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Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Ashutosh Ghimire Fathi Amsaad Tamzidul Hoque Kenneth Hopkinson Md Tauhidur Rahman Department of Computer Science and Engineering Wright State University Department of Electrical Engineering and Computer Science University of Kansas Department of Computer Science Air Force Institute of Technology Department of Electrical and Computer Engineering Florida International University

The detection of Hardware Trojans is crucial for ensuring trust in the semiconductor IC supply chain. However, existing detection methods that rely on side-channel analysis often require golden chips for verification. This paper presents a new approach to Hardware Trojan detection that utilizes unsupervised machine learning with side-channel analysis to eliminate the need for golden data. Trojans of varying sizes were implemented on an FPGA to evaluate the method to perform unsupervised clustering and detect anomalies. The proposed model achieved high accuracy, 93%, and improved the detection of small and short-triggered Trojans compared to competing approaches. The unsupervised learning techniques demonstrated a better false positive rate and similar accuracy to supervised approaches such as the KNN classifier, SVM, and Gaussian classifier which require golden data for training. This research contributes a new approach to Hardware Trojan detection that can improve the trustworthiness of semiconductor IC supply chains.

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Construction of Low-carbon Economic Development Level Model Based on BP Neural Network Algorithm 2

Construction of Low-carbon Economic Development Level Model ...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Nurullayevna, Nigmatullayeva Gulchekhra Parvin Begum, I. Alzubaidi, Laith H. Gnana Soundari, A. Santhosh, S. Naqvi, Aiman Tashkent State University of Economics Uzbekistan Department of Computer applications B.S.Abdur Rahman Crescent Institute of Science and Technology Tamil Nadu Vandalur600048 India College of Technical Engineering The Islamic University Najaf Iraq Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Tamil Nadu Chennai India New Prince Shri Bhavani College Of Engineering And Technology Tamilnadu India M S Ramaiah Institute Of Technology Msr Nagar Msrit Post Bangalore India

ISBN: (纸本)9798350305418

The Low-Carbon Economy (LCE) is an all-encompassing topic that combines economic, social, and environmental concerns with technological ones. It encompasses the ideas of low-carbon industry, low-carbon technology, low-carbon energy, low-carbon consumption, and carbon sinks. The expansion of LCE in India is hampered by a number of causes, one of which is India's illogical industrial structure, which is also an important component. As a result, the BPNN (BP neural network) technique is utilized in this study in order to construct the LCE development level model. First, an index system for controlling total carbon emissions should be created for LCE development. Next, an evaluation method that is based on the BPNN model should be developed. Examine the precision of the second generation prediction model by applying the test sample set to it, and continue this process until you have gotten the final prediction model that satisfies the convergence conditions. The findings of the research indicate that the maximum error that can occur between the actual output value of BPNN and the expected output value of BPNN is 8.36%, and the error is less than 10%, which indicates that the training results of the BPNN evaluation model constructed in this paper are good, and the evaluation results obtained are quite satisfactory. © 2023 IEEE.

关键词： BP neural network Index system Low carbon economy

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Adaptive Fourier Decomposition of the First Three SARS-CoV-2 Infection Waves with Epidemic Intervention—London,UK,2020-2022

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China CDC weekly 2024年第21期6卷 478-486,I0006-I0008页

作者： Zige Liu Guibin Lu Cheokieng Vong Zhiqi Zeng Wei He Zhijie Lin Cuiyun Lin Kaichin Hsieh Zifeng Yang Arlindo L.Oliveira Chitin Hon Department of Engineering Science Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao SARChina Queen Ethelburga’s Collegiate Thorpe Underwood EstateYorkUK State Key Laboratory of Respiratory Disease National Clinical Research Center for Respiratory DiseaseGuangzhou Institute of Respiratory HealthThe First Affiliated Hospital of Guangzhou Medical UniversityGuangzhou CityGuangdong ProvinceChina Respiratory Disease AI Laboratory on Epidemic and Medical Big Data Instrument Applications Department of Engineering ScienceFaculty of Innovation EngineeringMacao University of Science and TechnologyMacao SARChina Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases KingMed School of Laboratory MedicineGuangzhou Medical UniversityGuangzhou CityGuangdong ProvinceChina Faculty of Innovation Engineering School of Computer Science and EngineeringMacao University of Science and TechnologyMacao SARChina University College London UCL Faculty of Engineering SciencesLondonUK Guangzhou Laboratory Guangzhou CityGuangdong ProvinceChina Instituto de Engenharia de Sistemas e Computadores:Investigação e Desenvolvimento em Lisboa LisboaPortugal Instituto Superior Técnico Universidade de LisboaLisboaPortugal

Background:This study provides a detailed analysis of the daily fluctuations in coronavirus disease 2019(COVID-19)case numbers in London from January 31,2020 to February 24,*** primary objective was to enhance understanding of the interactions among government pandemic responses,viral mutations,and the subsequent changes in COVID-19 case ***:We employed the adaptive Fourier decomposition(AFD)method to analyze diurnal changes and further segmented the AFD into novel multi-component groups consisting of one to three *** restructured components were rigorously evaluated using Pearson correlation,and their effectiveness was compared with other signal analysis *** study introduced a novel approach to differentiate individual components across various time-frequency scales using basis decomposition ***:Analysis of London’s daily COVID-19 data using AFD revealed a strong correlation between the“stay at home”directive and high-frequency components during the first epidemic *** indicates the need for sustained implementation of vaccination policies to maintain their ***:The AFD component method provides a comprehensive analysis of the immediate and prolonged impact of governmental policies on the spread of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2).This robust tool has proven invaluable for analyzing COVID-19 pandemic data,offering critical insights that guide the formulation of future preventive and public health strategies.

关键词： offering sustained London

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Enriching Real-world Data with Social Determinants of Health for Health Outcomes and Health Equity: Successes, Challenges, and Opportunities

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Yearbook of Medical Informatics 2023年第1期32卷 253-263页

作者： He, Zhe Pfaff, Emily Guo, Serena Jingchuan Guo, Yi Wu, Yonghui Tao, Cui Stiglic, Gregor Bian, Jiang School of Information Florida State University United States Department of Behavioral Sciences and Social Medicine College of Medicine Florida State University United States Department of Medicine University of North Carolina at Chapel Hill School of Medicine United States Department of Pharmaceutical Outcomes and Policy College of Pharmacy University of Florida United States Department of Health Outcomes and Biomedical Informatics College of Medicine University of Florida United States School of Biomedical Informatics University of Texas Health Science Center at Houston United States Faculty of Health Science University of Maribor Slovenia Faculty of Electrical Engineering and Computer Science University of Maribor Slovenia Usher Institute University of Edinburgh United Kingdom

OBJECTIVE: To summarize the recent methods and applications that leverage real-world data such as electronic health records (EHRs) with social determinants of health (SDoH) for public and population health and health equity and identify successes, challenges, and possible solutions. METHODS: In this opinion review, grounded on a social-ecological-model-based conceptual framework, we surveyed data sources and recent informatics approaches that enable leveraging SDoH along with real-world data to support public health and clinical health applications including helping design public health intervention, enhancing risk stratification, and enabling the prediction of unmet social needs. RESULTS: Besides summarizing data sources, we identified gaps in capturing SDoH data in existing EHR systems and opportunities to leverage informatics approaches to collect SDoH information either from structured and unstructured EHR data or through linking with public surveys and environmental data. We also surveyed recently developed ontologies for standardizing SDoH information and approaches that incorporate SDoH for disease risk stratification, public health crisis prediction, and development of tailored interventions. CONCLUSIONS: To enable effective public health and clinical applications using real-world data with SDoH, it is necessary to develop both non-technical solutions involving incentives, policies, and training as well as technical solutions such as novel social risk management tools that are integrated into clinical workflow. Ultimately, SDoH-powered social risk management, disease risk prediction, and development of SDoH tailored interventions for disease prevention and management have the potential to improve population health, reduce disparities, and improve health equity. IMIA and Thieme. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long

关键词： electronic health records exposome public health informatics Social determinants of health

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Three-dimensional reconstruction of mobile binocular stereo vision based on push-broom line structured light for a workpiece surface

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Journal of the Optical Society of America A: Optics and Image science, and Vision 2023年第11期40卷 2096-2107页

作者： Wang, Yue Han, Xueyou Rui, Jing Zhang, Hailan Yin, Lei Zhang, Xuefeng Wang, Xiangjun School of Computer Science and Technology Anhui University of Technology Maanshan China Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Anhui University of Technology Maanshan243032 China School of Electrical and Information Engineering Anhui University of Technology Maanshan China The 53th Research Institute of China Electronics Technology Group Corporation Tianjin China State Key Laboratory of Precision Measuring Technology and Instruments Tianjin University Tianjin China Key Laboratory ofMOEMS of the Ministry of Education Tianjin University Tianjin China

Stereo vision technology based on line structured light can effectively solve the problem of a three-dimensional (3D) reconstruction of a smooth surface. A method for 3D reconstruction of mobile binocular stereo vision based on push-broom line structured light for a workpiece surface is presented in this paper. The subpixel coordinates of the light strip centers of the line structured light are obtained by the Steger algorithm while the binocular module moves along the guide rail, and the polar constraint is used to achieve the matching of the extracted light strip centers. As a result, the3Dcoordinates of the light strip centers in each location can be calculated because of the known interior and external parameters of the binocular module. To obtain the 3D point cloud data of the entire surface, a relative pose optimization method with respect to the initial frame is proposed, which accurately estimates the pose of the cameras in each location with respect to that in the initial location and unifies the 3D coordinates of the light strip centers in each location to the datum coordinates. The relative pose optimization method first estimates the rough values by using the direct linear transform method, and then iteratively calculates the refined solutions based on the principle of minimizing the re-projection errors. Simulation data and substantial experimental results validate the effectiveness of our method. Our method is compared to the direct linear transform method and the frame-by-frame transfer method, and the root mean square error (RMSE) of the distance from 3D point cloud to fitted plane is used to evaluate the 3D reconstruction accuracy. The repeatability experiment shows that the RMSE fromour method is as lowas 0.83mm. © 2023 Optica Publishing Group.

关键词： Location

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Automated Intrusion Detection and Classification using Binary Metaheuristics with Deep Learning on Smart Cities

Automated Intrusion Detection and Classification using Binar...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Haritha, V. Kumutha, R. Shobana, G. Sindhuja, R. Srivastava, Aviral Mary Sundararajan, Suma Christal Department of CSE RMK Engineering College Kavarapettai S. A. Engineering College Chennai India Department of Computer Applications College of Science and Humanities SRM Institute of Science and Technology SRM Nagar Kattankulathur-603 203 India Department of Medical Electronics Saveetha Engineering college College of information sciences and technology Pennsylvania state university State college Pennsylvania USA Department of Information Technology Panimalar Engineering College Poonamalle Chennai

ISBN: (纸本)9798350313987

With the dramatic increase of interconnected devices in smart cities, ensuring the integrity and security of the basic infrastructure has become a major concern. Intrusion detection plays a major role in protecting smart city systems against cyber-attacks and threats. Deep learning (DL) techniques, namely recurrent neural networks (RNN) or convolutional neural networks (CNNs), were employed for analyzing the network traffic data. The model was trained by labelled dataset, where network traffic instance was categorized as either intrusive or normal. This study proposes a novel automated Intrusion Detection and Classification design using Binary Metaheuristics with Deep Learning (AIDC-BMDL) techniques on Smart Cities. The AIDC-BMDL technique makes use of metaheuristic feature selection and DL based classification process. For the election of optimal features in the intrusion data, the AIDC-BMDL technique uses binary gray wolf optimizer (BGWO) algorithm. Besides, the AIDC-BMDL technique exploits Stacked Autoencoder (SAE) model for the effectual recognition and classification of the intrusions in the smart city environment. The simulation results illustrated the ability of the AIDC-BMDL technique to accurately identify intrusions in smart city environments. The AIDC-BMDL technique gained maximum performance, pointing out the significance of improving the security of smart cities. © 2023 IEEE.

关键词： Binary Grey Wolf Optimizer Deep Learning Intrusion Detection Security Smart City

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Efficient and Resilient Placement Strategy for Electric Vehicle Charging Stations, Incorporating Renewable Energy and Storage Equipment in Distribution Networks

Efficient and Resilient Placement Strategy for Electric Vehi...

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2023 International Conference for Technological engineering and its applications in Sustainable Development, ICTEASD 2023

作者： Kumar, Rakesh Begam, S. Shajathi Singh, Dev Lazizovich, Tuychiev Komiljon Muthubalaji, S. Al-Farouni, Mohammed Varanasi, Srinivas GLA University Department of Computer Engineering & Applications Mathura India E.G.S. Pillay Engineering College Department of Computer Science and Business System Tamilnadu Nagapattinam India Allenhouse Institute of Technology Mathematics Department Kanpur India Tashkent State University of Economics Department of Marketing Uzbekistan CMR College of Engineering & Technology Department of Electrical and Electronics Engineering Telangana Hyderabad501401 India The Islamic University Najaf Iraq GRIET Telangana Hyderabad India

ISBN: (纸本)9798350336474

The placement of Electric Vehicle Charging Stations (EVCS) is a significant obstacle to the widespread adoption of Electric Vehicles (EVs). However, integrating EVCS with distributed Renewable Energy (RE) sources may help mitigate the variability of RE production. This article focuses on studying a Microgrid (MG) that consists of power distribution sources such as Wind Turbines (WT) and photovoltaic (PV), as well as EVCSs and Energy Storage Structures (ESS). The analysis considers the ambiguities associated with the charging needs of electric vehicles and the production of distributed Renewable Energy Sources (RES). A comprehensive optimization model is provided for computing the best Charging Stations (CS) locations equipped with distributed electricity. This model combines the electrical network and the grid to ensure efficiency and reliability. The usage variation ratio is employed to assess the level of compatibility among the uncertain production curve of RE and the charging request curves to ascertain the suitable size of the WT and photovoltaic-producing method and energy storage system. The kernel prediction function is utilized to address the problem of excessive caution in adaptable optimizing methods. A simulation is conducted on an IEEE 33-nodal energy distributing system and a 50-nodal transit network. The findings from simulations provide evidence of the model's resilience and cost-effectiveness. © 2023 IEEE.

关键词： Electric power distribution

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Slow-light-enhanced on-chip 1D and 2D photonic crystal waveguide gas sensing in near-IR with an ultrahigh interaction factor

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Photonics Research 2023年第10期11卷 1647-1656页

作者： ZIHANG PENG YIJUN HUANG KAIYUAN ZHENG CHUANTAO ZHENG MINGQUAN PI HUAN ZHAO JIALIN JI YUTING MIN LEI LIANG FANG SONG YU ZHANG YIDING WANG FRANK K.TITTEL State Key Laboratory of Integrated Optoelectronics College of Electronic Science and EngineeringJilin UniversityChangchun 130012China Department of Electrical Engineering and Photonics Research Institute The Hong Kong Polytechnic UniversityHong Kong 518060China State Key Laboratory of Luminescence and Applications Changchun Institute of OpticsFine Mechanics and PhysicsChinese Academy of SciencesChangchun 130033China Department of Electrical and Computer Engineering Rice UniversityHoustonTexas 77005USA

Nanophotonic waveguides hold great promise to achieve chip-scale gas sensors. However, their performance is limited by a short light path and small light–analyte overlap. To address this challenge, silicon-based, slow-lightenhanced gas-sensing techniques offer a promising approach. In this study, we experimentally investigated the slow light characteristics and gas-sensing performance of 1D and 2D photonic crystal waveguides(PCWs) in the near-IR(NIR) region. The proposed 2D PCW exhibited a high group index of up to 114, albeit with a high propagation loss. The limit of detection(LoD) for acetylene(C_(2)H_(2)) was 277 parts per million(ppm) for a1 mm waveguide length and an averaging time of 0.4 s. The 1D PCW shows greater application potential compared to the 2D PCW waveguide, with an interaction factor reaching up to 288%, a comparably low propagation loss of 10 dB/cm, and an LoD of 706 ppm at 0.4 s. The measured group indices of the 2D and 1D waveguides are104 and 16, respectively, which agree well with the simulation results.

关键词： waveguide light interaction

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A Real-time Cutting Model Based on Finite Element and Order Reduction

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computer Systems science & engineering 2022年第10期43卷 1-15页

作者： Xiaorui Zhang Wenzheng Zhang Wei Sun Hailun Wu Aiguo Song Sunil Kumar Jha Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing210044China State Key Laboratory of Bioelectronics Jiangsu Key Lab of Remote Measurement and ControlSchool of Instrument Science and EngineeringSoutheast UniversityNanjing210096China IT Fundamentals and Education Technologies Applications University of Information Technology and Management in RzeszowRzeszow Voivodeship100031Poland

Telemedicine plays an important role in Corona Virus Disease 2019(COVID-19).The virtual surgery simulation system,as a key component in telemedicine,requires to compute in ***,this paper proposes a realtime cutting model based on finite element and order reduction method,which improves the computational speed and ensure the real-time *** proposed model uses the finite element model to construct a deformation model of the virtual ***,a model order reduction method combining proper orthogonal decomposition and Galerkin projection is employed to reduce the amount of deformation *** addition,the cutting path is formed according to the collision intersection position of the surgical instrument and the lesion area of the virtual ***,the Bezier curve is adopted to draw the incision outline after the virtual lung has been ***,the simulation system is set up on the PHANTOM OMNI force haptic feedback device to realize the cutting simulation of the virtual *** results show that the proposed model can enhance the real-time performance of telemedicine,reduce the complexity of the cutting simulation and make the incision smoother and more natural.

关键词： Virtual surgery cutting model finite element model model order reduction Bezier curve

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Hybrid Swarm Intelligence Based QoS Aware Clustering with Routing Protocol for WSN

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computers, Materials & Continua 2021年第9期68卷 2995-3013页

作者： M.S.Maharajan T.Abirami Irina V.Pustokhina Denis A.Pustokhin K.Shankar Department of Computer Science and Engineering GRT Institute of Engineering and TechnologyTiruttani631209India Department of Information Technology Kongu Engineering CollegeErode638060India Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia Department of Computer Applications Alagappa UniversityKaraikudi630001India

Wireless Sensor Networks(WSN)started gaining attention due to its wide application in the fields of data collection and information *** recent advancements in multimedia sensors demand the Quality of Service(QoS)be maintained up to certain *** restrictions and requirements in QoS management completely depend upon the nature of target *** of the major QoS parameters in WSN are energy efficiency,network lifetime,delay and *** this scenario,clustering and routing are considered as the most effective techniques to meet the demands of *** they are treated as NP(Non-deterministic Polynomial-time)hard problem,Swarm Intelligence(SI)techniques can be *** current research work introduces a new QoS aware Clustering and Routing-based technique using Swarm Intelligence(QoSCRSI)*** proposed QoSCRSI technique performs two-level clustering and proficient ***,the fuzzy is hybridized with Glowworm Swarm Optimization(GSO)-based clustering(HFGSOC)technique for optimal selection of Cluster Heads(CHs).Here,Quantum Salp Swarm optimization Algorithm(QSSA)-based routing technique(QSSAR)is utilized to select the possible routes in the *** order to evaluate the performance of the proposed QoSCRSI technique,the authors conducted extensive simulation analysis with varying node *** experimental outcomes,obtained from the proposed QoSCRSI technique,apparently proved that the technique is better compared to other state-of-the-art techniques in terms of energy efficiency,network lifetime,overhead,throughput,and delay.

关键词： Quality of service clustering routing energy efficiency wireless sensor networks swarm intelligence

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