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arXiv 2020年

作者： Chang, Celesta S. Tanen, Nicholas Protasenko, Vladimir Asel, Thaddeus J. Mou, Shin Xing, Huili Grace Jena, Debdeep Muller, David A. Department of Physics Cornell University IthacaNY14853 United States School of Applied and Engineering Physics Cornell University IthacaNY14853 United States Department of Materials Science and Engineering Cornell University IthacaNY14853 United States School of Electrical and Computer Engineering Cornell University IthacaNY14853 United States Air Force Research Laboratory Materials and Manufacturing Directorate Wright Patterson AFB OH45433 United States Kavli Institute at Cornell for Nanoscale Science Cornell University IthacaNY14853 United States

β-Ga2O3 is a promising ultra-wide bandgap semiconductor whose properties can be further enhanced by alloying with Al. Here, using atomic-resolution scanning transmission electron microscopy (STEM), we find the thermodynamically-unstable γ-phase is a ubiquitous defect in both β-(AlxGa1–x)2O3 films and doped β-Ga2O3 films grown by molecular beam epitaxy. For undoped β-(AlxGa1–x)2O3 films we observe γ-phase inclusions between nucleating islands of the β-phase at lower growth temperatures (~400-600 °C). In doped β-Ga2O3, a thin layer of the γ-phase is observed on the surfaces of films grown with a wide range of n-type dopants and dopant concentrations. The thickness of the γ-phase layer was most strongly correlated with the growth temperature, peaking at about 600 °C. Ga interstitials are observed in β-phase, especially near the interface with the γ-phase. By imaging the same region of the surface of a Sn-doped β-(AlxGa1–x)2O3 after ex-situ heating up to 400 °C, a γ-phase region is observed to grow above the initial surface, accompanied by a decrease in Ga interstitials in the β-phase. This suggests that the diffusion of Ga interstitials towards the surface is likely the mechanism for growth of the surface γ-phase, and more generally that the more-open γ-phase may offer diffusion pathways to be a kinetically-favored and early-forming phase in the growth of Ga2O3. © 2020, CC BY.

关键词： Growth temperature

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Deep reinforcement learning for fresh data collection in UAV-assisted IoT networks

arXiv

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arXiv 2020年

作者： Yi, Mengjie Wang, Xijun Liu, Juan Zhang, Yan Bai, Bo State Key Lab of Integrated Service Networks Information Science Institute Xidian University Xi’an Shaanxi710071 China Science and Technology on Communication Network Laboratory Shijiazhuang Hebei050081 China School of Electronics and Communication Engineering Sun Yat-sen University Guangzhou510006 China Key Laboratory of Wireless Sensor Network & Communication Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences 865 Changning Road Shanghai200050 China School of Electrical Engineering and Computer Science Ningbo University Zhejiang315211 China 2012 Labs Huawei Technologies Co. Ltd. Hong Kong Hong Kong

Due to the flexibility and low operational cost, dispatching unmanned aerial vehicles (UAVs) to collect information from distributed sensors is expected to be a promising solution in Internet of Things (IoT), especially for time-critical applications. How to maintain the information freshness is a challenging issue. In this paper, we investigate the fresh data collection problem in UAV-assisted IoT networks. Particularly, the UAV flies towards the sensors to collect status update packets within a given duration while maintaining a non-negative residual energy. We formulate a Markov Decision Process (MDP) to find the optimal flight trajectory of the UAV and transmission scheduling of the sensors that minimizes the weighted sum of the age of information (AoI). A UAV-assisted data collection algorithm based on deep reinforcement learning (DRL) is further proposed to overcome the curse of dimensionality. Extensive simulation results demonstrate that the proposed DRL-based algorithm can significantly reduce the weighted sum of the AoI compared to other baseline algorithms. Copyright © 2020, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Role of hole transport layers in normal and inverted bulk heterojunction organic solar cells: A light intensity dependent capacitance-voltage analysis

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Materials science in Semiconductor Processing 2025年 198卷

作者： Shailendra Kumar Gupta Durgesh C. Tripathi Amit Kumar Neha Garg Chinmay K. Gupta Vandana Yadav Sandeep Sharma Sanjeev Kumar Ashish Garg Amity School of Physical Sciences Amity University Punjab IT City Sector 82A Mohali 140308 India The Zispel Nano Electronic Center Department of Electrical Engineering Technion-Israel Institute of Technology Haifa 32000 Israel Clarendon Laboratory Department of Physics University of Oxford Oxford OX1 3PU United Kingdom Department of Computer Science and Engineering Manav Rachna International Institute of Research and Studies Faridabad 121004 India Department of Physics National Post Graduate College Barhalganj 273402 Gorakhpur India Physics Department Punjab Engineering College (Deemed to be University) Chandigarh 160012 India Department of Sustainable Energy Engineering Indian Institute of Technology Kanpur Kanpur 208016 India Chandrakanta Kesavan Center for Energy Policy and Climate Solutions Indian Institute of Technology Kanpur Kanpur 208016 India

In organic solar cells (OSC) hole transport layers (HTL) are essential for boosting the power conversion efficiency (PCE); therefore, it is crucial to determine the underlying mechanisms. In the present manuscript, we report a study that examines the effects of two HTL such as PEDOT:PSS and MoO 3 , on the electrical characteristics of P3HT:PC 61 BM (active layer) based bulk heterojunction (BHJ) in normal (ITO/PEDOT:PSS/P3HT:PCBM/Al) and inverted (ITO/ZnO/P3HT:PCBM/MoO 3 /Ag) configurations. Current density-voltage ( J-V ) and illumination intensity dependent capacitance-voltage ( C-V) measurements have been performed and compared for with and without HTL (PEDOT:PSS in normal and MoO 3 in inverted) devices. Results suggest that the extracted photovoltaic parameters (short circuit current density ( J sc ), open circuit voltage ( V oc ) and fill factor ( FF )) of HTL containing devices are superior to without HTL devices in both normal and inverted OSC, due to unfavourable charge carrier accumulation and band bending in HTL free devices. Such charge build-up at active layer/anode interface generates photovoltage contributing as loss factor to PCE and has been investigated in C-V characteristics measured under varied illumination intensities (0/dark, 50 and 100 mWcm −2 ). Parameters such as built-in-voltage (V bi ), effective built-in-voltage (V bi, eff ), accumulated carrier concentration and surface photovoltage (V spv = V bi, eff - V bi ) are extracted as a function of illumination intensity. Our findings reveal that HTL free devices, in both conventional and inverted OSCs structures, marked with low J sc , V oc and FF values generate higher SPV . The SPV calculation method used here has been proven to be crucial in recommending a suitable HTL for OSC device architectures.

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A 28nm 512Kb adjacent 2T2R RRAM PUF with interleaved cell mirroring and self-adaptive splitting for extremely low bit error rate of cryptographic key

A 28nm 512Kb adjacent 2T2R RRAM PUF with interleaved cell mi...

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IEEE Asian Conference on Solid-State Circuits (ASSCC)

作者： Xiaoyong Xue Jianguo Yang Yuejun Zhang Mingyu Wang Hangbing Lv Xiaoyang Zeng Ming Liu ASIC and System State Key Laboratory School of Microelectronics Fudan University China Key Laboratory of Microelectronics Devices and Integrated Technology Institute of Microelectronics of the Chinese Academy of Sciences China School of Electrical Engineering and Computer Science Ningbo University Ningbo China

ISBN: (数字)9781728151069

ISBN: (纸本)9781728151076

Reliability and cost efficiency are of utmost importance for cryptographic key generation and storage in IoT applications. This paper presents a 2T2R RRAM PUF scheme with assisting circuit techniques for dense and reliable cryptographic key generation in 28nm logic process. The set time mismatch in two adjacent RRAM devices is exploited for PUF output with better tolerance against PVTA variations. Array architecture featuring interleaved cell mirroring improves the randomness by eliminating the systematic deviation. Self-adaptive splitting with current limiter eliminates the negative effect of mismatch in the peripheral circuits and suppresses the undue duration of large operation current. Silicon data demonstrates our RRAM PUF passes NIST test with intrachip Hamming distance (HD) of 0, inter-chip HD of 0.496 and bit error rate of -5@0-120 °C. The cell size of 2T2R PUF is 0.54 μm 2 and can be reduced when the selector is shared or the RRAM set/reset voltages are optimized below the core supply voltage.

关键词： computer architecture Microprocessors Resistance Systematics Bit error rate Cryptography Current limiters

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Artificial intelligence reveals brain aging patterns in 27,402 individuals without diagnosed cognitive impairment that are linked to genetics, biomedical measures, and cognitive decline

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Alzheimer's & Dementia 2023年第S17期19卷

作者： Ioanna Skampardoni Ilya M. Nasrallah Junhao Wen Yuhan Cui Ahmed Abdulkadir Zhijian Yang Guray Erus Elizabeth Mamourian Ashish Singh Haochang Shou Li Shen Konstantina Nikita Christos Davatzikos iSTAGING consortium School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Centre for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA USA Correspondece Department of Radiology University of Pennsylvania Philadelphia PA USA Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL) Center for and Data Science for Integrated Diagnostics (AI2D) Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL) Center for and Data Science for Integrated Diagnostics (AI2D) Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Laboratory for Research in Neuroimaging Department of Clinical Neurosciences Lausanne University Hospital (CHUV) and University of Lausanne Lausanne Switzerland 1Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL) Center for and Data Science for Integrated Diagnostics (AI2D) Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Biostatistics Epidemiology & Informatics University of Pennsylvania Philadelphia PA USA University of Pennsylvania Philadelphia PA USA

Background Understanding heterogeneity of structural brain changes in aging may provide insights into susceptibility to neurodegenerative diseases. We characterize the genetics underlying brain structural heterogeneity within cognitively unimpaired (CU) individuals using data-driven machine learning applied to a diverse dataset of 27,402 individuals from 11 neuroimaging studies from the iSTAGING consortium. Method Structural brain morphologic patterns of CU individuals were independently examined in four decade-long intervals spanning ages 45 to 85. Within each interval, Smile-GAN (Yang et al., 2021) was trained on baseline anatomic and white matter hyperintensity (WMH) volumes. Smile-GAN probability scores were used as phenotypes in genome-wide association studies (GWAS). Specifically, we performed multiple linear regressions controlling for confounders (e.g., age) via Plink (Purcell et al., 2007). We observed longitudinal clustering stability across decades, so individuals from adjacent age groups were combined into broader age groups ([45,65), [65,85)) due to the large sample requirement of GWAS. Genomic loci, represented by the top leading single nucleotide polymorphisms (SNPs), were defined considering linkage disequilibrium. We investigated associations of SNPs with clinical traits and mapped them to genes using the GWAS Catalog (Buniello et al., 2019). Result Three structural brain aging patterns, relative to resilient agers (A0), consistent across decades, emerged: A1, or ‘typical’ aging with low atrophy and WMHs, and two ‘advanced’ aging patterns, one showing elevated WMHs and modest atrophy (A2) and the other displaying severe, widespread atrophy and moderate WMH load (A3) (Figure 1). GWAS discovered eight and six genomic loci in [45,65) and [65,85) age groups, respectively (Table 1, Figure 2). The lead SNPs for A1 and A2 were previously associated with several cardiometabolic risk factors, WMHs, and regional brain volumes. Interestingly, rs4843552, previo

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Author Correction: Limits to the strain engineering of layered square-planar nickelate thin films

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Nature communications 2024年第1期15卷 5660页

作者： Dan Ferenc Segedin Berit H Goodge Grace A Pan Qi Song Harrison LaBollita Myung-Chul Jung Hesham El-Sherif Spencer Doyle Ari Turkiewicz Nicole K Taylor Jarad A Mason Alpha T N'Diaye Hanjong Paik Ismail El Baggari Antia S Botana Lena F Kourkoutis Charles M Brooks Julia A Mundy Department of Physics Harvard University Cambridge MA USA. School of Applied and Engineering Physics Cornell University Ithaca NY USA. Kavli Institute at Cornell for Nanoscale Science Cornell University Ithaca NY USA. Department of Physics Arizona State University Tempe AZ USA. The Rowland Institute Harvard University Cambridge MA USA. School of Engineering and Applied Science Harvard University Cambridge MA USA. Department of Chemistry and Chemical Biology Harvard University Cambridge MA USA. Advanced Light Source Lawrence Berkeley National Laboratory Berkeley CA USA. Platform for the Accelerated Realization Analysis and Discovery of Interface Materials (PARADIM) Cornell University Ithaca NY USA. School of Electrical and Computer Engineering University of Oklahoma Norman OK USA. Department of Physics Harvard University Cambridge MA USA. mundy@fas.harvard.edu.

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Blockchain Technology Based Decentralized Energy Trading for Multiple-Microgrid Systems

Blockchain Technology Based Decentralized Energy Trading for...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Ting Li Wei Zhang Ning Chen Minhui Qian Ying Xu School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems China EPRI Nanjing China Department of Electrical Engineering & Computer Science University of Central Florida Orlando USA

ISBN: (数字)9781728131375

ISBN: (纸本)9781728131382

With the emerging technology for distributed generation and urge of improving quality of service of power supply for energy users, more and more Microgrids (MGs) are integrated into the distributed networks to serve the energy users. These Microgrids are gradually formulating a Multi-Microgrid System (MMGS, Multi-Microgrid System), which will play an important role for the future energy supply. Building a centralized control center not only increases the expense of investment, but also brings the issues of maintaining fairness among energy users. To address these problems, this paper proposes a peer-to-peer method for energy trading of MMGS, based on the idea of decentralized trading. An auction-based trading mechanism suitable for peer-to-peer energy trading is proposed first. For this mechanism, the MGs firstly declare their energy buying bids or selling quotations. Then, the market-clearing price is determined by using a unified weight clearing algorithm in a decentralized manner. By applying the edge technology of Blockchain, the implementation of the proposed peer-to-peer energy trading method, including the architecture, procedures, security check, etc., is also discussed. The proposed Blockchain-based energy trading platform can realize the decentralized and autonomous energy trading of MGs within an MMGS. A case study with an MMGS with 10 MG units is provided to demonstrate the effectiveness of the proposed approach.

关键词： Peer-to-peer computing Microgrids Blockchain Distributed power generation Electronic commerce Power distribution economics Pricing

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A Spatio-Spectral Hybrid Convolutional Architecture for Hyperspectral Document Authentication

A Spatio-Spectral Hybrid Convolutional Architecture for Hype...

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International Conference on Document Analysis and Recognition

作者： Muhammad Jaleed Khan Khurram Khurshid Faisal Shafait Department of Electrical Engineering Institute of Space Technology (IST) Islamabad Pakistan School of Electrical Engineering and Computer Science (SEECS) National University of Sciences and Technology (NUST) Islamabad Pakistan Deep Learning Laboratory National Center of Artificial Intelligence (NCAI) Islamabad Pakistan

Hyperspectral Document Image (HSDI) analysis allows for efficient and accurate differentiation of inks with visually similar color but unique spectral response, which is a crucial step in authentication of documents. Various HSDI based ink discrimination methods are available in the current literature, however, more accurate and robust methods are required to empower document authentication. Contrary to the former ink mismatch detection methods based on spectral features only, we present a novel method based on deep learning that exploits the spectral correlation as well as the spatial context to enhance ink mismatch detection. Spectral responses of the target pixel and its neighboring pixels are organized in an image format and fed to a Convolutional Neural Network (CNN) for classification. The proposed method achieves the highest accuracy among the other ink mismatch detection methods on the UWA Writing Ink Hyperspectral Images database (WIHSI), which demonstrates the effectiveness of deep learning models employing spatio-spectral hybrid features for document authentication. Detailed experimental analysis for selection of appropriate CNN architecture, spatio-spectral data format and training ratio is presented along with a comparison with the previous methods on this subject.

关键词： Ink Hyperspectral imaging Feature extraction Authentication Deep learning Databases Text analysis

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Just-in-Time Reconstruction: Inpainting Sparse Maps using Single View Depth Predictors as Priors

Just-in-Time Reconstruction: Inpainting Sparse Maps using Si...

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IEEE International Conference on Robotics and Automation

作者： Chamara Saroj Weerasekera Thanuja Dharmasiri Ravi Garg Tom Drummond Ian Reid School of Computer Science Australian Center for Robotic Vision Australia Department of Electrical and Computer Systems Engineering Australian Center for Robotic Vision Australia

We present "just-in-time reconstruction" as real-time image-guided inpainting of a map with arbitrary scale and sparsity to generate a fully dense depth map for the image. In particular, our goal is to inpaint a sparse map - obtained from either a monocular visual SLAM system or a sparse sensor - using a single-view depth prediction network as a virtual depth sensor. We adopt a fairly standard approach to data fusion, to produce a fused depth map by performing inference over a novel fully-connected Conditional Random Field (CRF) which is parameterized by the input depth maps and their pixel-wise confidence weights. Crucially, we obtain the confidence weights that parameterize the CRF model in a data-dependent manner via Convolutional Neural Networks (CNNs) which are trained to model the conditional depth error distributions given each source of input depth map and the associated RGB image. Our CRF model penalises absolute depth error in its nodes and pairwise scale-invariant depth error in its edges, and the confidence-based fusion minimizes the impact of outlier input depth values on the fused result. We demonstrate the flexibility of our method by real-time inpainting of ORB-SLAM, Kinect, and LIDAR depth maps acquired both indoors and outdoors at arbitrary scale and varied amount of irregular sparsity.

关键词： Image reconstruction Simultaneous localization and mapping Visualization Three-dimensional displays Real-time systems Uncertainty

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Mega-Reward: Achieving human-level play without extrinsic rewards

arXiv

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arXiv 2019年

作者： Song, Yuhang Wang, Jianyi Lukasiewicz, Thomas Xu, Zhenghua Zhang, Shangtong Xu, Mai Department of Computer Science University of Oxford United Kingdom State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology China School of Electronic and Information Engineering Beihang University China

Intrinsic rewards are introduced to simulate how human intelligence works;they are usually evaluated by intrinsically-motivated play, i.e., playing games without extrinsic rewards but evaluated with extrinsic rewards. However, none of the existing intrinsic reward approaches can achieve human-level performance under this very challenging setting of intrinsically-motivated play. In this work, we propose a novel megalomania-driven intrinsic reward (called mega-reward), which, to our knowledge, is the first approach that achieves human-level performance in intrinsically-motivated play. Intuitively, mega-reward comes from the observation that infants' intelligence develops when they try to gain more control on entities in an environment;therefore, mega-reward aims to maximize the control capabilities of agents on given entities in a given environment. To formalize mega-reward, a relational transition model is proposed to bridge the gaps between direct and latent control. Experimental studies show that mega-reward can (i) greatly outperform all state-of-the-art intrinsic reward approaches, (ii) generally achieves the same level of performance as Ex-PPO and professional human-level scores;and (iii) has also superior performance when it is incorporated with extrinsic reward. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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