Electronics

9 pages, 2568 KiB

Open AccessArticle

Impact of Laser Attacks on the Switching Behavior of RRAM Devices

by Daniel Arumí, Salvador Manich, Álvaro Gómez-Pau, Rosa Rodríguez-Montañés, Víctor Montilla, David Hernández, Mireia Bargalló González and Francesca Campabadal

Electronics 2020, 9(1), 200; https://doi.org/10.3390/electronics9010200 - 20 Jan 2020

Cited by 7 | Viewed by 3905

Abstract

The ubiquitous use of critical and private data in electronic format requires reliable and secure embedded systems for IoT devices. In this context, RRAMs (Resistive Random Access Memories) arises as a promising alternative to replace current memory technologies. However, their suitability for this [...] Read more.

The ubiquitous use of critical and private data in electronic format requires reliable and secure embedded systems for IoT devices. In this context, RRAMs (Resistive Random Access Memories) arises as a promising alternative to replace current memory technologies. However, their suitability for this kind of application, where the integrity of the data is crucial, is still under study. Among the different typology of attacks to recover information of secret data, laser attack is one of the most common due to its simplicity. Some preliminary works have already addressed the influence of laser tests on RRAM devices. Nevertheless, the results are not conclusive since different responses have been reported depending on the circuit under testing and the features of the test. In this paper, we have conducted laser tests on individual RRAM devices. For the set of experiments conducted, the devices did not show faulty behaviors. These results contribute to the characterization of RRAMs and, together with the rest of related works, are expected to pave the way for the development of suitable countermeasures against external attacks. Full article

(This article belongs to the Special Issue Challenges and Applications of Non-volatile Memory)

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13 pages, 2912 KiB

Open AccessArticle

A Power-Efficient Pipelined ADC with an Inherent Linear 1-Bit Flip-Around DAC

by Peiyuan Wan, Limei Su, Hongda Zhang and Zhijie Chen

Electronics 2020, 9(1), 199; https://doi.org/10.3390/electronics9010199 - 20 Jan 2020

Cited by 3 | Viewed by 4533

Abstract

An unity-gain 1-bit flip-around digital-to-analog converter (FADAC), without any capacitor matching issue, is proposed as the front-end input stage in a pipelined analog-to-digital converter (ADC), allowing an input signal voltage swing up to be doubled. This large input swing, coupled with the inherent [...] Read more.

An unity-gain 1-bit flip-around digital-to-analog converter (FADAC), without any capacitor matching issue, is proposed as the front-end input stage in a pipelined analog-to-digital converter (ADC), allowing an input signal voltage swing up to be doubled. This large input swing, coupled with the inherent large feedback factor (ideally β = 1) of the proposed FADAC, enables a power-efficient low-voltage high-resolution pipelined ADC design. The 1-bit FADAC is exploited in a SHA-less and opamp-sharing pipelined ADC, exhibiting 12-bit resolution with an input swing of 1.8 Vpp under a 1.1 V power supply. Fabricated in a 0.13-μm CMOS process, the prototype ADC achieves a measured signal-to-noise plus distortion ratio (SNDR) of 66.4 dB and a spurious-free dynamic range (SFDR) of 76.7 dB at 20 MS/s sampling rate. The ADC dissipates 5.2 mW of power and occupies an active area of 0.44 mm². The measured differential nonlinearity (DNL) is +0.72/−0.52 least significant bit (LSB) and integral nonlinearity (INL) is +0.84/−0.75 LSB at a 3-MHz sinusoidal input. Full article

(This article belongs to the Special Issue Low-Voltage Integrated Circuits Design and Application)

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16 pages, 5464 KiB

Open AccessArticle

Real-Time Image Stabilization Method Based on Optical Flow and Binary Point Feature Matching

by Zilong Deng, Dongxiao Yang, Xiaohu Zhang, Yuguang Dong, Chengbo Liu and Qiang Shen

Electronics 2020, 9(1), 198; https://doi.org/10.3390/electronics9010198 - 20 Jan 2020

Cited by 12 | Viewed by 6619

Abstract

The strap-down missile-borne image guidance system can be easily affected by the unwanted jitters of the motion of the camera, and the subsequent recognition and tracking functions are also influenced, thus severely affecting the navigation accuracy of the image guidance system. So, a [...] Read more.

The strap-down missile-borne image guidance system can be easily affected by the unwanted jitters of the motion of the camera, and the subsequent recognition and tracking functions are also influenced, thus severely affecting the navigation accuracy of the image guidance system. So, a real-time image stabilization technology is needed to help improve the image quality of the image guidance system. To satisfy the real-time and accuracy requirements of image stabilization in the strap-down missile-borne image guidance system, an image stabilization method based on optical flow and image matching with binary feature descriptors is proposed. The global motion of consecutive frames is estimated by the pyramid Lucas-Kanade (LK) optical flow algorithm, and the interval frames image matching based on fast retina keypoint (FREAK) algorithm is used to reduce the cumulative trajectory error. A Kalman filter is designed to smooth the trajectory, which is conducive to fitting to the main motion of the guidance system. Simulations have been carried out, and the results show that the proposed algorithm improves the accuracy and real-time performance simultaneously compared to the state-of-art algorithms. Full article

(This article belongs to the Section Computer Science & Engineering)

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16 pages, 2330 KiB

Open AccessFeature PaperArticle

From Hotel Reviews to City Similarities: A Unified Latent-Space Model

by Luca Cagliero, Moreno La Quatra and Daniele Apiletti

Electronics 2020, 9(1), 197; https://doi.org/10.3390/electronics9010197 - 20 Jan 2020

Cited by 12 | Viewed by 3462

Abstract

A large portion of user-generated content published on the Web consists of opinions and reviews on products, services, and places in textual form. Many travellers and tourists routinely rely on such content to drive their choices, shaping trips and visits to any place [...] Read more.

A large portion of user-generated content published on the Web consists of opinions and reviews on products, services, and places in textual form. Many travellers and tourists routinely rely on such content to drive their choices, shaping trips and visits to any place on earth, and specifically to select hotels in large cities. In the context of hospitality management, a challenging research problem is to identify effective strategies to explain hotel reviews and ratings and their correlation with the urban context. Under this umbrella, the paper investigates the use of sentence-based embedding models to deeply explore the similarities and dissimilarities between cities in terms of the corresponding hotel reviews and the surrounding points of interests. Reviews and point of interest (POI) descriptions are jointly modelled in a unified latent space, allowing us to deeply investigate the dependencies between guest feedbacks and the hotel neighborhood at different aggregation levels. The experiments performed on public TripAdvisor hotel-review datasets confirm the applicability and effectiveness of the proposed approach. Full article

(This article belongs to the Special Issue Big Data Analytics for Smart Cities)

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22 pages, 2495 KiB

Open AccessArticle

Spatiotemporal Feature Learning Based Hour-Ahead Load Forecasting for Energy Internet

by Liufeng Du, Linghua Zhang and Xu Wang

Electronics 2020, 9(1), 196; https://doi.org/10.3390/electronics9010196 - 20 Jan 2020

Cited by 14 | Viewed by 2578

Abstract

In this paper, we analyze the characteristics of the load forecasting task in the Energy Internet context and the deficiencies of existing methods and then propose a data driven approach for one-hour-ahead load forecasting based on the deep learning paradigm. The proposed scheme [...] Read more.

In this paper, we analyze the characteristics of the load forecasting task in the Energy Internet context and the deficiencies of existing methods and then propose a data driven approach for one-hour-ahead load forecasting based on the deep learning paradigm. The proposed scheme involves three aspects. First, we formulate a historical load matrix (HLM) with spatiotemporal correlation combined with the EI scenario and then create a three-dimensional historical load tensor (HLT) that contains the HLMs for multiple consecutive time points before the forecasted hour. Second, we preprocess the HLT leveraging a novel low rank decomposition algorithm and different load gradients, aiming to provide a forecasting model with richer input data. Third, we develop a deep forecasting framework (called the 3D CNN-GRU) featuring a feature learning module followed by a regression module, in which the 3D convolutional neural network (3D CNN) is used to extract the desired feature sequences with time attributes, while the gated recurrent unit (GRU) is responsible for mapping the sequences to the forecast values. By feeding the corresponding load label into the 3D CNN-GRU, our proposed scheme can carry out forecasting tasks for any zone covered by the HLM. The results of self-evaluation and a comparison with several state-of-the-art methods demonstrate the superiority of the proposed scheme. Full article

(This article belongs to the Section Power Electronics)

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10 pages, 1454 KiB

Open AccessArticle

Area-Efficient Error Detection Structure for Linear Feedback Shift Registers

by Hwasoo Shin, Soyeon Choi, Jiwoon Park, Byeong Yong Kong and Hoyoung Yoo

Electronics 2020, 9(1), 195; https://doi.org/10.3390/electronics9010195 - 20 Jan 2020

Cited by 6 | Viewed by 3936

Abstract

This paper presents a novel error detection linear feedback shift register (ED-LFSR), which can be used to realize error detection with a small hardware overhead for various applications such as error-correction codes, encryption algorithms and pseudo-random number generation. Although the traditional redundancy methods [...] Read more.

This paper presents a novel error detection linear feedback shift register (ED-LFSR), which can be used to realize error detection with a small hardware overhead for various applications such as error-correction codes, encryption algorithms and pseudo-random number generation. Although the traditional redundancy methods allow the incorporation of the error detection/correction capability in the original LFSRs, they suffer from a considerable amount of hardware overheads. The proposed ED-LFSR alleviates such problems by employing the parity check technique. The experimental results indicate that the proposed ED-LFSR requires an additional area of only 31.1% compared to that required by the conventional LFSR and it saves 39.1% and 31.9% of the resources compared to the corresponding utilization of the hardware and time redundancy methods. Full article

(This article belongs to the Section Circuit and Signal Processing)

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12 pages, 2873 KiB

Open AccessArticle

Design of SWB MIMO Antenna with Extremely Wideband Isolation

by Habib Ullah, Saeed Ur Rahman, Qunsheng Cao, Ijaz Khan and Hamid Ullah

Electronics 2020, 9(1), 194; https://doi.org/10.3390/electronics9010194 - 20 Jan 2020

Cited by 29 | Viewed by 3993

Abstract

This paper presents a compact planar multiple input multiple output (MIMO) antenna for super wide band (SWB) applications. The presented MIMO antenna comprises two identical patches on the same substrate. Dimensions of the MIMO antenna are 0.17λ × 0.20λ × 0.006λ mm³ [...] Read more.

This paper presents a compact planar multiple input multiple output (MIMO) antenna for super wide band (SWB) applications. The presented MIMO antenna comprises two identical patches on the same substrate. Dimensions of the MIMO antenna are 0.17λ × 0.20λ × 0.006λ mm³, with respect to the lowest resonance of 1.30 GHz. The SWB antenna was manufactured using F4B substrate having a dielectric constant of 2.65 that provides a percent impedance bandwidth and bandwidth ratio of 187% and 30.76:1, respectively. The mutual coupling between the antenna elements is suppressed by placing a T-shaped corrugated strip in the mid of two antenna elements. The proposed MIMO antenna exhibits maximum diversity gain of 10 dB, low mutual coupling (<−20 dB), low envelope correlation coefficient (ECC < 0.02), efficiency >80%, and low reflection coefficient (<−10 dB) in the SWB frequency range (1.30 GH–40 GHz). The presented antenna is a good candidate for SWB applications. The designed antenna has been experimentally validated, and the simulated results were also verified. Full article

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

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19 pages, 7651 KiB

Open AccessArticle

Vehicular Navigation Based on the Fusion of 3D-RISS and Machine Learning Enhanced Visual Data in Challenging Environments

by Yunlong Sun, Lianwu Guan, Menghao Wu, Yanbin Gao and Zhanyuan Chang

Electronics 2020, 9(1), 193; https://doi.org/10.3390/electronics9010193 - 20 Jan 2020

Cited by 10 | Viewed by 2678

Abstract

Based on the 3D Reduced Inertial Sensor System (3D-RISS) and the Machine Learning Enhanced Visual Data (MLEVD), an integrated vehicle navigation system is proposed in this paper. In demanding conditions such as outdoor satellite signal interference and indoor navigation, this work incorporates vehicle [...] Read more.

Based on the 3D Reduced Inertial Sensor System (3D-RISS) and the Machine Learning Enhanced Visual Data (MLEVD), an integrated vehicle navigation system is proposed in this paper. In demanding conditions such as outdoor satellite signal interference and indoor navigation, this work incorporates vehicle smooth navigation. Firstly, a landmark is set up and both of its size and position are accurately measured. Secondly, the image with the landmark information is captured quickly by using the machine learning. Thirdly, the template matching method and the Extended Kalman Filter (EKF) are then used to correct the errors of the Inertial Navigation System (INS), which employs the 3D-RISS to reduce the overall cost and ensuring the vehicular positioning accuracy simultaneously. Finally, both outdoor and indoor experiments are conducted to verify the performance of the 3D-RISS/MLEVD integrated navigation technology. Results reveal that the proposed method can effectively reduce the accumulated error of the INS with time while maintaining the positioning error within a few meters. Full article

(This article belongs to the Section Computer Science & Engineering)

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16 pages, 237 KiB

Open AccessReview

Identification of Daily Activites and Environments Based on the AdaBoost Method Using Mobile Device Data: A Systematic Review

by José M. Ferreira, Ivan Miguel Pires, Gonçalo Marques, Nuno M. Garcia, Eftim Zdravevski, Petre Lameski, Francisco Flórez-Revuelta and Susanna Spinsante

Electronics 2020, 9(1), 192; https://doi.org/10.3390/electronics9010192 - 20 Jan 2020

Cited by 11 | Viewed by 3593

Abstract

Using the AdaBoost method may increase the accuracy and reliability of a framework for daily activities and environment recognition. Mobile devices have several types of sensors, including motion, magnetic, and location sensors, that allow accurate identification of daily activities and environment. This paper [...] Read more.

Using the AdaBoost method may increase the accuracy and reliability of a framework for daily activities and environment recognition. Mobile devices have several types of sensors, including motion, magnetic, and location sensors, that allow accurate identification of daily activities and environment. This paper focuses on the review of the studies that use the AdaBoost method with the sensors available in mobile devices. This research identified the research works written in English about the recognition of daily activities and environment recognition using the AdaBoost method with the data obtained from the sensors available in mobile devices that were published between 2012 and 2018. Thus, 13 studies were selected and analysed from 151 identified records in the searched databases. The results proved the reliability of the method for daily activities and environment recognition, highlighting the use of several features, including the mean, standard deviation, pitch, roll, azimuth, and median absolute deviation of the signal of motion sensors, and the mean of the signal of magnetic sensors. When reported, the analysed studies presented an accuracy higher than 80% in recognition of daily activities and environments with the Adaboost method. Full article

(This article belongs to the Special Issue Machine Learning Techniques for Assistive Robotics)

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10 pages, 1223 KiB

Open AccessFeature PaperArticle

A Recent Electronic Control Circuit to a Throttle Device

by Leonardo Acho, Gisela Pujol-Vázquez and José Gibergans-Báguena

Electronics 2020, 9(1), 191; https://doi.org/10.3390/electronics9010191 - 19 Jan 2020

Cited by 5 | Viewed by 4347

Abstract

The main objective of this paper was to conceive a new electronic control circuit to the throttle device. The throttle mechanical actuator is the most important part in an automotive gasoline engine. Among the different control strategies recently reported, an easy to implement [...] Read more.

The main objective of this paper was to conceive a new electronic control circuit to the throttle device. The throttle mechanical actuator is the most important part in an automotive gasoline engine. Among the different control strategies recently reported, an easy to implement control scheme is an open research topic in the analog electronic engineering field. Hence, we propose using the nonlinear dwell switching control theory for an analog electronic control unit, to manipulate an automotive throttle plate. Due to the switching mechanism commuting between a stable and an unstable controllers, the resultant closed-loop system is robust enough to the control objective. This fact is experimentally evidenced. The proposed electronic controller uses operational amplifiers along with an Arduino unit. This unit is just employed to generate the related switching signal that can be replaced by using, for instance, the timer IC555. Thus, this study is a contribution on design and realization of an electronic control circuit to the throttle device. Full article

(This article belongs to the Special Issue Sensor-Based Navigation and Control with Applications)

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12 pages, 2391 KiB

Open AccessArticle

Fusion High-Resolution Network for Diagnosing ChestX-ray Images

by Zhiwei Huang, Jinzhao Lin, Liming Xu, Huiqian Wang, Tong Bai, Yu Pang and Teen-Hang Meen

Electronics 2020, 9(1), 190; https://doi.org/10.3390/electronics9010190 - 19 Jan 2020

Cited by 17 | Viewed by 4033

Abstract

The application of deep convolutional neural networks (CNN) in the field of medical image processing has attracted extensive attention and demonstrated remarkable progress. An increasing number of deep learning methods have been devoted to classifying ChestX-ray (CXR) images, and most of the existing [...] Read more.

The application of deep convolutional neural networks (CNN) in the field of medical image processing has attracted extensive attention and demonstrated remarkable progress. An increasing number of deep learning methods have been devoted to classifying ChestX-ray (CXR) images, and most of the existing deep learning methods are based on classic pretrained models, trained by global ChestX-ray images. In this paper, we are interested in diagnosing ChestX-ray images using our proposed Fusion High-Resolution Network (FHRNet). The FHRNet concatenates the global average pooling layers of the global and local feature extractors—it consists of three branch convolutional neural networks and is fine-tuned for thorax disease classification. Compared with the results of other available methods, our experimental results showed that the proposed model yields a better disease classification performance for the ChestX-ray 14 dataset, according to the receiver operating characteristic curve and area-under-the-curve score. An ablation study further confirmed the effectiveness of the global and local branch networks in improving the classification accuracy of thorax diseases. Full article

(This article belongs to the Section Artificial Intelligence)

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17 pages, 404 KiB

Open AccessFeature PaperArticle

Developing Efficient Discrete Simulations on Multicore and GPU Architectures

by Daniel Cagigas-Muñiz, Fernando Diaz-del-Rio, Manuel Ramón López-Torres, Francisco Jiménez-Morales and José Luis Guisado

Electronics 2020, 9(1), 189; https://doi.org/10.3390/electronics9010189 - 19 Jan 2020

Cited by 3 | Viewed by 3328

Abstract

In this paper we show how to efficiently implement parallel discrete simulations on multicore and GPU architectures through a real example of an application: a cellular automata model of laser dynamics. We describe the techniques employed to build and optimize the implementations using [...] Read more.

In this paper we show how to efficiently implement parallel discrete simulations on multicore and GPU architectures through a real example of an application: a cellular automata model of laser dynamics. We describe the techniques employed to build and optimize the implementations using OpenMP and CUDA frameworks. We have evaluated the performance on two different hardware platforms that represent different target market segments: high-end platforms for scientific computing, using an Intel Xeon Platinum 8259CL server with 48 cores, and also an NVIDIA Tesla V100 GPU, both running on Amazon Web Server (AWS) Cloud; and on a consumer-oriented platform, using an Intel Core i9 9900k CPU and an NVIDIA GeForce GTX 1050 TI GPU. Performance results were compared and analyzed in detail. We show that excellent performance and scalability can be obtained in both platforms, and we extract some important issues that imply a performance degradation for them. We also found that current multicore CPUs with large core numbers can bring a performance very near to that of GPUs, and even identical in some cases. Full article

(This article belongs to the Section Computer Science & Engineering)

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22 pages, 4157 KiB

Open AccessArticle

Leukemia Image Segmentation Using a Hybrid Histogram-Based Soft Covering Rough K-Means Clustering Algorithm

by Hannah Inbarani H., Ahmad Taher Azar and Jothi G

Electronics 2020, 9(1), 188; https://doi.org/10.3390/electronics9010188 - 19 Jan 2020

Cited by 52 | Viewed by 5192

Abstract

Segmenting an image of a nucleus is one of the most essential tasks in a leukemia diagnostic system. Accurate and rapid segmentation methods help the physicians identify the diseases and provide better treatment at the appropriate time. Recently, hybrid clustering algorithms have started [...] Read more.

Segmenting an image of a nucleus is one of the most essential tasks in a leukemia diagnostic system. Accurate and rapid segmentation methods help the physicians identify the diseases and provide better treatment at the appropriate time. Recently, hybrid clustering algorithms have started being widely used for image segmentation in medical image processing. In this article, a novel hybrid histogram-based soft covering rough k-means clustering (HSCRKM) algorithm for leukemia nucleus image segmentation is discussed. This algorithm combines the strengths of a soft covering rough set and rough k-means clustering. The histogram method was utilized to identify the number of clusters to avoid random initialization. Different types of features such as gray level co-occurrence matrix (GLCM), color, and shape-based features were extracted from the segmented image of the nucleus. Machine learning prediction algorithms were applied to classify the cancerous and non-cancerous cells. The proposed strategy is compared with an existing clustering algorithm, and the efficiency is evaluated based on the prediction metrics. The experimental results show that the HSCRKM method efficiently segments the nucleus, and it is also inferred that logistic regression and neural network perform better than other prediction algorithms. Full article

(This article belongs to the Special Issue Computational Intelligence in Healthcare)

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Graphical abstract

12 pages, 3717 KiB

Open AccessArticle

A Multi-Feature Representation of Skeleton Sequences for Human Interaction Recognition

by Xiaohang Wang and Hongmin Deng

Electronics 2020, 9(1), 187; https://doi.org/10.3390/electronics9010187 - 19 Jan 2020

Cited by 8 | Viewed by 3566

Abstract

Inspired from the promising performances achieved by recurrent neural networks (RNN) and convolutional neural networks (CNN) in action recognition based on skeleton, this paper presents a deep network structure which combines both CNN for classification and RNN to achieve attention mechanism for human [...] Read more.

Inspired from the promising performances achieved by recurrent neural networks (RNN) and convolutional neural networks (CNN) in action recognition based on skeleton, this paper presents a deep network structure which combines both CNN for classification and RNN to achieve attention mechanism for human interaction recognition. Specifically, the attention module in this structure is utilized to give various levels of attention to various frames by different weights, and the CNN is employed to extract the high-level spatial and temporal information of skeleton data. These two modules seamlessly form a single network architecture. In addition, to eliminate the impact of different locations and orientations, a coordinate transformation is conducted from the original coordinate system to the human-centric coordinate system. Furthermore, three different features are extracted from the skeleton data as the inputs of three subnetworks, respectively. Eventually, these subnetworks fed with different features are fused as an integrated network. The experimental result shows the validity of the proposed approach on two widely used human interaction datasets. Full article

(This article belongs to the Special Issue Computer Vision and Machine Learning in Human-Computer Interaction)

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14 pages, 5850 KiB

Open AccessArticle

A Novel 183 GHz Solid-State Sub-Harmonic Mixer

by Guangyu Ji, Dehai Zhang, Jin Meng, Siyu Liu and Changfei Yao

Electronics 2020, 9(1), 186; https://doi.org/10.3390/electronics9010186 - 18 Jan 2020

Cited by 2 | Viewed by 3200

Abstract

This paper proposes a novel sub-harmonic mixing topology. Based on the proposed topology and the precise three-dimensional electromagnetic model of the Schottky barrier diode; a novel 183 GHz solid-state sub-harmonic mixer is designed and measured. By adding a compact low-pass filter near the [...] Read more.

This paper proposes a novel sub-harmonic mixing topology. Based on the proposed topology and the precise three-dimensional electromagnetic model of the Schottky barrier diode; a novel 183 GHz solid-state sub-harmonic mixer is designed and measured. By adding a compact low-pass filter near the ground of the mixer’s circuit, the effect on the mixer’s RF performance of the random error of the conductive adhesive in assembling is effectively decreased. The test results show that the optimal single-sideband conversion loss of the mixer is 8.1dB@183GHz when the local oscillator signal is 4mw@91GHz. In the RF bandwidth from 173 GHz to 191 GHz, the single-sideband conversion loss is less than −10.6 dB. At the same time, the RF port return loss is less than 9.8 dB. Full article

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

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18 pages, 3713 KiB

Open AccessArticle

Discrete Sliding Mode Speed Control of Induction Motor Using Time-Varying Switching Line

by Grzegorz Tarchała and Teresa Orłowska-Kowalska

Electronics 2020, 9(1), 185; https://doi.org/10.3390/electronics9010185 - 18 Jan 2020

Cited by 10 | Viewed by 4000

Abstract

Sliding mode control (SMC) of electric drives constitutes a very popular control method for nonlinear multivariable and time-varying systems, e.g., induction motor (IM) drives. Nowadays, IM are the most popular electrical machines (EM) applied in many industrial applications as motion control devices, including [...] Read more.

Sliding mode control (SMC) of electric drives constitutes a very popular control method for nonlinear multivariable and time-varying systems, e.g., induction motor (IM) drives. Nowadays, IM are the most popular electrical machines (EM) applied in many industrial applications as motion control devices, including electrical and hybrid vehicles. Nowadays, the control systems of EM are mostly realized using digital techniques (microprocessors and microcontrollers). Therefore, all control algorithms should be discretized or the whole control system should be designed in the discrete-time domain. This paper deals with a discrete-time sliding mode control (DSMC) for IM drives. The discrete algorithms for sliding mode control of the motor speed and rotor flux are derived in detail and next tested in simulation research. The simulation tests include the discrete nature of the power converter supplying the IM and present excellent performance of the developed control structure. To obtain the rotor speed regulation invariant to external disturbances, like load torque or inertia, especially during the reaching phase of the switching line, the discrete version of a time-varying switching line was introduced. It is shown that the assumed dynamics of the IM flux and speed is achieved and the proposed control algorithm can be realized using commonly available microcontrollers. The paper is illustrated with comprehensive simulation results for 1.5 kW IM drive, which are verified by experimental tests. Full article

(This article belongs to the Special Issue Advanced Modelling and Control of Complex Nonlinear Mechatronic Systems)

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17 pages, 421 KiB

Open AccessArticle

Optimized Distributed Subgraph Matching Algorithm Based on Partition Replication

by Ling Yuan, Jiali Bin and Peng Pan

Electronics 2020, 9(1), 184; https://doi.org/10.3390/electronics9010184 - 18 Jan 2020

Cited by 1 | Viewed by 3248

Abstract

At present, with the explosive growth of data scale, subgraph matching for massive graph data is difficult to satisfy with efficiency. Meanwhile, the graph index used in existing subgraph matching algorithm is difficult to update and maintain when facing dynamic graphs. We propose [...] Read more.

At present, with the explosive growth of data scale, subgraph matching for massive graph data is difficult to satisfy with efficiency. Meanwhile, the graph index used in existing subgraph matching algorithm is difficult to update and maintain when facing dynamic graphs. We propose a distributed subgraph matching algorithm based on Partition Replica (noted as PR-Match) to process the partition and storage of large-scale data graphs. The PR-Match algorithm first splits the query graph into sub-queries, then assigns the sub-query to each node for sub-graph matching, and finally merges the matching results. In the PR-Match algorithm, we propose a heuristic rule based on prediction cost to select the optimal merging plan, which greatly reduces the cost of merging. In order to accelerate the matching speed of the sub-query graph, a vertex code based on the vertex neighbor label signature is proposed, which greatly reduces the search space for the subquery. As the vertex code is based on the increment, the problem that the feature-based graph index is difficult to maintain in the face of the dynamic graph is solved. An abundance of experiments on real and synthetic datasets demonstrate the high efficiency and strong scalability of the PR-Match algorithm when handling large-scale data graphs. Full article

(This article belongs to the Section Computer Science & Engineering)

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17 pages, 5433 KiB

Open AccessArticle

Stator Inductance Identification Based on Low-Speed Tests for Three-Level NPC Inverter-Fed Induction Motor Drives

by Yerganat Khojakhan, Kyoung-Min Choo and Chung-Yuen Won

Electronics 2020, 9(1), 183; https://doi.org/10.3390/electronics9010183 - 18 Jan 2020

Cited by 6 | Viewed by 3962

Abstract

This paper proposes a stator inductance identification process for three-level neutral point clamped (NPC), inverter-fed Induction Motor (IM) drives based on a low-speed test drive. Conventionally, the stator inductance of an IM is identified by methods based on standstill or rotational tests. Since [...] Read more.

This paper proposes a stator inductance identification process for three-level neutral point clamped (NPC), inverter-fed Induction Motor (IM) drives based on a low-speed test drive. Conventionally, the stator inductance of an IM is identified by methods based on standstill or rotational tests. Since conventional standstill test-based methods have several practical problems when used with three-level inverters because of their nonlinearity, an identification method based on rotational tests is superior in such applications. However, conventional rotational tests cause unintended behavior because of the high speeds used during the test. In the proposed stator inductance identification process, the stator inductance is identified based on a low-speed test drive. In the proposed method, the stator flux is estimated using the instantaneous reactive power of the IM during low-frequency sinusoidal current excitation, and the stator inductance is then identified based upon this. Therefore, the proposed identification process is safer than conventional approaches, as it uses only a low-speed test. The accuracy and reliability of this method are verified by simulation and experiment using three motors with different rated voltage and power. Full article

(This article belongs to the Section Power Electronics)

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14 pages, 1084 KiB

Open AccessArticle

FPGA-Based Hardware Matrix Inversion Architecture Using Hybrid Piecewise Polynomial Approximation Systolic Cells

by Javier Vázquez-Castillo, Alejandro Castillo-Atoche, Roberto Carrasco-Alvarez, Omar Longoria-Gandara and Jaime Ortegón-Aguilar

Electronics 2020, 9(1), 182; https://doi.org/10.3390/electronics9010182 - 18 Jan 2020

Cited by 6 | Viewed by 5089

Abstract

The hardware of the matrix inversion architecture using QR decomposition with Givens Rotations (GR) and a back substitution (BS) block is required for many signal processing algorithms. However, the hardware of the GR algorithm requires the implementation of complex operations, such as the [...] Read more.

The hardware of the matrix inversion architecture using QR decomposition with Givens Rotations (GR) and a back substitution (BS) block is required for many signal processing algorithms. However, the hardware of the GR algorithm requires the implementation of complex operations, such as the reciprocal square root (RSR), which is typically implemented using LookUp Table (LUT) and COordinate Rotation DIgital Computer (CORDICs), among others, conveying to either high-area consumption or low throughput. This paper introduces an Field-Programmable Gate Array (FPGA)-based full matrix inversion architecture using hybrid piecewise polynomial approximation systolic cells. In the design, a hybrid segmentation technique was incorporated for the implementation of piecewise polynomial systolic cells. This hybrid approach is composed by an external and internal segmentation, where the first is nonuniform and the second is uniform, fitting the curve shape of the complex functions achieving a better signal-quantization-to noise-ratio; furthermore, it improves the time performance and area resources. Experimental results reveal a well-balanced improvement in the design achieving high throughput and, hence, less resource utilization in comparison to state-of-the-art FPGA-based architectures. In our study, the proposed design achieves 7.51 Mega-Matrices per second for performing 4 × 4 matrix operations with a latency of 12 clock cycles; meanwhile, the hardware design requires only 1474 slice registers, 1458 LUTs in an FPGA Virtex-5 XC5VLX220T, and 1474 slice registers and 1378 LUTs when a FPGA Virtex-6 XC6VLX240T is used. Full article

(This article belongs to the Special Issue Approximate Computing: Design, Acceleration, Validation and Testing of Circuits, Architectures and Algorithms in Future Systems)

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24 pages, 1274 KiB

Open AccessArticle

A Novel Intrusion Detection Model Using a Fusion of Network and Device States for Communication-Based Train Control Systems

by Yajie Song, Bing Bu and Li Zhu

Electronics 2020, 9(1), 181; https://doi.org/10.3390/electronics9010181 - 18 Jan 2020

Cited by 11 | Viewed by 4441

Abstract

Security is crucial in cyber-physical systems (CPS). As a typical CPS, the communication-based train control (CBTC) system is facing increasingly serious cyber-attacks. Intrusion detection systems (IDSs) are vital to protect the system against cyber-attacks. The traditional IDS cannot distinguish between cyber-attacks and system [...] Read more.

Security is crucial in cyber-physical systems (CPS). As a typical CPS, the communication-based train control (CBTC) system is facing increasingly serious cyber-attacks. Intrusion detection systems (IDSs) are vital to protect the system against cyber-attacks. The traditional IDS cannot distinguish between cyber-attacks and system faults. Furthermore, the design of the traditional IDS does not take the principles of CBTC systems into consideration. When deployed, it cannot effectively detect cyber-attacks against CBTC systems. In this paper, we propose a novel intrusion detection method that considers both the status of the networks and those of the equipment to identify if the abnormality is caused by cyber-attacks or by system faults. The proposed method is verified on a hardware-in-the-loop simulation platform of CBTC systems. Simulation results indicate that the proposed method has achieved 97.64% true positive rate, which can significantly improve the security protection level of CBTC systems. Full article

(This article belongs to the Special Issue Security and Privacy in Cyber Physical Systems)

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16 pages, 255 KiB

Open AccessArticle

Activities of Daily Living and Environment Recognition Using Mobile Devices: A Comparative Study

by José M. Ferreira, Ivan Miguel Pires, Gonçalo Marques, Nuno M. García, Eftim Zdravevski, Petre Lameski, Francisco Flórez-Revuelta, Susanna Spinsante and Lina Xu

Electronics 2020, 9(1), 180; https://doi.org/10.3390/electronics9010180 - 18 Jan 2020

Cited by 12 | Viewed by 4499

Abstract

The recognition of Activities of Daily Living (ADL) using the sensors available in off-the-shelf mobile devices with high accuracy is significant for the development of their framework. Previously, a framework that comprehends data acquisition, data processing, data cleaning, feature extraction, data fusion, and [...] Read more.

The recognition of Activities of Daily Living (ADL) using the sensors available in off-the-shelf mobile devices with high accuracy is significant for the development of their framework. Previously, a framework that comprehends data acquisition, data processing, data cleaning, feature extraction, data fusion, and data classification was proposed. However, the results may be improved with the implementation of other methods. Similar to the initial proposal of the framework, this paper proposes the recognition of eight ADL, e.g., walking, running, standing, going upstairs, going downstairs, driving, sleeping, and watching television, and nine environments, e.g., bar, hall, kitchen, library, street, bedroom, living room, gym, and classroom, but using the Instance Based k-nearest neighbour (IBk) and AdaBoost methods as well. The primary purpose of this paper is to find the best machine learning method for ADL and environment recognition. The results obtained show that IBk and AdaBoost reported better results, with complex data than the deep neural network methods. Full article

(This article belongs to the Special Issue Machine Learning Techniques for Assistive Robotics)

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25 pages, 893 KiB

Open AccessArticle

Energy Management of Solar-Powered Aircraft-Based High Altitude Platform for Wireless Communications

by Steve Chukwuebuka Arum, David Grace, Paul Daniel Mitchell, Muhammad Danial Zakaria and Nils Morozs

Electronics 2020, 9(1), 179; https://doi.org/10.3390/electronics9010179 - 18 Jan 2020

Cited by 30 | Viewed by 5860

Abstract

With the increasing interest in wireless communications from solar-powered aircraft-based high altitude platforms (HAPs), it is imperative to assess the feasibility of their deployment in different locations with the constraints on energy consumption and payload weight under consideration. This paper considers the energy [...] Read more.

With the increasing interest in wireless communications from solar-powered aircraft-based high altitude platforms (HAPs), it is imperative to assess the feasibility of their deployment in different locations with the constraints on energy consumption and payload weight under consideration. This paper considers the energy management of solar-powered aircraft-based HAPs for wireless communications service provisioning in equatorial regions and regions further up the northern hemisphere. The total solar energy harvested and consumed on the shortest day of the year is analyzed, and it is explained how this determines the feasibility of long endurance, semi-permanent missions. This takes into account the different aircraft-based HAPs and the energy storage systems currently available, and how these can be deployed for wireless communications. We show that the solar-powered HAPs are energy and weight limited, and this depends largely on the platform’s wingspan available for the deployment of solar collectors. Our analysis show that services can be provided for a duration of 15–24 h/day using current platforms, with wingspans ranging between 25–35 m, depending on the configuration and coverage radius. Furthermore, we show that doubling an aircraft’s wingspan can increase its payload capacity by a factor of 6, which in turn enhances its feasibility for wireless communications. Full article

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

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30 pages, 2652 KiB

Open AccessArticle

Maximized Privacy-Preserving Outsourcing on Support Vector Clustering

by Yuan Ping, Bin Hao, Xiali Hei, Jie Wu and Baocang Wang

Electronics 2020, 9(1), 178; https://doi.org/10.3390/electronics9010178 - 17 Jan 2020

Cited by 6 | Viewed by 2982

Abstract

Despite its remarkable capability in handling arbitrary cluster shapes, support vector clustering (SVC) suffers from pricey storage of kernel matrix and costly computations. Outsourcing data or function on demand is intuitively expected, yet it raises a great violation of privacy. We propose maximized [...] Read more.

Despite its remarkable capability in handling arbitrary cluster shapes, support vector clustering (SVC) suffers from pricey storage of kernel matrix and costly computations. Outsourcing data or function on demand is intuitively expected, yet it raises a great violation of privacy. We propose maximized privacy-preserving outsourcing on SVC (MPPSVC), which, to the best of our knowledge, is the first all-phase outsourceable solution. For privacy-preserving, we exploit the properties of homomorphic encryption and secure two-party computation. To break through the operation limitation, we propose a reformative SVC with elementary operations (RSVC-EO, the core of MPPSVC), in which a series of designs make selective outsourcing phase possible. In the training phase, we develop a dual coordinate descent solver, which avoids interactions before getting the encrypted coefficient vector. In the labeling phase, we design a fresh convex decomposition cluster labeling, by which no iteration is required by convex decomposition and no sampling checks exist in connectivity analysis. Afterward, we customize secure protocols to match these operations for essential interactions in the encrypted domain. Considering the privacy-preserving property and efficiency in a semi-honest environment, we proved MPPSVC’s robustness against adversarial attacks. Our experimental results confirm that MPPSVC achieves comparable accuracies to RSVC-EO, which outperforms the state-of-the-art variants of SVC. Full article

(This article belongs to the Section Computer Science & Engineering)

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34 pages, 474 KiB

Open AccessEditorial

Acknowledgement to Reviewers of Electronics in 2019

by Electronics Editorial Office

Electronics 2020, 9(1), 177; https://doi.org/10.3390/electronics9010177 - 17 Jan 2020

Viewed by 3881

Abstract

n/a Full article

14 pages, 5000 KiB

Open AccessEditor’s ChoiceArticle

Application of a Stub-Loaded Square Ring Resonator for Wideband Bandpass Filter Design

by Ping Zhang, Liqin Liu, Deli Chen, Min-Hang Weng and Ru-Yuan Yang

Electronics 2020, 9(1), 176; https://doi.org/10.3390/electronics9010176 - 17 Jan 2020

Cited by 20 | Viewed by 3802

Abstract

In this paper, a stub-loaded square ring resonator (SLSRR) is analyzed and applied to design a very simple and compact wideband bandpass filter structure. Resonant modes dependent on the structure parameters of the SLSRR are analyzed first, and then the first two modes [...] Read more.

In this paper, a stub-loaded square ring resonator (SLSRR) is analyzed and applied to design a very simple and compact wideband bandpass filter structure. Resonant modes dependent on the structure parameters of the SLSRR are analyzed first, and then the first two modes are used to achieve a required passband. The input and output terminals are supplied with high impedance and strong coupling to provide sufficient coupling energy. Two wideband filter examples are designed, manufactured, and measured using the SLSRRs. The first filter is a wideband filter with a wide upper stopband, and the second filter is a dual wideband filter with a notched stopband between two passbands. The two filter examples are designed, fabricated, and measured to verify the design concept and present the advantages of easy design and a simple and compact structure. Full article

(This article belongs to the Special Issue RF/Mm-Wave Circuits Design and Applications)

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12 pages, 799 KiB

Open AccessFeature PaperArticle

Analysis of the Critical Bits of a RISC-V Processor Implemented in an SRAM-Based FPGA for Space Applications

by Luis Alberto Aranda, Nils-Johan Wessman, Lucana Santos, Alfonso Sánchez-Macián, Jan Andersson, Roland Weigand and Juan Antonio Maestro

Electronics 2020, 9(1), 175; https://doi.org/10.3390/electronics9010175 - 17 Jan 2020

Cited by 27 | Viewed by 4169

Abstract

One of the traditional issues in space missions is the reliability of the electronic components on board spacecraft. There are numerous techniques to deal with this, from shielding and rad-hard fabrication to ad-hoc fault-tolerant designs. Although many of these solutions have been extensively [...] Read more.

One of the traditional issues in space missions is the reliability of the electronic components on board spacecraft. There are numerous techniques to deal with this, from shielding and rad-hard fabrication to ad-hoc fault-tolerant designs. Although many of these solutions have been extensively studied, the recent utilization of FPGAs as the target architecture for many electronic components has opened new possibilities, partly due to the distinct nature of these devices. In this study, we performed fault injection experiments to determine if a RISC-V soft processor implemented in an FPGA could be used as an onboard computer for space applications, and how the specific nature of FPGAs needs to be tackled differently from how ASICs have been traditionally handled. In particular, in this paper, the classic definition of the cross-section is revisited, putting into perspective the importance of the so-called “critical bits” in an FPGA design. Full article

(This article belongs to the Special Issue Fault-Tolerant Digital Circuits: Protection Techniques, CAD Tools and Emerging Applications)

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16 pages, 10178 KiB

Open AccessArticle

Motor Imagery Based Continuous Teleoperation Robot Control with Tactile Feedback

by Baoguo Xu, Wenlong Li, Xiaohang He, Zhiwei Wei, Dalin Zhang, Changcheng Wu and Aiguo Song

Electronics 2020, 9(1), 174; https://doi.org/10.3390/electronics9010174 - 17 Jan 2020

Cited by 33 | Viewed by 7283

Abstract

Brain computer interface (BCI) adopts human brain signals to control external devices directly without using normal neural pathway. Recent study has explored many applications, such as controlling a teleoperation robot by electroencephalography (EEG) signals. However, utilizing the motor imagery EEG-based BCI to perform [...] Read more.

Brain computer interface (BCI) adopts human brain signals to control external devices directly without using normal neural pathway. Recent study has explored many applications, such as controlling a teleoperation robot by electroencephalography (EEG) signals. However, utilizing the motor imagery EEG-based BCI to perform teleoperation for reach and grasp task still has many difficulties, especially in continuous multidimensional control of robot and tactile feedback. In this research, a motor imagery EEG-based continuous teleoperation robot control system with tactile feedback was proposed. Firstly, mental imagination of different hand movements was translated into continuous command to control the remote robotic arm to reach the hover area of the target through a wireless local area network (LAN). Then, the robotic arm automatically completed the task of grasping the target. Meanwhile, the tactile information of remote robotic gripper was detected and converted to the feedback command. Finally, the vibrotactile stimulus was supplied to users to improve their telepresence. Experimental results demonstrate the feasibility of using the motor imagery EEG acquired by wireless portable equipment to realize the continuous teleoperation robot control system to finish the reach and grasp task. The average two-dimensional continuous control success rates for online Task 1 and Task 2 of the six subjects were 78.0% ± 6.1% and 66.2% ± 6.0%, respectively. Furthermore, compared with the traditional EEG triggered robot control using the predefined trajectory, the continuous fully two-dimensional control can not only improve the teleoperation robot system’s efficiency but also give the subject a more natural control which is critical to human–machine interaction (HMI). In addition, vibrotactile stimulus can improve the operator’s telepresence and task performance. Full article

(This article belongs to the Special Issue The Challenges in Brain-Computer Interface (BCI) - toward Practical BCI)

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18 pages, 1588 KiB

Open AccessEditor’s ChoiceArticle

Hybrid Intrusion Detection System Based on the Stacking Ensemble of C5 Decision Tree Classifier and One Class Support Vector Machine

by Ansam Khraisat, Iqbal Gondal, Peter Vamplew, Joarder Kamruzzaman and Ammar Alazab

Electronics 2020, 9(1), 173; https://doi.org/10.3390/electronics9010173 - 17 Jan 2020

Cited by 134 | Viewed by 13062

Abstract

Cyberttacks are becoming increasingly sophisticated, necessitating the efficient intrusion detection mechanisms to monitor computer resources and generate reports on anomalous or suspicious activities. Many Intrusion Detection Systems (IDSs) use a single classifier for identifying intrusions. Single classifier IDSs are unable to achieve high [...] Read more.

Cyberttacks are becoming increasingly sophisticated, necessitating the efficient intrusion detection mechanisms to monitor computer resources and generate reports on anomalous or suspicious activities. Many Intrusion Detection Systems (IDSs) use a single classifier for identifying intrusions. Single classifier IDSs are unable to achieve high accuracy and low false alarm rates due to polymorphic, metamorphic, and zero-day behaviors of malware. In this paper, a Hybrid IDS (HIDS) is proposed by combining the C5 decision tree classifier and One Class Support Vector Machine (OC-SVM). HIDS combines the strengths of SIDS) and Anomaly-based Intrusion Detection System (AIDS). The SIDS was developed based on the C5.0 Decision tree classifier and AIDS was developed based on the one-class Support Vector Machine (SVM). This framework aims to identify both the well-known intrusions and zero-day attacks with high detection accuracy and low false-alarm rates. The proposed HIDS is evaluated using the benchmark datasets, namely, Network Security Laboratory-Knowledge Discovery in Databases (NSL-KDD) and Australian Defence Force Academy (ADFA) datasets. Studies show that the performance of HIDS is enhanced, compared to SIDS and AIDS in terms of detection rate and low false-alarm rates. Full article

(This article belongs to the Special Issue Machine Learning Techniques for Intelligent Intrusion Detection Systems)

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18 pages, 4300 KiB

Open AccessArticle

Current Control Methods for an Asymmetric Six-Phase Permanent Magnet Synchronous Motor

by Zhihong Wu, Weisong Gu, Yuan Zhu and Ke Lu

Electronics 2020, 9(1), 172; https://doi.org/10.3390/electronics9010172 - 16 Jan 2020

Cited by 6 | Viewed by 4686

Abstract

Via the vector space decomposition (VSD) transformation, the currents in an asymmetric six-phase permanent magnet synchronous motor (ASP_PMSM) can be decoupled into three orthogonal subspaces. Control of α–β currents in α–β subspace is important for torque regulation, while control [...] Read more.

Via the vector space decomposition (VSD) transformation, the currents in an asymmetric six-phase permanent magnet synchronous motor (ASP_PMSM) can be decoupled into three orthogonal subspaces. Control of α–β currents in α–β subspace is important for torque regulation, while control of x-y currents in x-y subspace can suppress the harmonics due to the dead time of converters and other nonlinear factors. The zero-sequence components in O₁-O₂ subspace are 0 due to isolated neutral points. In α–β subspace, a state observer is constructed by introducing the error variable between the real current and the internal model current based on the internal model control method, which can improve the current control performance compared to the traditional internal model control method. In x–y subspace, in order to suppress the current harmonics, an adaptive-linear-neuron (ADALINE)-based control algorithm is employed to generate the compensation voltage, which is self-tuned by minimizing the estimated current distortion through the least mean square (LMS) algorithm. The modulation technique to implement the four-dimensional current control based on the three-phase SVPWM is given. The experimental results validate the robustness and effectiveness of the proposed control method. Full article

(This article belongs to the Section Systems & Control Engineering)

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14 pages, 5805 KiB

Open AccessFeature PaperArticle

Design and Implementation of a Test Fixture for ELF Schumann Resonance Magnetic Antenna Receiver and Magnetic Permeability Measurements

by Giorgos Tatsis, Vasilis Christofilakis, Spyridon K. Chronopoulos, Panos Kostarakis, Hector E. Nistazakis, C. Repapis and V. Tritakis

Electronics 2020, 9(1), 171; https://doi.org/10.3390/electronics9010171 - 16 Jan 2020

Cited by 8 | Viewed by 7488

Abstract

This paper presents a prototype test fixture for the absolute calibration and estimation of the equivalent magnetic flux noise of the extremely low frequency (ELF) Schumann resonant (SR) magnetic antenna receiver and rods’ magnetic permeability measurement. The test fixture, for ELF the SR [...] Read more.

This paper presents a prototype test fixture for the absolute calibration and estimation of the equivalent magnetic flux noise of the extremely low frequency (ELF) Schumann resonant (SR) magnetic antenna receiver and rods’ magnetic permeability measurement. The test fixture, for ELF the SR detector’s calibration, consists of a constructed coil, the signal generator, and the oscilloscope. The ELF SR detector used has been operating since 2016 near the Doliana village in the Ioannina prefecture, Northwestern Greece. At precisely this spot, far away from electromagnetic noise, the whole setup and experiment took place. The experiments performed with the proposed test fixture showed a sensitivity of 70 nV/pT/Hz and an apparent magnetic permeability at around 250 for the magnetic antenna. The total sensitivity of the ELF receiver was 210 mV/pT near 20 Hz, while the total input noise was around 0.04 pT. Full article

(This article belongs to the Section Circuit and Signal Processing)

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