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arXiv 2022年

作者： Niazi, Muhammad Umar B. Alanwar, Amr Chong, Michelle S. Johansson, Karl Henrik Division of Decision and Control Systems Digital Futures School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Computer Science and Electrical Engineering Jacobs University Bremen Germany Control Systems Technology Section Department of Mechanical Engineering Eindhoven University of Technology Netherlands

This paper considers the problem of set-based state estimation for linear time-invariant (LTI) systems under time-varying sensor attacks. Provided that the LTI system is stable and observable via every single sensor and that at least one sensor is uncompromised, we guarantee that the true state is always contained in the estimated set. We use zonotopes to represent these sets for computational efficiency. However, we show that intelligently designed stealthy attacks may cause exponential growth in the algorithm’s worst-case complexity. We present several strategies to handle this complexity issue and illustrate our resilient zonotope-based state estimation algorithm on a rotating target system. Copyright © 2022, The Authors. All rights reserved.

关键词： Linear time-invariant system

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Improving the Security of the IEEE 802.15.6 Standard for Medical BANs

arXiv

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arXiv 2022年

作者： Siddiqi, Muhammad A.L.I. Hahn, Georg Hamdioui, Said Serdijn, Wouter A. Strydis, Christos Quantum & Computer Engineering Department Delft University of Technology Netherlands Department of Neuroscience Erasmus Medical Center Rotterdam Netherlands Section Bioelectronics Delft University of Technology Netherlands

A Medical Body Area Network (MBAN) is an ensemble of collaborating, potentially heterogeneous, medical devices located inside, on the surface of or around the human body with the objective of tackling one or multiple medical conditions of the MBAN host. These devices - which are a special category of Wireless Body Area Networks (WBANs) - collect, process and transfer medical data outside of the network, while in some cases they also administer medical treatment autonomously. Since communication is so pivotal to their operation, the newfangled IEEE 802.15.6 standard is aimed at the communication aspects of WBANs. It places a set of physical and communication constraints while it also includes association/disassociation protocols and security services that WBAN applications need to comply with. However, the security specifications put forward by the standard can be easily shown to be insufficient when considering realistic MBAN use cases and need further enhancements. The present work addresses these shortcomings by, first, providing a structured analysis of the IEEE 802.15.6 security features and, afterwards, proposing comprehensive and tangible recommendations on improving the standard's security. © 2022, CC BY.

关键词： Wireless local area networks (WLAN)

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Thick Film Interdigitated Electrode Graphite and Carbon Nanotube on Flexible Substrate for Hydrogen Gas Detection

Thick Film Interdigitated Electrode Graphite and Carbon Nano...

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2023 IEEE International Conference on Sensors and Nanotechnology, SENNANO 2023

作者： Abu Bakar, Azlinda Nizar Hamidon, Mohd Yaacob, Mohd Hanif Paiman, Suriati Jaafar, Haslina Wan Jusoh, Wan Nursheila Universiti Putra Malaysia Funtional Device Laboratory Institute of Nanoscience and Nanotechnology Selangor Serdang Malaysia Universiti Putra Malaysia Faculty of Engineering Department of Computer and Communication Systems Selangor Serdang Malaysia Universiti Putra Malaysia Faculty of Science Department of Physics Selangor Serdang Malaysia Universiti Putra Malaysia Faculty of Engineering Department of Electrical and Electronic Selangor Serdang Malaysia Universiti Kuala Lumpur Section of Aerospace Malaysians Institute of Aviation Technology Selangor Dengkil Malaysia

ISBN: (纸本)9798350333312

The comparison on the performance of interdigitated electrode (IDE) graphite and carbon nanotube (CNT) using titanium dioxide-multiwall carbon nanotube (TiO2-MWCNT) composite as sensing materials to detect hydrogen gas is presented in this research work. Screen printing technique has been selected to deposited IDE graphite and CNT conductive pastes as the electrode thick film on a flexible substrate, which is continued by firing at 120°C each. TiO2-MWCNT paste was printed as a sensing layer on top of IDE, followed by firing at 350°C. FESEM was used to characterize the morphological analysis of the IDE film. The operating temperature was varied at 27°C, 50°C and 100°C and concentration varied from 100-1000 ppm for gas hydrogen. The gas sensor demonstrated as n-type gas characteristics where the response of the gas sensor produced increased current from the results. The results purpose when the sensor was exposed to hydrogen gas. Therefore, the ability of IDE graphite and CNT as an electrode to harvest the electron from sensing materials were successfully proven. The IDE CNT showed better response compare to IDE graphite, but both electrodes were able to sense 100 - 1000 ppm at the operating temperature of 27°C, 50°C and 100°C. The optimal of operating temperature for IDE graphite is 50°C and IDE CNT is at room temperature for the hydrogen gas detections in this research work. © 2023 IEEE.

关键词： Thick films

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CMOS photonic integrated source of broadband polarization-entangled photons

Optica Quantum

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Optica Quantum 2024年第4期2卷 254-259页

作者： Alexander Miloshevsky Lucas M. Cohen Karthik V. Myilswamy Muneer Alshowkan Saleha Fatema Hsuan-Hao Lu Andrew M. Weiner Joseph M. Lukens Quantum Information Science Section Computational Sciences and Engineering Division Oak Ridge National Laboratoryhttps://***/01qz5mb56 Oak Ridge Tennessee 37831 USA School of Electrical and Computer Engineering and Purdue Quantum Science and Engineering Institute Purdue University West Lafayette Indiana 47907 USA Research Technology Office and Quantum Collaborative Arizona State University Tempe Arizona 85287 USA

We showcase a fully on-chip CMOS-fabricated silicon photonic integrated circuit employing a bidirectionally pumped microring and polarization splitter-rotators tailored for the generation of broadband (>9 THz), high-fidelity (90–98%) polarization-entangled photons. Spanning the optical C+L-band and producing over 116 frequency-bin pairs on a 38.4-GHz-spaced grid, this source is ideal for flex-grid wavelength-multiplexed entanglement distribution in multiuser networks.

关键词： Beam splitters Entanglement distribution Optical directional couplers Parametric down conversion Silicon photonics Superconducting nanowire single photon detectors

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AI-Enabled Predictive Maintenance for Industrial Equipment in the Era of IoT

AI-Enabled Predictive Maintenance for Industrial Equipment i...

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International Conference on Science technology engineering and Management (ICONSTEM)

作者： Muthu Raman Subbiah Kabirdoss Devi Dhanamalar M Ashraf Mohammed Shareef Taha Raad AI-Shaikhli Sudheer Nidamanuri Department of Engineering MIE Section University of Technology and Applied sciences Muscat Oman School of Management Studies Vels Institute-of Science Technology and Advanced Studies Pallavaram Chennai Tamil Nadu India Department of Computer Science Kristu Jayanti College Bengaluru Karnataka India College of Pharmacy / National University of Science and Technology DhiQar Iraq Department of Medical Laboratories Technology AL-Nisour University College Baghdad Iraq Computer Science and Technology Madanapalle Institute of Technology and Science Madanapalle Andhra Pradesh India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

Modern digital technology breakthroughs include machine learning. It's used in medical, image processing, manufacturing, aviation, autonomics, and more. Our main goal is to find an automated system that can accurately rate production machines at any given moment. The critical energy meter characteristics needed to create power reports in automation system to solve production difficulties at defined intervals were also not captured. We apply machine learning to estimate manufacturing machine accuracy in this study. We employed supervised machine learning of binary decision tree using CART method and power report, obtained data using RS232 to RS485 convertor through Modbus communication protocol, to overcome these challenges. This study presents issue formulation, energy meter data analysis and retrieval, and ML approaches used to estimate production machine accuracy. After retrieving the parameters, we provide power reports for each machine and a graphical warning of deteriorating performance.

关键词： Meters Automation Accuracy Protocols Service robots Production Machine learning

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Performance Analysis of Machine Learning Algorithm for the Credit Risk Analysis in the Banking Sector

Performance Analysis of Machine Learning Algorithm for the C...

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International Conference on Computing Methodologies and Communication (ICCMC)

作者： Sandeep Kumar Hegde Rajalaxmi Hegde Krishna Priya R Sree Southry S A.V.G.A. Marthanda K. Logu Department of Computer Science and Engineering NMAM Institute of Technology Nitte (Deemed to be University) Karkala Taluk Udupi District India Department of Engineering Electrical Section University of Technology and Applied Sciences Shinas campus Oman Department of ECE Sona College of Technology Salem India Department of EEE LakiReddy BaliReddy college of Engineering Mylavaram Permanently affiliated to JNTU Kakinada Andhrapradesh India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu India

The banking sector has advanced in recent years. Thus, there is an increase in the demand for bank loans. The bank must distribute and sell each of the limited number of available slots to a select group of people. As a result, a usual stage is to identify who will be unable to return the loan and who will prove to be a more trustworthy option to the bank. In order to save the bank time and costs, in the proposed paper machine learning based approach is introduced to reduce the risk involved with finding the safe individual. In order to decide whether or not to grant someone a loan, this paper presents a method of loan approval based on predetermined criteria. The machine learning model for credit approval was implemented using logistic regression, XG Boost, random forest and naïve bayes model. The experimental results indicates that logistic regression model is more accurate for the credit risk analysis.

关键词： Analytical models Machine learning algorithms Costs Computational modeling Banking Performance analysis Risk analysis

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Machine Learning Techniques Applied to Development of Flexible Electronic Antireflective Film

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Sensors and Materials 2024年第1期36卷 17-23页

作者： Lin, Shih-Hung Wang, Yuan-Ting Wang, Yao-Chin Department of Electronics Engineering National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin64002 Taiwan Bachelor of Science and Technology National Chi Nan University 1 University Road Puli Town Nantou County545 Taiwan Department of Computer Science and Information Engineering Cheng Shiu University 840 Chengcing Road Niaosong District Kaohsiung City83347 Taiwan

In this study, we aim to optimize the process for developing antireflective and refractive-index-matching films on flexible substrates. The development of these films is crucial in light of the increasing demand in the market for flexible electronics, which are poised to be the next emerging technology and application after semiconductors and flat panel displays. Our research involves the production of these films by physical vapor deposition and sputtering techniques, which can also be applied to various optical thin films. The effectiveness of our coating process has been verified and refined on the basis of feedback. The results of this study are applicable to related industrial technologies and will contribute to improving industry competitiveness. © 2024 M Y U Scientific Publishing Division. All rights reserved.

关键词： Machine learning

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Feedforward Control in the Presence of Input Nonlinearities: A Learning-based Approach

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IFAC-PapersOnLine 2022年第37期55卷 235-240页

作者： Jilles van Hulst Maurice Poot Dragan Kostić Kai Wa Yan Jim Portegies Tom Oomen Control Systems Technology Section Dept. of Mechanical Engineering Eindhoven University of Technology The Netherlands Center of Competency ASM Pacific Technology The Netherlands CASA Dept. of Mathematics and Computer Science Eindhoven University of Technology The Netherlands Delft Center for Systems and Control Delft University of Technology The Netherlands

Advanced feedforward control methods enable mechatronic systems to perform varying motion tasks with extreme accuracy and throughput. The aim of this paper is to develop a data-driven feedforward controller that addresses input nonlinearities, which are common in typical applications such as semiconductor back-end equipment. The developed method consists of parametric inverse-model feedforward that is optimized for tracking error reduction by exploiting ideas from iterative learning control. Results on a simulated set-up indicate improved performance over existing identification methods for systems with nonlinearities at the input.

关键词： Nonlinear system identification Identification for control Iterative learning control Data-based control Motion Control Applications in semiconductor manufacturing

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Enhancing thyroid nodule assessment with deep learning and ultrasound imaging

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e-Prime - Advances in Electrical engineering, Electronics and Energy 2025年 11卷

作者： Kumar, Jatinder Panda, Surya Narayan Dayal, Devi Sharma, Manish Computer Section F Block Nehru Hospital Sector-12 PGIMER Chandigarh India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Endocrinology and Diabetes Unit Department of Paediatrics PGIMER Chandigarh India

The thyroid is a tiny, butterfly-shaped gland in the neck which produces hormones that are essential for controlling the body's various metabolic processes. Thyroid nodules, which are abnormal growths or lumps in the thyroid gland, are common thyroid illnesses, as are hypothyroidism, hyperthyroidism, and both. Thyroid issues are most commonly identified and categorised using thyroid ultrasonography (USG) images. They can have a range of effects on the body's metabolism and overall health. Developments in artificial intelligence (AI), particularly deep learning (DL), are helping to identify and measure patterns in clinical images because of DL's capacity towards pull out hierarchical attribute representations from images without the need for annotated images. Minimizing unnecessary fine needle aspiration (FNA) requires the essential identification of as many malignant thyroid nodules as possible, distinguishing them from benign ones. This research work introduces a technique for thyroid nodule identification in USGs, employing DL to extract relevant features. Three pre-trained DL models, namely ResNet-18, VGG-19 and AlexNet were fine-tuned before using for classification of thyroid USG images. The models' testing and training were done with Digital Database of Thyroid Ultrasound Images (DDTI) which is gold standard dataset. The results demonstrate a classification accuracy of 97.13%, 90.31% and 83.59% with ResNet-18, VGG-19 and AlexNet, respectively. The experimental findings affirm that the pre-trained network model ResNet-18 achieves superior classification performance compared to VGG-19 and AlexNet. © 2025 The Author(s)

关键词： AlexNet Benign Deep learning Malignant ResNet-18 VGG-19

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NIR-Responsive Methotrexate-Modified Iron Selenide Nanorods for Synergistic Magnetic Hyperthermic, Photothermal, and Chemodynamic Therapy

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ACS Applied Materials and Interfaces 2024年第20期16卷 25622-25636页

作者： Thirumurugan, Senthilkumar Muthiah, Kayalvizhi Samuvel Lin, Yu-Chien Dhawan, Udesh Liu, Wai-Ching Wang, An-Ni Liu, Xinke Hsiao, Michael Tseng, Ching-Li Chung, Ren-Jei No. 1 Section 3 Zhongxiao East Road Taipei10608 Taiwan Centre for the Cellular Microenvironment Division of Biomedical Engineering James Watt School of Engineering Mazumdar-Shaw Advanced Research Centre University of Glasgow GlasgowG116EW United Kingdom Faculty of Science and Technology Technological and Higher Education Institute of Hong Kong Hong Kong New Territories999077 Hong Kong Scrona AG Grubenstrasse 9 Zürich8045 Switzerland College of Materials Science and Engineering Chinese Engineering and Research Institute of Microelectronics Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Genomics Research Center Academia Sinica Taipei115 Taiwan Department and Graduate Institute of Veterinary Medicine School of Veterinary Medicine National Taiwan University Taipei10617 Taiwan Graduate Institute of Biomedical Materials and Tissue Engineering College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan International Ph.D. Program in Biomedical Engineering College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan Research Center of Biomedical Device College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan International Ph.D. Program in Cell Therapy and Regenerative Medicine College of Medicine Taipei Medical University Taipei11031 Taiwan Taipei10608 Taiwan

Breast cancer is a malignant tumor with a high mortality rate among women. Therefore, it is necessary to develop novel therapies to effectively treat this disease. In this study, iron selenide nanorods (FeSe2 NRs) were designed for use in magnetic hyperthermic, photothermal, and chemodynamic therapy (MHT/PTT/CDT) for breast cancer. To illustrate their efficacy, FeSe2 NRs were modified with the chemotherapeutic agent methotrexate (MTX). MTX-modified FeSe2 (FeSe2-MTX) exhibited excellent controlled drug release properties. Fe2+ released from FeSe2 NRs induced the release of •OH from H2O2 via a Fenton/Fenton-like reaction, enhancing the efficacy of CDT. Under alternating magnetic field (AMF) stimulation and 808 nm laser irradiation, FeSe2-MTX exerted potent hyperthermic and photothermal effects by suppressing tumor growth in a breast cancer nude mouse model. In addition, FeSe2 NRs can be used for magnetic resonance imaging in vivo by incorporating their superparamagnetic characteristics into a single nanomaterial. Overall, we presented a novel technique for the precise delivery of functional nanosystems to tumors that can enhance the efficacy of breast cancer treatment. © 2024 The Authors. Published by American Chemical Society

关键词： Magnetic resonance imaging

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