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

作者： El-Den, Niveen Nasr Elsharkawy, Mohamed Ghazal, Mohammed Mahmoud, Ali Sandhu, Harpal Mahdi, Hani El-Baz, Ayman Department of Computer and System Engineering Ain Shams University Egypt Department of Bioengineering University of Louisville LouisvilleKY United States Electrical Computer and Biomedical Engineering Department Abu Dhabi University Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350368741

Accurate early diagnosis is crucial in addressing Age-related Macular Degeneration (AMD), a chronic retinal disease that is a leading cause of blindness among the elderly. Medical imaging, particularly fundus imaging, is essential in facilitating timely detection and intervention. Due to the variability in image sizes within our dataset, this paper introduces the SABiT-MNet model, which effectively discriminates between healthy retinas, dry AMD, and wet AMD. The model integrates a novel scale-adaptive (SA) approach by combining an autoencoder with Big Transfer (BiT) as its backbone. Unlike traditional resizing methods, which often result in the loss of critical diagnostic information, the SA model dynamically adjusts to varying image sizes, preserving key retinal features essential for accurate diagnosis. The primary aim of this architecture is to retain crucial details in fundus images to ensure precise classification. In this study, 648 subjects were recruited through the Comparisons of AMD Treatments Trials study group, sponsored by the University of Pennsylvania. Experimental results demonstrate that the proposed SABiT-MNet model outperforms state-of-the-art approaches, including transformer-based models, achieving superior diagnostic accuracy. The model recorded performance metrics of 94% accuracy, 97% sensitivity, and 93.94% specificity. To further validate the robustness of the system, we tested it on the public ODiR dataset, where it achieved similarly promising results, confirming the effectiveness of our approach. © 2025 IEEE.

关键词： AMD Deep Learning fundus photography

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Stereolithography 3D printing of an accelerometer embedded nightguard for TMD monitoring 12

Stereolithography 3D printing of an accelerometer embedded n...

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Laser 3D Manufacturing XII 2025

作者： McLaughlin, Elizabeth M. Gerding, Maya Wu, Yongji Xiao, Hai Yao, Hai Department of Bioengineering Clemson University ClemsonSC29631 United States Photonics Technology Laboratory Holcombe Department of Electrical and Computer Engineering Clemson University AndersonSC29625 United States

ISBN: (纸本)9781510684560

There are currently no effective take-home jaw motion tracking devices to monitor the symptoms in temporomandibular joint disorder (TMD). Existing methods consist of bulky lab equipment that lack a standard for TMD diagnosis. These devices are unable to provide critical data that occurs within a patient’s natural day-to-day life which can lead to inadequate management of TMD. This study presents the fabrication techniques explored to create a take-home wearable device capable of detecting jaw movements to aid in remote patient monitoring of TMD symptoms. The nightguard prototypes are 3D modeled using denture model STL files and printed in various dental and biomedical resins on a Form 3B+ stereolithography 3D printer by Formlabs. Embedded accelerometers are used to quantify jaw movements, and a CAD-modeled, 3D-printed denture model is used to simulate these movements. Extensive testing is conducted throughout the fabrication process to ensure accuracy and reliability. This proof-of-concept study aims to establish the feasibility of the device for future clinical trials, where AI integration for data analysis and further optimization of manufacturing methods will be explored to validate the device’s efficacy and user requirements. The potential impact of this device includes improved TMD management through continuous, at-home monitoring, leading to better patient outcomes. Future work will focus on refining the design and expanding the testing to a larger cohort to further validate the device’s performance and reliability. © 2025 SPIE.

关键词： Stereolithography

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Infants Sucking Pattern Identification Using Machine-Learned Computational Modeling

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Journal of engineering and Science in Medical Diagnostics and Therapy 2025年第3期8卷 031003页

作者： Olapojoye, Abdullahi Singh, Abhishek Nishi, Eri Fei, Baowei Nostratinia, Aria Hassanipour, Fatemeh Department of Mechanical Engineering University of Texas at Dallas RichardsonTX75080 United States Department of Electrical Engineering Setsunan University Osaka Prefecture Neyagawa572-8508 Japan Department of Bioengineering University of Texas at Dallas RichardsonTX75080 United States Department of Radiology UT Southwestern Medical Center DallasTX75390 United States Department of Electrical and Computer Engineering University of Texas at Dallas RichardsonTX75080 United States

Breastfeeding involves a complex coordination of swallowing, breathing, and sucking, with the infant's sucking proficiency being crucial for adequate nutrient intake. However, real-time assessment of milk intake is difficult, and issues with sucking often become apparent after the infant shows signs of nutrient deficiency. Traditional assessments by clinicians rely on the expertise and subjective judgment of healthcare professionals, enabling personalized evaluations. In this research, we introduce a novel approach to identifying sucking patterns by leveraging data collected from infants during breastfeeding sessions. This method utilizes artificial nipple-based sensors to capture the tongue forces exerted by infants, generating valuable clinical data. In the analysis of the collected time-series data, we applied machine-learned computational modeling (MLCM) algorithms to extract pertinent features and identify distinctive sucking patterns. The best-performing model demonstrated an accuracy of 90%, an 80% recall score, a perfect 100% precision score, a 0.90 f1-score, and an area under the curve (AUC) of 0.80. The proposed classification system has the potential to serve as a reliable decision-support tool for clinicians, offering valuable insights into infants' sucking behaviors. By integrating machine learning (ML)-based computational modeling into clinical practice, we aim to enhance the early identification of unhealthy sucking patterns, allowing for timely interventions and pro-active healthcare management. © 2025 by ASME.

关键词： Diagnosis

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Information-Theoretic Design for High-Dimensional Computational Imaging

Information-Theoretic Design for High-Dimensional Computatio...

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Computational Optical Sensing and Imaging, COSI 2024 - Part of Optica Imaging Congress

作者： Markley, Eric Kabuli, Leyla Chien, Tiffany Pinkard, Henry Waller, Laura UCB/UCSF Joint Graduate Program in Bioengineering University of California Berkeley United States Department of Electrical Engineering and Computer Sciences University of California Berkeley United States

We present a technique for information-theoretic optimization of computational imaging systems demonstrated in snapshot 3D microscopy. By directly evaluating measurement quality and decoupling optimization from downst... 详细信息

关键词： Decoding

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SpikeNAS-Bench: Benchmarking NAS Algorithms for Spiking Neural Network Architecture

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第6期6卷 1614-1625页

作者： Sun, Genchen Liu, Zhengkun Gan, Lin Su, Hang Li, Ting Zhao, Wenfeng Sun, Biao Tianjin University School of Electrical and Information Engineering Tianjin300072 China Northwestern Polytechnical University School of Automation Xi'an710072 China Politecnico di Milano Department of Electronics Information and Bioengineering Milan20133 Italy Chinese Academy of Medical Science Institute of Biomedical Engineering Pecking Union Medical College Tianjin300020 China Binghamton University Department of Electrical and Computer Engineering State University of New York BinghamtonNY United States

In recent years, Neural Architecture Search (NAS) has marked significant advancements, yet its efficacy is marred by the dependence on substantial computational resources. To mitigate this, the development of NAS benchmarks has emerged, offering datasets that enumerate all potential network architectures and their performances within a predefined search space. Nonetheless, these benchmarks predominantly focus on convolutional architectures, which are criticized for their limited interpretability and suboptimal hardware efficiency. Recognizing the untapped potential of Spiking Neural Networks (SNNs) - often hailed as the third generation of neural networks for their biological realism and computational thrift - this study introduces SpikeNAS-Bench. As a pioneering benchmark for SNN, SpikeNAS-Bench utilizes a cell-based search space, integrating leaky integrate-and-fire (LIF) neurons with variable thresholds as candidate operations. It encompasses 15,625 candidate architectures, rigorously evaluated on CIFAR10, CIFAR100 and Tiny-ImageNet datasets. This paper delves into the architectural nuances of SpikeNAS-Bench, leveraging various criteria to underscore the benchmark's utility and presenting insights that could steer future NAS algorithm designs. Moreover, we assess the benchmark's consistency through three distinct proxy types: zero-cost-based, early-stop-based, and predictor-based proxies. Additionally, the paper benchmarks seven contemporary NAS algorithms to attest to SpikeNAS-Bench's broad applicability. We commit to providing training logs, diagnostic data for all candidate architectures, and the promise to release all code and datasets post-acceptance, aiming to catalyze further exploration and innovation within the SNN domain. SpikeNAS-Bench is open source at https://***/XXX (hidden for double anonymous review). © 2020 IEEE.

关键词： HTTP

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Speckle Structured Illumination of Dynamic Samples with a Neural Space-time Model

Speckle Structured Illumination of Dynamic Samples with a Ne...

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2023 Computational Optical Sensing and Imaging, COSI 2023 in Optica Imaging Congress - Part of Imaging and Applied Optics Congress 2023

作者： Cao, Ruiming Meng, Guanghan Waller, Laura Department of Bioengineering University of California BerkeleyCA United States Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA United States

We propose a structured illumination microscopy (SIM) method, Speckle Flow SIM, that uses static speckle illumination with a dynamic sample and reconstructs super-resolved images via a neural space-time model, without... 详细信息

ISBN: (纸本)9781957171289

关键词： Speckle

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Analysis of a Broadband Metamaterial Absorber at Terahertz Frequencies 6

Analysis of a Broadband Metamaterial Absorber at Terahertz F...

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6th International Conference on electrical Information and Communication Technology, EICT 2023

作者： Jyoti, Oishi Sarkar, Pritu Parna Habib, Md. Samiul Rajshahi University of Engineering & Technology Department of Electrical & Computer Engineering Rajshahi Bangladesh University of Texas Rio Grande Valley Department of Bioengineering & Biomedical Engineering EdinburgTX United States Rajshahi University of Engineering & Technology Department of Electrical & Electronic Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350370836

In this paper, we present a vanadium dioxide (VO2) based metamaterial absorber at terahertz (THz) frequencies, achieving near unity absorption from 3.5 THz to 5 THz through full vector numerical simulation which refers to a computational method that considers complete vector nature of electromagnetic fields in three dimensions. Our proposed absorber is based on a single layered elliptic shaped ring resonator, which provides more design freedom than the existing absorbers and is easier to fabricate. Our numerical simulations show that modifying the conductivity of VO2 significantly enhances the absorption peak, and reaches near-perfect absorption when the conductivity of VO2 reaches the metallic phase. We illustrate that for a specific conductivity of the metal layer, the absorption peak of the absorber can be controlled by adjusting the width of the ring, the ellipticity of the ring, and the thickness of the dielectric. We also explain the physical mechanism and origin of broadband absorption through impedance matching theory. We believe that our proposed absorber may find applications in sensing and imaging technology. © 2023 IEEE.

关键词： Vanadium dioxide

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Control Strategies for Electric Vehicle Drives: FOC Implementation with MTPA and Field Weakening 15

Control Strategies for Electric Vehicle Drives: FOC Implemen...

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15th International Conference Elektro, ELEKTRO 2024

作者： Sadaf, Majda Farooq, Adil Ullah, Zahid Ahsan, Muhammad School of Electrical and Electronic Engineering Newcastle University United Kingdom Maynooth University Department of Electronic Engineering Ireland Politecnico di Milano Department of Electronics Information and Bioengineering Italy University of Alabama at Birmingham Department of Computer Science United States

ISBN: (纸本)9798350372359

The reliability and performance of Electric vehicles (EVs) rely on effective control of electric drives, thereby improving the overall driving experience. To efficiently control the electric drive, this paper implemented a Field Oriented Control (FOC) scheme for controlling a permanent magnet synchronous motor (PMSM) drive designed for EVs. The FOC scheme is integrated with Maximum Torque per Ampere (MTPA) and field weakening control strategies to broaden the speed range of the drive at rated power density and maximum torque. The entire FOC control scheme and MTPA control reference function block are modeled using MATLAB/SIMULINK. The paper comprehensively reviews the mathematical equations governing FOC, field weakening control, and MTPA methods. The research also incorporates simulation results obtained under various driving conditions for EVs, demonstrating the effectiveness of the implemented control strategies. © 2024 IEEE.

关键词： Vector control (Electric machinery)

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Microdevice for directional axodendritic connectivity between micro 3D neuronal cultures

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Microsystems & Nanoengineering 2021年第5期7卷 227-239页

作者： Yixuan Ming Md Joynal Abedin Svetlana Tatic-Lucic Yevgeny Berdichevsky Department of Electrical&Computer Engineering Lehigh UniversityBethlehemPAUSA Department of Bioengineering Lehigh UniversityBethlehemPAUSA

Neuronal cultures are widely used in neuroscience ***,the randomness of circuits in conventional cultures prevents accurate in vitro modeling of cortical development and of the pathogenesis of neurological and psychiatric disorders.A basic feature of cortical circuits that is not captured in standard cultures of dissociated cortical cells is directional *** this work,a polydimethylsiloxane(PDMS)-based device that achieves directional connectivity between micro 3D cultures is *** device consists of through-holes for micro three-dimensional(n3D)clusters of cortical cells connected by microtrenches for axon and dendrite *** design of the trenches relies in part on the concept of axonal edge guidance,as well as on the novel concept of specific dendrite *** replicates dominant excitatory connectivity in the cortex,enables the guidance of the axon after it forms a synapse in passing(an"en passant"synapse),and ensures that directional selectivity is preserved over the lifetime of the *** directionality of connections was verified morphologically and *** were dependent on glutamatergic *** design of this device has the potential to serve as a building block for the reconstruction of more complex cortical circuits in vitro.

关键词： connectivity directional lifetime

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Learning cortico-muscular dependence through orthonormal decomposition of density ratios 24

Learning cortico-muscular dependence through orthonormal dec...

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

作者： Shihan Ma Bo Hu Tianyu Jia Alexander Kenneth Clarke Blanka Zicher Arnault H. Caillet Dario Farina José C. Principe Department of Bioengineering Imperial College London Department of Electrical and Computer Engineering University of Florida

ISBN: (纸本)9798331314385

The cortico-spinal neural pathway is fundamental for motor control and movement execution, and in humans it is typically studied using concurrent electroencephalography (EEG) and electromyography (EMG) recordings. However, current approaches for capturing high-level and contextual connectivity between these recordings have important limitations. Here, we present a novel application of statistical dependence estimators based on orthonormal decomposition of density ratios to model the relationship between cortical and muscle oscillations. Our method extends from traditional scalar-valued measures by learning eigenvalues, eigenfunctions, and projection spaces of density ratios from realizations of the signal, addressing the interpretability, scalability, and local temporal dependence of cortico-muscular connectivity. We experimentally demonstrate that eigenfunctions learned from cortico-muscular connectivity can accurately classify movements and subjects. Moreover, they reveal channel and temporal dependencies that confirm the activation of specific EEG channels during movement. Our code is available at https://***/bohu615/corticomuscular-eigen-encoder.

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