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Quantum qubit-optical cavity node: Effects of the temperature and coupling strengths on the cavity Fock-state distribution occupation probability and entropy of the subsystems using Dicke model

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Results in Physics 2025年 72卷

作者： Barhoumi, Mohamed Bassoli, Riccardo Fitzek, Frank H.P. Laboratory of Condensed Matter and Nanosciences (LMCN) Department of Physics Faculty of Sciences of Monastir University of Monastir Environment Street Monastir 5019 Tunisia Institut Supérieur d'Informatique et de Mathématiques de Monastir (ISIMM) University of Monastir Avenue de la Corniche Monastir 5000 Tunisia Deutsche Telekom Chair of Communication Networks Institute of Communication Technology Faculty of Electrical and Computer Engineering Technische Universität Dresden Dresden Germany Centre for Tactile Internet with Human-in-the-Loop (CeTI) Cluster of Excellence Dresden Germany Quantum Communication Networks (QCNets) research group Institute of Communication Technology Faculty of Electrical and Computer Engineering Technische Universität Dresden Dresden Germany

One of the most attractive systems for scalable quantum processing is the trapped atom in the optical cavity, which is easily manipulable with lasers. The qubit cavity system represents an attractive structure for quantum optics, with possibilities for applications in quantum detecting, quantum computation, and quantum communication. In addition, the investigation of the cavity-atom system under temperature is a fundamental concept in quantum optics and quantum information science. Here we continue in this quantum way, investigating different qubit(s)-cavity systems (where we increase the numbers of the qubit inside the optical cavity from one to four). Using the Dicke model, we calculate the probability of occupation of the ground state of the cavity 〈aˆ^†aˆ〉 and the expectation values 〈J_z〉 for each system. We find that these probabilities increase as the coupling strength C_s increases. This leads to reaching the ultra-strong coupling regime, which depends on the number of qubits. To describe the quantum state, we examine the cavity Wigner function WF as a function of the coupling strength C_s. Under the same parameters (e.g. C_s and the dissipation rates), we find that the separation of the quantum state in the WFs representation is different from one system to another. Furthermore, since understanding these kinds of systems is very important in order to improve the quantum network system, we study the entropy S of the subsystems (such as S of qubit) in each system. Also, we move beyond by studying the effect of the temperature on the Fock-state distribution occupation probability and entropy of the subsystems. Our results present another step to understanding the qubit(s)-cavity interaction to improve the performance of future quantum networks based on this system. Where the interplay between temperature, coupling strengths, and the Fock-state occupation probabilities contributes significantly to the understandi

关键词： Cavity Dicke model Quantum communication networks Quantum nodes Quantum state Qubit Qubit–cavity system Temperature

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Neural Network-Assisted Hybrid Model Based Message Passing for Parametric Holographic MIMO Near Field Channel Estimation

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IEEE Transactions on Wireless communications 2025年

作者： Yuan, Zhengdao Guo, Yabo Gao, Dawei Guo, Qinghua Wang, Zhongyong Huang, Chongwen Jin, Ming Wong, Kai-Kit Henan Open University Artificial Intelligence Technology Engineering Research Center Zhengzhou450002 China University of Wollongong School of Electrical Computer and Telecommunications Engineering WollongongNSW2522 Australia Zhengzhou University School of Information Engineering Zhengzhou450002 China Xidian University Hangzhou Institute of Technology Hangzhou311200 China University of Wollongong School of Electrical Computer and Telecommunications Engineering NSW2522 Australia Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Zhejiang Provincial Key Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing Hangzhou310027 China Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China University College London Department of Electronic and Electrical Engineering Torrington Place WC1E 7JE United Kingdom Yonsei University Yonsei Frontier Lab Seoul Korea Republic of

Holographic multiple-input and multiple-output (HMIMO) is a promising technology with the potential to achieve high energy and spectral efficiencies, enhance system capacity and diversity, etc. In this work, we address the challenge of HMIMO near field (NF) channel estimation, which is complicated by the intricate model introduced by the dyadic Green's function. Despite its complexity, the channel model is governed by a limited set of parameters. This makes parametric channel estimation highly attractive, offering substantial performance enhancements and enabling the extraction of valuable sensing parameters, such as user locations, which are particularly beneficial in mobile networks. However, the relationship between these parameters and channel gains is nonlinear and compounded by integration, making the estimation a formidable task. To tackle this problem, we propose a novel neural network (NN) assisted hybrid method. With the assistance of NNs, we first develop a novel hybrid channel model with a significantly simplified expression compared to the original one, thereby enabling parametric channel estimation. Using the readily available training data derived from the original channel model, the NNs in the hybrid channel model can be effectively trained offline. Then, building upon this hybrid channel model, we formulate the parametric channel estimation problem with a probabilistic framework and design a factor graph representation for Bayesian estimation. Leveraging the factor graph representation and unitary approximate message passing (UAMP), we develop an effective message passing-based Bayesian channel estimation algorithm. Extensive simulations demonstrate the superior performance of the proposed method. © 2025 IEEE.

关键词： Green's function

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Cybersecurity Using Hybrid Type Model for Classification Through SCO Optimization Technique

Cybersecurity Using Hybrid Type Model for Classification Thr...

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Computing, Power and communication Technologies (IC2PCT), IEEE International Conference on

作者： Jothi Prabha Appadurai Lekshmi S Raveendran R Latha E. Gangadevi M.Lawanya Shri Shilpa Gite Department of CSE(Networks) Kakatiya Institute of Technology and Science Warangal Telangana India Sathyabama Institute of Science and Technology Chennai India Electronics & Communication Engineering Saveetha Engineering College Saveetha Nagar Thandalam Chennai India Department of Computer Science Loyola College Chennai India SITE Vellore Institute of Technology Vellore India AIML department Symbiosis Institute of Technology Pune. Symbiosis International (Deemed University) Pune India

The effectiveness of Network Intrusion Detection Systems (NIDS) in recognizing complex attacks is hindered by evasion strategies like polymorphic malware and encrypted traffic. Vulnerabilities in NIDS algorithms are regularly exploited by cybercriminals, highlighting the dynamic relationship between changing attack tactics and cybersecurity defenses. An enhanced approach to strengthen Network Intrusion Detection Systems is suggested by this study. The methodology begins with a rigorous preprocessing phase that includes normalization functions to progress the accuracy and consistency of the input data. Optimizing the NIDS’s efficacy is the aim of the Single Candidate Optimization (SCO) algorithm for feature selection. We utilize a hybrid model that incorporates ANN, Wavelet Transform, and Long Short-Term Memory (LSTM) for the classification phase. This model is designed to identify sequential dependencies in the network traffic data that we have. A second iteration of the SCO is devoted to hyperparameter optimization in order to attain optimal model performance and to further optimize the model. For our review, we opted to use the BoT-IoT dataset because it is a gold standard in the field. The suggested approach demonstrates how SCO can improve hyperparameter optimization and feature selection, leading to a more accurate and cyberattack-resistant NIDS. Consequences from experiments and evaluations of performance demonstrate that the proposed strategy is effective; it achieves a remarkable 99.6 percent accuracy. This proves how successful it is in judgement to current representations, which is a significant step towards consolidation cybersecurity defenses against changing threat landscapes.

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Integrated high-performance error correction for continuous-variable quantum key distribution

arXiv

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arXiv 2024年

作者： Zhou, Chuang Li, Yang Ma, Li Yang, Jie Huang, Wei Sun, Ao Wang, Heng Luo, Yujie Li, Yong Chen, Ziyang Lau, Francis C.M. Zhang, Yichen Yu, Song Guo, Hong Xu, Bingjie National Key Laboratory of Security Communication Institute of Southwestern Communication Chengdu610041 China College of Computer Science Chongqing University Chongqing400044 China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Center for Quantum Information Technology Peking University Beijing100871 China Department of Electronic and Information Engineering Hong Kong Polytechnic University Hong Kong State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing100876 China

An integrated error-correction scheme with high throughput, low frame errors rate (FER) and high reconciliation efficiency under low signal to noise ratio (SNR) is one of the major bottlenecks to realize high-performance and low-cost continuous variable quantum key distribution (CV-QKD). To solve this long-standing problem, a novel two-stage error correction method with limited precision that is suitable for integration given limited on-chip hardware resource while maintaining excellent decoding performance is proposed, and experimentally verified on a commercial FPGA. Compared to state-of-art results, the error-correction throughput can be improved more than one order of magnitude given FER © 2024, CC BY.

关键词： Error correction

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A New Agent-Based Intelligent Network Architecture

arXiv

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arXiv 2022年

作者： Arzo, Sisay Tadesse Scotece, Domenico Bassoli, Riccardo Granelli, Fabrizio Foschini, Luca Fitzek, Frank H.P. University of Trento Trento Italy University of Bologna Bologna Italy The Deutsche Telekom Chair of Communication Networks Institute of Communication Technology Faculty of Electrical and Computer Engineering Technische Universität Dresden Dresden Germany Cluster of Excellence Dresden Germany

The advent of 5G and the design of its architecture has become possible because of the previous individual scientific works and standardization efforts on cloud computing and network softwarization. Software-defined Networking and Network Function Virtualization started separately to find their convolution into 5G network architecture. Then, the ongoing design of the future beyond 5G (B5G) and 6G network architecture cannot overlook the pivotal inputs of different independent standardization efforts about autonomic networking, service-based communication systems, and multi-access edge computing. This article provides the design and the characteristics of an agent-based, softwarized, and intelligent architecture, which coherently condenses and merges the independent proposed architectural works by different standardization working groups and bodies. This novel work is a helpful means for the design and standardization process of the futureB5G and 6G network architecture. Copyright © 2022, The Authors. All rights reserved.

关键词： Standardization

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A New Agent-Based Intelligent Network Architecture

TechRxiv

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TechRxiv 2022年

关键词： Queueing networks

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Photonic integrated spiking neuron chip based on a self-pulsating DFB laser with a saturable absorber

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Photonics Research 2023年第8期11卷 1382-1389页

作者： YUECHUN SHI SHUIYING XIANG XINGXING GUO YAHUI ZHANG HONGJI WANG DIANZHUANG ZHENG YUNA ZHANG YANAN HAN YONG ZHAO XIAOJUN ZHU XIANGFEI CHEN XUN LI YUE HAO Yongjiang Laboratory Ningbo 315202China State Key Laboratory of Integrated Service Networks State Key Discipline Laboratory of Wide Bandgap Semiconductor TechnologyXidian UniversityXi’an 710071China Key Laboratory of Intelligent Optical Sensing and Manipulation Ministry of EducationNational Laboratory of Solid State MicrostructuresCollege of Engineering and Applied SciencesInstitute of Optical Communication EngineeringNanjing UniversityNanjing 210023China School of Science Jiangnan UniversityWuxi 214122China School of Information Science and Technology Nantong UniversityNantong 226019China Department of Electrical and Computer Engineering McMaster UniversityHamiltonOantario L8S 4K1Canada

We proposed and experimentally demonstrated a simple and novel photonic spiking neuron based on a distributed feedback(DFB)laser chip with an intracavity saturable absorber(SA).The DFB laser with an intracavity SA(DFBSA)contains a gain region and an SA *** gain region is designed and fabricated by the asymmetric equivalentπ-phase shift based on the reconstruction-equivalent-chirp *** properly injected current in the gain region and reversely biased voltage in the SA region,periodic self-pulsation was experimentally observed due to the Q-switching *** self-pulsation frequency increases with the increase of the bias current and is within the range of several *** the bias current is below the self-pulsation threshold,neuronlike spiking responses appear when external optical stimulus pulses are *** results show that the spike threshold,temporal integration,and refractory period can all be observed in the fabricated DFB-SA *** numerically verify the experimental findings,a time-dependent coupled-wave equation model was developed,which described the physics processes inside the gain and SA *** numerical results agree well with the experimental *** further experimentally demonstrated that the weighted sum output can readily be encoded into the self-pulsation frequency of the DFB-SA *** also benchmarked the handwritten digit classification task with a simple single-layer fully connected neural *** using the experimentally measured dependence of the self-pulsation frequency on the bias current in the gain region as an activation function,we can achieve a recognition accuracy of 92.2%,which bridges the gap between the continuous valued artificial neural networks and spike-based neuromorphic *** the best of our knowledge,this is the first experimental demonstration of a photonic integrated spiking neuron based on a DFB-SA,which shows great potential to realizing large-scale mult

关键词： technique laser saturable

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Hybrid Deep Learning Algorithm For MRI Brain Alzheimer’s Disease Prediction

Hybrid Deep Learning Algorithm For MRI Brain Alzheimer’s Di...

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Energy, Materials and communication engineering (ICEMCE), International Conference on

作者： D. Karthikeyan R. Nivasini Kiruba Thangam Raja D. Madhivadhani B. Sivakumar A.T.R. Krishna Priya Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Kattankulathur Chennai Guvi Geek Networks IITM Research Park Chennai Tamilnadu India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems VIT University Vellore India Department of Electronics and Communication Engineering Kings Engineering College Sriperumbudur Chennai Department of Computer Science and Engineering Rohini College of Engineering and Technology Kanyakumari India

Magnetic resonance imaging (MRI) has been used to study the structural makeup of the brain and analyse several neurological disorders and diseased areas. For the adoption of preventative measures, early recognition of Alzheimer’s disease (AD) patients is essential. Here, a thorough inspection of the tissue arrangements obtained by MRI images of Outcome and Assessment Information (OASIS) dataset enables an exact characterization of certain brain diseases. There have been a number of division techniques for diagnosing AD that range in complexity. Compassion has been tested by deep learning techniques used to segment the structure of the brain and classify AD because they have the potential to uncover important information from vast amounts of data. In this paper the deep learning technique of Hybrid Dragonfly based GWO convolutional Neural Network (CNN) is achieved promising result for the diagnosis of AD. At image preprocessing wiener filter is used for removing the additive noises and the Gray Level Co-Occurrence Matrix (GLCM) extraction is implemented for texture analyzing. As a result, hybrid deep learning methods of CNN has the accuracy as 90% and the result of the image prediction are presented in this paper.

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Explainable Asymmetric Auto-Encoder for End-to-End Learning of IoBNT communications

Explainable Asymmetric Auto-Encoder for End-to-End Learning ...

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Machine Learning for communication and Networking (ICMLCN), IEEE International Conference on

作者： Roya Khanzadeh Stefan Angerbauer Jorge Torres Gomez Pit Hofmann Falko Dressler Frank H. P. Fitzek Andreas Springer Werner Haselmayr Institute for Communications Engineering and RF-Systems JKU LIT SAL eSPML Lab Johannes Kepler University Linz Linz Austria School of Electrical Engineering and Computer Science TU Berlin Berlin Germany Deutsche Telekom Chair of Communication Networks Technische Universität Dresden Dresden Germany Centre for Tactile Internet With Human-in-the-Loop (CeTI) Dresden Germany Institute for Communications Engineering and RF-Systems Johannes Kepler University Linz Linz Austria

ISBN: (数字)9798350343199

ISBN: (纸本)9798350343205

The Internet of Bio-Nano Things (IoBNT) is envisioned to be a heterogeneous network of artificial and natural units that are connected to the Internet. Hence, it extends the connectivity and control to unconventional domains, such as the human body. A potential use case for IoBNT is the communication from the outside to the inside of the human body. In this scenario, typically the Receiver (RX) inside the human body has limited computational complexity, while the Transmitter (TX) outside has large computational resources. In this paper, we address this scenario and propose a novel Asymmetric Auto-Encoder (AAEC) architecture for end-to-end learning of a Molecular communication (MC) system. It applies a Neural Network (NN) at the TX and a low-complexity slope detector at the RX. We discuss the different layers of the NN-based TX and the corresponding training approach. Moreover, we investigate the explainability of the NN-based TX and show through the use of meta modeling that it can be approximated by a linear model. In addition, we demonstrate that the proposed AAEC resembles an MC system with Zero Forcing (ZF) precoding for low and moderate Inter Symbol Interference (ISI). Finally, through numerical results, we confirmed the aforementioned findings and showed that the proposed AAEC outperforms MC systems with and without ZF precoding, especially in high ISI scenarios.

关键词： Training Transmitters Precoding Symbols Molecular communication Artificial neural networks Receivers

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Salient Object Detection in Optical Remote Sensing Images Driven by Transformer

arXiv

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arXiv 2023年

作者： Li, Gongyang Bai, Zhen Liu, Zhi Zhang, Xinpeng Ling, Haibin Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai200444 China School of Communication and Information Engineering Shanghai University Shanghai200444 China Wenzhou Institute of Shanghai University Wenzhou325000 China The Department of Computer Science Stony Brook University Stony BrookNY11794 United States

Existing methods for Salient Object Detection in Optical Remote Sensing Images (ORSI-SOD) mainly adopt Convolutional Neural networks (CNNs) as the backbone, such as VGG and ResNet. Since CNNs can only extract features within certain receptive fields, most ORSI-SOD methods generally follow the local-to-contextual paradigm. In this paper, we propose a novel Global Extraction Local Exploration Network (GeleNet) for ORSI-SOD following the global-to-local paradigm. Specifically, GeleNet first adopts a transformer backbone to generate four-level feature embeddings with global long-range dependencies. Then, GeleNet employs a Direction-aware Shuffle Weighted Spatial Attention Module (D-SWSAM) and its simplified version (SWSAM) to enhance local interactions, and a Knowledge Transfer Module (KTM) to further enhance cross-level contextual interactions. D-SWSAM comprehensively perceives the orientation information in the lowest-level features through directional convolutions to adapt to various orientations of salient objects in ORSIs, and effectively enhances the details of salient objects with an improved attention mechanism. SWSAM discards the direction-aware part of D-SWSAM to focus on localizing salient objects in the highest-level features. KTM models the contextual correlation knowledge of two middle-level features of different scales based on the self-attention mechanism, and transfers the knowledge to the raw features to generate more discriminative features. Finally, a saliency predictor is used to generate the saliency map based on the outputs of the above three modules. Extensive experiments on three public datasets demonstrate that the proposed GeleNet outperforms relevant state-of-the-art methods. The code and results of our method are available at https://***/MathLee/GeleNet. © 2023, CC BY-NC-ND.

关键词： Convolution

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