viewed by 1237

Abstract

With the rapid growth in the number of IoT devices at the edge of the network, fast, flexible and secure edge computing has emerged, but the disadvantage of the insufficient computing power of edge servers is evident when dealing with massive computing tasks. [...] Read more.

With the rapid growth in the number of IoT devices at the edge of the network, fast, flexible and secure edge computing has emerged, but the disadvantage of the insufficient computing power of edge servers is evident when dealing with massive computing tasks. To address this situation, firstly, a software-defined edge-computing architecture (SDEC) is proposed, merging the control layer of the software-defined architecture with the edge layer of edge computing, where multiple controllers share global information about the network state through an east–west message exchange, providing global state for the collaboration of edge servers. Secondly, a reinforcement-learning-based software-defined edge task allocation algorithm (RL-SDETA) is proposed in the software-defined IoT, which enables controllers to allocate computational tasks to the most appropriate edge servers for execution based on the global network information they have obtained. Simulation results show that the RL-SDETA algorithm can effectively reduce the finding cost of the optimal edge server and reduce the task completion time and its energy consumption compared to various task allocation methods such as random and uniform. Full article

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11 pages, 4091 KiB

Open AccessArticle

Fuzzy Controller in the Products Collecting System of the Jig for Minerals Beneficiation

by Sebastian Jendrysik, Daniel Kowol, Piotr Matusiak, Andrzej Dymarek, Krzysztof Kędzia, Bartosz Polnik, Marcin Szczygieł, Tomasz Trawiński and Mariusz Starak

Electronics 2023, 12(3), 772; https://doi.org/10.3390/electronics12030772 - 03 Feb 2023

Cited by 1 | Viewed by 1094

Abstract

The fuzzy controller of the bottom product collecting system of the pulsating jig is presented. The primary purpose of the research work was to design and properly adapt the fuzzy controller so that it would enable the correct operation of the jig with [...] Read more.

The fuzzy controller of the bottom product collecting system of the pulsating jig is presented. The primary purpose of the research work was to design and properly adapt the fuzzy controller so that it would enable the correct operation of the jig with appropriate control properties in all compartments of the jig. The results of industrial tests, in which selected indicators of regulation quality were considered, were analyzed. A comparative analysis of the tested fuzzy controller and the classic PID controller was also performed. Full article

(This article belongs to the Special Issue Modeling and Optimization of Energy Efficiency in the Light of Energy Security)

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19 pages, 1106 KiB

Open AccessFeature PaperArticle

DOA Estimation Based on Convolutional Autoencoder in the Presence of Array Imperfections

by Dah-Chung Chang and Yan-Ting Liu

Electronics 2023, 12(3), 771; https://doi.org/10.3390/electronics12030771 - 03 Feb 2023

Cited by 2 | Viewed by 1356

Abstract

Array imperfections may exist in an antenna system subject to non-ideal design and practical limitations. It is difficult to accurately model array imperfections, and thus complicated algorithms are usually inevitable for model-based methods to estimate the direction of arrival (DOA) with imperfect arrays. [...] Read more.

Array imperfections may exist in an antenna system subject to non-ideal design and practical limitations. It is difficult to accurately model array imperfections, and thus complicated algorithms are usually inevitable for model-based methods to estimate the direction of arrival (DOA) with imperfect arrays. Deep neural network (DNN)-based methods do not need to rely on pre-modeled antenna array geometries, and have been explored to handle flawed array models because of their better flexibility than model-based methods. The DNN autoencoder (DAE) method has been proposed for the array imperfection problem, which decomposes the input into multiple components in different spatial subregions. These components have more concentrated distributions than the original input, which avoid a large number of connections and nodes used in the layers to realize DOA estimation classifiers. In this paper, we study the convolutional AE (CAE) method that substantially focuses on the learning of local features in a different manner from the previous DAE method. The advantage of the convolutional operation using a kernel in CAE is to capture features in a more efficient manner than the DAE, and thus be able to reduce the number of parameters that are required to be trained in the neural networks. From the numerical evaluation of DOA estimation accuracy, the proposed CAE method is also more resistant to the noise effect than the DAE method such that the CAE method has better accuracy at a lower signal-to-noise ratio. Full article

(This article belongs to the Special Issue Smart Antenna Optimization Techniques for Wireless Applications)

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13 pages, 3425 KiB

Open AccessArticle

Investigation of Source/Drain Recess Engineering and Its Impacts on FinFET and GAA Nanosheet FET at 5 nm Node

by Dawei Wang, Xin Sun, Tao Liu, Kun Chen, Jingwen Yang, Chunlei Wu, Min Xu and Wei (David) Zhang

Electronics 2023, 12(3), 770; https://doi.org/10.3390/electronics12030770 - 03 Feb 2023

Cited by 2 | Viewed by 4643

Abstract

Impacts of source/drain (S/D) recess engineering on the device performance of both the gate-all-around (GAA) nanosheet (NS) field-effect transistor (FET) and FinFET have been comprehensively studied at 5 nm node technology. TCAD simulation results show that the device off-leakage, including subthreshold leakage through [...] Read more.

Impacts of source/drain (S/D) recess engineering on the device performance of both the gate-all-around (GAA) nanosheet (NS) field-effect transistor (FET) and FinFET have been comprehensively studied at 5 nm node technology. TCAD simulation results show that the device off-leakage, including subthreshold leakage through the channel (I_sub) and punch-through leakage (I_PT) in the sub-channel, is strongly related to the S/D recess process. Firstly, device electrical characteristics such as current density distributions, On/Off-state current (I_on, I_off), subthreshold swing (SS), RC delay, and gate capacitance (C_gg) are investigated quantitatively for DC/AC performance evaluation and comparison according to S/D lateral recess depth (L_rcs) variations. For both device types, larger L_rcs will result in a shorter effective channel length (L_eff), so that the I_on and I_off simultaneously increase. At the constant I_off, the L_rcs can be optimized to enhance the device’s drivability by ~3% and improve the device’s RC delay by ~1.5% due to a larger C_gg as a penalty. Secondly, S/D over recess depth (H_rcs) in the vertical direction severely affects the punch-through leakage in the Sub-Fin or bottom parasitic channel region. The NSFET exhibits less I_off sensitivity provided that it can be well controlled under 12 nm since the bottom parasitic channel is still gated. Furthermore, with both H_rcs and L_rcs accounted for in the device fabrication, the NSFET still shows better control of the off-leakage in the intrinsic and bottom parasitic channel regions and ~37% leakage reduction compared with FinFETs, which would be critical to enable further scaling and the low standby power application. Finally, the S/D recess engineering strategy has been given: a certain lateral recess could be optimized to obtain the best drive current and RC delay, while the vertical over-recess should be in tight management to keep the static power dissipation as low as possible. Full article

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18 pages, 5017 KiB

Open AccessArticle

Improved Salp Swarm Algorithm for Tool Wear Prediction

by Yu Wei, Weibing Wan, Xiaoming You, Feng Cheng and Yuxuan Wang

Electronics 2023, 12(3), 769; https://doi.org/10.3390/electronics12030769 - 03 Feb 2023

Cited by 4 | Viewed by 1128

Abstract

To address the defects of the salp swarm algorithm (SSA) such as the slow convergence speed and ease of falling into a local minimum, a new salp swarm algorithm combining chaotic mapping and decay factor is proposed and combined with back propagation (BP) [...] Read more.

To address the defects of the salp swarm algorithm (SSA) such as the slow convergence speed and ease of falling into a local minimum, a new salp swarm algorithm combining chaotic mapping and decay factor is proposed and combined with back propagation (BP) neural network to achieve an effective prediction of tool wear. Firstly, the chaotic mapping is used to enhance the formation of the population, which facilitates the iterative search and reduces the trapping in the local optimum; secondly, the decay factor is introduced to improve the update of the followers so that the followers can be updated adaptively with the iterations, and the theoretical analysis and validation of the improved SSA are carried out using benchmark test functions. Finally, the improved SSA with a strong optimization capability to solve BP neural networks for the optimal values of hyperparameters is used. The validity of this is verified by using the actual tool wear data set. The test results of the benchmark test function show that the algorithm presented has a better convergence speed and solution accuracy. Meanwhile, compared with the original algorithm, the R² value of the part life prediction model proposed is improved from 0.962 to 0.989, the MSE value is reduced from the original 34.4 to 9.36, which is a 72% improvement compared with the original algorithm, and a better prediction capability is obtained. Full article

(This article belongs to the Special Issue Artificial Intelligence Based on Data Mining)

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15 pages, 3000 KiB

Open AccessArticle

Fault Diagnosis for Rolling Bearings Based on Multiscale Feature Fusion Deep Residual Networks

by Xiangyang Wu, Haibin Shi and Haiping Zhu

Electronics 2023, 12(3), 768; https://doi.org/10.3390/electronics12030768 - 03 Feb 2023

Cited by 8 | Viewed by 1500

Abstract

Deep learning, due to its excellent feature-adaptive capture ability, has been widely utilized in the fault diagnosis field. However, there are two common problems in deep-learning-based fault diagnosis methods: (1) many researchers attempt to deepen the layers of deep learning models for higher [...] Read more.

Deep learning, due to its excellent feature-adaptive capture ability, has been widely utilized in the fault diagnosis field. However, there are two common problems in deep-learning-based fault diagnosis methods: (1) many researchers attempt to deepen the layers of deep learning models for higher diagnostic accuracy, but degradation problems of deep learning models often occur; and (2) the use of multiscale features can easily be ignored, which makes the extracted data features lack diversity. To deal with these problems, a novel multiscale feature fusion deep residual network is proposed in this paper for the fault diagnosis of rolling bearings, one which contains multiple multiscale feature fusion blocks and a multiscale pooling layer. The multiple multiscale feature fusion block is designed to automatically extract the multiscale features from raw signals, and further compress them for higher dimensional feature mapping. The multiscale pooling layer is constructed to fuse the extracted multiscale feature mapping. Two famous rolling bearing datasets are adopted to evaluate the diagnostic performance of the proposed model. The comparison results show that the diagnostic performance of the proposed model is superior to not only several popular models, but also other advanced methods in the literature. Full article

(This article belongs to the Special Issue Recent Advances in Prognostics and Health Management in Industry 4.0 Era)

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18 pages, 6549 KiB

Open AccessArticle

Promoting Adversarial Transferability via Dual-Sampling Variance Aggregation and Feature Heterogeneity Attacks

by Yang Huang, Yuling Chen, Xuewei Wang, Jing Yang and Qi Wang

Electronics 2023, 12(3), 767; https://doi.org/10.3390/electronics12030767 - 03 Feb 2023

Cited by 6 | Viewed by 1457

Abstract

At present, deep neural networks have been widely used in various fields, but their vulnerability requires attention. The adversarial attack aims to mislead the model by generating imperceptible perturbations on the source model, and although white-box attacks have achieved good success rates, existing [...] Read more.

At present, deep neural networks have been widely used in various fields, but their vulnerability requires attention. The adversarial attack aims to mislead the model by generating imperceptible perturbations on the source model, and although white-box attacks have achieved good success rates, existing adversarial samples exhibit weak migration in the black-box case, especially on some adversarially trained defense models. Previous work for gradient-based optimization either optimizes the image before iteration or optimizes the gradient during iteration, so it results in the generated adversarial samples overfitting the source model and exhibiting poor mobility to the adversarially trained model. To solve these problems, we propose the dual-sample variance aggregation with feature heterogeneity attack; our method is optimized before and during iterations to produce adversarial samples with better transferability. In addition, our method can be integrated with various input transformations. A large amount of experimental data demonstrate the effectiveness of the proposed method, which improves the attack success rate by 5.9% for the normally trained model and 11.5% for the adversarially trained model compared with the current state-of-the-art migration-enhancing attack methods. Full article

(This article belongs to the Special Issue AI in Knowledge-Based Information and Decision Support Systems)

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32 pages, 3491 KiB

Open AccessArticle

Industry 4.0-Based Framework for Real-Time Prediction of Output Power of Multi-Emitter Laser Modules during the Assembly Process

by Nikolaos Grigorios Markatos, Alireza Mousavi, Giulia Pippione and Roberto Paoletti

Electronics 2023, 12(3), 766; https://doi.org/10.3390/electronics12030766 - 02 Feb 2023

Viewed by 1335

Abstract

The challenges of defects in manufacturing and assembly processes in optoelectronic industry continue to persist. Defective products cause increased time to completion (cycle time), energy consumption, cost, and loss of precious material. A complex laser assembly process is studied with the aim of [...] Read more.

The challenges of defects in manufacturing and assembly processes in optoelectronic industry continue to persist. Defective products cause increased time to completion (cycle time), energy consumption, cost, and loss of precious material. A complex laser assembly process is studied with the aim of minimising the generation of defective laser modules. Subsequently, relevant data were gathered to investigate machine learning and artificial intelligence methods to predict the output beam power of the module during the assembly process. The assembly process was divided into a number of chain steps, where we implemented a bespoke framework of hybrid feature selection method alongside artificial neural networks (ANNs) to formulate the statistical inferences. A review of existing learning methods in manufacturing and assembly processes enabled us to select XGBoost and random forest regression (RFR) as the two methods to be compared with ANN, based on their capabilities; ANN outperformed both of them, as it avoided overfitting and scored similar test metrics in the majority of the assembly steps. The results of the proposed solution have been validated in a real production dataset, even showing good predictive capability in the early steps of the assembly process where the available information is limited. Furthermore, the transferability of the framework was validated by applying the proposed framework to another product that follows a similar assembly process. The results indicated that the proposed framework has the potential to serve as the foundation for further research on laser modules’ sophisticated and multi-step assembly lines. Full article

(This article belongs to the Section Optoelectronics)

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3 pages, 185 KiB

Open AccessEditorial

Memristive Devices and Systems: Modeling, Properties and Applications

by Chun Sing Lai, Zhekang Dong and Donglian Qi

Electronics 2023, 12(3), 765; https://doi.org/10.3390/electronics12030765 - 02 Feb 2023

Viewed by 1309

Abstract

The memristor is considered to be a promising candidate for next-generation computing systems due to its nonvolatility, high density, low power, nanoscale geometry, nonlinearity, binary/multiple memory capacity, and negative differential resistance [...] Full article

(This article belongs to the Special Issue Memristive Devices and Systems: Modelling, Properties & Applications)

12 pages, 6139 KiB

Open AccessArticle

Novel Pulsating-DC High-Voltage Linear Driving Scheme for GaN LED General Lighting

by Xiu Zhang, Baoxing Wang, Kai Fu, Rui Yue, Haojie Guo, Shuqi Li and Yong Cai

Electronics 2023, 12(3), 764; https://doi.org/10.3390/electronics12030764 - 02 Feb 2023

Viewed by 1272

Abstract

This work investigates a novel pulsating DC high-voltage linear driving scheme for GaN-based Light-emitting diode (GaN LED) general lighting to save costs and alleviate flicker. The superiority and practicality of this scheme in three-phase AC power grids were demonstrated for the first time. [...] Read more.

This work investigates a novel pulsating DC high-voltage linear driving scheme for GaN-based Light-emitting diode (GaN LED) general lighting to save costs and alleviate flicker. The superiority and practicality of this scheme in three-phase AC power grids were demonstrated for the first time. Compared to applications for single-phase AC grids, linear driving of GaN LEDs for three-phase AC grids can provide superior performance for general lighting. The DC component of the three-phase AC rectified voltage reaches 90.7%, which effectively alleviates the flicker problem. In this paper, we balanced GaN LED power and driving efficiency by optimizing the GaN LED distribution of the linear multi-string GaN LED driving scheme while taking the effects of grid voltage fluctuations into account. In addition, we constructed a double-string GaN LED lighting system as a modular prototype with scalability. The experimental results exhibit high driving efficiency (~94% @380 V line voltage), high power factor (~0.952), flicker-free, and high reliability at a very low cost (~$0.005/W). Full article

(This article belongs to the Special Issue Recent Advances in Wide Bandgap Semiconductors)

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17 pages, 3866 KiB

Open AccessArticle

An Attention-Based Uncertainty Revising Network with Multi-Loss for Environmental Microorganism Segmentation

by Hengyuan Na, Dong Liu and Shengsheng Wang

Electronics 2023, 12(3), 763; https://doi.org/10.3390/electronics12030763 - 02 Feb 2023

Viewed by 1432

Abstract

The presence of environmental microorganisms is inevitable in our surroundings, and segmentation is essential for researchers to identify, understand, and utilize the microorganisms; make use of their benefits; and prevent harm. However, the segmentation of environmental microorganisms is challenging because their vague margins [...] Read more.

The presence of environmental microorganisms is inevitable in our surroundings, and segmentation is essential for researchers to identify, understand, and utilize the microorganisms; make use of their benefits; and prevent harm. However, the segmentation of environmental microorganisms is challenging because their vague margins are almost transparent compared with those of the environment. In this study, we propose a network with an uncertainty feedback module to find ambiguous boundaries and regions and an attention module to localize the major region of the microorganism. Furthermore, we apply a mid-pred module to output low-resolution segmentation results directly from decoder blocks at each level. This module can help the encoder and decoder capture details from different scales. Finally, we use multi-loss to guide the training. Rigorous experimental evaluations on the benchmark dataset demonstrate that our method achieves higher scores than other sophisticated network models (95.63% accuracy, 89.90% Dice, 81.65% Jaccard, 94.68% recall, 0.59 ASD, 2.24 HD95, and 85.58% precision) and outperforms them. Full article

(This article belongs to the Special Issue Deep Learning in Computer Vision and Image Processing)

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20 pages, 11022 KiB

Open AccessArticle

Robust Motion Planning in Robot-Assisted Surgery for Nonlinear Incision Trajectory

by Shailu Sachan and Pankaj Swarnkar

Electronics 2023, 12(3), 762; https://doi.org/10.3390/electronics12030762 - 02 Feb 2023

Cited by 8 | Viewed by 1260

Abstract

In the era of digital OTs (operating theatres), the developments in robot-assisted surgery (RAS) can greatly benefit the medical field. RAS is a method of technological advancement that uses robotic articulations to assist in complicated surgeries. Its implementation improves the ability of the [...] Read more.

In the era of digital OTs (operating theatres), the developments in robot-assisted surgery (RAS) can greatly benefit the medical field. RAS is a method of technological advancement that uses robotic articulations to assist in complicated surgeries. Its implementation improves the ability of the specialized doctor to perform surgery to a great extent. The paper addresses the dynamics and control of the highly non-linear 3DOF surgical robot manipulator in the event of external disturbances and uncertainties. The integration of non-linear robust SMC (sliding mode control) with a smoothing mechanism, a FOPID (fractional-order proportional integral derivative) controller, and a fuzzy controller provides a high degree of robustness and minimal chatter. The addition of fuzzy logic to the controller, named intelligent fuzzy-SFOSMC (smoothing fractional order sliding mode controller) improves the system’s performance by ruling out the disturbances and uncertainties. The prototype model is developed in a laboratory and its outcomes are validated on OP5600, a real-time digital simulator. Simulation and experimental results of the proposed fuzzy-SFOSMC are compared with conventional controllers, which illustrates the efficacy and superiority of the proposed controller’s performance during the typical surgical situations. The proposed fuzzy-SFOSMC outperforms conventional controllers by providing greater precision and robustness to time-varying nonlinear multi-incision trajectories. Full article

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26 pages, 1465 KiB

Open AccessFeature PaperArticle

Enhancing MIMO Spatial-Multiplexing and Parallel-Decoding under Interference by Computational Feedback

by Avner Elgam, Yossi Peretz and Yosef Pinhasi

Electronics 2023, 12(3), 761; https://doi.org/10.3390/electronics12030761 - 02 Feb 2023

Viewed by 1418

Abstract

In this paper, we propose a new digital Hard-Successive-Interference-Cancellation (HSIC), the Alternating Projections-HSIC (AP-HSIC), an innovative fast computational feedback algorithm that deals with various destructive phenomena from different types of interferences. The correctness and convergence of the proposed algorithm are provided, and its [...] Read more.

In this paper, we propose a new digital Hard-Successive-Interference-Cancellation (HSIC), the Alternating Projections-HSIC (AP-HSIC), an innovative fast computational feedback algorithm that deals with various destructive phenomena from different types of interferences. The correctness and convergence of the proposed algorithm are provided, and its complexity is given. The proposed algorithm possesses the functionality of canceling digital interference without the aid of physical feedback between the receiver and the transmitter or the loading of learning information about the state of the Multiple Input–Multiple Output (MIMO) channel to the transmitter. The proposed AP-HSIC algorithm enables a parallel decoding process from the parallel transmission of Orthogonal- Space–Time-Block-Coding (OSTBC) under a complex and challenging wireless environment to facilitate the Dynamic Spectrum Sharing (DSS) capability. We present a performance comparison of the proposed algorithm with the algorithm for Multi-Group-Space–Time-Coding (MGSTC) under MIMO fading channels and general interference or high-level Additive White Gaussian Noise (AWGN). Mathematical analysis and real-time simulations show the advantages of the proposed algorithm compared to the MGSTC decoding algorithm. Full article

(This article belongs to the Special Issue Massive MIMO Systems for 5G and beyond Networks)

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13 pages, 7304 KiB

Open AccessArticle

A Study on the Development of a White Light Source Module for a Large-Capacity Searchlight Using a Blue Laser Diode

by Seung-Min Lee, Joon-Ho Cho and Wan-Bum Lee

Electronics 2023, 12(3), 760; https://doi.org/10.3390/electronics12030760 - 02 Feb 2023

Viewed by 1585

Abstract

In this paper, a large-capacity white light source module using a high-power blue laser diode and a reflective spaced phosphor was designed. The reflective spacing phosphor ensured thermal stability. The proposed white light module is a reflective phosphor structure with a bi-directional optical [...] Read more.

In this paper, a large-capacity white light source module using a high-power blue laser diode and a reflective spaced phosphor was designed. The reflective spacing phosphor ensured thermal stability. The proposed white light module is a reflective phosphor structure with a bi-directional optical system based on a rhombus prism lens. The rhombus prism optical system can greatly narrow the blue laser beam width and a long-wavelength band-pass filter of 500

nm

or more is applied to change the movement path of the laser beam and transmit white light excited by the phosphor. A dichroic filter was applied to the fold mirror part and a planar convex lens was designed to focus the blue laser beam so that the phosphor was irradiated. Finally, a high-power white light source is obtained from the pre-optics unit to which the dichroic filter is applied. In order to use the proposed white light source as a searchlight, a divergence angle of 4° or less is required. For this implementation, a large-area collimating lens combining an aspheric condensing lens and an achromatic lens was applied. It was confirmed that the divergence angle of 4 degrees or less was satisfied at the focal length (FL) of 38.5 to 42.5 mm of the optical lens of the laser white light module emitter and the collimating optical system. Full article

(This article belongs to the Section Optoelectronics)

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13 pages, 21780 KiB

Open AccessArticle

A Dual Long Short-Term Memory Model in Forecasting the Number of COVID-19 Infections

by Jung-Pin Lai and Ping-Feng Pai

Electronics 2023, 12(3), 759; https://doi.org/10.3390/electronics12030759 - 02 Feb 2023

Viewed by 1111

Abstract

Since the outbreak of the Coronavirus Disease 2019 (COVID-19), the spread of the epidemic has been a major international public health issue. Hence, various forecasting models have been used to predict the infectious spread of the disease. In general, forecasting problems often involve [...] Read more.

Since the outbreak of the Coronavirus Disease 2019 (COVID-19), the spread of the epidemic has been a major international public health issue. Hence, various forecasting models have been used to predict the infectious spread of the disease. In general, forecasting problems often involve prediction accuracy decreasing as the horizon increases. Thus, to extend the forecasting horizon without decreasing performance or prediction, this study developed a Dual Long Short-Term Memory (LSTM) with Genetic Algorithms (DULSTMGA) model. The model employed predicted values generated by LSTM models in short-forecasting horizons as inputs for the long-term prediction of LSTM in a rolling manner. Genetic algorithms were applied to determine the parameters of LSTM models, allowing long-term forecasting accuracy to increase as long as short-term forecasting was accurate. In addition, the compartment model was utilized to simulate the state of COVID-19 and generate numbers of infectious cases. Infectious cases in three countries were employed to examine the feasibility and performance of the proposed DULSTMGA model. Numerical results indicated that the DULSTMGA model could obtain satisfactory forecasting accuracy and was superior to many previous studies in terms of the mean absolute percentage error. Therefore, the designed DULSTMGA model is a feasible and promising alternative for forecasting the number of infectious COVID-19 cases. Full article

(This article belongs to the Special Issue Recent Advances in Data Science and Information Technology)

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16 pages, 8744 KiB

Open AccessArticle

Modeling and Investigation of Rear-Passivated Ultrathin CIGS Solar Cell

by Nour El I. Boukortt, Salvatore Patanè and Mabrouk Adouane

Electronics 2023, 12(3), 758; https://doi.org/10.3390/electronics12030758 - 02 Feb 2023

Cited by 1 | Viewed by 1256

Abstract

In this paper, we use numerical simulations to investigate ultrathin Cu (In_1−xGa_x) Se₂ solar cells. In the first part, we focus on the cell configuration in which the PV parameters fit and match the fabricated cell characteristics. Our [...] Read more.

In this paper, we use numerical simulations to investigate ultrathin Cu (In_1−xGa_x) Se₂ solar cells. In the first part, we focus on the cell configuration in which the PV parameters fit and match the fabricated cell characteristics. Our goal is to investigate the impact of different loss mechanisms, such as interface trap density (D_it) and absorber trap density (N_t), in different cell pitch sizes on cell performances. D_it defines the number of carrier traps at CIGS/Al₂O₃ interfaces to recombine with photogenerated carriers. N_t defines the number of carrier traps in the absorber layer. Recombination through traps has been found to be the primary loss process in the investigated cell. Additional numerical simulations reveal appreciable gains in cell performance for various cell pitch sizes, absorber doping densities, Ga content, and graded bandgap under AM1.5 illumination. Research during the recent decade has clarified that the most promising strategy to achieve maximum efficiency consists of the so-called tandem configuration. Therefore, we here propose a u-CIGS/PERT silicon device employing, as a top cell, a u-CIGS cell optimized to take into account the above procedure. The results of these simulations provide insights into the optimization of ultrathin-film CIGS solar cells. Full article

(This article belongs to the Special Issue Power Electronics for Renewable Energy Systems: Latest Advances and Prospects)

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13 pages, 4396 KiB

Open AccessArticle

A Fast Power Lines RCS Calculation Method Combining IEDG with CM-SMWA

by Chunfeng Chen, Changyu Hu and Jianjiang Zhou

Electronics 2023, 12(3), 757; https://doi.org/10.3390/electronics12030757 - 02 Feb 2023

Viewed by 1128

Abstract

The existing methods for calculating electromagnetic scattering can be used to obtain the RCS of power lines. However, these methods do not take advantage of the periodicity of power lines. We propose a fast electromagnetic scattering calculation method combining the integral equation discontinuous [...] Read more.

The existing methods for calculating electromagnetic scattering can be used to obtain the RCS of power lines. However, these methods do not take advantage of the periodicity of power lines. We propose a fast electromagnetic scattering calculation method combining the integral equation discontinuous Galerkin (IEDG) method and the characteristic modes-Sherman–Morrison–Woodbury algorithm (CM-SMWA) exploiting the power lines with stranded structure. We adopt the IEDG to discretize the electric field integral equation (EFIE) so that the EFIE can deal with non-conformal grids and significantly increase the flexibility of the CM-SMWA. Combing with the periodic property of power lines, the modeling and grid generation shall be carried out within one cycle (stranding) of the power line, and the grids of the rest cycle of the power line can be spliced by translating the grid of the divided sections. The advantage of the proposed method lies in that only the CM of one segment needs to be calculated, and the result can be applied to other segments to avoid repeated calculation of the CMs. The simulation results of the RCS of power lines show that the calculation time of our method is cut down by 50% as compared to the conventional CM-SMWA. Full article

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

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23 pages, 1797 KiB

Open AccessFeature PaperReview

Applications of the Internet of Medical Things to Type 1 Diabetes Mellitus

by Ignacio Rodríguez-Rodríguez, José-Víctor Rodríguez and María Campo-Valera

Electronics 2023, 12(3), 756; https://doi.org/10.3390/electronics12030756 - 02 Feb 2023

Cited by 2 | Viewed by 3704

Abstract

Type 1 Diabetes Mellitus (DM1) is a condition of the metabolism typified by persistent hyperglycemia as a result of insufficient pancreatic insulin synthesis. This requires patients to be aware of their blood glucose level oscillations every day to deduce a pattern and anticipate [...] Read more.

Type 1 Diabetes Mellitus (DM1) is a condition of the metabolism typified by persistent hyperglycemia as a result of insufficient pancreatic insulin synthesis. This requires patients to be aware of their blood glucose level oscillations every day to deduce a pattern and anticipate future glycemia, and hence, decide the amount of insulin that must be exogenously injected to maintain glycemia within the target range. This approach often suffers from a relatively high imprecision, which can be dangerous. Nevertheless, current developments in Information and Communication Technologies (ICT) and innovative sensors for biological signals that might enable a continuous, complete assessment of the patient’s health provide a fresh viewpoint on treating DM1. With this, we observe that current biomonitoring devices and Continuous Glucose Monitoring (CGM) units can easily obtain data that allow us to know at all times the state of glycemia and other variables that influence its oscillations. A complete review has been made of the variables that influence glycemia in a T1DM patient and that can be measured by the above means. The communications systems necessary to transfer the information collected to a more powerful computational environment, which can adequately handle the amounts of data collected, have also been described. From this point, intelligent data analysis extracts knowledge from the data and allows predictions to be made in order to anticipate risk situations. With all of the above, it is necessary to build a holistic proposal that allows the complete and smart management of T1DM. This approach evaluates a potential shortage of such suggestions and the obstacles that future intelligent IoMT-DM1 management systems must surmount. Lastly, we provide an outline of a comprehensive IoMT-based proposal for DM1 management that aims to address the limits of prior studies while also using the disruptive technologies highlighted before. Full article

(This article belongs to the Special Issue Wireless Sensors Networks in the IoT Era: Advanced Technologies, Recent Challenges, Smart Applications & Future Prospects)

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14 pages, 7949 KiB

Open AccessArticle

A Monostable Physically Unclonable Function Based on Improved RCCMs with 0–1.56% Native Bit Instability at 0.6–1.2 V and 0–75 °C

by Riccardo Della Sala, Davide Bellizia, Francesco Centurelli and Giuseppe Scotti

Electronics 2023, 12(3), 755; https://doi.org/10.3390/electronics12030755 - 02 Feb 2023

Cited by 7 | Viewed by 1204

Abstract

In this work, a Physically Unclonable Function (PUF) based on an improved regulated cascode current mirror (IRCCM) is presented. The proposed IRCCM improves the loop-gain of the gain-boosting branch over the conventional RCCM PUF, thereby increasing the output resistance and amplifying the mismatches [...] Read more.

In this work, a Physically Unclonable Function (PUF) based on an improved regulated cascode current mirror (IRCCM) is presented. The proposed IRCCM improves the loop-gain of the gain-boosting branch over the conventional RCCM PUF, thereby increasing the output resistance and amplifying the mismatches due to random variations. The introduction of an explicit reference current in the biasing branch of the IRCCM results in lower native unstable bits, good robustness against environmental variations and very stable power consumption. The proposed PUF has been validated through measurement results on a test-chip implemented in a 130 nm CMOS process. The PUF performance was measured for supply voltages between 0.6 and 1.2V, and temperatures ranging from 0 °C to 75 °C. A comparison against similar designs from the literature has shown that the proposed PUF exhibits state of the art performance with improved reliability under supply voltage variations. Full article

(This article belongs to the Special Issue Ultra-Low-Voltage and Ultra-Low-Power Integrated Circuits and Systems Evolution)

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16 pages, 5908 KiB

Open AccessArticle

Memory-Tree Based Design of Optical Character Recognition in FPGA

by Ke Yu, Minguk Kim and Jun Rim Choi

Electronics 2023, 12(3), 754; https://doi.org/10.3390/electronics12030754 - 02 Feb 2023

Cited by 2 | Viewed by 3045

Abstract

As one of the fields of Artificial Intelligence (AI), Optical Character Recognition (OCR) systems have wide application in both industrial production and daily life. Conventional OCR systems are commonly designed and implement data computation on the basis of microprocessors; the performance of the [...] Read more.

As one of the fields of Artificial Intelligence (AI), Optical Character Recognition (OCR) systems have wide application in both industrial production and daily life. Conventional OCR systems are commonly designed and implement data computation on the basis of microprocessors; the performance of the processor relates to the effect of the computation. However, due to the “Memory-wall” problem and Von Neumann bottlenecks, the drawbacks of traditional processor-based computing for OCR systems are gradually becoming apparent. In this paper, an approach based on the Memory-Centric Computing and “Memory-Tree” algorithm has been proposed to perform hardware optimization of traditional OCR systems. The proposed algorithm was first designed in software implementation using C/C++ and OpenCV to verify the feasibility of the idea and then the RTL conversion of the algorithm was done using the Xilinx Vitis High Level Synthesis (HLS) tool to implement the hardware. This work chose Xilinx Alveo U50 FPGA Accelerator to complete the hardware design, which can be connected to the x86 CPU in the PC by PCIe to form heterogeneous computing. The results of the hardware implementation show that the system this work designed can recognize characters of English capital letters and numbers within 34.24 us. The power of FPGA is 18.59 W, which saves 77.87% of energy consumption compared to the 84 W of the processor in PC. Full article

(This article belongs to the Special Issue FPGAs Based Hardware Design)

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14 pages, 1262 KiB

Open AccessFeature PaperArticle

Modelling and Simulation of Quasi-Resonant Inverter for Induction Heating under Variable Load

by Enrico Spateri, Fredy Ruiz and Giambattista Gruosso

Electronics 2023, 12(3), 753; https://doi.org/10.3390/electronics12030753 - 02 Feb 2023

Cited by 2 | Viewed by 1959

Abstract

Single-switch quasi-resonant DC inverters are preferred in low-power induction-heating applications for their cheapness. However, they pose difficulties in enforcing soft-switching and show limited controllability. A good design of these converters must proceed in parallel with the characterization of the load and the operating [...] Read more.

Single-switch quasi-resonant DC inverters are preferred in low-power induction-heating applications for their cheapness. However, they pose difficulties in enforcing soft-switching and show limited controllability. A good design of these converters must proceed in parallel with the characterization of the load and the operating conditions. The control of the switching frequency has a critical relationship to the non-linear behavior of the load due to electro-thermal coupling and geometrical anisotropies. Finite element methods enable the analysis of this kind of multiphysics coupled systems, but the simulation of transient dynamics is computationally expensive. The goal of this article is to propose a time-domain simulation strategy to analyze the behavior of induction heating systems with a quasi-resonant single-ended DC inverter using pulse frequency modulation and variable load. The load behavior is estimated through frequency stationary analysis and integrated into the time-domain simulations as a non-linear equivalent impedance parametrized by look-up tables. The model considers variations in temperature dynamics, the presence of work-piece anisotropies, and current harmonic waveforms. The power regulation strategy based on the control of the switch turn-on time is tested in a case study with varying load and it is shown that it is able to maintain the converter in the safe operation region, handling variations up to of

22 %

in the equivalent load resistance. Full article

(This article belongs to the Special Issue Power Electronics in Italy—Emerging Electronic Power Technologies and Electronic Devices in the Industrial 4.0 Era)

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14 pages, 4464 KiB

Open AccessArticle

A Chinese BERT-Based Dual-Channel Named Entity Recognition Method for Solid Rocket Engines

by Zhiqiang Zheng, Minghao Liu and Zhi Weng

Electronics 2023, 12(3), 752; https://doi.org/10.3390/electronics12030752 - 02 Feb 2023

Cited by 1 | Viewed by 1603

Abstract

With the Chinese data for solid rocket engines, traditional named entity recognition cannot be used to learn both character features and contextual sequence-related information from the input text, and there is a lack of research on the advantages of dual-channel networks. To address [...] Read more.

With the Chinese data for solid rocket engines, traditional named entity recognition cannot be used to learn both character features and contextual sequence-related information from the input text, and there is a lack of research on the advantages of dual-channel networks. To address this problem, this paper proposes a BERT-based dual-channel named entity recognition model for solid rocket engines. This model uses a BERT pre-trained language model to encode individual characters, obtaining a vector representation corresponding to each character. The dual-channel network consists of a CNN and BiLSTM, using the convolutional layer for feature extraction and the BiLSTM layer to extract sequential and sequence-related information from the text. The experimental results showed that the model proposed in this paper achieved good results in the named entity recognition task using the solid rocket engine dataset. The accuracy, recall and F1-score were 85.40%, 87.70% and 86.53%, respectively, which were all higher than the results of the comparison models. Full article

(This article belongs to the Section Artificial Intelligence)

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18 pages, 6590 KiB

Open AccessFeature PaperArticle

Inertial Measurement Units’ Reliability for Measuring Knee Joint Angle during Road Cycling

by Saša Obradović and Sara Stančin

Electronics 2023, 12(3), 751; https://doi.org/10.3390/electronics12030751 - 02 Feb 2023

Viewed by 1806

Abstract

We explore the reliability of joint angles in road cycling obtained using inertial measurement units. The considered method relies on 3D accelerometer and gyroscope measurements obtained from two such units, appropriately attached to two adjacent body parts, measuring the angle of the connecting [...] Read more.

We explore the reliability of joint angles in road cycling obtained using inertial measurement units. The considered method relies on 3D accelerometer and gyroscope measurements obtained from two such units, appropriately attached to two adjacent body parts, measuring the angle of the connecting joint. We investigate the effects of applying a simple drift compensation technique and an error-state Kalman filter. We consider the knee joint angle in particular, and conduct two measurement trials, a 5 and a 20 minute one, for seven subjects, in a closed, supervised laboratory environment and use optical motion tracking system measurements as reference. As expected from an adaptive solution, the Kalman filter gives more stable results. The root mean square errors per pedalling cycle are below 3.2°, for both trials and for all subjects, implying that inertial measurement units are not only reliable for short measurements, as is usually assumed, but can be reliably used for longer measurements as well. Considering the accuracy of the results, the presented method can be reasonably extended to open, unsupervised environments and other joint angles. Implementing the presented method supports the development of cheaper and more efficient monitoring equipment, as opposed to using expensive motion tracking systems. Consequently, cyclists can have an affordable way of position tracking, leading to not only better bicycle fitting, but to the avoidance and prevention of certain injuries as well. Full article

(This article belongs to the Special Issue Pattern Recognition and Sensor Fusion Solutions in Intelligent Sensor Systems)

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25 pages, 4178 KiB

Open AccessReview

Technological Advancements and Elucidation Gadgets for Healthcare Applications: An Exhaustive Methodological Review-Part-I (AI, Big Data, Block Chain, Open-Source Technologies, and Cloud Computing)

by Sridhar Siripurapu, Naresh K. Darimireddy, Abdellah Chehri, B. Sridhar and A. V. Paramkusam

Electronics 2023, 12(3), 750; https://doi.org/10.3390/electronics12030750 - 02 Feb 2023

Cited by 4 | Viewed by 2326

Abstract

In the realm of the emergence and spread of infectious diseases with pandemic potential throughout the history, plenty of pandemics (and epidemics), from the plague to AIDS (1981) and SARS (in 2003) to the bunch of COVID variants, have tormented mankind. Though plenty [...] Read more.

In the realm of the emergence and spread of infectious diseases with pandemic potential throughout the history, plenty of pandemics (and epidemics), from the plague to AIDS (1981) and SARS (in 2003) to the bunch of COVID variants, have tormented mankind. Though plenty of technological innovations are overwhelmingly progressing to curb them—a significant number of such pandemics astounded the world, impacting billions of lives and posing uncovered challenges to healthcare organizations and clinical pathologists globally. In view of addressing these limitations, a critically exhaustive review is performed to signify the prospective role of technological advancements and highlight the implicit problems associated with rendering best quality lifesaving treatments to the patient community. The proposed review work is conducted in two parts. Part 1 is essentially focused upon discussion of advanced technologies akin to artificial intelligence, Big Data, block chain technology, open-source technology, cloud computing, etc. Research works governing applicability of these technologies in solving many uncovered healthcare issues prominently faced by doctors and surgeons in the fields of cardiology, medicine, neurology, orthopaedics, paediatrics, gynaecology, psychiatry, plastic surgery, etc., as well as their role in curtailing the spread of numerous infectious, pathological, neurotic maladies is thrown light off. Boundary conditions and implicitly associated challenges substantiated by remedies coupled with future directions are presented at the end. Full article

(This article belongs to the Special Issue Feature Papers in "Networks" Section)

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