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22nd International Conference on Solid-State Sensors, Actuators and Microsystems, Transducers 2023

作者： Levy, Joshua A. Straker, Michael A. Beardslee, Luke A. Ghodssi, Reza University Of Maryland Department Of Materials Science And Engineering College ParkMD United States University Of Maryland Institute For Systems Research College ParkMD United States University Of Maryland Robert E. Fischell Institute For Biomedical Devices College ParkMD United States University Of Maryland Fischell Department Of Bioengineering College ParkMD United States University Of Maryland Department Of Electrical And Computer Engineering College ParkMD United States

ISBN: (纸本)9784886864352

This manuscript demonstrates the first cantilever actuator for highly targeted and directionally specific delivery of drug-eluting microneedles to the gastrointestinal (GI) tract from ingestible devices. The actuator achieves the following advantages over current technology: on-command deployment in under 500 ms, low power consumption, spatial conservation, and prolonged deployment shelf life. These advancements enable integration of multiple actuators per device for multidirectional deployment. These attributes are achieved by the optimization of heater design and use of low-profile and highly relaxation resistant PEEK polymers. In all, these features enable higher delivery precision in both longitudinal and polar directions, potentiating site targeting in the GI tract. © 2023 IEEJ.

关键词： Cantilever Actuator Drug Delivery Microneedles Stress Relaxation

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Computational optical sensor with a paper-based peptide panel assay for point-of-care testing of Lyme disease

Computational optical sensor with a paper-based peptide pane...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Joung, Hyou-Arm Ghosh, Rajesh Goncharov, Artem Ngo, Kevin Palanisamy, Barath Horn, Elizabeth J. Arnaboldi, Paul M. Dattwyler, Raymond J. Garner, Omai B. Di Carlo, Dino Ozcan, Aydogan Department of Electrical and Computer Engineering University of California Los Angeles United States California NanoSystems Institute University of California Los Angeles United States Department of Bioengineering University of California Los Angeles United States Department of Pathology and Medicine University of California Los Angeles United States Lyme Disease Biobank PortlandOR United States Department of Microbiology/Immunology New York Medical College ValhallaNY United States

We present a computational paper-based optical sensor for point-of-care serodiagnosis of Lyme disease. Using blindly tested human serum samples, we achieved 91.7% sensitivity and 100% specificity against two-tier sero... 详细信息

ISBN: (纸本)9781957171258

关键词： Optical sensors

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A Test Statistic Estimation-based Approach for Establishing Self-interpretable CNN-based Binary Classifiers

arXiv

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arXiv 2023年

作者： Sengupta, Sourya Anastasio, Mark A. the Department of Electrical and Computer Engineering University of Illinois Urbana–Champaign UrbanaIL61801 United States the Department of Bioengineering University of Illinois Urbana–Champaign UrbanaIL61801 United States

Interpretability is highly desired for deep neural network-based classifiers, especially when addressing high-stake decisions in medical imaging. Commonly used post-hoc interpretability methods have the limitation that they can produce plausible but different interpretations of a given model, leading to ambiguity about which one to choose. To address this problem, a novel decision-theory-inspired approach is investigated to establish a self-interpretable model, given a pre-trained deep binary black-box medical image classifier. This approach involves utilizing a self-interpretable encoder-decoder model in conjunction with a single-layer fully connected network with unity weights. The model is trained to estimate the test statistic of the given trained black-box deep binary classifier to maintain a similar accuracy. The decoder output image, referred to as an equivalency map, is an image that represents a transformed version of the to-be-classified image that, when processed by the fixed fully connected layer, produces the same test statistic value as the original classifier. The equivalency map provides a visualization of the transformed image features that directly contribute to the test statistic value and, moreover, permits quantification of their relative contributions. Unlike the traditional post-hoc interpretability methods, the proposed method is self-interpretable, quantitative. Detailed quantitative and qualitative analyses have been performed with three different medical image binary classification tasks. © 2023, CC BY.

关键词： Decision theory

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Fluorescence-based multiplexed point-of-care sensor using deep learning

Fluorescence-based multiplexed point-of-care sensor using de...

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Bio-Optics: Design and Application, BODA 2023 - Part of Optica Biophotonics Congress: Optics in the Life Sciences 2023

作者： Goncharov, Artem Joung, Hyou-Arm Ghosh, Rajesh Han, Gyeo-Re Ballard, Zachary S. Maloney, Quinn Bell, Alexandra Aung, Chew Tin Zar Garner, Omai B. Di Carlo, Dino Ozcan, Aydogan Electrical & Computer Engineering Department University of California Los AngelesCA90095 United States Bioengineering Department University of California Los AngelesCA90095 United States University of California Los AngelesCA90095 United States University of California Los AngelesCA90095 United States Department of Pathology and Laboratory Medicine University of California Los AngelesCA90095 United States

We present a multiplexed paper-based point-of-care sensor processed by a hand-held fluorescence reader and neural networks that quantify three cardiovascular disease biomarkers from human serum samples, achieving a li... 详细信息

ISBN: (纸本)9781957171210

关键词： Fluorescence

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Image Denoising Using Diffractive Optical Processors

Image Denoising Using Diffractive Optical Processors

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Conference on Lasers and Electro-Optics (CLEO)

作者： Çağatay Işıl Tianyi Can F. Onuralp Ardic Koray Mentesoglu Jagrit Digani Huseyin Karaca Hanlong Chen Jingxi Li Deniz Mengu Mona Jarrahi Kaan Akşit Aydogan Ozcan Electrical and Computer Engineering Department University of California Los Angeles CA USA Bioengineering Department University of California Los Angeles CA USA California NanoSystems Institute (CNSI) University of California Los Angeles CA USA Department of Computer Science University College London London United Kingdom

We report an image denoising analog processor composed of passive diffractive layers engineered through deep learning to filter out various types of noise from input images, instantly projecting denoised images at the... 详细信息

ISBN: (纸本)9798350369311

关键词： Optical filters Deep learning Optical diffraction Laser noise Lasers and electrooptics Optical imaging Optical noise Electro-optical waveguides Image denoising

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Fabric-substrated capacitive biopotential sensors enhanced by dielectric nanoparticles

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Nano Research 2021年第9期14卷 3248-3252页

作者： Xiangjun Chen Xiaoxiang Gao Akihiro Nomoto Keren Shi Muyang Lin Hongjie Hu Yue Gu Yangzhi Zhu Zhuohong Wu Xue Chen Xinyu Wang Baiyan Qi Sai Zhou Hong Ding Sheng Xu Materials Science and Engineering Program University of California San DiegoLa JollaCA 92093USA Department of NanoEngineering University of California San DiegoLa JollaCA 92093USA Department of Electrical and Computer Engineering University of California San DiegoLa JollaCA 92093USA Department of Bioengineering University of California San DiegoLa JollaCA 92093USA

Wearable biopotential sensing devices are essential to long-term and real-time monitoring of human ***-contact,capacitive sensing electrodes prevent potential skin iritations,and are thus beneficial for long-term *** capacitive electrodes are either connected to a separate control circuit via external wires or have limited sensing capacitances,which leads to low signal *** study demonstrates a stretchable capacitive sensing device with integrated electrodes and control electronics,with enhanced signal *** electrodes and the control electronics are fabricated on a common fabric substrate for breathability and strain-limiting *** stretchable electrodes are based on an island-bridge design with a stretchability as high as-100%,and an area ratio as high as-80%.By using a dielectric calcium copper titanate(CCTO)composite as the adhesive layer,the electrode capacitance can be increased,yielding an enhanced signal-to-noise ratio(SNR)in the acquired *** device offers a convenient and comfortable approach for long-term non-contact monitoring of biopotential signals.

关键词： capacitive sensing biopotential stretchable calcium copper titanate(CCTO) signal-to-noise ratio(SNR)

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Cadmium Zinc Telluride (CZT)-Based PET Scanner for Plant Rhizosphere Imaging

Cadmium Zinc Telluride (CZT)-Based PET Scanner for Plant Rhi...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： M.N. Ullah A. Groll D. Innes S. Abbaszadeh C. S. Levin Radiology Stanford University Stanford California United States of America Department of Electrical and Computer Engineering University of California Santa Cruz United States of America Electrical Engineering Bioengineering Physics Stanford University Stanford California United States of America

The soil interacts with the plant’s roots in the rhizosphere, altering its physical, chemical, and biological processes. Understanding and simulating the behavior of the soil-root interface (Rhizosphere) will help us understand the limitations of the existing Earth System Model (ESM), which make it difficult to predict climate change with sufficient accuracy under different conditions. We are currently developing a cadmium zinc telluride (CZT) based high resolution positron emission tomography (PET) scanner for plant rhizosphere imaging. In this paper, we report the preliminary imaging performance of a two panel CZT-PET scanner. In this phantom study, the roots of the plant were represented by a custom manufactured container that houses 5-capillary tubes with inner diameter ~1.3 mm placed at various distances from the center. Two cases were investigated. 1), F-18 filled capillaries in air with no soil; 2) F-18 filled capillaries with organic soil present to simulate the root system of a plant. All 5 capillary rods could be resolved in both cases with 2.04 ± 0.18 mm and 2.29 ± 0.24 mm FWHM resolution measured from a line profile taken through the capillaries in the tomographic images in the x and y directions (i.e., x and y form a plane orthogonal to the capillary tube direction). Deconvolving the X.Y mm capillary inner diameter yields 1.85 ±0.29 and 1.57±-.23 mm FWHM, respectively, indicating excellent resolution required for resolving biological information in individual root structures. In our next experiment, this two-panel system will be utilized to collect images of a real plant rhizosphere.

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Recurrent neural network-based volumetric fluorescence microscopy

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Light(Science & Applications) 2021年第4期10卷 620-635页

作者： Luzhe Huang Hanlong Chen Yilin Luo Yair Rivenson Aydogan Ozcan Electrical and Computer Engineering Department University of CaliforniaLos AngelesCA 90095USA Bioengineering Department University of CaliforniaLos AngelesCA 90095USA California Nano Systems Institute(CNSI) University of CaliforniaLos AngelesCA 90095USA David Geffen School of Medicine University of CaliforniaLos AngelesCA 90095USA

Volumetric imaging of samples using fluorescence microscopy plays an important role in various fields including physical,medical and life *** we report a deep learning-based volumetric image inference framework that uses 2D images that are sparsely captured by a standard wide-field fluorescence microscope at arbitrary axial positions within the sample *** a recurrent convolutional neural network,which we term as Recurrent-MZ,2D fluorescence information from a few axial planes within the sample is explicitly incorporated to digitally reconstruct the sample volume over an extended *** experiments on *** and nanobead samples,Recurrent-MZ is demonstrated to significantly increase the depth-of-field of a 63×/1.4NA objective lens,also providing a 30-fold reduction in the number of axial scans required to image the same sample *** further illustrated the generalization of this recurrent network for 3D imaging by showing its resilience to varying imaging conditions,including e.g.,different sequences of input images,covering various axial permutations and unknown axial positioning *** also demonstrated wide-field to confocal cross-modality image transformations using Recurrent-MZ framework and performed 3D image reconstruction of a sample using a few wide-field 2D fluorescence images as input,matching confocal microscopy images of the same sample ***-MZ demonstrates the first application of recurrent neural networks in microscopic image reconstruction and provides a flexible and rapid volumetric imaging framework,overcoming the limitations of current 3D scanning microscopy tools.

关键词： neural network networks

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Learnable Data Augmentation for One-Shot Unsupervised Domain Adaptation

arXiv

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arXiv 2023年

作者： Carrazco, Julio Ivan Davila Morerio, Pietro Del Bue, Alessio Murino, Vittorio Italian Institute of Technology Genoa Italy Department of Computer Science University of Verona Verona Italy Department of Computer Science and Technology Bioengineering Robotics and Systems Engineering University of Genova Genoa Italy Department of Marine Electrical Electronic and Telecommunications Engineering Robotics and Systems Engineering University of Genova Genoa Italy

This paper presents a classification framework based on learnable data augmentation to tackle the One-Shot Unsupervised Domain Adaptation (OS-UDA) problem. OS-UDA is the most challenging setting in Domain Adaptation, as only one single unlabeled target sample is assumed to be available for model adaptation. Driven by such single sample, our method LearnAug-UDA learns how to augment source data, making it perceptually similar to the target. As a result, a classifier trained on such augmented data will generalize well for the target domain. To achieve this, we designed an encoder-decoder architecture that exploits a perceptual loss and style transfer strategies to augment the source data. Our method achieves state-of-the-art performance on two well-known Domain Adaptation benchmarks, DomainNet and VisDA. The project code is available at https://***/IIT-PAVIS/LearnAug-UDA. © 2023, CC BY.

关键词： Benchmarking

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Optical Generative Models

arXiv

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arXiv 2024年

作者： Chen, Shiqi Li, Yuhang Chen, Hanlong Ozcan, Aydogan Electrical and Computer Engineering Department University of California Los AngelesCA90095 United States Bioengineering Department University of California Los AngelesCA90095 United States University of California Los AngelesCA90095 United States

Generative models cover various application areas, including image, video and music synthesis, natural language processing, and molecular design, among many others. As digital generative models become larger, scalable inference in a fast and energy-efficient manner becomes a challenge. Here, we present optical generative models inspired by diffusion models, where a shallow and fast digital encoder first maps random noise into phase patterns that serve as optical generative seeds for a desired data distribution;a jointly-trained free-space-based reconfigurable decoder all-optically processes these generative seeds to create novel images (never seen before) following the target data distribution. Except for the illumination power and the random seed generation through a shallow encoder, these optical generative models do not consume computing power during the synthesis of novel images. We report the optical generation of monochrome and multicolor novel images of handwritten digits, fashion products, butterflies, and human faces, following the data distributions of MNIST, Fashion MNIST, Butterflies-100, and Celeb-A datasets, respectively, achieving an overall performance comparable to digital neural network-based generative models. To experimentally demonstrate optical generative models, we used visible light to generate, in a snapshot, novel images of handwritten digits and fashion products. These optical generative models might pave the way for energy-efficient, scalable and rapid inference tasks, further exploiting the potentials of optics and photonics for artificial intelligence-generated content. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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