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Electronics, Volume 11, Issue 11 (June-1 2022) – 138 articles

Cover Story (view full-size image): Collaborative robots are meant to help humans in dangerous or mundane tasks. In this paper, we review the current use of extended reality (XR) as a way to test and develop collaboration scenarios with robots. We focus on virtual reality (VR) in simulating collaboration scenarios and the use of cobot digital twins. VR is especially useful for simulating dangerous scenarios and allows combining human self-reports with objective data such as biosignals representing stress. We provide a summary of other potential applications of XR and list critical variables for most human–robot collaboration testing frameworks. The use of XR has the potential to shape the way we design and test cobots in a broad range of domains: from industry through healthcare to space operations. View this paper
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Article
Modified Predictive Direct Torque Control ASIC with Multistage Hysteresis and Fuzzy Controller for a Three-Phase Induction Motor Drive
Electronics 2022, 11(11), 1802; https://doi.org/10.3390/electronics11111802 - 06 Jun 2022
Viewed by 447
Abstract
This paper proposes a modified predictive direct torque control (MPDTC) application-specific integrated circuit (ASIC) with multistage hysteresis and fuzzy controller to address the ripple problem of hysteresis controllers and to have a low power consumption chip. The proposed MPDTC ASIC calculates the stator’s [...] Read more.
This paper proposes a modified predictive direct torque control (MPDTC) application-specific integrated circuit (ASIC) with multistage hysteresis and fuzzy controller to address the ripple problem of hysteresis controllers and to have a low power consumption chip. The proposed MPDTC ASIC calculates the stator’s magnetic flux and torque by detecting three-phase currents, three-phase voltages, and the rotor speed. Moreover, it eliminates large ripples in the torque and flux by passing through the modified discrete multiple-voltage vector (MDMVV), and four voltage vectors were obtained on the basis of the calculated flux and torque in a cycle. In addition, the speed error was converted into a torque command by using the fuzzy PID controller, and rounding-off calculation was employed to decrease the calculation error of the composite flux. The proposed MDMVV switching table provides 294 combined voltage vectors to the following inverter. The proposed MPDTC scheme generates four voltage vectors in a cycle that can quickly achieve DTC function. The Verilog hardware description language (HDL) was used to implement the hardware architecture, and an ASIC was fabricated with a TSMC 0.18 μm 1P6M CMOS process by using a cell-based design method. Measurement results revealed that the proposed MPDTC ASIC performed with operating frequency, sampling rate, and dead time of 10 MHz, 100 kS/s, and 100 ns, respectively, at a supply voltage of 1.8 V. The power consumption and chip area of the circuit were 2.457 mW and 1.193 mm × 1.190 mm, respectively. The proposed MPDTC ASIC occupied a smaller chip area and exhibited a lower power consumption than the conventional DTC system did in the adopted FPGA development board. The robustness and convenience of the proposed MPDTC ASIC are especially advantageous. Full article
(This article belongs to the Section Power Electronics)
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Article
Measuring the Energy and Performance of Scientific Workflows on Low-Power Clusters
Electronics 2022, 11(11), 1801; https://doi.org/10.3390/electronics11111801 - 06 Jun 2022
Viewed by 318
Abstract
Scientific problems can be formulated as workflows to allow them to take advantage of cluster computing resources. Generally, the assumption is that the greater the resources dedicated to completing these tasks the better. This assumption does not take into account the energy cost [...] Read more.
Scientific problems can be formulated as workflows to allow them to take advantage of cluster computing resources. Generally, the assumption is that the greater the resources dedicated to completing these tasks the better. This assumption does not take into account the energy cost of performing the computation and the specific characteristics of each workflow. In this paper, we present a unique approach to evaluating the energy consumption of scientific workflows on compute clusters. Two workflows from different domains, Astronomy and Bioinformatics, are presented and their execution is analyzed on a cluster of low powered small board computers. The paper presents a theoretical analysis of an energy-aware execution of workflows that can reduce the energy consumption of workflows by up to 68% compared to normal execution. We demonstrate that there are limitations to the benefits of increasing cluster sizes and there are trade-offs when considering energy vs. performance of the workflows and that the performance and energy consumption of any scientific workflow is heavily dependent on its underlying structure. The study concludes that the energy consumption of workflows can be optimized to improve both aspects of the workflow and motivates the development of an energy-aware scheduler. Full article
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Review
Recent Advances in Machine Learning Applied to Ultrasound Imaging
Electronics 2022, 11(11), 1800; https://doi.org/10.3390/electronics11111800 - 06 Jun 2022
Viewed by 399
Abstract
Machine learning (ML) methods are pervading an increasing number of fields of application because of their capacity to effectively solve a wide variety of challenging problems. The employment of ML techniques in ultrasound imaging applications started several years ago but the scientific interest [...] Read more.
Machine learning (ML) methods are pervading an increasing number of fields of application because of their capacity to effectively solve a wide variety of challenging problems. The employment of ML techniques in ultrasound imaging applications started several years ago but the scientific interest in this issue has increased exponentially in the last few years. The present work reviews the most recent (2019 onwards) implementations of machine learning techniques for two of the most popular ultrasound imaging fields, medical diagnostics and non-destructive evaluation. The former, which covers the major part of the review, was analyzed by classifying studies according to the human organ investigated and the methodology (e.g., detection, segmentation, and/or classification) adopted, while for the latter, some solutions to the detection/classification of material defects or particular patterns are reported. Finally, the main merits of machine learning that emerged from the study analysis are summarized and discussed. Full article
(This article belongs to the Special Issue Ultrasonic Pattern Recognition by Machine Learning)
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Article
Band Bending and Trap Distribution along the Channel of Organic Field-Effect Transistors from Frequency-Resolved Scanning Photocurrent Microscopy
Electronics 2022, 11(11), 1799; https://doi.org/10.3390/electronics11111799 - 06 Jun 2022
Viewed by 339
Abstract
The scanning photocurrent microscopy (SPCM) method is applied to pentacene field-effect transistors (FETs). In this technique, a modulated laser beam is focused and scanned along the channel of the transistors. The resulting spatial photocurrent profile is attributed to extra free holes generated from [...] Read more.
The scanning photocurrent microscopy (SPCM) method is applied to pentacene field-effect transistors (FETs). In this technique, a modulated laser beam is focused and scanned along the channel of the transistors. The resulting spatial photocurrent profile is attributed to extra free holes generated from the dissociation of light-created excitons after their interaction with trapped holes. The trapped holes result from the local upward band bending in the accumulation layer depending on the applied voltages. Thus, the photocurrent profile along the conducting channel of the transistors reflects the pattern of the trapped holes and upward band bending under the various operating conditions of the transistor. Moreover, it is found here that the frequency-resolved SPCM (FR-SPCM) is related to the interaction of free holes via trapping and thermal release from active probed traps of the first pentacene monolayers in the accumulation layer. The active probed traps are selected by the modulation frequency of the laser beam so that the FR-SPCM can be applied as a spectroscopic technique to determine the energy distribution of the traps along the transistor channel. In addition, a crossover is found in the FR-SPCM spectra that signifies the transition from empty to partially empty probed trapping states near the corresponding trap quasi-Fermi level. From the frequency of this crossover, the energy gap from the quasi-Fermi Etp level to the corresponding local valence band edge Ev, which is bent up by the gate voltage, can be estimated. This allows us to spatially determine the magnitude of the band bending under different operation conditions along the channel of the organic transistors. Full article
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Article
DroidFDR: Automatic Classification of Android Malware Using Model Checking
Electronics 2022, 11(11), 1798; https://doi.org/10.3390/electronics11111798 - 06 Jun 2022
Viewed by 415
Abstract
Android faces an increasing threat of malware attacks. The few existing formal detection methods have drawbacks such as complex code modeling, incomplete and inaccurate expression of family properties, and excessive manual participation. To this end, this paper proposes a formal detection method, called [...] Read more.
Android faces an increasing threat of malware attacks. The few existing formal detection methods have drawbacks such as complex code modeling, incomplete and inaccurate expression of family properties, and excessive manual participation. To this end, this paper proposes a formal detection method, called DroidFDR, for Android malware classification based on communicating sequential processes (CSP). In this method, the APK file of an application is converted to an easy-to-analyze representation, namely Jimple, in order to model the code behavior with CSP. The process describing the behavior of a sample is inputted to an FDR model checker to be simplified and verified against a process that is automatically abstracted from the malware to express the property of a family. The sample is classified by detecting whether it has the typical behavior of any family property. DroidFDR can capture the behavioral characteristics of malicious code such as control flow, data flow, procedure calls, and API calls. The experimental results show that the automated method can characterize the behavior patterns of applications from the structure level, with a high family classification accuracy of 99.06% in comparison with another formal detection method. Full article
(This article belongs to the Section Networks)
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Article
GRU with Dual Attentions for Sensor-Based Human Activity Recognition
Electronics 2022, 11(11), 1797; https://doi.org/10.3390/electronics11111797 - 06 Jun 2022
Viewed by 321
Abstract
Human Activity Recognition (HAR) is nowadays widely used in intelligent perception and medical detection, and the use of traditional neural networks and deep learning methods has made great progress in this field in recent years. However, most of the existing methods assume that [...] Read more.
Human Activity Recognition (HAR) is nowadays widely used in intelligent perception and medical detection, and the use of traditional neural networks and deep learning methods has made great progress in this field in recent years. However, most of the existing methods assume that the data has independent identical distribution (I.I.D.) and ignore the data variability of different individual volunteers. In addition, most deep learning models are characterized by many parameters and high resources consumption, making it difficult to run in real time on embedded devices. To address these problems, this paper proposes a Gate Recurrent Units (GRU) network fusing the channel attention and the temporal attention for human activity recognition method without I.I.D. By using channel attention to mitigate sensor data bias, GRU and the temporal attention are used to capture important motion moments and aggregate temporal features to reduce model parameters. Experimental results show that our model outperforms existing methods in terms of classification accuracy on datasets without I.I.D., and reduces the number of model parameters and resources consumption, which can be easily used in low-resource embedded devices. Full article
(This article belongs to the Special Issue New Frontiers in Edge Computing for Internet of Things)
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Article
A Modified RL-IGWO Algorithm for Dynamic Weapon-Target Assignment in Frigate Defensing UAV Swarms
Electronics 2022, 11(11), 1796; https://doi.org/10.3390/electronics11111796 - 06 Jun 2022
Viewed by 316
Abstract
Unmanned aerial vehicle (UAV) swarms have significant advantages in terms of cost, number, and intelligence, constituting a serious threat to traditional frigate air defense systems. Ship-borne short-range anti-air weapons undertake terminal defense tasks against UAV swarms. In traditional air defense fire control systems, [...] Read more.
Unmanned aerial vehicle (UAV) swarms have significant advantages in terms of cost, number, and intelligence, constituting a serious threat to traditional frigate air defense systems. Ship-borne short-range anti-air weapons undertake terminal defense tasks against UAV swarms. In traditional air defense fire control systems, a dynamic weapon-target assignment (DWTA) is disassembled into several static weapon target assignments (SWTAs), but the relationship between DWTAs and SWTAs is not supported by effective analytical proof. Based on the combat scenario between a frigate and UAV swarms, a model-based reinforcement learning framework was established, and a DWAT problem was disassembled into several static combination optimization (SCO) problems by means of the dynamic programming method. In addition, several variable neighborhood search (VNS) operators and an opposition-based learning (OBL) operator were designed to enhance the global search ability of the original Grey Wolf Optimizer (GWO), thereby solving SCO problems. An improved grey wolf algorithm based on reinforcement learning (RL-IGWO) was established for solving DWTA problems in the defense of frigates against UAV swarms. The experimental results show that RL-IGWO had obvious advantages in both the decision making time and solution quality. Full article
(This article belongs to the Special Issue Modeling and Simulation Methods: Recent Advances and Applications)
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Review
A Review on Different State of Battery Charge Estimation Techniques and Management Systems for EV Applications
Electronics 2022, 11(11), 1795; https://doi.org/10.3390/electronics11111795 - 06 Jun 2022
Viewed by 361
Abstract
Electric vehicles (EVs) have acquired significant popularity in recent decades due to their performance and efficiency. EVs are already largely acknowledged as the most promising solutions to global environmental challenges and CO2 emissions. Li-ion batteries are most frequently employed in EVs due [...] Read more.
Electric vehicles (EVs) have acquired significant popularity in recent decades due to their performance and efficiency. EVs are already largely acknowledged as the most promising solutions to global environmental challenges and CO2 emissions. Li-ion batteries are most frequently employed in EVs due to their various benefits. An effective Battery Management System (BMS) is essential to improve the battery performance, including charging–discharging control, precise monitoring, heat management, battery safety, and protection, and also an accurate estimation of the State of Charge (SOC). The SOC is required to provide the driver with a precise indication of the remaining range. At present, different types of estimation algorithms are available, but they still have several challenges due to their performance degradation, complex electrochemical reactions, and inaccuracy. The estimating techniques, average error, advantages, and disadvantages were examined methodically and independently for this paper. The article presents advanced SOC estimating techniques, such as LSTM, GRU, and CNN-LSMT, and hybrid techniques to estimate the average error of the SOC. A detailed comparison is presented with merits and demerits, which helped the researchers in the implementation of EV applications. This research also identified several factors, challenges, and potential recommendations for an enhanced BMS and efficient estimating approaches for future sustainable EV applications. Full article
(This article belongs to the Special Issue Charging Systems for Electric Vehicles)
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Article
Low-Phase-Noise CMOS Relaxation Oscillators for On-Chip Timing of IoT Sensing Platforms
Electronics 2022, 11(11), 1794; https://doi.org/10.3390/electronics11111794 - 06 Jun 2022
Viewed by 296
Abstract
The design of low-phase-noise fully integrated frequency references is often a critical aspect in the development of low-cost integrated circuits for communication interfaces, sensing platforms, and biomedical applications. This work first discusses relaxation oscillator topologies and design approaches aimed at minimizing the phase [...] Read more.
The design of low-phase-noise fully integrated frequency references is often a critical aspect in the development of low-cost integrated circuits for communication interfaces, sensing platforms, and biomedical applications. This work first discusses relaxation oscillator topologies and design approaches aimed at minimizing the phase noise; then, a single-comparator low-phase-noise RC relaxation oscillator is proposed, featuring a novel comparator self-threshold-adjustment technique. The oscillator was designed for a 10 MHz oscillation frequency. Electrical simulations performed on a 0.18 μm CMOS design confirmed that the proposed technique effectively rejects the flicker component of the comparator noise, allowing for a 152 dBc/Hz figure of merit at a 1 kHz offset frequency. The standard deviation of the jitter accumulated across 10k oscillation cycles is lower than 4 ns. The simulated current consumption of the circuit is equal to 50.8 μA with a 1.8 V supply voltage. The temperature sensitivity of the oscillation frequency is also notably low, as its worst-case value across process corners is equal to −20.8 ppm/°C from −55 °C to 125 °C. Full article
(This article belongs to the Special Issue Analog IC Design—Analog and RF Interface for Electronic Systems)
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Article
A Fusion Decision-Making Architecture for COVID-19 Crisis Analysis and Management
Electronics 2022, 11(11), 1793; https://doi.org/10.3390/electronics11111793 - 06 Jun 2022
Viewed by 394
Abstract
The COVID-19 outbreak has had considerably harsh impacts on the global economy, such as shutting down and paralyzing industrial production capacity and increasing the unemployment rate. For enterprises, relying on past experiences and strategies to respond to such an unforeseen financial crisis is [...] Read more.
The COVID-19 outbreak has had considerably harsh impacts on the global economy, such as shutting down and paralyzing industrial production capacity and increasing the unemployment rate. For enterprises, relying on past experiences and strategies to respond to such an unforeseen financial crisis is not appropriate or sufficient. Thus, there is an urgent requirement to reexamine and revise an enterprise’s inherent crisis management architecture so as to help it recover sooner after having encountered extremely negative economic effects. To fulfill this need, the present paper introduces a fusion architecture that integrates artificial intelligence and multiple criteria decision making to exploit essential risk factors and identify the intertwined relations between dimensions/criteria for managers to prioritize improvement plans and deploy resources to key areas without any waste. The result indicated the accurate improvement priorities, which ran in the order of financial sustainability (A), customer and stakeholders (B), enablers’ learning and growth (D), and internal business process (C) based on the measurement of the impact. The method herein will help to effectively and efficiently support crisis management for an organization confronting COVID-19. Among all the criteria, maintaining fixed reserves was the most successful factor regarding crisis management. Full article
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Article
The Enhanced Energy Density of rGO/TiO2 Based Nanocomposite as Electrode Material for Supercapacitor
Electronics 2022, 11(11), 1792; https://doi.org/10.3390/electronics11111792 - 06 Jun 2022
Viewed by 336
Abstract
TiO2 electrode material is a poor choice for supercapacitor electrodes because it has low conductivity, poor cyclic stability, and a low capacitance value. It is inevitable to enhance electrode materials of this kind by increasing the surface area and combining high electronic [...] Read more.
TiO2 electrode material is a poor choice for supercapacitor electrodes because it has low conductivity, poor cyclic stability, and a low capacitance value. It is inevitable to enhance electrode materials of this kind by increasing the surface area and combining high electronic conductivity materials. In the current research work, it was proposed to combine reduced graphene oxide (rGO) as it might provide a large surface area for intercalation and deintercalation, and also, it could establish the shorter paths to ion transfer, leading to a reduction in ionic resistance. The size, surface morphology, and crystalline structure of as-prepared rGO/TiO2 nanocomposites were studied using HRTEM, FESEM, and XRD, respectively. Using an electrochemical workstation, the capacitive behaviors of the rGO/TiO2 electrode materials were assessed with respect to scan rate and current density. The capacitances obtained through cyclic voltammetry and galvanostatic charge-discharge techniques were found to be higher when compared to TiO2 alone. Furthermore, the as-synthesized nanocomposites were able to achieve a higher energy density and better cycle stability. Full article
(This article belongs to the Special Issue Advanced Design of RF/Microwave Circuit)
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Article
The Design of a Low Noise and Low Power Current Readout Circuit for Sub-pA Current Detection Based on Charge Distribution Model
Electronics 2022, 11(11), 1791; https://doi.org/10.3390/electronics11111791 - 05 Jun 2022
Viewed by 371
Abstract
In this article, we proposed an analytical model based on charge distribution for switched-capacitor trans-impedance amplifiers (SCTIAs). The changes in the load state of the amplifier under different operating conditions and the influence of the gain of the operational amplifier (Opamp) on the [...] Read more.
In this article, we proposed an analytical model based on charge distribution for switched-capacitor trans-impedance amplifiers (SCTIAs). The changes in the load state of the amplifier under different operating conditions and the influence of the gain of the operational amplifier (Opamp) on the trans-impedance gain are analyzed to improve the design theory of switched-capacitor trans-impedance amplifiers. According to the conclusion drawn from the analysis, the trans-impedance amplifier (TIA) has been designed by adopting “correlated double sampling technology” and “cross-connection technology” to optimize input-referred noise current, power consumption, and trans-impedance gain. As a result, the trans-impedance gain reaches up to 206 dB, while the bandwidth is 3 kHz. The current readout system achieves an input-referred noise current floor of 2.96 fA/Hz at 1 kHz, and the power consumption of the system is 0.643 mW. The circuit has been simulated with the technology of 0.18 μm, and the layout area is 1000 μm × 500 μm. Full article
(This article belongs to the Section Circuit and Signal Processing)
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Article
Three-Dimensional Reconstruction Method for Bionic Compound-Eye System Based on MVSNet Network
Electronics 2022, 11(11), 1790; https://doi.org/10.3390/electronics11111790 - 05 Jun 2022
Viewed by 343
Abstract
In practical scenarios, when shooting conditions are limited, high efficiency of image shooting and success rate of 3D reconstruction are required. To achieve the application of bionic compound eyes in small portable devices for 3D reconstruction, auto-navigation, and obstacle avoidance, a deep learning [...] Read more.
In practical scenarios, when shooting conditions are limited, high efficiency of image shooting and success rate of 3D reconstruction are required. To achieve the application of bionic compound eyes in small portable devices for 3D reconstruction, auto-navigation, and obstacle avoidance, a deep learning method of 3D reconstruction using a bionic compound-eye system with partial-overlap fields was studied. We used the system to capture images of the target scene, then restored the camera parameter matrix by solving the PnP problem. Considering the unique characteristics of the system, we designed a neural network based on the MVSNet network structure, named CES-MVSNet. We fed the captured image and camera parameters to the trained deep neural network, which can generate 3D reconstruction results with good integrity and precision. We used the traditional multi-view geometric method and neural networks for 3D reconstruction, and the difference between the effects of the two methods was analyzed. The efficiency and reliability of using the bionic compound-eye system for 3D reconstruction are proved. Full article
(This article belongs to the Topic Computer Vision and Image Processing)
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Article
Study on Co-Estimation of SoC and SoH for Second-Use Lithium-Ion Power Batteries
Electronics 2022, 11(11), 1789; https://doi.org/10.3390/electronics11111789 - 05 Jun 2022
Viewed by 351
Abstract
Lithium-ion batteries are an ideal power supplier for electric vehicles (EVs) due to their high-power density and wide operating voltage, but their performance decays to 80% before retirement from EVs. Nevertheless, they still have a particular use value after decommissioning, so recycling the [...] Read more.
Lithium-ion batteries are an ideal power supplier for electric vehicles (EVs) due to their high-power density and wide operating voltage, but their performance decays to 80% before retirement from EVs. Nevertheless, they still have a particular use value after decommissioning, so recycling the retired power battery in cascade can be considered. Therefore, accurate estimation of battery state-of-charge (SoC) and state-of-health (SoH) is crucial for extending the service life and echelon utilization of power lithium-ion battery packs. This paper proposes a comprehensive co-estimation scheme of battery SoC/SoH for the second-use of lithium-ion power batteries in EVs under different cycles using an adaptive extended Kalman filter (AEKF). First, according to the collected battery test data at different aging cycle levels, the external battery characteristics are analyzed, and then a cycle-dependent equivalent circuit model (cECM) is built up. Next, the parameter estimation of this battery model is performed via a recursive least square (RLS) algorithm. Meanwhile, the variations in internal battery parameters of the cycle numbers are fitted and synthesized. Moreover, validation of the estimated parameters is further carried out. Based on this enhanced battery model, the AEKF algorithm is utilized to fulfill battery SoC/SoH estimation simultaneously. The estimated results of SoC/SoH are obtained for a LiCoO2 cell in the case of CCC (constant current condition) under different cycle times. The results show that this proposed co-estimation scheme can predict battery SoC and SoH well, wherein the peak values of the SoC errors are less than 2.2%, and the peak values of SoH, calculated by the estimated capacity and internal resistance, are less than 1.7% and 2.2%, respectively. Hence, this has important guiding significance for realizing the cascade utilization of lithium-ion power batteries. Full article
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Article
Active Disturbance Rejection Adaptive Control for Hydraulic Lifting Systems with Valve Dead-Zone
Electronics 2022, 11(11), 1788; https://doi.org/10.3390/electronics11111788 - 05 Jun 2022
Viewed by 321
Abstract
In this article, the motion control problem of hydraulic lifting systems subject to parametric uncertainties, unmodeled disturbances, and a valve dead-zone is studied. To surmount the problem, an active disturbance rejection adaptive controller was developed for hydraulic lifting systems. Firstly, the dynamics, including [...] Read more.
In this article, the motion control problem of hydraulic lifting systems subject to parametric uncertainties, unmodeled disturbances, and a valve dead-zone is studied. To surmount the problem, an active disturbance rejection adaptive controller was developed for hydraulic lifting systems. Firstly, the dynamics, including both mechanical dynamics and hydraulic actuator dynamics with a valve dead-zone of the hydraulic lifting system, were modeled. Then, by adopting the system model and a backstepping technique, a composite parameter adaptation law and extended state disturbance observer were successfully combined, which were employed to dispose of the parametric uncertainties and unmodeled disturbances, respectively. This much decreased the learning burden of the extended state disturbance observer, and the high-gain feedback issue could be shunned. An ultimately bounded tracking performance can be assured with the developed control method based on the Lyapunov theory. A simulation example of a hydraulic lifting system was carried out to demonstrate the validity of the proposed controller. Full article
(This article belongs to the Special Issue High Performance Control and Industrial Applications)
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Article
A Compact CSRR-Based Sensor for Characterization of the Complex Permittivity of Dielectric Materials
Electronics 2022, 11(11), 1787; https://doi.org/10.3390/electronics11111787 - 04 Jun 2022
Viewed by 407
Abstract
A sensor is proposed to characterize the complex permittivity of dielectric materials in a non-destructive and non-invasive way. The proposed sensor is based on a rectangular patch microstrip two-port circuit with a complementary split-ring resonator (CSRR) element. The slotted CSRR element of the [...] Read more.
A sensor is proposed to characterize the complex permittivity of dielectric materials in a non-destructive and non-invasive way. The proposed sensor is based on a rectangular patch microstrip two-port circuit with a complementary split-ring resonator (CSRR) element. The slotted CSRR element of the sensor plays a key role in determining the electrical properties of the materials under test (MUT). The sensitivity analysis is determined by varying the permittivity of the MUT. The proposed sensor is simulated and analyzed using Ansoft HFSS software. A prototype was fabricated and measurements were made on two different samples of dielectric materials with complex permittivity values available in the literature. The simulated and measured results showed good agreement. Full article
(This article belongs to the Special Issue RF/Microwave Circuits for 5G and Beyond)
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Article
Implementation of Machine Learning Algorithms on Multi-Robot Coordination
Electronics 2022, 11(11), 1786; https://doi.org/10.3390/electronics11111786 - 04 Jun 2022
Viewed by 570
Abstract
Occasionally, professional rescue teams encounter issues while rescuing people during earthquake collapses. One such issue is the localization of wounded people from the earthquake. Machines used by rescue teams may cause crucial issues due to misleading localization. Usually, robot technology is utilized to [...] Read more.
Occasionally, professional rescue teams encounter issues while rescuing people during earthquake collapses. One such issue is the localization of wounded people from the earthquake. Machines used by rescue teams may cause crucial issues due to misleading localization. Usually, robot technology is utilized to address this problem. Many research papers addressing rescue operations have been published in the last two decades. In the literature, there are few studies on multi-robot coordination. The systems designed with a single robot should also overcome time constraints. A sophisticated algorithm should be developed for multi-robot coordination to solve that problem. Then, a fast rescuing operation could be performed. The distinctive property of this study is that it proposes a multi-robot system using a novel heuristic bat-inspired algorithm for use in search and rescue operations. Bat-inspired techniques gained importance in soft-computing experiments. However, there are only single-robot systems for robot navigation. Another original aspect of this paper is that this heuristic algorithm is employed to coordinate the robots. The study is devised to encourage extended work related to earthquake collapse rescue operations. Full article
(This article belongs to the Section Computer Science & Engineering)
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Article
Deep Learning-Based Context-Aware Video Content Analysis on IoT Devices
Electronics 2022, 11(11), 1785; https://doi.org/10.3390/electronics11111785 - 04 Jun 2022
Viewed by 351
Abstract
Integrating machine learning with the Internet of Things (IoT) enables many useful applications. For IoT applications that incorporate video content analysis (VCA), deep learning models are usually used due to their capacity to encode the high-dimensional spatial and temporal representations of videos. However, [...] Read more.
Integrating machine learning with the Internet of Things (IoT) enables many useful applications. For IoT applications that incorporate video content analysis (VCA), deep learning models are usually used due to their capacity to encode the high-dimensional spatial and temporal representations of videos. However, limited energy and computation resources present a major challenge. Video captioning is one type of VCA that describes a video with a sentence or a set of sentences. This work proposes an IoT-based deep learning-based framework for video captioning that can (1) Mine large open-domain video-to-text datasets to extract video-caption pairs that belong to a particular domain. (2) Preprocess the selected video-caption pairs including reducing the complexity of the captions’ language model to improve performance. (3) Propose two deep learning models: A transformer-based model and an LSTM-based model. Hyperparameter tuning is performed to select the best hyperparameters. Models are evaluated in terms of accuracy and inference time on different platforms. The presented framework generates captions in standard sentence templates to facilitate extracting information in later stages of the analysis. The two developed deep learning models offer a trade-off between accuracy and speed. While the transformer-based model yields a high accuracy of 97%, the LSTM-based model achieves near real-time inference. Full article
(This article belongs to the Special Issue Image and Video Analysis and Understanding)
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Article
Using Breast Tissue Information and Subject-Specific Finite-Element Models to Optimize Breast Compression Parameters for Digital Mammography
Electronics 2022, 11(11), 1784; https://doi.org/10.3390/electronics11111784 - 04 Jun 2022
Viewed by 369
Abstract
Digital mammography has become a first-line diagnostic tool for clinical breast cancer screening due to its high sensitivity and specificity. Mammographic compression force is closely associated with image quality and patient comfort. Therefore, optimizing breast compression parameters is essential. Subjects were recruited for [...] Read more.
Digital mammography has become a first-line diagnostic tool for clinical breast cancer screening due to its high sensitivity and specificity. Mammographic compression force is closely associated with image quality and patient comfort. Therefore, optimizing breast compression parameters is essential. Subjects were recruited for digital mammography and breast magnetic resonance imaging (MRI) within a month. Breast MRI images were used to calculate breast volume and volumetric breast density (VBD) and construct finite element models. Finite element analysis was performed to simulate breast compression. Simulated compressed breast thickness (CBT) was compared with clinical CBT and the relationships between compression force, CBT, breast volume, and VBD were established. Simulated CBT had a good linear correlation with the clinical CBT (R2 = 0.9433) at the clinical compression force. At 10, 12, 14, and 16 daN, the mean simulated CBT of the breast models was 5.67, 5.13, 4.66, and 4.26 cm, respectively. Simulated CBT was positively correlated with breast volume (r > 0.868) and negatively correlated with VBD (r < –0.338). The results of this study provides a subject-specific and evidence-based suggestion of mammographic compression force for radiographers considering image quality and patient comfort. Full article
(This article belongs to the Special Issue Recent Advances in Biomedical Image Processing and Analysis)
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Article
SFQ: Constructing and Querying a Succinct Representation of FASTQ Files
Electronics 2022, 11(11), 1783; https://doi.org/10.3390/electronics11111783 - 04 Jun 2022
Viewed by 346
Abstract
A large and ever increasing quantity of high throughput sequencing (HTS) data is stored in FASTQ files. Various methods for data compression are used to mitigate the storage and transmission costs, from the still prevalent general purpose Gzip to state-of-the-art specialized methods. However, [...] Read more.
A large and ever increasing quantity of high throughput sequencing (HTS) data is stored in FASTQ files. Various methods for data compression are used to mitigate the storage and transmission costs, from the still prevalent general purpose Gzip to state-of-the-art specialized methods. However, all of the existing methods for FASTQ file compression require the decompression stage before the HTS data can be used. This is particularly costly with the random access to specific records in FASTQ files. We propose the sFASTQ format, a succinct representation of FASTQ files that can be used without decompression (i.e., the records can be retrieved and listed online), and that supports random access to individual records. The sFASTQ format can be searched on the disk, which eliminates the need for any additional memory resources. The searchable sFASTQ archive is of comparable size to the corresponding Gzip file. sFASTQ format outputs (interleaved) FASTQ records to the STDOUT stream. We provide SFQ, a software for the construction and usage of the sFASTQ format that supports variable length reads, pairing of records, and both lossless and lossy compression of quality scores. Full article
(This article belongs to the Section Computer Science & Engineering)
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Article
Cost-Aware Bandits for Efficient Channel Selection in Hybrid Band Networks
Electronics 2022, 11(11), 1782; https://doi.org/10.3390/electronics11111782 - 03 Jun 2022
Viewed by 320
Abstract
Recently, hybrid band communications have received much attention to fulfil the exponentially growing user demands in next-generation communication networks. Still, determining the best band to communicate over is a challenging issue, especially in the dynamic channel conditions in multi-band wireless systems. In this [...] Read more.
Recently, hybrid band communications have received much attention to fulfil the exponentially growing user demands in next-generation communication networks. Still, determining the best band to communicate over is a challenging issue, especially in the dynamic channel conditions in multi-band wireless systems. In this paper, we manipulate a practical online-learning-based solution for the best band/channel selection in hybrid radio frequency and visible light communication (RF/VLC) wireless systems. The best band selection difficulty is formulated as a multi-armed bandit (MAB) with cost subsidy, in which the learner (transmitter) endeavors not only to increase his total reward (throughput) but also reduce his cost (energy consumption). Consequently, we propose two hybrid band selection (HBS) algorithms, named cost subsidy upper confidence bound (CSUCB-HBS) and cost subsidy Thompson sampling (CSTS-HBS), to efficiently handle this problem and obtain the best band with high throughput and low energy consumption. Extensive simulations confirm that CSTS-/CSUCB-HBS outperform the naive TS/UCB and heuristic HBS approaches regarding energy consumption, energy efficiency, throughput, and convergence speed. Full article
(This article belongs to the Special Issue Deep Learning for Next-Generation Wireless Networks)
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Article
Design of Capacitor-Less High Reliability LDO Regulator with LVTSCR Based ESD Protection Circuit Using Current Driving Buffer Structure
Electronics 2022, 11(11), 1781; https://doi.org/10.3390/electronics11111781 - 03 Jun 2022
Viewed by 325
Abstract
The peak voltage depending on the load current can be affected by the external capacitors installed in the output stage of the LDO regulator. However, the capacitor-less LDO regulator proposed in this study was applied a current driving buffer structure between the output [...] Read more.
The peak voltage depending on the load current can be affected by the external capacitors installed in the output stage of the LDO regulator. However, the capacitor-less LDO regulator proposed in this study was applied a current driving buffer structure between the output stage of the error amplifier and the path transistor. Therefore, the proposed LDO regulator maintained a stable output voltage regardless of the load current by controlling an effective overshoot/undershoot voltage. In addition, the proposed LDO regulator has a built-in LVTSCR based on the ESD protection circuit. As most IC circuits are malfunctioned and destroyed by the ESD phenomenon, the reliability was verified through the built-in ESD protection circuit of the proposed LDO regulator. The proposed LDO regulator with the current driving buffer structure can effectively control the peak voltage. As a result of the measurement, the undershoot voltage of 22 mV and the overshoot voltage of 19 mV were maintained when the load current of 250 mA was provided under the conditions of 3.3 V to 4.5 V and the output power voltage of 3 V. The proposed ESD protection circuit is also guaranteed to function at temperatures as high as 500 K. Full article
(This article belongs to the Section Semiconductor Devices)
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Article
Deepsign: Sign Language Detection and Recognition Using Deep Learning
Electronics 2022, 11(11), 1780; https://doi.org/10.3390/electronics11111780 - 03 Jun 2022
Viewed by 396
Abstract
The predominant means of communication is speech; however, there are persons whose speaking or hearing abilities are impaired. Communication presents a significant barrier for persons with such disabilities. The use of deep learning methods can help to reduce communication barriers. This paper proposes [...] Read more.
The predominant means of communication is speech; however, there are persons whose speaking or hearing abilities are impaired. Communication presents a significant barrier for persons with such disabilities. The use of deep learning methods can help to reduce communication barriers. This paper proposes a deep learning-based model that detects and recognizes the words from a person’s gestures. Deep learning models, namely, LSTM and GRU (feedback-based learning models), are used to recognize signs from isolated Indian Sign Language (ISL) video frames. The four different sequential combinations of LSTM and GRU (as there are two layers of LSTM and two layers of GRU) were used with our own dataset, IISL2020. The proposed model, consisting of a single layer of LSTM followed by GRU, achieves around 97% accuracy over 11 different signs. This method may help persons who are unaware of sign language to communicate with persons whose speech or hearing is impaired. Full article
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Article
Target Assignment Algorithm for Joint Air Defense Operation Based on Spatial Crowdsourcing Mode
Electronics 2022, 11(11), 1779; https://doi.org/10.3390/electronics11111779 - 03 Jun 2022
Viewed by 359
Abstract
Spatial crowdsourcing is a mode that uses distributed artificial computing power to solve specific function sets through Internet outsourcing. It has broad application value in the networked command and control of current joint air defense operations. In this paper, we introduce the spatial [...] Read more.
Spatial crowdsourcing is a mode that uses distributed artificial computing power to solve specific function sets through Internet outsourcing. It has broad application value in the networked command and control of current joint air defense operations. In this paper, we introduce the spatial crowdsourcing theory into the field of target allocation for joint air defense operations and establish a weapon-target assignment model based on spatial crowdsourcing mode, which is more appropriate to the real situation and highlights the system cooperation capability of joint air defense operations. To solve the model, we propose a heuristic variable weight nonlinear learning factor particle swarm optimization (VWNF-PSO). This algorithm can significantly improve the efficiency and adaptability to weapon-target assignment problems under large-scale extreme conditions. Finally, we establish two kinds of joint air defense operation scenarios to verify the proposed model, then compare the proposed algorithm with variable weight PSO (VWPSO) and adaptive learning factor PSO (AFPSO), to validate the effectiveness and efficiency of the VWNF-PSO algorithm proposed in this paper. Full article
(This article belongs to the Section Systems & Control Engineering)
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Article
Multi-Modal Alignment of Visual Question Answering Based on Multi-Hop Attention Mechanism
Electronics 2022, 11(11), 1778; https://doi.org/10.3390/electronics11111778 - 03 Jun 2022
Viewed by 342
Abstract
The alignment of information between the image and the question is of great significance in the visual question answering (VQA) task. Self-attention is commonly used to generate attention weights between image and question. These attention weights can align two modalities. Through the attention [...] Read more.
The alignment of information between the image and the question is of great significance in the visual question answering (VQA) task. Self-attention is commonly used to generate attention weights between image and question. These attention weights can align two modalities. Through the attention weight, the model can select the relevant area of the image to align with the question. However, when using the self-attention mechanism, the attention weight between two objects is only determined by the representation of these two objects. It ignores the influence of other objects around these two objects. This contribution proposes a novel multi-hop attention alignment method that enriches surrounding information when using self-attention to align two modalities. Simultaneously, in order to utilize position information in alignment, we also propose a position embedding mechanism. The position embedding mechanism extracts the position information of each object and implements the position embedding mechanism to align the question word with the correct position in the image. According to the experiment on the VQA2.0 dataset, our model achieves validation accuracy of 65.77%, outperforming several state-of-the-art methods. The experimental result shows that our proposed methods have better performance and effectiveness. Full article
(This article belongs to the Special Issue Image and Video Analysis and Understanding)
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Article
Research on the SDIF Failure Principle for RF Stealth Radar Signal Design
Electronics 2022, 11(11), 1777; https://doi.org/10.3390/electronics11111777 - 03 Jun 2022
Viewed by 340
Abstract
Radio frequency (RF) stealth is one of the essential research hotspots in the radar field. The anti-sorting signal is an important direction of the RF stealth signal. Theoretically speaking, the anti-sorting signal design is based on the failure principle of the radar signal [...] Read more.
Radio frequency (RF) stealth is one of the essential research hotspots in the radar field. The anti-sorting signal is an important direction of the RF stealth signal. Theoretically speaking, the anti-sorting signal design is based on the failure principle of the radar signal sorting algorithm, and the SDIF algorithm is a core sorting algorithm widely used in engineering. Thus, in this paper, the SDIF algorithm is first analyzed in detail. It is pointed out that the threshold function of the SDIF algorithm will fail when the signal pulse repetition interval (PRI) value obeys the interval distribution whose length is 20 times larger than the minimum interval of PRI. Secondly, the correctness of the failure principle of SDIF threshold separation is proved by the formula. Finally, the correctness is further verified by the signal design case. The principle of SDIF sorting threshold failure provides theoretical support for anti-sorting RF stealth signal design. It also complements the shortcoming of the casual design for the anti-sorting signal. Furthermore, the principle of SDIF sorting threshold failure helps improve anti-sorting signal design efficiency. Compared with the Dwell & Switch (D&S) signal and jitter signal, the anti-sorting ability of the signal designed by using the sorting failure principle is notably enhanced through simulation and experimentation. Full article
(This article belongs to the Section Microwave and Wireless Communications)
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Article
A Novel Approach for the Implementation of Fast Frequency Control in Low-Inertia Power Systems Based on Local Measurements and Provision Costs
Electronics 2022, 11(11), 1776; https://doi.org/10.3390/electronics11111776 - 02 Jun 2022
Viewed by 351
Abstract
Transitioning towards carbon-free energy has brought severe difficulties related to reduced inertia in electric power systems. Regarding frequency stability, low-inertia systems are more sensitive to disturbance, and traditional frequency control is becoming insufficient to maintain frequency within acceptable limits. Consequently, there is a [...] Read more.
Transitioning towards carbon-free energy has brought severe difficulties related to reduced inertia in electric power systems. Regarding frequency stability, low-inertia systems are more sensitive to disturbance, and traditional frequency control is becoming insufficient to maintain frequency within acceptable limits. Consequently, there is a necessity for faster frequency support that can be activated before the primary frequency control and that can decelerate further frequency decay. This paper proposes a local control strategy for a multi-stage fast frequency response (FFR) provided as an ancillary service that considers the location of the disturbance and the distribution of system inertia. The novelty of the presented control strategy is the ranking of FFR resources by price, which takes the economic component into consideration. The proposed control is simple, based only on RoCoF measurements that trigger the activation of FFR resources. Its advantage over other methods is the ability to adapt the FFR resource response to the disturbance without complex calculations and the ability to ensure a bigger response closer to the disturbance, as well as in low-inertia parts of the system. In that way, there is a bigger activation of resources in the parts of the system that are more endangered by disturbances, which, as a result, minimizes the propagation of the disturbance’s impact on system stability. The applicability of the presented method is demonstrated in a simple 3-area power system and IEEE 68-bus system implemented in MATLAB/Simulink. The results show that the proposed control enables the largest response closer to the disturbance, thus mitigating the propagation of the disturbance. Furthermore, the results confirm that the proposed control enables lower provision costs and more support in low-inertia areas that are more vulnerable to disturbances. Full article
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Article
CXAI: Explaining Convolutional Neural Networks for Medical Imaging Diagnostic
Electronics 2022, 11(11), 1775; https://doi.org/10.3390/electronics11111775 - 02 Jun 2022
Viewed by 424
Abstract
Deep learning models have been increasingly applied to medical images for tasks such as lesion detection, segmentation, and diagnosis. However, the field suffers from the lack of concrete definitions for usable explanations in different settings. To identify specific aspects of explainability that may [...] Read more.
Deep learning models have been increasingly applied to medical images for tasks such as lesion detection, segmentation, and diagnosis. However, the field suffers from the lack of concrete definitions for usable explanations in different settings. To identify specific aspects of explainability that may catalyse building trust in deep learning models, we will use some techniques to demonstrate many aspects of explaining convolutional neural networks in a medical imaging context. One important factor influencing clinician’s trust is how well a model can justify its predictions or outcomes. Clinicians need understandable explanations about why a machine-learned prediction was made so they can assess whether it is accurate and clinically useful. The provision of appropriate explanations has been generally understood to be critical for establishing trust in deep learning models. However, there lacks a clear understanding on what constitutes an explanation that is both understandable and useful across different domains such as medical image analysis, which hampers efforts towards developing explanatory tool sets specifically tailored towards these tasks. In this paper, we investigated two major directions for explaining convolutional neural networks: feature-based post hoc explanatory methods that try to explain already trained and fixed target models and preliminary analysis and choice of the model architecture with an accuracy of 98% ± 0.156% from 36 CNN architectures with different configurations. Full article
(This article belongs to the Special Issue Electronic Devices and Systems for Biomedical Applications)
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Article
An Enhanced DV-Hop Localization Scheme Based on Weighted Iteration and Optimal Beacon Set
Electronics 2022, 11(11), 1774; https://doi.org/10.3390/electronics11111774 - 02 Jun 2022
Viewed by 314
Abstract
Node localization technology has become a research hotspot for wireless sensor networks (WSN) in recent years. The standard distance vector hop (DV-Hop) is a remarkable range-free positioning algorithm, but the low positioning accuracy limits its application in certain scenarios. To improve the positioning [...] Read more.
Node localization technology has become a research hotspot for wireless sensor networks (WSN) in recent years. The standard distance vector hop (DV-Hop) is a remarkable range-free positioning algorithm, but the low positioning accuracy limits its application in certain scenarios. To improve the positioning performance of the standard DV-Hop, an enhanced DV-Hop based on weighted iteration and optimal beacon set is presented in this paper. Firstly, different weights are assigned to beacons based on the per-hop error, and the weighted minimum mean square error (MMSE) is performed iteratively to find the optimal average hop size (AHS) of beacon nodes. After that, the approach of estimating the distance between unknown nodes and beacons is redefined. Finally, considering the influence of beacon nodes with different distances to the unknown node, the nearest beacon nodes are given priority to compute the node position. The optimal coordinates of the unknown nodes are determined by the best beacon set derived from a grouping strategy, rather than all beacons directly participating in localization. Simulation results demonstrate that the average localization error of our proposed DV-Hop reaches about 3.96 m, which is significantly lower than the 9.05 m, 7.25 m, and 5.62 m of the standard DV-Hop, PSO DV-Hop, and Selective 3-Anchor DV-Hop. Full article
(This article belongs to the Special Issue Advanced Applications of Sensor Network and Wireless Communication)
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Article
A Formal Modeling and Verification Scheme with an RNN-Based Attacker for CAN Communication System Authenticity
Electronics 2022, 11(11), 1773; https://doi.org/10.3390/electronics11111773 - 02 Jun 2022
Viewed by 304
Abstract
To enhance the attack resistance of the Controller Area Network (CAN) system and optimize the communication software design, a comprehensive model that combines a variable attacker with the CAN bus (VACB) is proposed to evaluate the bus communication risk. The VACB model consists [...] Read more.
To enhance the attack resistance of the Controller Area Network (CAN) system and optimize the communication software design, a comprehensive model that combines a variable attacker with the CAN bus (VACB) is proposed to evaluate the bus communication risk. The VACB model consists of a variable attacker and the CAN bus model. A variable attacker is a visualized generation of the attack traffic based on a recurrent neural network (RNN), which is used to evaluate the anti-attack performance of the CAN bus; the CAN bus model combines the data link layer and the application layer to analyze the anomalies in CAN bus data transmission after the attack message. The simulation results indicate that the transmission accuracy and successful response rate decreased by 1.8% and 4.3% under the constructed variable attacker. The CAN bus’s authenticity was promoted after the developers adopted this model to analyze and optimize the software design. The transmission accuracy and the successful response rate were improved by 2.5% and 5.1%, respectively. Moreover, the model can quantify the risk of potential attacks on the CAN bus, prompting developers to avoid it in early development to reduce the loss caused by system crashes. The comprehensive model can provide theoretical guidance for the timing design of embedded software. Full article
(This article belongs to the Section Computer Science & Engineering)
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