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arXiv 2024年

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

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 neuromorphic systems that fully exploit the computational advantages of sparse SNNs. © 2024, CC BY.

关键词： Neural network models

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Multiplexed Nanoscopy via Buffer Exchange

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ACS Nano 2024年第34期18卷 23445-23456页

作者： Chang, Ting-Jui Ben Yang, T. Tony Department of Electrical Engineering National Taiwan University Taipei10617 Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei10617 Taiwan Department of Physics National Taiwan University Taipei10617 Taiwan Nano Science and Technology Program Taiwan International Graduate Program Academia Sinica and National Taiwan University Taipei10617 Taiwan

Understanding cellular functions, particularly in their intricate complexity, can greatly benefit from the spatial mapping of diverse molecules through multitarget single-molecule localization microscopy (SMLM). Existing methodologies, primarily restricting the encoding dimensions to color and lifetime or requiring cyclic staining, often involve broad chromatic detection, specialized optical configurations, or sophisticated labeling techniques. Here, we propose a simple approach called buffer-exchange stochastic optical reconstruction microscopy (beSTORM), which introduces an additional dimension to differentiate between single molecules irrespective of their spectral properties. This method leverages the distinguishable photoblinking responses to distinct buffer conditions, offering a straightforward yet effective means of fluorophore discrimination. Through buffer exchanges, beSTORM achieves multitarget SMLM imaging with minimal crosstalk. Direct integration with expansion microscopy (ExM) demonstrates its capability to resolve up to six proteins at the molecular level within a single emission color without chromatic aberration. Overall, beSTORM presents a highly compatible imaging platform, promising significant advancements in highly multiplexed nanoscopy for exploring multiple targets in biological systems with nanoscale precision. © 2024 The Authors. Published by American Chemical Society.

关键词： Stochastic systems

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Grating Coupled Attachment of Optical Fiber Arrays for in situ Photonics Experimentation

Grating Coupled Attachment of Optical Fiber Arrays for in si...

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CLEO: Applications and Technology in CLEO 2024, CLEO: A and T 2024 - Part of Conference on Lasers and Electro-Optics

作者： Arnold, Kellen P. Slaby, Joel B. Dattilo, Hannah M. Kaylor, C. Alex Schrimpf, Ronald D. Fleetwood, Daniel M. Ralph, Stephen E. Reed, Robert Weiss, Sharon M. Interdisciplinary Materials Science Program Vanderbilt University 2201 West End Ave NashvilleTN37235 United States School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States Department of Electrical and Computer Engineering Vanderbilt University 2201 West End Ave NashvilleTN37235 United States

We report a simple, vacuum-compatible fiber attach process for in situ study of grating-coupled photonic devices. The robustness of this technique is demonstrated on grating-coupled waveguides exposed to multiple X-ra... 详细信息

关键词： Photonic devices

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132-MVAr 35-kV STATCOM Equipped With a Novel Power Oscillation Damping Control Strategy

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IEEE Journal of Emerging and Selected Topics in Power Electronics 2025年

作者： Finotti, Claudio Granata, Samuele Borghetti, Giovanni Corsi, Giacomo Guarda, Filippo Postiglione, Gianluca Santagiuliana, Massimo Pace, Luca De Vercesi, Francesco Fregonese, Alberto Zanchetta, Pericle NIDEC Conversion Milan20092 Italy University of Pavia Department of Electrical Computer and Biomedical Engineering Pavia27100 Italy

This paper presents a 132-MVAr 35-kV STATCOM equipped with a novel Power Oscillation Damping (POD) controller which is able to damp the network inter-area low-frequency oscillations. The transmission system is modeled as a 3-bus network to which the STATCOM is connected and whose dynamics are captured by means of the small-signal approach. Then, the relevant system transfer functions are derived. Through the analysis, it is found that the upstream network exhibits and undamped oscillatory response in presence of perturbations. Thus, the control strategy proposed here first introduces a damping action. Then, to improve the transient performances, a proportional-derivative (PD) regulation on the PCC voltage angle is presented. An algorithm able to extract and filter the upstream network variables involved in the control process is also provided. The effectiveness of the POD control strategy developed in this work is therefore verified in simulation and its regulation performances are discussed in detail. Finally, the converter has been deployed and connected to a high-voltage substation. The experimental tests carried out on field demonstrates the validity and the proper operation of the POD control proposed in this article, under both inductive and capacitive modes of the converter. © 2025 IEEE. All rights reserved.

关键词： Damping

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Automatic Nuclei Segmentation of Label-free Chromatin-sensitive Partial Wave Spectroscopic Microscopy using Convolution Neural Network with Transformer

Automatic Nuclei Segmentation of Label-free Chromatin-sensit...

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Label-Free biomedical Imaging and Sensing, LBIS 2025

作者： Alom, Md Shahin Daneshkhah, Ali Acosta, Nicolas Liwag, Emily Pujadas Kuo, Tiffany Ye, Rachel Pritchard, Joshua A. Bhattarai, Narayan Backman, Vadim Gaire, Sunil Kumar Department of Electrical and Computer Engineering North Carolina Agricultural and Technical State University GreensboroNC27411 United States Department of Biomedical Engineering Northwestern University EvanstonIL60208 United States Department of Chemical Biological and Bioengineering North Carolina Agricultural and Technical State University GreensboroNC27411 United States

ISBN: (纸本)9781510684102

Chromatin-sensitive Partial Wave Spectroscopic (csPWS) microscopy is a label-free spectroscopic optical nanosensing technique used to study the dynamic chromatin packing structure in the cell nucleus. Chromatin, composed of DNA wrapped around histones to form nucleosomes, organizes into supranucleosomal disordered chromatin chains, which assemble into packing domains (PDs). Dysregulation of these chromatin PDs is a common factor in the transcriptional phenotypic plasticity by which cancer cells change their state without changing their genes. csPWS microscopy quantifies the relationship between the genomic physical scales of these nanoscale chromatin PDs, enabling the evaluation of cancer-promoting markers in the nucleus for cancer screening or treatment care. Nucleus segmentation is a crucial task to ensure accurate analysis of chromatin PDs and spatial analysis, such as assessment of peripheral regions and nucleus shape. Traditional manual nuclei segmentation is labor-intensive, time-consuming, and prone to user bias, leading to errors due to over or under-segmentation. We present a novel hybrid convolutional neural networks-transformer-based approach for automatic cell nuclei segmentation with enhanced accuracy and speed for csPWS microscopy. Our method learns both local and global features from the input images for precise cell nuclei segmentation. We trained our model with several hundred csPWS images (>15,000 nuclei) from HCT116 and U2OS cell lines. With weighted-focal and dice loss, our model achieved median IoU and DSC scores of 0.77 and 0.89 for HCT116 cells and 0.75 and 0.85 for U2OS cells. This automated, rapid, and accurate segmentation streamlines csPWS microscopy data analysis, significantly aiding chromatin research for cancer diagnosis and treatment. Our deep-learning technique can also be leveraged by other microscopy techniques requiring segmentation, broadening its potential across various optical imaging modalities in biomedical research. © 2025

关键词： Cell culture

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Hybrid Deep Learning Model for Accurate Short-Term Electricity Price Forecasting

Hybrid Deep Learning Model for Accurate Short-Term Electrici...

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Moradzadeh, Arash Mouhammadpourfard, Mostafa Weng, Yang Pol, Suhas Muyeen, S.M. Qatar University Department of Electrical Engineering Doha Qatar National Wind Institute Texas Tech University LubbockTX United States School of Electrical Computer and Energy Engineering Arizona State University TempeAZ United States Texas Tech University Renewable Energy Program LubbockTX United States

ISBN: (纸本)9798331541125

Accurate short-term electricity price forecasting (STEPF) is critical for efficient energy market operations, guiding investment strategies, resource allocation, and consumer behavior. This study introduces a hybrid deep learning approach specifically designed to improve STEPF accuracy by leveraging historical Hourly Ontario Energy Price (HOEP) data from 2017 to 2019. The model integrates advanced techniques, including data preprocessing and denoising through a Stacked Denoising Autoencoder (SDAE), along with enhanced temporal modeling via Bidirectional Long Short-Term Memory (BiLSTM) and Gated Recurrent Unit (GRU) networks. By capturing the complex dynamics inherent in electricity pricing data, the proposed hybrid model significantly enhances forecasting accuracy. Trained on data from 2017 and 2018, with 2019 used for testing, the model achieves a strong correlation coefficient (R = 99.86%) and substantially lowers forecasting errors. Comparative evaluations against established forecasting methods highlight the model's superior performance. This work demonstrates the practical value of deep learning techniques in the energy sector, particularly in responding to the volatility of demand and supply in real-time electricity markets. © 2025 IEEE.

关键词： Electricity market forecasting Energy management Hybrid deep learning models Short-term electricity price forecasting Temporal data analysis

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CRESSim–MPM: A Material Point Method Library for Surgical Soft Body Simulation with Cutting and Suturing

arXiv

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arXiv 2025年

作者： Ou, Yafei Tavakoli, Mahdi Department of Electrical and Computer Engineering University of Alberta EdmontonAB Canada Department of Biomedical Engineering University of Alberta EdmontonAB Canada

A number of recent studies have focused on developing surgical simulation platforms to train machine learning (ML) agents or models with synthetic data for surgical assistance. While existing platforms excel at tasks such as rigid body manipulation and soft body deformation, they struggle to simulate more complex soft body behaviors like cutting and suturing. A key challenge lies in modeling soft body fracture and splitting using the finite-element method (FEM), which is the predominant approach in current platforms. Additionally, the two-way suture needle/thread contact inside a soft body is further complicated when using FEM. In this work, we use the material point method (MPM) for such challenging simulations and propose new rigid geometries and soft-rigid contact methods specifically designed for them. We introduce CRESSim-MPM, a GPU-accelerated MPM library that integrates multiple MPM solvers and incorporates surgical geometries for cutting and suturing, serving as a specialized physics engine for surgical applications. It is further integrated into Unity, requiring minimal modifications to existing projects for soft body simulation. We demonstrate the simulator’s capabilities in real-time simulation of cutting and suturing on soft tissue and provide an initial performance evaluation of different MPM solvers when simulating varying numbers of particles. Copyright © 2025, The Authors. All rights reserved.

关键词： Grafts

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Digital Twin-Empowered Network Planning for Multi-Tier Computing

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Journal of Communications and Information Networks 2022年第3期7卷 221-238页

作者： Conghao Zhou Jie Gao Mushu Li Xuemin(Sherman)Shen Weihua Zhuang Department of Electrical and Computer Engineering University of WaterlooWaterlooONN2L 3G1Canada School of Information Technology Carleton UniversityOttawaONK1S 5B6Canada Department of Electrical Computerand Biomedical EngineeringToronto Metropolitan UniversityTorontoONM5B 2K3Canada

In this paper,we design a resource management scheme to support stateful applications,which will be prevalent in sixth generation(6G)*** from stateless applications,stateful applications require context data while executing computing tasks from user terminals(UTs).Using a multi-tier computing paradigm with servers deployed at the core network,gateways,and base stations to support stateful applications,we aim to optimize long-term resource reservation by jointly minimizing the usage of computing,storage,and communication resources and the cost of reconfiguring resource *** coupling among different resources and the impact of UT mobility create challenges in resource *** address the challenges,we develop digital twin(DT)empowered network planning with two elements,i.e.,multi-resource reservation and resource reservation ***,DTs are designed for collecting UT status data,based on which UTs are grouped according to their mobility ***,an algorithm is proposed to customize resource reservation for different groups to satisfy their different resource ***,a Meta-learning-based approach is developed to reconfigure resource reservation for balancing the network resource usage and the reconfiguration *** results demonstrate that the proposed DTempowered network planning outperforms benchmark frameworks by using less resources and incurring lower reconfiguration costs.

关键词： 6G digital twin network planning multitier computing Meta-learning

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Intention-Aware Reverse Passivity-Based Teleoperation Stabilizer for Physical Human-(tele)Robot Interaction

Intention-Aware Reverse Passivity-Based Teleoperation Stabil...

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American Control Conference (ACC)

作者： Xingyuan Zhou Peter Paik S. Farokh Atashzar Department of Electrical and Computer Engineering New York University (NYU) New York NY USA Department of Electrical and Computer Engineering Mechanical and Aerospace Engineering Biomedical Engineering NYU NYU WIRELESS Center NYU CUSP

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

In networked robotic systems, specifically haptics-enabled teleoperation, ensuring stability and trackig performance is of paramount importance. Recently, several stabilizers have leveraged the concept of “excess of passivity” (EoP) from non-linear control theory to decode and incorporate the dissi-pative energetic behavior of human biomechanics in the design of the stabilizers. This is done to counterbalance the effect of energy accumulation in the system due to the suboptimal non-passive communication behavior (which includes delays, jitter and packet losses). However, the dissipative behavior of human biomechanics naturally degrades the perceived force transparency when considering the “intended force” as the desired signal to be tracked. In other words, there is a “force gap” between the tracked forces and the intended forces. This is because parts of energy production are compensated for to move human biomechanics. This paper focuses on filling the gap by designing a networked robotic architecture that recovers parts of the dissipated active force of the operator so that the remote task is conducted according to the intended action of the operator rather than dissipated action. This requires a reformulation of the telerobotic architecture and the corresponding controllers. In this paper, we mathematically formulate a reverse telerobotic design and synthesize a new passivity-based stabilizer, named Intention-aware reverse Time Domain Passivity-Based teleoperation stabilizer (ITDPB) so that system stability is guaranteed while perceived transparency is recovered. In addition, we conduct extensive grid simulations, comparing the results of our proposed stabilizer to the state-of-the-art approach. The results indicate that the proposed approach has superior performance in terms of maximizing the ratio between the force intended by the user and the actual force transmitted to the environment while guaranteeing the system's stability. The proposed stabilizer is suita

关键词： Biomechanics Accuracy Tracking Force Stability criteria Human-robot interaction Power system stability

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A Dual-Track Framework for Enhancing Age-Related Macular Degeneration Diagnosis Through Embedding and Vision Classification 22

A Dual-Track Framework for Enhancing Age-Related Macular Deg...

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22nd IEEE International Symposium on biomedical Imaging, ISBI 2025

作者： Balaha, Hossam Magdy Aboudessouki, Ahmed Mahmoud, Ali Ghazal, Mohammed El-Baz, Ayman J.B. Speed School of Engineering University of Louisville Bioengineering Department LouisvilleKY40292 United States Abu Dhabi University Electrical Computer and Biomedical Engineering Department Abu Dhabi United Arab Emirates

ISBN: (纸本)9798331520526

Age-related macular degeneration (AMD) is a leading cause of vision loss in older adults, making timely and accurate diagnosis crucial for effective treatment. This study suggests a new framework for AMD diagnosis that integrates embedding extraction and direct vision classification to improve diagnostic performance. We employ a dual-track methodology, utilizing advanced pretrained models for embedding extraction (utilizing Virchow2) alongside a vision-based approach (utilizing Nomic) to classify fundus images into distinct AMD categories: (1) normal, (2) intermediate, (3) geographic atrophy, and (4) wet AMD. Our experiments achieved the best results, with an accuracy of 98.03%, an F1 score of 98.05%, and a precision of 98.08%. By experimentally investigating the applicability of pretrained embedding models originally designed for histopathological patches to fundus images, we bridge a gap in multimodal image analysis, potentially paving the way for improved diagnostic tools in ophthalmology. The experimental results demonstrate the potential of combining embedding extraction and direct vision classification to improve the accuracy of AMD detection, addressing the urgent need for automated diagnostic systems in the clinical environment. © 2025 IEEE.

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