Electronics

Journal Information
ISSN / EISSN : 2079-9292 / 2079-9292
Current Publisher: MDPI (10.3390)
Total articles ≅ 4,092
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Vedrana Jerković Štil, Toni Varga, Tin Benšić, Marinko Barukčić
Published: 28 October 2020
by MDPI
Electronics, Volume 9; doi:10.3390/electronics9111788

Abstract:
Multi-motor systems are strong coupled multiple-input–multiple-output systems. The main objective in multi-motor drive control is to achieve synchronized operation of all motors in the system. In this paper, multi-motor systems are classified in accordance with their control demands. This paper also provides a systematic categorization of multi-motor synchronization techniques. The review of recent research literature indicates that fuzzy algorithms are widely used in multi-motor control. Finally, in this paper, a review of fuzzy logic controllers and their functionalities in multi-motor control is given.
Jie Meng, HouJun Wang, Peng Ye, Yu Zhao, Lianping Guo, Hao Zeng, Yu Tian
Published: 28 October 2020
by MDPI
Electronics, Volume 9; doi:10.3390/electronics9111787

Abstract:
The in-phase/quadrature (I/Q) imbalance encountered in the zero-IF receiver leads to incomplete image frequency suppression, which severely deteriorates image rejection ratio (IRR) of the receiver system and must be improved using additional analog or digital signal processing. The I/Q linear phase imbalance (LPI) is the key of the I/Q imbalance, which consists of the time delay deviation (TDD) and the local oscillator (LO) phase offset. TDD is negligible in most literature, but it degrades system performance largely for wideband communication systems. This paper proposes a method based on the cross-power spectrum between the I/Q signal to address the estimation problem of LPI. Compared with other conventional methods, the proposed approach calculates LPI parameters simultaneously without any additional hardware. The MATLAB simulation is utilized to evaluate the effectiveness of the presented method. Moreover, the experimental platform of detailed design demonstrates the feasibility of the proposed estimation method, and IRR of the system before and after compensation shows that LPI has been accurately estimated and eliminated with the help of an appropriate compensation structure. Both reveal that the proposed method offers an effective solution to the LPI problem.
Ignacio Bravo-Muñoz, Alfredo Gardel-Vicente, José Lázaro-Galilea
Published: 28 October 2020
by MDPI
Electronics, Volume 9; doi:10.3390/electronics9111789

Abstract:
Nowadays, the digital world demands continuous technological evolutions
Diego Paredes-Páliz, Guillermo Royo, Francisco Aznar, Concepción Aldea, Santiago Celma
Published: 27 October 2020
by MDPI
Electronics, Volume 9; doi:10.3390/electronics9111785

Abstract:
Wireless broadband access networks have been positioning themselves as a good solution for manufacturers and users of IoT (internet of things) devices, due mainly to the high data transfer rate required over terminal devices without restriction of information format. In this work, a review of two Radio over Fiber strategies is presented. Both have excellent performance and even offer the possibility to extend wireless area coverage where mobile networks do not reach or the 802.11 network presents issues. Radio Frequency over Fiber (RFoF) and intermediate Frequency over Fiber (IFoF) are two transmission strategies compatible with the required new broadband services and both play a key role in the design of the next generation integrated optical–wireless networks, such as 5G and Satcom networks, including on RAU (Remote Antenna Unit) new functionalities to improve their physical dimensions, employing a microelectronic layout over nanometric technologies.
Ting Kang, Yuxin Ye, Yuncong Jia, Yanmei Kong, Binbin Jiao
Published: 27 October 2020
by MDPI
Electronics, Volume 9; doi:10.3390/electronics9111778

Abstract:
This study introduces an enhanced thermal management strategy for efficient heat dissipation from GaN power amplifiers with high power densities. The advantages of applying an advanced liquid-looped silicon-based micro-pin fin heat sink (MPFHS) as the mounting plate for GaN devices are illustrated using both experimental and 3D finite element model thermal simulation methods, then compared against traditional mounting materials. An IR thermography system was equipped to obtain the temperature distribution of GaN mounted on three different plates. The influence of mass flow rate on a MPFHS was also investigated in the experiments. Simulation results showed that GaN device performance could be improved by increasing the thermal conductivity of mounting plates’ materials. The dissipated power density of the GaN power amplifier increased 17.5 times when the mounting plate was changed from LTCC (Low Temperature Co-fired Ceramics) (k = 2 Wm−1 K−1) to HTCC (High-Temperature Co-fired Ceramics) (k = 180 Wm−1 K−1). Experiment results indicate that the GaN device performance was significantly improved by applying liquid-looped MPFHS, with the maximum dissipated power density reaching 7250 W/cm2. A thermal resistance model for the whole system, replacing traditional plates (PCB (Printed Circuit Board), silicon wafer and LTCC/HTCC) with an MPFHS plate, could significantly reduce θjs (thermal resistance of junction to sink) to its theoretical limitation value.
DaoHua Pan, Hongwei Liu, Dongming Qu, Zhan Zhang
Published: 27 October 2020
by MDPI
Electronics, Volume 9; doi:10.3390/electronics9111780

Abstract:
The livelihood problem, especially the medical wisdom, has played an important role during the process of the building of smart cities. For the medical wisdom, the fall detection has attracted the considerable attention from the global researchers and medical institutions. It is very difficult for the traditional fall detection strategies to realize the intelligent detection with the following three reasons: (i) the data collection cannot reach the real-time level; (ii) the adopted detection methods cannot satisfy the enough stability; and (iii) the computation overhead of collection device is very high, which causes the barely satisfactory detection effect. Therefore, this paper proposes Convolutional Neural Network (CNN)-based fall detection strategy with edge computing consideration, where the global network view ability of Software-Defined Networking (SDN) is used to collect the generated data from smartphone. Meanwhile, on one hand, the edge computing is exploited to put some computation tasks at the edge server by the scheduling technique. On the other hand, CNN is equipped with both edge server and smartphone, and it is leveraged to train the related data and further give the guidance of fall detection. The experimental results show that the novel fall detection strategy has a more accurate rate, transmission delay, and stability than two cutting-edge strategies.
Aurelio López-Fernández, Domingo Rodríguez-Baena, Francisco Gómez-Vela
Published: 27 October 2020
by MDPI
Electronics, Volume 9; doi:10.3390/electronics9111782

Abstract:
Nowadays, Biclustering is one of the most widely used machine learning techniques to discover local patterns in datasets from different areas such as energy consumption, marketing, social networks or bioinformatics, among them. Particularly in bioinformatics, Biclustering techniques have become extremely time-consuming, also being huge the number of results generated, due to the continuous increase in the size of the databases over the last few years. For this reason, validation techniques must be adapted to this new environment in order to help researchers focus their efforts on a specific subset of results in an efficient, fast and reliable way. The aforementioned situation may well be considered as Big Data context. In this sense, multiple machine learning techniques have been implemented by the application of Graphic Processing Units (GPU) technology and CUDA architecture to accelerate the processing of large databases. However, as far as we know, this technology has not yet been applied to any bicluster validation technique. In this work, a multi-GPU version of one of the most used bicluster validation measure, Mean Squared Residue (MSR), is presented. It takes advantage of all the hardware and memory resources offered by GPU devices. Because of to this, gMSR is able to validate a massive number of biclusters in any Biclustering-based study within a Big Data context.
John Rodríguez, Santiago Durán, Daniel Díaz-López, Javier Pastor-Galindo, Félix Mármol
Published: 27 October 2020
by MDPI
Electronics, Volume 9; doi:10.3390/electronics9111779

Abstract:
Prevention of cybercrime is one of the missions of Law Enforcement Agencies (LEA) aiming to protect and guarantee sovereignty in the cyberspace. In this regard, online sex crimes are among the principal ones to prevent, especially those where a child is abused. The paper at hand proposes C3-Sex, a smart chatbot that uses Natural Language Processing (NLP) to interact with suspects in order to profile their interest regarding online child sexual abuse. This solution is based on our Artificial Conversational Entity (ACE) that connects to different online chat services to start a conversation. The ACE is designed using generative and rule-based models in charge of generating the posts and replies that constitute the conversation from the chatbot side. The proposed solution also includes a module to analyze the conversations performed by the chatbot and calculate a set of 25 features that describes the suspect’s behavior. After 50 days of experiments, the chatbot generated a dataset with 7199 profiling vectors with the features associated to each suspect. Afterward, we applied an unsupervised method to describe the results that differentiate three groups, which we categorize as indifferent, interested, and pervert. Exhaustive analysis is conducted to validate the applicability and advantages of our solution.
Siti Rosli, Hasliza Rahim, Khairul Abdul Rani, Ruzelita Ngadiran, R. Ahmad, Nor Yahaya, MohamedFareq Abdulmalek, Muzammil Jusoh, Mohd Yasin, Thennarasan Sabapathy, et al.
Published: 27 October 2020
by MDPI
Electronics, Volume 9; doi:10.3390/electronics9111786

Abstract:
The metaheuristic algorithm is a popular research area for solving various optimization problems. In this study, we proposed two approaches based on the Sine Cosine Algorithm (SCA), namely, modification and hybridization. First, we attempted to solve the constraints of the original SCA by developing a modified SCA (MSCA) version with an improved identification capability of a random population using the Latin Hypercube Sampling (LHS) technique. MSCA serves to guide SCA in obtaining a better local optimum in the exploitation phase with fast convergence based on an optimum value of the solution. Second, hybridization of the MSCA (HMSCA) and the Cuckoo Search Algorithm (CSA) led to the development of the Hybrid Modified Sine Cosine Algorithm Cuckoo Search Algorithm (HMSCACSA) optimizer, which could search better optimal host nest locations in the global domain. Moreover, the HMSCACSA optimizer was validated over six classical test functions, the IEEE CEC 2017, and the IEEE CEC 2014 benchmark functions. The effectiveness of HMSCACSA was also compared with other hybrid metaheuristics such as the Particle Swarm Optimization–Grey Wolf Optimization (PSOGWO), Particle Swarm Optimization–Artificial Bee Colony (PSOABC), and Particle Swarm Optimization–Gravitational Search Algorithm (PSOGSA). In summary, the proposed HMSCACSA converged 63.89% faster and achieved a shorter Central Processing Unit (CPU) duration by a maximum of up to 43.6% compared to the other hybrid counterparts.
Javier Cuenca, José-Matías Cutillas-Lozano, Domingo Giménez, Alberto Pérez-Bernabeu, José López-Espín
Published: 27 October 2020
by MDPI
Electronics, Volume 9; doi:10.3390/electronics9111781

Abstract:
In the last years, the huge amount of data available in many disciplines makes the mathematical modeling, and, more concretely, econometric models, a very important technique to explain those data. One of the most used of those econometric techniques is the Vector Autoregression Models (VAR) which are multi-equation models that linearly describe the interactions and behavior of a group of variables by using their past. Traditionally, Ordinary Least Squares and Maximum likelihood estimators have been used in the estimation of VAR models. These techniques are consistent and asymptotically efficient under ideal conditions of the data and the identification problem. Otherwise, these techniques would yield inconsistent parameter estimations. This paper considers the estimation of a VAR model by minimizing the difference between the dependent variables in a certain time, and the expression of their own past and the exogenous variables of the model (in this case denoted as VARX model). The solution of this optimization problem is approached through hybrid metaheuristics. The high computational cost due to the huge amount of data makes it necessary to exploit High-Performance Computing for the acceleration of methods to obtain the models. The parameterized, parallel implementation of the metaheuristics and the matrix formulation ease the simultaneous exploitation of parallelism for groups of hybrid metaheuristics. Multilevel and heterogeneous parallelism are exploited in multicore CPU plus multiGPU nodes, with the optimum combination of the different parallelism parameters depending on the particular metaheuristic and the problem it is applied to.
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