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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Dat T. Nguyen Qihang Zeng Xi Tian John S. Ho Integrative Sciences and Engineering Program National University of Singapore Graduate School Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore SIA-NUS Digital Aviation Corporate Laboratory and the Institute for Health Innovation and Technology National University of Singapore Singapore Department of Electrical and Computer Engineering Institute for Health Innovation and Technology N.1 Institute for Health at the National University of Singapore Singapore

Contactless sensors embedded in the ambient environment have broad applications in unobtrusive, long-term health monitoring for preventative and personalized healthcare. Microwave radar sensors are an attractive candidate for ambient sensing due to their high sensitivity to physiological motions, ability to penetrate through obstacles and privacy-preserving properties, but practical applications in complex real-world environments have been limited because of challenges associated with background clutter and interference. In this work, we propose a thin and soft textile sensor based on microwave metamaterials that can be easily integrated into ordinary furniture for contactless ambient monitoring of multiple cardiovascular signals in a localized manner. Evaluations of our sensor’s performance in human subjects show high accuracy of heartbeat and arterial pulse detection, with ≥ 96.5% sensitivity and < 5% mean absolute relative error (MARE) across all subjects. We demonstrate our sensor’s utility for cuffless blood pressure monitoring on a human subject over a continuous 10-minute period. Our results highlight the potential of metamaterial textile sensors in ambient health and wellness monitoring *** relevance—The contactless metamaterial textile sensors demonstrated in this paper provide unobtrusive, convenient and long-term monitoring of multiple cardiovascular health metrics, including heart rate, pulse rate and cuffless blood pressure, which can facilitate preventative and personalized healthcare.

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Extendedsketch+: Super Host Identification and Network Host Trust Evaluation with Memory Efficiency and High Accuracy

SSRN

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SSRN 2022年

作者： Han, Hui Jing, Xuyang Yan, Zheng Pedrycz, Witold State Key Laboratory on Integrated Services Networks School of Cyber Engineering Xidian University China Department of Electrical and Computer Engineering University of Alberta Canada

Host cardinality estimation is one crucial task in network traffic measurement. Super host is the host that exhibits anomalies in host cardinality and it is usually related to network abnormal events. Therefore, accurate host caidinality estimation is the premise of super host identification and it can be used to measure the trust degree of a network. Sketches have been widely used in super host identification. However, expanding network scale and increasing link rate bring challenges to the efficiency and accuracy of sketch-based super host identification. The size of the counters in most sketches requires a trade-off between memory resources and accuracy. To meet the need of measuring high cardinalities, large size counters are applied, which leads to memory waste in monitoring low cardinality hosts. On the other hand, most sketches have high computational overhead when tracking superhosts, resulting in inefficiency. In order to address these issues, we propose a novel memory efficient and reversible sketch, named ExtendedSketch+, to provide accurate host cardinality estimation with the purpose of super host identification and network host trust evaluation. ExtendedSketch+ achieves both high memory utilization efficiency and high accuracy, in addition to high super host identification efficiency. It applies extensible counters to record unbalanced host cardinality distribution, by adaptively expanding the counters with the increase of cardinality. It adopts a lossless traffic information transfer strategy during counter extension to ensure the accuracy of cardinality estimation. It can directly tracks the host with the highest cardinality in each bucket, which greatly improves the identification efficiency of super hosts. Based on accurate cardinality estimation of ExtendedSketch+, we can further accurately evaluate host trust degree. We theoretically analyze ExtendedSketch+ with regard to its space and time complexities and estimation accuracy. We conduct perform

关键词： Efficiency

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Braided Convolutional Self-orthogonal Codes with Double Sliding Window Decoding

Braided Convolutional Self-orthogonal Codes with Double Slid...

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Topics in Coding (ISTC), International Symposium on

作者： Min Zhu Andrew D. Cummins David G. M. Mitchell Michael Lentmaier Daniel J. Costello State Key Laboratory of ISN Xidian University Xi’an P. R. China Klipsch School of Electrical and Computer Engineering New Mexico State University Las Cruces NM USA Department of Electrical and Information Technology Lund University Lund Sweden Department of Electrical Engineering University of Notre Dame Notre Dame IN USA

In this paper, we investigate a class of braided convolutional codes (BCCs), where the component codes are convolutional self-orthogonal codes (CSOCs), called braided convolutional self-orthogonal codes. Compared to conventional BCCs, the advantages of braided CSOCs include the availability of several low-complexity decoding methods and the relative ease of extending these methods to high rates More specifically, to construct high-rate braided codes, it is necessary to use higher-rate component codes, which results in exponentially higher decoding complexity with conventional BCJR decoding, whereas the complexity of the CSOC decoding methods proposed here grows only linearly with rate. In particular, we introduce a double sliding window decoding method based on belief propagation (BP) for braided CSOCs, which exhibits good performance while maintaining low-complexity decoding for the higher rates required in many applications.

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Strong Long-Wave Infrared Optical Response in a Topological Semiconductor with a Mexican Hat Band Structure

arXiv

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arXiv 2025年

作者： Polking, Mark J. Xu, Haowei Sankar, Raman Grossklaus, Kevin Li, Ju Advanced Materials and Microsystems Group Massachusetts Institute of Technology Lincoln Laboratory 02421 United States Department of Nuclear Science and Engineering Massachusetts Institute of Technology 02139 United States Institute of Physics Academia Sinica 11529 Taiwan Department of Electrical and Computer Engineering Tufts University 02155 United States Department of Materials Science and Engineering Massachusetts Institute of Technology 02139 United States

Light sources and photodetectors operating in the far- to mid-infrared (FIR/MIR) band (8-12 μm, 0.1-0.15 eV) remain relatively poorly developed compared to their counterparts operating in the visible and near-infrared ranges, despite extensive application potential for thermal imaging, standoff sensing, and other technologies. This is attributable in part to the lack of narrow-gap materials (10 larger than that of HgxCd1-xTe (MCT), the dominant material for FIR/MIR photodetectors. A previous theoretical investigation indicated that chalcogen p and metal d band inversion in this material creates a Mexican hat band structure (MHBS), which results in a dramatic increase in the joint density of states at the optical transition edge compared to typical semiconductors. This prediction is experimentally validated here using single-crystal specimens of Pb0.7Sn0.3Se measured using temperature-dependent spectroscopic ellipsometry over a wavelength range of 1.7-20 μm (0.73-0.062 eV). These measurements demonstrate a large enhancement in extinction coefficient and refractive index characteristic of a MHBS in the vicinity of the absorption edge, in agreement with theoretical predictions. The realization of topological semiconductors with a MHBS is expected to lead to high-efficiency detectors operating in the FIR/MID range. © 2025, CC BY.

关键词： Single crystals

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Continual Reinforcement Learning for Digital Twin Synchronization Optimization

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IEEE Transactions on Mobile Computing 2025年

作者： Tong, Haonan Chen, Mingzhe Zhao, Jun Hu, Ye Yang, Zhaohui Liu, Yuchen Yin, Changchuan Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing Advanced Information Network Laboratory Beijing China College of Computing and Data Science Singapore University of Miami Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing Coral Gables33146 United States University of Miami Department of Industrial and Systems Engineering Coral Gables33146 United States Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China North Carolina State University Department of Computer Science RaleighNC27695 United States Beijing Key Laboratory of Network System Architecture and Convergence China Beijing University of Posts and Telecommunications Beijing Advanced Information Network Laboratory Beijing100876 China

This article investigates the adaptive resource allocation scheme for digital twin (DT) synchronization optimization over dynamic wireless networks. In our considered model, a base station (BS) continuously collects factory physical object state data from wireless devices to build a real-time virtual DT system for factory event analysis. Due to continuous data transmission, maintaining DT synchronization must use extensive wireless resources. To address this issue, a subset of devices is selected to transmit their sensing data, and resource block (RB) allocation is optimized. This problem is formulated as a constrained Markov process (CMDP) problem that minimizes the long-term mismatch between the physical and virtual systems. To solve this CMDP, we first transform the problem into a dual problem that refines RB constraint impacts on device scheduling strategies. We then propose a continual reinforcement learning (CRL) algorithm to solve the dual problem. The CRL algorithm learns a stable policy across historical experiences for quick adaptation to dynamics in physical states and network capacity. Simulation results show that the CRL can adapt quickly to network capacity changes and reduce normalized root mean square error (NRMSE) between physical and virtual states by up to 55.2%, using the same RB number as traditional methods. © 2025 IEEE. All rights reserved.

关键词： Markov processes

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AI/ML Based Detection and Categorization of Covert Communication in IPv6 Network

arXiv

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arXiv 2025年

作者： Rahman, Mohammad Wali Ur Lin, Yu-Zheng Weeks, Carter Ruddell, David Gabriellini, Jeff Hayes, Bill Hariri, Salim Ziegler, Edward V. Department of Electrical and Computer Engineering University of Arizona TucsonAZ85721 United States Department of Management Information Systems University of Arizona TucsonAZ85721 United States Department of Systems and Industrial Engineering University of Arizona TucsonAZ85721 United States Laboratory for Advanced Cybersecurity Research National Security Agency United States

The flexibility and complexity of IPv6 extension headers allow attackers to create covert channels or bypass security mechanisms, leading to potential data breaches or system compromises. The mature development of machine learning has become the primary detection technology option used to mitigate covert communication threats. However, the complexity of detecting covert communication, evolving injection techniques, and scarcity of data make building machine-learning models challenging. In previous related research, machine learning has shown good performance in detecting covert communications, but oversimplified attack scenario assumptions cannot represent the complexity of modern covert technologies and make it easier for machine learning models to detect covert communications. To bridge this gap, in this study, we analyzed the packet structure and network traffic behavior of IPv6, used encryption algorithms, and performed covert communication injection without changing network packet behavior to get closer to real attack scenarios. In addition to analyzing and injecting methods for covert communications, this study also uses comprehensive machine learning techniques to train the model proposed in this study to detect threats, including traditional decision trees such as random forests and gradient boosting, as well as complex neural network architectures such as CNNs and LSTMs, to achieve detection accuracy of over 90%. This study details the methods used for dataset augmentation and the comparative performance of the applied models, reinforcing insights into the adaptability and resilience of the machine learning application in IPv6 covert communication. In addition, we also proposed a Generative AI-assisted interpretation concept based on prompt engineering as a preliminary study of the role of Generative AI agents in covert communication. © 2025, CC BY-NC-ND.

关键词： Decision trees

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Voronoi-diagram-enabled arbitrary-path topological transport

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Physical Review Applied 2025年第5期23卷 054041-054041页

作者： You Yang Zhixia Xu Shiqiang Fu Yun Zhou Jie Chang Shuo Bao Xianghong Kong Haotian Wu School of Information Science and Technology Dalian Maritime University Dalian 116026 China State Key Laboratory of Millimeter Waves Southeast University Nanjing 210096 China Department of Mechanical and Aerospace Engineering University of California San Diego San Diego California 92093 USA Department of Electrical and Computer Engineering National University of Singapore Singapore 117583 Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore 639798 Singapore

Topological physics in electromagnetics has enabled topological edge-state waveguides, yet traditional propagation path designs of topological photonics are restricted by the geometry of the underlying periodic lattice. Here, we propose a topological waveguide scheme based on Voronoi diagrams, allowing for arbitrary propagation paths and offering greater flexibility beyond the constraints of conventional periodic structures. Focusing on gyromagnetic photonic topological insulators and valley topological insulators, we detail the construction and implementation process. Our method ensures smooth connections between unit cells and efficient wave transmission, which are validated through both simulations and experiments. The proposed scheme greatly expands the flexibility for topological transport and enables more applications for complex topological devices and systems.

关键词： Geometry

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Consumption Manner Effect on Renewable DG Allocation in Reconfigurable Distribution Systems

Consumption Manner Effect on Renewable DG Allocation in Reco...

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Renewable Energy and Hydrogen Technologies (GlobConHT), IEEE IAS Global Conference on

作者： Meisam Mahdavi Francisco Jurado Konrad Schmitt Ricardo Alan Verdú Ramos Department of Electrical Engineering University of Jaén Linares Jaén Spain Electrical and Computer Engineering Department Texas Tech University Lubbock TX USA Bioenergy Research Institute (IPBEN) Associated Laboratory of Ilha Solteira são Paulo State University Ilha Solteira SP Brazil

Distributed generation (DG) allocation in reconfigurable electric energy systems is an efficient way for energy loss reduction in modern power systems. On the other side, load profile and electricity consumption method affect the amount of energy losses in such systems. Consequently, power demand fluctuations have a fundamental role in determining energy loss value and, therefore, the network topology and optimal generation of DG units. Nevertheless, load variability consideration in DG allocation of reconfigurable networks significantly increases the computational efforts and processing time, while its neglect causes inaccurate calculation of energy losses. Accordingly, the present paper intends to assess the impact of consumption patterns on renewable DG allocation and switching plans to find out the importance of load variations consideration in minimizing power losses via DG placement and network topology modification. The AMPL, as an important classic optimization tool, was used for the analysis of the proposed idea in several distribution systems.

关键词： Energy loss Renewable energy sources Power demand Network topology Switches Companies Load management

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Machine learning predicts long-term mortality after acute myocardial infarction using systolic time intervals and routinely collected clinical data

arXiv

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arXiv 2024年

作者： Roodini, Bijan Khajehpiri, Boshra Moghaddam, Hamid Abrishami Forouzanfar, Mohamad Department of Systems Engineering École de technologie supérieure Université du Québec MontréalQC Canada Laboratory Faculty of Electrical and Computer Engineering K. N. Toosi University of Technology Tehran Iran Montréal Canada

Background: Precise estimation of cardiac patients’ current and future comorbidities is an important factor in prioritizing continuous physiological monitoring and new therapies. Machine learning (ML) models have shown satisfactory performance in short-term mortality prediction of patients with heart disease, while their utility in long-term predictions is limited. This study aims to investigate the performance of tree-based ML models on long-term mortality prediction and the effect of two recently introduced biomarkers on long-term mortality. Methods: This study utilized publicly available data from the Collaboration Center of Health Information Application (CCHIA) at the Ministry of Health and Welfare, Taiwan, China1. The data pertained to patients admitted to the cardiac care unit for acute myocardial infarction (AMI) between November 2003 and September 2004. Included in the dataset were patients over 20 years old diagnosed with type 1 AMI. Exclusion criteria encompassed patients with missing data for brachial pre-ejection period (bPEP) and brachial ejection time (bET), as well as those with atrial fibrillation or extremity amputations, resulting in a final cohort of 139 AMI patients. We collected and analyzed mortality data up to December 2018. All patients had provided informed consent, in accordance with the Declaration of Helsinki. Medical records were used to gather demographic and clinical data, including age, gender, body mass index (BMI), percutaneous coronary intervention (PCI) status, and comorbidities such as hypertension, dyslipidemia, ST-segment elevation myocardial infarction (STEMI), and non-ST segment elevation myocardial infarction (NSTEMI). Using medical and demographic records as well as two recently introduced biomarkers, bPEP and bET, collected from 139 patients with acute myocardial infarction, we investigated the performance of advanced ensemble tree-based ML algorithms (random forest, AdaBoost, and XGBoost) to predict all-cause mortality w

关键词： Adaptive boosting

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Quantitative particle agglutination assay using mobile holographic imaging and neural networks

Quantitative particle agglutination assay using mobile holog...

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CLEO: Applications and Technology, A and T 2022

作者： Luo, Yi Joung, Hyou-Arm Esparza, Sarah Rao, Jingyou Garner, Omai Ozcan, Aydogan Electrical and Computer Engineering Department University of California Los AngelesCA90095 United States Bioengineering Department University of California Los AngelesCA90095 United States University of California Los AngelesCA90095 United States Computer Science Department University of California Los AngelesCA90095 United States Department of Pathology and Laboratory Medicine University of California Los AngelesCA90095 United States

A quantitative particle agglutination assay using mobile holography and neural networks is demonstrated to rapidly and accurately measure C-reactive protein (CRP) concentration in human serum samples. © Optica Pu... 详细信息

ISBN: (纸本)9781957171050

关键词： Holography

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