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arXiv 2024年

作者： Jang, Hyun-June Joung, Hyou-Arm Goncharov, Artem Kanegusuku, Anastasia Gant Chan, Clarence W. Yeo, Kiang-Teck Jerry Zhuang, Wen Ozcan, Aydogan Chen, Junhong Pritzker School of Molecular Engineering University of Chicago ChicagoIL60637 United States Chemical Sciences and Engineering Division Physical Sciences and Engineering Directorate Argonne National Laboratory LemontIL60439 United States Department of Electrical and Computer Engineering University of California Los AngelesCA90095 United States Department of Pathology Laboratory Medicine Loyola University Medical Center MaywoodIL60153 United States Department of Pathology The University of Chicago ChicagoIL60637 United States Pritzker School of Medicine The University of Chicago ChicagoIL60637 United States Department of Bioengineering University of California Los AngelesCA90095 United States University of California Los AngelesCA90095 United States Department of Surgery David Geffen School of Medicine University of California Los AngelesCA90095 United States

This study explores the fusion of a field-effect transistor (FET), a paper-based analytical cartridge, and the computational power of deep learning (DL) for quantitative biosensing via kinetic analyses. The FET sensors address the low sensitivity challenge observed in paper analytical devices, enabling electrical measurements with kinetic data. The paper-based cartridge eliminates the need for surface chemistry required in FET sensors, ensuring economical operation (cost 2 value of > 0.976 for cholesterol testing when blindly compared to results obtained from a CLIA-certified clinical laboratory. These integrated technologies can create a new generation of FET-based biosensors, potentially transforming point-of-care diagnostics and at-home testing through enhanced accessibility, ease-of-use, and accuracy. © 2024, CC BY.

关键词： Cholesterol

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Spatially-varying microscope calibration from unstructured sparse inputs

Spatially-varying microscope calibration from unstructured s...

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Computational Optical Sensing and Imaging, COSI 2020 - Part of Imaging and Applied Optics Congress 2020

作者： Kuo, Grace Monakhova, Kristina Yanny, Kyrollos Ng, Ren Waller, Laura Department of Electrical Engineering and Computer Sciences University of California Berkeley BerkeleyCA94720 United States UCB/UCSF Joint Graduate Program in Bioengineering University of California Berkeley BerkeleyCA94720 United States

We propose a method based on blind deconvolution to calibrate the spatially-varying point spread functions of a coded-aperture microscope system. From easy-to-acquire measurements of unstructured fluorescent beads, we... 详细信息

ISBN: (纸本)9781557528209

关键词： Optical transfer function

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Neuromorphic Deep Spiking Neural Networks for Seizure Detection

TechRxiv

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TechRxiv 2022年

作者： Yang, Yikai Eshraghian, Jason K. Truong, Nhan Duy Nikpour, Armin Kavehei, Omid The School of Biomedical Engineering The Australian Research Council Training Centre for Innovative BioEngineering Faculty of Engineering The University of Sydney NSW2006 Australia The Department of Electrical and Computer Engineering University of California Santa Cruz Santa CruzCA95064 United States The Comprehensive Epilepsy Services Department of Neurology The Royal Prince Alfred Hospital NSW2050 Australia The Faculty of Medicine and Health The University of Sydney NSW2006 Australia

The vast majority of studies that process and analyze neural signals are conducted on cloud computing resources, which is often necessary for the demanding requirements of Deep Neural Network (DNN) workloads. However, applications such as epileptic seizure detection stand to benefit from edge devices that can securely analyze sensitive medical data in real-time and personalised manner. In this work, we propose a novel neuromorphic computing approach to seizure detection using a surrogate gradient-based deep Spiking Neural Network (SNN), which consists of a novel Spiking ConvLSTM unit (SPCLU). We have trained, validated, and rigorously tested the proposed SNN model across three publicly accessible datasets, including Boston Children’s Hospital–MIT (CHB-MIT) dataset from the U.S., and the Freiburg (FB) and EPILEPSIAE intracranial EEG (iEEG) datasets from Germany. The average leave-one-out cross-validation AUC score for FB, CHB-MIT, and EPILEPSIAE datasets can reach 92.7%, 89.0%, and 81.1%, respectively, while the computational overhead and energy consumption are significantly reduced when compared to alternative state-of-the-art models, showing the potential for building an accurate hardware-friendly, low-power neuromorphic system. This is the first feasibility study using a deep Spiking Neural Network for seizure detection on several reliable public datasets. © 2022, CC BY-NC-SA.

关键词： Energy utilization

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electrical stimulation in the cochlea: Influence of modiolar microstructures on the activation of auditory nerve fibres

Electrical stimulation in the cochlea: Influence of modiolar...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Siwei Bai Albert Croner Jörg Encke Werner Hemmert Department of Electrical and Computer Engineering and Munich School of BioEngineering Technical University of Munich Garching Germany

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

Existing computational studies of cochlear implants have demonstrated that the structural detail of three-dimensional (3D) cochlear models exerts influence on the current spread within the cochlea. Nevertheless, the significance of including the microstructures inside the modiolar bone in a cochlear model is still unclear in the literature. We employed two different multi-compartment neuron models to simulate auditory nerve fibres, and compared response characteristics of the fibre population between a detailed and a simplified 3D cochlear model. Results showed that although the prediction of firing is dependent on the details of the neuron model, the responses of the fibre population to the electrical stimulus, especially the location of the initiation of action potential, varied between the detailed and the simplified models. Therefore, the inclusion of the modiolar microstructures in a cochlear model may be necessary for fully understanding the firing of auditory nerve fibres.

关键词： Computational modeling Biological system modeling Solid modeling Electrodes Neurons Sociology Statistics

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Haptic Guidance and Haptic Error Amplification in a Virtual Surgical Robotic Training Environment

arXiv

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arXiv 2023年

作者： Oquendo, Yousi A. Coad, Margaret M. Wren, Sherry M. Lendvay, Thomas S. Nisky, Ilana Jarc, Anthony M. Okamura, Allison M. Chua, Zonghe The Hospital for Special Surgery New YorkNY United States The Department of Aerospace and Mechanical Engineering The University of Notre Dame South BendIN United States The Department of Surgery The School of Medicine Stanford University StanfordCA United States The Department of Urology The School of Medicine The University of Washington SeattleWA United States The Department of Bioengineering The School of Brain Sciences and Cognition Ben-Gurion University of the Negev Israel Intuitive Surgical Inc. SunnyvaleCA United States The Department of Mechanical Engineering Stanford University StanfordCA United States The Department of Electrical Computer and Systems Engineering Case Western Reserve University ClevelandOH United States

Teleoperated robotic systems have introduced more intuitive control for minimally invasive surgery, but the optimal method for training remains unknown. Recent motor learning studies have demonstrated that exaggeration of errors helps trainees learn to perform tasks with greater speed and accuracy. We hypothesized that training in a force field that pushes the operator away from a desired path would improve their performance on a virtual reality ring-on-wire task. Forty surgical novices trained under a no-force, guidance, or error-amplifying force field over five days. Completion time, translational and rotational path error, and combined error-time were evaluated under no force field on the final day. The groups significantly differed in combined error-time, with the guidance group performing the worst. Error-amplifying field participants showed the most improvement and did not plateau in their performance during training, suggesting that learning was still ongoing. Guidance field participants had the worst performance on the final day, confirming the guidance hypothesis. Participants with high initial path error benefited more from guidance. Participants with high initial combined error-time benefited more from guidance and error-amplifying force field training. Our results suggest that error-amplifying and error-reducing haptic training for robot-assisted telesurgery benefits trainees of different abilities differently. Copyright © 2023, The Authors. All rights reserved.

关键词： Errors

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High-level synthesis design of scalable ultrafast ultrasound beamformer with single FPGA

arXiv

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arXiv 2022年

作者： Kou, Zhengchang You, Qi Kim, Jihun Dong, Zhijie Lowerison, Matthew R. Chandra Sekaran, Nathiya V. Llano, Daniel A. Song, Pengfei Oelze, Michael L. The Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign UrbanaIL61820 United States The Department of Electrical and Computer Engineering United States The Carle Illinois College of Medicine United States The Department of Bioengineering United States The Department of Molecular and Integrative Physiology University of Illinois Urbana-Champaign UrbanaIL61801 United States The Division of ICT Convergence Engineering/Major in Electronic Engineering Kangnam University Korea Republic of

Ultrafast ultrasound imaging is essential for advanced ultrasound imaging techniques such as ultrasound localization microscopy (ULM) and functional ultrasound (fUS). Current ultrafast ultrasound imaging is challenged by the ultrahigh data bandwidth associated with the radio frequency (RF) signal, and by the latency of the computationally expensive beamforming process. As such, continuous ultrafast data acquisition and beamforming remain elusive with existing software beamformers based on CPUs or GPUs. To address these challenges, the proposed work introduces a novel method of implementing an ultrafast ultrasound beamformer specifically for ultrafast plane wave imaging (PWI) on a field programmable gate array (FPGA) by using high-level synthesis. A parallelized implementation of the beamformer on a single FPGA was proposed by 1) utilizing a delay compression technique to reduce the delay profile size, which enables both run-time pre-calculated delay profile loading from external memory and delay reuse 2) vectorizing channel data fetching which is enabled by delay reuse, and 3) using fixed summing networks to reduce consumption of logic resources. Our proposed method presents two unique advantages over current FPGA beamformers: 1) high scalability that allows fast adaptation to different FPGA resources and beamforming speed demands by using Xilinx High-Level Synthesis as the development tool, and 2) allow a compact form factor design by using a single FPGA to complete the beamforming instead of multiple FPGAs. Current Xilinx FPGA provides the capabilities of connecting up to 1024 ultrasound channels with a single FPGA and the newest JESD204B interface analog front end (AFE). This channel count is much more than the channel count needed by current linear arrays, which normally have 128 or 256 channels. With the proposed method, a sustainable average beamforming rate of 4.83 G samples/second in terms of input raw RF sample was achieved. The resulting image quality of t

关键词： Beamforming

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Misalignment Tolerant Diffractive Optical Networks

Misalignment Tolerant Diffractive Optical Networks

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Conference on Lasers and Electro-Optics (CLEO)

作者： Deniz Mengu Yifan Zhao Nezih T. Yardimci Yair Rivenson Mona Jarrahi Aydogan Ozcan Electrical and Computer Engineering Department University of California Angeles CA USA Bioengineering Department University of California Los Angeles CA USA California NanoSystems Institute (CNSI) University of California Los Angeles CA USA

Design of diffractive optical networks that are resilient against physical misalignments is reported. The success of this design framework is also experimentally demonstrated using 3D printed diffractive networks that... 详细信息

ISBN: (纸本)9781943580910

关键词： Laser theory Three-dimensional displays Optical fiber networks Lasers and electrooptics Electrooptical waveguides

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Barrier layer design reduces top electrode ion migration in perovskite solar cells

EES Solar

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EES Solar 2025年第3期1卷 345-355页

作者： Saivineeth Penukula Megh N. Khanal Mohin Sharma Mritunjaya Parashar Ross A. Kerner Min Chen Melissa A. Davis Rafikul Ali Saha Eduardo Solano Maarten B. J. Roeffaers Joseph J. Berry Joseph M. Luther Julian A. Steele Axel Palmstrom Vincent R. Whiteside Bibhudutta Rout Ian R. Sellers Nicholas Rolston Renewable Energy Materials and Devices Lab School of Electrical Computer and Energy Engineering (ECEE) Arizona State University Tempe Arizona 85281 USA Department of Electrical Engineering University at Buffalo SUNY Buffalo 230 Davis Hall New York 14260 USA Department of Physics Ion Beam Laboratory University of North Texas Denton Texas 76203 USA National Renewable Energy Laboratory Golden Colorado 80401 USA cMACS Department of Microbial and Molecular Systems KU Leuven Celestijnenlaan 200F 3001 Heverlee Belgium NCD-SWEET Beamline ALBA Synchrotron Light Source 08290 Cerdanyola del Vallès Barcelona Spain Department of Physics University of Colorado Boulder Boulder Colorado 80309 USA Renewable and Sustainable Energy Institute University of Colorado Boulder Boulder Colorado 80309 USA Australian Institute of Bioengineering and Nanotechnology The University of Queensland St Lucia QLD 4072 Australia School of Mathematics and Physics The University of Queensland Brisbane QLD 4072 Australia

We report on an examination of mobile ion concentration (N0) in perovskite solar cells (PSCs) as a function of temperature and device architecture. We find that lower initial N0 is correlated to devices with higher th...

We report on an examination of mobile ion concentration (N₀) in perovskite solar cells (PSCs) as a function of temperature and device architecture. We find that lower initial N₀ is correlated to devices with higher thermal performance through in situ measurements up to 450 K. Changes in N₀ are observed upon thermal aging and are impacted by the changes made at the electron collecting interface. We examine the extent to which various top electrode materials (Ag, Au, carbon) impact N₀ as well as the effects of tin oxide (SnO₂) or an ozone-nucleated SnO₂ (O₃–SnO₂) barrier layer between the ETL and top electrode. Upon thermal aging, we confirm the involvement of Ag ion diffusion through the ETL dependent on the device details. We are able to quantify the degree to which Ag ions migrate or are blocked from migrating into the underlying device layers in the PSC stack. X-ray scattering shows improved suppression of the degradation products formed in the bulk of the perovskite when a blocking layer, particularly the O₃–SnO₂ is employed.

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Plastic Contaminant Detection in Aerial Imagery of Cotton Fields with Deep Learning

SSRN

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SSRN 2023年

作者： Yadav, Pappu Kumar Thomasson, J. Alex Hardin, Robert G. Searcy, Stephen W. Braga-Neto, Ulisses Popescu, Sorin C. Rodriguez, Roberto Martin, Daniel E. Enciso, Juan Meza, Karem White, Emma L. Department of Biological & Agricultural Engineering Texas A&M University College StationTX United States Department of Agricultural & Biological Engineering Mississippi State University Mississippi State MS United States Department of Electrical & Computer Engineering Texas A&M University College StationTX United States Department of Ecology & Conservation Biology Texas A&M University College StationTX United States Department of Molecular Biosciences and Bioengineering University of Hawaii at Manoa HonoluluHI United States Aerial Application Technology Research U.S.D.A. Agriculture Research Service College StationTX United States Department of Civil & Environmental Engineering Utah State University LoganUT United States

Plastic shopping bags are often discarded as litter and can get carried away from roadsides and tangled on cotton plants in farm fields. This rubbish plastic can end up in cotton at the gin if not removed before the harvest. Such bags may not only cause problems in the ginning process but might also get embodied in cotton fibers reducing its quality and marketable value. Therefore, it is required to detect, locate, and remove the bags before cotton is harvested. Manually detecting and locating these bags in cotton fields is labor intensive, time-consuming and a costly process. To solve these challenges, we present application of four variants of YOLOv5 (YOLOv5s, YOLOv5m, YOLOv5l and YOLOv5x) for detecting plastic shopping bags using Unmanned Aircraft Systems (UAS)-acquired RGB (Red, Green, and Blue) images. We also show fixed effect model tests of color of plastic bags as well as YOLOv5-variant on average precision (AP) , mean average precision (mAP@50) and accuracy. In addition, we also consider the effect of the height of plastic bags on plants in terms of detection accuracy. It was found that color of bags had significant effect (p © 2023, The Authors. All rights reserved.

关键词： computer vision

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Early detection and classification of live bacteria using time-lapse coherent imaging and deep learning

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Light(Science & Applications) 2020年第1期9卷 890-906页

作者： Hongda Wang Hatice Ceylan Koydemir Yunzhe Qiu Bijie Bai Yibo Zhang Yiyin Jin Sabiha Tok Enis Cagatay Yilmaz Esin Gumustekin Yair Rivenson Aydogan Ozcan Electrical and Computer Engineering Department University of CaliforniaLos AngelesCA 90095USA Bioengineering Department University of CaliforniaLos AngelesCA 90095USA California NanoSystems Institute University of CaliforniaLos AngelesCA 90095USA Department of Biophysics Istanbul Medical FacultyIstanbul UniversityIstanbul 22000Turkey Department of Microbiology Immunologyand Molecular GeneticsUniversity of CaliforniaLos AngelesCA 90095USA Department of Surgery David Geffen School of MedicineUniversity of CaliforniaLos AngelesCA 90095USA

Early identification of pathogenic bacteria in food,water,and bodily fluids is very important and yet challenging,owing to sample complexities and large sample volumes that need to be rapidly *** screening methods based on plate counting or molecular analysis present various tradeoffs with regard to the detection time,accuracy/sensitivity,cost,and sample preparation ***,we present a computational live bacteria detection system that periodically captures coherent microscopy images of bacterial growth inside a 60-mm-diameter agar plate and analyses these time-lapsed holograms using deep neural networks for the rapid detection of bacterial growth and the classification of the corresponding *** performance of our system was demonstrated by the rapid detection of Escherichia coli and total coliform bacteria(i.e.,Klebsiella aerogenes and Klebsiella pneumoniae ***)in water samples,shortening the detection time by >12 h compared to the Environmental Protection Agency(EPA)-approved *** the preincubation of samples in growth media,our system achieved a limit of detection(LOD)of ~1 colony forming unit(CFU)/L in≤9 h of total test *** platform is highly cost-effective(~$0.6/test)and has high-throughput with a scanning speed of 24 cm2/min over the entire plate surface,making it highly suitable for integration with the existing methods currently used for bacteria detection on agar *** by deep learning,this automated and cost-effective live bacteria detection platform can be transformative for a wide range of applications in microbiology by significantly reducing the detection time and automating the identification of colonies without labelling or the need for an expert.

关键词： coherent Early lapse

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