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arXiv 2022年

作者： Wu, Wen Qu, Kaige Yang, Peng Zhang, Ning Shen, Xuemin Sherman Zhuang, Weihua Frontier Research Center Peng Cheng Laboratory Shenzhen China Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada School of Electronic Information and Communications Huazhong University of Science and Technology China Department of Electrical and Computer Engineering University of Windsor Windsor Canada

In this paper, we study a network slicing problem for edge-cloud orchestrated vehicular networks, in which the edge and cloud servers are orchestrated to process computation tasks for reducing network slicing cost while satisfying the quality of service requirements. We propose a two-stage network slicing framework, which consists of 1) network planning stage in a large timescale to perform slice deployment, edge resource provisioning, and cloud resource provisioning, and 2) network operation stage in a small timescale to perform resource allocation and task dispatching. Particularly, we formulate the network slicing problem as a two-timescale stochastic optimization problem to minimize the network slicing cost. Since the problem is NP-hard due to coupled network planning and network operation stages, we develop a Two timescAle netWork Slicing (TAWS) algorithm by collaboratively integrating reinforcement learning (RL) and optimization methods, which can jointly make network planning and operation decisions. Specifically, by leveraging the timescale separation property of decisions, we decouple the problem into a large-timescale network planning subproblem and a small-timescale network operation subproblem. The former is solved by an RL method, and the latter is solved by an optimization method. Simulation results based on real-world vehicle traffic traces show that the TAWS can effectively reduce the network slicing cost as compared to the benchmark scheme. © 2022, CC BY-NC-SA.

关键词： Cost effectiveness

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Silicon Nitride Integrated Photonics with Intrinsic Quantum Emitters

Silicon Nitride Integrated Photonics with Intrinsic Quantum ...

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Optica Quantum 2.0 Conference and Exhibition, Quantum 2.0

作者： Senichev, Alexander Martin, Zachariah O. Peana, Samuel Yesilyurt, Omer Matthiessen, Owen M. Sychev, Demid Lawrie, Benjamin Lagutchev, Alexei S. Boltasseva, Alexandra Shalaev, Vladimir M. Elmore Family School of Electrical and Computer Engineering Birck Nanotechnology Center Purdue Quantum Science and Engineering Institute Purdue University West LafayetteIN47907 United States Quantum Science Center Department of Energy a National Quantum Information Science Research Center of the U.S. Oak RidgeTN37931 United States Materials Science and Technology Division Oak Ridge National Laboratory Oak RidgeTN37831 United States

We create intrinsic quantum emitters in silicon nitride, study their structure and temperature-dependent optical properties, and demonstrate monolithic integration with photonic waveguides to evaluate the potential of... 详细信息

ISBN: (纸本)9781957171272

关键词： Integrated photonics Optical properties Photonic integrated circuits Quantum information Quantum light sources Silicon nitride

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Assessing the Robustness of nnU-Net in the Detection of Prostate Lesions via Bi-Parametric MRI

Assessing the Robustness of nnU-Net in the Detection of Pros...

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Data Science and engineering in Healthcare, Medicine and Biology, IEEE EMBS Special Topic Conference on

作者： Dimitrios I. Zaridis Eugenia Mylona Nikolaos Tachos Charalampos Kalantzopoulos Vasileios C. Pezoulas Dimitrios D. Koutsouris George K. Matsopoulos Kostas Marias Manolis Tsiknakis Dimitrios I. Fotiadis Biomedical Engineering Laboratory School of Electrical and Computer Engineering National Technical University of Athens GR15773 Athens Greece Biomedical Research Institute FORTH Ioannina Greece University of Ioannina Ioannina Greece Institute of Computer Science FORTH Heraklion Greece Department of Electrical and Computer Engineering Hellenic Mediterranean University Heraklion Crete Greece FORTH-IMBB Ioannina Greece Materials Science and Engineering Department Unit of Medical Technology and Intelligent Information Systems University of Ioannina Ioannina GR 45110 Greece

Within the scope of prostate cancer diagnostic imaging, distinguishing lesions is challenging due to their subtle appearance and the prostate gland’s complexity. This study employed the nnU-Net, a state-of-the-art medical image segmentation model, on a dataset of 301 patients from 2 openly available datasets to identify whether lesion sizes are affecting the model’s performance. By applying t-SNE dimensionality reduction algorithm among dice score and respective lesion sizes, we found that nnU-Net behaves differently for lesions smaller than 9mm compared to lesions larger than 15mm. These insights can inform specialized training approaches for future deep learning models in prostate lesion *** Relevance: The main contributions of this study is, to investigate whether lesion sizes affects nnU-Net’s deficiency leading to the development of more specialized models in respect to these factors.

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Vertical‐organic‐nanocrystal‐arrays for crossbar memristors with tuning switching dynamics toward neuromorphic computing

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SmartMat 2021年第1期2卷 99-108页

作者： Fangxu Yang Lingjie Sun Qingxi Duan Huanli Dong Zhaokun Jing Yuchao Yang Rongjin Li Xiaotao Zhang Wenping Hu Leon Chua Key Laboratory of Molecular Optoelectronic Sciences School of ScienceTianjin UniversityTianjinChina Beijing National Research Center for Molecular Sciences Chinese Academy of SciencesBeijingChina Joint School of National University of Singapore and Tianjin University International Campus of Tianjin UniversityBinhai New CityFuzhouChina Key Laboratory of Microelectronic Devices and Circuits(MOE) Institute of MicroelectronicsPeking UniversityBeijingChina Department of Electrical Engineering and Computer Sciences University of CaliforniaBerkeleyCaliforniaUSA

Memristors proposed by Leon Chua provide a new type of memory device for novel neuromorphic computing ***,the approaching of distinct multi‐intermediate states for tunable switching dynamics,the con-trolling of conducting filaments(CFs)toward high device repeatability and reproducibility,and the ability for large‐scale preparation devices,remain full of ***,we show that vertical‐organic‐nanocrystal‐arrays(VONAs)could make a way toward the *** perfect one‐dimensional structure of the VONAs could confine the CFs accurately with fine‐tune resistance states in a broad range of 103 *** availability of large‐area VONAs makes the fabrication of large‐area crossbar memristor arrays facilely,and the analog switching characteristic of the memristors is to effectively imitate different kinds of synaptic plasticity,indicating their great potential in future applications.

关键词： conducting filament memristor organic electronics organic single crystal

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A Novel Spatial-Temporal Variational Quantum Circuit to Enable Deep Learning on NISQ Devices

arXiv

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arXiv 2023年

作者： Li, Jinyang Wang, Zhepeng Hu, Zhirui Date, Prasanna Li, Ang Jiang, Weiwen George Mason University Department of Electrical and Computer Engineering VA United States George Mason University Quantum Science and Engineering Center VA United States Oak Ridge National Laboratory TN United States Pacific Northwest National Laboratory WA United States

Quantum computing presents a promising approach for machine learning with its capability for extremely parallel computation in high-dimension through superposition and entanglement. Despite its potential, existing quantum learning algorithms, such as Variational Quantum Circuits (VQCs), face challenges in handling more complex datasets, particularly those that are not linearly separable. What's more, it encounters the deployability issue, making the learning models suffer a drastic accuracy drop after deploying them to the actual quantum devices. To overcome these limitations, this paper proposes a novel spatial-temporal design, namely "ST-VQC", to integrate nonlinearity in quantum learning and improve the robustness of the learning model to noise. Specifically, ST-VQC can extract spatial features via a novel block-based encoding quantum sub-circuit coupled with a layer-wise computation quantum sub-circuit to enable temporal-wise deep learning. Additionally, a SWAP-Free physical circuit design is devised to improve robustness. These designs bring a number of hyperparameters. After a systematic analysis of the design space for each design component, an automated optimization framework is proposed to generate the ST-VQC quantum circuit. The proposed ST-VQC has been evaluated on two IBM quantum processors, ibm_cairo with 27 qubits and ibmq_lima with 7 qubits to assess its effectiveness. The results of the evaluation on the standard dataset for binary classification show that ST-VQC can achieve over 30% accuracy improvement compared with existing VQCs on actual quantum computers. Moreover, on a non-linear synthetic dataset, the ST-VQC outperforms a linear classifier by 27.9%, while the linear classifier using classical computing outperforms the existing VQC by 15.58%. Copyright © 2023, The Authors. All rights reserved.

关键词： Learning algorithms

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Stealthy Attacks on Sampled-Data Control and Encryption-Based Countermeasures

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IEEE Transactions on Automatic Control 2025年

作者： Shang, Jun Li, Yuzhe Chen, Tongwen Tongji University Department of Control Science and Engineering Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai200092 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110004 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada

This paper investigates stealthy attacks on sampled-data control systems, where a continuous process is sampled periodically, and the resultant discrete output and control signals are transmitted through dual channels in wireless communication networks. Acknowledging the vulnerability of these transmitted data to cyber threats, we analyze two types of stealthy attacks that compromise either one or both channels. We explicitly present the necessary and sufficient conditions for the existence of stealthy single-channel attacks. To mitigate these security risks, we implement encryption-based countermeasures, including linear time-invariant and periodic encryption methods. We detail sufficient conditions under which these encryption strategies effectively thwart single- and dual-channel stealthy attacks. The effectiveness of these approaches and the theoretical findings are demonstrated through numerical simulations. © 1963-2012 IEEE.

关键词： Cyber attacks

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All-passive upconversion of incoherent near-infrared light at intensities down to ~10-7 W/cm2

arXiv

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arXiv 2024年

作者： Hamid, Rabeeya Feng, Demeng Narayanan, Pournima Edwards, Justin S. Hu, Manchen Belliveau, Emma Kim, Minjeong Deshpande, Sanket Wan, Chenghao Pucurimay, Linda Czaplewski, David A. Congreve, Daniel N. Kats, Mikhail A. Department of Electrical and Computer Engineering University of Wisconsin-Madison Madison53706 United States Department of Chemistry Stanford University StanfordCA94305 United States Department of Physics University of Wisconsin-Madison Madison53706 United States Department of Electrical Engineering Stanford University StanfordCA94305 United States Department of Materials Science and Engineering Stanford University StanfordCA94305 United States Center for Nanoscale Materials Argonne National Laboratory ArgonneIL60439 United States

Frequency upconversion, which converts low-energy photons into higher-energy ones, typically requires intense coherent illumination to drive nonlinear processes or the use of externally driven optoelectronic devices. Here, we demonstrate an upconversion system that converts low-intensity (down to ~10-7 W/cm2) incoherent near-infrared (NIR) light into the visible, reaching intensities perceptible by the human eye, without the use of any external power input. Our upconverting element is enabled by the following ingredients: (1) photon upconversion via triplet-triplet annihilation in a bulk heterojunction of the organic semiconductors Y6 and rubrene;(2) plasmonic enhancement of absorption and field intensity in the heterojunction layer;(3) collection enhancement using a dichroic thin-film assembly. To enable high-resolution imaging, the upconverting element is inserted at an intermediate image plane of a dual-wavelength telescope system, which preserves the relative directionality of rays between the incident NIR light and output visible light. Our all-passive upconversion imaging system will enable NIR imaging and sensing in low-light environments under energy constraints. Copyright © 2024, The Authors. All rights reserved.

关键词： Heterojunctions

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Thermal-Sensitive Fluorescence of Ho3+ in Oxyfluoride Nanocrystals and Derivative Embedded Fibers

SSRN

引用

SSRN 2023年

作者： Yu, Jing Zhao, Xin Shen, Lifan Pun, Edwin Yue Bun Lin, Hai School of Information Science and Engineering Dalian Polytechnic University Dalian116034 China College of Microelectronics Key Laboratory of Optoelectronics Technology Faculty of Information Technology Beijing University of Technology Beijing100124 China Department of Electrical Engineering State Key Laboratory of Terahertz and Millimeter Waves City University of Hong Kong 999077 Hong Kong

(Y1-x-yHoxYby)OF/polyacrylonitrile (YOF-HxYy/PAN) composite fibers prepared by electrostatic spinning have been demonstrated to possess strong upconversion (UC) luminescence and sensitive temperature feedback properties for laser display and real-time temperature monitoring in complex environments. When the excitation power density is increased to 73 mW/mm2, the total luminous flux reaches 3.35 mlm, demonstrating that the Ho3+/Yb3+ co-doped YOF nanocrystals embedded in the composite fibers are efficient luminous materials. The quantum yields (QYs) of green and red emissions from Ho3+ are derived to be 2.97×10-5 and 1.40×10-5 respectively when the pump power density arrives at 73 mW/mm2 under 977 nm laser, and the high photon generation efficiency ensures sufficient fluorescence intensity for tracing temperature feedback. The maximum relative sensitivities appear at 303 K, while the absolute sensitivity (SA) and relative sensitivity (SR) of the nanofibers retain 13.77% K-1 and 0.343% K-1 at 433 K respectively, demonstrating that the nanofibers have superior optical temperature sensing properties. The composite fibers with effective luminescence and high temperature sensitivity as luminescent materials provide a potential option for the field of laser display and temperature sensing. © 2023, The Authors. All rights reserved.

关键词： Nanocrystals

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Slow-Wave Hybrid Magnonics

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Physical Review Letters 2024年第11期132卷 116701-116701页

作者： Jing Xu Changchun Zhong Shihao Zhuang Chen Qian Yu Jiang Amin Pishehvar Xu Han Dafei Jin Josep M. Jornet Bo Zhen Jiamian Hu Liang Jiang Xufeng Zhang Center for Nanoscale Materials Argonne National Laboratory Lemont Illinois 60439 USA Pritzker School of Molecular Engineering University of Chicago Chicago Illinois 60637 USA Department of Materials Science and Engineering University of Wisconsin—Madison Madison Wisconsin 53706 USA Department of Physics and Astronomy University of Pennsylvania Philadelphia Pennsylvania 19104 USA Department of Electrical and Computer Engineering Northeastern University Boston Massachusetts 02115 USA Department of Physics and Astronomy University of Notre Dame Notre Dame Indiana 46556 USA Department of Physics Northeastern University Boston Massachusetts 02115 USA

Cavity magnonics is an emerging research area focusing on the coupling between magnons and photons. Despite its great potential for coherent information processing, it has been long restricted by the narrow interaction bandwidth. In this Letter, we theoretically propose and experimentally demonstrate a novel approach to achieve broadband photon-magnon coupling by adopting slow waves on engineered microwave waveguides. To the best of our knowledge, this is the first time that slow wave is combined with hybrid magnonics. Its unique properties promise great potentials for both fundamental research and practical applications, for instance, by deepening our understanding of the light-matter interaction in the slow wave regime and providing high-efficiency spin wave transducers. The device concept can be extended to other systems such as optomagnonics and magnomechanics, opening up new directions for hybrid magnonics.

关键词： Magnons Spin waves Surface plasmon polariton Waveguides

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Short-Term Power Load Forecasting Using MOGOA and ConvBiLSTM During COVID-19 Pandemic

Short-Term Power Load Forecasting Using MOGOA and ConvBiLSTM...

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International Conference on Power System Technology (POWERCON)

作者： Da Xu Bowen Liu Chi-Seng Lam Zhangyou Huang State Key Laboratory of Analog and Mixed-Signal VLSI University of Macau Macao China Institute of Microelectronics University of Macau Macao China School of Automation China University of Geosciences Wuhan China School of Automation China University of Geosciences Wuhan China Department of Electrical and Computer Engineering FST University of Macau Macao China

Due to the high restrictions of the global COVID-19 on society, economy and production, the electrical supply-demand balance is facing a great challenge. This paper proposes a short-term load forecasting (STLF) method based on multi-factor fusion during the COVID-19. After fully analyzing and screening the nonlinear coupling interactions among cross-domain weather and public health safety features, a convolutional bi-directional long short-term memory neural network (ConvBiLSTM) is developed based on the convolutional and bidirectional loop enhancements of long short-term memory (LSTM). To avoid early termination of training the global optimal solution, multi-objective grasshopper optimization algorithm (MOGOA) is adopted to select the inherent hyperparameters of the forecasting network. Attention mechanism is further introduced to enhance the learning ability of the key features in temporal data, improving the forecasting accuracy and robustness of ConvBiLSTM. Comparisons on cross-domain COVID-19 dataset over classical methods are performed to verify its effective and superior performances.

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