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Optica Quantum 2025年第1期3卷 45-54页

作者： Stephen D Chapman Suparna Seshadri Joseph M Lukens Nicholas A Peters Jason D McKinney Andrew M Weiner Hsuan-Hao Lu Elmore Family School of Electrical and Computer Engineering and Purdue Quantum Science and Engineering Institute Purdue University West Lafayette Indiana 47907 USA Research Technology Office and Quantum Collaborative Arizona State University Tempe Arizona 85287 USA Quantum Information Science Section Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA

We demonstrate nonlocal modulation of entangled photons with truly distributed radio frequency (RF) clocks. Leveraging a custom radio-over-fiber (RFoF) system characterized via classical spectral interference, we validate its effectiveness for quantum networking by multiplexing the RFoF clock with one photon from a frequency-bin-entangled pair and distributing the coexisting quantum-classical signals over fiber. Phase modulation of the two photons reveals nonlocal correlations in excellent agreement with theory: in-phase modulation produces additional sidebands in the joint spectral intensity, while out-of-phase modulation is nonlocally canceled. Our simple, feedback-free design attains subpicosecond synchronization—namely, drift less than ∼0.5 ps in a 5.5 km fiber over 30 min (fractionally only ∼2×10 −8 of the total fiber delay)—and should facilitate frequency-encoded quantum networking protocols such as high-dimensional quantum key distribution and entanglement swapping, unlocking frequency-bin qubits for practical quantum communications in deployed metropolitan-scale networks.

关键词： Entanglement distribution Laser communications Phase modulation Quantum communications Quantum key distribution Quantum teleportation

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Crowding and Mutation Improvements in an EA for Flight Control Correction in a Flapping-Wing Vehicle

Crowding and Mutation Improvements in an EA for Flight Contr...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： John C. Gallagher Michael W. Oppenheimer Eric T. Matson Electrical and Computer Enginnering University of Cincinnati Cincinnati OH USA Autonomous Control Branch Wright-Patterson Air Force Base Dayton OH USA Computer and Information Technology Purdue University West Lafayette IN

Small Flapping-Wing Micro Air Vehicles (FW-MA Vs) may be subjected to either or both of manufacturing defects or in-service damage that render their pre-designed controllers less than adequately effective. Even minor damage to wings, for example, can remove the vehicle's ability to reliably follow waypoint trails even if that same damage does not result in a catastrophic loss of altitude. One solution to this problem is to adapt the core wing motion scripts (wing gaits) in an attempt to use wing motion to compensate for losses of force and torque generation due to in-service damage or manufacturing faults. This approach presents a number of challenges - especially if it is to be deployed in a resource restricted vehicle in an online mode during an actual mission. Prior to this paper, we had presented only anecdotal treatment of fully unrestricted, 3D flight. In this paper, we will definitively establish the utility of EA adaptation of flight control in unrestricted flight in a pendulum-stable FW-MA V. We will, additionally, introduce mutation and crowding modifications that provide demonstrable utility in a manner amenable to implementation on a resource-restricted micro vehicle. The paper will conclude with a discussion of open-issues and the potential application of the reported methods to other problems.

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Innovation of software engineering course with the background of engineering education professional certification 16

Innovation of software engineering course with the backgroun...

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16th IEEE International Conference on computer Science and Education, ICCSE 2021

作者： Guanbo, Feng Jia, Yue Purdue University Fort Wayne Computer Science Department Fort WayneIN United States Dalian Neusoft University of Information Software Technology Department Dalian China

ISBN: (纸本)9781665414685

Taking the practice teaching of software engineering an example, this paper discusses the innovative teaching design of the practice course of software engineering under the background of 'new engineering' and 'engineering education certification'. With the aim of training students to solve complex engineering problems, this paper elaborates on the overall design, teaching effect, reflection and improvement of the course teaching, in order to provide reference for the teaching reform under the relevant training objectives of new engineering. © 2021 IEEE.

关键词： Engineering education

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Multiphysics Numerical Method for Modeling Josephson Traveling-Wave Parametric Amplifiers

arXiv

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arXiv 2024年

作者： Elkin, Samuel T. Haider, Michael Roth, Thomas E. The Elmore Family School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States The Purdue Quantum Science and Engineering Institute West LafayetteIN47907 United States The TUM School of Computation Information and Technology Technical University of Munich Garching85748 Germany

Josephson traveling-wave parametric amplifiers (JTWPAs) are wideband, ultralow-noise amplifiers used to enable the readout of superconducting qubits. While individual JTWPAs have achieved high performance, behavior between devices is inconsistent due to wide manufacturing tolerances. Amplifier designs could be modified to improve resilience towards variations in amplifier components;however, existing device models often rely on analytical techniques that typically fail to incorporate component variations. To begin addressing this issue, a 1D numerical method for modeling JTWPAs is introduced in this work. The method treats the Josephson junctions and transmission lines in an amplifier as coupled subsystems and can easily incorporate arbitrary parameter variations. We discretize the transmission line subsystem with a finite element time domain method and the Josephson junction subsystem with a finite difference method, with leapfrog time marching used to evolve the system in time. We validate our method by comparing the computed gain to an analytical model for a traditional JTWPA architecture and one with resonant phase matching. We then use our method to demonstrate the impact of variations in Josephson junctions and phase-matching resonators on amplification. In future work, the method will be adjusted to incorporate additional amplifier architectures and extended to a 3D full-wave approach. © 2024, CC BY.

关键词： Numerical methods

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Motion Planning Integrated with Vehicle-Terrain Interactions for Off-Road Autonomous Ground Vehicles 27

Motion Planning Integrated with Vehicle-Terrain Interactions...

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Yin, Jianhua Zhan, Xianyuan Ji, Tianying Xu, Bingrong He, Yi Zhang, Daqing Li, Lingxi Wuhan University of Technology Intelligent Transportation Systems Research Center Wuhan430063 China Engineering Research Center of Transportation Information and Safety Ministry of Education Wuhan430063 China Chongqing Research Institute Wuhan University of Technology Chongqing China Institute for Ai Industry Research Tsinghua University Beijing100084 China Shanghai Artificial Intelligence Laboratory Shanghai200232 China Tsinghua University Department of Computer Science and Technology Beijing100084 China School of Automation Wuhan University of Technology Wuhan430070 China College of Mechanical and Electrical Engineering Hunan Agricultural University Changsha410128 China Sunward Intelligent Equipment Co. Ltd. Changsha410100 China Elmore Family School of Electrical and Computer Engineering College of Engineering Purdue University IN46202 United States

ISBN: (纸本)9798331505929

Off-road autonomous ground vehicles (AGVs) work in unstructured environments with varied elevation and deformable ground, where vehicle-terrain interactions are more complex resulting in unpredictable dynamics behavior. Current sensor-based hazards/obstacle identification methods mainly focus on geometric hazards/obstacles with explicit geometry, which might be adequate for vehicles in on-road conditions. However, in the off-road environment, vehicle-terrain interactions could introduce non-geometric hazards that cannot be detected by current geometric hazards-oriented sensing techniques, e.g. vehicle stuck in mud. To enable AGVs to traverse the target terrain and meet the goal while avoiding non-geometric hazards, this work proposes a motion planning method integrated with coupled vehicle-terrain dynamics response. Specifically, terramechanics-based modeling and simulation (M&S) is utilized to characterize vehicle-terrain interactions. Based on the dynamics response, a reinforcement learning (RL) algorithm with a specialized reward function is used to navigate the AGV to find the goal with high mobility and safety. We demonstrate the proposed method with two case studies, 1) high mobility is the foremost concern with less safety requirement, and 2) both high mobility and safety are required. The results show that the proposed motion planning method is effective for navigating the AGV in off-road terrains. © 2024 IEEE.

关键词： Off road vehicles

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A practical adversarial attack on contingency detection of smart energy systems

arXiv

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arXiv 2021年

作者： Sabounchi, Moein Wei-Kocsis, Jin Department of Computer and Information Technology Purdue University

Due to the advances in computing and sensing, deep learning (DL) has widely been applied in smart energy systems (SESs). These DL-based solutions have proved their potentials in improving the effectiveness and adaptiveness of the control systems. However, in recent years, increasing evidence shows that DL techniques can be manipulated by adversarial attacks with carefully-crafted perturbations. Adversarial attacks have been studied in computer vision and natural language processing. However, there is very limited work focusing on the adversarial attack deployment and mitigation in energy systems. In this regard, to better prepare the SESs against potential adversarial attacks, we propose an innovative adversarial attack model that can practically compromise dynamical controls of energy system. We also optimize the deployment of the proposed adversarial attack model by employing deep reinforcement learning (RL) techniques. In this paper, we present our first-stage work in this direction. In simulation section, we evaluate the performance of our proposed adversarial attack model using standard IEEE 9-bus system. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep learning

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Creating Single Color Centers in Nanodiamonds with Ion Implantation

Creating Single Color Centers in Nanodiamonds with Ion Impla...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Xu, Xiaohui Martin, Zachariah O. Titze, Michael Wang, Yongqiang Sychev, Demid Henshaw, Jacob Lagutchev, Alexei S. Htoon, Han Bielejec, Ed Bogdanov, Simeon Shalaev, Vladimir M. Boltasseva, Alexandra School ofMaterials Engineering Birck Nanotechnology Center Purdue University West LafayetteIN47907 United States Elmore Family School of Electrical and Computer Engineering Birck Nanotechnology Center Purdue University West LafayetteIN47907 United States SandiaNationalLaboratories AlbuquerqueNM87123 United States Los Alamos National Laboratory Los AlamosNM87545 United States Department of Electrical and Computer Engineering Nick Holonyak Jr. Micro and Nanotechnology Laboratory Illinois Quantum Information Science and Technology Center University of Illinois at Urbana-Champaign UrbanaIL60801 United States

We report the first demonstration of single silicon vacancy center creation in 20 nm nanodiamonds using silicon ion implantation combined with thermal annealing. Room-temperature single photon emission with linewidth ... 详细信息

ISBN: (纸本)9781957171258

关键词： Nanodiamonds

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Surgical Tool Detection: A Comparative Study of Supervised and Semi-Supervised Learning Approaches 10th

Surgical Tool Detection: A Comparative Study of Supervised ...

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10th International Conference on Health Informatics and Medical Systems, HIMS 2024 and 10th International Conference on Biomedical Engineering, BIOENG 2024, held as part of the World Congress in computer Science, computer Engineering and Applied Computing, CSCE 2024

作者： Shokouhmand, Shiva Rahman, Md Motiur Bhatt, Smriti Panigrahi, Suranjan Faezipour, Miad School of Engineering Technology Electrical and Computer Engineering Technology Purdue University West LafayetteIN United States Department of Computer and Information Technology Purdue University West LafayetteIN47907 United States

ISBN: (纸本)9783031859076

Surgical tool detection and classification is of great importance in enhancing the efficacy, safety of surgical procedures, and providing advanced surgical assistance, especially applicable to robotic/automatic surgery. Current methodologies predominantly rely on supervised techniques necessitating extensive annotated datasets. However, the availability of labeled datasets remains a persistent challenge. In this research, the objective is to detect and classify surgical instruments while evaluating the performance of supervised machine learning and deep learning models against a semi-supervised approach in image classification. Also, we investigate and contrast the significance of preprocessing, transfer learning, and semi-supervised learning techniques. Our findings demonstrate that while thorough preprocessing mechanisms can significantly impact the performance of machine learning models, employing transfer learning with deep learning models yields superior results. Additionally, training data on semi-labeled datasets exhibits comparable, or in some cases better performance than supervised deep learning methods, particularly in scenarios where labeled data is scarce. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semi-supervised learning

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Soundscape Analytics: A New Frontier of Knowledge Discovery in Soundscape Data

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Current Landscape Ecology Reports 2024年第4期9卷 88-107页

作者： Pijanowski, Bryan C. Fuenzalida, Francisco Rivas Banerjee, Subham Minghim, Rosane Lima, Samantha L. Bowers-Sword, Ruth Guzman, Santiago Ruiz Revuelta-Acosta, Josept Adeniji, Adebola Esther Grimes, Sarah E. Sarker, Swapan Kumar Hossain, Md. Rifat Anika, Tabia Tasnim Savage, David Department of Forestry and Natural Resources Purdue University West Lafayette USA Center for Global Soundscapes Purdue University West Lafayette USA School of Computer Science and Information Technology University College Cork Cork Ireland Department of Biological Sciences University of Pittsburgh Pittsburgh USA Department of Civil Engineering Universidad Veracruzana Veracruz Mexico Department of Forestry and Environmental Science Shahjalal University of Science and Technology Sylhet Bangladesh Indiana Utility Regulatory Commission Indianapolis USA

Here, we describe a new evolving research area of focus for soundscape ecology called soundscape analytics. Soundscape analytics follows traditional, as well as state-of-the-art, data and visual analytics that chain together tools and approaches to explore and analyze massive data. The theoretical underpinning of soundscape analytics is anchored in recent advances in machine learning that have been very successful in other applications, such as business, medicine, and psychology. We present and summarize four main components – data processing, data mining, integration and interpretation - of soundscape analytics pipelines that are being used today by soundscape ecologists. In the last five years, the number of tools advancing our ability to analyze big acoustic data for soundscape ecology research has increased considerably, especially those leveraging generic deep learning methods. A considerable portion of this work has focused on using soundscape recordings to assess biodiversity trends across space and time. Many of these implementations are based on R and Python routines designed to be executed on supercomputers with specialized data storage arrays as well as cloud-based user interface software. Quick porting of interactive data visualizations to the web enables scientists around the world to collaborate and share information for management and with the public. Big data in ecology has arrived, illustrated by the massive amounts of acoustic data being collected by soundscape ecologists. The challenge of soundscape analytics is to make the most of each available computational resource so many application problems can be solved from similar data.

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Resource Optimization for Blockchain-based Federated Learning in Mobile Edge Computing

arXiv

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arXiv 2022年

作者： Wang, Zhilin Hu, Qin Xiong, Zehui The Department of Computer & Information Science Indiana University-Purdue University Indianapolis United States The Pillar of Information Systems Technology & Design Singapore University of Technology Design Singapore Singapore

With the development of mobile edge computing (MEC) and blockchain-based federated learning (BCFL), a number of studies suggest deploying BCFL on edge servers. In this case, resource-limited edge servers need to serve both mobile devices for their offloading tasks and the BCFL system for model training and blockchain consensus in a cost-efficient manner without sacrificing the service quality to any side. To address this challenge, this paper proposes a resource allocation scheme for edge servers, aiming to provide the optimal services with the minimum cost. Specifically, we first analyze the energy consumed by the MEC and BCFL tasks, and then use the completion time of each task as the service quality constraint. Then, we model the resource allocation challenge into a multivariate, multi-constraint, and convex optimization problem. To solve the problem in a progressive manner, we design two algorithms based on the alternating direction method of multipliers (ADMM) in both the homogeneous and heterogeneous situations with equal and on-demand resource distribution strategies, respectively. The validity of our proposed algorithms is proved via rigorous theoretical analysis. Through extensive experiments, the convergence and efficiency of our proposed resource allocation schemes are evaluated. To the best of our knowledge, this is the first work to investigate the resource allocation dilemma of edge servers for BCFL in MEC. Copyright © 2022, The Authors. All rights reserved.

关键词： Blockchain

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