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arXiv 2023年

作者： Boddeti, Ashwin K. Wang, Yi Juarez, Xitlali G. Boltasseva, Alexandra Odom, Teri W. Shalaev, Vladimir Alaeian, Hadiseh Jacob, Zubin Elmore Family School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States Birck Nanotechnology Center Purdue University West LafayetteIN47907 United States Graduate Program in Applied Physics Northwestern University EvanstonIL60208 United States Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Department of Chemistry Northwestern University EvanstonIL60208 United States School of Physics and Astronomy Purdue University West LafayetteIN47907 United States

Dimensionality plays a crucial role in long-range dipole-dipole interactions (DDIs). We demonstrate that a resonant nanophotonic structure modifies the apparent dimensionality in an interacting ensemble of emitters, as revealed by population decay dynamics. Our measurements on a dense ensemble of interacting quantum emitters in a resonant nanophotonic structure with long-range DDIs reveal an effective dimensionality reduction to d¯ = 2.20(12), despite the emitters being distributed in 3D. This contrasts the homogeneous environment, where the apparent dimension is d¯ = 3.00. Our work presents a promising avenue to manipulate dimensionality in an ensemble of interacting emitters. © 2023, CC0.

关键词： Dipole moment

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Imaging depth limit analysis in multiphoton microscopy using the beam propagation method

Imaging depth limit analysis in multiphoton microscopy using...

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Optics and the Brain, BRAIN 2020

作者： Cheng, Xiaojun Sadegh, Sanaz Zilpelwar, Sharvari Devor, Anna Tian, Lei Boas, David A. Department of Biomedical Engineering Boston University MA02215 United States Department of Electrical and Computer Engineering Boston University MA02215 United States Neurophotonics Center Boston University MA02215 United States Departments of Neurosciences and Radiology University of California San DiegoCA92093 United States

We apply our recently developed beam propagation model to simulate wave propagation in scattering biological tissue. The imaging depth limit is estimated for two-photon, three-photon, and non-degenerate two-photon mic... 详细信息

ISBN: (纸本)9781943580743

关键词： Photons

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Calibrationless MRI reconstruction with a plug-in denoiser

arXiv

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arXiv 2020年

作者： Zhao, Shen Potter, Lee C. Ahmad, Rizwan Department of Electrical and Computer Engineering Ohio State University United States Department of Biomedical Engineering Ohio State University United States

Magnetic Resonance Imaging (MRI) is a noninvasive imaging technique that provides excellent soft-tissue contrast without using ionizing radiation. MRI’s clinical application may be limited by long data acquisition time;therefore, MR image reconstruction from highly under-sampled k-space data has been an active research area. Calibrationless MRI not only enables a higher acceleration rate but also increases flexibility for sampling pattern design. To leverage non-linear machine learning priors, we pair our High-dimensional Fast Convolutional Framework (HICU) [1] with a plug-in denoiser and demonstrate its feasibility using 2D brain data. © 2020, CC BY.

关键词： Magnetic resonance imaging

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Advanced 4D-bioprinting technologies for brain tissue modeling and study

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International Journal of Smart and Nano Materials 2019年第3期10卷 177-204页

作者： Timothy J.Esworthy Shida Miao Se-Jun Lee Xuan Zhou Haitao Cui Yi Y.Zuo Lijie Grace Zhang Department of Mechanical and Aerospace Engineering The George Washington UniversityWashingtonDCUSA Department of Mechanical Engineering University of Hawaii at ManoaHonoluluHIUSA Department of Medicine The George Washington UniversityWashingtonDCUSA Department of Biomedical Engineering The George Washington UniversityWashingtonDCUSA Department of Electrical and Computer Engineering The George Washington UniversityWashingtonDCUSA

Although the process by which the cortical tissues of the brain fold has been the subject of considerable study and debate over the past few decades,a single mechanistic description of the phenomenon has yet to be fully ***,two competing explanations of cortical folding have arisen in recent years;known as the axonal tension and the differential tangential expansion *** the present review,these two models are introduced by analyzing the computational,theoretical,materials-based,and cell studies which have yielded *** Four-dimensional bioprinting is presented as a powerful technology which can not only be used to test both models of cortical folding de novo,but can also be used to explore the reciprocal effects that folding associated mechanical stresses may have on neural ***,the fabrication of‘smart’tissue models which can accurately simulate the in vivo folding process and recapitulate physiologically relevant stresses are *** also provide a general description of both cortical neurobiology as well as the cellular basis of cortical *** discussion also entails an overview of both 3D and 4D bioprinting technologies,as well as a brief commentary on recent advancements in printed central nervous system tissue engineering.

关键词： 4D bioprinting smart materials brain cortical folding gyrification

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Investigation of Different Membrane Materials Effects in CMUT Membrane Behaviour 3

Investigation of Different Membrane Materials Effects in CMU...

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3rd International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2019

作者： Yasar, Abdullah Irfan Yildiz, Fikret TOBB University Biomedical Engineering Graduate Program Ankara Turkey Hakkari University Electrical and Electronics Engineering Department Hakkari Turkey

ISBN: (纸本)9781728137896

Research on Capacitive Micromachined Ultrasonic Transducers (CMUT), which is MEMS-based new transducer technology compared to piezoelectric in ultrasound applications, has recently increased. It is an important fact that micro and nano technologies provide convenience in many areas. CMUT technology has many advantages, such as its small size, the ease of integration with electronic materials and its compatibility with 2-dimensional geometries. In this study, the effect of different membrane material properties on displacement and output pressure of the CMUT membrane was investigated by assuming that it works in CMUT transmission mode and in the water media. SiC, Si, diamond, Si3N4, and polysilicon were used as membrane material. The membrane radius and thickness were selected as 60 μm and 2.6 μm, respectively. Collapse voltage and impedance values were calculated according to the structural properties of these materials. In addition, the mechanical structure of CMUT was taken into consideration and modeling was performed. Using this model created in the SIMULINK, the displacement and output pressure of CMUT membrane under various DC and AC voltages were analyzed. Under the same DC voltage, silicon membrane had the highest displacement and diamond membrane had the lowest displacement. Square shape wave was used as the AC excitation signal because the square wave was more efficient than the others. Some properties of the AC signal were changed and their effects were investigated. The most suitable material and working environment were tried to be found. Moreover, Si3Ni4 membrane behavior also investigated under different electric field to understand effects of electrode size using same model in air. As a result, this study aims to contribute to efficient CMUT design and determination of optimum parameters and membrane material selection for various applications in the immersion and air. © 2019 IEEE.

关键词： Silicon carbide

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Minimizing Number of Nodes in Decision Trees with Hypotheses

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Procedia computer Science 2021年 192卷 232-240页

作者： Mohammad Azad Igor Chikalov Shahid Hussain Mikhail Moshkov Jouf University College of Computer and Information Sciences Department of Computer Science Sakaka 72441 Saudi Arabia Intel Corporation 5000 W Chandler Blvd Chandler AZ 85226 USA Habib University Dhanani School of Science and Engineering Computer Science Program Karachi 75290 Pakistan King Abdullah University of Science and Technology (KAUST) Computer Electrical and Mathematical Sciences & Engineering Division Thuwal 23955-6900 Saudi Arabia

In this paper, we consider decision trees that use both conventional queries based on one attribute each and queries based on hypotheses about values of all attributes. This approach is similar to one studied in exact learning, where membership and equivalence queries are considered. We propose dynamic programming algorithms for the minimization of the number of nodes in such decision trees and discuss results of computer experiments.

关键词： Decision tree Hypothesis Number of nodes

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A Bus Authentication and Anti-Probing Architecture Extending Hardware Trusted Computing Base Off CPU Chips and Beyond

A Bus Authentication and Anti-Probing Architecture Extending...

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Annual International Symposium on computer Architecture, ISCA

作者： Zhenyu Xu Thomas Mauldin Zheyi Yao Shuyi Pei Tao Wei Qing Yang Department of Electrical Computer and Biomedical Engineering University of Rhode Island Kingston Rhode Island USA

ISBN: (数字)9781728146614

ISBN: (纸本)9781728146621

Tamper-proof hardware designs present a great challenge to computer architects. Most existing research limits hardware trusted computing base (TCB) to a CPU chip and anything off the CPU chip is vulnerable to probing and tampering. This paper introduces a new hardware design that provides strong defenses against physical attacks on interconnecting buses between chips in a computer system thereby extending the hardware TCB beyond CPU chips. The new approach is referred to as DIVOT: Detecting Impedance Variations Of Transmission-lines (Tx-lines). Every Tx-line in a computer system, such as a bus and interconnection wire has a unique, intrinsic, and fingerprint-like property: Impedance Inhomogeneity Pattern (IIP), i.e. the impedance distribution over distance. Such unpredictable, uncontrollable, and non-reproducible IIP fingerprints can be used to authenticate a Tx-line to ensure the confidentiality and integrity of data being transmitted. In addition, physical probes perturb the electromagnetic (EM) field around a Tx-line, leading to an altered IIP. As a result, runtime monitoring of IIPs can also be used to actively detect physical probing, snooping, and wire-tapping on buses. While the physics behind the IIP is known, the major technical breakthrough of DIVOT is the new integrated time domain reflectometer, iTDR, that is capable of carrying out in-situ and runtime monitoring of a Tx-line without interfering with normal data transfers. The iTDR is based on two innovations: analog-to-probability conversion (APC) and probability density modulation (PDM). The iTDR performs runtime IIP measurements noninvasively and is CMOS-compatible allowing it to be integrated with any interface logic connected to a bus. DIVOT is a generic, scalable, cost-effective, and low-overhead security solution for any computer system from servers to embedded computers in smart mobile devices and IoTs. To demonstrate the proposed architecture, a working prototype of DIVOT has been built on an FP

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Prospectively accelerated dynamic speech MRI at 3 Tesla using a self-navigated spiral based manifold regularized scheme

arXiv

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arXiv 2022年

作者： Rusho, Rushdi Zahid Ahmed, Abdul Haseeb Kruger, Stanley Alam, Wahidul Meyer, David Howard, David Story, Brad Jacob, Mathews Lingala, Sajan Goud Roy J Carver Department of Biomedical Engineering University of Iowa Iowa city IA52242 United States Department of Electrical and Computer Engineering University of Iowa Iowa city IA52242 United States Department of Radiology University of Iowa Iowa city IA52242 United States Janette Ogg Voice Research Center Shenandoah University WinchesterVA22601 United States Department of Electronic Engineering Royal Holloway University of London United Kingdom Department of Speech Language and Hearing Sciences University of Arizona AZ85721 United States

This work develops and evaluates a self-navigated variable density spiral (VDS) based manifold regularization scheme to prospectively improve dynamic speech MRI at 3 Tesla. Short readout duration spirals (1.3 ms long) were used to minimize sensitivity to off-resonance. A custom 16-channel speech coil was used for improved parallel imaging of vocal tract structures. The manifold model leveraged similarities between frames sharing similar vocal tract postures without explicit motion binning. The self-navigating capability of VDS was leveraged to learn the Laplacian structure of the manifold. Reconstruction was posed as a SENSE-based non-local soft weighted temporal regularization scheme. Our approach was compared against view-sharing, low-rank, finite difference, extra-dimension-based sparsity reconstruction constraints. Under-sampling experiments were conducted on five volunteers performing repetitive and arbitrary speaking tasks at different speaking rates. Quantitative evaluation in terms of mean square error over moving edges were performed in a retrospective under-sampling experiment on one volunteer. For prospective under-sampling, blinded image quality evaluation in the categories of alias artifacts, spatial blurring, and temporal blurring were performed by three experts in voice research. Region of interest (ROI) analysis at articulator boundaries were performed in both the experiments to assess articulatory motion. Improved performance with manifold reconstruction constraints was observed over existing constraints. With prospective under-sampling, a spatial resolution of 2.4mm2/pixel and a temporal resolution of 17.4 ms/frame for single slice imaging, and 52.2 ms/frame for concurrent 3-slice imaging were achieved. We demonstrated implicit motion binning by analyzing the mechanics of the Laplacian matrix. Manifold regularization demonstrated superior image quality scores in reducing spatial and temporal blurring compared to all other reconstruction constraints

关键词： Navigation

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Deep networks as logical circuits: Generalization and interpretation

arXiv

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arXiv 2020年

作者： Snyder, Christopher Vishwanath, Sriram Department of Biomedical Engineering University of Texas Austin United States Department of Electrical and Computer Engineering University of Texas Austin United States

Not only are Deep Neural Networks (DNNs) black box models, but also we frequently conceptualize them as such. We lack good interpretations of the mechanisms linking inputs to outputs. Therefore, we find it difficult to analyze in human-meaningful terms (1) what the network learned and (2) whether the network learned. We present a hierarchical decomposition of the DNN discrete classification map into logical (AND/OR) combinations of intermediate (True/False) classifiers of the input. Those classifiers that can not be further decomposed, called atoms, are (interpretable) linear classifiers. Taken together, we obtain a logical circuit with linear classifier inputs that computes the same label as the DNN. This circuit does not structurally resemble the network architecture, and it may require many fewer parameters, depending on the configuration of weights. In these cases, we obtain simultaneously an interpretation and generalization bound (for the original DNN), connecting two fronts which have historically been investigated separately. Unlike compression techniques, our representation is exact. We motivate the utility of this perspective by studying DNNs in simple, controlled settings, where we obtain superior generalization bounds despite using only combinatorial information (e.g. no margin information). We demonstrate how to "open the black box" on the MNIST dataset. We show that the learned, internal, logical computations correspond to semantically meaningful (unlabeled) categories that allow DNN descriptions in plain English. We improve the generalization of an already trained network by interpreting, diagnosing, and replacing components within the logical circuit that is the DNN. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Dose Requirements for Fused Multi-Modal Electron Tomography

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Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada 2023年第1期29卷 1968-1969页

作者： Manassa, Jason Schwartz, Jonathan Jiang, Yi Zheng, Huihuo Fessler, Jeffrey A. Wendy Di, Zichao Hovden, Robert Department of Materials Science and Engineering University of Michigan Ann Arbor MI United States Advanced Photon Source Facility Argonne National Laboratory Lemont IL United States Argonne Leadership Computing Facility Argonne National Laboratory Lemont IL United States Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI United States Mathematics and Computer Science Division Argonne National Laboratory Lemont IL United States Applied Physics Program University of Michigan Ann Arbor MI United States

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