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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Luis H. Cubillos Guy Revach Matthew J. Mender Joseph T. Costello Hisham Temmar Aren Hite Diksha Zutshi Dylan M. Wallace Xiaoyong Ni Madison M. Kelberman Matthew S. Willsey Ruud J.G. van Sloun Nir Shlezinger Parag Patil Anne Draelos Cynthia A. Chestek Departments of Electrical & Computer Engineering Biomedical Engineering Robotics Computational Medicine & Bioinformatics and Neurosurgery University of Michigan Department of Information Technology and Electrical Engineering ETH Zürich Switzerland Departments of Electrical & Computer Engineering Biomedical Engineering Robotics Computational Medicine & Bioinformatics and Neurosurgery University of Michigan and Biointerfaces Institute and Neuroscience Institute University of Michigan Department of Electrical Engineering Eindhoven University of Technology Netherlands School of Electrical and Computer Engineering Ben-Gurion University Israel

ISBN: (纸本)9798331314385

People with brain or spinal cord-related paralysis often need to rely on others for basic tasks, limiting their independence. A potential solution is brain-machine interfaces (BMIs), which could allow them to voluntarily control external devices (e.g., robotic arm) by decoding brain activity to movement commands. In the past decade, deep-learning decoders have achieved state-of-the-art results in most BMI applications, ranging from speech production to finger control. However, the 'black-box' nature of deep-learning decoders could lead to unexpected behaviors, resulting in major safety concerns in real-world physical control scenarios. In these applications, explainable but lower-performing decoders, such as the Kalman filter (KF), remain the norm. In this study, we designed a BMI decoder based on Kalman-Net, an extension of the KF that augments its operation with recurrent neural networks to compute the Kalman gain. This results in a varying "trust" that shifts between inputs and dynamics. We used this algorithm to predict finger movements from the brain activity of two monkeys. We compared KalmanNet results offline (pre-recorded data, n =13 days) and online (real-time predictions, n = 5 days) with a simple KF and two recent deep-learning algorithms: tcFNN (non-ReFIT version) and LSTM. KalmanNet achieved comparable or better results than other deep learning models in offline and online modes, relying on the dynamical model for stopping while depending more on neural inputs for initiating movements. We further validated this mechanism by implementing a heteroscedastic KF that used the same strategy, and it also approached state-of-the-art performance while remaining in the explainable domain of standard KFs. However, we also see two downsides to KalmanNet. KalmanNet shares the limited generalization ability of existing deep-learning decoders, and its usage of the KF as an inductive bias limits its performance in the presence of unseen noise distributions. Despite th

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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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biomedical Osteosarcoma Image Classification Using Elephant Herd Optimization and Deep Learning

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computers, Materials & Continua 2022年第12期73卷 6443-6459页

作者： Areej A.Malibari Jaber S.Alzahrani Marwa Obayya Noha Negm Mohammed Abdullah Al-Hagery Ahmed S.Salama Anwer Mustafa Hilal Department of Industrial and Systems Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Faculty of Science Mathematics and Computer Science DepartmentMenoufia UniversityEgypt Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Electrical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

Osteosarcoma is a type of malignant bone tumor that is reported across the *** advancements in Machine Learning(ML)and Deep Learning(DL)models enable the detection and classification of malignancies in biomedical *** this regard,the current study introduces a new biomedical Osteosarcoma Image Classification using Elephant Herd Optimization and Deep Transfer Learning(BOIC-EHODTL)*** presented BOIC-EHODTL model examines the biomedical images to diagnose distinct kinds of *** the initial stage,Gabor Filter(GF)is applied as a pre-processing technique to get rid of the noise from *** addition,Adam optimizer with MixNet model is also employed as a feature extraction technique to generate feature ***,EHOalgorithm is utilized along with Adaptive Neuro-Fuzzy Classifier(ANFC)model for recognition and categorization of *** algorithm is utilized to fine-tune the parameters involved in ANFC model which in turn helps in accomplishing improved classification *** design of EHO with ANFC model for classification of osteosarcoma is the novelty of current *** order to demonstrate the improved performance of BOIC-EHODTL model,a comprehensive comparison was conducted between the proposed and existing models upon benchmark dataset and the results confirmed the better performance of BOIC-EHODTL model over recent methodologies.

关键词： biomedical imaging osteosarcoma classification deep transfer learning parameter tuning fuzzy logic

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A 170GHz 5.5dB NF Low-Noise Amplifier in 55nm SiGe BiCMOS

A 170GHz 5.5dB NF Low-Noise Amplifier in 55nm SiGe BiCMOS

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Ph.D. Research in Microelectronics and Electronics (PRIME)

作者： Guglielmo De Filippi Lorenzo Piotto Melchiorre Bruccoleri Andrea Mazzanti Department of Electrical Computer and Biomedical Engineering University of Pavia Italy STMicroelectronics Milan Italy

ISBN: (数字)9798350386301

ISBN: (纸本)9798350386318

Silicon low-noise amplifiers (LNA) are key building blocks for the development of phased-array systems. BiCMOS is a promising technology, levering both the higher speed and power handling capability of HBTs and the high density of CMOS nodes, enabling the realization of complex systems on chip working in both the digital and RF domains. This work illustrates a two-stage D-band LNA that targets the integration in a receiver front-end module for dense phased-array systems. Realized in SiGe Bi55X by STM, the amplifier achieves 17.5 dB gain with 50 GHz bandwidth centered at 170GHz and a noise figure ranging from 5.2 dB to 5.5 dB, the lowest value reported in literature to date at the same frequency and with similar technologies. The power consumption is 20 mW from a 2V supply and the area occupation is 0.06 mm 2 , Measured results compare favorably against state-of-the-art.

关键词： Noise figure Low-noise amplifiers Semiconductor device measurement Power measurement Receivers Transmission line measurements BiCMOS integrated circuits

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Visual Scene Understanding for Enhanced EMG Gesture Recognition 46

Visual Scene Understanding for Enhanced EMG Gesture Recognit...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Chamberland, Felix Labbé, Thomas Tam, Simon Scheme, Erik Gosselin, Benoit Université Laval Department of Electrical and Computer Engineering QuébecQCG1V 0A6 Canada University of New Brunswick Institute of Biomedical Engineering FrederictonNBE3B 5A3 Canada

ISBN: (纸本)9798350371499

This work presents a multimodal approach combining electromyography (EMG) and computer vision (CV) for robust real-time gesture recognition in a real-world setting. A context-aware framework is proposed for myoelectric prosthesis control, wherein EMG hand gesture recognition is augmented by the visual detection of objects of interest in a scene, effectively mitigating risks of false movements. By supporting EMG gesture predictions produced by a Siamese deep convolution neural network (SDCNN) with context derived from object detection using a tailored YOLO computer vision model, the system prevents false detection during gesture onset and during static gesture maintenance. In a pilot experiment, this multimodal sensor fusion is shown to effectively augment human volitional control by enhancing both the robustness of the gesture control interface and the user's ability to maintain full command over it. © 2024 IEEE.

关键词： Electromyography

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A Novel Deep Learning Prospective Urban Growth Model Applied to Multispectral Satellite Data over the City of Córdoba, Argentina

A Novel Deep Learning Prospective Urban Growth Model Applied...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： María Sol Villella Paolo Gamba Instituto Gulich CONAE/UNC Department of Electrical Computer and Biomedical Engineering University of Pavia

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Prospective urban growth models serve as tools aimed at supporting sustainable city development, informed decision-making and proactive planning, contributing to enhancing livability and resilience in expanding urban *** research develops a prospective urban growth model for the Metropolitan Area of Córdoba (Argentina) by integrating multispectral remote sensing data and open-source GIS data within a Deep Learning framework, projecting the urban extent for the year 2030. It is observed that the DL model effectively replicates edge expansion by utilizing neighborhood information. Additionally, it accurately simulates outlying growth via isolated patches, all of which follow similar patterns to those observed in the past.

关键词： Deep learning Satellites Urban areas Decision making Data models Planning Remote sensing

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A Hybrid EEG-EMG Framework for Humanoid Control using Deep Learning Transformers

A Hybrid EEG-EMG Framework for Humanoid Control using Deep L...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Olikkal, Parthan Pillai, Branesh M. Suthakorn, Jackrit Ali, Habib Vinjamuri, Ramana University of Maryland Baltimore County Vinjamuri lab Department of Computer Science and Electrical Engineering BaltimoreMD21220 United States Mahidol University Center for Biomedical and Robotics Technology Nakorm Pathom Salaya Thailand

ISBN: (纸本)9781665481090

Brain-computer interfaces (BCIs) offer promising solutions for upper limb rehabilitation. Despite advancements in deep learning, traditional models for motor rehabilitation using electroencephalography (EEG) or electromyography (EMG) to control assistive devices require enhancement. This study aims to enhance motor control capabilities by integrating EEG and EMG signals using a Transformer-based deep learning model. Ten able-bodies subjects performed center-out tasks on a low-cost upper limb rehabilitation table, capturing 2D kinematic data, EEG, and EMG signals simultaneously. The tasks varied in complexity across four levels. Preprocessed EEG and EMG signals were fused and given as input to the proposed model, which was evaluated using three performance metrics. Results showed that the EEG-EMG combined model achieved 87.27% accuracy across all the four levels. Furthermore, the model's output successfully controlled a humanoid robot to replicate similar movements. These findings highlight the efficacy of combined EEG-EMG data in improving accuracy and performance in BCI applications, advancing assistive technologies and neurorehabilitation interventions. © 2024 IEEE.

关键词： Anthropomorphic robots

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Terminal-ileum Centerline Extraction from Magnetic Resonance Enterography Data of Crohn's Disease Patients

Terminal-ileum Centerline Extraction from Magnetic Resonance...

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2023 IEEE EMBS Special Topic Conference on Data Science and engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Benisty, Rotem Shinnawi, Faten Haj Ali Porat, Moshe Illivitzki, Anat Freiman, Moti Faculty of Electrical & Computer Engineering Technion - Israel Institute of Technology Haifa Israel Rambam Health Care Campus Department of Radiology Haifa Israel Faculty of Biomedical Engineering Technion Israel Institute of Technology Haifa Israel

ISBN: (纸本)9798350383386

Crohn's disease (CD), a chronic inflammatory bowel disorder, often affects the terminal ileum (TI) and leads to digestive tract inflammation and complications like bowel obstruction. Accurately determining the 3D extent of CD from 2D Magnetic Resonance Enterography (MRE) images requires approximations, as no automated 3D measurement system exists. We developed an intelligent MRE reading application for virtual unfolding and 3D visualization of MRE data. We introduce a semi-automatic algorithm to predict the TI centerline from MRE data to reduce radiologist interaction time. The algorithm involves constructing an orientation classifier and implementing a shortest path algorithm to determine the TI centerline. We evaluated the algorithm's effectiveness on a database of 123 MRE scans using a k-fold cross-validation experimental setup, comparing the predicted centerline with a radiologist-annotated centerline considered as ground-truth. The results showed good alignment with the ground-truth centerline with minimal interaction *** relevance: This study introduces a semi-automatic algorithm that accurately predicts the terminal ileum centerline from MRE data, streamlining the diagnostic process for Crohn's disease. By reducing radiologist interaction time in assessing the 3D extent of the disease from 2D images, the algorithm holds promise for improving early diagnosis and treatment strategies for affected patients. © 2023 IEEE.

关键词： Magnetic resonance

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Mindful Breathing Coaches Using IoT-Integrated AI Systems for Personalized Anxiety Relief 1

Mindful Breathing Coaches Using IoT-Integrated AI Systems fo...

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1st International Conference on Innovations in Communications, electrical and computer engineering, ICICEC 2024

作者： Dhal, P.K. Sivakumar, T. Vasudevan, B. Lakshmi, V.T. Muthaiyan, Baskar Murugan, S. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electrical and Electronics Engineering Tami Nadu Chennai India Sri Eshwar College of Engineering Department of Computer Science and Engineering Tamil Nadu Coimbatore India SRM Institute of Science and Technology Department of Electronics and Communication Engineering Kattankulathur Tamil Nadu Chennai India Department of Mathematics Tamil Nadu Salem India Rajalakshmi Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376517

The proposed system focuses on personal anxiety alleviation through mindful breathing coaching, which investigates the use of Artificial Intelligence (AI) systems combined with the Internet of Things (IoT). Treatments for anxiety disorders should be easily available because of how common and crippling they are. The proposed solution integrates IoT devices with AI algorithms to provide personalized instructions for mindful breathing exercises and real-time tracking. Using a mixed-methods strategy, quantitative and qualitative user experiences and anxiety levels are analyzed. Anxiety symptoms were significantly reduced, and users were very satisfied with the tailored coaching, according to preliminary studies. There are scalable solutions to individual anxiety requirements that can be provided by integrating IoT and AI technology. It emphasizes the potential of technology to promote well-being and helps advance individualized therapies for mental health. It shows that it is possible to provide effective personalized anxiety reduction. By providing individualized assistance to those dealing with anxiety problems, this method has the potential to completely transform the way mental health treatment is provided. © 2024 IEEE.

关键词： Electronic health record

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