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Multiplexed quantum repeaters based on dual-species trapped-ion systems

Physical Review A 2022年第2期105卷 022623-022623页

作者： Prajit Dhara Norbert M. Linke Edo Waks Saikat Guha Kaushik P. Seshadreesan Wyant College of Optical Sciences University of Arizona Tucson Arizona 85721 USA Joint Quantum Institute and Department of Physics University of Maryland College Park Maryland 20742 USA Joint Quantum Institute and Department of Electrical and Computer Engineering University of Maryland College Park Maryland 20742 USA Department of Informatics and Networked Systems School of Computing and Information University of Pittsburgh Pittsburgh Pennsylvania 15260 USA

Trapped ions form an advanced technology platform for quantum information processing with long qubit coherence times, high-fidelity quantum logic gates, optically active qubits, and a potential to scale up in size while preserving a high level of connectivity between qubits. These traits make them attractive not only for quantum computing, but also for quantum networking. Dedicated, special-purpose trapped-ion processors in conjunction with suitable interconnecting hardware can be used to form quantum repeaters that enable high-rate quantum communications between distant trapped-ion quantum computers in a network. In this regard, hybrid traps with two distinct species of ions, where one ion species can generate ion-photon entanglement that is useful for optically interfacing with the network and the other has long memory lifetimes, useful for qubit storage, have been proposed for entanglement distribution. We consider an architecture for a repeater based on such dual-species trapped-ion systems. We propose and analyze a protocol based on spatial and temporal mode multiplexing for entanglement distribution across a line network of such repeaters. Our protocol offers enhanced rates compared to rates previously reported for such repeaters. We determine the ion resources required at the repeaters to attain the enhanced rates, and the best rates attainable when constraints are placed on the number of repeaters and the number of ions per repeater. Our results bolster the case for near-term trapped-ion systems as quantum repeaters for long distance quantum communications.

关键词： Quantum communication Quantum information architectures & platforms Quantum repeaters

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Improving partial weight bearing compliance with a smart insole: Validity, reliability, and feasibility study

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Foot and Ankle Surgery 2025年

作者： Avcı, Ender Ersin Akgün, Gazi Uygur, Mehmet Esat Polat, Mine Gülden Demirbüken, İlkşan Marmara University Health Sciences Faculty Department of Physiotherapy Education and Informatics Istanbul Turkey University of Tennessee at Chattanooga College of Engineering and Computer Science (CECS) Department of Engineering Management & Technology /Mechatronics United States Istanbul Medeniyet University Medicine Faculty Surgical Sciences Orthopaedic and Traumatology Istanbul Turkey

Background: Partial weight bearing (PWB) plays a critical role in early stages of rehabilitation following lower-extremity orthopedic surgery. However, it has been reported patients often have difficulty complying with PWB restrictions, particularly in home setting. The newly designed smart insole system aims to address this issue by remotely monitoring the patient's weight-bearing loads and providing real-time feedback for non-compliant weight-bearing to support PWB compliance. The aim of this study was to assess the validity and reliability of the system in measuring weight-bearing loads and to investigate its feasibility in improving PWB compliance. Methods: The research involved two phases: an experimental design to test validity and reliability in a clinical setting, and a randomized controlled design to test feasibility with remote monitoring at home. Validity and reliability were assessed on a healthy subject using simultaneous load measurements from a smart insole and a force plate under three metronome conditions (30,40, and 60bpm). In the feasibility phase, twelve patients who had undergone microfracture surgery for talar osteochondral lesions were recruited at 4 weeks postoperatively. Participants used Android smartphones and met Ferkel & Scaglione classification criteria I, IIA, and IIB. They were randomized into groups with feedback (FB) or without feedback (NFB) and their compliance with PWB limits was monitored remotely 24-hours for one week using the Insole system. Results: The insole demonstrated high reliability (SEM:1.67 N, ICC_1.1:0.97) and, excellent agreement with the force plate (R²=0.9175, p < 0.001). The FB group achieved higher compliance with prescribed PWB thresholds compared to the NFB group. Conclusions: The newly developed insole system is a valid and reliable rehabilitation tool for accurately measuring weight bearing loads. The system's ability to provide continuous remote patient monitoring and real-time feedbac

关键词： Feedback Gait Mobile Applications Patient Compliance Postoperative Care Rehabilitation

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High-Fidelity Coherent-One-Way QKD Simulation Framework for 6G Networks: Bridging Theory and Reality

arXiv

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arXiv 2025年

作者： Brazaola-Vicario, Aitor Kouvakis, Vasileios Trevlakis, Stylianos E. Ruiz, Alejandra Boulogeorgos, Alexandros-Apostolos A. Tsiftsis, Theodoros Niyato, Dusit Spain The Department of Research and Development InnoCube P.C. 79 17is Noemvriou Str. Thessaloniki55534 Greece The Department of Electrical and Computer Engineering University of Western Macedonia ZEP Area Kozani50100 Greece The Department of Informatics & Telecommunications University of Thessaly Lamia35100 Greece The College of Computing and Data Science Nanyang Technological University 639798 Singapore

Quantum key distribution (QKD) has been emerged as a promising solution for guaranteeing information-theoretic security. Inspired by this, a great amount of research effort has been recently put on designing and testing QKD systems as well as articulating preliminary application scenarios. However, due to the considerable high-cost of QKD equipment, a lack of QKD communication system design tools, wide deployment of such systems and networks is challenging. Motivated by this, this paper introduces a QKD communication system design tool. First we articulate key operation elements of the QKD, and explain the feasibility and applicability of coherent-one-way (COW) QKD solutions. Next, we focus on documenting the corresponding simulation framework as well as defining the key performance metrics, i.e., quantum bit error rate (QBER), and secrecy key rate. To verify the accuracy of the simulation framework, we design and deploy a real-world QKD setup. We perform extensive experiments for three deployments of diverse transmission distance in the presence or absence of a QKD eavesdropper. The results reveal an acceptable match between simulations and experiments rendering the simulation framework a suitable tool for QKD communication system design. © 2025, CC BY.

关键词： Bit error rate

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Discovering Predictable Latent Factors for Time Series Forecasting

arXiv

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arXiv 2023年

作者： Hou, Jingyi Dong, Zhen Zhou, Jiayu Liu, Zhijie The School of Intelligence Science and Technology University of Science and Technology Beijing Beijing100083 China The Institute of Artificial Intelligence University of Science and Technology Beijing Beijing100083 China The Key Laboratory of Intelligent Bionic Unmanned Systems Ministry of Education University of Science and Technology Beijing Beijing100083 China The College of Mathematics and Computer Science Yan’An University Yan’an716000 China The Department of Computer Science and Engineering Michigan State University East LansingMI48823 United States

Modern temporal modeling methods, such as Transformer and its variants, have demonstrated remarkable capabilities in handling sequential data from specific domains like language and vision. Though achieving high performance with large-scale data, they often have redundant or unexplainable structures. When encountering some real-world datasets with limited observable variables that can be affected by many unknown factors, these methods may struggle to identify meaningful patterns and dependencies inherent in data, and thus, the modeling becomes unstable and unpredictable. To tackle this critical issue, in this paper, we develop a novel algorithmic framework for inferring latent factors implied by the observed temporal data. The inferred factors are used to form multiple predictable and independent signal components that enable not only the reconstruction of future time series for accurate prediction but also sparse relation reasoning for long-term efficiency. To achieve this, we introduce three characteristics, i.e., predictability, sufficiency, and identifiability, and model these characteristics of latent factors via powerful deep latent dynamics models to infer the predictable signal components. Empirical results on multiple real datasets show the efficiency of our method for different kinds of time series forecasting tasks. Statistical analyses validate the predictability and interpretability of the learned latent factors. Copyright © 2023, The Authors. All rights reserved.

关键词： Efficiency

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Developing the Performance and Longevity of Electric Vehicle Batteries Through the Implementation of Thermal Management systems

Developing the Performance and Longevity of Electric Vehicle...

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Disruptive Technologies (ICDT), International Conference on

作者： N. Venkatesh Kumar Praveen Kumar. C B. Anandan B. A. Devan G. Sasikala V.R. Kavitha Department of Chemistry VSB Engineering College Karur Tamil Nadu India Department of Electronics and Instrumentation Engineering SRM Valliammai Engineering College Chengalpet Tamil Nadu India Department of Mechanical Engineering Siddharth Institute of Engineering & Technology Puttur Andhrapradesh India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Business Systems R.M.K. Engineering College Kavarapettai Tamil Nadu India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Electric vehicles, also known as EVs, are quickly becoming more popular as a more environmentally friendly alternative to conventional automobiles powered by internal combustion engines. The performance and longevity of electric vehicle batteries, on the other hand, are crucial aspects that can have a substantial impact on the adoption of these batteries and their overall efficiency. As a result of the fact that temperature fluctuations have a significant impact on the degradation of batteries, thermal management systems are absolutely necessary in order to keep batteries functioning at their best and to extend their lifespan. The development and implementation of better thermal management systems for electric vehicle batteries is the primary emphasis of this field of research. In the first step of this process, we will examine the current status of electric vehicle battery technology as well as the difficulties connected with thermal control. After that, we investigate a variety of thermal management solutions, such as air cooling, liquid cooling, and phase change materials, in order to determine which methods are the most efficient for a variety of battery chemistries and operating situations. The design and simulation of a unique thermal management system that blends liquid cooling with superior heat dissipation materials is an important part of our study. This system is designed to manage thermal energy. In order to analyze the heat transfer that occurs within the battery pack and to optimize the design parameters of the thermal management system, we make use of computational fluid dynamics (CFD) and finite element analysis (FEA). We analyze the effectiveness of the suggested thermal management system by conducting a number of experiments and simulations. Our results show that the system is effective in terms of temperature regulation, energy efficiency, and the impact it has on the life of the battery. According to the facts that we have gathered, the deploymen

关键词： Phase change materials Degradation Liquid cooling Computational fluid dynamics Thermal management Electric vehicles Regulation Batteries Thermal analysis Thermal degradation

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Digital Twins for Energy-Efficient Manufacturing

Digital Twins for Energy-Efficient Manufacturing

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Annual IEEE systems Conference

作者： Nader Mohamed Sanja Lazarova-Molnar Jameela Al-Jaroodi Department of Computer Science and Information Systems Pennsylvania Western University California PA USA Institute of Applied Informatics and Formal Description Methods Karlsruhe Institute of Technology Karlsruhe Germany Faculty of Engineering University of Southern Denmark Odense Denmark Department of Engineering Robert Morris University Pittsburgh PA USA

Some manufacturing industries consume massive energy for their manufacturing and logistics processes. The costs of manufacturing can increase significantly as the energy cost increases. Such increase amplifies the final cost of the created products on the consumers. This makes finding different approaches that utilize innovative technologies to improve energy efficiency in manufacturing extremely important. This paper investigates how digital twins can be utilized to enable improved energy efficiency in manufacturing. Different applications of digital twins for energy-efficient manufacturing are investigated. In addition, the paper discusses some challenges of utilizing digital twins for this purpose. A framework for utilizing digital twins for energy-efficient manufacturing is also discussed.

关键词： Manufacturing industries Costs Manufacturing processes Buildings Energy efficiency Raw materials Manufacturing

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Distribution Key Scheme for Secure Group Management in VANET Using Polynomial Interpolation

Distribution Key Scheme for Secure Group Management in VANET...

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7th International Symposium on Security and Privacy in Social Networks and Big Data, SocialSec 2021

作者： Kumar, Ankit Singh, Kamred Udham Hsieh, Sun-Yuan Kumar, V. D. Ambeth Kumar, Abhishek Department of Computer Science and Engineering Swami Keshvanand Institute of Technology Management and Gramothan Jaipur India Department of Computer Science and Information Engineering National Cheng Kung University Tainan701 Taiwan Department of Computer Science and Information Engineering Institute of Medical Information Institute of Manufacturing Information and Systems Center for Innovative FinTech Business Models and International Center for the Scientific Development of Shrimp Aquaculture National Cheng Kung University No. 1 University Road Tainan70101 Taiwan Department of Computer Science and Engineering Panimalar Engineering College Anna University Chennai India Bengaluru India

ISBN: (纸本)9789811679124

Security is a major concern for vehicular ad hoc networks (VANETs). Group-based communications in VANETs is a commonly investigated topic. Creating a group and distributing a key to numerous fast-moving vehicles for secure communication is a major security challenge for VANETs. Distributed keys in VANETs allow for the secure participation of vehicles that may not have been present when the group was formed. This paper presents a new distributed key scheme for secure group management in VANET by using polynomial interpolation. The proposed scheme not only provides a distributed key for vehicles to be created, to join, and to communicate in the group but also provides and manages the distribution of keys to maintain the security of VANETs. The experimental results reveal that the proposed scheme is a secure solution for group communication in VANETs. © 2021, Springer Nature Singapore Pte Ltd.

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Perceptual Video Summarization Using Keyframes Extraction Technique

Perceptual Video Summarization Using Keyframes Extraction Te...

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Innovative Practices in Technology and Management (ICIPTM), International Conference on

作者： Rajitha Jasmine R Padmaja Nimmagadda K. Sudhakar Benitha Christinal J P. Rajasekar Syed Musthafa A Department of Information Technology R. M. K. Engineering College Kavaraipettai India Department of Electronics and Communication Mohan Babu University Tirupati India Department of Computer Science Madanapalle Institute of Technology Madanapalle India Department of Information Technology Panimalar Engineering College Chennai India Department of Data Science and Business Systems SRM Institute of Science and Technology Chengalpattu India Department of Information Technology M. Kumarasamy College of Engineering Karur India

The growth of video content in recent years is a challenging problem due to increased memory storage and time consuming for analyzing content of the video. Therefore, there is a need to reduce the content for human usage. This paper presents a keyframes technique that uses co-occurrence matrix and permutation computation for perceptual video summarization (PVS). The Human Visual System (HVS) needs to incorporate the PVS. It helps to allow for the importance of perceptually significant contents. The proposed method uses different kinds of videos to evaluate the effectiveness of the work. The subjective evaluation scores have evaluated the proposed work.

关键词： Color Visual systems Feature extraction

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A Deep Learning Framework for Prognosis Patients with COVID-19

A Deep Learning Framework for Prognosis Patients with COVID-...

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IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS)

作者： Mohammad Khaja Shaik Kiranmai Vanaparthi Gundala Swarnalatha Pundru Chandra Shaker Reddy Rama Prasanna Dalai B Jayaram Electronics and Communication Engineering St. Ann's College of Engineering and Technology Chirala Andhra Pradesh India Department of CSE(AIML) Guru Nanak Institutions Technical Campus Hyderabad Telangana India Department of CSE (AIDS) Guru Nanak Institute of Technical Campus Hyderabad Telangana India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Electrical and Electronics Engineering Centurion University of Technology and Management Odisha India Computer Science and Business Systems RMK Engineering College Kavaraipettai Tamil Nadu India

ISBN: (数字)9798350361186

ISBN: (纸本)9798350361193

A branch of AI known as “deep learning” sifts through mountains of data in search of patterns and predictions using a machine learning approach called an artificial neural network. Rapid progress and widespread, successful application across many fields have been its hallmarks in recent years. A significant obstacle to healthcare transformation is the learning of useful information from complicated, high-dimensional, and diverse biological data. This research aims to give a concise synopsis of recent developments in deep learning, a survey of deep learning studies conducted on COVID-19, and some realworld applications of deep-learning(DL) strategies’ for COVID19 diagnosis, prognosis, and therapy management. DL has the potential to enhance the quality and speed of drug development by analyzing medicinal imaging data, lab test results, and other pertinent data to make diagnoses, assess the course and prognosis of illnesses, and even provide healing recommendations and drug-utilize regimens. In addition, it may guide policymakers in creating effective control and preventative strategies. Additionally, we evaluate the present state of deep learning's capabilities and shortcomings in terms of COVID-19 treatment accuracy, covering issues such as the dearth of phenotypically plentiful data and the necessity for deeper learning models that are easier to understand and work with. We wrap up by talking about how to get beyond the obstacles that are standing in the way of deep learning's potential future clinical uses.

关键词： Deep learning COVID-19 Surveys Time-frequency analysis Biological system modeling Time series analysis Signal processing algorithms

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Smoothing Matters: Momentum Transformer for Domain Adaptive Semantic Segmentation

arXiv

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arXiv 2022年

作者： Chen, Runfa Rong, Yu Guo, Shangmin Han, Jiaqi Sun, Fuchun Xu, Tingyang Huang, Wenbing State Key Lab on Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University China Tsinghua University China Tencent AI lab School of Informatics University of Edinburgh United Kingdom

After the great success of Vision Transformer variants (ViTs) in computer vision, it has also demonstrated great potentials in domain adaptive semantic segmentation. Unfortunately, straightforwardly applying local ViTs in domain adaptive semantic segmentation does not bring in expected improvement. We find that the pitfall of local ViTs is due to the severe high-frequency components generated during both the pseudo-label construction and features alignment for target domains. These high-frequency components make the training of local ViTs very unsmooth and hurts their transferability. In this paper, we introduce a low-pass filtering mechanism, momentum network, to smooth the learning dynamics of target domain features and pseudo labels. Furthermore, we propose a dynamic of discrepancy measurement to align the distributions in the source and target domains via dynamic weights to evaluate the importance of the samples. After tackling above issues, extensive experiments on sim2real benchmarks show that the proposed method outperforms the state-of-the-art methods. Our codes are available at https://***/alpc91/TransDA. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantic Segmentation

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