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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ahmed Sharafeldeen Hossam Magdy Balaha Ibrahim Shawky Farahat Mohammed Ghazal James Connelly Eric van Bogaert Ayman El-Baz Bioengineering Department J.B. Speed School of Engineering University of Louisville Louisville KY USA Department of Computer Science Mansoura University Mansoura Egypt Electrical Computer and Biomedical Engineering Department Abu Dhabi University Abu Dhabi UAE Department of Radiology Faculty of Medicine University of Louisville Louisville KY USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

A novel diagnostic system is introduced for assessing the required level of respiratory support for COVID-19 patients. It bases its assessments on the correlation between detected COVID-19 lesions and the respiratory support levels administered to the patients. The correlation with computed tomography (CT) scans will focus on three respiratory support levels, including minimal support (Level 0), non-invasive support (Level 1, such as soft oxygen), and invasive support (Level 3, e.g., mechanical ventilation). The system initially outlines the pulmonary regions from CT scans, then identifies COVID-19 lesions within these segmented lung regions. Subsequently, three second-order texture features are extracted from the identified COVID-19 lesion regions to detect differences and abnormalities across varying severity levels in COVID-19 cases. To obtain the suitable level of respiratory support for each patient, a fusion mechanism based on backpropagation neural network is utilized to integrate the diagnosis of these three features, individually generated by the support vector machine (SVM) classifier. The system’s performance is evaluated on 307 COVID-19 patients using a hold-out validation approach. This evaluation included various metrics, such as sensitivity, specificity, F1-score, Cohen’s kappa, and accuracy, demonstrating impressive results. Specifi-cally, it achieved a score of 97.25%, 98.56%, 97.26%, 95.79%, and 97.25%, respectively. The results demonstrate the effectiveness of the integrated system, which uses various second-order features, in predicting respiratory support needs for COVID-19 patients, outperforming both its individual components and other machine learning-based classification systems.

关键词： COVID-19 Support vector machines Correlation Computed tomography Lungs Signal processing Feature extraction Ventilation Lesions Speech processing

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Calcium Recording in Hippocampal CA1 Pyramidal Neurons with Enhanced Contrast Using OS-SIM

Calcium Recording in Hippocampal CA1 Pyramidal Neurons with ...

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Optics and the Brain, BRAIN 2024 - Part of Optica Biophotonics Congress: Biomedical Optics

作者： Speed, Forest Saladrigas, Catherine A. Teel, Alec Vieau, Sean Kumar, Vikrant Bright, Victor M. Kymissis, Ioannis Gopinath, Juliet T. Welle, Cristin G. Restrepo, Diego Gibson, Emily A. Department of Bioengineering University of Colorado Anschutz AuroraCO80045 United States Department of Electrical Computer and Energy Engineering University of Colorado Boulder BoulderCO80309 United States Department of Cell and Developmental Biology University of Colorado Anschutz AuroraCO80045 United States Department of Neurosurgery University of Colorado Anschutz AuroraCO80045 United States Department of Electrical Engineering Columbia University New York CityNY10027 United States Department of Mechanical Engineering University of Colorado Boulder BoulderCO80309 United States Department of Physics University of Colorado Boulder BoulderCO80309 United States Department of Physiology and Biophysics University of Colorado Anschutz AuroraCO80045 United States

We discuss optical sectioning structured illumination microscopy (OS-SIM) for functional GCaMP8f imaging in the CA1 hippocampal region of awake mice. We demonstrate OS-SIM implementation to reduce out-of-focus signal ... 详细信息

ISBN: (纸本)9781957171340

关键词： Fluorescence microscopy Gratings Illumination Optical systems Spatial frequency Structured illumination microscopy

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Can rubrics combat gender bias in faculty hiring? Some bias persisted, but rubric use should be encouraged

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Science 2022年第6601期377卷 35-37页

作者： Blair-Loy, Mary Mayorova, Olga V. Cosman, Pamela C. Fraley, Stephanie I. Department of Sociology University of California San Diego San diego CA United States Department of Electrical and Computer Engineering University of California San Diego San diego CA United States Department of Bioengineering University of California San Diego San diego CA United States

Research has documented the presence of bias against women in hiring, including in academic science, technology, engineering, and mathematics (STEM). Hiring rubrics (also called criterion checklists, decision support tools, and evaluation tools) are widely recommended as a precise, cost-effective remedy to counteract hiring bias, despite a paucity of evidence that they actually work (see table S8). Our in-depth case study of rubric usage in faculty hiring in an academic engineering department in a very research-active university found that the rate of hiring women increased after the department deployed rubrics and used them to guide holistic discussions. Yet we also found evidence of substantial gender bias persisting in some rubric scoring categories and evaluators’ written comments. We do not recommend abandoning rubrics. Instead, we recommend a strategic and sociologically astute use of rubrics as a department self-study tool within the context of a holistic evaluation of semifinalist candidates. © 2022 American Association for the Advancement of Science. All rights reserved.

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Neural network uncertainty quantification in inverse imaging problems using cycle consistency

Neural network uncertainty quantification in inverse imaging...

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CLEO: Applications and Technology in CLEO 2024, CLEO: A and T 2024 - Part of Conference on Lasers and Electro-Optics

作者： Huang, Luzhe Li, Jianing Ding, Xiaofu Zhang, Yijie 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 Computer Science Department University of California Los AngelesCA90095 United States Department of Mathematics University of California Los AngelesCA90095 United States David Geffen School of Medicine University of California Los Angeles Los AngelesCA90095 United States

We present an uncertainty quantification method for neural networks solving inverse imaging problems. We leverage the physical forward model to establish forward-backward cycles to quantify inference uncertainty and detect out-of-distribution data in computational imaging tasks. © Optica Publishing Group 2024, © 2024 The Author(s)

关键词： Neural network models

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Study of ASIC Readout Used for Detectors in an MR Compatible, RF-Penetrable TOF-PET Insert

Study of ASIC Readout Used for Detectors in an MR Compatible...

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2022 IEEE Nuclear Science Symposium, Medical Imaging Conference, and Room Temperature Semiconductor Detector Conference, IEEE NSS MIC RTSD 2022

作者： Dong, Qian Sajedi, Salar Sacco, Ilaria Fischer, Peter Levin, Craig S. Stanford University Departments of Electrical Engineering and Radiology StanfordCA United States Stanford University Departments of Radiology StanfordCA United States Heidelberg University Department of Computer Science Baden-Württemberg Germany Stanford University Department of Radiology Electrical Engineering Bioengineering and Physics United States StanfordCA94305 United States

ISBN: (纸本)9781665488723

Developing positron emission tomography (PET) systems with high channel number has driven the need for application-specific integrated circuits (ASICs) owing to their compactness, multi-channel readout capability, and excellent time and energy resolution performance. They can also be quite flexible by employing field-programmable gate arrays (FPGAs) in the readout circuit. In particular, the ASIC+FPGA approach provides a high performance and compact solution for TOF-PET applications. ASICs serve functions of extracting time and energy information from analog SiPM signals, and an FPGA is employed for other application-specific requirements like on-board chip configuration, communication with data acquisition (DAQ) systems, and system monitoring. Based on this foundation, we developed TOF-capable detector modules for our second generation RF-penetrable PET insert. During our previous experiments, we found that different trigger and readout methods lead to different noise pattern and intensity in MR environment, and also have different singles count loss ratio. This paper aims to study and compare different readout configurations for our MR-compatible detectors. We studied four trigger and readout methods: hit-standard, frequent-standard, hit-bypass, and frequent-bypass, and compared their singles count loss ratios with both theoretical models and experimental results. For all four methods, the theoretical results matched the experimental results well. The two methods which used bypass mode performed better than those using the standard mode approach. With the most optimized trigger and readout method (hit-bypass), we achieved 1.2% singles count loss ratio at a 20 kcps count rate per channel. © 2022 IEEE.

关键词： ASIC FPGA PET insert readout TOF-PET

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Analyzing Voltage Transients Under Different Stimulation Configurations and Rates in Rat Cortex

Analyzing Voltage Transients Under Different Stimulation Con...

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

作者： Christopher K. Nguyen Brandon S. Sturgill Sandeep Negi Stuart F. Cogan Department of Bioengineering University of Texas at Dallas Richardson TX USA Blackrock Neurotech Salt Lake City UT USA Department Electrical and Computer Engineering University of Utah Salt Lake City UT USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

This study introduces a method for measuring the voltage transients (VTs) in different stimulation configurations as a function of stimulation rate. Utilizing a stimulator with two-electrode setup and an isolated input oscilloscope, VTs across monopolar (MP), bipolar (BP), tripolar (TP), and partial tripolar (PTP) stimulation configurations could be measured with a proper reference electrode (Ag|AgCl) without a potentiostat or custom-built stimulator. We tested this methodology with Utah electrode arrays (UEAs) with sputtered iridium oxide film (SIROF) electrodes and amorphous silicon carbide (a-SiC) encapsulation in rat cortex at 24 weeks. Our investigation revealed the impact of quasi-reference electrode (QRE) polarization, which leads to underestimating the chargeinjection capacity—max(Q inj )—for electrode *** Relevance—This research offers advancement in understanding electrode behavior during neural stimulation, emphasizing the importance of protocol optimization for enhanced clinical outcomes in neural stimulation.

关键词： Electrodes Encapsulation Voltage measurement Protocols Oscilloscopes Amorphous silicon Iridium Transient analysis engineering in medicine and biology Optimization

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Deep Learning-enabled Autofocusing and Pixel Super-Resolution in Holographic Imaging

Deep Learning-enabled Autofocusing and Pixel Super-Resolutio...

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Frontiers in Optics + Laser Science 2023, FiO, LS 203: Part of Frontiers in Optics + Laser Science 2023

作者： Chen, Hanlong Huang, Luzhe Liu, Tairan 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 David Geffen School of Medicine University of California Los AngelesCA90095 United States

ISBN: (纸本)9781957171296

We introduce the enhanced Fourier Imager Network (eFIN), a deep learning framework for generalizable holographic image reconstruction, offering adaptive pixel super-resolution and autofocusing. Tested on new sample types, eFIN demonstrates superior generalizability and robustness. © Optica Publishing Group 2023, © 2023 The Author(s)

关键词： Deep learning Fourier transforms Image reconstruction Neural networks Phase retrieval Super resolution imaging

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Label-free hyperspectral multiphoton microscopy

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Optics Letters 2025年第5期50卷 1484-1487页

作者： De La Cadena, Alejandro Renteria, Carlos A. Aksamitiene, Edita Boppart, Stephen A. Beckman Institute for Advanced Science and Technology University of Illinois Urbana-Champaign UrbanaIL61801 United States Department of Bioengineering University of Illinois Urbana-Champaign UrbanaIL61801 United States Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign UrbanaIL61801 United States Interdisciplinary Health Science Institute University of Illinois Urbana-Champaign UrbanaIL61801 United States University of Illinois Urbana-Champaign NIH/NIBIB P41 Center for Label-Free Imaging and Multiscale Biophotonics United States

Label-free multiphoton microscopy is a powerful tool for investigating pristine biological specimens. This imaging modality leverages optical signals originating from the nonlinear response of native biomolecules to intense optical radiation, nonlinear signals that allow localizing and quantifying the constituents of specimens, driving applications in biology and medicine. However, since its inception over three decades ago, this approach has operated with a narrowband detection scheme, relying on narrow bandwidths from the entire spectra to derive imaging contrast. This detection scheme hinders the analytical power of the nonlinear microscope, preventing the rigorous unmixing of co-localized constituents with spectral overlap. In this Letter, we shift from the narrowband paradigm to the broadband, demonstrating label-free hyperspectral multiphoton imaging of biological specimens. We validate this configuration by disentangling the constituents of fresh murine tissues by virtue of nonlinear spectra. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Nonlinear optics

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The Impact of SBOM Generators on Vulnerability Assessment in Python: A Comparison and a Novel Approach

arXiv

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arXiv 2024年

作者： Benedetti, Giacomo Cofano, Serena Brighente, Alessandro Conti, Mauro Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Italy IMT School for Advanced Studies Lucca Italy Department of Mathematics University of Padua Italy Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Netherlands

The Software Supply Chain (SSC) security is a critical concern for both users and developers. Recent incidents, like the SolarWinds Orion compromise, proved the widespread impact resulting from the distribution of compromised software. The reliance on open-source components, which constitute a significant portion of modern software, further exacerbates this risk. To enhance SSC security, the Software Bill of Materials (SBOM) has been promoted as a tool to increase transparency and verifiability in software composition. However, despite its promise, SBOMs are not without limitations. Current SBOM generation tools often suffer from inaccuracies in identifying components and dependencies, leading to the creation of erroneous or incomplete representations of the SSC. Despite existing studies exposing these limitations, their impact on the vulnerability detection capabilities of security tools is still unknown. In this paper, we perform the first security analysis on the vulnerability detection capabilities of tools receiving SBOMs as input. We comprehensively evaluate SBOM generation tools by providing their outputs to vulnerability identification software. Based on our results, we identify the root causes of these tools' ineffectiveness and propose PIP-sbom, a novel pip-inspired solution that addresses their shortcomings. PIP-sbom provides improved accuracy in component identification and dependency resolution. Compared to best-performing state-of-the-art tools, PIP-sbom increases the average precision and recall by 60%, and reduces by ten times the number of false positives. © 2024, CC BY-SA.

关键词： Input output programs

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ADC-Less 3D-NAND Compute-in-Memory Architecture Using Margin Propagation

ADC-Less 3D-NAND Compute-in-Memory Architecture Using Margin...

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Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Aswin Chowdary Undavalli Gert Cauwenberghs Arun Natarajan Shantanu Chakrabartty Aravind Nagulu Department of Electrical and Systems Engineering Washington University in St. Louis MO USA Department of Bioengineering University of California at San Diego CA USA School of Electrical Engineering and Computer Science Oregon State University Corvallis OR USA

Compute In Memory (CIM) has gained significant attention in recent years due to its potential to overcome the memory bottleneck in Von-Neumann computing architectures. While most CIM architectures use non-volatile memory elements in a NOR-based configuration, NAND-based configuration, and in particular, 3D-NAND flash memories are attractive because of their potential in achieving ultra-high memory density and ultra-low cost per bit storage. Unfortunately, the standard multiply-and-accumulate (MAC) CIM-paradigm can not be directly applied to NAND-flash memories. In this paper, we report a NAND-Flash-based CIM architecture by combining the conventional 3D-NAND flash with a Margin-Propagation (MP) based approximate computing technique. We show its application for implementing matrix-vector multipliers (MVMs) that do not require analog-to-digital converters (ADCs) for read-out. Using simulation results we show that this approach has the potential to provide a 100 x improvement in compute density, read speed, and computation efficiency compared to the current state-of-the-art.

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