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IOT, Electronics and Mechatronics Conference (IEMTRONICS), IEEE International

作者： Dina Nawara Rasha Kashef Electrical Computer and Biomedical Engineering Department Ryerson Umiversity Toronto Ontario Canada

ISBN: (数字)9781728196152

ISBN: (纸本)9781728196169

Internet of Things (IoT) has emerged in many industries, such as health care, transportation, agriculture, manufacturing, smart homes, to name a few. It paves the path for massive applications on the user level to enhance the quality of life or service, and on the decision-makers' level to provide a sustainable increase in revenue. IoT principally connects different physical objects (e.g., sensors) and enables them to communicate, collect, and share data. In the Era of IoT, Recommendation systems provide personalized recommendations based on the user's historical datasets collected from the IoT devices. These recommendations enable an efficient decision-making process by suggesting relevant products, resources, and information. This paper provides an overview of various multi-layers IoT architectures, and IoT-based recommendation systems with an emphasis on their advantages, disadvantages, application domains, and validation metrics for quality assessment.

关键词： computer architecture Medical services Recommender systems Smart homes Internet of Things Sensors

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Missing value imputation affects the performance of machine learning: A review and analysis of the literature (2010–2021)

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Informatics in Medicine Unlocked 2021年 27卷

作者： Hasan, Md. Kamrul Alam, Md. Ashraful Roy, Shidhartho Dutta, Aishwariya Jawad, Md. Tasnim Das, Sunanda Department of Electrical and Electronic Engineering (EEE) Khulna University of Engineering & Technology (KUET) Khulna 9203 Bangladesh Department of Biomedical Engineering (BME) Khulna University of Engineering & Technology (KUET) Khulna 9203 Bangladesh Department of Computer Science and Engineering (CSE) Khulna University of Engineering & Technology (KUET) Khulna 9203 Bangladesh

Recently, numerous studies have been conducted on Missing Value Imputation (MVI), intending the primary solution scheme for the datasets containing one or more missing attribute's values. The incorporation of MVI reinforces the Machine Learning (ML) models’ performance and necessitates a systematic review of MVI methodologies employed for different tasks and datasets. It will aid beginners as guidance towards composing an effective ML-based decision-making system in various fields of applications. This article aims to conduct a rigorous review and analysis of the state-of-the-art MVI methods in the literature published in the last decade. Altogether, 191 articles, published from 2010 to August 2021, are selected for review using the well-known Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) technique. We summarize those articles with relevant definitions, theories, and analyses to provide essential information for building a precise decision-making framework. In addition, the evaluation metrics employed for MVI methods and ML-based classification models are also discussed and explored. Remarkably, the trends for the MVI method and its evaluation are also scrutinized from the last twelve years’ data. To come up with the conclusion, several ML-based pipelines, where the MVI schemes are incorporated for performance enhancement, are investigated and reviewed for many different datasets. In the end, informative observations and recommendations are addressed for future research directions and trends in related fields of interest. © 2021 The Authors

关键词： Imputation methods and evaluations Incomplete datasets Machine learning classifiers and evaluations PRISMA technique

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Z-Number Based Fuzzy Neural Network for System Identification

SSRN

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SSRN 2022年

作者： Abiyev, Rahib H. Kaynak, Okyay Aliev, Rafiq Near East University Department of Computer Engineering Mersin-10 North Cyprus Lefkosa Turkey Bogazici University Department of Electrical and Electronics Engineering Bebek Istanbul34342 Turkey Joint MBA Program Georgia State University United States Azerbaijan State Oil and Industry University 20 Azadliq ave. BakuAZ1010 Azerbaijan

In this paper, a novel Z-number based Fuzzy Neural Network (Z-FNN) is proposed for dynamic system identification. The architecture and learning algorithm of the proposed Z-FNN are designed. The inference mechanism of Z-FNN is based on distance measures and interpolative reasoning. The structure of the Z-FNN is accordingly designed using an interpolative reasoning scheme. Genetic Algorithms (GA) is applied to update the unknown parameters of the network. The proposed Z-FNN system is applied for dynamic plant identification. Its performance is compared with those obtained by the use of other approaches from the literature and it is proved that the proposed approach is a suitable alternative for practical applications that are characterized by uncertain and imprecise information. © 2022, The Authors. All rights reserved.

关键词： Fuzzy neural networks

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Spectral2Spectral: Image-spectral Similarity Assisted Deep Spectral CT Reconstruction without Reference

arXiv

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arXiv 2022年

作者： Guo, Xiaodong Li, Yonghui Chang, Dingyue He, Peng Feng, Peng Yu, Hengyong Wu, Weiwen The Key Lab of Optoelectronic Technology and Systems of the Education Ministry of China Chongqing University Chongqing China The China Academy of Engineering Physics Institute of Materials Mianyang China The Department of Electrical and Computer Engineering University of Massachusetts Lowell LowellMA01854 United States The School of Biomedical Engineering Sun Yat-Sen University Guangdong Shenzhen China The Henan Key Laboratory of Imaging and Intelligent Processing PLA Strategic Support Force Information Engineering University Zhengzhou China

Spectral computed tomography based on a photon-counting detector (PCD) attracts more and more attentions since it has the capability to provide more accurate identification and quantitative analysis for biomedical materials. The limited number of photons within narrow energy bins leads to imaging results of low signal-noise ratio. The existing supervised deep reconstruction networks for CT reconstruction are difficult to address these challenges because it is usually impossible to acquire noise-free clinical images with clear structures as references. In this paper, we propose an iterative deep reconstruction network to synergize unsupervised method and data priors into a unified framework, named as Spectral2Spectral. Our Spectral2Spectral employs an unsupervised deep training strategy to obtain high-quality images from noisy data in an end-to-end fashion. The structural similarity prior within image-spectral domain is refined as a regularization term to further constrain the network training. The weights of neural network are automatically updated to capture image features and structures within the iterative process. Three large-scale preclinical datasets experiments demonstrate that the Spectral2spectral reconstructs better image quality than other the state-of-the-art methods. © 2022, CC0.

关键词： Image reconstruction

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Consistency Regularization Improves Placenta Segmentation in Fetal EPI MRI Time Series

arXiv

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arXiv 2023年

作者： Liu, Yingcheng Karani, Neerav Dey, Neel Abulnaga, S. Mazdak Xu, Junshen Grant, P. Ellen Turk, Esra Abaci Golland, Polina Computer Science and Artificial Intelligence Lab Massachusetts Institute of Technology CambridgeMA United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Fetal-Neonatal Neuroimaging and Developmental Science Center Boston Children's Hospital Harvard Medical School BostonMA United States Martinos Center for Biomedical Imaging Harvard Medical School BostonMA United States

The placenta plays a crucial role in fetal development. Automated 3D placenta segmentation from fetal EPI MRI holds promise for advancing prenatal care. This paper proposes an effective semi-supervised learning method for improving placenta segmentation in fetal EPI MRI time series. We employ consistency regularization loss that promotes consistency under spatial transformation of the same image and temporal consistency across nearby images in a time series. The experimental results show that the method improves the overall segmentation accuracy and provides better performance for outliers and hard samples. The evaluation also indicates that our method improves the temporal coherency of the prediction, which could lead to more accurate computation of temporal placental biomarkers. This work contributes to the study of the placenta and prenatal clinical decision-making. Code is available at https://***/firstmover/cr-seg. Copyright © 2023, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Highway Traffic Control via Smart e-Mobility – Part II: Dutch A13 Case Study

arXiv

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arXiv 2021年

作者： Cenedese, Carlo Cucuzzella, Michele Scherpen, Jacquelien M.A. Grammatico, Sergio Cao, Ming Department of Information Technology and Electrical Engineering ETH Zürich Zurich Switzerland Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy Jan C. Willems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen Netherlands Delft Center for Systems and Control TU Delft Netherlands

In this paper, we study how to alleviate highway traffic congestions by encouraging plug-in electric and hybrid vehicles to stop at charging stations around peak congestion times. Specifically, we focus on a case study and simulate the adoption of a dynamic charging price depending on the traffic congestion. We use real traffic data of the A13 highway stretch between The Hague and Rotterdam, in The Netherlands, to identify the Cell Transmission Model. Then, we apply the algorithm proposed in [1](Part I: Theory) to different scenarios, validating the theoretical results and showing the benefits of our strategy in terms of traffic congestion alleviation. Finally, we carry out a sensitivity analysis of the proposed algorithm and discuss how to optimize its performance. Copyright © 2021, The Authors. All rights reserved.

关键词： Traffic congestion

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A comparative analysis of reactive power compensation using reactors and STATCOMs in primary substations: a case study in Milan, Italy

A comparative analysis of reactive power compensation using ...

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AEIT Annual Conference

作者： Luigi Sandrini Alessandro Bosisio Alessandro Cirocco Mario Turrisi Caterina Pasetti Andrea Morotti Luca Cavalletto Energy Planning Department Unareti S.p.A. Milan Italy Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy Technological Development Department A2A S.p.A. Milan Italy Planning Department Unareti S.p.A. Milan Italy Electricity Grid Development Unareti S.p.A. Milan Italy Asset Management Department A2A S.p.A. Milan Italy

The article aims to compare the benefits derived from the installation of two different technologies for reactive power compensation systems in distribution networks: reactors and static synchronous compensators. The analysis, conducted on the Unareti electrical distribution grid, explicitly evaluates these two technologies’ help when installed at the primary substation level, considering long-term scenarios to identify the technology that provides more significant benefits throughout its lifespan. Starting from the actual energy flows recorded over one year in the eleven primary substations of Milan and Rozzano, and considering the impact of the energy transition on electricity demand as well as the consequent predicted evolution of the electrical grid resulting from the expected commissioning of eight new primary substations, the article assesses the benefits provided by the two different technologies in both the current (AS IS) and the future (TO BE) scenario by 2050. The simulation results demonstrate that due to the ongoing energy transition and the consequent shift of energy consumption to the electrical vector, static synchronous compensators can offer greater flexibility in response to changes in the operating conditions that will affect distribution networks in the coming years.

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A Novel Galvanic Coupling Testbed Based on PC Sound Card for Intra-body Communication Links 14th

A Novel Galvanic Coupling Testbed Based on PC Sound Card for...

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14th EAI International Conference on Body Area Networks, BodyNets 2019

作者： Vizziello, Anna Savazzi, Pietro Kulsoom, Farzana Magenes, Giovanni Gamba, Paolo Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia27100 Italy

ISBN: (纸本)9783030348328

Intra-Body Communication (IBC) is an emerging research area that will transform the personalized medicine by allowing real time and in situ monitoring in daily life. A galvanic coupling (GC) technology is used in this work to send data through weak currents for intra-body links, as an energy efficient alternative to the current radio frequency (RF) solutions. A sound card based GC testbed is here designed and implemented, whose main features are: (i) low equipment requirements since it only employs two ordinary PCs and Matlab software, (ii) high flexibility because all the parameters setting may be modified through Matlab programs, and (iii) real time physiological data set transmissions. Experimental evaluation with a real chicken tissue are conducted in terms of bit error rate (BER) proving the feasibility of the proposed solution. The developed GC testbed may be easily replicated by the interested research community to carry out simulation-based experiments, thus fostering new research in this field. © 2019, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Sensor networks

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Nanoplasmonic microbubble and nanoplasmonic sensing – Two birds in one stone

Nanoplasmonic microbubble and nanoplasmonic sensing – Two b...

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Optical Sensors, Sensors 2020 - Part of Optical Sensors and Sensing Congress 2020

作者： Ohannesian, Nareg Misbah, Ibrahim Shih, Wei-Chuan Department of Electrical and Computer Engineering University of Houston 4800 Calhoun Rd HoustonTX77004 United States Department of Biomedical Engineering University of Houston 4800 Calhoun Rd HoustonTX77004 United States Program of Materials Science and Engineering University of Houston 4800 Calhoun Rd HoustonTX77004 United States Department of Chemistry University of Houston 4800 Calhoun Rd HoustonTX77004 United States

ISBN: (纸本)9781557528209

We present low power operated nanoplasmonic microbubble generation using near infrared light for particle concentrating on high-density arrayed nanoporous gold disks. Utilizing a spatial light modulator provides precise control of size and location of multiple microbubbles. After concentrating, various nanoplasmonic sensing techniques can be directly performed. © OSA 2020.

关键词： Infrared devices

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One-Shot Unsupervised Domain Adaptation for Object Detection

One-Shot Unsupervised Domain Adaptation for Object Detection

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International Joint Conference on Neural Networks (IJCNN)

作者： Zhiqiang Wan Lusi Li Hepeng Li Haibo He Zhen Ni Department of Electrical Computer and Biomedical Engineering University of Rhode Island Kingston USA Department of Computer and Electrical Engineering and Computer Science Florida Atlantic University Boca Raton USA

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

The existing unsupervised domain adaptation (UDA) methods require not only labeled source samples but also a large number of unlabeled target samples for domain adaptation. Collecting these target samples is generally time-consuming, which hinders the rapid deployment of these UDA methods in new domains. Besides, most of these UDA methods are developed for image classification. In this paper, we address a new problem called one-shot unsupervised domain adaptation for object detection, where only one unlabeled target sample is available. To the best of our knowledge, this is the first time this problem is investigated. To solve this problem, a one-shot feature alignment (OSFA) algorithm is proposed to align the low-level features of the source domain and the target domain. Specifically, the domain shift is reduced by aligning the average activation of the feature maps in the lower layer of CNN. The proposed OSFA is evaluated under two scenarios: adapting from clear weather to foggy weather; adapting from synthetic images to real-world images. Experimental results show that the proposed OSFA can significantly improve the object detection performance in target domain compared to the baseline model without domain adaptation.

关键词： Object detection Adaptation models Feature extraction Proposals Meteorology Training Data models

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