Electronics

Journal Information
EISSN: 20799292
Published by: MDPI
Total articles ≅ 11,741

Latest articles in this journal

Published: 7 December 2022
by MDPI
Journal: Electronics
Abstract:
Earth-moving vehicles (EMVs) are vital in numerous industries, including construction, forestry, mining, cleaning, and agriculture. The changing nature of the off-road environment in which they operate makes situational awareness for readiness and, consequently, mental stress crucial for drivers and requires a high level of controllability. Therefore, the monitoring of drivers’ acute stress patterns may be used as an input in identifying various levels of attentiveness. This research presents an experimental evaluation of a physiological-based system that can be useful to evaluate the readiness of a driver in different conditions. For the experimental validation, physiological signals such as electrocardiogram (ECG), galvanic skin response (GSR) and speech data were collected from nine participants throughout driving experiments of increasing complexity on a specific simulator. The experimental results show that the identified parameters derived from the acquired physiological signals can help us understand the driver status when performing different tasks, the engagement of which is related to different road environments. This multi-parameter approach can provide more reliable information compared to single parameter approaches (e.g., eye monitoring with a camera) and identify driver status variations, from relaxed to stressed or drowsy. The use of these signals allows for the development of a smart driving cockpit, which could communicate to the vehicle the driver’s status, to set up an innovative protection system aiming to increase road safety.
Published: 7 December 2022
by MDPI
Journal: Electronics
Abstract:
The concern over safety features in autonomous vehicles is increasing due to the rapid development and increasing use of autonomous driving technology. The safety evaluations performed for an autonomous driving system cannot depend only on existing safety verification methods, due to the lack of scenario reproducibility and the dynamic characteristics of the vehicle. Vehicle-In-the-Loop Simulation (VILS) utilizes both real vehicles and virtual simulations for the driving environment to overcome these drawbacks and is a suitable candidate for ensuring reproducibility. However, there may be differences between the behavior of the vehicle in the VILS and vehicle tests due to the implementation level of the virtual environment. This study proposes a novel VILS system that displays consistency with the vehicle tests. The proposed VILS system comprises virtual road generation, synchronization, virtual traffic manager generation, and perception sensor modeling, and implements a virtual driving environment similar to the vehicle test environment. Additionally, the effectiveness of the proposed VILS system and its consistency with the vehicle test is demonstrated using various verification methods. The proposed VILS system can be applied to various speeds, road types, and surrounding environments.
Published: 7 December 2022
by MDPI
Journal: Electronics
Abstract:
In this paper, a tone mapping algorithm is presented to map real-world luminance into displayed luminance. Our purpose is to reveal the local contrast of real-world scenes on a conventional monitor. Around this point, we propose a three-stage algorithm to visualize high dynamic range images. All pixels of high dynamic range images are classified into three groups. For the first stage, we introduce piecewise linear mapping as the global tone mapping operator to map the luminance of the first group, which provides overall impressions of luminance. For the second stage, the luminance of the second group is determined by the weighted average of its neighborhood pixels, which are derived from the first group’s pixels. For the third stage, the luminance of the third group is determined by the weighted average of its neighborhood pixels, which are derived from the second group’s pixels. Experimental results on several real-world images and the TMQI database show that our algorithm can improve the visibility of real-world scenes with about 12% and 9% higher scores of mean opinion score and tone-mapped image quality index than the closest competitive tone mapping methods. Compared to the existing tone mapping methods, our algorithm produces visually compelling results without halo artifacts and loss of detail.
Published: 7 December 2022
by MDPI
Journal: Electronics
Abstract:
In this paper, a periodic signal suppression method in position domain based on repetitive control (RC) is proposed to realize high-precision speed control for the gimbal servo system of the single gimbal control moment gyro (SGCMG). To reduce the volume and weight while outputting large torque, the gimbal servo system usually needs to install the harmonic drive. However, the nonlinear transmission characteristics of the harmonic drive are also introduced into the gimbal servo system and make the speed fluctuate. Considering the speed fluctuation signal shown as a fixed frequency in the position domain, a position domain RC method combined with acceleration feedback is designed to realize the speed fluctuation minimization. The position domain RC improves the dynamic characteristics, while the acceleration feedback increases the damping of the system. To analyze the stability, the position domain RC is converted into the time domain through the domain transformation method, and a phase compensator is designed to improve the stability and increase the bandwidth of the position domain RC by compensating for the phase lag of the middle and low frequency, respectively. The feasibility and effectiveness of the proposed method are verified by the simulation and experimental results. These results illustrate that after applying the proposed approach, the output speed fluctuation and harmonic components decrease more than 20% and 24.1%, respectively.
Published: 7 December 2022
by MDPI
Journal: Electronics
Abstract:
Energy efficiency presents a significant challenge to the reliability of Internet of Things (IoT) services. Wireless Sensor Networks (WSNs) present as an elementary technology of IoT, which has limited resources. Appropriate energy management techniques can perform increasing energy efficiency under variable workload conditions. Therefore, this paper aims to experimentally implement a hybrid energy management solution, combining Dynamic Voltage and Frequency Scaling (DVFS) and Duty-Cycling. The DVFS technique is implemented as an effective power management scheme to optimize the operating conditions during data processing. Moreover, the duty-cycling method is applied to reduce the energy consumption of the transceiver. Hardware optimization is performed by selecting the low-power microcontroller, MSP430, using experimental estimation and characterization. Another contribution is evaluating the energy-saving design by defining the normalized power as a metric to measure the consumed power of the proposed model per throughput. Extensive simulations and real-world implementations indicate that normalized power can be significantly reduced while sustaining performance levels in high-data IoT use cases.
Published: 7 December 2022
by MDPI
Journal: Electronics
Abstract:
In order to solve the problem of service interruption caused by user movement and the limited service range of edge nodes, a service migration algorithm based on the multi-attribute Markov decision process was proposed for mobile edge computing. By performing service migration, the distance between the user and the service is always kept to a small range. In addition, in order to prevent the service quality from being affected by the frequent migration of users, the return function of the model was defined by comprehensively considering the service quality, the resource demand of the service, the migration cost, and the movement income of users in each node, and on the premise of taking into account the migration cost and resource conditions, which did not only make up the deficiency of the service migration scheme based solely on distance. The number of migrations is also reduced, and a single migration target server is no longer used. The candidate server set was constructed based on the user’s motion trajectory, and the Q-Learning algorithm was used to solve the problem. Simulation results show that the proposed algorithm can reduce the number of migrations and ensure the balance between the number of migrations and the cost of migrations.
Published: 7 December 2022
by MDPI
Journal: Electronics
Abstract:
The semantic segmentation model usually provides pixel-wise category prediction for images. However, a massive amount of pixel-wise annotation images is required for model training, which is time-consuming and labor-intensive. An image-level categorical annotation is recently popular and attempted to overcome the above issue in this work. This is also termed weakly supervised semantic segmentation, and the general framework aims to generate pseudo masks with class activation mapping. This can be learned through classification tasks that focus on explicit features. Some major issues in these approaches are as follows: (1) Excessive attention on the specific area; (2) for some objects, the detected range is beyond the boundary, and (3) the smooth areas or minor color gradients along the object are difficult to categorize. All these problems are comprehensively addressed in this work, mainly to overcome the importance of overly focusing on significant features. The suppression expansion module is used to diminish the centralized features and to expand the attention view. Moreover, to tackle the misclassification problem, the saliency-guided module is adopted to assist in learning regional information. It limits the object area effectively while simultaneously resolving the challenge of internal color smoothing. Experimental results show that the pseudo masks generated by the proposed network can achieve 76.0%, 73.3%, and 73.5% in mIoU with the PASCAL VOC 2012 train, validation, and test set, respectively, and outperform the state-of-the-art methods.
Published: 7 December 2022
by MDPI
Journal: Electronics
Abstract:
Fuzz testing is the process of testing programs by continually producing unique inputs in order to detect and identify security flaws. It is often used in vulnerability mining. The most prevalent fuzzing approach is grey-box fuzzing, which combines lightweight code instrumentation with data-feedback-driven generation of fresh program input seeds. AFL (American Fuzzy Lop) is an outstanding grey-box fuzzing tool that is well known for its quick fork server execution, dependable genetic algorithm, and numerous mutation techniques. AFLGO proposes and executes power scheduling based on a simulated annealing process for a more appropriate energy allocation to seeds, however it is neither reliable nor successful. To tackle this issue, we offer an energy-dynamic scheduling strategy based on the algorithm of the fruit fly. Adjusting the energy of the seeds dynamically controls the production of test cases. The findings demonstrate that the approach suggested in this research can test the target region more rapidly and thoroughly and has a high application value for patch testing and vulnerability replication.
Published: 7 December 2022
by MDPI
Journal: Electronics
Abstract:
Agriculture provides a basis for social and economic development. It is therefore crucial for society and the economy to stabilize agricultural prices. Recent large increases and decreases in Chinese agricultural commodity prices have increased production risks, heightened fluctuations in the domestic agricultural supply, and impacted the stability of the global agricultural market. Meanwhile, big data technology has advanced quickly and now serves as a foundation for the investigation of time series bubbles. Identifying agricultural price bubbles is important for determining agricultural production decisions and policies that control agricultural prices. Using weekly agricultural price data from 2009 to 2021, this paper identifies agricultural price bubbles, pinpoints their time points, and examines their causes. According to our research, prices for corn, hog, green onions, pork, and ginger all have bubbles, but garlic do not. The quantity, length, time distribution, and type of bubbles differ significantly among corn, ginger, green onion, hog, and pork. The main causes for ginger and green onion price bubbles are speculation and natural disasters. Price bubbles for hog and pork are influenced by animal disease and rising costs. Conflicts between supply and demand and changes in price policy cause corn price bubbles to form. This paper advises that the government adopt various regulatory actions to stabilize agricultural prices depending on the characteristics and causes of the various types of agricultural price bubbles, it should also improve the early warning system and response mechanism for agricultural price bubbles and focus on how policies and market processes work together.
Published: 6 December 2022
by MDPI
Journal: Electronics
Abstract:
Fiber optic networks (FONs) are considered the backbone of telecom companies worldwide. However, the network elements of FONs are scattered over a wide area and managed through a centralized controller based on intelligent devices and the internet of things (IoT), with actuators used to perform specific tasks at remote locations. During the COVID-19 pandemic, many telecom companies advised their employees to manage the network using the public internet (e.g., working from home while connected to an IoT network). Theses IoT devices mostly have weak security algorithms that are easily taken-over by hackers, and therefore can generate Distributed Denial of Service (DDoS) attacks in FONs. A DDoS attack is one of the most severe cyberattack types, and can negatively affect the stability and quality of managing networks. Nowadays, software-defined networks (SDN) constitute a new approach that simplifies how the network can be managed through a centralized controller. Moreover, machine learning algorithms allow the detection of incoming malicious traffic with high accuracy. Therefore, combining SDN and ML approaches can lead to detecting and stopping DDoS attacks quickly and efficiently, especially compared to traditional methods. In this paper, we evaluated six ML models: Logistic Regression, K-Nearest Neighbor, Support Vector Machine, Naive Bayes, Decision Tree, and Random Forest. The accuracy reached 100% while detecting DDoS attacks in FON with two approaches: (1) using SVM with three features (SOS, SSIP, and RPF) and (2) using Random Forest with five features (SOS, SSIP, RPF, SDFP, and SDFB). The training time for the first approach was 14.3 s, whereas the second approach only requires 0.18 s; hence, the second approach was utilized for deployment.
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