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Importance of considering microscopic structures in modeling brain stimulation

Brain stimulation 2025年第4期18卷 1150-1152页

作者： Zhen Qi Gregory M Noetscher Alton Miles Konstantin Weise Thomas R Knösche Cameron R Cadman Alina R Potashinsky Kelu Liu William A Wartman Guillermo Nunez Ponasso Marom Bikson Hanbing Lu Zhi-De Deng Aapo Nummenmaa Sergey N Makaroff Electrical and Computer Engineering Department Worcester Polytechnic Institute Worcester MA USA. Electrical and Computer Engineering Department Worcester Polytechnic Institute Worcester MA USA. Electronic address: gregn@wpi.edu. Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany Leipzig University of Applied Sciences (HTWK) Faculty of Engineering Leipzig Germany. Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany. Department of Biomedical Engineering The City College of New York New York NY USA. Neuroimaging Research Branch NIDA Intramural Research Program National Institutes of Health Baltimore MD USA. Computational Neurostimulation Research Program Noninvasive Neuromodulation Unit Experimental Therapeutics and Pathophysiology Branch NIMH National Institutes of Health Baltimore MD USA. Massachusetts General Hospital Harvard Medical School Boston MA USA. Electrical and Computer Engineering Department Worcester Polytechnic Institute Worcester MA USA Massachusetts General Hospital Harvard Medical School Boston MA USA Department of Mathematical Sciences Worcester Polytechnic Institute Worcester MA USA.

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Synthesis of Proactive Sensor Placement In Probabilistic Attack Graphs

Synthesis of Proactive Sensor Placement In Probabilistic Att...

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American Control Conference (ACC)

作者： Lening Li Haoxiang Ma Shuo Han Jie Fu Department of Robotics Engineering Worcester Polytechnic Institute Worcester MA USA Department of Electrical & Computer Engineering University of Florida Gainesville FL USA Department of Electrical & Computer Engineering University of Illinois at Chicago Chicago IL USA

This paper studies the deployment of joint moving target defense (MTD) and deception against multi-stage cyber-attacks. Given the system equipped with MTD that randomizes between different configurations, we investigate how to allocate a bounded number of sensors in each configuration to optimize the probability of detecting the attack before the attacker achieves its objective. Specifically, two types of sensors are considered: intrusion detectors that are observable by the attacker and stealthy sensors that are not observable to the attacker. We propose a two-step optimization-based approach: Firstly, the defender allocates intrusion detectors assuming the attacker will best respond to evade detection. Secondly, the defender will allocate stealthy sensors, given the best response attack strategy computed in the first step, to further reduce the attacker’s chance of success. We illustrate the effectiveness of the proposed methods using a cyber defense example.

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InfantMotion2Vec: Unlabeled Data-Driven Infant Pose Estimation Using a Single Chest IMU

InfantMotion2Vec: Unlabeled Data-Driven Infant Pose Estimati...

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International Workshop on Wearable and Implantable Body Sensor Networks (BSN)

作者： Mohammad Nur Hossain Khan Nancy L. McElwain Mark Hasegawa-Johnson Bashima Islam Department of Electrical and Computer Engineering Worcester Polytechnic Institute Department of Human Development and Family Studies University of Illinois Beckrnan Institute for Advanced Science and Technology University of Illinois Department of Electrical and Computer Engineering University of Illinois

ISBN: (数字)9798331530143

ISBN: (纸本)9798331530150

Early identification of neuro-developmental risks in infants is crucial for timely intervention and improved quality of life. Current screening methods are costly, intrusive, and limited by artificial environments or require the infant to wear multiple sensors. To address these challenges, we propose a novel approach leveraging inertial measurement units (IMUs) to monitor infants' spontaneous motor abilities in natural settings. Our method introduces a hierarchical semi-supervised classifier and the InfantMotion2Vec embedding to capture detailed motion patterns, accommodating a wide age range (up to 36 months) while minimizing reliance on labeled data and cumbersome sensor setups. We collected labeled IMU data from 25 families and unlabeled data from 42 families using a single wearable sensor. Pretraining an embedding network using unlabeled data with a hierarchical pose estimator resulted in a 26% increase in F1-score and a 77.7% increase in Cohen's Kappa score compared to using only labeled data. The InfantMotion2Vec embedding adequately handles highly unbalanced labeled data, demonstrating its effectiveness in infant posture classification.

关键词： Body sensor networks Measurement units Pose estimation Inertial navigation Motors Wearable sensors Monitoring

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Training Strategies for Increased Temporal Resolution in Clinical Cardiac-gated SPECT Studies with Deep Learning Denoising 22

Training Strategies for Increased Temporal Resolution in Cli...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Zhang, Xirang Yang, Yongyi Brankov, Jovan G. Pretorius, P. Hendrik King, Michael A. Illinois Institute of Technology Department of Electrical and Computer Engineering ChicagoIL60616 United States University of Massachusetts Medical School Department of Radiology WorcesterMA01655 United States

ISBN: (纸本)9798331520526

Cardiac-gated SPECT studies are limited to the use of a small number of gated frames due to high imaging noise in clinical acquisitions. Here we investigate three strategies for training a deep learning (DL) network for suppressing the elevated imaging noise when the number of gate frames is doubled from conventional clinical studies. These training strategies are aimed at addressing the challenge of a lack of ground truth images in clinical acquisitions for use as the learning target by a DL network. In the experiments we investigated these strategies with a set of 237 clinical acquisitions. The results indicate that the trained DL network can effectively suppress both the spatial and temporal variability in the reconstructed myocardium in the gated frames. Moreover, surrogate training with gated data and noise-to-noise (N2N) training can yield better preservation of perfusion defects than with ungated data. © 2025 IEEE.

关键词： Image denoising

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Sound Tagging in Infant-centric Home Soundscapes

Sound Tagging in Infant-centric Home Soundscapes

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IEEE International Conference on Connected Health: Applications, Systems and engineering Technologies (CHASE)

作者： Mohammad Nur Hossain Khan Jialu Li Nancy L. McElwain Mark Hasegawa–Johnson Bashima Islam Department of Electrical and Computer Engineering Worcester Polytechnic Institute Department of Electrical and Computer Engineering University of Illinois Department of Human Development and Family Studies University of Illinois Beckman Institute for Advanced Science and Technology University of Illinois

ISBN: (数字)9798350345018

ISBN: (纸本)9798350345025

Certain environmental noises have been associated with negative developmental outcomes for infants and young children. Though classifying or tagging sound events in a domestic environment is an active research area, previous studies focused on data collected from a non-stationary microphone placed in the environment or from the perspective of adults. Further, many of these works ignore infants or young children in the environment or have data collected from only a single family where noise from the fixed sound source can be moderate at the infant's position or vice versa. Thus, despite the recent success of large pre-trained models for noise event detection, the performance of these models on infant-centric noise soundscapes in the home is yet to be explored. To bridge this gap, we have collected and labeled noises in home soundscapes from 22 families in an unobtrusive manner, where the data are collected through an infant-worn recording device. In this paper, we explore the performance of a large pre-trained model (Audio Spectrogram Transformer [AST]) on our noise-conditioned infant-centric environmental data as well as publicly available home environmental datasets. Utilizing different training strategies such as resampling, utilizing public datasets, mixing public and infant-centric training sets, and data augmentation using noise and masking, we evaluate the performance of a large pre-trained model on sparse and imbalanced infant-centric data. Our results show that fine-tuning the large pre-trained model by combining our collected dataset with public datasets increases the F1-score from 0.11 (public datasets) and 0.76 (collected datasets) to 0.84 (combined datasets) and Cohen's Kappa from 0.013 (public datasets) and 0.77 (collected datasets) to 0.83 (combined datasets) compared to only training with public or collected datasets, respectively.

关键词： Training Performance evaluation Pediatrics Working environment noise Noise Tagging Transformers

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Uncertainty Quantification for Recursive Estimation in Adaptive Safety-Critical Control

Uncertainty Quantification for Recursive Estimation in Adapt...

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American Control Conference (ACC)

作者： Max H. Cohen Makai Mann Kevin Leahy Calin Belta Department of Mechanical and Civil Engineering California Institute of Technology Pasadena CA Massachusetts Institute of Technology Lincoln Laboratory Lexington MA Department of Robotics Engineering Worcester Polytechnic Institute Worcester MA Department of Electrical and Computer Engineering University of Maryland College Park MD

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

In this paper, we present a framework for online parameter estimation and uncertainty quantification in the context of adaptive safety-critical control. The key insight enabling our approach is that the parameter estimate generated by the continuous-time recursive least squares (RLS) algorithm at any point in time is an affine transformation of the initial parameter estimate. This property allows for parameterizing such estimates using objects that are closed under affine transformation, such as zonotopes, and enables the efficient propagation of such set-based estimates as time progresses. We illustrate how such an approach facilitates the synthesis of safety-critical controllers for systems with parametric uncertainty and additive disturbances using control barrier functions, and demonstrate the utility of our approach through illustrative examples.

关键词： Uncertainty Parameter estimation Additives Control systems Recursive estimation

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Graph Coordinates and Conventional Neural Networks - An Alternative for Graph Neural Networks

Graph Coordinates and Conventional Neural Networks - An Alte...

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IEEE International Conference on Big Data

作者： Zheyi Qin Randy Paffenroth Anura P. Jayasumana Electrical and Computer Engineering Colorado State University Fort Collins USA Department of Data Science Program Worcester Polytechnic Institute Worcester MA USA

Graph-based data present unique challenges and opportunities for machine learning. Graph Neural Networks (GNNs), and especially those algorithms that capture graph topology through message passing for neighborhood aggregation, have been a leading solution. However, these networks often require substantial computational resources and may not optimally leverage the information contained in the graph’s topology, particularly for large-scale or complex *** propose Topology Coordinate Neural Network (TCNN) and Directional Virtual Coordinate Neural Network (DVCNN) as novel and efficient alternatives to message passing GNNs, that directly leverage the graph’s topology, sidestepping the computational challenges presented by competing algorithms. Our proposed methods can be viewed as a reprise of classic techniques for graph embedding for neural network feature engineering, but they are novel in that our embedding techniques leverage ideas in Graph Coordinates (GC) that are lacking in current *** results, benchmarked against the Open Graph Benchmark Leaderboard, demonstrate that TCNN and DVCNN achieve competitive or superior performance to message passing GNNs. For similar levels of accuracy and ROC-AUC, TCNN and DVCNN need far fewer trainable parameters than contenders of the OGBN Leaderboard. The proposed TCNN architecture requires fewer parameters than any neural network method currently listed in the OGBN Leaderboard for both OGBN-Proteins and OGBN-Products datasets. Conversely, our methods achieve higher performance for a similar number of trainable parameters. These results hold across diverse datasets and edge features, underscoring the robustness and generalizability of our methods. By providing an efficient and effective alternative to message passing GNNs, our work expands the toolbox of techniques for graph-based machine learning. A significantly lower number of tunable parameters for a given evaluation metric makes TCNN and DVCNN especiall

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A 0.18 μm CMOS Integrated Optical Wireless Power Transfer System for Implantable Biomedical Devices

A 0.18 μm CMOS Integrated Optical Wireless Power Transfer S...

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International Conference on Mixed Design of Integrated Circuits and Systems (MIXDES)

作者： Guido Di Patrizio Stanchieri Andrea De Marcellis Marco Faccio Elia Palange Ulkuhan Guler Department of Information Engineering Computer Science and Mathematics (DISIM) University of L’Aquila L’Aquila Italy Department of Electrical and Computer Engineering (ECE) Worcester Polytechnic Institute Worcester Massachusetts USA

This paper reports on a fully integrated optical wireless power transfer system designed for implantable biomedical devices. If compared with the solutions based on radiative and non-radiative methodologies, like the capacitive, inductive, magnetic couplings and radio frequency techniques, the optical systems reach the highest electromagnetic compatibility respect to the simultaneous presence of other electronic instrumentations. A further advantage common to all the radio frequencies, is the capability to transfer power to implantable and wearable biomedical devices also from long distances using environmental visible and near infrared light and/or LED-based lamps. The proposed solution has been designed at transistor level in TSMC 0.18 μm CMOS technology using inverter stages with the aim to maximize the output voltage value starting from low input voltages coming from a Si photodiode, also included in the overall layout of the system that requires a total Si area of about 0.466 mm 2 . By performing post-layout simulations, considering a minimum input voltage as low as 0.5 V, the achieved output voltage is higher than 2 V that increases up to 3.4 V for an input voltage of 0.7 V, with a best power conversion efficiency of about 70 %. The comparison of the main characteristics of the proposed solution with the State-of-Art demonstrates that, starting from the lower input voltages, the proposed system is capable to work with a reduced clock frequency achieving higher output voltages to supply external loads, satisfactory output currents and good power conversion efficiencies, requiring a reduced Si area.

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A Near Sensor Edge Computing System for Point Cloud Semantic Segmentation

arXiv

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arXiv 2022年

作者： Bai, Lin Zhao, Yiming Huang, Xinming Department of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA01609 United States

Point cloud semantic segmentation has attracted attentions due to its robustness to light condition. This makes it an ideal semantic solution for autonomous driving. However, considering the large computation burden and bandwidth demanding of neural networks, putting all the computing into vehicle Electronic Control Unit (ECU) is not efficient or practical. In this paper, we proposed a light weighted point cloud semantic segmentation network based on range view. Due to its simple pre-processing and standard convolution, it is efficient when running on deep learning accelerator like DPU. Furthermore, a near sensor computing system is built for autonomous vehicles. In this system, a FPGA-based deep learning accelerator core (DPU) is placed next to the LiDAR sensor, to perform point cloud pre-processing and segmentation neural network. By leaving only the post-processing step to ECU, this solution heavily alleviate the computation burden of ECU and consequently shortens the decision making and vehicles reaction latency. Our semantic segmentation network achieved 10 frame per second (fps) on Xilinx DPU with computation efficiency 42.5 GOP/W. Copyright © 2022, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Probabilistic Planning with Prioritized Preferences over Temporal Logic Objectives

arXiv

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arXiv 2023年

作者： Li, Lening Rahmani, Hazhar Fu, Jie The Department of Robotics Worcester Polytechnic Institute WorcesterMA01609 United States The Department of Electrical and Computer Engineering University of Florida GainesvilleFL32605 United States

This paper studies temporal planning in probabilistic environments, modeled as labeled Markov decision processes (MDPs), with user preferences over multiple temporal goals. Existing works reflect such preferences as a prioritized list of goals. This paper introduces a new specification language, termed prioritized qualitative choice linear temporal logic on finite traces, which augments linear temporal logic on finite traces with prioritized conjunction and ordered disjunction from prioritized qualitative choice logic. This language allows for succinctly specifying temporal objectives with corresponding preferences accomplishing each temporal task. The finite traces that describe the system's behaviors are ranked based on their dissatisfaction scores with respect to the formula. We propose a systematic translation from the new language to a weighted deterministic finite automaton. Utilizing this computational model, we formulate and solve a problem of computing an optimal policy that minimizes the expected score of dissatisfaction given user preferences. We demonstrate the efficacy and applicability of the logic and the algorithm on several case studies with detailed analyses for each. Copyright © 2023, The Authors. All rights reserved.

关键词： Specification languages

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