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Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Sara Alahmadi Kasem Khalil Magdy Bayoumi Center of Advanced Computer Studies University of Louisiana Lafayette USA Department of Electrical and Computer Engineering University of Mississippi USA Department of Electrical and Computer Engineering University of Louisiana Lafayette USA

ISBN: (数字)9798350387179

ISBN: (纸本)9798350387186

Physically Unclonable Functions (PUFs) have been promoted as a lightweight security solution for IoT devices, offering authentication without the need to store a secret key or perform complex cryptography. However, PUFs are susceptible to modeling attacks, where adversaries use machine learning to replicate the PUF. Research efforts have focused on improving PUF designs to enhance their resistance to these attacks. In this work, we propose a method to bolster the security of PUFs against modeling attacks. Our approach first obfuscates the challenges using an XOR operation, followed by transforming the challenges into constant weight vectors through encoding. This method introduces randomness by dynamically generating random bits for the XOR step and also uses these bits to generate the weight for encoding. By obscuring the relationship between Challenge-Response Pairs (CRPs), our method makes modeling significantly harder. Testing with varying numbers of CRPs, our results show that modeling accuracy changes only subtly, with a maximum accuracy of 56

关键词： Accuracy Computational modeling Machine learning Encoding Vectors Robustness Security

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On the chronological understanding of the homogeneous dielectric barrier discharge

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High Voltage 2023年第6期8卷 1132-1150页

作者： Xinpei Lu Zhi Fang Dong Dai Tao Shao Feng Liu Cheng Zhang Dawei Liu Lanlan Nie Chunqi Jiang State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic EngineeringHuazhong University of Science and TechnologyWuhanChina College of Electrical Engineering and Control Science Nanjing Tech TechnologyNanjingChina School of Electric Power South China University of TechnologyGuangzhouChina Institute of Electrical Engineering Chinese Academy of SciencesBeijingChina Frank Reidy Research Center for Bioelectrics Old Dominion UniversityNorfolkVirginiaUSA Department of Electrical and Computer Engineering Old Dominion UniversityNorfolkVirginiaUSA

Dielectric barrier discharges(DBD)are widely utilised non‐equilibrium atmospheric pressure plasmas with a diverse range of applications,such as material processing,surface treatment,light sources,pollution control,and *** the course of several decades,extensive research has been dedicated to the generation of homogeneous DBD(H‐DBD),focussing on understanding the transition from H‐DBD to filamentary DBD and exploring strategies to create and sustain H‐*** paper first discusses the in-fluence of various parameters on DBD,including gas flow,dielectric material,surface conductivity,and mesh ***,a chronological literature review is presented,highlighting the development of H‐DBD and the associated understanding of its un-derlying *** encompasses the generation of H‐DBD in helium,nitrogen,and ***,the paper provides a brief overview of multiple‐current‐pulse(MCP)behaviours in H‐*** objective of this article is to provide a chronological un-derstanding of homogeneous dielectric barrier discharge(DBD).This understanding will aid in the design of new experiments aimed at better comprehending the mechanisms behind H‐DBD generation and ultimately assist in achieving large‐volume H‐DBD in an air environment.

关键词： dielectric barrier pollution

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MPEC: Manifold-Preserved EEG Classification via an Ensemble of Clustering-Based Classifiers

MPEC: Manifold-Preserved EEG Classification via an Ensemble ...

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International CSI computer Conference, CSICC

作者： Shermin Shahbazi Mohammad–Reza Nasiri Majid Ramezani Department of Electrical and Computer Engineering University of Zanjan Zanjan Iran Department of Computer Science and Information Technology Institute for Advanced Studies in Basic Sciences (IASBS) Zanjan Iran

ISBN: (数字)9798331523114

ISBN: (纸本)9798331523121

Accurate classification of EEG signals is crucial for brain-computer interfaces (BCls) and neuroprosthetic applications, yet many existing methods fail to account for the non-Euclidean, manifold structure of EEG data, resulting in suboptimal performance. Preserving this manifold information is essential to capture the true geometry of EEG signals, but traditional classification techniques largely overlook this need. To this end, we propose MPEC (Manifold-Preserved EEG Classification via an Ensemble of Clustering-Based Classifiers), that introduces two key innovations: (1) a feature engineering phase that combines covariance matrices and Radial Basis Function (RBF) kernels to capture both linear and non-linear relationships among EEG channels, and (2) a clustering phase that employs a modified K-means algorithm tailored for the Riemannian manifold space, ensuring local geometric sensitivity. Ensembling multiple clustering-based classifiers, MPEC achieves superior results, validated by significant improvements on the BCI Competition IV dataset 2a.

关键词： Manifolds Geometry Support vector machines Technological innovation Accuracy Sensitivity Brain modeling Electroencephalography Brain-computer interfaces Reliability

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Automated Heart Disease Diagnosis and Monitoring System Using Similarity-Navigated Graph Neural Networks with Leopard Seal Optimization in IoT Healthcare Applications 4

Automated Heart Disease Diagnosis and Monitoring System Usin...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Raja, V. Sripathi Rawat, Ruchira Pandit, Karuna Dhumpati, Raghu Vekariya, Vipul Vijitha, S. B V Raju Institute of Technology Department of Biomedical Engineering Telangana Narsapur502313 India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Department of Information Science and Engineering Nitte Karkala Karnataka Udupi District India Bahrain Polytechnic Isa Town Department of Computer Science and Engineering Isa Town3339 Bahrain Parul Institute of Engineering and Technology Parul University Department of Computer Science and Engineering Post Limda Gujarat Waghodia391760 India Vels Institute of Science Technology and Advanced Studies Department of computer science and Engineering Pallavaram Tamil Nadu Chennai600043 India

ISBN: (纸本)9798331540364

One of the leading causes of death worldwide continues to be heart disease;therefore, early detection and precise diagnosis are essential to better patient outcomes. The conventional methods of diagnosing chemotherapy-induced nail changes involve a great deal of laboratory testing, which is more than exhaustive. In order to tackle these obstacles, this research introduces a novel methodology for the automated diagnosis and continuous tracking of heart diseases in the IoT -based health care system, integrated with deep learning algorithms. The proposed system includes several state-of-the art techniques to improve diagnostic reliability and speed. First of all, min-max normalization is used as a pre-processing technique that aims at pre-scaling the data of patients to a consistent level and, as a result, enhancing the performance of the model. The Mud Ring Algorithm is then used for feature selection, whereby the best features are chosen together with the reduction in dimensionality to decrease computational intensity, thus improving diagnostic accuracy. The core diagnostic engine is based on Similarity Navigated Graph Neural Networks (SNGNN) and has been enhanced with Leopard Seal Optimization (LSO), a new meta-method motivated by the hunting patterns of leopard seals. This combination enables the system to relate various features and give the correct prognosis for heart diseases. These techniques, applied within the IoT framework, enable real-time monitoring and diagnosis and therefore have the benefit of being a scalable solution for modern health care systems. The results show that the proposed method for providing diagnosis of heart disease is accurate, timely, and reliable and thus has the potential to revolutionize patient management in healthcare based on the IoT. © 2024 IEEE.

关键词： Chemotherapy

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Sound Tagging in Infant-centric Home Soundscapes

Sound Tagging in Infant-centric Home Soundscapes

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IEEE International Conference on Connected Health: Applications, Systems and engineering Technologies (CHASE)

作者： Mohammad Nur Hossain Khan Jialu Li Nancy L. McElwain Mark Hasegawa–Johnson Bashima Islam Department of Electrical and Computer Engineering Worcester Polytechnic Institute Department of Electrical and Computer Engineering University of Illinois Department of Human Development and Family Studies University of Illinois Beckman Institute for Advanced Science and Technology University of Illinois

ISBN: (数字)9798350345018

ISBN: (纸本)9798350345025

Certain environmental noises have been associated with negative developmental outcomes for infants and young children. Though classifying or tagging sound events in a domestic environment is an active research area, previous studies focused on data collected from a non-stationary microphone placed in the environment or from the perspective of adults. Further, many of these works ignore infants or young children in the environment or have data collected from only a single family where noise from the fixed sound source can be moderate at the infant's position or vice versa. Thus, despite the recent success of large pre-trained models for noise event detection, the performance of these models on infant-centric noise soundscapes in the home is yet to be explored. To bridge this gap, we have collected and labeled noises in home soundscapes from 22 families in an unobtrusive manner, where the data are collected through an infant-worn recording device. In this paper, we explore the performance of a large pre-trained model (Audio Spectrogram Transformer [AST]) on our noise-conditioned infant-centric environmental data as well as publicly available home environmental datasets. Utilizing different training strategies such as resampling, utilizing public datasets, mixing public and infant-centric training sets, and data augmentation using noise and masking, we evaluate the performance of a large pre-trained model on sparse and imbalanced infant-centric data. Our results show that fine-tuning the large pre-trained model by combining our collected dataset with public datasets increases the F1-score from 0.11 (public datasets) and 0.76 (collected datasets) to 0.84 (combined datasets) and Cohen's Kappa from 0.013 (public datasets) and 0.77 (collected datasets) to 0.83 (combined datasets) compared to only training with public or collected datasets, respectively.

关键词： Training Performance evaluation Pediatrics Working environment noise Noise Tagging Transformers

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InfantMotion2Vec: Unlabeled Data-Driven Infant Pose Estimation Using a Single Chest IMU

InfantMotion2Vec: Unlabeled Data-Driven Infant Pose Estimati...

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International Workshop on Wearable and Implantable Body Sensor Networks (BSN)

作者： Mohammad Nur Hossain Khan Nancy L. McElwain Mark Hasegawa-Johnson Bashima Islam Department of Electrical and Computer Engineering Worcester Polytechnic Institute Department of Human Development and Family Studies University of Illinois Beckrnan Institute for Advanced Science and Technology University of Illinois Department of Electrical and Computer Engineering University of Illinois

ISBN: (数字)9798331530143

ISBN: (纸本)9798331530150

Early identification of neuro-developmental risks in infants is crucial for timely intervention and improved quality of life. Current screening methods are costly, intrusive, and limited by artificial environments or require the infant to wear multiple sensors. To address these challenges, we propose a novel approach leveraging inertial measurement units (IMUs) to monitor infants' spontaneous motor abilities in natural settings. Our method introduces a hierarchical semi-supervised classifier and the InfantMotion2Vec embedding to capture detailed motion patterns, accommodating a wide age range (up to 36 months) while minimizing reliance on labeled data and cumbersome sensor setups. We collected labeled IMU data from 25 families and unlabeled data from 42 families using a single wearable sensor. Pretraining an embedding network using unlabeled data with a hierarchical pose estimator resulted in a 26% increase in F1-score and a 77.7% increase in Cohen's Kappa score compared to using only labeled data. The InfantMotion2Vec embedding adequately handles highly unbalanced labeled data, demonstrating its effectiveness in infant posture classification.

关键词： Body sensor networks Measurement units Pose estimation Inertial navigation Motors Wearable sensors Monitoring

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Prediction of Geopolymer Concrete Compressive Strength Using Convolutional Neural Networks

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computer Modeling in engineering & Sciences 2024年第5期139卷 1455-1486页

作者： Kolli Ramujee Pooja Sadula Golla Madhu Sandeep Kautish Abdulaziz S.Almazyad Guojiang Xiong Ali Wagdy Mohamed Department of Civil Engineering VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Information Technology VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Computer Science LBEF Campus(Asia Pacific University of Technology&InnovationMalaysia)Kathmandu44600Nepal Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmmanJordan

Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental *** attributes as a non-toxic,low-carbon,and economical substitute for conventional cement concrete,coupled with its elevated compressive strength and reduced shrinkage properties,position it as a pivotal material for diverse applications spanning from architectural structures to transportation *** this context,this study sets out the task of using machine learning(ML)algorithms to increase the accuracy and interpretability of predicting the compressive strength of geopolymer concrete in the civil engineering *** achieve this goal,a new approach using convolutional neural networks(CNNs)has been *** study focuses on creating a comprehensive dataset consisting of compositional and strength parameters of 162 geopolymer concrete mixes,all containing Class F fly *** selection of optimal input parameters is guided by two distinct *** first criterion leverages insights garnered from previous research on the influence of individual features on compressive *** second criterion scrutinizes the impact of these features within the model’s predictive *** to enhancing the CNN model’s performance is the meticulous determination of the optimal *** a systematic trial-and-error process,the study ascertains the ideal number of epochs for data division and the optimal value of k for k-fold cross-validation—a technique vital to the model’s *** model’s predictive prowess is rigorously assessed via a suite of performance metrics and comprehensive score ***,the model’s adaptability is gauged by integrating a secondary dataset into its predictive framework,facilitating a comparative evaluation against conventional prediction *** unravel the intricacies of the CNN model’s learning trajectory,a loss plot is deployed to elucidate its learning rat

关键词： Class F fly ash compressive strength geopolymer concrete prediction deep learning convolutional neural network

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Design and Implementation of Renewable Energy Applications Based Bi-Directional Buck-Boost Converter 3

Design and Implementation of Renewable Energy Applications B...

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3rd International Conference on Innovative Practices in Technology and Management, ICIPTM 2023

作者： Ramana, K. Venkata Arulkumar, S. Marathe, Asmita Beshir, Kedir Jaiganesh, V. Tamilselvi, K. Sudhakar, M. Vnr Vignana Jyothi Institute of Engineering & Technology Department of Computer Scinece and Engineering Hyderabad India Wachemo University Centre for Energy Studies Department of Electrical and Computer Engineering Ethiopia Savitribai Phule Pune University Department of Technology Pune India Wachemo University Department of Electrical and Computer Engineering 667 Ethiopia Bharath Institute of Higher Education and Research Department of Mechanical Engineering Tamil Nadu Chennai India Panimalar Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Sai Ram Engineering College Department of Mechanical Engineering Chennai India

ISBN: (纸本)9798350336238

Our main focus was on creating a Two directional power converter for a standalone photo-voltaic energy-generating technology as well as technology energy control when a lead-acid energy storage device is used to control the energy supply. We utilized a light bulb as the load to evaluate the effectiveness of the directional power converter developed as part of our project as well as the functionality of energy control technologies. The suggested technology was composed of an energy control technology, a lead-acid battery, a bulb, a two-directional buck-boost converter, an utmost power point tracking controller, and these components. An energy control technique was developed to grow the rate at which the Photo-voltaic power producing technology consumed energy. The circuits are intended to charge the battery between upper and lower voltage limits, as well as to continuously check the battery's state of charge and add or release current as necessary. The bidirectional buck-boost converter's ability to function as a charge controller on its own, along with the use of particular BUCK and BOOST converter properties to optimize the home application, is the primary distinction between the method used in the proposed technology and other techniques used in the past. The battery is charged and discharged using a bidirectional dc-dc converter. Measurement data are employed to support the viability of a photovoltaic air conditioning technology. © 2023 IEEE.

关键词： Buck-Boost converter

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Intelligent Energy Management Energy Storage Systems Using Machine Learning

Intelligent Energy Management Energy Storage Systems Using M...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Govindasamy, Chinnaraj Dayana, T.R Nisha Adkar, Siddhika Dilip Prakash, D. Shyam Gupta, A. Shilpa Trivedi, Sanjeev Kumar Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamilnadu Chennai602105 India Vels Institute of Science Technology and Advance Studies Department of Computer Science Chennai India School of Computer Science Engineering and Applications D y Patil International University Akurdi India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore India Department of Electrical and Electronics Engineering Aurora's Technological Research Institute Uppal Hyderabad India Khwaja Moinuddin Chishti Language University Faculty of Engineering and Technology Department of Electronics Lucknow India

ISBN: (纸本)9798350375237

A nevertheless-emerging generation called cloud computing permits customers to pay for services on a usage-based foundation. Internet-primarily based IT offerings are supplied through cloud computing, at the same time as virtualization enables the availability of PC sources. The muse of cloud computing is the information center, which is made up of networked computers, cables, electricity components, and different components that host and shop corporate records. With cloud facts centers, high overall performance has continually been the most critical concern, yet it compromises strength utilization. The key hassle is to lessen power consumption at the same time as preserving provider nice and performance a good way to stability device performance and strength intake. A detailed grasp of strength use styles within the cloud environment is needed for our suggested technique. We look at power consumption tendencies and reveal how, through the use of the right optimization standards based on our strength intake models, we can keep more strength in cloud records facilities. All through the prediction section, tablet optimization, which has a 97 percent accuracy rate, permits this era to provide extra correct future price forecasts. © 2024 IEEE.

关键词： Machine learning

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Microwave-induced cooling in double quantum dots: Achieving millikelvin temperatures to reduce thermal noise around spin qubits

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Physical Review B 2025年第1期111卷 014305-014305页

作者： Daryoosh Vashaee Jahanfar Abouie Department of Materials Science and Engineering and Department of Electrical and Computer Engineering North Carolina State University Raleigh North Carolina 27606 USA Department of Physics Institute for Advanced Studies in Basic Sciences Zanjan 45137-66731 Iran

Spin qubits in gate-defined quantum dots are emerging as a leading technology due to their scalability and long coherence times. However, maintaining these qubits at ultralow temperatures typically requires complex cryogenic systems. This paper proposes a gate-defined double quantum dot (DQD) cooling system, where the DQDs act as refrigerants to reduce the local phonon environment around computational qubits. The cooling process occurs in two distinct stages: the first step involves microwave-induced state depopulation combined with fast cyclic detuning to transfer the DQD's population to the ground state, effectively lowering the DQD's temperature. In the second step, the cooled DQD interacts with and absorbs phonons resonant with the DQD spin energy, thereby filtering out these phonons that contribute to spin-lattice relaxation in the surrounding environment. This study focuses on the first step, presenting detailed calculations and numerical results that demonstrate the feasibility of achieving local DQD temperatures below 10mK at a bath temperature of 1K. The sensitivity of the cooling performance to detuning energy, magnetic field strength, spin-orbit couplings, and diabatic return time is analyzed, while the phonon filtering in the second step will require further investigation.

关键词： Semiconductor quantum dots

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