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Survey of Spectral Amplitude Coding Optical Code Division Multiple Access Families

Journal of Lightwave technology 2025年

作者： Ullah, Rahat Ullah, Sibghat Ali, Aitizaz Jianxin, Ren Alwageed, Hathal Salamah Yaya, Mao Qi, Zhipeng Yu, Yongyi Imtiaz, Waqas A. Khan, Maqbool School of Physics and Optoelectronics Nanjing University of Information Science and Technology China Jiangsu Key Laboratory for Optoelectronic Detection of Atmosphere and Ocean Nanjing University of Information Science Technology Nanjing210044 China Jiangsu International Joint Laboratory on Meteorological Photonics and Optoelectronic Detection Nanjing University of Information Science Technology Nanjing210044 China School of Electronics Science and Technology Southeast University China School of Technology NSF Group Asia Pacific University Malaysia Saudi Arabia College of Computer Science Jouf University Saudi Arabia Electrical Engineering Department University of Engineering and Technology Peshawar Pakistan Pak-Austra Fachhochschule Haripur Pakistan

Quest for high capacity communication have led the research community to explore a plethora of technologies. Among these, optical code division multiple access (OCDMA) technique stands tall with support for high data rate connectivity in terms of data, reach and the number of subscribers. The development of OCDMA based networks started with the design and implementation of one dimensional (1D) codes, which provides effective correlation properties to reduce the effect of interference due to multiple users (MAI) and phase-induced-intensity-noise (PIIN) in order to provide the required system capacity. Several families of codes have been developed to determine the most suitable combination between code length, weight against the auto- and cross-correlation property respectively. This survey paper will analyze the most commonly used families of codes from spectral amplitude coding (SAC) based OCDMA systems names Double Weight (DW) codes, Diagonal codes , zero cross-correlation (ZCC) codes, and hybrid codes in terms of different performance parameters and identify the most suitable combination of code length (L), code weight (w), and cross-correlation (λC) to be utilized for next generation communication applications that can provide high capacity and cardinality. © 1983-2012 IEEE.

关键词： Passive optical networks

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Design and Implementation of Takeaway Ordering Recommendation system Based on Python and Flask 1

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2nd International Conference on Applied Intelligence, ICAI 2024

作者： Cao, Junjie Yu, Wendong Wei, Hongwei Yuan, Lin Li, Zhujun Shandong Jinan250353 China Shandong Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Shandong Jinan250353 China Shandong Tianyi Information Technology Co. Ltd. Shandong Jinan250101 China Jinan Springs Patent and Trademark Office Shandong Jinan250014 China

ISBN: (数字)9789819619047

ISBN: (纸本)9789819619030

With the rapid development of the economy, people are getting busier and busier, and it is difficult to find time to cook, so ordering takeout or dining at restaurants has become the norm. In the 2020 work report, the government proposed to fully implement the "Internet +" process, which also indicates the arrival of the advantages of the digital economy. Therefore, it has become an inevitable trend to use Internet technology to change our daily life and production methods. Driven by this trend, the catering industry has begun to move towards informatization. In order to make it more convenient for merchants and customers to meet their respective needs and save time and energy, a takeaway ordering recommendation system has emerged. This paper uses a collaborative filtering recommendation algorithm to recommend personalized foods that users may like, so as to provide more convenient services for catering services. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Bottles

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Robust Adversarial Defenses in Federated Learning: Exploring the Impact of Data Heterogeneity

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IEEE Transactions on information Forensics and security 2025年 20卷 6005-6018页

作者： Li, Qian Wu, Di Zhou, Dawei Lin, Chenhao Liu, Shuai Wang, Cong Shen, Chao Xi’an Jiaotong University Ministry of Education Key Laboratory for Intelligent Networks and Network Security School of Cyber Science and Engineering Xi’an 710049 China Xi’an Jiaotong University School of Computer Science and Technology Xi’an 710049 China Xidian University Xi’an 710071 China Xi’an Jiaotong University School of Software Xi’an 710049 China City University of Hong Kong Department of Computer Science Hong Kong

Federated Learning (FL) enables geographically distributed clients to collaboratively train machine learning models by exchanging local model parameters while preserving data privacy. In practice, FL faces two critical challenges. First, it is vulnerable to security issues as malicious clients would artificially harm the functionality of FL by launching poisoning attacks. Second, the inherent data heterogeneity among clients (termed Non-IID data in FL) naturally arises from distributed data ownership and significantly degrades model convergence and accuracy. However, with studies separately devoted to these two research lines, the interplay between data heterogeneity and security remains poorly understood. In this paper, we systematically investigate the relationship between data heterogeneity and adversarial robustness in FL. Specifically, we propose novel data partitioning algorithms that simulate Label-Conditional Non-IID and Feature-Conditional Non-IID with quantifiable heterogeneity levels. Further, we conduct extensive experiments to evaluate classical defense methods in the practical FL environment under state-of-the-art untargeted attacks. With results in various settings, we separately analyze the connection between Non-IID to defenses and attacks. Regarding attacks, with similar effects on models, Non-IID impacts the training in a different way compared with attacks. The interaction between attacks and Non-IID provides an opportunity to cause severe damage to FL. Regarding defenses, Non-IID induces heterogeneity in model distribution among clients which raises the difficulty of maintaining fidelity and robustness for defense methods. © 2005-2012 IEEE.

关键词： Adversarial Defenses Federated Learning Poisoning attacks security

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A method of fundamental matrix estimation based on nsga-ii and improved quasi-affine transform

Journal of Network Intelligence

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Journal of Network Intelligence 2021年第2期6卷 313-327页

作者： Fan, Yi-Kai Liu, Shi-Jian Kong, Ling-Ping Pan, Jeng-Shyang College of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China VSB-Technical University of Ostrava Czech Republic Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Department of Information Management Chaoyang University of Technology Taiwan

The fundamental matrix is the mainstream solution to computer vision problems such as 3D reconstruction, real-time location and map building. Accuracy and efficiency are two main measurement indexes in fundamental matrix estimation. When the accuracy is not enough, it often needs to be corrected through back-end optimization and other costly ways, and low efficiency will affect the real-time performance of the system. In order to solve this problem, this paper proposes a new estimation method of fundamental matrix based on improved quasi affine transformation. Specifically, based on the QUATRE algorithm, this method first proposes a population cooperation method based on a specific "gene-chromosome" pattern. Secondly, combining the advantages of NSGA-II in solving multi-objective problems, the Pareto dominance relationship of the population chromosome was firstly calculated according to the objective function of mean polarity distance and internal points in the way of NSGA-II, and the crowding degree was also calculated. The operations of population initialization, mutation and crossover in the discrete solution space represented by the homogeneous coordinate system are redefined. The selection operation is then performed according to the elite policy of NSGA-II. In addition, a confidence-based method to determine the number of iterations is proposed to accelerate the algorithm. Experimental results show that the proposed method can effectively eliminate noise and mismatching, and is superior to the current mainstream methods in accuracy and efficiency, and can effectively solve the problem of fundamental matrix estimation. © 2021 Global Research Online. All rights reserved.

关键词： Efficiency

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A new quantum machine learning algorithm: split hidden quantum Markov model inspired by quantum conditional master equation

arXiv

引用

arXiv 2023年

作者： Li, Xiao-Yu Zhu, Qin-Sheng Hu, Yong Wu, Hao Yang, Guo-Wu Yu, Lian-Hui Chen, Geng School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China School of Physics University of Electronic Science and Technology of China Chengdu610054 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610103 China Institute of Electronics and Information Industry Technology of Kash Kash 844000 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu610054 China

The Hidden Quantum Markov Model (HQMM) has significant potential for analyzing time-series data and studying stochastic processes in the quantum domain as an upgrading option with potential advantages over classical Markov models. In this paper, we introduced the split HQMM (SHQMM) for implementing the hidden quantum Markov process, utilizing the conditional master equation with a fine balance condition to demonstrate the interconnections among the internal states of the quantum system. The experimental results suggest that our model outperforms previous models in terms of scope of applications and robustness. Additionally, we establish a new learning algorithm to solve parameters in HQMM by relating the quantum conditional master equation to the HQMM. Finally, our study provides clear evidence that the quantum transport system can be considered a physical representation of HQMM. The SHQMM with accompanying algorithms present a novel method to analyze quantum systems and time series grounded in physical implementation. © 2023, CC BY-NC-SA.

关键词： Stochastic systems

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Tree-based Search Graph for Approximate Nearest Neighbor Search

arXiv

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arXiv 2022年

作者： Fan, Xiaobin Wang, Xiaoping Lu, Kai Xue, Lei Zhao, Jinjing Hunan University Changsha410082 China National University of Defense Technology Changsha410073 China National Key Laboratory of Science and Technology on Information System Security Beijing China

Nearest neighbor search supports important applications in many domains, such as database, machine learning, computer vision. Since the computational cost for accurate search is too high, the community turned to the research of approximate nearest neighbor search (ANNS). Among them, graph-based algorithm is one of the most important branches. Research by Fu et al. shows that the algorithms based on Monotonic Search Network (MSNET), such as NSG and NSSG, have achieved the state-of-the-art search performance in efficiency. The MSNET is dedicated to achieving monotonic search with minimal out-degree of nodes to pursue high efficiency. However, the current MSNET designs did not optimize the probability of the monotonic search, and the lower bound of the probability is only 50%. If they fail in monotonic search stage, they have to suffer tremendous backtracking cost to achieve the required accuracy. This will cause performance problems in search efficiency. To address this problem, we propose (r,p)-MSNET, which achieves guaranteed probability on monotonic search. Due to the high building complexity of a strict (r,p)-MSNET, we propose TBSG, which is an approximation with low complexity. Experiments conducted on four million-scaled datasets show that TBSG yields better or comparable search performance than all recent state-of-the-art algorithms. Our code has been released on Github. Copyright © 2022, The Authors. All rights reserved.

关键词： Graphic methods

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NLOS/LOS Classification by Constructing Indirect Environment Interaction From GNSS Receiver Measurements Using a Transformer-Based Deep Learning Model

NLOS/LOS Classification by Constructing Indirect Environment...

引用

2023 International Technical Meeting of The Institute of Navigation, ITM 2023

作者： Li, Zhenni Zeng, Kungan Wang, Lujia Kan, Xie Yuan, Rong Xie, Shengli School of Automation Guangdong University of Technology Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China School of Software and Engineering Sun Yat-sen University Hong Kong Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Techtotop Microelectronics Technology Co. Ltd Guangzhou510006 China Guangdong Key Laboratory of IoT Information Technology Guangzhou510006 China

ISBN: (纸本)9781713871378

The blockage of the direct signal by high-rise buildings degrades the positioning performance of the global navigation satellite system (GNSS) in urban canyons. Traditional methods usually distinguish the line-of-sight (LOS), and non-line-of-sight (NLOS) signals using the satellite’s elevation angle or signal strength to mitigate the issue, which cannot obtain satisfactory classification accuracy in complex urban environments. Recently, machine learning-based methods using GNSS measurements have been developed to improve GNSS signal classification. However, dynamic changes in environments and the limited features from the measurements raise a challenge to the classification performance of algorithms. To address the above issues, a transformer-based deep learning model in this paper is proposed for LOS and NLOS signal classification. The innovation of our proposed model is using the self-attention mechanism to construct the indirect environment interaction for learning the representation of environmental information. First, the measurements of captured satellites in the same epoch will be fed into the model. The model’s feature extraction layer can learn the fundamental representation. Then, the self-attention module can construct the indirect environment interaction by computing the relevance between the satellites. Finally, the module’s output is transferred to the output layer for classification. A comparative experimental study is conducted with real-world data collected in urban canyons. The results demonstrate that our model can keep more than 90% classification accuracy while support vector machine (SVM) and multilayer perception (MLP) decline to about 80% with the sites increasing. © 2023 Proceedings of the International Technical Meeting of The Institute of Navigation, ITM.

关键词： Satellites

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Mixed Motivation Driven Social Multi-Agent Reinforcement Learning for Autonomous Driving

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IEEE/CAA Journal of Automatica Sinica 2025年第6期12卷 1272-1282页

作者： Long Chen Peng Deng Lingxi Li Xuemin Hu State Key Laboratory of Multimodal Artificial Intelligence Systems and the State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing WAYTOUS Inc. Beijing Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China School of Computer Science and Information Engineering Hubei University Wuhan China Purdue School of Engineering and Technology Indiana University-Purdue University Indianapolis Indianapolis IN USA School of Artificial Intelligence Hubei University Wuhan Key Laboratory of Intelligent Sensing System and Security (Hubei University) Ministry of Education Wuhan China

Despite great achievement has been made in autonomous driving technologies, autonomous vehicles (AVs) still exhibit limitations in intelligence and lack social coordination, which is primarily attributed to their reliance on single-agent technologies, neglecting inter-AV interactions. Current research on multi-agent autonomous driving (MAAD) predominantly focuses on either distributed individual learning or centralized cooperative learning, ignoring the mixed-motive nature of MAAD systems, where each agent is not only self-interested in reaching its own destination but also needs to coordinate with other traffic participants to enhance efficiency and safety. Inspired by the mixed motivation of human driving behavior and their learning process, we propose a novel mixed motivation driven social multi-agent reinforcement learning method for autonomous driving. In our method, a multi-agent reinforcement learning (MARL) algorithm, called Social Learning Policy Optimization (SoLPO), which takes advantage of both the individual and social learning paradigms, is proposed to empower agents to rapidly acquire self-interested policies and effectively learn socially coordinated behavior. Based on the proposed SoLPO, we further develop a mixed-motive MARL method for autonomous driving combined with a social reward integration module that can model the mixed-motive nature of MAAD systems by integrating individual and neighbor rewards into a social learning objective for improved learning speed and effectiveness. Experiments conducted on the MetaDrive simulator show that our proposed method outperforms existing state-of-the-art MARL approaches in metrics including the success rate, safety, and efficiency. More-over, the AVs trained by our method form coordinated social norms and exhibit human-like driving behavior, demonstrating a high degree of social coordination.

关键词： Measurement Reinforcement learning Safety Driver behavior Autonomous vehicles Optimization

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Membership information Leakage in Well-Generalized Auto Speech Recognition systems

Membership Information Leakage in Well-Generalized Auto Spee...

引用

Data science and Network security (ICDSNS), International Conference on

作者： Honglei Li Xinlong Zhao School of Cyberspace Security (School of Cryptology) Hainan University Haikou China Key Laboratory of Internet Information Retrieval of Hainan Province Haikou China School of computer science and technology Hainan University Haikou China

Membership inference attack (MIA) attempts to determine whether a particular data sample has participated in the training process of the target model, and is regarded as basic form of privacy breach in machine learning. This paper provides a label-only membership inference attack to well-generalized automatic speech recognition (ASR) systems, while only being granted black-box access permissions for the target system. Initially, we develop shadow models as substitution of the target ASR model. Subsequently, data augmentation techniques are employed to generate additional speech samples for the member and non-member sets elements of the shadow models. These augmentation techniques include modifying speech speed, cropping, adding noise, and so on. We query the shadow models with all the samples above, and we can derive a series of features from the model's output. All the features and their corresponding membership labels are then utilized to train an audit model. This audit model will be used to judge the membership of a given sample. The experimental results indicate that the performance metrics of this attack are significantly higher when the adversary has knowledge about the data distribution of target model's training set and the target model's architecture. However, if the adversary is lack of this knowledge, the probability of successful MIA is comparable to random guessing. The main contribution is that we investigate the membership leakage in well-generalized auto speech recognition system, and propose a data augmentation-based membership inference attack. We also discuss the transferability of the proposed scheme under various attack uncertainty.

关键词：

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Robust Imagined Speech Production from Electrocorticography with Adaptive Frequency Enhancement

Robust Imagined Speech Production from Electrocorticography ...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Xu Xu Chong Fu Junxin Chen Gwanggil Jeon David Camacho School of Computer Science and Engineering Northeastern University Shenyang China School of Software Dalian University of Technology Dalian China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China Engineering Research Center of Security Technology of Complex Network System Ministry of Education Shenyang China College of Information Technology Incheon National University Incheon Korea Department of Computer Systems Engineering Universidad Politécnica de Madrid Madrid Spain

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Imagined speech production with electrocorticography (ECoG) plays a crucial role in brain-computer interface system. A challenging issue is the great variation underlying the frequency bands of the ECoG signals’ encode information, which makes current methods difficult to generate imagined speech with stable quality among different persons. To this end, we propose a robust model to generate high-quality imagined speech from ECoG. A frequency enhancement branch is first designed to adaptively modulate the frequency information, whose product is fed into the following multi-scale channel attention module for robust feature extraction and fusion. By incorporating both the mel-spectrum and audio as training constraints, a multi-constraint decoder branch is finally constructed for imagined speech production. The performance of our model is evaluated on a high-quality dateset, i.e, Single Word Production Dutch-iBIDS. It yields Pearson correlation scores that are all above 0.8, and the standard deviationsare are all below 0.2 in different volunteers. Experimental results demonstrate that our model is effective and robust for ECoG based imagined speech production, and has advantages over peer methods.

关键词： Training Adaptation models Frequency modulation Correlation Neuroscience Production Speech enhancement

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