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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： An, Delin Du, Pan Gu, Pengfei Wang, Jian-Xun Wang, Chaoli University of Notre Dame Department of Computer Science and Engineering Notre Dame IN United States University of Texas Rio Grande Valley Department of Computer Science Edinburg TX United States University of Notre Dame Department of Aerospace and Mechanical Engineering Notre Dame IN United States Sibley School of Mechanical and Aerospace Engineering Cornell University Ithaca NY United States

ISBN: (纸本)9798331520526

Accurate segmentation of the aorta and its associated arch branches is crucial for diagnosing aortic diseases. While deep learning techniques have significantly improved aorta segmentation, they remain challenging due to the intricate multiscale structure and the complexity of the surrounding tissues. This paper presents a novel approach for enhancing aorta segmentation using a Bayesian neural network-based hierarchical Laplacian of Gaussian (LoG) model. Our model consists of a 3D U-Net stream and a hierarchical LoG stream: the former provides an initial aorta segmentation, and the latter enhances blood vessel detection across varying scales by learning suitable LoG kernels, enabling self-adaptive handling of different parts of the aorta vessels with significant scale differences. We employ a Bayesian method to parameterize the LoG stream and provide confidence intervals for the segmentation results, ensuring robustness and reliability of the prediction for vascular medical image analysts. Experimental results show that our model can accurately segment main and supra-aortic vessels, yielding at least a 3% gain in the Dice coefficient over state-of-the-art methods across multiple volumes drawn from two aorta datasets, and can provide reliable confidence intervals for different parts of the aorta. The code is available at https://***/adlsn/LoGBNet. © 2025 IEEE.

关键词： Image segmentation

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Generative Adversarial Network with Adaptive Synthesis for Brain-computer Interfaces in Motor Imagery Classification

Generative Adversarial Network with Adaptive Synthesis for B...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhou, Qiaoli Ye, Xiyuan Li, Shurui Zhao, Yi Du, Qiang Ke, Li School of Computer Science Shenyang Aerospace University Liaoning Shenyang110136 China School of Electrical Engineering Shenyang University of Technology Liaoning Shenyang110870 China

ISBN: (纸本)9798350368741

Motor Imagery (MI) is essential in Brain-computer Interfaces (BCIs), highlighting the central role of electroencephalography (EEG) in this technology. However, the amount of raw EEG data is often limited. Raw EEG data contains significant noise caused by individual and task-specific differences. These complications often necessitate the removal of noise, making it crucial to develop robust models capable of maintaining performance. To achieve this, synthetic data is essential to obtain cleaner signals. This paper presents a novel a novel contrastive diffusion model to adaptive synthesize clean signals for Motor Imagery Classification (CDASC-MI), achieved through three components: Clean Signal Extractor(CSE) for clean signal feature learning, Adapative Noise Extractor(ANE) for adaptively extracting subject noise and task noise, and Contrastive Module for further separating noise to obtain cleaner signals. Specifically, we first proposed the novel Cell-UNET in CSE, which is an architecture with progressive learning capabilities to effectively capture subtle changes in EEG signals. Additionally, ANE incorporates a novel Subject Feature Learning Network(SFLNet) and Cross-Domain Electrode Attention Network (CDANet). SFLNet and CDANet dynamically capturing variation subject noise and task noise. Ultimately, a Contrastive Module is designed to use contrastive learning during the denoising process to amplify the differences between clean signals and noise, ensuring robust denoising and clearer feature representation. According to experimental results from two public datasets, the proposed method achieved superior accuracies of 96.19% and 89.19%, outperforming existing approaches. It highlights its potential as a powerful method for augmenting EEG datasets and improving the accuracy and highlight its broad application values. © 2025 IEEE.

关键词： DDPM Deep Learning Domain-attention EEG

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Parameter Identification for Electrochemical Models of Lithium-Ion Batteries Using Bayesian Optimization

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IFAC-PapersOnLine 2024年第28期58卷 180-185页

作者： Jianzong Pi Samuel Filgueira da Silva Mehmet Fatih Ozkan Abhishek Gupta Marcello Canova Department of Electrical and Computer Engineering The Ohio State University Columbus OH Department of Mechanical and Aerospace Engineering The Ohio State University Columbus OH

Efficient parameter identification of electrochemical models is crucial for accurate monitoring and control of lithium-ion cells. This process becomes challenging when applied to complex models that rely on a considerable number of interdependent parameters that affect the output response. Gradient-based and metaheuristic optimization techniques, although previously employed for this task, are limited by their lack of robustness, high computational costs, and susceptibility to local minima. In this study, Bayesian Optimization is used for tuning the dynamic parameters of an electrochemical equivalent circuit battery model (E-ECM) for a nickel-manganese-cobalt (NMC)-graphite cell. The performance of the Bayesian Optimization is compared with baseline methods based on gradient-based and metaheuristic approaches. The robustness of the parameter optimization method is tested by performing verification using an experimental drive cycle. The results indicate that Bayesian Optimization outperforms Gradient Descent and PSO optimization techniques, achieving reductions on average testing loss by 28.8% and 5.8%, respectively. Moreover, Bayesian optimization significantly reduces the variance in testing loss by 95.8% and 72.7%, respectively.

关键词： Parameter Identification Bayesian Optimization Electrochemical Models Lithium-ion Batteries

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A Synthetic Nervous System for on and Off Motion Detection Inspired by the Drosophila melanogaster Optic Lobe 12th

A Synthetic Nervous System for on and Off Motion Detection...

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Proceedings of the 12th International Conference on Biomimetic and Biohybrid Systems, Living Machines 2023

作者： Nourse, William R. P. Szczecinski, Nicholas S. Quinn, Roger D. Department of Electrical Computer and Systems Engineering Case Western Reserve University ClevelandOH44106 United States Department of Mechanical and Aerospace Engineering West Virginia University MorgantownWV26506 United States Department of Mechanical and Aerospace Engineering Case Western Reserve University ClevelandOH44106 United States

ISBN: (纸本)9783031388569

In this work, we design and simulate a synthetic nervous system which is capable of computing optic flow throughout a visual field, inspired by recent advances in the neural anatomy of Drosophila melanogaster found through connectomics. We present methods for tuning the network for desired stimuli, and benchmark its temporal properties and capability for directional selectivity. This network acts as a stepping point towards visual locomotion control in a hexapod robot inspired by the anatomy of Drosophila. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Optical flows

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Enhancing Campus Safety: A Comprehensive Approach with IoT and OpenCV Technology 4

Enhancing Campus Safety: A Comprehensive Approach with IoT a...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Presskila, X. Arogya Kumari, D. Abitha Darney, P. Ebby Sundararajan, S. Sankar Ram, C. Ramasamy Sangeetha, A. Scad College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India Rmk Engineering College Department of Computer Science and Engineering Thiruvallur India Rajarajeswari College of Engineering Department of Electrical and Electronics Engineering Bangalore India Scad College of Engineering and Technology Department of Mechanical Engineering Cheranmahadevi India University College of Engineering Anna University Department of Computer Science and Engineering Tiruchirappalli India PSNA College of Engineering and Technology Department of Information Technology Dindigul India

ISBN: (纸本)9798350300888

The COVID-19 pandemic has brought about a significant shift in people's lifestyles, prompting a complete change in their way of life. Prioritizing teacher and student health before permitting them to visit college grounds has become crucial because the virus is evolving and posing a threat to human survival. In order to do this a three-stage examining process that entails the following phases was developed. The system first verifies to validate if the visitor has received all necessary vaccinations. Second, the system keeps tabs on the people health during the second phase. Lastly, nobody is allowed on the college campus without a mask. The device also provides hand sanitizing solutions to the guests, ensuring everyone is as safe and protected as possible. © 2023 IEEE.

关键词： Viruses

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Multi-Agent Autonomy: Advancements and Challenges in Subterranean Exploration

Field Robotics

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Field Robotics 2025年 2卷 1068-1104页

作者： Michael T. Ohradzansky Eugene R. Rush Danny G. Riley Andrew B. Mills Shakeeb Ahmad Steve McGuire Harel Biggie Kyle Harlow Michael J. Miles Eric W. Frew Christoffer Heckman J. Sean Humbert Aerospace Engineering Sciences University of Colorado Boulder CO USA Mechanical Engineering University of Colorado Boulder CO USA Computer Science University of Colorado Boulder CO USA Electrical & Computer Engineering University of California Santa Cruz CA USA

Artificial intelligence has undergone immense growth and maturation in recent years, though autonomous systems have traditionally struggled when fielded in diverse and previously unknown environments. DARPA is seeking to change that with the Subterranean Challenge, by providing roboticists the opportunity to support civilian and military first responders in complex and high-risk underground scenarios. The subterranean domain presents a handful of challenges, such as limited communication, diverse topology and terrain, and degraded sensing. Team MARBLE proposes a solution for autonomous exploration of unknown subterranean environments in which coordinated agents search for artifacts of interest. The team presents two navigation algorithms in the form of a metric-topological graph-based planner and a continuous frontier-based planner. To facilitate multi-agent coordination, agents share and merge new map information and candidate goal points. Agents deploy communication beacons at different points in the environment, extending the range at which maps and other information can be shared. Onboard autonomy reduces the load on human supervisors, allowing agents to detect and localize artifacts and explore autonomously outside established communication networks. Given the scale, complexity, and tempo of this challenge, a range of lessons was learned, most importantly, that frequent and comprehensive field testing in representative environments is key to rapidly refining system performance.

关键词： Robots Robot kinematics Robot sensing systems Location awareness Land vehicles Base stations Planning Navigation Graphical user interfaces Autonomous aerial vehicles

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Automated segmentation of central airway obstruction from endoscopic video stream with deep learning

Automated segmentation of central airway obstruction from en...

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Medical Imaging 2025: Image-Guided Procedures, Robotic Interventions, and Modeling

作者： Li, Hao Wang, Jiacheng Kumar, Nithin Lu, Daiwei Acar, Ayberk Han, John Yang, Qingyun Ertop, Tayfun Efe Wu, Jie Ying Webster, Robert J. Oguz, Ipek Department of Computer Science Vanderbilt University Nashville United States Department of Mechanical Engineering Vanderbilt University Nashville United States Department of Mechanical Aerospace and Biomedical Engineering University of Tennessee Knoxville United States

ISBN: (纸本)9781510685949

Central airway obstruction (CAO) can disrupt normal breathing and pose significant risks, with treatments often involving surgical removal. Accurate image segmentation is crucial for identifying the target region to assist surgeons or robotic surgery system during operations. Although manual annotation is the gold standard, it is time-consuming and subjective, making a robust automated segmentation algorithm desirable. In this paper, we propose an automated deep learning framework for CAO segmentation. As a benchmark study, we build a custom CAO phantom model and acquire endoscopy videos. We then establish inter-rater variability. To assess the effectiveness of the proposed method, a 4-fold cross-validation is performed with The Dice score as an evaluation metric. The proposed CAO segmentation method yields mean binary Dice scores of 0.95 and 0.89 against annotations from two human raters, respectively, compared to an inter-rater variability Dice score of 0.88. This indicates that the proposed framework can provide robust CAO segmentation from endoscopic video. © 2025 SPIE

关键词： autonomous surgical robot central airway obstruction (CAO) Image segmentation inter-rater variability phantom endoscopy video

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Timing Mechanism for a Low-Cost, Single Use, Nucleic Acid Amplification Diagnostic Test

Timing Mechanism for a Low-Cost, Single Use, Nucleic Acid Am...

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2024 IEEE MIT Undergraduate Research Technology Conference, URTC 2024

作者： Gordon, Savannah Jaffe, Ellie Garcia, Sebastian Kim, Suhan Kang, Ziliang Hanumara, Nevan C. Traverso, Giovanni Massachusetts Institute of Technology Dept. of Mechanical Engineering CambridgeMA02139 United States Massachusetts Institute of Technology Dept. of Electrical Engineering & Computer Science CambridgeMA02139 United States

ISBN: (纸本)9798331531003

Nucleic Acid Amplification Tests (NAATs) are used to diagnose infectious diseases such as tuberculosis (TB), SARS-CoV-2, and Human Papillomavirus (HPV). Conventional testing requires a bench-top system that limits its accessibility to potential patients. A self-contained, low-cost, single use NAAT would increase worldwide access to critical diagnostics. In support of an ongoing NAAT diagnostic project, we prototyped a timing mechanism to control the critical heating step that effects amplification. Herein we present a design consisting of a micro controller, a dual-channel Mosfet, and LEDs to visualize the operation. We selected electrical components that were low cost and tested the design for accuracy under two conditions: 1) the elevated temperatures required for the NAAT process and 2) a fluctuating power source, as would result from batteries suitable for a disposable device. We prototyped iteratively, ultimately demonstrating a design by creating a flex PCB approximately 3 cm by 2 cm with a cost less than 1 USD at scale. With this work, we aim to help further the development of more distributed and accessible diagnostic tests. © 2024 IEEE.

关键词： Microfluidics

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The UbiLAB Framework for Remote Laboratories 30

The UbiLAB Framework for Remote Laboratories

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30th International Conference on Systems, Signals and Image Processing, IWSSIP 2023

作者： Kalendar, Marija Kokolanski, Zivko Gerazov, Branislav Nadzinski, Gorjan Poposka, Marija Zlatkova, Aleksandra Donchevski, Filip Markovska, Marija Sapeha, Anastasiia Karpov, Kirill Efnusheva, Danijela Gleich, Dusan Sarjas, Andrej Pongrac, Blaz Siemens, Eduard Ss Cyril and Methodius University Faculty of Electrical Engineering and Information Technologies Skopje Macedonia Anhalt University of Applied Sciences Department of Electrical Mechanical and Industrial Engineering Köthen Germany University of Maribor Faculty of Electrical Engineering and Computer Science Maribor Slovenia

ISBN: (纸本)9798350337297

Starting from the period of the Covid-19 pandemic, when the presence of students in the university laboratories was very limited, the research work on remote laboratories significantly increased. The most affected aspect of students' study experience at that time were the laboratory experiments intended to be conducted on-premises, in the university laboratories. The UbiLAB framework was envisioned as a digital space for implementing and conducting laboratory work and experiments that are rarely completely transferred into the digital world. Here we have identified two most difficult challenges: achieving a nearly realistic substitute for learning experiences in the actual physical laboratories, and a nearly realistic substitute for the social element of collaborative learning and 'making friends' in the process. The innovative cloud-based UbiLAB framework addresses both challenges, digitizing various and diverse actual laboratory experiments through a simple and widely available platform, while enhancing the reality of students' experience in the process. © 2023 IEEE.

关键词： Students

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Using Deep Reinforcement Learning and Formal Verification in Safety Critical Systems: Strategies and Challenges 23

Using Deep Reinforcement Learning and Formal Verification in...

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23rd IEEE International Conference on Software Quality, Reliability, and Security Companion, QRS-C 2023

作者： Sharma, Satyam Rahim, Muhammad Abdul Basit Ur Hussain, Shahid Abid, Muhammad Rizwan Liu, Tairan California State University Computer Engineering and Computer Science Long BeachCA United States Computer Science and Software Engineering Penn State University Behrend PA United States Florida Polytechnic University Computer Science LakelandFL United States Mechanical and Aerospace Engineering California State University Long BeachCA United States

ISBN: (纸本)9798350359398

Deep Reinforcement Learning (DRL) is critical in modern Artificial Intelligence (AI), powering innovations from gaming to autonomous vehicles. As DRL continues its rapid ascent, ensuring its systems are both trustworthy and effective is crucial. This research focuses on different DRL techniques and the challenges faced in real-life scenarios. The paper also describes various formal verification techniques and the challenges related to their application. It sheds light on the different frameworks and tools that can enhance the credibility of systems. We performed an extensive literature survey to present the existing methodologies, tools, and frameworks. The analysis systematically reviews and categorizes various formal verification techniques and frameworks employed in DRL. The insights garnered from this study are anticipated to foster an enriched understanding of the processes and contribute to decision-making in Safety Critical Systems using DRL and verification. © 2023 IEEE.

关键词： Decision making

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