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Time-Resolved Hanbury Brown–Twiss Interferometry of On-Chip Biphoton Frequency Combs Using Vernier Phase Modulation

Physical Review Applied 2023年第3期19卷 034019-034019页

作者： Karthik V. Myilswamy Suparna Seshadri Hsuan-Hao Lu Mohammed S. Alshaykh Junqiu Liu Tobias J. Kippenberg Andrew M. Weiner Joseph M. Lukens School of Electrical and Computer Engineering and Purdue Quantum Science and Engineering Institute Purdue University West Lafayette Indiana 47907 USA Quantum Information Science Section Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA Electrical Engineering Department King Saud University Riyadh 11421 Saudi Arabia Institute of Physics Swiss Federal Institute of Technology Lausanne (EPFL) Lausanne 1015 Switzerland Research Technology Office and Quantum Collaborative Arizona State University Tempe Arizona 85287 USA

Biphoton frequency combs (BFCs) are promising quantum sources for large-scale and high-dimensional quantum information and networking systems. In this context, the spectral purity of individual frequency bins will be critical for realizing quantum networking protocols like teleportation and entanglement swapping. Measurement of the temporal autocorrelation function of the unheralded signal or idler photons comprising the BFC is a key tool for characterizing their spectral purity and in turn verifying the utility of the biphoton state for networking protocols. Yet the experimentally obtainable precision for measuring BFC correlation functions is often severely limited by detector jitter. The fine temporal features in the correlation function—not only of practical value in quantum information, but also of fundamental interest in the study of quantum optics—are lost as a result. We propose a scheme to circumvent this challenge through electro-optic phase modulation, experimentally demonstrating time-resolved Hanbury Brown–Twiss characterization of BFCs generated from an integrated 40.5-GHz Si3N4 microring, up to a 3×3-dimensional two-qutrit Hilbert space. Through slight detuning of the electro-optic drive frequency from the comb’s free spectral range, our approach leverages Vernier principles to magnify temporal features, which would otherwise be averaged out by detector jitter. We demonstrate our approach under both continuous-wave and pulsed-pumping regimes, finding excellent agreement with theory. Our method reveals not only the collective statistics of the contributing frequency bins but also their temporal shapes—features lost in standard fully integrated autocorrelation measurements.

关键词： Quantum coherence & coherence measures Quantum communication Quantum correlations in quantum information Quantum entanglement Quantum information processing Quantum networks Quantum optics

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SceNDD: A Scenario-based Naturalistic Driving Dataset

arXiv

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arXiv 2022年

作者： Prabu, Avinash Ranjan, Nitya Li, Lingxi Tian, Renran Chien, Stanley Chen, Yaobin Sherony, Rini United States The Department of Electrical and Computer Engineering Indiana University Purdue University Indianapolis United States The Department of Computer Information and Graphics Technology Indiana University Purdue University Indianapolis United States Toyota Motor North America Ann ArborMI United States

In this paper, we propose SceNDD: a scenario-based naturalistic driving dataset that is built upon data collected from an instrumented vehicle in downtown Indianapolis. The data collection was completed in 68 driving sessions with different drivers, where each session lasted about 20–40 minutes. The main goal of creating this dataset is to provide the research community with real driving scenarios that have diverse trajectories and driving behaviors. The dataset contains ego-vehicle’s waypoints, velocity, yaw angle, as well as non-ego actor’s waypoints, velocity, yaw angle, entry-time, and exit-time. Certain flexibility is provided to users so that actors, sensors, lanes, roads, and obstacles can be added to the existing scenarios. We used a Joint Probabilistic Data Association (JPDA) tracker to detect non-ego vehicles on the road. We present some preliminary results of the proposed dataset and a few applications associated with it. The complete dataset is expected to be released by early 2023. © 2022, CC BY.

关键词： Vehicles

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Transfer Learning to Detect COVID-19 Coughs with Incremental Addition of Patient Coughs to Healthy People’s Cough Detection Models

arXiv

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arXiv 2023年

作者： Vhaduri, Sudip Paik, Seungyeon Huber, Jessica E. Computer and Information Technology Department United States Speech Language and Hearing Sciences Department Purdue University West LafayetteIN47907 United States Communicative Disorders and Sciences Department University at Buffalo BuffaloNY14214 United States

Millions of people have died worldwide from COVID-19. In addition to its high death toll, COVID-19 has led to unbearable suffering for individuals and a huge global burden to the healthcare sector. Therefore, researchers have been trying to develop tools to detect symptoms of this human-transmissible disease remotely to control its rapid spread. Coughing is one of the common symptoms that researchers have been trying to detect objectively from smartphone microphone-sensing. While most of the approaches to detect and track cough symptoms rely on machine learning models developed from a large amount of patient data, this is not possible at the early stage of an outbreak. In this work, we present an incremental transfer learning approach that leverages the relationship between healthy peoples’ coughs and COVID-19 patients’ coughs to detect COVID-19 coughs with reasonable accuracy using a pre-trained healthy cough detection model and a relatively small set of patient coughs, reducing the need for large patient dataset to train the model. This type of model can be a game changer in detecting the onset of a novel respiratory virus. © 2023, CC BY.

关键词： COVID-19

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A Comprehensive Review of Wireless Charging Technologies with Emphasis on V2V Integration

SSRN

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SSRN 2024年

作者： Aboharga, Gehad Mukhtar, Husameldin Himeur, Yassine Gadhafi, Rida Copiaco, Abigail Hadi, Sabina Abdul Gan, Dongming Taha, Tarek Ritz, Christian Atalla, Shadi Mansoor, Wathiq Al-Ahmad, Hussain College of Engineering and Information Technology University of Dubai Emirates Rd Academic City Dubai14143 United Arab Emirates Dubai Future Labs Dubai Future Foundation Dubai14143 United Arab Emirates School of Electrical Computer and Telecommunications Engineering University of Wollongong Australia School of Engineering Technology Purdue University West Lafayette United States

Recent studies have identified common factors that negatively impact customer demand for fully electric vehicles, including the slow development of long-lasting batteries and the limited availability of plug-in power hubs. Therefore, mobile charging systems are recently being explored as a potential solution. This paper thoroughly reviews diverse classes of electric vehicle wireless charging along with their respective methods, standards, and key performance metrics with a deeper focus on vehicle-to-vehicle wireless charging. The paper compares a range of wireless charging infrastructure systems, communication subsystems, charging coil designs, wireless power transfer challenges and solutions, and the role of mobile chargers in enhancing the charging experience. Several experimental techniques for providing safe wireless charging for electric vehicles have been identified and assessed, starting from short-range power transfer methods such as inductive magnetic power transfer and its resonant form to long-range power transfer methods such as microwave or laser rays. Potential future directions are discussed based on the identified research challenges. © 2024, The Authors. All rights reserved.

关键词： Energy transfer

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FEDERATED LEARNING WITH DYNAMIC CLIENT ARRIVAL AND DEPARTURE: CONVERGENCE AND RAPID ADAPTATION VIA INITIAL MODEL CONSTRUCTION

arXiv

引用

arXiv 2024年

作者： Chang, Zhan-Lun Han, Dong-Jun Parasnis, Rohit Hosseinalipour, Seyyedali Brinton, Christopher G. Elmore Family School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States Department of Computer Science and Engineering Yonsei University Seoul Korea Republic of The Laboratory for Information and Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering University at Buffalo-SUNY BuffaloNY14260 United States

While most existing federated learning (FL) approaches assume a fixed set of clients in the system, in practice, clients can dynamically leave or join the system depending on their needs or interest in the specific task. This dynamic FL setting introduces several key challenges: (1) the objective function dynamically changes depending on the current set of clients, unlike traditional FL approaches that maintain a static optimization goal;(2) the current global model may not serve as the best initial point for the next FL rounds and could potentially lead to slow adaptation, given the possibility of clients leaving or joining the system. In this paper, we consider a dynamic optimization objective in FL that seeks the optimal model tailored to the currently active set of clients. Building on our probabilistic framework that provides direct insights into how the arrival and departure of different types of clients influence the shifts in optimal points, we establish an upper bound on the optimality gap, accounting for factors such as stochastic gradient noise, local training iterations, non-IIDness of data distribution, and deviations between optimal points caused by dynamic client pattern. We also propose an adaptive initial model construction strategy that employs weighted averaging guided by gradient similarity, prioritizing models trained on clients whose data characteristics align closely with the current one, thereby enhancing adaptability to the current clients. The proposed approach is validated on various datasets and FL algorithms, demonstrating robust performance across diverse client arrival and departure patterns, underscoring its effectiveness in dynamic FL environments. © 2024, CC BY.

关键词： Federated learning

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A Traffic Signal Control Method Based on Improved Deep Reinforcement Learning*

A Traffic Signal Control Method Based on Improved Deep Reinf...

引用

Chinese Automation Congress (CAC)

作者： Ye Bao-Lin Chen Dong Wu Peng Wu Weimin Lingxi Li Chen Bin School of Information Science and Engineering Jiaxing University Jiaxing China School of Information Science and Engineering Zhejiang Sci-Tech University Hangzhou China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China Elmore Family School of Electrical and Computer Engineering Purdue University West Lafayette USA Jiaxing Key Laboratory of Smart Transportations Jiaxing University Jiaxing China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

In order to solve the problem that the existing traffic signal control methods usually need to predefine the phase sequence or phase duration, a single intersection traffic signal control method based on improved deep reinforcement learning Double DQN (DDQN) was proposed. Firstly, a traffic signal control model based on deep reinforcement learning DDQN was constructed to optimize the estimated value of action-value function and the iterative process of target value. Secondly, the original one-dimensional action space was discretized into a three-dimensional action space composed of phase selection, phase duration and loss factor, and the next phase and phase green time to be executed were intelligently decided. Finally, a new loss function is designed based on the loss factor, which can dynamically adjust the loss weight of different actions according to the difference of importance. The simulation test is carried out based on the microscopic simulation platform SUMO. The experimental results show that, compared with the baseline method, the proposed method reduces the average waiting time by 28.1%, the average queue length by 29.7%, and the average number of parking vehicles by 18.8%.

关键词： Training Microscopy Roads Neural networks Process control Aerospace electronics Traffic control Deep reinforcement learning Feature extraction Data models

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A Privacy Preserving and Byzantine Robust Collaborative Federated Learning Method Design

A Privacy Preserving and Byzantine Robust Collaborative Fede...

引用

IEEE International Conference on Communications (ICC)

作者： Nuocheng Yang Sihua Wang Mingzhe Chen Changchuan Yin Christopher G. Brinton Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA School of Electrical and Computer Engineering Purdue University West Lafayette IN USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Collaborative federated learning (CFL) enables device cooperation in training shared machine learning models without reliance on a parameter server. However, the absence of a parameter server also impacts vulnerabilities associated with adversarial attacks, including privacy inference and Byzantine attacks. In this context, this paper introduces a novel CFL framework that enables each device to individually determine the subset of devices to transmit FL parameters to over the wireless network, based on its neighboring devices' location, current loss, and connection information, to achieve privacy protection and robust aggregation. This is formulated as an optimization problem whose goal is to minimize CFL training loss while satisfying the privacy preservation, robust aggregation, and transmission delay requirements. To solve this problem, a proximal policy optimization (PPO)-based reinforcement learning (RL) algorithm integrated with a graph neural network (GNN) is proposed. Compared to traditional algorithms that use global information with high computational complexity, the proposed GNN-RL method can be deployed on devices based on neighboring information with lower computational overhead. Simulation results show that the proposed algorithm can protect data privacy and increase identification accuracy by 15% compared to an algorithm in which devices are partially clustered for model aggregation.

关键词： Training Privacy Federated learning Simulation Collaboration Clustering algorithms Propagation losses

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Unorthodox parallelization for Bayesian quantum state estimation

arXiv

引用

arXiv 2025年

作者： Nguyen, Hanson H. Law, Kody J.H. Lukens, Joseph M. School of Electrical Computer and Energy Engineering Research Technology Office Arizona State University TempeAZ85287 United States School of Mathematics University of Manchester ManchesterM13 9PL United Kingdom Elmore Family School of Electrical and Computer Engineering Purdue Quantum Science and Engineering Institute Purdue University West LafayetteIN47907 United States Quantum Information Science Section Oak Ridge National Laboratory Oak RidgeTN37831 United States

Quantum state tomography (QST) allows for the reconstruction of quantum states through measurements and some inference technique under the assumption of repeated state preparations. Bayesian inference provides a promising platform to achieve both efficient QST and accurate uncertainty quantification, yet is generally plagued by the computational limitations associated with long Markov chains. In this work, we present a novel Bayesian QST approach that leverages modern distributed parallel computer architectures to efficiently sample a D-dimensional Hilbert space. Using a parallelized preconditioned Crank–Nicholson Metropolis-Hastings algorithm, we demonstrate our approach on simulated data and experimental results from IBM Quantum systems up to four qubits, showing significant speedups through parallelization. Although highly unorthodox in pooling independent Markov chains, our method proves remarkably practical, with validation ex post facto via diagnostics like the intrachain autocorrelation time. We conclude by discussing scalability to higher-dimensional systems, offering a path toward efficient and accurate Bayesian characterization of large quantum systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Markov chains

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CMOS photonic integrated source of ultrabroadband polarization-entangled photons

arXiv

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arXiv 2024年

作者： Miloshevsky, Alexander Cohen, Lucas M. Myilswamy, Karthik V. Alshowkan, Muneer Fatema, Saleha Lu, Hsuan-Hao Weiner, Andrew M. Lukens, Joseph M. Quantum Information Science Section Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN37831 United States School of Electrical and Computer Engineering Purdue Quantum Science and Engineering Institute Purdue University West LafayetteIN47907 United States Research Technology Office and Quantum Collaborative Arizona State University TempeAZ85287 United States

We showcase a fully on-chip CMOS-fabricated silicon photonic integrated circuit employing a bidirectionally pumped microring and polarization splitter-rotators tailored for the generation of ultrabroadband (>9 THz), high-fidelity (90–98%) polarization-entangled photons. Spanning the optical C+L-band and producing over 116 frequency-bin pairs on a 38.4 GHz-spaced grid, this source is ideal for flex-grid wavelength-multiplexed entanglement distribution in multiuser networks. Copyright © 2024, The Authors. All rights reserved.

关键词： Polarization

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Towards Text-based phishing detection

arXiv

引用

arXiv 2021年

作者： Park, Gilchan Taylor, Julia M. Department of Computer and Information Technology Purdue University West LafayetteIN47907 United States

This paper reports on an experiment into text-based phishing detection using readily available resources and without the use of semantics. The developed algorithm is a modified version of previously published work that works with the same tools. The results obtained in recognizing phishing emails are considerably better than the previously reported work;but the rate of text falsely identified as phishing is slightly worse. It is expected that adding semantic component will reduce the false positive rate while preserving the detection accuracy. © 2021, CC BY.

关键词： computer crime

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