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52nd European Microwave Conference, EuMC 2022

作者： Aslan, Yanki Delft University of Technology Microwave Sensing Signals and Systems Group Faculty of Electrical Engineering Mathematics and Computer Science Department of Microelectronics Delft Netherlands

ISBN: (数字)9782874870699

ISBN: (纸本)9782874870699

Optimal design of uniformly-fed aperiodic millimeter-wave phased array topologies for site-specific and quasi interference-free operation is presented. Several use cases with different number of line-of-sight cells in the close vicinity of the base station, and for various widths and shapes of the cells, are analyzed. The arrays are synthesized by applying an iterative convex optimization technique. The simulation results of 256-element arrays indicate strong intra-cell and inter-cell isolation with around -40 dB maximal side lobe level. The proposed technique is found to be especially useful in the application scenarios supporting sparsely distributed narrow communication cells. © 2022 European Microwave Association (EuMA).

关键词： Millimeter waves

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Fuzzy Logic in computer Technology: Applications and Advancements 3

Fuzzy Logic in Computer Technology: Applications and Advance...

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3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Pareek, Shashank Gupta, Hemant Kaur, Jaspreet Kumar, Raman Chohan, Jasgurpreet Singh Chandigarh University Department of Computer Science and Engineering Mohali140413 India Gla University Department of Electrical Engineering Mathura281406 India Gulzar Group of Institutes Computer Science and Engineering Department Khanna141401 India Chandigarh University Department of Mechanical Engineering Mohali140413 India University Centre for Research and Development Chandigarh University Mohali140413 India

ISBN: (纸本)9798350322842

The development of Fuzzy Logic has had a profound impact on the field of computing. A crucial part of any decision-making process is the use of this mathematical method for dealing with uncertainty and imprecision. In this article, it will go over what Fuzzy Logic is, how it may be used in computing, and what recent developments have been achieved in the field. The strengths and weaknesses of using Fuzzy Logic in IT are also discussed. The introduction of Fuzzy Logic into the realm of computer science was a game-changer, since it provided a robust mathematical strategy for addressing uncertainty and imprecision in decision-making. This article introduces readers to Fuzzy Logic by discussing its background, basic ideas, and potential uses in the field of information technology. Artificial intelligence, control systems, decision-making, data analysis, and image processing are just some of the many areas where Fuzzy Logic has been put to use. In addition, this article discusses contemporary developments in Fuzzy Logic, such as those in machine learning, control systems, and robotics. Although this study extols Fuzzy Logic's numerous benefits, it also addresses the method's drawbacks. Workers in the IT industry would do well to familiarize themselves with the merits and drawbacks of Fuzzy Logic, as this knowledge may inform the development of superior products. This study's overarching goal is to introduce readers to Fuzzy Logic in the context of computer technology, including its advantages and disadvantages. Fuzzy logic has been more useful in many areas of computer science and technology. Used in areas such as control systems, image processing, robotics, and decision making, fuzzy logic allows for the processing of imprecise or uncertain input. This study gives a thorough introduction to fuzzy logic in computer technology, including its history, current developments, and potential future uses. The first section of the study provides a foundational understanding of fu

关键词： Fuzzy logic

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Hybrid Deep Learning Algorithm For MRI Brain Alzheimer's Disease Prediction

Hybrid Deep Learning Algorithm For MRI Brain Alzheimer's Dis...

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2023 International Conference on Energy, Materials and Communication engineering, ICEMCE 2023

作者： Karthikeyan, D. Nivasini, R. Raja, Kiruba Thangam Madhivadhani, D. Sivakumar, B. Krishna Priya, A.T.R. Srm Institute of Science and Technology Department of Electrical and Electronics Engineering Kattankulathur Chennai India Iitm Research Park Guvi Geek Networks Tamilnadu Chennai India Vit University School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Vellore India Kings Engineering College Department of Electronics and Communication Engineering Sriperumbudur Chennai India Rohini College of Engineering and Technology Department of Computer Science and Engineering Kanyakumari India

ISBN: (纸本)9798350393378

Magnetic resonance imaging (MRI) has been used to study the structural makeup of the brain and analyse several neurological disorders and diseased areas. For the adoption of preventative measures, early recognition of Alzheimer's disease (AD) patients is essential. Here, a thorough inspection of the tissue arrangements obtained by MRI images of Outcome and Assessment Information (OASIS) dataset enables an exact characterization of certain brain diseases. There have been a number of division techniques for diagnosing AD that range in complexity. Compassion has been tested by deep learning techniques used to segment the structure of the brain and classify AD because they have the potential to uncover important information from vast amounts of data. In this paper the deep learning technique of Hybrid Dragonfly based GWO convolutional Neural Network (CNN) is achieved promising result for the diagnosis of AD. At image preprocessing wiener filter is used for removing the additive noises and the Gray Level Co-Occurrence Matrix (GLCM) extraction is implemented for texture analyzing. As a result, hybrid deep learning methods of CNN has the accuracy as 90% and the result of the image prediction are presented in this paper. © 2023 IEEE.

关键词： Magnetic resonance imaging

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A Comparative Study of Power Electronics and Control Techniques for Renewable Energy Integration in Smart Grids 3

A Comparative Study of Power Electronics and Control Techniq...

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3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Pareek, Shashank Kumar, Yogendra Kaur, Harpreet Kumar, Raman Chohan, Jasgurpreet Singh Chandigarh University Department of Computer Science and Engineering Mohali140413 India Gla University Department of Electrical Engineering Mathura281406 India Gulzar Group of Institutes Computer Science and Engineering Department Khanna141401 India Chandigarh University Department of Mechanical Engineering Mohali140413 India University Centre for Research and Development Chandigarh University Mohali140413 India

ISBN: (纸本)9798350322842

The development of smart grid technology for more effective and sustainable energy management has been prompted by the growing use of renewable energy sources like solar and wind power. In order to integrate renewable energy sources into the grid, power electronics and control approaches are essential. The purpose of this study is to examine the pros and cons of the various power electronics and control approaches now in use for integrating renewable energy sources into smart grids. The development of smart grid technology for more effective and sustainable energy management has been facilitated by the use of renewable energy sources like solar and wind power. When it comes to incorporating renewable energy sources into the grid, power electronics and control approaches play a crucial role. This article provides a comprehensive analysis of the literature on the topic of renewable energy integration in smart grids, with a particular focus on power electronics and control strategies. The study is based on a review of relevant literature and an analysis of the benefits and drawbacks of each method. Efficiency, dependability, cost-effectiveness, scalability, and compatibility with various renewable energy sources are some of the characteristics included in the comparison study. The research shows that each method has advantages and disadvantages, and that the choice of method is context-specific. The work has significant significance for the field of electrical and computer engineering and aids in the development of more efficient and sustainable energy management systems for the future smart grid. © 2023 IEEE.

关键词： Wind power

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A Distributed Framework for Large-scale Protein-protein Interaction Data Analysis and Prediction Using MapReduce

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 160-172页

作者： Lun Hu Shicheng Yang Xin Luo Huaqiang Yuan Khaled Sedraoui MengChu Zhou School of Computer Science and Technology Dongguan University of TechnologyDongguan 523808China School of Computer Science and Technology Wuhan University of TechnologyWuhan 430070China Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent ComputingChongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of SciencesUrumqi 830000China Center of Research Excellence in Renewable Energy and Power Systems and the Department of Electrical and Computer EngineeringFaculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA

Protein-protein interactions are of great significance for human to understand the functional mechanisms of *** the rapid development of high-throughput genomic technologies,massive protein-protein interaction(PPI)data have been generated,making it very difficult to analyze them *** address this problem,this paper presents a distributed framework by reimplementing one of state-of-the-art algorithms,i.e.,CoFex,using *** do so,an in-depth analysis of its limitations is conducted from the perspectives of efficiency and memory consumption when applying it for large-scale PPI data analysis and *** solutions are then devised to overcome these *** particular,we adopt a novel tree-based data structure to reduce the heavy memory consumption caused by the huge sequence information of *** that,its procedure is modified by following the MapReduce framework to take the prediction task distributively.A series of extensive experiments have been conducted to evaluate the performance of our framework in terms of both efficiency and *** results well demonstrate that the proposed framework can considerably improve its computational efficiency by more than two orders of magnitude while retaining the same high accuracy.

关键词： Distributed computing large-scale prediction machine learning MapReduce protein-protein interaction(PPI)

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Design of a Rat Robotic Forelimb 13th

Design of a Rat Robotic Forelimb

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13th International Conference on Biomimetic and Biohybrid systems, Living Machines 2024

作者： Pham, Huy Jackson, Clayton B. Armstead, Benjamin Nourse, William R. P. Andrada, Emanuel Quinn, Roger D. Department of Mechanical and Aerospace Engineering Case Western Reserve University ClevelandOH44106 United States Institute of Zoology and Evolutionary Research Friedrich-Schiller University Jena Germany Department of Electrical Computer and Systems Engineering Case Western Reserve University ClevelandOH44106 United States

ISBN: (纸本)9783031725968

This paper presents the design of robotic rat forelimbs, scaled to 2.5 times the size of a female Sprague-Dawley rat. Based on a previous rat hindlimb model, significant enhancements were implemented to facilitate manufacturing, robustness, improve overall performance, and enhance biological accuracy. The two front legs are each equipped with five motors to enable sagittal rotations of the scapula, elbow, and wrist joints, along with scapula abduction and adduction. The leg segment dimensions are based on a scanned rat bones model, but the shapes are modified for 3D printing and assembly. Parts are printed using micro carbon fiber filled nylon, strengthened by various amounts of continuously inlaid carbon fiber. Revolute joints are driven by motors, and a pulley-belt transmission system is incorporated to move wrist and elbow motors higher up in order to reduce the inertia of the leg. Furthermore, the foot structure comprises two segments that are passively controlled by two torsion springs to enhance flexibility and facilitate a more naturalistic movement. Additionally, a new electrical board is integrated to enhance operational smoothness and align with the control scheme. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Machine design

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Swallowing Assessment using High-Resolution Cervical Auscultations and Transformer-based Neural Networks 46

Swallowing Assessment using High-Resolution Cervical Auscult...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Anwar, Ayman Khalifa, Yassin Lucatorto, Erin Coyle, James L. Sejdic, Ervin University of Toronto Faculty of Applied Science & Engineering Department of Electrical and Computer Engineering TorontoON Canada University of Pittsburgh Center for Research Computing and Information Technology Analytics PittsburghPA United States Cairo University Faculty of Engineering Systems and Biomedical Engineering Department Giza Egypt University of Pittsburgh School of Health and Rehabilitation Sciences Department of Communication Science and Disorders PittsburghPA United States University of Toronto North York General Hospital Faculty of Applied Science & Engineering Department of Electrical and Computer Engineering TorontoON Canada

ISBN: (纸本)9798350371499

Swallowing assessment is a crucial task to reveal swallowing abnormalities. There are multiple modalities to analyze swallowing kinematics, such as videofluoroscopic swallow studies (VFSS), which is the gold standard method, and high-resolution cervical auscultation (HRCA), which is a noninvasive technique that uses a triaxial accelerometer attached to the patient's neck. Deep learning models play an essential role in data driven analysis of swallowing landmarks using VFSS and/or HRCA as input data. Most of these models utilize convolutional and recurrent neural networks. Here, we investigate the ability of transformers to analyze swallowing kinematics;specifically upper esophageal sphincter opening and laryngeal vestibule closure using HRCA signals. We tested the model using an independent test dataset to assess the generalizability of the proposed network. The proposed network achieved an average detection accuracy higher than 90% and 85% for both segmentation tasks, which outperform the hybrid neural networks from the literature, and the model obtained high-performance measures for the independent dataset, showing the transformers' ability to generalize on unseen data. © 2024 IEEE.

关键词： Recurrent neural networks

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A 1.65 to 1.84 GHz Filtering SPDT Switch With Fully Tunable Insertion Phase Using NRN and Mixed Electromagnetic Coupling Techniques

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IEEE Transactions on Circuits and systems II: Express Briefs 2023年第9期70卷 3353-3357页

作者： Wei, Zhihua Chi, Pei-Ling Xu, Ruimin Yang, Tao School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu Sichuan611731 China Department of Electrical and Computer Engineering National Chiao Tung University Hsinchu300 Taiwan Research Center of Advanced RF Chips and Systems Nanhu Laboratory Jiaxing Zhejiang314000 China

This brief presents a novel filtering single-pole-double-throw (SPDT) switch with continuously tunable center frequency and insertion phase. It is comprised of six varactor-loaded microstrip resonators and three tunable non-resonating nodes (NRNs) at source and loads. The controllable mixed electromagnetic couplings are introduced on the two main paths of each channel of the switch. At ON-state, the designed filtering switch can generate a 4th-order bandpass filtering performance with high selectivity, and the insertion phase can be continuously tuned with a full 360° tuning range by changing the reactances of NRNs and switching the dominated modes of the mixed couplings. At OFF-state, the mixed electromagnetic couplings are set to zero, and hence high isolation can be realized. To validate the proposed technique, a circuit prototype is designed and fabricated. The measured results suggest that the designed filtering SPDT switch can operate with a tunable center frequency ranging from 1.65 GHz to 1.84 GHz, and its insertion phase at ON-state channel can be continuously tuned from -180° to 180°. During the whole tuning process, the measured in-band isolation for OFF-state channel keeps better than 40 dB. © 2023 IEEE.

关键词： Bandpass filters

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Understanding the Impact of Unobservable Variables on the Performance of Predictive Models: The Need for Feature Space Partitioning and Fusion

Understanding the Impact of Unobservable Variables on the Pe...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Juarez Garcia, Ezequiel Stephens, Chad L. Napoli, Nicholas J. Laboratory Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States Crew Systems & Aviation Operations Branch and System-Wide Safety Project NASA Langley Research Center HamptonVA23681 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

When developing predictive models over a dataset, the model is globally optimized across the entire feature space to learn a decision boundary. However, when unobservable variables—which cannot be measured or estimated—interact with the observable variables, this can negatively impact the optimization applied to the decision boundary since the data samples introduced by unobservable variables may have little to no association with the applied global optimization. This, consequently, penalizes the entire decision boundary and model performance. This paper examines some of the detrimental effects of unobservable variables, particularly their role in creating new modes in the distribution of observable variables and reducing the separ ability of class distributions. Such challenges result in unrepresentative training data, complex skewedor warped decision boundaries, and decreased accuracy of model predictions, particularly for interpretable models like logistic regression and decision trees. Through two illustrative case examples, we highlight the need to address the challenges imposed by unobservable variables. We propose a strategy to mitigate these challenges by creating local regions with in the feature space through partitioning. This enables the optimization of local models with inthe regions to overcome the impact of un observ ability and complex decision boundaries indifferent feature space localities. research into a more sophisticated partitioning strategy and where the partition should be relative to the sample of interest is left as future work. Through the analysis of the impact of un observability and the development of a partitioning method, we demonstrate the clear need for a partitioning strategy that integrates knowledge from multiple local models to estimate risk factors using information fusion. Thus, we establish the foundation and motivation for using partitioning and information fusion to overcome the effects of unobserv ability in predictive mo

关键词： Information fusion

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Deep Learning Model for Classifying Public Opinions on Energy Sector IHSG Stocks on the Twitter Social Media Platform

Deep Learning Model for Classifying Public Opinions on Energ...

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2023 International Conference on Artificial Intelligence Robotics, Signal and Image Processing, AIRoSIP 2023

作者： Sulistio, Bambang Heryadi, Yaya Lukas Suparta, Wayan Sonata, Ilvico Binus Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Universitas Katolik Indonesia Atma Jaya Cognitive Engineering Research Group Jakarta Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350327472

Capital market transactions provide an opportunity for investors to acquire ownership of company shares and capital gains, as well as dividends. However, alongside the benefits, there are risks of capital loss and liquidation, leading to stress and depression due to profit targets and decision-making errors. To mitigate the risk of decision-making errors in investment, data analysis is needed, including sentiment analysis, which influences stock prices. This study aims to develop a new deep learning model to classify Indonesian public opinion on JCI stocks, especially the Energy sector, obtained from the Twitter social media platform. The model will perform sentiment analysis and categorize opinions as negative, neutral, or positive. We created a dataset that was trained using Bidirectional Encoder Representations from Transformers (BERT) to summarize the analysis of public sentiment above so that it can assist investors in studying public sentiment as a reference for investing with a yield precision of 76%, Recall of 77%, and F1-score on 76%. © 2023 IEEE.

关键词： Tweets

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