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17th IEEE International Symposium on Embedded Multicore/Many-core Systems-on-Chip, MCSoC 2024

作者： Aliyev, Ilkin Svoboda, Kama Adegbija, Tosiron Fellous, Jean-Marc University of Arizona Department of Electrical & Computer Engineering TucsonAZ United States University of Arizona Departments of Psychology and Biomedical Engineering TucsonAZ United States

ISBN: (纸本)9798331530471

Spiking Neural Networks (SNNs) are inspired by the sparse and event-driven nature of biological neural processing, and offer the potential for ultra-low-power artificial intelligence. However, realizing their efficiency benefits requires specialized hardware and a co-design approach that effectively leverages sparsity. We explore the hardware-software co-design of sparse SNNs, examining how sparsity representation, hardware architectures, and training techniques influence hardware efficiency. We analyze the impact of static and dynamic sparsity, discuss the implications of different neuron models and encoding schemes, and investigate the need for adaptability in hardware designs. Our work aims to illuminate the path towards embedded neuro-morphic systems that fully exploit the computational advantages of sparse SNNs. © 2024 IEEE.

关键词： Neural network models

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Learning from Future: Prediction-based Data Augmentation to Enhance Power Grids Fault Detection 11

Learning from Future: Prediction-based Data Augmentation to ...

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11th IEEE International Conference on Smart Energy Grid engineering, SEGE 2023

作者： Rogers, Jennifer Danilczyk, William Lin, Hui Sun, Yan Lindsay University of Rhode Island Department of Electrical Computer and Biomedical Engineering KingstonRI United States

ISBN: (纸本)9798350340716

As power grids modernize to include wide area monitoring and advanced metering infrastructures, the applications for data-driven methods based on artificial intelligence (AI) for situational awareness, reliability, and security continue to grow. However, obtaining effective training data that captures normal and anomalous operations in power systems remains a great challenge. This paper proposes a data-augmentation method that can efficiently build effective training data sets, benefiting various data-driven applications used in power grids without increasing the size of training data set. By leveraging load prediction in smart grids, we obtain the knowledge of future operating conditions and potential anomalies, which are integrated with the historical data in the training data set. By utilizing this augmented training data set, we significantly increase the accuracy of data-driven fault detection, e.g., 8.6% on average, compared to the results trained based on historical data only. © 2023 IEEE.

关键词： Fault detection

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Scaling of vagus nerve stimulation parameters does not achieve equivalent nerve responses across species

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Bioelectronic Medicine 2025年第1期11卷 1-17页

作者： Musselman, Eric D. Raha, Ishani Pelot, Nicole A. Grill, Warren M. Department of Biomedical Engineering Duke University DurhamNC United States Department of Electrical and Computer Engineering Duke University DurhamNC United States Department of Neurobiology Duke University DurhamNC United States Department of Neurosurgery Duke University DurhamNC United States

Background: Previous efforts to translate vagus nerve stimulation (VNS) therapies from preclinical studies to human clinical applications (e.g., for stroke, heart failure, and inflammatory diseases) did not account for individual- or species-specific differences in nerve responses when selecting stimulation parameters. Lack of explicit consideration for producing equivalent nerve responses could contribute to clinical outcomes not replicating promising results from preclinical animal studies. Methods: We used models of VNS built with ASCENT (Musselman, PLoS Comput Biol 17:e1009285, 2021) to quantify nerve responses across species and simulate translation of VNS therapies via either recycling or linear scaling of stimulation parameters. For humans (n = 9) and pigs (n = 12), we used previously validated computational models with the standard clinical helical cuff electrode on individual-specific nerve morphologies (Musselman, J Neural Eng 20:acda64, 2023b). We also modeled rat VNS (n = 9) with the Micro-Leads Neuro bipolar cuff. We calculated thresholds for fiber activation (A-, B-, and C-fibers) with biphasic rectangular pulses (0.13, 0.25, 0.5 ms). We defined "K" as the ratio of activation thresholds between a pair of individuals. We used a mixed model ANOVA on the natural logarithm of K to test for differences in inter-species Ks across fiber types and pulse widths. Lastly, using the same nerve morphologies and application-specific device design (cuff and waveform), we developed models to predict nerve responses in chronic human and rat VNS studies for treatment of stroke, inflammation, and heart failure. Results: Depending on the individual and species, the activation amplitude required to produce a given nerve response varied widely. Thus, applying the same VNS parameters across individuals within a species produced a large range of nerve responses. Further, applying the same or linearly scaled stimulation amplitudes across species also produced highly variable r

关键词： Rats

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A Real Time Physics-Based Industrial Control System Honeynet Architecture for the Smart Grid 8

A Real Time Physics-Based Industrial Control System Honeynet...

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8th International Conference on Information and Network Technologies, ICINT 2023

作者： De Pace, Gabriel Lin, Hui Sun, Yan Lindsay University of Rhode Island Department of Electrical Computer and Biomedical Engineering KingstonRI United States

ISBN: (纸本)9798350301458

Many details of cyber attacks occurred to Industrial Control Systems (ICS) such as power grids remain as a secret. To reveal those secrets and benefit a broad research community, we propose an original cyber-physical honeypot architecture that can seamlessly couple IT and OT components in an power grid. Specifically, we will provide high interaction with adversaries as physical processes of an ICS move forward, revealing attack strategies according to adversaries' knowledge on physical trajectories of the target system. Our preliminary evaluations demonstrate that the proposed CPS honeypot can present realistic device fingerprints and application-layer payload that piggybacks meaningful measurements conforming to the physical model of a power grid. In addition, by leveraging runtime high-fidelity OPAL-RT simulator, our CPS honeypot causes small latency, at least one order of magnitude smaller compared to the existing work. © 2023 IEEE.

关键词： Smart power grids

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Microfluidic compartmentalization of rat vagal afferent neurons to model gut-brain axis

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Bioelectronic Medicine 2024年第1期10卷 1-13页

作者： Girardi, Gregory Zumpano, Danielle Raybould, Helen Seker, Erkin Department of Biomedical Engineering University of California – Davis DavisCA95616 United States Department of Anatomy Physiology and Cell Biology University of California – Davis DavisCA95616 United States Department of Electrical and Computer Engineering University of California – Davis DavisCA95616 United States

Background: Vagal afferent neurons represent the key neurosensory branch of the gut-brain axis, which describes the bidirectional communication between the gastrointestinal system and the brain. These neurons are important for detecting and relaying sensory information from the periphery to the central nervous system to modulate feeding behavior, metabolism, and inflammation. Confounding variables complicate the process of isolating the role of the vagal afferents in mediating these physiological processes. Therefore, we developed a microfluidic model of the sensory branch of the gut-brain axis. We show that this microfluidic model successfully compartmentalizes the cell body and neurite terminals of the neurons, thereby simulates the anatomical layout of these neurons to more accurately study physiologically-relevant processes. Methods: We implemented a primary rat vagal afferent neuron culture into a microfluidic platform consisting of two concentric chambers interconnected with radial microchannels. The microfluidic platform separated cell bodies from neurite terminals of vagal afferent neurons. We then introduced physiologically-relevant gastrointestinal effector molecules at the nerve terminals and assessed their retrograde transport along the neurite or capacity to elicit an electrophysiological response using live cell calcium imaging. Results: The angle of microchannel outlets dictated the probability of neurites growing into a chamber versus tracking along chamber walls. When the neurite terminals were exposed to fluorescently-labeled cholera toxin subunit B, the proteins were taken up and retrogradely transported along the neurites over the course of 24 h. Additionally, mechanical perturbation (e.g., rinsing) of the neurite terminals significantly increased intracellular calcium concentration in the distal soma. Finally, membrane-displayed receptor for capsaicin was expressed and trafficked along newly projected neurites, as revealed by confocal microscopy

关键词： Microfluidics

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Automated lumpy skin grading in bovine images using novel deep convolutional neural networks

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Multimedia Tools and Applications 2024年 1-26页

作者： Muhammad, Abdul Malik ur Rehman, Muhammad Zia Mushtaq, Zohaib Qureshi, Muhammad Farrukh Department of Biomedical Engineering Riphah International University Islamabad44000 Pakistan Department of Electrical Electronics and Computer Systems University of Sargodha Sargodha40100 Pakistan Department of Electrical Engineering Namal University Mianwali Mianwali42250 Pakistan Center of AI and BigData Namal University Mianwali Mianwali42250 Pakistan

The diagnosis and prevention of lumpy skin disease, a viral ailment affecting cattle and buffalo, present significant financial implications for the livestock industry. Traditional methods for identifying lumpy skin disease rely on manual visual inspection by veterinarians, which can be labor-intensive, subjective, and prone to errors. To address these challenges, this study proposes a novel deep convolutional neural network (DCNN) model for the automatic recognition and grading of lumpy skin disease from bovine images. The primary contributions of this research include the development of a DCNN architecture specifically tailored for this task, comprising five convolutional layers, five max pooling layers, two fully connected layers with ReLU activation, and a final fully connected layer with softmax activation. The model’s detection accuracy is further enhanced by applying image cropping and patching techniques, which divide each input image into 12 patches to improve local feature extraction. The proposed model was trained and tested using a publicly available dataset from Kaggle. Comparative analysis was conducted against several state-of-the-art models, including InceptionV3, ResNet50, MobileNetV3, VGG19, and Xception. The DCNN model demonstrated superior performance, achieving the highest validation accuracy of 0.96875, outperforming the compared models in terms of accuracy, precision, recall, and F1 score. Additionally, the study explores the potential of transitioning from binary to multiclass classification, which would allow for the assessment of the severity of lumpy skin disease. This future direction aims to provide more nuanced and actionable information for veterinary diagnostics. The significance of this research lies in its potential to offer an objective, efficient, and scalable solution for early disease detection and prevention in livestock, thereby presenting considerable economic benefits for farmers and the livestock industry as a whole. The me

关键词： Convolutional neural networks

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Assessing the stiffness of intact bone marrow with non-contact optical coherence elastography 12

Assessing the stiffness of intact bone marrow with non-conta...

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Optical Elastography and Tissue Biomechanics XII 2025

作者： Singh, Amandeep Singh, Manmohan Aglyamov, Salavat R. Mayerich, David Larin, Kirill V. Department of Biomedical Engineering University of Houston TX77204 United States Department of Mechanical and Aerospace Engineering University of Houston TX77204 United States Department of Electrical and Computer Engineering University of Houston TX77204 United States

ISBN: (纸本)9781510683907

Bone marrow plays an important role in regulating immunity for homeostasis and controlling stromal cell trafficking. Bone marrow cells are sustained by the framework of connective tissues that preserve the mechanical and functional integrity of the tissue. Optical coherence elastography (OCE) is a potential method to measure the biomechanical properties of bone marrow with high spatio-temporal resolution. In this work, we demonstrate the quantification of wave speed at different locations along the diaphysis of mice femur samples using non-contact air-coupled ultrasound OCE (ACUS-OCE). The preliminary results from the current approach show that the ACUS-OCE can characterize the mechanical stiffness of intact bone marrow and can be used for minimally invasive and ex vivo clinical applications. © 2025 SPIE.

关键词： Bone

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Event-triggered robust cooperative output regulation for a class of linear multi-agent systems with an unknown exosystem

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Journal of Automation and Intelligence 2024年第2期3卷 119-127页

作者： Yangyang Qian Lu Liu Charles L.Brown Department of Electrical and Computer Engineering University of VirginiaCharlottesvilleVA 22904USA Department of Biomedical Engineering City University of Hong KongKowloonHong Kong999077China

This paper investigates the robust cooperative output regulation problem for a class of heterogeneousuncertain linear multi-agent systems with an unknown exosystem via event-triggered control (ETC). By utilizingthe internal model approach and the adaptive control technique, a distributed adaptive internal model isconstructed for each agent. Then, based on this internal model, a fully distributed ETC strategy composed ofa distributed event-triggered adaptive output feedback control law and a distributed dynamic event-triggeringmechanism is proposed, in which each agent updates its control input at its own triggering time instants. It isshown that under the proposed ETC strategy, the robust cooperative output regulation problem can be solvedwithout requiring either the global information associated with the communication topology or the bounds ofthe uncertain or unknown parameters in each agent and the exosystem. A numerical example is provided toillustrate the effectiveness of the proposed control strategy.

关键词： Adaptive control Event-triggered control Internal model Multi-agent systems Output regulation

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A Physical-Based Artificial Neural Networks Compact Modeling Framework for Emerging FETs

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IEEE Transactions on Electron Devices 2024年第1期71卷 223-230页

作者： Yang, Ya-Shu Li, Yiming Kola, Sekhar Reddy Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory The Institute of Communications Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Institute of Communications Engineering Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan

We report a compact modeling framework based on the Grove-Frohman (GF) model and artificial neural networks (ANNs) for emerging gate-all-around (GAA) MOSFETs. The framework consists of two ANNs;the first ANN constructed with the drain current model not only can capture the main trend of device I - V characteristics but also can predict its variation even when the amount of training data for the ANN is insufficient or outside the range of applied biases. The second one is then designed to improve the model accuracy by further minimizing the errors between the target and the model outputs. We implement the proposed framework to accurately model emerging GAA nanosheet (NS) MOSFETs and complementary FETs (CFETs) without suffering from divergent issues in circuit simulation. In addition, nonphysical behaviors, such as nonzero current at zero bias, do not occur in the modeling framework. Compared to recently reported machine-learning (ML) models, our approach can achieve a similar level of model accuracy with merely 20% amount of the training data. © 1963-2012 IEEE.

关键词： Neural networks

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A Proposed Model for Calculate the Number of Mammography Machines for the South-West Area of Mexico 9th

A Proposed Model for Calculate the Number of Mammography Mac...

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9th Latin American Congress on biomedical engineering, CLAIB 2022 and 28th Brazilian Congress on biomedical engineering, CBEB 2022

作者： Martinez-Licona, Fabiola M. Martinez-Vazquez, Cipactli M. Electrical Engineering Department Universidad Autonoma Metropolitana Iztapalapa Mexico City Mexico Universidad Autonoma Metropolitana Biomedical Engineering Academic Program Iztapalapa Mexico City Mexico

ISBN: (纸本)9783031494093

Breast cancer is a global health problem. Mexican statistics show that 28.2% of new cancer cases in women come from the breast, so efforts must be made to control the burden of the disease. Early detection is an essential part of the social programs aimed at female populations at potential risk, and mammography machines are the technology used to perform screening methods. The possible coverage of a region is mainly affected by the available technological resources. This paper presents a proposal model to determine the number of mammography machines required to meet the State’s requirements of Guerrero, Oaxaca and Chiapas, as those in which there are municipalities with the highest social backwardness. The socioeconomic and demographic determinants, as well as the screening mammography demand data, were extracted from official Mexican agencies. The model variables were selected from the categories of target population, epidemiological indicators and infrastructure. Under the author’s heuristic assumptions, the analytic hierarchy process produced the weight that led to selecting the final set of variables. The resource allocation principles were used to adapt the Resource Allocation Working Party (RAWP) focus formula from population to equipment to calculate the State Weighted Machines using the mentioned category adjustments. Model results with 2017 and 2020 data showed that, on average, 50% more mammography machines are needed to cover around 46% of the female population 50–64 years old in the studied States. Further study and analysis of the determinants of access to the service and the equipment proposals are necessary. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Resource allocation

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