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IEEE International Conference on Consumer Electronics (ICCE)

作者： Chang-Ming Yang Shu-Cing Wu Ji-Jer Huang Chi-Chun Chen Ming Young Biomedical Corp. Miaoli Taiwan Prospective Technology of Electrical Engineering and Computer Science National Chin-Yi University of Technology Taichung Taiwan Department of Electrical Engineering Southern Taiwan University of Science and Technology Tainan Taiwan Dept. of Electronic Engineering National Chin-Yi University of Technology Taichung Taiwan

This paper introduces a novel standard 3lead ECG monitoring system, designed for enhanced comfort in daily wear. The system utilizes innovative dry electrodes, seamlessly integrated into both underwear and shirts. These electrodes, made of non-filtering silver material, are specifically engineered to maximize wearer comfort while reliably capturing standard 3-lead ECG signals. This design allows for accurate ECG signal acquisition in both stationary and ambulatory states. The system uniquely records life behavior patterns by analyzing the noise components within the 3-lead ECG signals. For physical activities, the system switches to a filtering silver electrode, ensuring the capture of high-fidelity ECG signals without compromising comfort. The primary goal of this research is to develop a versatile standard 3-lead ECG monitoring system suitable for various daily activities, including exercise, Holter monitoring, and sleep tracking.

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DCGAN-based Medical Image Augmentation to Improve ELM Classification Performance

DCGAN-based Medical Image Augmentation to Improve ELM Classi...

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IEEE International Conference on Communication, Networks and Satellite (ComNetSat)

作者： Rando Noor Akhmad Setiawan Adhistya Erna Permanasari Riries Rulaningtyas Andriyan Bayu Suksmono Imas Sukaesih Sitanggang Dept. of Electrical and Information Engineering Universitas Gadjah Mada Yogyakarta Indonesia Dept. of Physics Universitas Airlangga Biomedical Engineering Program Surabaya Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Department of Computer Science IPB University Bogor Indonesia

ISBN: (数字)9781665460309

ISBN: (纸本)9781665460316

Cervical cancer is one of the deadliest diseases in women. One of the cervical cancer screening methods is pap smear method. However, using a pap smear method to detect cervical cancer takes a long time for a pathologist to diagnose. Hence, a rapid development of medical computerization for early detection to get the results quickly is needed. This paper proposes synthetic data augmentation by using Deep Convolutional Generative Adversarial Network (DCGAN) to increase number of pap smear samples in dataset. Gray Level Co-occurrence Matrix (GLCM) is employed to extract features from dataset. Classification of 3 classes which are Adenocarcinoma, High-Grade Squamous Intraepithelial Lesion (HSIL), and Squamous Cell Carcinoma (SCC) is conducted using Extreme Learning Machine (ELM). The result shows that the addition of synthetic data improves the performance of ELM with the accuracy of 90%. This accuracy is better than the accuracy of ELM using only the original dataset which is 85%.

关键词： Satellites Extreme learning machines Medical services Feature extraction Generative adversarial networks Entropy Lesions

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Using Unlabeled Data in Self-Supervised Training Improves Automatic Vertebral Body Fracture Assessment

Using Unlabeled Data in Self-Supervised Training Improves Au...

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IEEE International Symposium on biomedical Imaging

作者： Julian Lukas Laue Christian Adolph Eren Bora Yilmaz Carsten Meyer Department of Computer Science Ostfalia University of Applied Sciences Wolfenbüttel Germany L3S Research Center Leibniz University Hannover Hannover Germany Deptartment of Computer Science Faculty of Engineering Kiel University Germany Dept. of Radiology and Neuroradiology Section Biomedical Imaging UKSH Kiel Germany

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Vertebral body (VB) fractures can have severe implications for the patient’s well-being, but often remain undetected. Automatic vertebral fracture assessment is challenging due to severe class imbalance in fracture grades, scarcity of annotated data, and often subtle visual differences between the grades. In this paper, we show that leveraging unlabeled data, collected from eight diverse, publicly available datasets in a self-supervised way via the BYOL framework, can noticeably improve performance. Our pre-trained models outperformed the non-pre-trained baselines in 17 out of 18 comparisons, 11 of which were statistically significant, when evaluated on the widely-used VerSe dataset, our in-house dataset, and combinations thereof. Furthermore, our models reached an average AUROC value of 99.6% on the VerSe test corpus, the highest recorded in the literature so far.

关键词： Training Visualization Sensitivity Biological system modeling Context modeling biomedical imaging

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Probabilistic Decomposed Linear Dynamical Systems for Robust Discovery of Latent Neural Dynamics

arXiv

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arXiv 2024年

作者： Chen, Yenho Mudrik, Noga Johnsen, Kyle A. Alagapan, Sankaraleengam Charles, Adam S. Rozell, Christopher J. Machine Learning Center Georgia Institute of Technology United States School of Electrical and Computer Engineering Georgia Institute of Technology United States Coulter Dept. of Biomedical Engineering Emory University Georgia Institute of Technology United States Department of Biomedical Engineering Mathematical Institute for Data Science Center for Imaging Science Kavli Neuroscience Discovery Institute Johns Hopkins University United States

Time-varying linear state-space models are powerful tools for obtaining mathematically interpretable representations of neural signals. For example, switching and decomposed models describe complex systems using latent variables that evolve according to simple locally linear dynamics. However, existing methods for latent variable estimation are not robust to dynamical noise and system nonlinearity due to noise-sensitive inference procedures and limited model formulations. This can lead to inconsistent results on signals with similar dynamics, limiting the model’s ability to provide scientific insight. In this work, we address these limitations and propose a probabilistic approach to latent variable estimation in decomposed models that improves robustness against dynamical noise. Additionally, we introduce an extended latent dynamics model to improve robustness against system nonlinearities. We evaluate our approach on several synthetic dynamical systems, including an empirically-derived brain-computer interface experiment, and demonstrate more accurate latent variable inference in nonlinear systems with diverse noise conditions. Furthermore, we apply our method to a real-world clinical neurophysiology dataset, illustrating the ability to identify interpretable and coherent structure where previous models cannot. © 2024, CC BY-SA.

关键词： State space methods

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Synergistic Integration of Photovoltaic and the Grid: An Intelligent ANFIS-Governed Interleaved Boost Converter Approach

Synergistic Integration of Photovoltaic and the Grid: An Int...

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Advancement in Renewable Energy and Intelligent Systems (AREIS), International Conference on

作者： Balaji V M. Ganga Barathi K Pramod Kumar Gouda Rajesh Prasad Mammula Venkatesh Department of Electrical and Computer Engineering MAI-NEFHI College of Engineering and Technology Asmara Eritrea Department of Biomedical Engineering Hindusthan College of Engineering and Technology Coimbatore India Dept. of EEE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu India Dept. of EEE Aditya Institute of Technology and Management Srikakulam Andhra Pradesh Dept. of EEE Stella Mary's College of Engineering Tamil Nadu India Dept. of EEE Avanthi's Research and Technological Academy Vizianagaram A. P India

ISBN: (数字)9798350387230

ISBN: (纸本)9798350387247

The transition towards Renewable Energy Sources (RES), mainly solar power is crucial in addressing the depletion of fossil fuel-based energy supplies and mitigating environmental concerns. This paper proposes an innovative approach to enhance efficiency of PhotoVoltaic (PV) systems by integrating a two-phase Interleaved Boost (IB) converter with an Adaptive Neuro Fuzzy Inference System (ANFIS) controller. The ANFIS controller optimizes the operation of the IB converter by adjusting the duty cycle based on two key input parameters: solar irradiation and temperature. This adaptive control method effectively transforms solar energy into electrical power, maximizing the PV system's power production. By using the ANFIS mechanism, the suggested system improves total output power, decreases overshoot, and produces a higher voltage output than conventional techniques, all of which lead to increased performance and dependability. The IB converter operates with an efficiency of 96.15% and minimized settling time.

关键词： Photovoltaic systems Renewable energy sources Radiation effects Systems operation Transforms Solar energy Production Stability analysis Temperature control Thermal stability

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Large Language Models: A Concise Review of Types and Considerations

Large Language Models: A Concise Review of Types and Conside...

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International Conference on Parallel Distributed and Grid Computing (PDGC)

作者： Apeksha Aggarwal Naveen Kumar Gupta Ajay Agarwal Pawan Kumar Dept. of CSE and IT Jaypee Institute of Information Technology Noida India Dept. of Information Technology Center of Energy and Environment Dr B R Ambedkar National Institute of Technology Jalandhar Jalandhar India Department of Computer Science and Engineering R. D. Engineering College Ghaziabad India Biomedical Engineering Indian Institute of Technology Bombay Mumbai India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

This research paper explores nuances of large language models along with a detailed analysis of various model types, and their associated advantages and drawbacks. The primary objective of this paper is to provide a brief knowledge about prevailing popular LLMs. This study further offers a thorough summary of the most recent survey articles that have been published on Large Language Models (LLMs) till date. Furthermore, a comparative study of the latest LLMs launched by popular organizations is also done, along with their evolution over a period of time. Challenges of LLMs are also outlined in this work.

关键词： Surveys Analytical models Reviews Large language models Organizations Transformers Grid computing Natural language processing

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Numerical analysis of free convection under the influence of radiation and inclined MHD in a triangular cavity filled with hybrid nanofluid and a porous fin

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International Journal of Thermofluids 2024年 24卷

作者： Hassan, Ahmed M. Alomari, Mohammed Azeez Al-Salami, Qusay H. Alyousuf, Farah Q.A. Alqurashi, Faris Flayyih, Mujtaba A. Department of Mechanical Engineering University of Al-Qadisiyah Al-Qadisiyah58001 Iraq Department of Mechanical Engineering University of Al-Qadisiyah Ad-Diwaniyah58001 Iraq College of Engineering University of Warith Al-Anbiyaa Karbala Iraq Department of Business Administration College of Administrative and Financial Sciences Cihan University Erbil Iraq Department of Computer Network College of Engineering and Computer Science Lebanese French University Kurdistan Region Erbil Iraq Mechanical Engineering Dept. College of Engineering University of Bisha Bisha Saudi Arabia Biomedical Engineering Department College of Engineering and Technologies Al-Mustaqbal University Hillah Iraq

This study presents a novel investigation into the thermal behavior of a hybrid nanofluid (MgO − Ag − H2O) in natural convection around a porous fin in a triangular enclosure under the influence of radiation and magnetohydrodynamic effects. The research uniquely combines these complex phenomena, addressing a significant gap in the literature. This configuration has potential applications in advanced solar thermal collectors, electronic cooling systems for high-power devices, and compact heat exchangers in various industries. The main objectives are to understand how various parameters influence heat transfer and fluid flow behavior and to optimize the design for enhanced thermal performance. The study considers a range of variables including Rayleigh number (103 − − 106), Hartmann number (0 – 50), Aspect ratio (0.3 – 0.6), radiation parameters (Rd = 1 − − 5, λ = 1 − 5), and volume concentration (0- 0.05), which have been numerically analyzed using the finite element method (FEM). The findings reveal that increasing the Darcy number (Da) enhances heat transfer at low Rayleigh numbers (Ra = 103,104). However, at higher Ra (Ra = 106), the impact of Da becomes more complex, with a critical Da beyond which heat transfer efficiency decreases due to an increase in flow resistance. The nanoparticle volume concentration plays a vital role, as higher concentrations lead to improved heat transfer efficiency, especially at higher Ra, through enhanced thermal conductivity and thermal dispersion. The length of the porous fin greatly impacts fluid flow patterns and heat transfer rates, with longer fins creating more complex flow patterns, promoting enhanced heat transfer and stronger thermal plumes. Thermal radiation, represented by the radiation parameters (Rd and λ), significantly influences both the heat transfer rate and the convective flow patterns within the enclosure. This study also incorporates a comprehensive entropy generation analysis, providing novel insights into sys

关键词： Rayleigh number

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Hardware-Aware DNN Compression via Diverse Pruning and Mixed-Precision Quantization

arXiv

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arXiv 2023年

作者： Balaskas, Konstantinos Karatzas, Andreas Sad, Christos Siozios, Kostas Anagnostopoulos, Iraklis Zervakis, Georgios Henkel, Jörg The Department of Physics Aristotle University of Thessaloniki Thessaloniki54124 Greece Karlsruhe Institute of Technology Karlsruhe76131 Germany The School of Electrical Computer and Biomedical Engineering Southern Illinois University Carbondale CarbondaleIL62901 United States The Dept. of Computer Engineering & Informatics University of Patras Patras26504 Greece

Deep Neural Networks (DNNs) have shown significant advantages in a wide variety of domains. However, DNNs are becoming computationally intensive and energy hungry at an exponential pace, while at the same time, there is a vast demand for running sophisticated DNN-based services on resource constrained embedded devices. In this paper, we target energy-efficient inference on embedded DNN accelerators. To that end, we propose an automated framework to compress DNNs in a hardware-aware manner by jointly employing pruning and quantization. We explore, for the first time, per-layer fine- and coarse-grained pruning, in the same DNN architecture, in addition to low bit-width mixed-precision quantization for weights and activations. Reinforcement Learning (RL) is used to explore the associated design space and identify the pruning-quantization configuration so that the energy consumption is minimized whilst the prediction accuracy loss is retained at acceptable levels. Using our novel composite RL agent we are able to extract energy-efficient solutions without requiring retraining and/or fine tuning. Our extensive experimental evaluation over widely used DNNs and the CIFAR-10/100 and ImageNet datasets demonstrates that our framework achieves 39% average energy reduction for 1.7% average accuracy loss and outperforms significantly the state-of-the-art approaches. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Numerical analysis of unsteady free convection under the combined influence of inclined magnetohydrodynamic and exothermic chemical reaction in an enclosure filled with nanofluid

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International Journal of Thermofluids 2024年 23卷

作者： Alaydamee, Hussein H. Alomari, Mohammed Azeez Al-Salami, Qusay H. Alyousuf, Farah Q.A. Alqurashi, Faris Flayyih, Mujtaba A. Department of Chemical Engineering University of Al-Qadisiyah College of Engineering Al-Qadisiyah58002 Iraq Department of Mechanical Engineering University of Al-Qadisiyah Ad-Diwaniyah58001 Iraq College of Engineering University of Warith Al-Anbiyaa Karbala Iraq Department of Business Administration College of Administrative and Financial Sciences Cihan University-Erbil Iraq Department of Computer Network College of Engineering and Computer Science Lebanese French University Erbil Kurdistan Region Iraq Mechanical Engineering Dept. College of Engineering University of Bisha Bisha Saudi Arabia Biomedical Engineering Department College of Engineering and Technologies Al-Mustaqbal University Hillah Iraq

Unsteady study of the natural convection of aluminum oxide-water nanofluid within a trapezoidal geometry containing a circular cylinder located at its center. Finite Element method has been considered for the numerical analysis. The proposed investigation handled the impact of Rayleigh number (103–105), chemical reaction parameter (0–4), aluminum oxide nanoparticles volume fraction (0–0.06), magnetic field (0–63) and its inclination angle (0°–90°), and circular obstacle diameter (0.3–0.7) effects on time-dependent natural convection of Al2O3–H2O nanofluid. On the other hand, the value of Prandtl number has kept constant at (Pr = 6.2). Since the nanofluid mobility at φ = 0.02, Ha = 3, and Fk = 1 significantly improved, the heat transfer rate achieved its maximum intensity at Ra = 105. Research also reveals a little effect on heat transfer by increasing the fraction of nanoparticles. Additionally, as Ha intensifies from 0 to 63, a final change in the mean Nusselt number of 28.65 % is displayed. Finally, as the magnetic field angle of rotation is diminished, more enhancement in heat transmission is achieved. This research provides insights into the intricate relationship between natural convection and exothermic reaction under the influences of various conditions. This can illustrate the flow and thermal behaviors of nanofluid in such non-uniform shapes in many engineering applications. © 2024

关键词： Natural convection

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Technical Debt Management: The Road Ahead for Successful Software Delivery

arXiv

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arXiv 2024年

作者： Avgeriou, Paris Ozkaya, Ipek Chatzigeorgiou, Alexander Ciolkowski, Marcus Ernst, Neil A. Koontz, Ronald J. Poort, Eltjo Shull, Forrest Dept. of Computing Science University of Groningen Groningen Netherlands Software Engineering Institute Carnegie Mellon University PittsburghPA United States Dept. of Applied Informatics University of Macedonia Thessaloniki Greece QAware GmbH München Germany Department of Computer Science University of Victoria Victoria Canada The Boeing Company MesaAZ United States CGI Rotterdam Netherlands

Technical Debt, considered by many to be the 'silent killer' of software projects, has undeniably become part of the everyday vocabulary of software engineers. We know it compromises the internal quality of a system, either deliberately or inadvertently. We understand Technical Debt is not all derogatory, often serving the purpose of expediency. But, it is associated with a clear risk, especially for large and complex systems with extended service life: if we do not properly manage Technical Debt, it threatens to "bankrupt" those systems. Software engineers and organizations that develop softwareintensive systems are facing an increasingly more dire future state of those systems if they do not start incorporating Technical Debt management into their day to day practice. But how? What have the wins and losses of the past decade of research and practice in managing Technical Debt taught us and where should we focus next? In this paper, we examine the state of the art in both industry and research communities in managing Technical Debt;we subsequently distill the gaps in industrial practice and the research shortcomings, and synthesize them to define and articulate a vision for what Technical Debt management looks like five years hence. © 2024, CC BY-NC-ND.

关键词： Industrial research

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