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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： H.S. Shim H. Kang J. Kim J.S. Lee Y. Jeong Interdisciplinary Program of Bioengineering Seoul National University Seoul Republic of Korea Integrated Major in Innovative Medical Science Seoul National University Seoul Republic of Korea Department of Electrical and Computer Engineering Seoul National University Seoul Republic of Korea

This study aims to investigate the characteristics of Cherenkov radiation with respect to wavelength and refractive index. Refractive index is a function of wavelength, and when it changes, not only does the gain of Cherenkov radiation increase, but also the photon loss in the medium increases. Therefore, we examine how photons of different wavelengths behave under changing refractive index conditions. We adjust the refractive index to maintain a constant emission angle of Cherenkov radiation based on the deceleration rate of electrons moving in the medium. When electrons generating Cherenkov radiation move a total of 200 μm, both long-wavelength and short-wavelength photons are emitted at the initial stage; however, only short-wavelength photons are emitted at the late stage. Although the shorter-wavelength photons arrive first among the photons that reach the detector, the longer-wavelength photons are detected more, forming a significant peak, which is due to the fact that the former experience more absorption-based photon loss in the medium than the latter.

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ProxNF: Neural Field Proximal Training for High-Resolution 4D Dynamic Image Reconstruction

arXiv

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arXiv 2024年

作者： Lozenski, Luke Cam, Refik Mert Pagel, Mark D. Anastasio, Mark A. Villa, Umberto Department of Electrical and Systems Engineering Washington University in St. Louis St. LouisMO63130 United States Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign UrbanaIL61801 United States Department of Medical Physics The University of Wisconsin–Madison MadisonWI53705 United States Department of Bioengineering University of Illinois Urbana-Champaign UrbanaIL61801 United States Oden Institute for Computational Engineering and Sciences University of Texas at Austin AustinTX78712 United States

Accurate spatiotemporal image reconstruction methods are needed for a wide range of biomedical research areas but face challenges due to data incompleteness and computational burden. Data incompleteness arises from the undersampling often required to increase frame rates, while computational burden emerges due to the memory footprint of high-resolution images with three spatial dimensions and extended time horizons. Neural fields (NFs), an emerging class of neural networks that act as continuous representations of spatiotemporal objects, have previously been introduced to solve these dynamic imaging problems by reframing image reconstruction as a problem of estimating network parameters. Neural fields can address the twin challenges of data incompleteness and computational burden by exploiting underlying redundancies in these spatiotemporal objects. This work proposes ProxNF, a novel neural field training approach for spatiotemporal image reconstruction leveraging proximal splitting methods to separate computations involving the imaging operator from updates of the network parameters. Specifically, ProxNF evaluates the (subsampled) gradient of the data-fidelity term in the image domain and uses a fully supervised learning approach to update the neural field parameters. This method is demonstrated in two numerical phantom studies and an in-vivo application to tumor perfusion imaging in small animal models using dynamic contrast-enhanced photoacoustic computed tomography (DCE PACT). © 2024, CC BY-NC-ND.

关键词： Digital elevation model

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SLIFER: Investigating Performance and Robustness of Malware Detection Pipelines

arXiv

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arXiv 2024年

作者： Ponte, Andrea Trizna, Dmitrijs Demetrio, Luca Biggio, Battista Ogbu, Ivan Tesfai Roli, Fabio Department of Informatics Bioengineering Robotics and Systems Engineering University of Genova Viale Causa 13 Genova16145 Italy Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Rome00185 Italy Department of Electrical and Electronic Engineering University of Cagliari Via Marengo 3 Cagliari09123 Italy Rina Consulting S.p.A. Via Antonio Cecchi 6 Genova16129 Italy

As a result of decades of research, Windows malware detection is approached through a plethora of techniques. However, there is an ongoing mismatch between academia – which pursues an optimal performances in terms of detection rate and low false alarms – and the requirements of real-world scenarios. In particular, academia focuses on combining static and dynamic analysis within a single or ensemble of models, falling into several pitfalls like (i) firing dynamic analysis without considering the computational burden it requires;(ii) discarding impossible-to-analyze samples;and (iii) analyzing robustness against adversarial attacks without considering that malware detectors are complemented with more non-machine-learning components. Thus, in this paper we bridge these gaps, by investigating the properties of malware detectors built with multiple and different types of analysis. To do so, we develop SLIFER, a Windows malware detection pipeline sequentially leveraging both static and dynamic analysis, interrupting computations as soon as one module triggers an alarm, requiring dynamic analysis only when needed. Contrary to the state of the art, we investigate how to deal with samples that impede analyzes, showing how much they impact performances, concluding that it is better to flag them as legitimate to not drastically increase false alarms. Lastly, we perform a robustness evaluation of SLIFER. Counter-intuitively, the injection of new content is either blocked more by signatures than dynamic analysis, due to byte artifacts created by the attack, or it is able to avoid detection from signatures, as they rely on constraints on file size disrupted by attacks. As far as we know, we are the first to investigate the properties of sequential malware detectors, shedding light on their behavior in real production environment. Copyright © 2024, The Authors. All rights reserved.

关键词： Malware

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Long-term regulation of prolonged epidemic outbreaks in large populations via adaptive control: A singular perturbation approach

arXiv

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arXiv 2021年

作者： Al-Radhawi, M. Ali Sadeghi, Mahdiar Sontag, Eduardo D. The Department of Electrical & Computer Engineering Northeastern University BostonMA United States The Department of Bioengineering Northeastern University

In order to control highly-contagious and prolonged outbreaks, public health authorities intervene to institute social distancing, lock-down policies, and other Non-Pharmaceutical Interventions (NPIs). Given the high social, educational, psychological, and economic costs of NPIs, authorities tune them, alternatively tightening up or relaxing rules, with the result that, in effect, a relatively flat infection rate results. For example, during the summer in parts of the United States, daily COVID-19 infection numbers dropped to a plateau. This paper approaches NPI tuning as a control-theoretic problem, starting from a simple dynamic model for social distancing based on the classical SIR epidemics model. Using a singular-perturbation approach, the plateau becomes a Quasi-Steady-State (QSS) of a reduced two-dimensional SIR model regulated by adaptive dynamic feedback. It is shown that the QSS can be assigned and it is globally asymptotically stable. Interestingly, the dynamic model for social distancing can be interpreted as a nonlinear integral controller. Problems of data fitting and parameter identifiability are also studied for this model. The paper also discusses how this simple model allows for a meaningful study of the effect of population size, vaccinations, and the emergence of second waves. © 2021, CC BY.

关键词： Population statistics

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Computational Spectroscopy via Large Aperture Meta-Optics

Computational Spectroscopy via Large Aperture Meta-Optics

引用

Conference on Lasers and Electro-Optics (CLEO)

作者： Johannes E. Fröch Shane Colburn Alan Zhan Zheyi Han Zhuoran Fang Abhi Saxena Luocheng Huang Karl F. Böhringer Arka Majumdar Department of Physics University of Washington Seattle WA USA Department of Electrical and Computer Engineering University of Washington Seattle WA USA Tunoptix Seattle WA USA Department of Bioengineering University of Washington Seattle WA USA Institute for Nano-Engineered Systems University of Washington Seattle WA USA

We demonstrate the concept of a meta-optic based computational spectrometer, combining a meta-optic, which encodes the spectrum of transmitted light into a spatial intensity pattern, that is retrievable through a comp... 详细信息

ISBN: (纸本)9781957171258

关键词： Diffractive optical elements Extended depth of field Light intensity Optical components Point spread function Spectroscopy

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Simultaneous Multi-heartbeat ECGI Solution with a Time-Varying Forward Model: A Joint Inverse Formulation 11th

Simultaneous Multi-heartbeat ECGI Solution with a Time-Varyi...

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11th International Conference on Functional Imaging and Modeling of the Heart, FIMH 2021

作者： Bergquist, Jake A. Coll-Font, Jaume Zenger, Brian Rupp, Lindsay C. Good, Wilson W. Brooks, Dana H. MacLeod, Rob S. Biomedical Engineering Department University of Utah Salt Lake CityUT84112 United States Cardiovascular Bioengineering and Imaging Lab Massachusetts General Hospital and Harvard Medical School BostonMA United States Department of Electrical and Computer Engineering Northeastern University BostonMA United States

ISBN: (纸本)9783030787097

Electrocardiographic imaging (ECGI) is an effective tool for noninvasive diagnosis of a range of cardiac dysfunctions. ECGI leverages a model of how cardiac bioelectric sources appear on the torso surface (the forward problem) and uses recorded body surface potential signals to reconstruct the bioelectric source (the inverse problem). Solutions to the inverse problem are sensitive to noise and variations in the body surface potential (BSP) recordings such as those caused by changes or errors in cardiac position. Techniques such as signal averaging seek to improve ECGI solutions by incorporating BSP signals from multiple heartbeats into an averaged BSP with a higher SNR to use when estimating the cardiac bioelectric source. However, signal averaging is limited when it comes to addressing sources of BSP variability such as beat to beat differences in the forward solution. We present a novel joint inverse formulation to solve for the cardiac source given multiple BSP recordings and known changes in the forward solution, here changes in the heart position. We report improved ECGI accuracy over signal averaging and averaged individual inverse solutions using this joint inverse formulation across multiple activation sequence types and regularization techniques with measured canine data and simulated heart motion. Our joint inverse formulation builds upon established techniques and consequently can easily be applied with many existing regularization techniques, source models, and forward problem formulations. © 2021, Springer Nature Switzerland AG.

关键词： Inverse problems

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Electrowetting lens for focus-tunable three-photon excitation microscopy

Electrowetting lens for focus-tunable three-photon excitatio...

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IEEE Photonics Conference (IPC)

作者： S. D. Gilinsky D. N. Jung G. L. Futia M. Zohrabi T. A. Welton E. A. Gibson D. Restrepo V. M. Bright J. T. Gopinath Department Electrical Computer and Energy Engineering University of Colorado Boulder Boulder USA Department Mechanical Engineering University of Colorado Boulder Boulder USA Department Bioengineering University of Colorado Anschutz Medical Campus Aurora USA Department of Cell and Developmental Biology University of Colorado Anschutz Medical Campus Aurora USA Department of Physics University of Colorado Boulder Boulder USA

We demonstrate an electrowetting lens suitable for 3-photon microscopy using ionic liquids and 1-phenyl-1-cyclohexene. These devices are over 90% transmissive from 400-1600 nm and tunable from 133−48° with an applied voltage of 0−60 V. Using this device, we demonstrate 3-photon imaging at two focal planes.

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GranatumX:A Community-engaging, Modularized, and Flexible Webtool for Single-cell Data Analysis

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Genomics, Proteomics & Bioinformatics 2021年第3期19卷 452-460页

作者： David G.Garmire Xun Zhu Aravind Mantravadi Qianhui Huang Breck Yunits Yu Liu Thomas Wolfgruber Olivier Poirion Tianying Zhao Cédric Arisdakessian Stefan Stanojevic Lana X.Garmire Department of Electrical Engineering&Computer Science University of MichiganAnn ArborMI 48109USA Epidemiology Program University of Hawaii Cancer CenterHonoluluHI 96813USA Department of Biostatistics University of MichiganAnn ArborMI 48109USA Department of Computational Medicine and Bioinformatics University of MichiganAnn ArborMI 48105USA Molecular Biosciences and Bioengineering Graduate Program University of Hawaii at ManoaHonoluluHI 96822USA

We present GranatumX,a next-generation software environment for single-cell RNA sequencing(scRNA-seq)data *** is inspired by the interactive webtool *** enables biologists to access the latest scRNA-seq bioinformatics methods in a web-based graphical *** also offers software developers the opportunity to rapidly promote their own tools with others in customizable *** architecture of GranatumX allows for easy inclusion of plugin modules,named Gboxes,which wrap around bioinformatics tools written in various programming languages and on various *** can be run on the cloud or private servers and generate reproducible *** is a community-engaging,flexible,and evolving software ecosystem for scRNA-seq analysis,connecting developers with bench *** is freely accessible at http://***/granatumx/app.

关键词： Single-cell RNA sequencing Analysis Pipeline Webtool Module

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Modeling and Learning-Based Control for Super-Coiled Polymer-Driven Robotic Eye

Modeling and Learning-Based Control for Super-Coiled Polymer...

引用

American Control Conference (ACC)

作者： Sunil Kumar Rajendran Qi Wei Ningshi Yao Feitian Zhang Department of Electrical and Computer Engineering George Mason University Fairfax VA USA Department of Bioengineering George Mason University Fairfax VA USA Department of Advanced Manufacturing and Robotics Peking University Beijing China

ISBN: (数字)9781665451963

ISBN: (纸本)9781665494809

A newly-developed type of artificial muscles – Super-Coiled Polymer (SCP), comparatively offers many advantages in terms of cost, size, flexibility, fabrication, and power-to-weight ratio, potentially making SCPs a great fit for deployment in bioinspired robots. Development of bioinspired robots incorporating artificial muscles, increasingly necessitates derivation of precise dynamic models for motion prediction and controller design. Nevertheless, the process of modeling the system dynamics of such sophisticatedly evolving robots becomes difficult due to their continuum dynamics and high dimensionality. To address the problems of high nonlinearity and intrinsically infinite system dimension, contemporary artificial intelligence techniques, specifically reinforcement learning algorithms, are employed to design learning-based controllers. This necessity of developing intelligent control serves as the motivation to not only mimic the biomechanisms, but also mimic the cognitive abilities of these biological life forms, which is where learning-based controllers will play a major role in such bioinspired robotic systems. Our research aims at designing and developing a motivating SCP-driven bioinspired robotic eye, which aims to aid ophthalmologists, ocularists, and biomedical researchers in understanding better, the movement of the extraocular muscles of the human eye. Consequently, this could give comprehensive insights in studying the neuro-biomechanics of oculomotor disorders such as misalignment of the eyes in patients with strabismus, thus enabling diagnostic researchers to profoundly investigate the fundamental cause and effect of such disorders. Concurrently, we focus on modeling its system dynamics, and developing a robust learning-based controller to achieve various objectives such as orientation and perceptive control. Our proposed learning-based control design employs the use of deep-deterministic policy gradient (DDPG) reinforcement learning algorithm to trai

关键词： Bio-inspired robotics Regulators System dynamics Heuristic algorithms Dynamics Reinforcement learning Muscles

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MICROFLUIDIC PLATFORM FOR MULTI-FREQUENCY VISCOELASTIC PHENOTYPING OF SINGLE CELLS 25

MICROFLUIDIC PLATFORM FOR MULTI-FREQUENCY VISCOELASTIC PHENO...

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25th International Conference on Miniaturized Systems for Chemistry and Life Sciences, MicroTAS 2021

作者： Lai, Andre Dong, Alan Lustig, Michael Sohn, Lydia L. UC Berkeley-UC San Francisco Graduate Program in Bioengineering University of California BerkeleyCA United States Electrical Engineering and Computer Sciences University of California BerkeleyCA United States Department of Mechanical Engineering University of California BerkeleyCA United States

ISBN: (纸本)9781733419031

Biomechanics plays an integral role in cancer development and progression. Accurately quantifying the mechanical properties of cells would enable a more comprehensive understanding of cancer, complementing conventional biochemical and genetic analyses. Here, we introduce a microfluidic platform for measuring single cell viscoelasticity at multiple frequencies in a high-throughput and label-free manner. Using the Coulter principal and a unique microfluidic channel geometry, our device records resistance pulses in order to estimate a cell's viscoelastic phenotype. We demonstrate our platform's capabilities by measuring three breast epithelial cell lines. © 2021 MicroTAS 2021 - 25th International Conference on Miniaturized Systems for Chemistry and Life Sciences. All rights reserved.

关键词： Biomechanics

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