Electronics

26 pages, 1505 KiB

Open AccessEditor’s ChoiceReview

The Mechanical Effects Influencing on the Design of RF MEMS Switches

by Igor E. Lysenko, Alexey V. Tkachenko, Olga A. Ezhova, Boris G. Konoplev, Eugeny A. Ryndin and Elena V. Sherova

Electronics 2020, 9(2), 207; https://doi.org/10.3390/electronics9020207 - 22 Jan 2020

Cited by 18 | Viewed by 6258

Abstract

Radio-frequency switches manufactured by microelectromechanical systems technology are now widely used in aerospace systems and other mobile installations for various purposes. In these operating conditions, these devices are often exposed to intense mechanical environmental influences that have a strong impact on their operation. [...] Read more.

Radio-frequency switches manufactured by microelectromechanical systems technology are now widely used in aerospace systems and other mobile installations for various purposes. In these operating conditions, these devices are often exposed to intense mechanical environmental influences that have a strong impact on their operation. These negative effects can lead to unwanted short-circuit or open-circuit in the radio-frequency transmission line or to irreversible damage to structural elements. Such a violation in the operation of radio-frequency microelectromechanical switches leads to errors and improper functioning of the electronic equipment in which they are integrated. Thus, this review is devoted to the analysis of the origin of these negative intense mechanical effects of the environment, their classification, and analysis, as well as a review of methods to reduce or prevent their negative impact on the design of radio-frequency microelectromechanical switches. Full article

(This article belongs to the Special Issue Progress in MEMS/NEMS Devices)

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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 158 | Viewed by 15054

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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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 4386

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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14 pages, 2705 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Design and Validation of 100 nm GaN-On-Si Ka-Band LNA Based on Custom Noise and Small Signal Models

by Lorenzo Pace, Sergio Colangeli, Walter Ciccognani, Patrick Ettore Longhi, Ernesto Limiti, Remy Leblanc, Marziale Feudale and Fabio Vitobello

Electronics 2020, 9(1), 150; https://doi.org/10.3390/electronics9010150 - 13 Jan 2020

Cited by 25 | Viewed by 5192

Abstract

In this paper a GaN-on-Si MMIC Low-Noise Amplifier (LNA) working in the Ka-band is shown. The chosen technology for the design is a 100 nm gate length HEMT provided by OMMIC foundry. Both small-signal and noise models had been previously extracted by the [...] Read more.

In this paper a GaN-on-Si MMIC Low-Noise Amplifier (LNA) working in the Ka-band is shown. The chosen technology for the design is a 100 nm gate length HEMT provided by OMMIC foundry. Both small-signal and noise models had been previously extracted by the means of an extensive measurement campaign, and were then employed in the design of the presented LNA. The amplifier presents an average noise figure of 2.4 dB, a 30 dB average gain value, and input/output matching higher than 10 dB in the whole 34–37.5 Ghz design band, while non-linear measurements testify a minimum output 1 dB compression point of 23 dBm in the specific 35–36.5 GHz target band. This shows the suitability of the chosen technology for low-noise applications. Full article

(This article belongs to the Section Microelectronics)

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19 pages, 3531 KiB

Open AccessEditor’s ChoiceArticle

Towards a Lightweight Detection System for Cyber Attacks in the IoT Environment Using Corresponding Features

by Yan Naung Soe, Yaokai Feng, Paulus Insap Santosa, Rudy Hartanto and Kouichi Sakurai

Electronics 2020, 9(1), 144; https://doi.org/10.3390/electronics9010144 - 11 Jan 2020

Cited by 98 | Viewed by 7661

Abstract

The application of a large number of Internet of Things (IoT) devices makes our life more convenient and industries more efficient. However, it also makes cyber-attacks much easier to occur because so many IoT devices are deployed and most of them do not [...] Read more.

The application of a large number of Internet of Things (IoT) devices makes our life more convenient and industries more efficient. However, it also makes cyber-attacks much easier to occur because so many IoT devices are deployed and most of them do not have enough resources (i.e., computation and storage capacity) to carry out ordinary intrusion detection systems (IDSs). In this study, a lightweight machine learning-based IDS using a new feature selection algorithm is designed and implemented on Raspberry Pi, and its performance is verified using a public dataset collected from an IoT environment. To make the system lightweight, we propose a new algorithm for feature selection, called the correlated-set thresholding on gain-ratio (CST-GR) algorithm, to select really necessary features. Because the feature selection is conducted on three specific kinds of cyber-attacks, the number of selected features can be significantly reduced, which makes the classifiers very small and fast. Thus, our detection system is lightweight enough to be implemented and carried out in a Raspberry Pi system. More importantly, as the really necessary features corresponding to each kind of attack are exploited, good detection performance can be expected. The performance of our proposal is examined in detail with different machine learning algorithms, in order to learn which of them is the best option for our system. The experiment results indicate that the new feature selection algorithm can select only very few features for each kind of attack. Thus, the detection system is lightweight enough to be implemented in the Raspberry Pi environment with almost no sacrifice on detection performance. Full article

(This article belongs to the Section Networks)

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17 pages, 3543 KiB

Open AccessEditor’s ChoiceArticle

Deep Learning-Based Stacked Denoising and Autoencoder for ECG Heartbeat Classification

by Siti Nurmaini, Annisa Darmawahyuni, Akhmad Noviar Sakti Mukti, Muhammad Naufal Rachmatullah, Firdaus Firdaus and Bambang Tutuko

Electronics 2020, 9(1), 135; https://doi.org/10.3390/electronics9010135 - 10 Jan 2020

Cited by 120 | Viewed by 11692

Abstract

The electrocardiogram (ECG) is a widely used, noninvasive test for analyzing arrhythmia. However, the ECG signal is prone to contamination by different kinds of noise. Such noise may cause deformation on the ECG heartbeat waveform, leading to cardiologists’ mislabeling or misinterpreting heartbeats due [...] Read more.

The electrocardiogram (ECG) is a widely used, noninvasive test for analyzing arrhythmia. However, the ECG signal is prone to contamination by different kinds of noise. Such noise may cause deformation on the ECG heartbeat waveform, leading to cardiologists’ mislabeling or misinterpreting heartbeats due to varying types of artifacts and interference. To address this problem, some previous studies propose a computerized technique based on machine learning (ML) to distinguish between normal and abnormal heartbeats. Unfortunately, ML works on a handcrafted, feature-based approach and lacks feature representation. To overcome such drawbacks, deep learning (DL) is proposed in the pre-training and fine-tuning phases to produce an automated feature representation for multi-class classification of arrhythmia conditions. In the pre-training phase, stacked denoising autoencoders (DAEs) and autoencoders (AEs) are used for feature learning; in the fine-tuning phase, deep neural networks (DNNs) are implemented as a classifier. To the best of our knowledge, this research is the first to implement stacked autoencoders by using DAEs and AEs for feature learning in DL. Physionet’s well-known MIT-BIH Arrhythmia Database, as well as the MIT-BIH Noise Stress Test Database (NSTDB). Only four records are used from the NSTDB dataset: 118 24 dB, 118 −6 dB, 119 24 dB, and 119 −6 dB, with two levels of signal-to-noise ratio (SNRs) at 24 dB and −6 dB. In the validation process, six models are compared to select the best DL model. For all fine-tuned hyperparameters, the best model of ECG heartbeat classification achieves an accuracy, sensitivity, specificity, precision, and F1-score of 99.34%, 93.83%, 99.57%, 89.81%, and 91.44%, respectively. As the results demonstrate, the proposed DL model can extract high-level features not only from the training data but also from unseen data. Such a model has good application prospects in clinical practice. Full article

(This article belongs to the Special Issue Sensing and Signal Processing in Smart Healthcare)

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16 pages, 1709 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Novel Extensions to Enhance Scalability and Reliability of the IEEE 802.15.4-DSME Protocol

by Filippo Battaglia, Mario Collotta, Luca Leonardi, Lucia Lo Bello and Gaetano Patti

Electronics 2020, 9(1), 126; https://doi.org/10.3390/electronics9010126 - 9 Jan 2020

Cited by 17 | Viewed by 3516

Abstract

The Deterministic and Synchronous Multichannel Extension (DSME) of the IEEE 802.15.4 standard was designed to fulfill the requirements of commercial and industrial applications. DSME overcomes the IEEE 802.15.4 limitation on the maximum number of Guaranteed Time Slots (GTS) in a superframe and it [...] Read more.

The Deterministic and Synchronous Multichannel Extension (DSME) of the IEEE 802.15.4 standard was designed to fulfill the requirements of commercial and industrial applications. DSME overcomes the IEEE 802.15.4 limitation on the maximum number of Guaranteed Time Slots (GTS) in a superframe and it also exploits channel diversity to increase the communication reliability. However, DSME suffers from scalability problems, as its multi-superframe structure does not efficiently handle GTS in networks with a high number of nodes and periodic flows. This paper proposes the enhanceD DSME (D-DSME), which consists of two extensions that improve the DSME scalability and reliability exploiting a GTS within the multi-superframe to accommodate multiple flows or multiple retransmissions of the same flow. The paper describes the proposed extensions and the performance results of both OMNeT simulations and experiments with real devices implementing the D-DSME. Full article

(This article belongs to the Special Issue Emerging Trends in Industrial Communication)

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10 pages, 2326 KiB

Open AccessEditor’s ChoiceArticle

Numerical Analysis on Effective Mass and Traps Density Dependence of Electrical Characteristics of a-IGZO Thin-Film Transistors

by Jihwan Park, Do-Kyung Kim, Jun-Ik Park, In Man Kang, Jaewon Jang, Hyeok Kim, Philippe Lang and Jin-Hyuk Bae

Electronics 2020, 9(1), 119; https://doi.org/10.3390/electronics9010119 - 8 Jan 2020

Cited by 17 | Viewed by 10442

Abstract

We have investigated the effect of electron effective mass (m_e*) and tail acceptor-like edge traps density (N_TA) on the electrical characteristics of amorphous-InGaZnO (a-IGZO) thin-film transistors (TFTs) through numerical simulation. To examine the credibility of our simulation, [...] Read more.

We have investigated the effect of electron effective mass (m_e*) and tail acceptor-like edge traps density (N_TA) on the electrical characteristics of amorphous-InGaZnO (a-IGZO) thin-film transistors (TFTs) through numerical simulation. To examine the credibility of our simulation, we found that by adjusting m_e* to 0.34 of the free electron mass (m_o), we can preferentially derive the experimentally obtained electrical properties of conventional a-IGZO TFTs through our simulation. Our initial simulation considered the effect of m_e* on the electrical characteristics independent of N_TA. We varied the m_e* value while not changing the other variables related to traps density not dependent on it. As m_e* was incremented to 0.44 m_o, the field-effect mobility (µ_fe) and the on-state current (I_on) decreased due to the higher sub-gap scattering based on electron capture behavior. However, the threshold voltage (V_th) was not significantly changed due to fixed effective acceptor-like traps (N_TA). In reality, since the magnitude of N_TA was affected by the magnitude of m_e*, we controlled m_e* together with N_TA value as a secondary simulation. As the magnitude of both m_e* and N_TA increased, µ_fe and Ion deceased showing the same phenomena as the first simulation. The magnitude of V_th was higher when compared to the first simulation due to the lower conductivity in the channel. In this regard, our simulation methods showed that controlling m_e* and N_TA simultaneously would be expected to predict and optimize the electrical characteristics of a-IGZO TFTs more precisely. Full article

(This article belongs to the Section Semiconductor Devices)

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15 pages, 2107 KiB

Open AccessEditor’s ChoiceArticle

An Automatic Diagnosis of Arrhythmias Using a Combination of CNN and LSTM Technology

by Zhenyu Zheng, Zhencheng Chen, Fangrong Hu, Jianming Zhu, Qunfeng Tang and Yongbo Liang

Electronics 2020, 9(1), 121; https://doi.org/10.3390/electronics9010121 - 8 Jan 2020

Cited by 81 | Viewed by 6710

Abstract

Electrocardiogram (ECG) signal evaluation is routinely used in clinics as a significant diagnostic method for detecting arrhythmia. However, it is very labor intensive to externally evaluate ECG signals, due to their small amplitude. Using automated detection and classification methods in the clinic can [...] Read more.

Electrocardiogram (ECG) signal evaluation is routinely used in clinics as a significant diagnostic method for detecting arrhythmia. However, it is very labor intensive to externally evaluate ECG signals, due to their small amplitude. Using automated detection and classification methods in the clinic can assist doctors in making accurate and expeditious diagnoses of diseases. In this study, we developed a classification method for arrhythmia based on the combination of a convolutional neural network and long short-term memory, which was then used to diagnose eight ECG signals, including a normal sinus rhythm. The ECG data of the experiment were derived from the MIT-BIH arrhythmia database. The experimental method mainly consisted of two parts. The input data of the model were two-dimensional grayscale images converted from one-dimensional signals, and detection and classification of the input data was carried out using the combined model. The advantage of this method is that it does not require performing feature extraction or noise filtering on the ECG signal. The experimental results showed that the implemented method demonstrated high classification performance in terms of accuracy, specificity, and sensitivity equal to 99.01%, 99.57%, and 97.67%, respectively. Our proposed model can assist doctors in accurately detecting arrhythmia during routine ECG screening. Full article

(This article belongs to the Section Bioelectronics)

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19 pages, 3999 KiB

Open AccessEditor’s ChoiceCommunication

Floating Car Data Adaptive Traffic Signals: A Description of the First Real-Time Experiment with “Connected” Vehicles

by Vittorio Astarita, Vincenzo Pasquale Giofré, Demetrio Carmine Festa, Giuseppe Guido and Alessandro Vitale

Electronics 2020, 9(1), 114; https://doi.org/10.3390/electronics9010114 - 7 Jan 2020

Cited by 24 | Viewed by 7625

Abstract

The future of traffic management will be based on “connected” and “autonomous” vehicles. With connected vehicles it is possible to gather real-time information. The main potential application of this information is in real-time adaptive traffic signal control. Despite the feasibility of using Floating [...] Read more.

The future of traffic management will be based on “connected” and “autonomous” vehicles. With connected vehicles it is possible to gather real-time information. The main potential application of this information is in real-time adaptive traffic signal control. Despite the feasibility of using Floating Car Data (FCD), for signal control, there have been practically no real experiments with all “connected” vehicles to regulate traffic signals in real-time. Most of the research in this field has been carried out with simulations. The purpose of this study is to present a dedicated system that was implemented in the first experiment of an FCD-based adaptive traffic signal. For the first time in the history of traffic management, a traffic signal has been regulated in real time with real “connected” vehicles. This paper describes the entire path of software and system development that has allowed us to make the steps from just simulation test to a real on-field implementation. Results of the experiments carried out with the presented system prove the feasibility of FCD adaptive traffic signals with commonly-used technologies and also establishes a test-bed that may help others to develop better regulation algorithms for these kinds of new “connected” intersections. Full article

(This article belongs to the Special Issue Intelligent Transportation Systems (ITS))

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16 pages, 3041 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Mobilities in Network Topology and Simulation Reproducibility of Sightseeing Vehicle Detected by Low-Power Wide-Area Positioning System

by Keigo Yamamoto, Jun Yoshida, Shigeyuki Miyagi, Shinsuke Minami, Daisuke Minami and Osamu Sakai

Electronics 2020, 9(1), 116; https://doi.org/10.3390/electronics9010116 - 7 Jan 2020

Cited by 3 | Viewed by 3025

Abstract

Vehicle mobilities for passengers in a city’s downtown area or in the countryside are significant points to characterize their functions and outputs. We focus on commercial sightseeing vehicles in a Japanese city where many tourists enjoy sightseeing. Such mobilities and their visualizations make [...] Read more.

Vehicle mobilities for passengers in a city’s downtown area or in the countryside are significant points to characterize their functions and outputs. We focus on commercial sightseeing vehicles in a Japanese city where many tourists enjoy sightseeing. Such mobilities and their visualizations make tourist activities smoother and richer. We design and install a low-power, wide-area positioning system on a rickshaw, which is a human-pulled, two- or three-wheeled cart, and monitor its mobility in Hikone City. All the spatial locations, which are recorded in a time sequence on a cloud server, are currently available as open data on the internet. We analyze such sequential data using graph topology, which reflects the information of corresponding geographical maps, and reproduce it in cyberspace using an agent-based model with similar probabilities to the accumulated rickshaw records from one spatial node to another. Although the numerical results of the agent traced in a simulated city are partially consistent with the rickshaw’s record, we identify some significant differences. We conclude that the rickshaw’s mobility observed at the actual sightseeing sites is partially in the random motion; some cases are strongly biased by memory routes. Such non-randomness in the rickshaw’s mobility indicates the existence of specific features in tourism sources that are identified for each sightseeing activity and affected by local sightseeing resources. Full article

(This article belongs to the Section Networks)

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18 pages, 11088 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Interference of Spread-Spectrum EMI and Digital Data Links under Narrowband Resonant Coupling

by Paolo Stefano Crovetti and Francesco Musolino

Electronics 2020, 9(1), 60; https://doi.org/10.3390/electronics9010060 - 1 Jan 2020

Cited by 17 | Viewed by 4555

Abstract

In this paper, the effects of electromagnetic interference (EMI) coupled to a radio-frequency (RF) communication channel by resonant mechanisms are investigated and described in the framework of Shannon information theory in terms of an equivalent channel capacity loss so that to analyze and [...] Read more.

In this paper, the effects of electromagnetic interference (EMI) coupled to a radio-frequency (RF) communication channel by resonant mechanisms are investigated and described in the framework of Shannon information theory in terms of an equivalent channel capacity loss so that to analyze and compare the effects of non-modulated and random Spread Spectrum (SS) modulated EMI. The analysis reveals a higher EMI-induced capacity loss for SS-modulated compared to non modulated EMI under practical values of the quality factor Q, while a modest improvement in the worst case capacity loss is observed only for impractical values of Q. Simulations on a 4-quadrature amplitude modulation (4-QAM) digital link featuring Turbo coding under EMI resonant coupling reveal that SS-modulated EMI gives rise to higher bit error rate (BER) at lower EMI power compared non-modulated EMI in the presence of resonant coupling for practical values of Q, thus suggesting a worse interfering potential of SS-modulated EMI. Full article

(This article belongs to the Special Issue Electromagnetic Interference and Compatibility)

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13 pages, 4935 KiB

Open AccessEditor’s ChoiceArticle

4-Port MIMO Antenna with Defected Ground Structure for 5G Millimeter Wave Applications

by Mahnoor Khalid, Syeda Iffat Naqvi, Niamat Hussain, MuhibUr Rahman, Fawad, Seyed Sajad Mirjavadi, Muhammad Jamil Khan and Yasar Amin

Electronics 2020, 9(1), 71; https://doi.org/10.3390/electronics9010071 - 1 Jan 2020

Cited by 352 | Viewed by 14594

Abstract

We present a 4-port Multiple-Input-Multiple-Output (MIMO) antenna array operating in the mm-wave band for 5G applications. An identical two-element array excited by the feed network based on a T-junction power combiner/divider is introduced in the reported paper. The array elements are rectangular-shaped slotted [...] Read more.

We present a 4-port Multiple-Input-Multiple-Output (MIMO) antenna array operating in the mm-wave band for 5G applications. An identical two-element array excited by the feed network based on a T-junction power combiner/divider is introduced in the reported paper. The array elements are rectangular-shaped slotted patch antennas, while the ground plane is made defected with rectangular, circular, and a zigzag-shaped slotted structure to enhance the radiation characteristics of the antenna. To validate the performance, the MIMO structure is fabricated and measured. The simulated and measured results are in good coherence. The proposed structure can operate in a 25.5–29.6 GHz frequency band supporting the impending mm-wave 5G applications. Moreover, the peak gain attained for the operating frequency band is 8.3 dBi. Additionally, to obtain high isolation between antenna elements, the polarization diversity is employed between the adjacent radiators, resulting in a low Envelope Correlation Coefficient (ECC). Other MIMO performance metrics such as the Channel Capacity Loss (CCL), Mean Effective Gain (MEG), and Diversity gain (DG) of the proposed structure are analyzed, and the results indicate the suitability of the design as a potential contender for imminent mm-wave 5G MIMO applications. Full article

(This article belongs to the Special Issue Antennas and Propagation Aspects for Emerging Wireless Communication Technologies)

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16 pages, 3904 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Common Mode Voltage Elimination for Quasi-Switch Boost T-Type Inverter Based on SVM Technique

by Duc-Tri Do, Minh-Khai Nguyen, Van-Thuyen Ngo, Thanh-Hai Quach and Vinh-Thanh Tran

Electronics 2020, 9(1), 76; https://doi.org/10.3390/electronics9010076 - 1 Jan 2020

Cited by 16 | Viewed by 3843

Abstract

In this paper, the effect of common-mode voltage generated in the three-level quasi-switched boost T-type inverter is minimized by applying the proposed space-vector modulation technique, which uses only medium vectors and zero vector to synthesize the reference vector. The switching sequence is selected [...] Read more.

In this paper, the effect of common-mode voltage generated in the three-level quasi-switched boost T-type inverter is minimized by applying the proposed space-vector modulation technique, which uses only medium vectors and zero vector to synthesize the reference vector. The switching sequence is selected smoothly for inserting the shoot-through state for the inverter branch. The shoot-through vector is added within the zero vector in order to not affect the active vectors as well as the output voltage. In addition, the shoot-through control signal of active switches of the impedance network is generated to ensure that its phase is shifted 90 degrees compared to shoot through the signal of the inverter leg, which provides an improvement in reducing the inductor current ripple and enhancing the voltage gain. The effectiveness of the proposed method is verified through simulation and experimental results. In addition, the superiority of the proposed scheme is demonstrated by comparing it to the conventional pulse-width modulation technique. Full article

(This article belongs to the Special Issue Power Converters in Power Electronics)

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14 pages, 10153 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Low-Speed Performance Improvement of Direct Torque Control for Induction Motor Drives Fed by Three-Level NPC Inverter

by Samer Saleh Hakami, Ibrahim Mohd Alsofyani and Kyo-Beum Lee

Electronics 2020, 9(1), 77; https://doi.org/10.3390/electronics9010077 - 1 Jan 2020

Cited by 12 | Viewed by 4003

Abstract

Classical direct torque control (DTC) is considered one of the simplest and fastest control algorithms in motor drives. However, it produces high torque and flux ripples due to the implementation of the three-level hysteresis torque regulator (HTR). Although, increasing the level of HTR [...] Read more.

Classical direct torque control (DTC) is considered one of the simplest and fastest control algorithms in motor drives. However, it produces high torque and flux ripples due to the implementation of the three-level hysteresis torque regulator (HTR). Although, increasing the level of HTR and utilizing multilevel inverters has a great contribution in torque and flux ripples reduction, stator flux magnitude of induction motor (IM) droops at every switching sector transition, particularly at low-speed operation. This problem occurs due to the utilization of a zero voltage vector, where the domination of stator resistance is very high. A simple flux regulation strategy (SFRS) is applied for low-speed operation for DTC of IM. The proposed DTC-SFRS modifies the output status of the five-level HTR depending on the flux error, torque error, and stator flux position. This method regulates the stator flux for both forward and reverse rotational directions of IM with retaining the basic structure of classical DTC. By using the proposed algorithm, the stator flux is regulated, hence pure sinusoidal current waveform is achieved, which results in lower total harmonics distortion (THD). The effectiveness of the proposed DTC-SFRS is verified through simulation and experimental results. Full article

(This article belongs to the Special Issue High Power Electric Traction Systems)

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16 pages, 4395 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Development of a Compact, IoT-Enabled Electronic Nose for Breath Analysis

by Akira Tiele, Alfian Wicaksono, Sai Kiran Ayyala and James A. Covington

Electronics 2020, 9(1), 84; https://doi.org/10.3390/electronics9010084 - 1 Jan 2020

Cited by 51 | Viewed by 9177

Abstract

In this paper, we report on an in-house developed electronic nose (E-nose) for use with breath analysis. The unit consists of an array of 10 micro-electro-mechanical systems (MEMS) metal oxide (MOX) gas sensors produced by seven manufacturers. Breath sampling of end-tidal breath is [...] Read more.

In this paper, we report on an in-house developed electronic nose (E-nose) for use with breath analysis. The unit consists of an array of 10 micro-electro-mechanical systems (MEMS) metal oxide (MOX) gas sensors produced by seven manufacturers. Breath sampling of end-tidal breath is achieved using a heated sample tube, capable of monitoring sampling-related parameters, such as carbon dioxide (CO₂), humidity, and temperature. A simple mobile app was developed to receive real-time data from the device, using Wi-Fi communication. The system has been tested using chemical standards and exhaled breath samples from healthy volunteers, before and after taking a peppermint capsule. Results from chemical testing indicate that we can separate chemical standards (acetone, isopropanol and 1-propanol) and different concentrations of isobutylene. The analysis of exhaled breath samples demonstrate that we can distinguish between pre- and post-consumption of peppermint capsules; area under the curve (AUC): 0.81, sensitivity: 0.83 (0.59–0.96), specificity: 0.72 (0.47–0.90), p-value: <0.001. The functionality of the developed device has been demonstrated with the testing of chemical standards and a simplified breath study using peppermint capsules. It is our intention to deploy this system in a UK hospital in an upcoming breath research study. Full article

(This article belongs to the Special Issue Design and Application of Biomedical Circuits and Systems)

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18 pages, 3546 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Face–Iris Multimodal Biometric Identification System

by Basma Ammour, Larbi Boubchir, Toufik Bouden and Messaoud Ramdani

Electronics 2020, 9(1), 85; https://doi.org/10.3390/electronics9010085 - 1 Jan 2020

Cited by 82 | Viewed by 10141

Abstract

Multimodal biometrics technology has recently gained interest due to its capacity to overcome certain inherent limitations of the single biometric modalities and to improve the overall recognition rate. A common biometric recognition system consists of sensing, feature extraction, and matching modules. The robustness [...] Read more.

Multimodal biometrics technology has recently gained interest due to its capacity to overcome certain inherent limitations of the single biometric modalities and to improve the overall recognition rate. A common biometric recognition system consists of sensing, feature extraction, and matching modules. The robustness of the system depends much more on the reliability to extract relevant information from the single biometric traits. This paper proposes a new feature extraction technique for a multimodal biometric system using face–iris traits. The iris feature extraction is carried out using an efficient multi-resolution 2D Log-Gabor filter to capture textural information in different scales and orientations. On the other hand, the facial features are computed using the powerful method of singular spectrum analysis (SSA) in conjunction with the wavelet transform. SSA aims at expanding signals or images into interpretable and physically meaningful components. In this study, SSA is applied and combined with the normal inverse Gaussian (NIG) statistical features derived from wavelet transform. The fusion process of relevant features from the two modalities are combined at a hybrid fusion level. The evaluation process is performed on a chimeric database and consists of Olivetti research laboratory (ORL) and face recognition technology (FERET) for face and Chinese academy of science institute of automation (CASIA) v3.0 iris image database (CASIA V3) interval for iris. Experimental results show the robustness. Full article

(This article belongs to the Special Issue Recent Advances in Biometrics and its Applications)

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28 pages, 9694 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Design of an Intrinsically Safe Series-Series Compensation WPT System for Automotive LiDAR

by Luiz A. Lisboa Cardoso, Vítor Monteiro, José Gabriel Pinto, Miguel Nogueira, Adérito Abreu, José A. Afonso and João L. Afonso

Electronics 2020, 9(1), 86; https://doi.org/10.3390/electronics9010086 - 1 Jan 2020

Cited by 2 | Viewed by 3820

Abstract

The earliest and simplest impedance compensation technique used in inductive wireless power transfer (WPT) design is the series-series (SS) compensation circuit, which uses capacitors in series with both primary and secondary coils of an air-gapped transformer. Despite of its simplicity at the resonant [...] Read more.

The earliest and simplest impedance compensation technique used in inductive wireless power transfer (WPT) design is the series-series (SS) compensation circuit, which uses capacitors in series with both primary and secondary coils of an air-gapped transformer. Despite of its simplicity at the resonant condition, this configuration exhibits a major sensitivity to variations of the load attached to the secondary, especially when higher coupling coefficients are used in the design. In the extreme situation that the secondary coil is left at open circuit, the current at the primary coil may increase above the safety limits for either the power converter driving the primary coil or the components in the primary circuit, including the coil itself. An approach often used to minimize this problem is detuning, but this also reduces the electrical efficiency of the power transfer. In low power, fixed-distance stationary WPT, a fair trade-off between efficiency and safety must be verified. This paper aims to consolidate a simple design procedure for such a SS-compensation, exemplifying its use in the prototype of a WPT system for automotive light detection and ranging (LiDAR) equipment. The guidelines herein provided should equally apply to other low power applications. Full article

(This article belongs to the Section Power Electronics)

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9 pages, 3402 KiB

Open AccessEditor’s ChoiceArticle

A Compact 3.3–3.5 GHz Filter Based on Modified Composite Right-/Left-Handed Resonator Units

by Shanwen Hu, Yunqing Hu, Haiyu Zheng, Weiguang Zhu, Yiting Gao and Xiaodong Zhang

Electronics 2020, 9(1), 1; https://doi.org/10.3390/electronics9010001 - 18 Dec 2019

Cited by 12 | Viewed by 4566

Abstract

In the RF (Radio Frequency) front-end of a communication system, bandpass filters (BPFs) are used to send passband signals and reject stopband signals. Substrate-integrated waveguides (SIW) are widely used in RF filter designs due to their low loss and low cost and the [...] Read more.

In the RF (Radio Frequency) front-end of a communication system, bandpass filters (BPFs) are used to send passband signals and reject stopband signals. Substrate-integrated waveguides (SIW) are widely used in RF filter designs due to their low loss and low cost and the flexibility of their integration properties. However, SIW filters under 6 GHz are still too large to meet the requirement of portable communication devices due to their long wavelength. In this paper, a very compact fully integrated SIW filter is proposed and designed with RT6010 dielectric material to meet the small size requirement of portable devices for next-generation sub-6 G applications. The proposed filter contains two sawtooth-shaped composite right-/left-handed (CRLH) resonator units, instead of traditional rectangular-shaped CRLH resonator units, which makes the filter more compact and cost effective. The filter is designed and fabricated on an RT6010 substrate, with a size of only 10 mm × 7.4 mm. The measurement results illustrated that the proposed BPF shows a passband covering the frequency range of 3.25–3.45 GHz; the minimum passband insertion loss is only 2.4 dB; the stopband rejection is better than −20 dB throughout the frequencies below 3.0 GHz and above 3.8 GHz; S11 is as low as −37 dB at 3.35 GHz; and the group delay variation is only 1.4 ns throughout the operation bandwidth. Full article

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

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18 pages, 5456 KiB

Open AccessEditor’s ChoiceArticle

A Deep Learning-Based Scatter Correction of Simulated X-ray Images

by Heesin Lee and Joonwhoan Lee

Electronics 2019, 8(9), 944; https://doi.org/10.3390/electronics8090944 - 27 Aug 2019

Cited by 34 | Viewed by 7819

Abstract

X-ray scattering significantly limits image quality. Conventional strategies for scatter reduction based on physical equipment or measurements inevitably increase the dose to improve the image quality. In addition, scatter reduction based on a computational algorithm could take a large amount of time. We [...] Read more.

X-ray scattering significantly limits image quality. Conventional strategies for scatter reduction based on physical equipment or measurements inevitably increase the dose to improve the image quality. In addition, scatter reduction based on a computational algorithm could take a large amount of time. We propose a deep learning-based scatter correction method, which adopts a convolutional neural network (CNN) for restoration of degraded images. Because it is hard to obtain real data from an X-ray imaging system for training the network, Monte Carlo (MC) simulation was performed to generate the training data. For simulating X-ray images of a human chest, a cone beam CT (CBCT) was designed and modeled as an example. Then, pairs of simulated images, which correspond to scattered and scatter-free images, respectively, were obtained from the model with different doses. The scatter components, calculated by taking the differences of the pairs, were used as targets to train the weight parameters of the CNN. Compared with the MC-based iterative method, the proposed one shows better results in projected images, with as much as 58.5% reduction in root-mean-square error (RMSE), and 18.1% and 3.4% increases in peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM), on average, respectively. Full article

(This article belongs to the Special Issue Deep Neural Networks and Their Applications)

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20 pages, 36563 KiB

Open AccessEditor’s ChoiceArticle

Fallen People Detection Capabilities Using Assistive Robot

by Saturnino Maldonado-Bascón, Cristian Iglesias-Iglesias, Pilar Martín-Martín and Sergio Lafuente-Arroyo

Electronics 2019, 8(9), 915; https://doi.org/10.3390/electronics8090915 - 21 Aug 2019

Cited by 43 | Viewed by 8139

Abstract

One of the main problems in the elderly population and for people with functional disabilities is falling when they are not supervised. Therefore, there is a need for monitoring systems with fall detection functionality. Mobile robots are a good solution for keeping the [...] Read more.

One of the main problems in the elderly population and for people with functional disabilities is falling when they are not supervised. Therefore, there is a need for monitoring systems with fall detection functionality. Mobile robots are a good solution for keeping the person in sight when compared to static-view sensors. Mobile-patrol robots can be used for a group of people and systems are less intrusive than ones based on mobile robots. In this paper, we propose a novel vision-based solution for fall detection based on a mobile-patrol robot that can correct its position in case of doubt. The overall approach can be formulated as an end-to-end solution based on two stages: person detection and fall classification. Deep learning-based computer vision is used for person detection and fall classification is done by using a learning-based Support Vector Machine (SVM) classifier. This approach mainly fulfills the following design requirements—simple to apply, adaptable, high performance, independent of person size, clothes, or the environment, low cost and real-time computing. Important to highlight is the ability to distinguish between a simple resting position and a real fall scene. One of the main contributions of this paper is the input feature vector to the SVM-based classifier. We evaluated the robustness of the approach using a realistic public dataset proposed in this paper called the Fallen Person Dataset (FPDS), with 2062 images and 1072 falls. The results obtained from different experiments indicate that the system has a high success rate in fall classification (precision of 100% and recall of 99.74%). Training the algorithm using our Fallen Person Dataset (FPDS) and testing it with other datasets showed that the algorithm is independent of the camera setup. Full article

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

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18 pages, 3513 KiB

Open AccessEditor’s ChoiceArticle

Multivariate Temporal Convolutional Network: A Deep Neural Networks Approach for Multivariate Time Series Forecasting

by Renzhuo Wan, Shuping Mei, Jun Wang, Min Liu and Fan Yang

Electronics 2019, 8(8), 876; https://doi.org/10.3390/electronics8080876 - 7 Aug 2019

Cited by 290 | Viewed by 27651

Abstract

Multivariable time series prediction has been widely studied in power energy, aerology, meteorology, finance, transportation, etc. Traditional modeling methods have complex patterns and are inefficient to capture long-term multivariate dependencies of data for desired forecasting accuracy. To address such concerns, various deep learning [...] Read more.

Multivariable time series prediction has been widely studied in power energy, aerology, meteorology, finance, transportation, etc. Traditional modeling methods have complex patterns and are inefficient to capture long-term multivariate dependencies of data for desired forecasting accuracy. To address such concerns, various deep learning models based on Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) methods are proposed. To improve the prediction accuracy and minimize the multivariate time series data dependence for aperiodic data, in this article, Beijing PM2.5 and ISO-NE Dataset are analyzed by a novel Multivariate Temporal Convolution Network (M-TCN) model. In this model, multi-variable time series prediction is constructed as a sequence-to-sequence scenario for non-periodic datasets. The multichannel residual blocks in parallel with asymmetric structure based on deep convolution neural network is proposed. The results are compared with rich competitive algorithms of long short term memory (LSTM), convolutional LSTM (ConvLSTM), Temporal Convolution Network (TCN) and Multivariate Attention LSTM-FCN (MALSTM-FCN), which indicate significant improvement of prediction accuracy, robust and generalization of our model. Full article

(This article belongs to the Special Issue Deep Learning Applications with Practical Measured Results in Electronics Industries)

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34 pages, 364 KiB

Open AccessEditor’s ChoiceReview

Machine Learning Interpretability: A Survey on Methods and Metrics

by Diogo V. Carvalho, Eduardo M. Pereira and Jaime S. Cardoso

Electronics 2019, 8(8), 832; https://doi.org/10.3390/electronics8080832 - 26 Jul 2019

Cited by 1250 | Viewed by 68241

Abstract

Machine learning systems are becoming increasingly ubiquitous. These systems’s adoption has been expanding, accelerating the shift towards a more algorithmic society, meaning that algorithmically informed decisions have greater potential for significant social impact. However, most of these accurate decision support systems remain complex [...] Read more.

Machine learning systems are becoming increasingly ubiquitous. These systems’s adoption has been expanding, accelerating the shift towards a more algorithmic society, meaning that algorithmically informed decisions have greater potential for significant social impact. However, most of these accurate decision support systems remain complex black boxes, meaning their internal logic and inner workings are hidden to the user and even experts cannot fully understand the rationale behind their predictions. Moreover, new regulations and highly regulated domains have made the audit and verifiability of decisions mandatory, increasing the demand for the ability to question, understand, and trust machine learning systems, for which interpretability is indispensable. The research community has recognized this interpretability problem and focused on developing both interpretable models and explanation methods over the past few years. However, the emergence of these methods shows there is no consensus on how to assess the explanation quality. Which are the most suitable metrics to assess the quality of an explanation? The aim of this article is to provide a review of the current state of the research field on machine learning interpretability while focusing on the societal impact and on the developed methods and metrics. Furthermore, a complete literature review is presented in order to identify future directions of work on this field. Full article

(This article belongs to the Section Artificial Intelligence)

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