Electronics

Journal Information
ISSN / EISSN : 2079-9292 / 2079-9292
Published by: MDPI (10.3390)
Total articles ≅ 7,044
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Latest articles in this journal

Published: 26 October 2021
by MDPI
Abstract:
Heterogeneous networks are powerful tools for describing different types of entities and relationships and are more relevant models of complex networks. The study of heterogeneous network defense is of great practical significance for protecting useful networks such as military combat networks and critical infrastructure networks. However, a large amount of current research on complex network defense focuses on homogeneous networks under complete information conditions, which often ignore the real conditions such as incomplete information and heterogeneous networks. In this paper, we propose firstly a new adversarial hiding deception strategy for heterogeneous network defense under incomplete information conditions. Secondly, we propose an adversarial hiding deception network optimization method based on a genetic algorithm and design node importance index and a fitness function, which take into account the graph structure information and information about the type of nodes. Finally, we conduct comparison experiments for different defense strategies, and the results show that the proposed strategy and network optimization method are effective at hiding the critical nodes and inducing the attacker to attack the non-important nodes. The generated adversarial hiding deception network has a similar graph structure to the real network.
Published: 26 October 2021
by MDPI
Abstract:
Due to their maneuverability, unmanned aerial vehicles (UAVs) have grown into a promising enabler of the Internet of Things (IoTs). In addition to the benefits of the bandwidth and communication quality of millimeter-wave (mmWave) systems, a UAV-aided mmWave multiple-input and multiple-output (MIMO) communication system is investigated in this paper for the data collection of IoT systems, in which single-antenna IoT devices are divided into several clusters, and the UAV aided mmWave base station (UAV-BS) collects data from each cluster using the time division scheme. The joint optimization of the beam selection, UAV trajectory, user clustering, power allocation and transmission duration is studied in this paper to improve the data collection efficiency. The solution of the problem is then given in three steps. Firstly, the incremental K-means clustering and ant colony optimization algorithm are utilized to handle the UAV trajectory planning and user clustering problem. Secondly, an incremental beam selection scheme is employed to ensure that all the devices in each cluster can communicate with the UAV. Thirdly, an iterative algorithm is proposed by alternately optimizing the power allocation and transmission duration of the IoT devices. Finally, the simulation results demonstrate the effectiveness of the proposed solution for the UAV-aided mmWave communication system.
Published: 26 October 2021
by MDPI
Abstract:
Distributed tracing allows tracking user requests that span across multiple services and machines in a distributed application. However, typical cloud applications rely on abstraction layers that can hide the root cause of latency happening between processes or in the kernel. Because of its focus on high-level events, existing methodologies in applying distributed tracing can be limited when trying to detect complex contentions and relate them back to the originating requests. Cross-level analyses that include kernel-level events are necessary to debug problems as prevalent as mutex or disk contention, however cross-level analysis and associating events in the kernel and distributed tracing data is complex and can add a lot of overhead. This paper describes a new solution for combining distributed tracing with low-level software tracing in order to find the latency root cause better. We explain how we achieve a hybrid trace collection to capture and synchronize both kernel and distributed request events. Then, we present our design and implementation for a critical path analysis. We show that our analysis describes precisely how each request spends its time and what stands in its critical path while limiting overhead.
Published: 26 October 2021
by MDPI
Abstract:
Artificial intelligence allows computer systems to make decisions similar to those of humans. However, the expert knowledge that artificial intelligence systems have is rarely used to teach non-expert humans in a specific knowledge domain. In this paper, we want to explore this possibility by proposing a tool which presents and explains recommendations for playing board games generated by a Monte Carlo Tree Search algorithm combined with Neural Networks. The aim of the aforementioned tool is to showcase the information in an easily interpretable way and to effectively transfer knowledge: in this case, which movements should be avoided, and which action is recommended. Our system displays the state of the game in the form of a tree, showing all the movements available from the current state and a set of their successors. To convince and try to teach people, the tool offers a series of queries and all information available about every possible movement. In addition, it produces a brief textual explanation for those which are recommended or not advisable. To evaluate the tool, we performed a series of user tests, observing and assessing how participants learn while using this system.
Published: 26 October 2021
by MDPI
Abstract:
The utilization of wind energy sources with energy storage systems has been increased in the power sector to satisfy the consumer’s energy demand with minimum price. This paper presents the impact of a wind farm (WF) and pumped hydroelectric storage (PHS) system in the competitive electricity market under a congested transmission system. The PHS system is used to compensate for the deviation of WF generation in the real-time electricity market. To investigate the impact of the proposed method, initially, the market-clearing power problem is solved without consideration of WF and PHS systems, and again it is solved with the WF and PHS systems. The optimal location of the WF and PHS systems is decided by the bus sensitivity factor (BSF) of these systems. The analysis is carried out by using generator sensitivity factor (GSF) with the help of the moth flame optimization (MFO) algorithm and thereby calculating market clearing price (MCP) and market clearing volume (MCV). The MFO algorithm is used here for the first time for solving the congested market-clearing power problem with the integration of WF and PHS systems under deregulated environment. The presented approach shows the improvement of social welfare after the placement of WF and PHS in the congested deregulated system. Modified IEEE 30 bus system is used to solve the market-clearing power problem and results obtained from the MFO algorithm are compared with the firefly algorithm (FA). Three different real-time wind speed data have been considered here to verify the proposed approach with uncertainty and the continuously changing nature of wind flow. It is discovered that social welfare is improved with the quantity addition of wind power, regardless of optimization techniques.
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Published: 26 October 2021
by MDPI
Abstract:
For the battery pack’s limited remaining power, two energy-aware ecological driving problems are discussed. A real-time energy-aware ecological driving control strategy is proposed to optimize energy consumption and meet the ECO driving demand. First, the vehicle longitudinal driving dynamics model and energy consumption model are established. Then, the optimal control problem is constructed with the maximum driving distance and the shortest driving time as the objective functions, respectively. With the multinomial Radau pseudo-spectral method, the optimization results of residual power, vehicle speed, and acceleration are obtained. The results show that in the case of in-vehicle driving the remaining power of the battery pack can be sensed in real-time, and the driving of intelligent electric vehicles can be planned in real-time to realize the most ecological driving with the largest driving distance and shortest driving time. The energy consumptions of vehicles, traveling at the same distance, are compared. The consumption obtained through optimization, is 26% less than the consumption of the vehicle that has not been optimized. The results show that the optimization process has certain advantages. In the future, as one of intelligent vehicles’ autonomous driving control strategies, the results have guiding and practical significance.
Published: 26 October 2021
by MDPI
Abstract:
Ontologies, and especially formal ones, have traditionally been investigated as a means to formalize an application domain so as to carry out automated reasoning on it. The union of the terminological part of an ontology and the corresponding assertional part is known as a Knowledge Graph. On the other hand, database technology has often focused on the optimal organization of data so as to boost efficiency in their storage, management and retrieval. Graph databases are a recent technology specifically focusing on element-driven data browsing rather than on batch processing. While the complementarity and connections between these technologies are patent and intuitive, little exists to bring them to full integration and cooperation. This paper aims at bridging this gap, by proposing an intermediate format that can be easily mapped onto the formal ontology on one hand, so as to allow complex reasoning, and onto the graph database on the other, so as to benefit from efficient data handling.
Published: 26 October 2021
by MDPI
Abstract:
The emerging computational storage drives (CSDs) provide new opportunities by moving data computation closer to the storage. Performing computation within storage drives enables data pre/post-processing without expensive data transfers. Moreover, large amounts of data can be processed in parallel thanks to the nature of the field-programmable gate array (FPGA) included in CSDs. In a CSD, there are several implementation techniques that support parallel processing, each of which provides a different degree of parallelism. However, without sufficient understanding of the parallel processing techniques of CSD, it can lead to overhead due to misuse rather than benefiting from task offloading. Thus, to exploit the best performance of CSDs, it is important to properly adjust the degree of parallelism of each implementation technique. In this paper, we focus on the study of the differences in CSD performance according to various combinations of parallel processing techniques. To investigate the performance differences, we implement and offload the data verification algorithm to the CSD and analyze the performance and resource utilization. The experimental results show that implementing the data verification algorithm with a sufficient understanding of CSD’s parallel processing techniques can improve the performance by up to 20 times. Moreover, even with the same degree of parallelism, the performance can differ by 59% depending on the combination of implementation techniques. These results imply that proper orchestration of different implementation techniques leads to better performance and efficient resource utilization.
Published: 26 October 2021
by MDPI
Abstract:
The fast growth of wireless technology for mobile communication devices requires broad bandwidth, high data rate facilities and compact device size. The solution to the next generation user equipment is high data rate 4G/5G technologies. In this research, wideband antenna design was analyzed and evaluated for 4G, 5G and NB-IoT applications. CST microwave studio was used for simulations and investigations of the performance parameters. The antenna was designed as a folded dipole with a tunable bandwidth and resonates for 5G NR n78, NR-IoT bands B1, B2, and B25, and eleven TDD LTE frequency bands with a bandwidth percentage and minimum scattering loss of 69.02% and −42 dB respectively. Additionally, the designed antenna is small (35 × 48 × 1.62 mm3) and planar in structure and can be easily integrated with radio equipment. The antenna design was also investigated for SAR minimization and gain enhancement using metamaterial integration. For all operating frequency bands, the antenna design results in a considerable gain improvement. The metamaterial was shown to be an excellent absorber of radiation, particularly in high frequency regions. This research also included a SAR examination with and without metamaterial integration. SAR values were found to be significantly reduced throughout all operating bands. The results were validated by fabricating the design prototype on FR-4 substrate for 4G, 5G and IoT bands. The antenna will be possibly used for communication in high data rate applications.
Published: 26 October 2021
by MDPI
Abstract:
This paper proposes a single-stage wireless battery charging circuit with a coupling coefficient prediction method. The proposed circuit consists of only two stages: full bridge inverter with transmitter coil in the first stage and full bridge rectifier with receiver coil in the second stage. This circuit implements the constant current (CC) charging mode at the resonant frequency of two coils and the constant voltage (CV) charging mode at a specific frequency that is dependent on the coupling coefficient of two coils. The operation at a specific frequency guarantees the CV operation regardless of load condition and reduces the switching losses than the operation at the resonant frequency owing to a zero-voltage switching (ZVS) operation. In CC-CV modes, the phase-shift technique is additionally appied to improve the output voltage/current regulation. Unlike other approaches, the proposed single-stage wireless battery charging circuit does not require multiple stages of power conversion, or additional components, a pre-measured coupling coefficient or a complex control algorithm for CC-CV charging operation. The prototype proposed circuit was tested under various coil alignment conditions, and successfully implemented the CC-CV charging operation for a 36 V battery pack. The predicted coupling coefficient had an error of ≤0.62% in the coil alignment condition, and the circuit had errors of ≤0.32%, ≤0.1% in the output current and voltage regulation, respectively.
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