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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Alyounis, Sona Khandoker, Ahsan Stefanini, Cesare Hadjileontiadis, Leontios Scuola Superiore Sant'Anna Creative Engineering Design Lab The BioRobotics Institute Pontedera Italy Khalifa University of Science and Technology Healthcare Engineering Innovation Center Department of Biomedical Engineering P.O. Box 127788 Abu Dhabi United Arab Emirates Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Thessaloniki Greece

ISBN: (纸本)9798350371499

Heart failure (HF) is the leading etiology for hospital admissions and ranks among the foremost contributors to mortality. This complex clinical syndrome with various phenotypes is categorized by left ventricle ejection fraction levels (LVEF), namely preserved (HFpEF), mid-range (HFmEF), and reduced (HFrEF). This study investigates the prognostic impact of serum creatinine and serum sodium levels in HF patients across these three classes using machine learning tools. A comprehensive dataset of HF patients' medical records including serum sodium and serum creatinine was utilized. Machine learning regression models were employed to predict the LVEF levels. Additionally, classification models were implemented to categorize patients into HFpEF, HFmEF, and HFrEF classes. Regression analyses revealed the predictive capabilities of serum sodium and serum creatinine in estimating the progression of HF severity. Furthermore, classification models successfully differentiated between the three EF classes, providing valuable insights into the classification patterns of HF patients based on these biomarkers. The results demonstrated the significance of serum sodium serum creatinine as prognostic markers in HF, and this contributes to a more nuanced approach to HF management, paving the way for targeted interventions and improved patient outcomes. Moreover, this study highlights the potential of machine learning techniques to enhance risk stratification and classification in HF patients, enabling personalized prognostication and treatment strategies. © 2024 IEEE.

关键词： Creatinine

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LanCOPE: Language-Guided Category-Level Object Pose Estimation from a Single RGB Image

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IEEE Robotics and Automation Letters 2025年第7期10卷 7555-7562页

作者： Yang, Hui Sun, Wei Liu, Jian Zheng, Jin Dai, Zhenqi Mian, Ajmal Hunan University National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Changsha 410082 China Central South University School of Architecture and Art Changsha 410082 China The University of Western Australia Department of Computer Science and Software Engineering 6009 WA Australia

Monocular RGB-based category-level object pose estimation is more practical and cost-effective for robotics. However, existing methods do not fully exploit the rich semantic and contextual information in multimodal data (e.g. language) that provides additional object attributes to guide the model in extracting category features more reliably. We propose a language-guided category-level object pose estimation method (LanCOPE), taking a single RGB image as input. Our method uses DINOv2 to recover depth from a single RGB image and converts it into point cloud to perceive the object's geometry. We then introduce language descriptions for the RGB image, estimated point cloud and overall scene to better guide the point cloud encoder and image encoder in learning category features. We develop a cross-modal differential perception feature fusion network to fuse multimodal features. This network employs a differential perception module to eliminate redundant information across different modalities, highlighting signifcant semantic differences and similarities. Furthermore, it uses a cross-attention mechanism to fuse the semantic information of the language and vision features, improving the overall perception. Finally, we design a denoising network based on the skip fusion transformer to recover the object pose accurately. Extensive experiments on REAL275 and Wild6D datasets show that LanCOPE achieves state-of-the-art performance. Our code is available at LanCOPE. © 2016 IEEE.

关键词： category-level object pose estimation languag RGB image

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A Review on Active Customers'Participation in Smart Grids

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Journal of Modern Power Systems and Clean Energy 2023年第1期11卷 3-16页

作者： Konrad Schmitt Rabindra Bhatta Manohar Chamana Mahtab Murshed Ilham Osman Stephen Bayne Luciane Canha Electrical and Computer Engineering Department Texas Tech UniversityLubbockTX79409USA Wind Energy Program Texas Tech UniversityLubbockTX79409USA Electromechanical and Power Systems Department Federal University of Santa MariaSanta MariaRS97105-900Brazil Design Release Engineer at Electrification and Power conversion General MotorsGM Tech Center RoadWarrenMI48092USAl IEEE

Industrial,commercial,and residential facilities are progressively adopting automation and generation *** having flexible demand and renewable energy generation,traditional passive customers are becoming active participants in electric power system *** profound coordination among grid operators and active customers,the facilities’capability for demand response(DR)and distributed energy resource(DER)management will be valuable asset for ancillary services(ASs).To comply with the increasing demand and flexible energy,utilities urgently require standards,regulations,and programs to efficiently handle load-side resources without trading off stability and *** study reviews different types of customers’flexibilities for DR,highlighting their capabilities and limitations in performing local ancillary services(LASs),which should benefit the power grid by profiting from it through incentive *** financial incentives and techniques employed around the world are presented and *** potential barriers in technical and regulatory aspects are successfully identified and potential solutions along with future guidance are discussed.

关键词： Active distribution network(ADN) ancillary services energy management flexible demand resiliency

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Efficient Multi-user Offloading of Personalized Diffusion Models: A DRL-Convex Hybrid Solution

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IEEE Transactions on Mobile Computing 2025年

作者： Yang, Wanting Xiong, Zehui Guo, Song Mao, Shiwen Kim, Dong In Debbah, Merouane Singapore University of Technology and Design Pillar of Engineering Systems and Design Singapore Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong Hong Kong Auburn University Department of Electrical and Computer Engineering Auburn36830 United States Sungkyunkwan University Department of Electrical and Computer Engineering Suwon16419 Korea Republic of Khalifa University KU 6G Research Center Department of Computer and Information Engineering Abu Dhabi127788 United Arab Emirates University Paris-Saclay CentraleSupelec Gif-sur-Yvette91192 France

Generative diffusion models like Stable Diffusion are at the forefront of the thriving field of generative models today, celebrated for their robust training methodologies and high-quality photorealistic generation capabilities. These models excel in producing rich content, establishing them as essential tools in the industry. Building on this foundation, the field has seen the rise of personalized content synthesis as a particularly exciting application. However, the large model sizes and iterative nature of inference make it difficult to deploy personalized diffusion models broadly on local devices with heterogeneous computational power. To address this, we propose a novel framework for efficient multi-user offloading of personalized diffusion models. This framework accommodates a variable number of users, each with different computational capabilities, and adapts to the fluctuating computational resources available on edge servers. To enhance computational efficiency and alleviate the storage burden on edge servers, we propose a tailored multi-user hybrid inference approach. This method splits the inference process for each user into two phases, with an optimizable split point. Initially, a cluster-wide model processes low-level semantic information for each user's prompt using batching techniques. Subsequently, users employ their personalized models to refine these details during the later phase of inference. Given the constraints on edge server computational resources and users' preferences for low latency and high accuracy, we model the joint optimization of each user's offloading request handling and split point as an extension of the Generalized Quadratic Assignment Problem (GQAP). Our objective is to maximize a comprehensive metric that balances both latency and accuracy across all users. To solve this NP-hard problem, we transform the GQAP into an adaptive decision sequence, model it as a Markov decision process, and develop a hybrid solution combining dee

关键词： Markov processes

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An Efficient and Secure Post-Quantum Multi-Authority Ciphertext-Policy Attribute-Based Encryption Method Using Lattice

An Efficient and Secure Post-Quantum Multi-Authority Ciphert...

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2023 IEEE INFOCOM Conference on computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Bagchi, Prithwi Bera, Basudeb Maheshwari, Raj Das, Ashok Kumar Yau, David K. Y. Sikdar, Biplab International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Singapore University of Technology and Design Information Systems Technology and Design Singapore487372 Singapore National University of Singapore Department of Electrical and Computer Engineering Singapore117583 Singapore

ISBN: (纸本)9781665494274

This article deals with designing an efficient post-quantum lattice based encryption scheme that relies on the multi-authority Ciphertext-Policy Attribute-Based Encryption (CP-ABE). The security of the proposed scheme is based on the hardness of the ring learning with errors (RLWE) problem. The construction of the proposed scheme is done using the Shamir's threshold secret sharing along with the Lagrange interpolation during the key generation and decryption processes in order to achieve the segmentation and restoration of private keys. A comparative study with the existing state of art schemes has been performed to show the feasibility and efficiency of the proposed scheme. Furthermore, experiments on the proposed scheme have been conducted to illustrate the computational time required during the key generation, encryption and decryption processes. © 2023 IEEE.

关键词： Quantum cryptography

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A Multi-Agent Deep Reinforcement Learning Framework for Detecting and Mitigating DDoS Attacks in IoT Networks 4

A Multi-Agent Deep Reinforcement Learning Framework for Dete...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Shaharkar, Bhushan Mittal, Tanusha Sunkara, Krishna Chaitanya Shukla, Vishal Desai, Ishan Mookherjee, Ujal Walchand Institute of Technology Department of Information Technology Solapur India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India Oracle Cloud Engineering Data Center Design & Modeling Raleigh United States Ramdeobaba University Department of Mechanical Engineering Nagpur India Parul institute of Engineering and Technology Parul University Faculty of Engineering and Technology Department of Electrical Engineering Post Limda India Faculty of Law Pune India

ISBN: (纸本)9798350352931

The rise of Internet of Things (IoT) networks has introduced new opportunities for innovation but also increased vulnerability to cyberattacks, particularly Distributed Denial-of-Service (DDoS) attacks. Traditional detection and mitigation mechanisms often struggle to handle the dynamic and distributed nature of IoT systems. In this study, we propose a Multi-Agent Deep Reinforcement Learning (MADRL) framework designed to detect and mitigate DDoS attacks in IoT networks in real-time. The framework consists of multiple collaborative agents, each deployed at different network layers or nodes, trained using deep reinforcement learning to identify attack patterns and take immediate mitigation actions. Through cooperative learning, these agents adapt to evolving attack strategies and optimize defense policies. Experimental evaluations demonstrate that the proposed MADRL framework outperforms traditional techniques by achieving higher detection accuracy, faster response times, and reduced false positive rates. Our results indicate that this framework can effectively secure IoT networks against complex DDoS attacks, maintaining network stability and availability. © 2024 IEEE.

关键词： Deep reinforcement learning

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Diffusion as Sound Propagation: Physics-inspired Model for Ultrasound Image Generation

arXiv

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arXiv 2024年

作者： Domínguez, Marina Velikova, Yordanka Navab, Nassir Azampour, Mohammad Farid Computer Aided Medical Procedures Technical University of Munich Germany Munich Center for Machine Learning Munich Germany Department of Electrical Engineering Sharif University of Technology Tehran Iran

Deep learning (DL) methods typically require large datasets to effectively learn data distributions. However, in the medical field, data is often limited in quantity, and acquiring labeled data can be costly. To mitigate this data scarcity, data augmentation techniques are commonly employed. Among these techniques, generative models play a pivotal role in expanding datasets. However, when it comes to ultrasound (US) imaging, the authenticity of generated data often diminishes due to the oversight of ultrasound physics. We propose a novel approach to improve the quality of generated US images by introducing a physics-based diffusion model that is specifically designed for this image modality. The proposed model incorporates an US-specific scheduler scheme that mimics the natural behavior of sound wave propagation in ultrasound imaging. Our analysis demonstrates how the proposed method aids in modeling the attenuation dynamics in US imaging. We present both qualitative and quantitative results based on standard generative model metrics, showing that our proposed method results in overall more plausible images. Our code is available at ***/marinadominguez/diffusion-for-us-images. © 2024, CC BY-NC-SA.

关键词： Diffusion

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Twenty-one days of bed rest alter motor unit properties and neuromuscular junction transmission in young adults

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Journal of Applied Physiology 2025年第6期138卷 1411-1424页

作者： Sarto, Fabio Kalc, Miloš Motanova, Evgeniia S. Franchi, Martino V. Stashuk, Daniel Murks, Nina Valli, Giacomo Negro, Samuele Prašnikar, Tomaž Gasparini, Mladen Martino, Giovanni De Vito, Giuseppe Holobar, Aleš Simunič, Boštjan Pišot, Rado Narici, Marco V. Department of Biomedical Sciences University of Padova Padova Italy Centre of Studies and Activities for Space "Giuseppe Colombo Padova Italy Science and Research Center Koper Institute for Kinesiology Research Koper Slovenia CIR-MYO Myology Center University of Padova Padova Italy Department of Systems Design Engineering University of Waterloo Waterloo ON Canada Faculty of Electrical Engineering and Computer Science University of Maribor Maribor Slovenia Department of Clinical and Experimental Sciences University of Brescia Brescia Italy Izola General Hospital Izola Slovenia

Previous studies showed that properties of higher-threshold motor units (MUs) and neuromuscular junction (NMJ) function are preserved during short-term disuse. This study aimed to test how a longer disuse period affects MU properties, NMJ transmission, and NMJ morphology remodeling. Nine young healthy males (age: 18-29 yr) underwent 21 days of horizontal bed rest. Pre- (BR0) and postbed rest (BR21), quadriceps maximal voluntary contraction (MVC), and size were assessed. We combined intramuscular electromyography (iEMG) and high-density surface electromyography (HDsEMG) recordings on the vastus lateralis to assess MU properties at 25% and 50% of MVC. Muscle biopsies and blood samples were also collected. Quadriceps MVC and size decreased at BR21. We found alterations in MU properties at both contraction intensities, including reduced discharge rate, MU potential area changes, and increased complexity. NMJ transmission was found to be reduced at BR21 at 25% MVC. This functional NMJ impairment was biochemically corroborated by an increase in serum C-terminal agrin fragment concentration, a biomarker of NMJ instability. In addition, a direct assessment of NMJ morphology revealed the presence of some denervated NMJs exclusively at BR21. In conclusion, 21-day bed rest altered MU properties across different contraction intensities and impaired NMJ transmission with initial signs of remodeling/denervation. Disuse duration appears to be a critical factor, as previous shorter studies failed to detect some of these changes. We believe these findings are clinically relevant for disuse after trauma, surgery, or illness and may support the development of effective *** & NOTEWORTHY Leveraging both intramuscular and high-density surface EMG recordings in the vastus lateralis, we identified alterations in motor unit (MU) properties in young adults after 21 days of bed rest. These included reduced discharge rates and changes in MU potential size and complexity, observ

关键词： disuse electromyography electrophysiology physical inactivity unloading

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Topologically Protected Wavelength Conversion in a Photonic Valley Hall Insulator

Topologically Protected Wavelength Conversion in a Photonic ...

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

作者： Sohn, Byoung-Uk Choi, Ju Won Chen, George F.R. Gao, Hongwei Mitchell, William J. Ng, Doris K.T. Tan, Dawn T.H. Photonics Devices and System Group SUTD-MIT International Design Center Singapore University of Technology and Design Singapore487372 Singapore NanoFab Facility Electrical and Computer Engineering Department University of California Santa Barbara Santa BarbaraCA93106 United States Institute of Microelectronics A*STAR 2 Fusionopolis Way #08-02 Innovis Tower Singapore138634 Singapore

We explore nonlinear effects in a topological photonic valley Hall insulator with a Kagome system at telecommunications wavelengths, we achieve topologically protected -12dB parametric conversion with sub-milliwatt in... 详细信息

关键词： Landforms

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Topologically Protected Wavelength Conversion in a Photonic Valley Hall Insulator

Topologically Protected Wavelength Conversion in a Photonic ...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

ISBN: (纸本)9781957171395

关键词： Photonics

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