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Large-scale and high-quality III-nitride membranes through microcavity-assisted crack propagation by engineering tensile-stressed Ni layers

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Opto-Electronic science 2022年第10期1卷 51-61,42-50页

作者： Jung-Hong Min Kwangjae Lee Tae-Hoon Chung Jung-Wook Min Kuang-Hui Li Chun Hong Kang Hoe-Min Kwak Tae-Hyeon Kim Youyou Yuan Kyoung-Kook Kim Dong-Seon Lee Tien Khee Ng Boon S.Ooi Photonics Laboratory ComputerElectrical and Mathematical Sciences and Engineering Division(CEMSE)King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Department of Electrical Engineering Stanford UniversityStanfordCA 94305USA Light Source Research Division Korea Photonics Technology Institute(KOPTI)Gwangju 61007Republic of Korea School of Electrical Engineering and Computer Science Gwangju Institute of Science and TechnologyGwangju 61005Republic of Korea Department of Advanced Convergence Technology Research Institute of Advanced Convergence TechnologyKorea Polytechnic University237 Sangidaehak-roSiheung-si 15073Republic of Korea King Abdullah University of Science and Technology(KAUST) Thuwal 23955-6900Saudi Arabia

Epitaxially grown III-nitride alloys are tightly bonded materials with mixed covalent-ionic *** tight bonding presents tremendous challenges in developing III-nitride membranes,even though semiconductor membranes can provide numerous advantages by removing thick,inflexible,and costly ***,cavities with various sizes were introduced by overgrowing target layers,such as undoped GaN and green LEDs,on nanoporous templates prepared by electrochemical etching of n-type *** large primary interfacial toughness was effectively reduced according to the design of the cavity density,and the overgrown target layers were then conveniently exfoliated by engineering tensile-stressed Ni *** resulting III-nitride membranes maintained high crystal quality even after exfoliation due to the use of GaN-based nanoporous templates with the same lattice *** microcavity-assisted crack propagation process developed for the current III-nitride membranes forms a universal process for developing various kinds of large-scale and high-quality semiconductor membranes.

关键词： III-nitride alloys membranes nanoporous Ni stressor light-emitting diodes ultraviolet photodetectors

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Flocking in the Cucker-Smale model with self-delay and nonsymmetric interaction weights

arXiv

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arXiv 2021年

作者： Haskovec, Jan Computer Electrical and Mathematical Sciences & Engineering King Abdullah University of Science and Technology Thuwal 23955 Saudi Arabia

We derive a sufficient condition for asymptotic flocking in the Cucker-Smale model with self-delay (also called reaction delay) and with non-symmetric interaction weights. The condition prescribes smallness of the delay length relative to the decay rate of the inter-agent communication weight. The proof is carried out by a bootstrapping argument combining a decay estimate for the group velocity diameter with a variant of the Gronwall-Halanay inequality. © 2021, CC BY-NC-ND.

关键词： Decay (organic)

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Asymptotic behavior of the linear consensus model with delay and anticipation

arXiv

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arXiv 2021年

作者： Haskovec, Jan Computer Electrical and Mathematical Sciences & Engineering King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia

We study asymptotic behavior of solutions of the first-order linear consensus model with delay and anticipation, which is a system of neutral delay differential equations. We consider both the transmission-type and reaction-type delay that are motivated by modeling inputs. Studying the simplified case of two agents, we show that, depending on the parameter regime, anticipation may have both a stabilizing and destabilizing effect on the solutions. In particular, we demonstrate numerically that moderate level of anticipation generically promotes convergence towards consensus, while too high level disturbs it. Motivated by this observation, we derive sufficient conditions for asymptotic consensus in the multiple-agent systems, which are explicit in the parameter values delay length and anticipation level, and independent of the number of agents. The proofs are based on construction of suitable Lyapunov-type functionals. Copyright © 2021, The Authors. All rights reserved.

关键词： Asymptotic analysis

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Coverage and rate analysis in coexisting Terahertz and RF finite wireless networks

arXiv

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arXiv 2021年

作者： Kouzayha, Nour Kishk, Mustafa A. Sarieddeen, Hadi Al-Naffouri, Tareq Y. Alouini, Mohamed-Slim Division of Computer Electrical and Mathematical Sciences and Engineering King Abdullah University of Science and Technology Thuwal Saudi Arabia

Wireless communications over Terahertz (THz)-band frequencies are vital enablers of ultra-high rate applications and services in sixth-generation (6G) networks. However, THz communications suffer from poor coverage because of inherent THz features such as high penetration losses, severe path loss, and significant molecular absorption. To surmount these critical challenges and fully exploit the THz band, we explore a coexisting radio frequency (RF) and THz finite indoor network in which THz small cells are deployed to provide high data rates, and RF macrocells are deployed to satisfy coverage requirements. Using stochastic geometry tools, we assess the performance of coexisting RF and THz networks in terms of coverage probability and average achievable rate. The accuracy of the analytical results is validated with Monte-Carlo simulations. Several insights are devised for accurate tuning and optimization of THz system parameters, including the fraction of THz access points (APs) to deploy, and the THz bias. The obtained results recognize a clear coverage/rate trade-off where a high fraction of THz AP improves the rate significantly but may degrade the coverage performance. Furthermore, the location of the user in the finite area highly affects the fraction of THz APs that optimizes the performance. © 2021, CC BY.

关键词： Radio waves

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Energy Efficient Capacitive Body Channel Access Schemes for Internet of Bodies

Energy Efficient Capacitive Body Channel Access Schemes for ...

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2021 IEEE Global Communications Conference (GLOBECOM)

作者： Abeer Alamoudi Abdulkadir Celik Ahmed M. Eltawil Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal KSA

ISBN: (纸本)9781728181059

The Internet of bodies is a network of wearable, ingestible, injectable, and implantable smart objects located in, on, and around the body. Although radio frequency (RF) systems are considered the default choice for implementing on-body communications, which need to be localized in the vicinity of the human body (typically < 5 cm), highly radiative RF propagations unnecessarily extend several meters beyond the human body. This intuitively degrades energy efficiency, leads to interference and co-existence issues, and exposes sensitive personal data to security threats. As an alternative, the capacitive body channel communication (BCC) couples the signal (between 10 kHz-100 MHz) to the human body, which is more conductive than air. Hence, BCC provides a lower propagation loss, better physical layer security, and nJ/bit to pJ/bit energy efficiency. Accordingly, this paper investigates orthogonal and non-orthogonal capacitive body channel access schemes for ultra-low-power IoB nodes. We present the optimal uplink and downlink power allocations in closed-form, which deliver better fairness and network lifetime than benchmark numerical solvers. For a given bandwidth and data rate requirement, we also derive the maximum affordable number of IoB nodes for both directions of orthogonal and non-orthogonal schemes.

关键词： Radio frequency Wireless communication NOMA Wireless sensor networks Simulation Mathematical models Energy efficiency

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Monocrystalline Si/β-Ga2O3p-n heterojunction diodes fabricated via grafting

arXiv

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arXiv 2023年

作者： Gong, Jiarui Kim, Donghyeok Jang, Hokyung Alema, Fikadu Wang, Qingxiao Ng, Tien Khee Qiu, Shuoyang Zhou, Jie Su, Xin Lin, Qinchen Singh, Ranveer Abbasi, Haris Chabak, Kelson Jessen, Gregg Cheung, Clincy Gambin, Vincent Pasayat, Shubhra S. Osinsky, Andrei Ooi, Boon S. Gupta, Chirag Ma, Zhenqiang Department of Electrical and Computer Engineering University of Wisconsin-Madison MadisonWI53706 United States Agnitron Technology Incorporated ChanhassenMN55317 United States Department of Electrical and Computer Engineering King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Air Force Research Laboratory DaytonOH45433 United States Northrop Grumman Redondo BeachCA90278 United States

The β-Ga2O3has exceptional electronic properties with vast potential in power and RF electronics. Despite the excellent demonstrations of high-performance unipolar devices, the lack of p-type doping in β-Ga2O3has hindered the development of Ga2O3-based bipolar devices. The approach of p-n diodes formed by polycrystalline p-type oxides with n-type β-Ga2O3can face severe challenges in further advancing the β-Ga2O3bipolar devices due to their unfavorable band alignment and the poor p-type oxide crystal quality. In this work, we applied the semiconductor grafting approach to fabricate monocrystalline Si/β-Ga2O3p-n diodes for the first time. With enhanced concentration of oxygen atoms at the interface of Si/β-Ga2O3, double side surface passivation was achieved for both Si and β-Ga2O3with an interface Ditvalue of 1-3 × 1012/cm2·eV. A Si/β-Ga2O3p-n diode array with high fabrication yield was demonstrated along with a diode rectification of 1.3 × 107at ±2 V, a diode ideality factor of 1.13 and avalanche reverse breakdown characteristics. The diodes C-V shows frequency dispersion-free characteristics from 10 kHz to 2 MHz. Our work has set the foundation toward future development of β-Ga2O3-based transistors. Copyright © 2023, The Authors. All rights reserved.

关键词： Gallium compounds

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BACKLINK: SUPERVISED LOCAL TRAINING WITH BACKWARD LINKS

arXiv

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arXiv 2022年

作者： Guo, Wenzhe Fouda, Mohammed E. Eltawil, Ahmed M. Salama, Khaled N. Department of Electrical and Computer Engineering King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia Center for Embedded & Cyber-physical Systems University of California IrvineCA92612 United States

Empowered by the backpropagation (BP) algorithm, deep neural networks have dominated the race in solving various cognitive tasks. The restricted training pattern in the standard BP requires end-to-end error propagation, causing large memory cost and prohibiting model parallelization. Existing local training methods aim to resolve the training obstacle by completely cutting off the backward path between modules and isolating their gradients to reduce memory cost and accelerate the training process. These methods prevent errors from flowing between modules and hence information exchange, resulting in inferior performance. This work proposes a novel local training algorithm, BackLink, which introduces inter-module backward dependency and allows errors to flow between modules. The algorithm facilitates information to flow backward along with the network. To preserve the computational advantage of local training, BackLink restricts the error propagation length within the module. Extensive experiments performed in various deep convolutional neural networks demonstrate that our method consistently improves the classification performance of local training algorithms over other methods. For example, in ResNet32 with 16 local modules, our method surpasses the conventional greedy local training method by 4.00% and a recent work by 1.83% in accuracy on CIFAR10, respectively. Analysis of computational costs reveals that small overheads are incurred in GPU memory costs and runtime on multiple GPUs. Our method can lead up to a 79% reduction in memory cost and 52% in simulation runtime in ResNet110 compared to the standard BP. Therefore, our method could create new opportunities for improving training algorithms towards better efficiency and biological plausibility. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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Ransomware Attacks Detection Methodology to Protect IoT-Enabled Critical Infrastructures

Ransomware Attacks Detection Methodology to Protect IoT-Enab...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Amit Kumar Mishra Mohammad S. Obaidat Harshit Bhajpai Pranjal Trivedi Mohammad Wazid D. P. Singh Joel J. P. C. Rodrigues Balqies Sadoun Department of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India King Abdullah II School of Information Technology The University of Jordan Amman Jordan University of Science and Technology Beijing Beijing China College of Computer Science and Technology China University of Petroleum (East China) Qingdao China Instituto de Telecomunições Covilhã Portugal College of Engineering Al- Balqa' Applied University Al-Salt Jordan

Critical infrastructure is a collection of physical and cyber systems, which are essentially required to support the day-to-day operations of our daily life. In the critical infrastructure, the computing systems (i.e., Internet of Things (loT) devices) communicate through the Internet. Therefore, most of the time, critical infrastructures are targeted by hackers by launching some cyber-attack, i.e., ransomware. Hence we require some security mechanisms to protect the data and systems of critical infrastructures. This paper proposes a scheme for the detection, analysis and mitigation of ransomware attacks to protect Internet of Things (loT)-enabled critical infrastructure (in short, RADM-ICI). We also practically demonstrated RADM-ICI and computed essential performance parameters, i.e., accuracy and F1-score under different machine learning models. The conducted security analysis of RADM-ICI proved its excellent security for the ransom ware attacks. During the performance comparison of the proposed RADM-ICI and other similar competing existing schemes, it has been observed that the proposed RADM-ICI achieved better accuracy than the other existing competing schemes.

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From Non-Expert to Expert: Recurrent Refined Learning for Medical Image Segmentation

From Non-Expert to Expert: Recurrent Refined Learning for Me...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Ruohui Jiang Changtai Li Xiaojuan Ban Shihua Yin Chao Yao Yu Guo Mohammad S. Obaidat Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing Beijing China School of Intelligence Science and Technology University of Science and Technology Beijing Beijing China Institute of Materials Intelligent Technology Liaoning Academy of Materials Shenyang China Second Affiliated Hospital of Guangxi Medical University Nanning China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China King Abdullah II School of Information Technology University of Jordan Amman Jordan Amity University Noida UP India

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Precise lesion segmentation is essential in computer-aided diagnosis and treatment. Prevalent deep-learning-based approaches need high-quality annotations to achieve satisfying performance. However, blurring effects and infiltration hamper the accurate delineation of lesions. A common solution is to divide the annotation process into initial annotation by non-specialized personnel and subsequent modification by expert physicians, where the latent correction patterns and non-expert labels are seldom utilized effectively. To explore their helpfulness, we propose ReReNet, a recurrent refined network for lesion segmentation that learns from non-expert to expert. It achieves progressively refined segmentation results through multiple iterations with tailored discrepancy-aware supervision. During training, as the iterative process perceives discrepancies between refining and expert labels, the model gradually grasps the knowledge of turning the barely correct into the clinically accurate. We validate ReReNet’s capability on three medical image segmentation (MIS) datasets, including magnetic resonance (MR) and computed tomography (CT) modalities. Comparison results indicate that the proposed approach achieves superior performance by introducing the designed recurrent mechanism and outperforms mainstream methods, demonstrating the effectiveness of mining hidden correction patterns by utilizing non-expert information.

关键词： Training Image segmentation Accuracy Annotations Computed tomography Refining computer architecture Turning Lesions Medical diagnostic imaging

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FALCON: Scalable Reasoning over Inconsistent ALC Ontologies

arXiv

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arXiv 2022年

作者： Hinnerichs, Tilman Tang, Zhenwei Peng, Xi Zhang, Xiangliang Hoehndorf, Robert Computational Bioscience Research Center Computer Electrical and Mathematical Sciences & Engineering Division King Abdullah University of Science and Technology Saudi Arabia University of Toronto Canada Delft University of Technology Netherlands University of Notre Dame United States

Ontologies are one of the richest sources of knowledge. Real-world ontologies often contain thousands of axioms and are often human-made. Hence, they may contain inconsistency and incomplete information which may impair classical reasoners to compute entailments that are considered as useful. To overcome these two challenges, we propose FALCON, a Fuzzy Ontology Neural reasoner to approximate reasoning over ALC ontologies. We provide an approximate technique for the model generation step in classical ALC reasoners. Our approximation is not guaranteed to construct exact logical models, but can approximate arbitrary models, which is notably faster for some large ontologies. Moreover, by sampling multiple approximate logical models, our technique supports approximate entailment also over inconsistent ontologies. Theoretical results show that more models generated lead to closer, i.e., faithful approximation of entailment over ALC entailments. Experimental results show that FALCON enables approximate reasoning and reasoning in the presence of inconsistency. Our experiments further demonstrate how ontologies can improve knowledge base completion in biomedicine by incorporating knowledge expressed in ALC. Copyright © 2022, The Authors. All rights reserved.

关键词： Ontology

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