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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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Data Processing of Functional Optical Microscopy for Neuroscience

arXiv

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arXiv 2022年

作者： Benisty, Hadas Song, Alexander Mishne, Gal Charles, Adam S. Yale Neuroscience New HavenCT06510 United States Max Planck Institute for Intelligent Systems Stuttgart Germany Halıcıoğlu Data Science Institute Department of Electrical and Computer Engineering Neurosciences Graduate Program UC San Diego 9500 Gilman Drive La Jolla CA92093 United States Department of Biomedical Engineering Kavli Neuroscience Discovery Institute Center for Imaging Science Department of Neuroscience Mathematical Institute for Data Science Johns Hopkins University BaltimoreMD21287 United States

Functional optical imaging in neuroscience is rapidly growing with the development of new optical systems and fluorescence indicators. To realize the potential of these massive spatiotemporal datasets for relating neuronal activity to behavior and stimuli and uncovering local circuits in the brain, accurate automated processing is increasingly essential. In this review, we cover recent computational developments in the full data processing pipeline of functional optical microscopy for neuroscience data and discuss ongoing and emerging challenges. © 2022, CC BY-NC-ND.

关键词： Fluorescence microscopy

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On the Selection of Neural Network Architecture for Supervised Motor Unit Identification from High-Density Surface EMG*

On the Selection of Neural Network Architecture for Supervis...

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

作者： Filip Urh Damjan Strnad Alex Clarke Dario Farina Aleš Holobar Faculty of Electrical Engineering and Computer Science University of Maribor Maribor Slovenia Department of Bioengineering Imperial College London London United Kingdom

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

In the last decade, accurate identification of motor unit (MU) firings received a lot of research interest. Different decomposition methods have been developed, each with its advantages and disadvantages. In this study, we evaluated the capability of three different types of neural networks (NNs), namely dense NN, long short-term memory (LSTM) NN and convolutional NN, to identify MU firings from high-density surface electromyograms (HDsEMG). Each type of NN was evaluated on simulated HDsEMG signals with a known MU firing pattern and high variety of MU characteristics. Compared to dense NN, LSTM and convolutional NN yielded significantly higher precision and significantly lower miss rate of MU identification. LSTM NN demonstrated higher sensitivity to noise than convolutional *** Relevance-MU identification from HDsEMG signals offers valuable insight into neurophysiology of motor system but requires relatively high level of expert knowledge. This study assesses the capability of self-learning artificial neural networks to cope with this problem.

关键词： Artificial neural networks Firing Convolution Training Electromyography Muscles Signal to noise ratio

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Multifunctional metaoptics based on bilayer metasurfaces

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Light(science & Applications) 2019年第1期8卷 466-474页

作者： You Zhou Ivan I.Kravchenko Hao Wang Hanyu Zheng Gong Gu Jason Valentine Interdisciplinary Materials Science Program Vanderbilt UniversityNashvilleTN 37212USA Center for Nanophase Materials Sciences Oak Ridge National LaboratoryOak RidgeTN 37831USA Min H.Kao Department of Electrical Engineering and Computer Science University of TennesseeKnoxvilleTN 37996USA Department of Electric Engineering and Computer Science Vanderbilt UniversityNashvilleTN 37212USA Department of Mechanical Engineering Vanderbilt UniversityNashvilleTN 37212USA

Optical metasurfaces have become versatile platforms for manipulating the phase,amplitude,and polarization of light.A platform for achieving independent control over each of these properties,however,remains elusive due to the limited engineering space available when using a single-layer *** instance,multiwavelength metasurfaces suffer from performance limitations due to space filling constraints,while control over phase and amplitude can be achieved,but only for a single ***,we explore bilayer dielectric metasurfaces to expand the design space for *** ability to independently control the geometry and function of each layer enables the development of multifunctional metaoptics in which two or more optical properties are independently *** a proof of concept,we demonstrate multiwavelength holograms,multiwavelength waveplates,and polarization-insensitive 3D holograms based on phase and amplitude *** proposed architecture opens a new avenue for designing complex flat optics with a wide variety of functionalities.

关键词： surface. optics independently

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Spatial atlas of the mouse central nervous system at molecular resolution (vol 622, pg 552, 2023)

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NATURE 2024年第7993期625卷 E6-E6页

作者： Shi, Hailing He, Yichun Zhou, Yiming Huang, Jiahao Maher, Kamal Wang, Brandon Tang, Zefang Luo, Shuchen Tan, Peng Wu, Morgan Lin, Zuwan Ren, Jingyi Thapa, Yaman Tang, Xin Chan, Ken Y. Deverman, Benjamin E. Shen, Hao Liu, Albert Liu, Jia Wang, Xiao Broad Institute of MIT and Harvard Cambridge MA USA Department of Chemistry Massachusetts Institute of Technology Cambridge MA USA John A. Paulson School of Engineering and Applied Sciences Harvard University Boston MA USA Computational and Systems Biology PhD Program Massachusetts Institute of Technology Cambridge MA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Department of Biology Massachusetts Institute of Technology Cambridge MA USA Klarman Cell Observatory Broad Institute of MIT and Harvard Cambridge MA USA Department of Chemistry and Chemical Biology Harvard University Cambridge MA USA Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge MA USA

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Unique material modifications of Ga2O3 enabled by ultrafast laser irradiation 11

Unique material modifications of Ga2O3 enabled by ultrafast ...

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Oxide-based Materials and Devices XI 2020

作者： Ahn, Minhyung Sarracino, Alex Ansari, Abdul Torralva, Ben Yalisove, Steven Phillips, Jamie Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States Applied Physics Program University of Michigan Ann ArborMI48109 United States Department of Climate and Space Sciences and Engineering University of Michigan Ann ArborMI48109 United States Materials Science and Engineering Department University of Michigan Ann ArborMI48109 United States

ISBN: (纸本)9781510633254

Ultrafast (femtosecond pulse) laser irradiation provides unique laser-material interactions associated with high instantaneous electric fields and short timescales that lead to a nonequilibrium state between excited electrons and phonons. These interactions result in material modifications that differ from conventional chemical, physical, and thermal processing at much longer timescales, and an opportunity to address material and device processing challenges associated with wide-bandgap materials such as Ga2O3. In this work, we explore ultrafast laser irradiation (Ti:sapphire, 150 fs pulse width) of bulk (010) Sn-doped β-Ga2O3 under two different wavelengths, fundamental (780 nm) and frequency-doubled (390 nm), and a range of laser fluences. We identified a regime for laser-induced damage threshold resulting in material ablation, thermally-induced straight crack formation, and recrystallization. Rastering on a β-Ga2O3 substrate created surface nanostructures including laser-induced periodic surface structures at a high spatial frequency (period ∼250 nm). These highly aligned periodic structures can be controlled by laser polarization and wavelength, presenting a means for direct writing of surface nanostructures. Enhanced atomic movement associated with a transient metallic state can provide a means for intentional generation of point defects via the laser irradiation. These point defects may offer a means of electrical modification, which was demonstrated as more than five orders of the magnitude enhancement of lateral conductance on rastered β-Ga2O3. Moreover, a hydrophobic surface of β-Ga2O3was achieved by ultrafast laser irradiation. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Hydrophobicity

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Observation of Temporal Reflections and Broadband Frequency Translations at Photonic Time-Interfaces

arXiv

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arXiv 2022年

作者： Moussa, Hady Xu, Gengyu Yin, Shixiong Galiffi, Emanuele Ra'di, Younes Alù, Andrea Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States Department of Electric Engineering City College of City University of New York New YorkNY10031 United States Department of Electrical Engineering and Computer Science Syracuse University SyracuseNY13244 United States Physics Program Graduate Center City University of New York New YorkNY10016 United States

Time-reflection is a uniform inversion of the temporal evolution of a signal, which arises when an abrupt change in the properties of the host material occurs uniformly in space.1-3 At such a time-interface, a portion of the input signal is time-reversed, and its frequency spectrum is homogeneously translated while its momentum is conserved, forming the temporal counterpart of a spatial interface. Combinations of time-interfaces, forming time-metamaterials and Floquet matter4, exploit the interference of multiple time-reflections for extreme wave manipulation, leveraging time as a new degree of freedom5. Here, we report the observation of photonic time-reflection and associated broadband frequency translation in a switched transmission-line metamaterial whose effective capacitance is homogeneously and abruptly changed via a synchronized array of switches. A pair of temporal interfaces are combined to demonstrate time-reflection-induced wave interference, realizing the temporal counterpart of a Fabry-Perot cavity6. Our results establish the foundational building blocks to realize time-metamaterials and Floquet photonic crystals, with opportunities for extreme photon manipulation in space and time7-8 © 2022, CC BY.

关键词： Metamaterials

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Machine learning for improved current-density reconstruction from two-dimensional vector magnetic images

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Physical Review Applied 2025年第3期23卷 034035-034035页

作者： Niko R. Reed Danyal Bhutto Matthew J. Turner Declan M. Daly Sean M. Oliver Jiashen Tang Kevin S. Olsson Nicholas Langellier Mark J.H. Ku Department of Physics University of Maryland College Park Maryland USA Quantum Technology Center University of Maryland College Park Maryland USA Joint Quantum Institute University of Maryland College Park Maryland USA Department of Biomedical Engineering Boston University Boston Massachusetts USA A. A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown Massachusetts USA Department of Electrical and Computer Engineering University of Maryland College Park Maryland USA The MITRE Corporation McLean Virginia 22102 USA Intelligence Community Postdoctoral Research Fellowship Program University of Maryland College Park Maryland 20742 USA Department of Physics Harvard University Cambridge Massachusetts USA Present address: VideaHealth Inc. Boston MA. Department of Materials Science and Engineering University of Delaware Newark Delaware 19716 USA Department of Physics and Astronomy University of Delaware Newark Delaware 19716 USA Present address: Northrop Grumman Mission Systems Linthicum Maryland 21090 USA.

The reconstruction of electrical current densities from magnetic field measurements is an important technique with applications in materials science, circuit design, quality control, plasma physics, and biology. Analytic reconstruction methods exist for planar currents, but break down in the presence of high-spatial-frequency noise or large standoff distance, restricting the types of systems that can be studied. Here, we demonstrate the use of a deep convolutional neural network for current density reconstruction from two-dimensional images of vector magnetic fields acquired by a quantum diamond microscope . Trained network performance significantly exceeds analytic reconstruction for data with high noise or large standoff distances. This machine learning technique can perform quality inversions on lower-signal-to-noise-ratio data, significantly reducing the data collection time and permitting reconstructions of weaker and three-dimensional current sources.

关键词： Signal to noise ratio

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Aerogel from sustainably grown bacterial cellulose pellicle as thermally insulative film for building envelope

arXiv

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arXiv 2022年

作者： Fleury, Blaise Abraham, Eldho De La Cruz, Joshua A. Chandrasekar, Varun S. Senyuk, Bohdan Liu, Qingkun Cherpak, Vladyslav Park, Sungoh ten Hove, Jan Bart Smalyukh, Ivan I. Department of Physics University of Colorado Boulder CO80309 United States Materials Science and Engineering Program University of Colorado Boulder CO80309 United States Soft Materials Research Center and Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO80309 United States Renewable and Sustainable Energy Institute National Renewable Energy Laboratory University of Colorado Boulder CO80303 United States

Improving building energy performance requires the development of new highly insulative materials. An affordable retrofitting solution comprising a thin film could improve the resistance to heat flow in both residential and commercial buildings and reduce overall energy consumption. Here we propose cellulose aerogel films formed from pellicles produced by the bacteria Gluconacetobacter hansenii as insulation materials. We studied the impact of density and nanostructure on the aerogels' thermal properties. Thermal conductivity as low as 13 mW/(K·m) was measured for native pellicle-based aerogels dried as-is with minimal post-treatment. The use of waste from the beer brewing industry as a solution to grow the pellicle maintained the cellulose yield obtained with standard Hestrin-Schramm medium, making our product more affordable and sustainable. In the future, our work can be extended through further diversification of the sources of substrate among food wastes, facilitating larger potential production and applications. © 2022, CC BY-NC-ND.

关键词： Aerogels

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Active Pixel TFT Arrays for Digital Fluoroscopy in a-Si:H Technology

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MRS Online Proceedings Library 2020年第1期862卷 2241-2246页

作者： Jackson Lai Nader Safavian Arokia Nathan John A. Rowlands Electrical and Computer Engineering Department University of Waterloo Waterloo Ontario Canada Imaging Research Program Sunnybrook Health Science Centre Toronto Ontario Canada

Major development challenges in application of hydrogenated amorphous silicon (a-Si:H) technology to large area digital X-ray imaging and technological attributes such as low temperature deposition and high uniformity over large area evolve around dynamic imaging modalities such as fluoroscopy, which demands both high speed readout and signal amplification capabilities in addition to long term device stability. This work reports on initial results of a variety of TFT active pixel sensor (APS) structures in a-Si:H technology, each demonstrating unique capabilities such as enhancements in signal gain, TFT threshold voltage immunity, and real-time high speed readout.

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