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Augment Reality-Based Teaching Practice

Biomedical engineering education 2021年第1期1卷 237-241页

作者： You Wu Mingzhen Zhang Xiaosi Li Yu Gan Chao Zhao Department of Chemical and Biological Engineering The University of Alabama P. O. Box870203 Tuscaloosa AL 35401 USA. Computational Structural Biology Section Basic Science Program Frederick National Laboratory for Cancer Research Frederick MD 21702 USA. Department of Electrical and Computer Engineering The University of Alabama P. O. Box870286 Tuscaloosa AL 35401 USA.

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Impact of deep learning-based image super-resolution on binary signal detection

arXiv

引用

arXiv 2021年

作者： Zhang, Xiaohui Kelkar, Varun A. Granstedt, Jason Li, Hua Anastasio, Mark A. University of Illinois at Urbana-Champaign Department of Bioengineering UrbanaIL61801 United States University of Illinois at Urbana-Champaign Department of Electrical and Computer Engineering UrbanaIL61801 United States University of Illinois at Urbana-Champaign Department of Computer Science UrbanaIL61801 United States University of Illinois at Urbana-Champaign Cancer Center at Illinois UrbanaIL61801 United States Carle Cancer Center Carle Foundation Hospital UrbanaIL61801 United States

Purpose: Deep learning-based image super-resolution (DL-SR) has shown great promise in medical imaging applications. To date, most of the proposed methods for DL-SR have only been assessed by use of traditional measures of image quality (IQ) that are commonly employed in the field of computer vision. However, the impact of these methods on objective measures of image quality that are relevant to medical imaging tasks remains largely unexplored. In this study, we investigate the impact of DL-SR methods on binary signal detection performance. Approach: Two popular DL-SR methods, the super-resolution convolutional neural network (SRCNN) and the super-resolution generative adversarial network (SRGAN), were trained by use of simulated medical image data. Binary signal-known-exactly with background-known-statistically (SKE/BKS) and signal-known-statistically with background-known-statistically (SKS/BKS) detection tasks were formulated. Numerical observers, which included a neural network-approximated ideal observer and common linear numerical observers, were employed to assess the impact of DL-SR on task performance. The impact of the complexity of the DL-SR network architectures on task-performance was quantified. In addition, the utility of DL-SR for improving the task-performance of sub-optimal observers was investigated. Results: Our numerical experiments confirmed that, as expected, DL-SR could improve traditional measures of IQ. However, for many of the study designs considered, the DL-SR methods provided little or no improvement in task performance and could even degrade it. It was observed that DL-SR could improve the task-performance of suboptimal observers under certain conditions. Conclusions: The presented study highlights the urgent need for the objective assessment of DL-SR methods and suggests avenues for improving their efficacy in medical imaging applications. Copyright © 2021, The Authors. All rights reserved.

关键词： Medical imaging

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Schottky barrier height engineering In β-Ga2O3 using SiO2interlayer dielectric

arXiv

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arXiv 2020年

作者： Bhattacharyya, Arkka Ranga, Praneeth Saleh, Muad Roy, Saurav Scarpulla, Michael A. Lynn, Kelvin G. Krishnamoorthy, Sriram Department of Electrical and Computer Engineering University of Utah Salt Lake CityUT84112 United States Department of Electrical and Computer Engineering Department of Materials Science and Engineering University of Utah Salt Lake CityUT84112 United States Materials Science & Engineering Program Center for Materials Research Washington State University PullmanWA99164 United States Materials Science & Engineering Program Center for Materials Research School of Mechanical and Materials Engineering Department of Physics Washington State University PullmanWA99164 United States

This paper reports on the modulation of Schottky barrier heights (SBH) on three different orientations of βGa2O3 by insertion of an ultra-thin SiO2 dielectric interlayer at the metal-semiconductor junction, which can potentially lower the Fermi-level pinning (FLP) effect due to metal-induced gap states (MIGS). Pt and Ni metal-semiconductor (MS) and metal-interlayer-semiconductor (MIS) Schottky barrier diodes were fabricated on bulk n-type doped β-Ga2O3 single crystal substrates along the (010), (-201) and (100) orientations and were characterized by room temperature current-voltage (I-V) and capacitance-voltage (C-V) measurements. Pt MIS diodes exhibited 0.53 eV and 0.37 eV increment in SBH along the (010) and (-201) orientations respectively as compared to their respective MS counterparts. The highest SBH of 1.81 eV was achieved on the (010)-oriented MIS SBD using Pt metal. The MIS SBDs on (100)-oriented substrates exhibited a dramatic increment (>1.5×) in SBH as well as reduction in reverse leakage current. The use of thin dielectric interlayers can be an efficient experimental method to modulate SBH of metal/Ga2O3 junctions. Copyright © 2020, The Authors. All rights reserved.

关键词： Schottky barrier diodes

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Roadmap on Deep Learning for Microscopy

arXiv

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arXiv 2023年

作者： Volpe, Giovanni Wählby, Carolina Tian, Lei Hecht, Michael Yakimovich, Artur Monakhova, Kristina Waller, Laura Sbalzarini, Ivo F. Metzler, Christopher A. Xie, Mingyang Zhang, Kevin Lenton, Isaac C.D. Rubinsztein-Dunlop, Halina Brunner, Daniel Bai, Bijie Ozcan, Aydogan Midtvedt, Daniel Wang, Hao Sladoje, Nataša Lindblad, Joakim Smith, Jason T. Ochoa, Marien Barroso, Margarida Intes, Xavier Qiu, Tong Yu, Li-Yu You, Sixian Liu, Yongtao Ziatdinov, Maxim A. Kalinin, Sergei V. Sheridan, Arlo Manor, Uri Nehme, Elias Goldenberg, Ofri Shechtman, Yoav Moberg, Henrik K. Langhammer, Christoph Špačková, Barbora Helgadottir, Saga Midtvedt, Benjamin Argun, Aykut Thalheim, Tobias Cichos, Frank Bo, Stefano Hubatsch, Lars Pineda, Jesus Manzo, Carlo Bachimanchi, Harshith Selander, Erik Homs-Corbera, Antoni Fränzl, Martin de Haan, Kevin Rivenson, Yair Korczak, Zofia Adiels, Caroline Beck Mijalkov, Mite Veréb, Dániel Chang, Yu-Wei Pereira, Joana B. Matuszewski, Damian Kylberg, Gustaf Sintorn, Ida-Maria Caicedo, Juan C. Cimini, Beth A. Bell, Muyinatu Lediju A. Saraiva, Bruno M. Jacquemet, Guillaume Henriques, Ricardo Ouyang, Wei Le, Trang Gómez-De-Mariscal, Estibaliz Sage, Daniel Muñoz-Barrutia, Arrate Lindqvist, Ebba Josefson Bergman, Johanna Department of Physics University of Gothenburg Gothenburg41296 Sweden Dept. IT and SciLifeLab Uppsala University Sweden Department of Electrical and Computer Engineering Boston University BostonMA02215 United States Department of Biomedical Engineering Boston University BostonMA02215 United States Görlitz Germany Department of Renal Medicine Division of Medicine University College London Royal Free Hospital Campus London United Kingdom Artificial Intelligence for Life Sciences CIC Dorset United Kingdom Roche Pharma International Informatics Roche Diagnostics GmbH Mannheim Germany Department of Electrical Engineering and Computer Sciences UC Berkeley United States Technische Universität Dresden Faculty of Computer Science Dresden Germany Max Planck Institute of Molecular Cell Biology and Genetics Center for Systems Biology Dresden Dresden Germany University of Maryland College Park United States Klosterneuburg3400 Austria School of Mathematics and Physics The University of Queensland Brisbane4072 Australia Australian Research Council Centre of Excellence for Engineered Quantum Systems The University of Queensland Brisbane4072 Australia Département d’Optique P. M. Duffieux Institut FEMTO-ST Université Franche-Comté CNRS UMR 6174 Besançon France Department of Electrical and Computer Engineering University of California Los Angeles Los AngelesCA United States Department of Information Technology Uppsala University Sweden Department of Biomedical Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Elephas 1 Erdman Pl. Madison53705 United States University of Wisconsin–Madison Department of Medical Physics MadisonWI53705 United States Department of Molecular and Cellular Physiology Albany Medical College AlbanyNY12208 United States Research Laboratory of Electronics Electrical Engineering and Computer Science Massachusetts Institute of Technology BostonMA United States Center for Nanophase Materials Sciences Oak R

Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasing resolution and throughput. Artificial intelligence, deep neural networks, and machine learning are all niche terms describing computational methods that have gained a pivotal role in microscopy-based research over the past decade. This Roadmap is written collectively by prominent researchers and encompasses selected aspects of how machine learning is applied to microscopy image data, with the aim of gaining scientific knowledge by improved image quality, automated detection, segmentation, classification and tracking of objects, and efficient merging of information from multiple imaging modalities. We aim to give the reader an overview of the key developments and an understanding of possibilities and limitations of machine learning for microscopy. It will be of interest to a wide cross-disciplinary audience in the physical sciences and life sciences. Copyright © 2023, The Authors. All rights reserved.

关键词： Image enhancement

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Miniaturized Microarray-Format Digital ELISA Enabled by Lithographic Protein Patterning

SSRN

引用

SSRN 2023年

作者： Stephens, Andrew D. Song, Yujing McClellan, Brandon L. Su, Shiuan-Haur Xu, Sonnet Chen, Kevin Castro, Maria G. Singer, Benjamin H. Kurabayashi, Katsuo Department of Mechanical Engineering University of Michigan Ann ArborMI48109 United States Department of Neurosurgery University of Michigan Medical School Ann ArborMI48109 United States Department of Cell and Developmental Biology University of Michigan Medical School Ann ArborMI48109 United States Graduate Program in Immunology University of Michigan Medical School Ann ArborMI48109 United States Department of Biomedical Engineering University of Michigan Ann ArborMI48109 United States Rogel Cancer Center University of Michigan Ann ArborMI48109 United States Department of Internal Medicine Division of Pulmonary and Critical Care Medicine University of Michigan Ann ArborMI48109 United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States Weil Institute for Critical Care Research and Innovation University of Michigan Ann ArborMI48109 United States

The search for reliable protein biomarker candidates is critical for early disease detection and treatment. However, current immunoassay technologies are failing to meet increasing demands for sensitivity and multiplexing. Here, the authors have created a highly sensitive protein microarray using the principle of single-molecule counting for signal amplification, capable of simultaneously detecting a panel of cancer biomarkers at sub-pg/mL levels. To enable this amplification strategy, the authors introduce a novel method of protein patterning using photolithography to subdivide addressable arrays of capture antibody spots into hundreds of thousands of individual microwells. This allows for the total sensor area to be miniaturized, increasing the total possible multiplex capacity. With the immunoassay realized on a standard 75x25 mm form factor glass substrate, sample volume consumption is minimized to © 2023, The Authors. All rights reserved.

关键词： Biomarkers

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Reduced recombination via tunable surface fields in perovskite thin films (aprl, 10.1038/s41560-024-01470-5, 2024)

引用

NATURE ENERGY 2024年第6期9卷 762-762页

作者： deQuilettes, Dane W. Yoo, Jason J. Brenes, Roberto Kosasih, Felix Utama Laitz, Madeleine Dou, Benjia Dak Graham, Daniel J. Ho, Kevin Shi, Yangwei Shin, Seong Sik Ducati, Caterina Bawendi, Moungi G. Bulovic, Vladimir Massachusetts Institute of Technology Research Laboratory of Electronics Cambridge USA (GRID:grid.116068.8) (ISNI:0000 0001 2341 2786) Massachusetts Institute of Technology Department of Chemistry Cambridge USA (GRID:grid.116068.8) (ISNI:0000 0001 2341 2786) Division of Advanced Materials Korea Research Institute of Chemical Technology Daejeon Republic of Korea (GRID:grid.29869.3c) (ISNI:0000 0001 2296 8192) Massachusetts Institute of Technology Research Laboratory of Electronics Cambridge USA (GRID:grid.116068.8) (ISNI:0000 0001 2341 2786) Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science Cambridge USA (GRID:grid.116068.8) (ISNI:0000 0001 2341 2786) University of Cambridge Department of Materials Science and Metallurgy Cambridge UK (GRID:grid.5335.0) (ISNI:0000 0001 2188 5934) University of Washington Department of Bioengineering Seattle USA (GRID:grid.34477.33) (ISNI:0000 0001 2298 6657) University of Washington Department of Chemistry Seattle USA (GRID:grid.34477.33) (ISNI:0000 0001 2298 6657) University of Washington Department of Chemistry Seattle USA (GRID:grid.34477.33) (ISNI:0000 0001 2298 6657) University of Washington Molecular Engineering and Sciences Institute Seattle USA (GRID:grid.34477.33) (ISNI:0000 0001 2298 6657) Division of Advanced Materials Korea Research Institute of Chemical Technology Daejeon Republic of Korea (GRID:grid.29869.3c) (ISNI:0000 0001 2296 8192) Sungkyunkwan University Department of Nano Engineering and Department of Nano Science and Technology SKKU Advanced Institute of Nanotechnology (SAINT) Suwon Republic of Korea (GRID:grid.264381.a) (ISNI:0000 0001 2181 989X) Sungkyunkwan University SKKU Institute of Energy Science and Technology (SIEST) Suwon Republic of Korea (GRID:grid.264381.a) (ISNI:0000 0001 2181 989X) Massachusetts Institute of Technology Department of Chemistry Cambridge USA (GRID:grid.116068.8) (ISNI:0000 0001 2341 2786)

The ability to reduce energy loss at semiconductor surfaces through passivation or surface field engineering is an essential step in the manufacturing of efficient photovoltaic (PV) and optoelectronic devices. Similarly, surface modification of emerging halide perovskites with quasi-two-dimensional (2D) heterostructures is now ubiquitous to achieve PV power conversion efficiencies (PCEs) >25%, yet a fundamental understanding to how these treatments function is still generally lacking. Here we use a unique combination of depth-sensitive nanoscale characterization techniques to uncover a tunable passivation strategy and mechanism found in perovskite PV devices that were the first to reach the >25% PCE milestone. Namely, treatment with hexylammonium bromide leads to the simultaneous formation of an iodide-rich 2D layer along with a Br halide gradient that extends from defective surfaces and grain boundaries into the bulk three-dimensional (3D) layer. This interface can be optimized to extend the charge carrier lifetime to record values >30 μs and to reduce interfacial recombination velocities to values as low as <7 cm s−*** et al. show that hexylammonium bromide forms an iodide-rich 2D structure and bromide gradient at the surface of 3D perovskite, both of which limit interfacial charge and energy losses in perovskite solar cells.

关键词： Passivation

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Macroscopically Self-Aligned and Chiralized Carbon Nanotubes: From Filtration to Innovation

arXiv

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arXiv 2023年

作者： Doumani, Jacques Zahn, Keshav Yu, Shengjie Barrios, Gustavo Rodriguez Sasmal, Somesh Kikuchi, Rikuta Kritzell, T. Elijah Xu, Hongjing Baydin, Andrey Kono, Junichiro Department of Electrical and Computer Engineering Rice University 6100 Main Street HoustonTX77005 United States Applied Physics Graduate Program Smalley–Curl Institute Rice University 6100 Main Street HoustonTX77005 United States Carbon Hub Rice University 6100 Main Street HoustonTX77005 United States Smalley–Curl Institute Rice University 6100 Main Street HoustonTX77005 United States Department of Physics and Astronomy Rice University 6100 Main Street HoustonTX77005 United States Department of Materials Science and NanoEngineering Rice University 6100 Main Street HoustonTX77005 United States

Because of their natural one-dimensional (1D) structure combined with intricate chiral variations, carbon nanotubes (CNTs) exhibit various exceptional physical properties, such as ultrahigh electrical and thermal conductivity, exceptional mechanical strength, and chirality-dependent metallicity. These properties make CNTs highly promising for diverse applications, including field-effect transistors, sensors, photodetectors, and thermoelectric devices. While CNTs excel individually at the nanoscale, their 1D and chiral nature can be lost on a macroscopic scale when they are randomly assembled. Therefore, the alignment and organization of CNTs in macroscopic structures is crucial for harnessing their full potential. In this review, we explore recent advancements in understanding CNT alignment mechanisms, improving CNT aligning methods, and demonstrating macroscopically 1D properties of ordered CNT assemblies. We also focus on a newly discovered class of CNT architectures, combining CNT alignment and twisting mechanisms to create artificial radial and chiral CNT films at wafer scales. Finally, we summarize recent developments related to aligned and chiral CNT films in optoelectronics, highlighting their unique roles in solar cells, thermal emitters, and optical modulators. Copyright © 2023, The Authors. All rights reserved.

关键词： Carbon nanotubes

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Electrostatic Properties and Ions Elimination Effect of Polyvinyl Chloride (PVC) Adhesive Tape Manufacturing

Electrostatic Properties and Ions Elimination Effect of Poly...

引用

International electrical engineering Congress (iEECON)

作者： Tatsanee Jenbanphue Komsan Hongesombut Kulsawasd Jitkajornwanich Siwapon Srisonphan Graduate program in safety engineering Kasetsart University Bangkok Thailand Department of Electrical Engineering Kasetsart University Bangkok Thailand Department of Computer Science King Mongkut’s Institute of Technology Ladkrabang Bangkok Thailand

ISBN: (数字)9781728130767

ISBN: (纸本)9781728130774

Electrostatic discharges (ESD) can release large amounts of energy to products and humans, causing product failure and pain, respectively. In this research, we elucidate the electrostatic charge generation, and discharge phenomena in real polyvinyl chloride (PVC) based electrical tape factory, which is primarily operated by the roll to roll manufacturing process. We perform the electrostatic potential measurement to identify the critical process of charge generation and to eliminate static charge at the source in order to prevent incidents caused by electrostatic discharge to operators or staff in the winding process. We also perform a numerical simulation-based finite element method (FEM) that provides modeling of an induced electric field, allowing the electrostatic discharge. The maximum potential is ~20 kV measured at 25 mm, indicating that the meter was induced by the electric field approximately ~0.8 MV/m, corresponding to surface charges density is ~ 10 μC/m 2 . Therefore, we introduced the ionization emitter bar in this case because it is feasible to install to the current infrastructure of the tested factory. The ionization emitter appears to reduce the electrostatic charge deposited on the PVC surface ~ 30% of the original charges. Therefore, installing a charge-dissipation technology at a proper location or combining with other methods, the electrostatic charge generation can be minimized even in roll to roll process.

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Efficient sampling of polycyclic aromatic compounds for free energy predictions through active learning

引用

Energy and AI 2025年 21卷

作者： Mohammed I. Radaideh Matt Raymond Paolo Elvati Jacob C. Saldinger Majdi I. Radaideh Angela Violi Department of Mechanical Engineering University of Michigan Ann Arbor MI 48109-2125 United States Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI 48109-2125 United States Department of Chemical Engineering University of Michigan Ann Arbor MI 48109-2125 United States Department of Nuclear Engineering and Radiological Sciences University of Michigan Ann Arbor MI 48109-1500 United States Biophysics Program University of Michigan Ann Arbor MI 48109-1500 United States

The physical growth of Polycyclic Aromatic Compounds (PACs) to soot particles plays a significant role in understanding the chemistry of soot formation. Insights into the process can be gained from PACs’ free energy of dimerization landscape. However, because the infeasibly large space of possible PAC dimers cannot be exhaustively simulated, researchers must train machine learning models on a subset of data to impute the rest. To this end, we propose and assess an active learning approach to discovering the optimal PACs for training a machine learning model to predict PACs’ association and dissociation free energies. The comparison between active learning and random sampling showed that active learning has faster loss convergence, requiring fewer training samples to reach the same level of accuracy. The trained model accurately modeled unseen PACs and exhibited robustness against changes in the sampling space used to train the model. More broadly, this work shows how active learning can optimize the design and improve the understanding of more expensive models in specific domains.

关键词： Free energy Polycyclic aromatic compounds (PACs) Active learning Gaussian process regression Representer theorem SHAP

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Quantum Approximation of Normalized Schatten Norms and Applications to Learning

arXiv

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arXiv 2022年

作者： Chen, Yiyou Miyahara, Hideyuki Bouchard, Louis-Serge Roychowdhury, Vwani Department of Computer Science University of California 404 Westwood Plaza Los AngelesCA90095 United States Department of Electrical and Computer Engineering University of California 420 Westwood Plaza Los AngelesCA90095 United States Department of Chemistry and Biochemistry University of California 607 Charles E Young Dr E Los AngelesCA90095 United States Center for Quantum Science and Engineering University of California Los Angeles Los AngelesCA90095 United States California NanoSystems Institute University of California 570 Westwood Plaza building 114 Los AngelesCA90095 United States Department of Bioengineering University of California 410 Westwood Plaza Los AngelesCA90095 United States The Molecular Biology Institute University of California 611 Charles E. Young Drive East Los AngelesCA90095 United States

Efficient measures to determine similarity of quantum states, such as the fidelity metric, have been widely studied. In this paper, we address the problem of defining a similarity measure for quantum operations that can be efficiently estimated. Given two quantum operations, U1 and U2, represented in their circuit forms, we first develop a quantum sampling circuit to estimate the normalized Schatten 2-norm of their difference (kU1 − U2kS2) with precision E, using only one clean qubit and one classical random variable. We prove a Poly(1E) upper bound on the sample complexity, which is independent of the size of the quantum system. We then show that such a similarity metric is directly related to a functional definition of similarity of unitary operations using the conventional fidelity metric of quantum states (F): If kU1 − U2kS2 is sufficiently small (e.g. ≤ 1+√2(1E/δ−1)) then the fidelity of states obtained by processing the same randomly and uniformly picked pure state, |ψi, is as high as needed (F(U1 |ψi, U2 |ψi) ≥ 1 − E) with probability exceeding 1−δ. We provide example applications of this efficient similarity metric estimation framework to quantum circuit learning tasks, such as finding the square root of a given unitary operation. © 2022, CC BY.

关键词： Timing circuits

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