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Navigating IoT Security:Insights into Architecture,Key Security Features,Attacks,Current Challenges and AI-Driven Solutions Shaping the Future of Connectivity

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computers, Materials & Continua 2024年第12期81卷 3499-3559页

作者： Ali Hassan N.Nizam-Uddin Asim Quddus Syed Rizwan Hassan Ateeq Ur Rehman Salil Bharany Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan Department of Biomedical Engineering HITEC UniversityTaxila47080Pakistan Department of Electronics Engineering University of ChakwalChakwal48800Pakistan Department of Computer Science NFC Institute of Engineering and Fertilizer ResearchFaisalabad38000Pakistan School of Computing Gachon UniversitySeongnam-si13120Republic of Korea Chitkara University Institute of Engineering and Technology Chitkara UniversityRajpura140401PunjabIndia

Enhancing the interconnection of devices and systems,the Internet of Things(IoT)is a paradigm-shifting *** security concerns are still a substantial concern despite its extraordinary *** paper offers an extensive review of IoT security,emphasizing the technology’s architecture,important security elements,and common *** highlights how important artificial intelligence(AI)is to bolstering IoT security,especially when it comes to addressing risks at different IoT architecture *** systematically examined current mitigation strategies and their effectiveness,highlighting contemporary challenges with practical solutions and case studies from a range of industries,such as healthcare,smart homes,and industrial *** results highlight the importance of AI methods that are lightweight and improve security without compromising the limited resources of devices and computational *** networks can ensure operational efficiency and resilience by proactively identifying and countering security risks by utilizing machine learning *** study provides a comprehensive guide for practitioners and researchers aiming to understand the intricate connection between IoT,security challenges,and AI-driven solutions.

关键词： Internet of Things(IoT) artificial intelligence(AI) IoT architecture security attacks in IoT

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Biomechanical Evaluation of Fingers Repetitive Voluntary Tasks in Chronic Stroke Survivors

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IEEE Access 2023年 11卷 143649-143656页

作者： Adeel, Muhammad Peng, Chih-Wei Lee, I-Jung Lin, Bor-Shing National Taipei University Department of Computer Science and Information Engineering New Taipei City237303 Taiwan Taipei Medical University School of Biomedical Engineering College of Biomedical Engineering Taipei11031 Taiwan Taipei Medical University School of Gerontology and Long-Term Care College of Nursing Taipei11031 Taiwan National Taipei University College of Electrical Engineering and Computer Science New Taipei City237303 Taiwan

A chronic stroke affects hand mobility limiting the normal functioning of the finger joints. The voluntary tasks with a repetitive motion can identify the limitation in the range of motion (ROM) to enhance the hand functioning of stroke survivors. Therefore, we compared two voluntary tasks fast ball squeezing (FBS) and slow ball squeezing (SBS), and analyzed the variability of these tasks for finger pressure and ROM in chronic stroke survivors. An experimental study with 22 healthy and 39 chronic stroke participants was conducted. All of them wore an upper limb motion capture device (UMCD) to record the active (AROM) of finger joints during FBS and SBS tasks for 20 repetitions. The data were analyzed into the joint angle and pressure to compare these tasks and their variability. While comparing the FBS and SBS tasks, left-side pressure and both sides' ROM attained the level of significance (p © 2013 IEEE.

关键词： Joints (anatomy)

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Laser Machined Fiber‑Based Microprobe: Application in Microscale Electroporation

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Advanced Fiber Materials 2022年第4期4卷 859-872页

作者： Jongwoon Kim Yajun Zhao Shuo Yang Ziang Feng Anbo Wang Rafael V.Davalos Xiaoting Jia Bradley Department of Electrical and Computer Engineering Virginia TechBlacksburgVA 24060USA Department of Biomedical Engineering and Mechanics Virginia TechBlacksburgVA 24061USA

Microscale electroporation devices are mostly restricted to in vitro experiments(i.e.,microchannel and microcapillary).Novel fiber-based microprobes enable in vivo microscale electroporation and arbitrarily select the cell groups of interest to *** developed a flexible,fiber-based microscale electroporation device through a thermal drawing process and femtosecond laser micromachining *** fiber consists of four copper electrodes(80μm),one microfluidic channel(30μm),and has an overall diameter of 400μ*** dimensions of the exposed electrodes and channel were customizable through a delicate femtosecond laser *** feasibility of the fiber probe was validated through numerical simulations and in vitro *** reversible and irreversible microscale electroporation was observed in a 3D collagen scaffold(seeded with U251 human glioma cells)using fluorescent *** ablation regions were estimated by performing the covariance error ellipse method and compared with the numerical *** computational and experimental results of the working fiber-based microprobe suggest the feasibility of in vivo microscale electroporation in space-sensitive areas,such as the deep brain.

关键词： Fiber probe Polymer Micromachine Irreversible electroporation Reversible electroporation Microscale electroporation

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What is Affective Touch Made Of? A Soft Capacitive Sensor Array Reveals the Interplay between Shear, Normal Stress and Individuality 24

What is Affective Touch Made Of? A Soft Capacitive Sensor Ar...

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37th Annual ACM Symposium on User Interface Software and Technology, UIST 2024

作者： Mclaren, Devyani Gao, Jian Yin, Xiulun Guerra, Rúbia Reis Vyas, Preeti Morton, Chrys Cang, Xi Laura Chen, Yizhong Sun, Yiyuan Li, Ying Madden, John David Wyndham Maclean, Karon E. Computer Science University of British Columbia VancouverBC Canada Electrical and Computer Engineering University of British Columbia VancouverBC Canada Biomedical Engineering University of British Columbia VancouverBC Canada

ISBN: (纸本)9798400706288

Humans physically express emotion by modulating parameters that register on mammalian skin mechanoreceptors, but are unavailable in current touch-sensing technology. Greater sensory richness combined with data on affect-expression composition is a prerequisite to estimating affect from touch, with applications including physical human-robot interaction. To examine shear alongside more easily captured normal stresses, we tailored recent capacitive technology to attain performance suitable for affective touch, creating a flexible, reconfigurable and soft 36-taxel array that detects multitouch normal and 2-dimensional shear at ranges of 1.5kPa-43kPa and ± 0.3-3.8kPa respectively, wirelessly at 43Hz (1548 taxels/s). In a deep-learning classification of 9 gestures (N=16), inclusion of shear data improved accuracy to 88%, compared to 80% with normal stress data alone, confirming shear stress's expressive centrality. Using this rich data, we analyse the interplay of sensed-touch features, gesture attributes and individual differences, propose affective-touch sensing requirements, and share technical considerations for performance and practicality. © 2024 Owner/Author.

关键词： Shear stress

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Mode-Division Multiplexing at Visible Wavelengths

Mode-Division Multiplexing at Visible Wavelengths

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Shah, Priyansh Mellouk, Elias Ben Levine, Jeremy Mohanty, Aseema Department of Electrical and Computer Engineering Tufts University MedfordMA02155 United States Department of Biomedical Engineering Tufts University MedfordMA02155 United States

We demonstrate mode-division multiplexing at visible wavelengths (473 nm) for the first time using adiabatic mode couplers. We measure less than -15 dB and -20 dB crosstalk for TE2 and TE3 higher-order mode couplers, ... 详细信息

ISBN: (纸本)9781957171395

关键词： Laser beams

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Mode-Division Multiplexing at Visible Wavelengths

Mode-Division Multiplexing at Visible Wavelengths

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CLEO: Science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

关键词： Tellurium compounds

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Monte Carlo modelling of Raman signal enhancement using power-efficient Apple processors 15

Monte Carlo modelling of Raman signal enhancement using powe...

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biomedical Light Scattering XV 2025

作者： Vladyko, Ilya Yakovlev, Vladislav Doronin, Alexander School of Engineering and Computer Science Victoria University of Wellington Wellington6140 New Zealand Department of Biomedical Engineering Department of Electrical and Computer Engineering Department of Physics and Astronomy Institute for Quantum Science and Engineering Texas A&M University College StationTX77843 United States

ISBN: (纸本)9781510683884

Modern optical technologies encompass classical light phenomena and non-linear effects, crucial for biomedical imaging and therapies. Despite substantial interest and many experimental studies, non-linear optical effects in inhomogeneous scattering media require extensive computer modeling to understand light-material interactions. This report focuses on Monte Carlo simulations of Raman scattering in turbid media, such as biological tissues, essential for clinical applications. We developed a novel, open-source, power-efficient Monte Carlo algorithm optimized for Apple s M-series chips. By incorporating single-photon tracking, the algorithm accurately models time-resolved light-Tissue interactions. Validated against experiments, it matches traditional methods accuracy while offering superior efficiency, advancing Raman functional imaging. © 2025 SPIE.

关键词： Tissue

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Performance Analysis of Heart Disease Detection Using Different Machine Learning Approaches 6

Performance Analysis of Heart Disease Detection Using Differ...

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6th International Conference on electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Tousif, Md. Noumil Rabbi Ushan, Md. Nahian Akhter, Rumana Mahmud, Sadia Rashid, Sakera Mohammad, Sakib University of Dhaka Department of Biomedical Physics and Technology Dhaka Bangladesh Ahsanullah University of Science and Technology Department of Electrical and Electronic Engineering Dhaka Bangladesh European University of Bangladesh Department of Electrical and Electronic Engineering Dhaka Bangladesh Southern Illinois University Carbondale Department of Electrical Computer and Biomedical Engineering CarbondaleIL United States

ISBN: (纸本)9798350385779

The leading cause of illness and mortality globally, heart disease is a serious global health problem. For successful prevention and intervention, timely and precise cardiac disease prediction is essential. Using the well-known Cleveland dataset from the UCI repository, we underwent an extensive performance examination of several ML approaches in the context of heart disease prediction in this study. The 303 cases and 14 features in this collection include essential clinical factors. The KNN, Random Forest, Naive Bayes, AdaBoost, Decision Tree, and Logistic Regression machine learning algorithms are among those being examined. Each is being assessed for its predictive efficacy. Our work reveals fascinating insights into algorithm performance through substantial data preparation, robust model training, and a thorough assessment utilizing measures like accuracy, sensitivity, specificity, and PPV, NPV. Notably, our results show that the KNN algorithm is the most effective at predicting heart disease by 93%. This finding has implications for improving clinical decision support systems and early heart disease detection, thereby lowering the prevalence of the condition and its negative effects on public health. This research highlights the crucial contribution of ML to the development of cardiovascular medicine and the significance of algorithm choice for predicting accuracy. © 2024 IEEE.

关键词： Decision trees

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Electroencephalography Machine Learning Features and Methods for Early Diagnosis and Classification of Parkinson’s Disease (2013–2023): A Review

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IEEE Sensors Journal 2025年第12期25卷 21017-21032页

作者： Emad-Ud-Din, Muhammad Almuteb, Ibrahim Wang, Ya Hubbard, James E. Texas A&M University Department of Computer Science College StationTX77843 United States Texas A&M University Department of Biomedical Engineering College StationTX77843 United States Department of Electrical and Computer Engineering College Station77843 United States Texas A&M University Department of Mechanical Engineering College StationTX77843 United States

Exploring the Machine Learning (ML) features and methods based on electroencephalography (EEG) and quantitative electroencephalographic (qEEG) holds the potential to early diagnosis and classification of Parkinson’s disease (PD) and can bring convenience to the decision-making process for healthcare professionals. PD is clinically heterogeneous, with high variability between patients in terms of motor and non-motor symptoms. This nature makes it extremely challenging for early diagnosis, and severity classification using traditionally questionnaire-based standards. EEG and qEEG provide objective signals with rich features that can potentially provide a translational alternative. In the past decade, a host of ML-based features and algorithms have been developed to detect and classify PD in patients using a range of EEG and qEEG datasets. Various methodologies, including graph-CNNs, deep RNNs, and hybrid models, are discussed within the context of early diagnosis and classification, each offering unique insights and performance levels. This review highlights the multifaceted nature of PD diagnosis and classification research. It addresses the challenges and limitations encountered in existing techniques, emphasizing the need for further improvements in accuracy, sensitivity, and specificity. This review provides a standardized insight into the evolving landscape of PD diagnosis and classification. It showcases the remarkable progress that has been made in utilizing EEG and qEEG data while emphasizing the ongoing quest for effective and convenient ML based methods for early diagnosis and classification. © 2001-2012 IEEE.

关键词： Electroencephalography

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On-chip hyperspectral detectors for fluorescence lifetime imaging 9

On-chip hyperspectral detectors for fluorescence lifetime im...

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High-Speed biomedical Imaging and Spectroscopy IX 2024

作者： Ahamed, Ahasan Rawat, Amita McPhillips, Lisa N. Marcu, Laura Saif Islam, M. Electrical and Computer Engineering Department University of California DavisCA95616 United States Biomedical Engineering Department University of California DavisCA95616 United States

ISBN: (纸本)9781510669659

Fluorescence Lifetime Imaging (FLIM) is a powerful technique that measures the decay time of fluorophores present in tissue samples alluding to their constituent molecules. FLIM has gained popularity in biomedical imaging for applications such as detecting cancerous tumors, surgical guidance, etc. However, conventional FLIM systems are limited by a reduced number of spectral bands and long acquisition time. Moreover, the large footprint, complexity, and cost of the instrumentation make it difficult for clinical applications. In this paper, we demonstrate a reconstruction-based hyperspectral detector that can resolve decay time and intensities in broad spectral ranges while providing high sensitivity, high gain, and fast response time. The hyperspectral detector is comprised of an array of efficient, ultrafast avalanche photodetectors integrated with nanophotonic structures. We utilize different nanostructures in the detectors to modulate light-matter interactions in spectral channels. This allows us to computationally reconstruct the spectral profile of the incoming fluorescence spectrum without the need for additional filters or dispersive optics. Also, the nanophotonic structures enhance efficiency (by a factor of 2 to 10 over different wavelengths) while providing fast response time. An innovative detector design has been employed to reduce the breakdown of the avalanche photodetectors to-7.8V while maintaining high gain (∼50) across the spectral range. Therefore, enabling low light detection with a high signal-To-noise ratio for FLIM applications. Added spectral channels would provide valuable information about tissue materials, morphology, and disease diagnosis. Such innovative hyperspectral sensors can now be integrated on-chip capable of miniaturizing the FLIM system and making it a commercially viable tool for clinical use. This technology has the potential to revolutionize the current FLIM system with improved detection capabilities opening doors for new ho

关键词： Hyperspectral imaging

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