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Chinese Optics Letters 2024年第5期22卷 36-40页

作者：周家豪张静赵雪王芮李劲江胡少华张倩武杨奇许渤邱昆 Key Laboratory of Optical Fiber Sensing and communication University of Electronic Science and Technology of ChinaChengdu 611731China Key Laboratory of Specialty Fiber Optics and Optical Access networks Shanghai UniversityShanghai 200444China School of Optical and Electronic Information Huazhong University of Science and TechnologyWuhan 430074China

We propose a trellis-compressed maximum likelihood sequence estimation(TC-MLSE)-assisted sliding-block decision feedback equalizer(DFE)to suppress the error propagation resulting from the DFE in high-speed *** use an out-ofrange detector to detect the end of burst errors from the DFE and activate the optional TC-MLSE to correct burst *** conduct experiments to transmit a 201-Gbit/s PAM-8 *** results show that the proposed method achieves a bit error rate of 3.65×10^(-3),which is close to that of *** optional MLSE is only activated when needed and processes 11.4%of the total ***,the proposed method compresses the maximum length of burst errors from 19 to 5.

关键词： intensity modulation direct detection sliding-block decision feedback equalizer error propagation maximum likelihood sequence estimation out-of-range detector

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Rethinking multi-spatial information for transferable adversarial attacks on speaker recognition systems

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CAAI Transactions on Intelligence technology 2024年第3期9卷 620-631页

作者： Junjian Zhang Hao Tan Le Wang Yaguan Qian Zhaoquan Gu Guangzhou University GuangzhouChina department of New networks Peng Cheng LaboratoryShenzhenChina Harbin Institute of technology(Shenzhen) ShenzhenChina Zhejiang University of Science and technology HangzhouChina

Adversarial attacks have been posing significant security concerns to intelligent systems,such as speaker recognition systems(SRSs).Most attacks assume the neural networks in the systems are known beforehand,while black-box attacks are proposed without such information to meet practical *** black-box attacks improve trans-ferability by integrating multiple models or training on multiple datasets,but these methods are *** by the optimisation strategy with spatial information on the perturbed paths and samples,we propose a Dual Spatial Momentum Iterative Fast Gradient Sign Method(DS-MI-FGSM)to improve the transferability of black-box at-tacks against ***,DS-MI-FGSM only needs a single data and one model as the input;by extending to the data and model neighbouring spaces,it generates adver-sarial examples against the integrating *** reduce the risk of overfitting,DS-MI-FGSM also introduces gradient masking to improve *** authors conduct extensive experiments regarding the speaker recognition task,and the results demonstrate the effectiveness of their method,which can achieve up to 92%attack success rate on the victim model in black-box scenarios with only one known model.

关键词： speaker recognition spoofing attacks

FMSA:a meta-learning framework-based fast model stealing attack technique against intelligent network intrusion detection systems

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Cybersecurity 2024年第1期7卷 110-121页

作者： Kaisheng Fan Weizhe Zhang Guangrui Liu Hui He School of Cyberspace Science Harbin Institute of TechnologyHarbin 150001China department of New networks Peng Cheng LaboratoryShenzhen 518055GuangdongChina

Intrusion detection systems are increasingly using machine *** machine learning has shown excellent performance in identifying malicious traffic,it may increase the risk of privacy *** paper focuses on imple-menting a model stealing attack on intrusion detection *** model stealing attacks are hard to imple-ment in practical network environments,as they either need private data of the victim dataset or frequent access to the victim *** this paper,we propose a novel solution called Fast Model Stealing Attack(FMSA)to address the problem in the field of model stealing *** also highlight the risks of using ML-NIDS in network ***,meta-learning frameworks are introduced into the model stealing algorithm to clone the victim model in a black-box ***,the number of accesses to the target model is used as an optimization term,resulting in minimal queries to achieve model ***,adversarial training is used to simulate the data distribution of the target model and achieve the recovery of privacy *** experiments on multiple public datasets,compared to existing state-of-the-art algorithms,FMSA reduces the number of accesses to the target model and improves the accuracy of the clone model on the test dataset to 88.9%and the similarity with the target model to 90.1%.We can demonstrate the successful execution of model stealing attacks on the ML-NIDS system even with protective measures in place to limit the number of anomalous queries.

关键词： AI security Model stealing attack Network intrusion detection Meta learning

High-Al-composition AlGaN/GaN MISHEMT on Si with fT of 320 GHz

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Science China(Information Sciences) 2024年第6期67卷 459-460页

作者： Hanghai DU Lu HAO Zhihong LIU Zeyu SONG Yachao ZHANG Kui DANG Jin ZHOU Jing NING Zan LI Jincheng ZHANG Yue HAO State Key Laboratory of Wide Bandgap Semiconductor Devices and Integrated technology School of MicroelectronicsXidian University Guangzhou Wide Bandgap Semiconductor Innovation Center Guangzhou Institute of TechnologyXidian University State Key Laboratory of Integrated Services networks School of Communication EngineeringXidian University

GaN-based metal-insulator-semiconductor high-electronmobility-transistors (MISHEMTs) have many excellent performances compared with the Si and Ga As counterparts,and are prime candidates for applications in communicat...

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Photoionisation detection of a single Er3+ ion with sub-100-ns time resolution

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National Science Review 2024年第4期11卷 38-45页

作者： Yangbo Zhang Wenda Fan Jiliang Yang Hao Guan Qi Zhang Xi Qin Changkui Duan Gabriele G.de Boo Brett C.Johnson Jeffrey C.Mc Callum Matthew J.Sellars Sven Rogge Chunming Yin Jiangfeng Du CAS Key Laboratory of Microscale Magnetic Resonance and School of Physical Sciences University of Science and Technology of China CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Hefei National Laboratory University of Science and Technology of China Centre of Excellence for Quantum Computation and communication technology School of Physics University of New South Wales Centre of Excellence for Quantum Computation and communication technology School of Engineering RMIT University Centre of Excellence for Quantum Computation and communication technology School of Physics University of Melbourne Centre of Excellence for Quantum Computation and communication technology Research School of Physics and Engineering Australian National University

Efficient detection of single optical centres in solids is essential for quantum information processing, sensing and single-photon generation *** this work,we use radio-frequency（RF） reflectometry to electrically detect the photoionisation induced by a single Er3+ion in *** high bandwidth and sensitivity of the RF reflectometry provide sub-100-ns time resolution for the photoionisation *** this technique,the optically excited state lifetime of a single Er3+ion in a Si nano-transistor is measured for the first time to be 0.49±0.04μ*** results demonstrate an efficient approach for detecting a charge state change induced by Er excitation and *** approach could be used for fast readout of other single optical centres in solids and is attractive for large-scale integrated optical quantum systems thanks to the multi-channel RF reflectometry demonstrated with frequency multiplexing techniques.

关键词： single optical centres erbium ions RF reflectometry photoionisation detection

LLM4CP:Adapting Large Language Models for Channel Prediction

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Journal of communications and Information networks 2024年第2期9卷 113-125页

作者： Boxun Liu Xuanyu Liu Shijian Gao Xiang Cheng Liuqing Yang State Key Laboratory of Advanced Optical communication Systems and networks School of ElectronicsPeking UniversityBeijing 100871China Internet of Things Thrust The Hong Kong University of Science and Technology(Guangzhou)Guangzhou 511400China Internet of Things Thrust and Intelligent Transportation Thrust The Hong Kong University of Science and Technology(Guangzhou)Guangzhou 511400China department of Electronic and Computer Engineering and department of Civil and Environmental Engineering The Hong Kong University of Science and TechnologyHong KongChina

Channel prediction is an effective approach for reducing the feedback or estimation overhead in massive multi-input multi-output (m-MIMO) systems. However, existing channel prediction methods lack precision due to model mismatch errors or network generalization issues. Large language models (LLMs) have demonstrated powerful modeling and generalization abilities, and have been successfully applied to cross-modal tasks, including the time series analysis. Leveraging the expressive power of LLMs, we propose a pre-trained LLM-empowered channel prediction(LLM4CP)method to predict the future downlink channel state information (CSI) sequence based on the historical uplink CSI sequence. We fine-tune the network while freezing most of the parameters of the pre-trained LLM for better cross-modality knowledge transfer. To bridge the gap between the channel data and the feature space of the LLM,preprocessor, embedding, and output modules are specifically tailored by taking into account unique channel characteristics. Simulations validate that the proposed method achieves state-of-the-art (SOTA) prediction performance on full-sample, few-shot, and generalization tests with low training and inference costs.

关键词： channel prediction massive multi-input multi-output(m-MIMO) large language models(LLMs) fine-tuning time-series

Simulation and implementation of a reconfigurable dual-function pixel

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Optoelectronics Letters 2024年第8期20卷 454-459页

作者： Shaher Dwik Gurusamy Sasikala department of Electronics&communication Engineering School of Electrical&CommunicationVel Tech Rangarajan Dr Sagunthala R&D Institute of Science and TechnologyChennai 600062India

This paper presents the simulation and implementation of a reconfigurable pixel that serves both data acquisition and energy harvesting *** main topic focuses on switching between the two operating modes of the photodiode:photoconductive and photovoltaic *** proposed model can be used to design novel optical sensors with energy harvesting capability,such as position sensitive device(PSD)and complementary metal oxide semiconductor(CMOS)image sensors,which can extend the battery lifetime of the whole optical ***,we can overcome power supply problems like wiring and changing batteries frequently,especially in hard-to-reach places like space(cube satellites)or even underwater wireless optical communication(UWOC).The proposed pixel architecture offers the advantage of a minimalistic design with only four ***,it does come with a drawback in the form of higher noise *** simulation was achieved using MATLAB,and the implementation was performed using the programmable system-on-chip(PSoC)*** results showed that the functionality of the dual-function pixel is correct,and the scheduling of both energy harvesting and signal sensing functions was successfully achieved.

关键词： implementation harvesting function

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Transmit Power Minimization for IRS-Assisted NOMA-UAV networks

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China communications 2024年第5期21卷 137-152页

作者： Zhao Chen Pang Xiaowei Tang Jie Liu Mingqian Zhao Nan Zhang Xiuyin Wang Xianbin School of Information and communication Engineering Dalian University of TechnologyDalian 116024China School of Electronic and Information Engineering South China University of TechnologyGuangzhou 510641China State Key Laboratory of Integrated Services networks Xidian UniversityXi’an 710071China department of Electrical and Computer Engineering Western UniversityLondonON N6A 5B9Canada

The flexibility of unmanned aerial vehicles(UAVs)allows them to be quickly deployed to support ground *** reflecting surface(IRS)can reflect the incident signal and form passive beamforming to enhance the signal in the specific *** by the promising benefits of both technologies,we consider a new scenario in this paper where a UAV uses non-orthogonal multiple access to serve multiple users with *** to their distance to the UAV,the users are divided into the close users and remote *** UAV hovers above the close users due to their higher rate requirement,while the IRS is deployed near the remote users to enhance their received *** aim at minimizing the transmit power of UAV by jointly optimizing the beamforming of UAV and the phase shift of IRS while ensuring the decoding ***,the problem is ***,we decompose it into two sub-problems,including the transmit beamforming optimization and phase shift optimization,which are transformed into second-order cone programming and semidefinite programming,*** propose an iterative algorithm to solve the two sub-problems *** results prove the effectiveness of the proposed scheme in minimizing the transmit power of UAV.

关键词： intelligent reflecting surface nonorthogonal multiple access phase shift optimization transmit beamforming optimization unmanned aerial vehicle

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Faster Region Convolutional Neural Network(FRCNN)Based Facial Emotion Recognition

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Computers, Materials & Continua 2024年第5期79卷 2427-2448页

作者： J.Sheril Angel A.Diana Andrushia TMary Neebha Oussama Accouche Louai Saker N.Anand department of Electronics and communication Engineering Karunya Institute of Technology and SciencesCoimbatore641114India College of Engineering and technology American University of the Middle EastEgaila54200Kuwait department of Civil Engineering Karunya Institute of Technology and SciencesCoimbatore641114India

Facial emotion recognition(FER)has become a focal point of research due to its widespread applications,ranging from human-computer interaction to affective *** traditional FER techniques have relied on handcrafted features and classification models trained on image or video datasets,recent strides in artificial intelligence and deep learning(DL)have ushered in more sophisticated *** research aims to develop a FER system using a Faster Region Convolutional Neural Network(FRCNN)and design a specialized FRCNN architecture tailored for facial emotion recognition,leveraging its ability to capture spatial hierarchies within localized regions of facial *** proposed work enhances the accuracy and efficiency of facial emotion *** proposed work comprises twomajor key components:Inception V3-based feature extraction and FRCNN-based emotion *** experimentation on Kaggle datasets validates the effectiveness of the proposed strategy,showcasing the FRCNN approach’s resilience and accuracy in identifying and categorizing facial *** model’s overall performance metrics are compelling,with an accuracy of 98.4%,precision of 97.2%,and recall of 96.31%.This work introduces a perceptive deep learning-based FER method,contributing to the evolving landscape of emotion recognition *** high accuracy and resilience demonstrated by the FRCNN approach underscore its potential for real-world *** research advances the field of FER and presents a compelling case for the practicality and efficacy of deep learning models in automating the understanding of facial emotions.

关键词： Facial emotions FRCNN deep learning emotion recognition face CNN

Digital twin modeling and controlling of optical power evolution enabling autonomous-driving optical networks: a Bayesian approach

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Advanced Photonics 2024年第2期6卷 69-80页

作者： Xiaomin Liu Yihao Zhang Yuli Chen Yichen Liu Meng Cai Qizhi Qiu Mengfan Fu Lilin Yi Weisheng Hu Qunbi Zhuge Shanghai Jiao Tong University State Key Laboratory of Advanced Optical Communication Systems and NetworksDepartment of Electronic Engineering

Optical networks are evolving toward ultrawide bandwidth and autonomous operation. In this scenario, it is crucial to accurately model and control optical power evolutions(OPEs) through optical amplifiers(OAs), as they directly affect the signal-to-noise ratio and fiber nonlinearities. However, a fundamental contradiction arises between the complex physical phenomena in optical transmission and the required precision in network control. Traditional theoretical methods underperform due to ideal assumptions, while data-driven approaches entail exorbitant costs associated with acquiring massive amounts of data to achieve the desired level of accuracy. In this work, we propose a Bayesian inference framework(BIF) to construct the digital twin of OAs and control OPE in a data-efficient manner. Only the informative data are collected to balance the exploration and exploitation of the data space, thus enabling efficient autonomous-driving optical networks(ADONs). Simulations and experiments demonstrate that the BIF can reduce the data size for modeling erbium-doped fiber amplifiers by 80% and Raman amplifiers by 60%. Within 30 iterations, the optimal controlling performance can be achieved to realize target signal/gain profiles in links with different types of OAs. The results show that the BIF paves the way to accurately model and control OPE for future ADONs.

关键词： optical fiber communications digital twin Bayesian inference optical amplifiers autonomous-driving optical networks