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Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

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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 11 | Viewed by 3710

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 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 24 | Viewed by 6058

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 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 32 | Viewed by 6538

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 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 - 07 Aug 2019

Cited by 192 | Viewed by 21665

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 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 804 | Viewed by 49891

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 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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