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Superior performance of ZnGaO solar-blind photodetectors by Implementing TFT structure and tunable ZnO cycle ratio

Materials Today Chemistry 2024年 38卷

作者： Fan, Hui-Chen Wang, Chen Xu, Yi-Hong Fan, Teng-Min Huang, Pao-Hsun Ruan, Yu-Jiao Wu, Ting-Zhu Kuo, Hao-Chung Wuu, Dong-Sing Lai, Feng-Min Lien, Shui-Yang Xiamen Key Laboratory of Development and Application for Advanced Semiconductor Coating Technology School of Opto-Electronic and Communication Engineering Xiamen University of Technology Xiamen 361024 China School of Ocean Information Engineering Jimei University Jimei District Xiamen 361021 Fujian China National Measurement and Testing Center for Flat Panel Display Industry Xiamen Institute of Measurement and Testing Xiamen 361024 China National Integrated Circuit Industry and Education Integration Innovation Platform Department of Electronic Science Xiamen University Xiamen 361005 China Department of Photonics & Graduate Institute of Electro-Optical Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu 30010 Taiwan Department of Applied Materials and Optoelectronic Engineering National Chi Nan University Nantou 54561 Taiwan Department of Biomedical Engineering Da-Yeh University Changhua 51591 Taiwan

In this study, plasma enhanced atomic layer deposition was used to prepare ZnGaO thin films with different ZnO cycle ratios from 0 % to 50 %, which were employed to fabricate the MSM-type and TFT-type solar-blind photodetector. The properties of the films and the performance of the solar blind photodetector are greatly affected by the ZnO cycle ratio. Appropriate Zn doping content decreases the ratio of oxygen vacancies and Zn interstitials in Ga₂O₃, while excessive Zn doping degrades the quality of the film. Compared to MSM-type photodetector, an obviously improved performance of ZnGaO TFT-type photodetector has been observed due to its self-amplified photocurrent and the reduced dark current controlled by gate voltage. At a ZnO cycle ratio of 40 %, a superior performance of TFT-type solar-blind photodetector with an ultra-low dark current ∼4.5 × 10⁻¹³ A, an excellent light switching ratio ∼2.2 × 10⁷, and an outstandingly high sensitivity ∼34.31 A/W has been obtained. These results demonstrate that the incorporation of Zn dopants can effectively modulate the performance of Ga₂O₃ TFT-type photodetector providing a great perspective for cost-effective and high-performance solar-blind PDs. © 2024 Elsevier Ltd

关键词： High performance Plasma enhanced atomic layer deposition TFT-Type solar-blind photodetector ZnGaO film ZnO cycle ratio

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Integrated path planning and tracking control of marine current turbine in uncertain ocean environments

arXiv

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arXiv 2021年

作者： Hasankhani, Arezoo Ondes, Ertugrul Baris Tang, Yufei Sultan, Cornel VanZwieten, James The Department of Computer & Electrical Engineering and Computer Science Florida Atlantic University Boca RatonFL33431 United States The Department of Aerospace and Ocean Engineering Virginia Tech 460 Old Turner St. BlacksburgVA24061 United States The Department of Civil Environmental and Geomatics Engineering Florida Atlantic University Boca RatonFL33431 United States

This paper presents an integrated path planning and tracking control of marine hydrokinetic energy harvesting devices. To address the highly nonlinear and uncertain oceanic environment, the path planner is designed based on a reinforcement learning (RL) approach by fully exploring the historical ocean current profiles. The planner will search for a path to optimize a chosen cost criterion, such as maximizing the total harvested energy for a given time. Model predictive control (MPC) is then utilized to design the tracking control for the optimal path command from the planner subject to problem constraints. The planner and the tracking control are accommodated in an integrated framework to optimize these two parts in a real-time manner. The proposed approach is validated on a marine current turbine (MCT) that executes vertical waypoint path searching to maximize the net power due to spatiotemporal uncertainties in the ocean environment, as well as the path following via an MPC tracking controller to navigate the MCT to the optimal path. Results demonstrate that the path planning increases harnessed power compared to the baseline (i.e., maintaining MCT at an equilibrium depth), and the tracking controller can successfully follow the reference path under different shear profiles. Copyright © 2021, The Authors. All rights reserved.

关键词： Motion planning

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Learning High-Resolution and Efficient Non-local Features for Brain Glioma Segmentation in MR Images 23rd

Learning High-Resolution and Efficient Non-local Features fo...

引用

23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2020

作者： Jia, Haozhe Xia, Yong Cai, Weidong Huang, Heng Research and Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen518057 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xian710072 China School of Computer Science University of Sydney SydneyNSW2006 Australia Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA15261 United States JD Finance America Corporation Mountain ViewCA94043 United States

ISBN: (纸本)9783030597184

Brain glioma segmentation using multi-parametric magnetic resonance (MR) imaging has significant clinical value. Although 3D convolutional neural networks (CNNs) have become increasingly prevalent in delivering this segmentation task, these models still suffer from an insufficient ability to high-resolution feature representation for small and irregular regions, limited local receptive fields, and poor long-range dependencies. In this paper, we propose a 3D High-resolution and Non-local Feature Network (HNF-Net) for brain glioma segmentation using multi-parametric MR imaging. We construct HNF-Net based mainly on the parallel multi-scale fusion (PMF) module, which helps produce strong high-resolution feature representation and aggregate multi-scale contextual information. We also introduce the expectation-maximization attention (EMA) module to HNF-Net, aiming to capture the long-range dependent contextual information and reduce the feature redundancy in a lightweight fashion. We evaluated our HNF-Net on the BraTS 2019 Challenge dataset against eight top-ranking methods listed on the challenge leaderboard. Our results suggest that the proposed HNF-Net achieves improved overall performance over these methods, and our ablation study demonstrates the effectiveness of the PMF module and EMA module. © 2020, Springer Nature Switzerland AG.

关键词： Magnetic resonance imaging

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An algorithm for constructing monotone quintic interpolating splines

An algorithm for constructing monotone quintic interpolating...

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2020 Spring Simulation Multiconference, SpringSim 2020

作者： Lux, Thomas C.H. Watson, Layne T. Chang, Tyler H. Xu, Li Wang, Yueyao Hong, Yili Departments of Computer Science Mathematics and Aerospace and Ocean Engineering Virginia Polytechnic Institute State University 2000 Torgersen Hall BlacksburgVA United States Department of Statistics Virginia Polytechnic Institute State University 213 Hutcheson Hall BlacksburgVA United States

ISBN: (纸本)9781713812883

A novel algorithm for computing monotone order six piecewise polynomial interpolants is proposed. Algebraic constraints for enforcing monotonicity are provided that align with quintic monotonicity theory. The algorithm is implemented, tested, and applied to several sample problems to demonstrate the improved accuracy of monotone quintic spline interpolants compared to the previous state-of-the-art monotone cubic spline interpolants. © 2020 Society for Modeling & Simulation International (SCS)

关键词： Interpolation

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A swarm of simple robots constructing planar shapes

arXiv

引用

arXiv 2020年

作者： Vardy, Andrew Ibrahim, Dalia S. Departments of Computer Science and Electrical & Computer Engineering Memorial University of Newfoundland St. John’s Canada Departments of Computer Science Memorial University of Newfoundland St. John’s Canada

We present a new version of our previously proposed algorithm enabling a swarm of robots to construct a desired shape from objects in the plane. We also describe a hardware realization for this system which makes use of simple and readily sourced components. We refer to the task as planar construction which is the gathering of ambient objects into some desired shape. As an example application, a swarm of robots could use this algorithm to not only gather waste material into a pile, but shape that pile into a line for easy collection. The shape is specified by an image known as the scalar field. The scalar field serves an analogous role to the template pheromones that guide the construction of complex natural structures such as termite mounds. In addition to describing the algorithm and hardware platform, we develop some performance insights using a custom simulation environment and present experimental results on physical robots. Copyright © 2020, The Authors. All rights reserved.

关键词： Piles

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Image-Based Benchmarking and Visualization for Large-Scale Global Optimization

arXiv

引用

arXiv 2020年

作者： Harrison, Kyle Robert Bidgoli, Azam Asilian Rahnamayan, Shahryar Deb, Kalyanmoy Lab Department of Electrical Computer and Software Engineering University of Ontario Institute of Technology Oshawa Canada Computational Optimization and Innovation [COIN Laboratory Departments of Electrical and Computer Engineering Computer Science and Engineering and Mechanical Engineering Michigan State University East Lansing United States

In the context of optimization, visualization techniques can be useful for understanding the behaviour of optimization algorithms and can even provide a means to facilitate human interaction with an optimizer. Towards this goal, an image-based visualization framework, without dimension reduction, that visualizes the solutions to large-scale global optimization problems as images is proposed. In the proposed framework, the pixels visualize decision variables while the entire image represents the overall solution quality. This framework affords a number of benefits over existing visualization techniques including enhanced scalability (in terms of the number of decision variables), facilitation of standard image processing techniques, providing nearly infinite benchmark cases, and explicit alignment with human perception. Furthermore, image-based visualization can be used to visualize the optimization process in real-time, thereby allowing the user to ascertain characteristics of the search process as it is progressing. To the best of the authors’ knowledge, this is the first realization of a dimension-preserving, scalable visualization framework that embeds the inherent relationship between decision space and objective space. The proposed framework is utilized with 10 different mapping schemes on an image-reconstruction problem that encompass continuous, discrete, binary, combinatorial, constrained, dynamic, and multi-objective optimization. The proposed framework is then demonstrated on arbitrary benchmark problems with known optima. Experimental results elucidate the flexibility and demonstrate how valuable information about the search process can be gathered via the proposed visualization framework. Copyright © 2020, The Authors. All rights reserved.

关键词： Visualization

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Pseudo-MRI-Guided PET Image Reconstruction Method Based on a Diffusion Probabilistic Model

arXiv

引用

arXiv 2024年

作者： Gan, Weijie Xie, Huidong von Gall, Carl Platsch, Günther Jurkiewicz, Michael T. Andrade, Andrea Anazodo, Udunna C. Kamilov, Ulugbek S. An, Hongyu Cabello, Jorge Department of Computer Science & Engineering Washington University in St. Louis St. LouisMO United States Department of Radiology and Biomedical Imaging Yale University New HavenCT United States Department of Medical Imaging Department of Medical Biophysics Western University 1151 Richmond St LondonONN6A 5C1 Canada Department of Pediatrics Department of Clinical Neurological Sciences Western University 1151 Richmond St LondonONN6A 5C1 Canada Lawson Health Research Institute 268 Grosvenor St LondonONN6A 4V2 Canada Neurology and Neurosurgery Montreal Neurological Institute McGill University MontrealQCH3A 2B4 Canada Siemens Healthineers AG Erlangen Germany Department of Computer Science & Engineering Department of Electrical & System Engineering Washington University in St. Louis St. LouisMO United States Institute of Radiology The Departments of Neurology Biomedical Engineering Electrical & System Engineering Division of Biology and Biomedical Sciences Washington University St. LouisMO United States Siemens Medical Solutions USA Inc. 810 Innovation Dr KnoxvilleTN37932 United States

Anatomically guided PET reconstruction using MRI information has been shown to have the potential to improve PET image quality. However, these improvements are limited to PET scans with paired MRI information. In this work we employed a diffusion probabilistic model (DPM) to infer T1-weightedMRI (deep-MRI) images from FDG-PET brain images. We then use the DPM-generated T1w-MRI to guide the PET reconstruction. The model was trained with brain FDG scans, and tested in datasets containing multiple levels of counts. Deep-MRI images appeared somewhat degraded than the acquired MRI images. Regarding PET image quality, volume of interest analysis in different brain regions showed that both PET reconstructed images using the acquired and the deep-MRI images improved image quality compared to OSEM. Same conclusions were found analysing the decimated datasets. A subjective evaluation performed by two physicians confirmed that OSEM scored consistently worse than the MRI-guided PET images and no significant differences were observed between the MRI-guided PET images. This proof of concept shows that it is possible to infer DPM-based MRI imagery to guide the PET reconstruction, enabling the possibility of changing reconstruction parameters such as the strength of the prior on anatomically guided PET reconstruction in the absence of MRI. Copyright © 2024, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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NLOS Identification and Machine Learning Methods for Predicting the Outcome of 60GHz Ranging System

引用

Chinese Journal of Electronics 2018年第1期27卷 175-182页

作者： LIANG Xiaolin JIN Yiheng ZHANG Hao LYU Tingting College of Information Science and Engineering Ocean University of China Department of Electrical Computer Engineering University of Victoria

Millimeter-wave(MMW) signals in 60 GHz band have shown immense potential for accurate range estimation with precise time and multipath resolution. Nonline of sight(NLOS) propagation is a primary factor affecting the range precision. To improve range estimation,an Energy detector(ED) based normalized threshold algorithm which employs a Neural network(NN) is developed on the basis of NLOS identification. The maximum curl and standard deviation of the received energy block values are used to determine NLOS environment and the normalized thresholds for different Signal-to-noise ratios(SNRs). The effects of the channel and integration period are *** results are presented which show that the proposed approach provides better precision and is more robust than other solutions over a wide range of SNRs for the CM1.1 and CM2.1 channel models in the IEEE802.15.3 c standard.

关键词： Non-line of sight(NLOS) identification Curl Standard deviation IEEE 802.15.3c Machine learning

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Experimental implementation of an efficient test of quantumness

arXiv

引用

arXiv 2022年

作者： Lewis, Laura Zhu, Daiwei Gheorghiu, Alexandru Noel, Crystal Katz, Or Harraz, Bahaa Wang, Qingfeng Risinger, Andrew Feng, Lei Biswas, Debopriyo Egan, Laird Vidick, Thomas Cetina, Marko Monroe, Christopher Institute for Quantum Information and Matter Department of Computing and Mathematical Sciences California Institute of Technology CA91125 United States Division of Physics Mathematics and Astronomy California Institute of Technology CA91125 United States Joint Quantum Institute Departments of Physics and Electrical and Computer Engineering University of Maryland College ParkMD20742 United States Joint Center for Quantum Information and Computer Science NIST University of Maryland College ParkMD20742 United States IonQ Inc. College ParkMD20740 United States Departments of Electrical and Computer Engineering University of Maryland College ParkMD20742 United States Institute for Theoretical Studies ETH Zürich CH 8001 Switzerland Duke Quantum Center Department of Physics Duke University DurhamNC27708 United States Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States Chemical Physics Program Institute for Physical Science and Technology University of Maryland College ParkMD20742 United States

A test of quantumness is a protocol where a classical user issues challenges to a quantum device to determine if it exhibits non-classical behavior, under certain cryptographic assumptions. Recent attempts to implement such tests on current quantum computers rely on either interactive challenges with efficient verification, or non-interactive challenges with inefficient (exponential time) verification. In this paper, we execute an efficient non-interactive test of quantumness on an ion-trap quantum computer. Our results significantly exceed the bound for a classical device’s success. Copyright © 2022, The Authors. All rights reserved.

关键词： Qubits

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AI-Derived Left Ventricular Mass From Noncontrast Cardiac CT: Correlation With Contrast CT Angiography and CMR

JACC: Advances

引用

JACC: Advances 2024年第10期3卷 101249-101249页

作者： Han, Donghee Shanbhag, Aakash Miller, Robert JH. Kwok, Nicholas Waechter, Parker Builoff, Valerie Newby, David E. Dey, Damini Berman, Daniel S. Slomka, Piotr Departments of Medicine (Division of Artificial Intelligence in Medicine) Imaging and Biomedical Sciences Cedars-Sinai Medical Center Los Angeles CA United States Signal and Image Processing Institute Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles CA United States Department of Cardiac Sciences University of Calgary Calgary AB Canada British Heart Foundation Centre for Cardiovascular Science University of Edinburgh Edinburgh United Kingdom

Background: Noncontrast computed tomography (CT) scans are not used for evaluating left ventricle myocardial mass (LV mass), which is typically evaluated with contrast CT or cardiovascular magnetic resonance imaging (CMR). Objectives: The purpose of the study was to assess the feasibility of LV mass estimation from standard, ECG-gated, noncontrast CT using an artificial intelligence (AI) approach and compare it with coronary CT angiography (CTA) and CMR. Methods: We enrolled consecutive patients who underwent coronary CTA, which included noncontrast CT calcium scanning and contrast CTA, and CMR. The median interval between coronary CTA and CMR was 22 days (interquartile range: 3-76). We utilized a no new UNet AI model that automatically segmented noncontrast CT structures. AI measurement of LV mass was compared to contrast CTA and CMR. Results: A total of 316 patients (age: 57.1 ± 16.7 years, 56% male) were included. The AI segmentation took on average 22 seconds per case. An excellent correlation was observed between AI and contrast CTA LV mass measures (r = 0.84, P < 0.001), with no significant differences (136.5 ± 55.3 g vs 139.6 ± 56.9 g, P = 0.133). Bland-Altman analysis showed minimal bias of 2.9. When compared to CMR, measured LV mass was higher with AI (136.5 ± 55.3 g vs 127.1 ± 53.1 g, P < 0.001). There was an excellent correlation between AI and CMR (r = 0.85, P < 0.001), with a small bias (−9.4). There were no statistical differences between the correlations of LV mass between contrast CTA and CMR or AI and CMR. Conclusions: The AI-based automated estimation of LV mass from noncontrast CT demonstrated excellent correlations and minimal biases when compared to contrast CTA and CMR. © 2024 The Authors

关键词： artificial intelligence cardiac magnetic resonance imaging computed tomography myocardial mass

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