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Fog-Enabled Multimodal Chest-Worn Device for Systolic Blood Pressure Monitoring

IEEE Access 2025年 13卷 90345-90357页

作者： Salas, Pamela Mejia-Munoz, Jose-Manuel Gonzalez-Landaeta, Rafael BIOCIM Research Group Department of Electrical and Computer Engineering Juarez Chihuahua32310 Mexico

This paper presents a Fog-enabled chest-worn device for estimating systolic blood pressure (SBP). The device integrates two sensors that simultaneously detect the phonocardiogram (PCG), ballistocardiogram (BCG), and seismocardiogram (SCG) from a single location on the chest. These signals are good indicators of the early stages of cardiovascular diseases (CVD). Here, a new fog-based deep learning architecture that extracts features directly from raw signals to estimate the SBP is proposed. Three signals were transmitted to a fog computing system via a socket. Four models were implemented for analysis: Random Forest (RF), AdaBoost, Gradient Boosting (GB), and a novel deep learning architecture. The RF, AdaBoost, and GB models focused on the time delays between the main waves of the PCG, BCG, and SCG, while the deep learning architecture extracted features directly from the signals using convolutional layers. The tests involved 36 healthy volunteers under both resting and physically active conditions. The device was built using surface-mount components and powered by a 3.7 V, 250 mAh battery. The dimensions were 65 mm × 54 mm, and the weight was 56 g. The three cardiac signals were simultaneously detected, with a signal-to-noise ratio higher than 41 dB. Among the four implemented models for SBP estimation in fog, the proposed deep learning architecture demonstrated the best performance, achieving an MAE of 3.5 mmHg without estimating the timing between the three detected signals. Integrating the methodology discussed here, the proposed device offers a novel method for estimating blood pressure through raw biosignals detected by a wearable device. © 2013 IEEE.

关键词： Surface mount technology

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Modified Frequency Scheduling Droop Control for Decentralized State of Charge Balancing of Battery in Islanded Microgrid 25

Modified Frequency Scheduling Droop Control for Decentralize...

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25th International Seminar on Intelligent Technology and Its Applications, ISITIA 2024

作者： Rahman, Irham Izzatur Miyauchi, Hajime Takano, Hirotaka Gifu University Department of Electrical Electronic and Computer Engineering Gifu Japan Kumamoto University Department of Computer Science and Electrical Engineering Kumamoto Japan

ISBN: (纸本)9798350378573

This study proposes a decentralized frequency scheduling method to achieve SoC balancing with no communication protocol. Since the frequency scheduling method is based on modifying droop control of inverter, to make the power output of inverter to be proportional to the SoC, the frequency output of inverter would be affected. This study considers to minimize the frequency deviation within European Norm EN50160 (±1% of the nominal value of the frequency), while also able to satisfy SoC balancing. To achieve it, we propose fuzzy logic controller to weight the value of droop coefficient ( ) instead of linear scaling in conventional frequency scheduling. The proposed method is studied in several test to show the performance and compare to conventional method. From the result, the proposed method performs better in SoC balancing, and results in better system reliability. We also study the proposed battery balancing system in various condition of PV and load profile, as well "plug and play"condition of microgrid. The proposed method can perform well in both conditions. © 2024 IEEE.

关键词： State of charge

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Secure signal and image transmissions using chaotic synchronization scheme under cyber-attack in the communication channel

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Soft Computing 2025年第1期29卷 339-353页

作者： Nobakht, Shaghayegh Ahmadi, Ali-Akbar Industrial Automation Laboratory Electrical and Computer Engineering Department Kharazmi University Tehran Iran

In this paper, problem of secure message (signal and image) transmission is studied. The message is encrypted by masking it with a chaotic system state and then transmitted to receiver-side via a communication channel. It is assumed that the communication channel may be prone to a cyber-attack. The transmission system dynamics in presence of the cyber-attack in the communication channel is modeled by a singular dynamical system. Then, at the receiver-side, a novel observer is designed to recover the message together with the cyber-attack signal. Estimation of the cyber-attack is crucial for monitoring the communication channel and enable a network supervisor to mitigate the adverse consequences and make an appropriate strategy. It is assumed neither restrictions on the message nor on the cyber-attack signals. This makes the proposed scheme, the best solution in the real applications. Moreover, comparing to the existing methods which two public and private channels are required, only one communication channel is used in this paper for transmission the encrypted signal which results in a cheaper solution. Solving the optimization problem can be easily performed with the help of the existing toolboxes such as YALMIP and CVX software in MATLAB. Finally, numerical simulations for signal and image transmission are presented to justify the applicability and superior performance of the proposed scheme. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Cyber attacks

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Security, Latency, and Throughput of Proof-of-Work Nakamoto Consensus

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IEEE Transactions on Information Theory 2025年第6期71卷 4708-4731页

作者： Cao, Shu-Jie Guo, Dongning Northwestern University Department of Electrical and Computer Engineering EvanstonIL60208 United States

This paper investigates the fundamental trade-offs between block safety, confirmation latency, and transaction throughput of proof-of-work (PoW) longest-chain fork-choice protocols, also known as PoW Nakamoto consensus. New upper and lower bounds are derived for the probability of block safety violations as a function of honest and adversarial mining rates, a block propagation delay limit, and confirmation latency measured in both time and block depth. The results include the first non-trivial closed-form finite-latency bound applicable across all delays and mining rates up to the ultimate fault tolerance. Notably, the gap between these upper and lower bounds is narrower than previously established bounds for a wide range of parameters relevant to Bitcoin and its derivatives, including Litecoin and Dogecoin, as well as Ethereum Classic. Additionally, the study uncovers a fundamental trade-off between transaction throughput and confirmation latency, ultimately determined by the desired fault tolerance and the rate at which block propagation delay increases with block size. © 1963-2012 IEEE.

关键词： Bitcoin

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Introduction to the Special Issue on computerModeling for Smart Cities Applications

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computer Modeling in engineering & sciences 2024年第2期138卷 1015-1017页

作者： Wenbing Zhao Chenxi Huang Yizhang Jiang Department of Electrical and Computer Engineering Cleveland State UniversityClevelandOH 44115USA School of Computer Science Xiamen UniversityXiamen36100China School of Artificial Intelligent and Computer Jiangnan UniversityWuxi224122China

A significant fraction of the world’s population is living in cities. With the rapid development ofinformation and computing technologies (ICT), cities may be made smarter by embedding ICT intotheir infrastructure. By smarter, we mean that the city operation will be more efficient, cost-effective,energy-saving, be more connected, more secure, and more environmentally friendly. As such, a smartcity is typically defined as a city that has a strong integration with ICT in all its components, includingits physical components, social components, and business components [1,2].

关键词： smart typically Smart

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Printing Stretchable Electronics With Fused Filament Fabrication

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2025年第2期4卷 98-107页

作者： Jahan, Sharmin Lazarus, Nathan University of Delaware Electrical and Computer Engineering Department NewarkDE19716 United States

Additive manufacturing is a powerful approach forintegrating flexible and stretchable conductors into complex3-D structures, but many current printing technologies, suchas direct ink writing (DIW), are expensive and challengingto access for many potential users, such as hobbyists andsmall companies. In this work, a low-cost commercial fusedfilament fabrication (FFF) 3-D printer is used to manufacturelow-resistivity stretchable serpentine conductors. By using dualextruder heads, serpentines are printed in a highly conductivemetal-particulate-based composite on a thermoplastic elastomer(TPE) part and demonstrated to survive mechanical strains of upto 25%, sufficient for use in close contact with the human *** electroless plating is also demonstrated to metalize theprinted traces, depositing a thin layer of copper for improvedelectrical conductivity. Using this approach, we demonstrateconductivity improvements by about three orders of magnitude(∼ 103) over past FFF printed serpentines, a major milestonein making stretchable electronics cheaper and more *** resulting stretchable conductors are then demonstrated toprint devices, including stretchable inductors and stretchableinterconnect on a soft robotic actuator. © 2022 IEEE.

关键词： Reinforced plastics

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Fabrication of LCE Microactuator Arrays Through Soft Lithography With Surface Alignment

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Journal of Microelectromechanical Systems 2025年第3期34卷 241-243页

作者： Li, Ke Kim, Jae-Jun Fernandes, Jayer Jiang, Hongrui University of Wisconsin–Madison Department of Electrical and Computer Engineering MadisonWI53705 United States University of Wisconsin–Madison Department of Electrical and Computer Engineering Department of Biomedical Engineering Department of Materials Science and Engineering Department of Ophthalmology and Visual Sciences McPherson Eye Research Institute MadisonWI53706 United States

Liquid crystal elastomers (LCEs) offer potentially programmable actuation through precise molecular alignment, making them ideal for microactuators in soft robotics and optical systems. However, achieving precise microscale alignment for LCE actuator arrays through scalable microfabrication approaches has been challenging. This letter introduces a low-cost surface alignment method to fabricate LCE microactuator arrays, reducing the dependency on expensive equipment, improving accessibility and manufactuarability compared to existing studies using field-assisted alignment methods. Thermal actuation tests demonstrated strong thermal responsiveness and stability. Our method offers a superior approach to integrating LCE microactuator arrays with modern microfabrication processes, promising multitudinous applications in MEMS and beyond. © 2025 IEEE. All rights reserved,.

关键词： Microfabrication

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Exploring Hyperdimensional Computing Robustness Against Hardware Errors

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IEEE Transactions on computers 2025年第6期74卷 1963-1977页

作者： Zhang, Sizhe Juretus, Kyle Jiao, Xun Villanova University Department of Electrical and Computer Engineering VillanovaPA19085 United States

Brain-inspired hyperdimensional computing (HDC) is an emerging machine learning paradigm leveraging high-dimensional spaces for efficient tasks like pattern recognition and medical diagnostics. As a lightweight alternative to deep neural networks, HDC offers smaller model sizes, reduced computation, and memory-centric processing. However, deploying HDC in safety-critical applications, such as healthcare and robotics, is challenged by hardware-induced errors. This paper investigates HDC's robustness to memory errors via extensive bit-flip injection experiments on item and associative memories. Results reveal that certain bit-flips severely degrade accuracy. To address this, we introduce the Hyperdimensional Bit-Flip Search (HD-BFS), a similarity-guided method for identifying vulnerabilities and crafting efficient attacks, where flipping just 6 critical bits—3.9% of random bit-flips—reduces accuracy to chance levels. We further propose Hyperdimensional Accelerated Bit-Flip Search (HD-ABFS), which narrows the search space by targeting critical dimensions and most significant bits (MSBs), achieving up to 282x speedup over HD-BFS. Finally, we develop an effective protection mechanism to enhance model safety. These insights highlight HDC's resilience to random errors, offer robust defenses against targeted attacks, and advance the security and reliability of HDC systems. © 1968-2012 IEEE.

关键词： Deep neural networks

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Noninvasive estimation of blood potassium concentration using ECG and FCM-ANFIS model

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Research on Biomedical engineering 2024年第3-4期40卷 647-658页

作者： Samandari, Zeynab Molaeezadeh, Seyyedeh Fatemeh Department of Electrical and Computer Engineering Jundi-Shapur University of Technology Dezful Iran

Purpose: Potassium imbalance, often symptomless but potentially fatal, is prevalent in patients with kidney or heart conditions. Traditional laboratory tests for potassium measurement are costly and require skilled technicians. Additionally, phlebotomy is challenging and painful for high-risk patients. To address these issues, this study proposes a noninvasive and bloodless approach that leverages the electrocardiogram (ECG) signal, which may be sensitive to potassium changes. The research introduces a data-driven framework for ECG-based potassium measurement. Methods: The proposed framework comprises five main components: Cohort Selection—Among 461,178 patients in the ECG-ViEW II database, those with simultaneous ECG, Age-adjusted Charlson Comorbidity Index (ACCI), and potassium measurements within a 5-min interval are chosen;Data Labeling—Three labels—hypokalemia, normal, and hyperkalemia—are defined;Feature Extraction—Relevant features such as RR interval, PR interval, QRS duration, QT interval, QTc interval, P axis, QRS axis, T axis, and ACCI are extracted;Feature Selection—The Kruskal–Wallis technique evaluates feature importance and selects discriminative ones;and Model Design—An ANFIS model based on FCM clustering (FCM-ANFIS) is developed using the selected features. Results: Among the 42 patients selected during cohort filtering, T axis demonstrated significant association with potassium levels (P © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2024.

关键词： Electrocardiograms

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Deep Reinforcement Learning Data Collection for Bayesian Inference of Hidden Markov Models

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1217-1232页

作者： Alali, Mohammad Imani, Mahdi Northeastern University Department of Electrical and Computer Engineering BostonMA02115 United States

Hidden Markov models (HMMs) are a powerful class of dynamical models for representing complex systems that are partially observed through sensory data. Existing data collection methods for HMMs, typically based on active learning or heuristic approaches, face challenges in terms of efficiency in stochastic domains with costly data. This article introduces a Bayesian lookahead data collection method for inferring HMMs with finite state and parameter spaces. The method optimizes data collection under uncertainty using a belief state that captures the joint distribution of system states and models. Unlike traditional approaches that prioritize short-term gains, this policy accounts for the long-term impact of data collection decisions to improve inference performance over time. We develop a deep reinforcement learning policy that approximates the optimal Bayesian solution by simulating system trajectories offline. This pretrained policy can be executed in real time, dynamically adapting to new conditions as data is collected. The proposed framework supports a wide range of inference objectives, including point based, distribution based, and causal inference. Experimental results across three distinct systems demonstrate significant improvements in inference accuracy and robustness, showcasing the effectiveness of the approach in uncertain and data-limited environments. © 2020 IEEE.

关键词： Deep reinforcement learning

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