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On-chip spiking neural networks based on add-drop ring microresonators and electrically reconfigurable phase-change material photonic switches

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Photonics Research 2024年第4期12卷 755-766页

作者： QIANG ZHANG NING JIANG YIQUN ZHANG ANRAN LI HUANHUAN XIONG GANG HU YONGSHENG CAO KUN QIU School of Information and communication Engineering University of Electronic Science and Technology of ChinaChengdu 611731China

We propose and numerically demonstrate a photonic computing primitive designed for integrated spiking neural networks (SNNs) based on add-drop ring microresonators (ADRMRs) and electrically reconfigurable phasechange material (PCM) photonic switches. In this neuromorphic system, the passive silicon-based ADRMR,equipped with a power-tunable auxiliary light, effectively demonstrates nonlinearity-induced dual neural dynamics encompassing spiking response and synaptic plasticity that can generate single-wavelength optical neural spikes with synaptic weight. By cascading these ADRMRs with different resonant wavelengths, weighted multiple-wavelength spikes can be feasibly output from the ADRMR-based hardware arrays when external wavelengthaddressable optical pulses are injected;subsequently, the cumulative power of these weighted output spikes is utilized to ascertain the activation status of the reconfigurable PCM photonic switches. Moreover, the reconfigurable mechanism driving the interconversion of the PCMs between the resonant-bonded crystalline states and the covalent-bonded amorphous states is achieved through precise thermal modulation. Drawing from the thermal properties, an innovative thermodynamic leaky integrate-and-firing (TLIF) neuron system is proposed. With the TLIF neuron system as the fundamental unit, a fully connected SNN is constructed to complete a classic deep learning task:the recognition of handwritten digit patterns. The simulation results reveal that the exemplary SNN can effectively recognize 10 numbers directly in the optical domain by employing the surrogate gradient algorithm. The theoretical verification of our architecture paves a whole new path for integrated photonic SNNs, with the potential to advance the field of neuromorphic photonic systems and enable more efficient spiking information processing.

关键词： neural networks precise

Improve Chinese Aspect Sentiment Quadruplet Prediction via Instruction Learning Based on Large Generate Models

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Computers, Materials & Continua 2024年第3期78卷 3391-3412页

作者： Zhaoliang Wu Yuewei Wu Xiaoli Feng Jiajun Zou Fulian Yin College of Information and communication Engineering Communication University of ChinaBeijing100024China The State Key Laboratory of Media Convergence and communication Communication University of ChinaBeijing100024China Department of electronic Engineering Tsinghua UniversityBeijing100084China

Aspect-Based Sentiment Analysis(ABSA)is a fundamental area of research in Natural Language Processing(NLP).Within ABSA,Aspect Sentiment Quad Prediction(ASQP)aims to accurately identify sentiment quadruplets in target sentences,including aspect terms,aspect categories,corresponding opinion terms,and sentiment ***,most existing research has focused on English ***,while ASQP has seen significant progress in English,the Chinese ASQP task has remained relatively *** inspiration from methods applied to English ASQP,we propose Chinese generation templates and employ prompt-based instruction learning to enhance the model’s understanding of the task,ultimately improving ASQP performance in the Chinese ***,under the same pre-training model configuration,our approach achieved a 5.79%improvement in the F1 score compared to the previously leading ***,when utilizing a larger model with reduced training parameters,the F1 score demonstrated an 8.14%***,we suggest a novel evaluation metric based on the characteristics of generative models,better-reflecting model *** results validate the effectiveness of our approach.

关键词： ABSA ASQP LLMs sentiment analysis Chinese comments

Rapid Phase Ambiguity Elimination Methods for DOA Estimator via Hybrid Massive MIMO Receive Array

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Chinese Journal of electronics 2024年第1期33卷 175-184页

作者： Xichao ZHAN Zhongwen SUN Feng SHU Yiwen CHEN Xin CHENG Yuanyuan WU Qi ZHANG Yifan LI Peng ZHANG School of Information and communication Engineering Hainan University School of electronic and Optical Engineering Nanjing University of Science and Technology

For a sub-connected hybrid multiple-input multiple-output(MIMO) receiver with K subarrays and N antennas, there exists a challenging problem of how to rapidly remove phase ambiguity in only single time-slot. A direction of arrival(DOA) estimator of maximizing received power(Max-RP) is proposed to find the maximum value of K-subarray output powers, where each subarray is in charge of one sector, and the center angle of the sector corresponding to the maximum output is the estimated true DOA. To make an enhancement on precision, Max-RP plus quadratic interpolation(Max-RP-QI) method is designed. In the proposed Max-RP-QI, a quadratic interpolation scheme is adopted to interpolate the three DOA values corresponding to the largest three receive powers of *** achieve the Cramer Rao lower bound, a Root-MUSIC plus Max-RP-QI scheme is developed. Simulation results show that the proposed three methods eliminate the phase ambiguity during one time-slot and also show low computational complexities. The proposed Root-MUSIC plus Max-RP-QI scheme can reach the Cramer Rao lower bound,and the proposed Max-RP and Max-RP-QI are still some performance losses 2–4 d B compared to the Cramer Rao lower bound.

关键词： Phased arrays Interpolation Wireless sensor networks Direction-of-arrival estimation Simulation Receiving antennas Massive MIMO

RFFsNet-SEI:a multidimensional balanced-RFFs deep neural network framework for specific emitter identification

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Journal of Systems Engineering and electronics 2024年第3期35卷 558-574,F0002页

作者： FAN Rong SI Chengke HAN Yi WAN Qun School of Information and communication Engineering University of Electronic Science and Technology of ChinaChengdu 611731China institute of electronic and Electrical Engineering Civil Aviation Flight University of ChinaGuanghan 618307China

Existing specific emitter identification(SEI)methods based on hand-crafted features have drawbacks of losing feature information and involving multiple processing stages,which reduce the identification accuracy of emitters and complicate the procedures of *** this paper,we propose a deep SEI approach via multidimensional feature extraction for radio frequency fingerprints(RFFs),namely,***,we extract multidimensional physical RFFs from the received signal by virtue of variational mode decomposition(VMD)and Hilbert transform(HT).The physical RFFs and I-Q data are formed into the balanced-RFFs,which are then used to train *** introducing model-aided RFFs into neural network,the hybrid-driven scheme including physical features and I-Q data is *** improves physical interpretability of ***,since RFFsNet-SEI identifies individual of emitters from received raw data in end-to-end,it accelerates SEI implementation and simplifies procedures of ***,as the temporal features and spectral features of the received signal are both extracted by RFFsNet-SEI,identification accuracy is ***,we compare RFFsNet-SEI with the counterparts in terms of identification accuracy,computational complexity,and prediction *** results illustrate that the proposed method outperforms the counterparts on the basis of simulation dataset and real dataset collected in the anechoic chamber.

关键词： specific emitter identification(SEI) deep learning(DL) radio frequency fingerprint(RFF) multidimensional feature extraction(MFE) variational mode decomposition(VMD)

CNN-LSTM based incremental attention mechanism enabled phase-space reconstruction for chaotic time series prediction

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Journal of electronic Science and Technology 2024年第2期22卷 77-90页

作者： Xiao-Qian Lu Jun Tian Qiang Liao Zheng-Wu Xu Lu Gan Bidding and Purchasing Center University of Electronic Science and Technology of ChinaChengdu611731China Yibin Research institute University of Electronic Science and Technology of ChinaYibin644000China School of Information and communication Engineering University of Electronic Science and Technology of ChinaChengdu611731China

To improve the prediction accuracy of chaotic time series and reconstruct a more reasonable phase space structure of the prediction network,we propose a convolutional neural network-long short-term memory(CNN-LSTM)prediction model based on the incremental attention ***,a traversal search is conducted through the traversal layer for finite parameters in the phase ***,an incremental attention layer is utilized for parameter judgment based on the dimension weight criteria(DWC).The phase space parameters that best meet DWC are selected and fed into the input ***,the constructed CNN-LSTM network extracts spatio-temporal features and provides the final prediction *** model is verified using Logistic,Lorenz,and sunspot chaotic time series,and the performance is compared from the two dimensions of prediction accuracy and network phase space ***,the CNN-LSTM network based on incremental attention is compared with long short-term memory(LSTM),convolutional neural network(CNN),recurrent neural network(RNN),and support vector regression(SVR)for prediction *** experiment results indicate that the proposed composite network model possesses enhanced capability in extracting temporal features and achieves higher prediction ***,the algorithm to estimate the phase space parameter is compared with the traditional CAO,false nearest neighbor,and C-C,three typical methods for determining the chaotic phase space *** experiments reveal that the phase space parameter estimation algorithm based on the incremental attention mechanism is superior in prediction accuracy compared with the traditional phase space reconstruction method in five networks,including CNN-LSTM,LSTM,CNN,RNN,and SVR.

关键词： Chaotic time series Incremental attention mechanism Phase-space reconstruction

Performance Analysis and Optimization of Energy Harvesting Modulation for Multi-User Integrated Data and Energy Transfer

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China communications 2024年第1期21卷 148-162页

作者： Yizhe Zhao Yanliang Wu Jie Hu Kun Yang School of Information and communication Engineering University of Electronic Science and Technology of ChinaChengdu 611731China

Integrated data and energy transfer(IDET)enables the electromagnetic waves to transmit wireless energy at the same time of data delivery for lowpower *** this paper,an energy harvesting modulation(EHM)assisted multi-user IDET system is studied,where all the received signals at the users are exploited for energy harvesting without the degradation of wireless data transfer(WDT)*** joint IDET performance is then analysed theoretically by conceiving a practical time-dependent wireless *** the aid of the AO based algorithm,the average effective data rate among users are maximized by ensuring the BER and the wireless energy transfer(WET)*** results validate and evaluate the IDET performance of the EHM assisted system,which also demonstrates that the optimal number of user clusters and IDET time slots should be allocated,in order to improve the WET and WDT performance.

关键词： energy harvesting modulation(EHM) integrated data and energy transfer(IDET) performance analysis wireless data transfer(WDT) wireless energy transfer(WET)

Probabilistic modeling of multifunction radars with autoregressive kernel mixture network

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Defence Technology（防务技术） 2024年第5期35卷 275-288页

作者： Hancong Feng Kaili.Jiang Zhixing Zhou Yuxin Zhao Kailun Tian Haixin Yan Bin Tang School of Information and communication Engineering University of Electronic Science and Technology of ChinaChengdu 611731China

The task of modeling and analyzing intercepted multifunction radars(MFRs)pulse trains is vital for cognitive electronic *** methodologies predominantly rely on prior information or heavily constrained models,posing challenges for non-cooperative *** paper introduces a novel approach to model MFRs using a Bayesian network,where the conditional probability density function is approximated by an autoregressive kernel mixture network(ARKMN).Utilizing the estimated probability density function,a dynamic programming algorithm is proposed for denoising and detecting change points in the intercepted MFRs pulse *** results affirm the proposed method's efficacy in modeling MFRs,outperforming the state-of-the-art in pulse train denoising and change point detection.

关键词： Probabilistic forecasting Multifunction radar Unsupervised learning Change point detection Outlier detection

SAR regional all-azimuth observation orbit design for target 3D reconstruction

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Journal of Systems Engineering and electronics 2024年第3期35卷 609-618页

作者： WANG Yanan ZHOU Chaowei LIU Aifang MAO Qin Beijing institute of Tracking and communication Technology Beijing 100094China Nanjing Research institute of electronics Technology Nanjing 210012China Shanghai Aerospace electronic Technology institute Shanghai 201109China

Three-dimensional(3D) synthetic aperture radar(SAR)extends the conventional 2D images into 3D features by several acquisitions in different aspects. Compared with 3D techniques via multiple observations in elevation, e.g. SAR interferometry(InSAR) and SAR tomography(TomoSAR), holographic SAR can retrieve 3D structure by observations in azimuth. This paper focuses on designing a novel type of orbit to achieve SAR regional all-azimuth observation(AAO) for embedded targets detection and holographic 3D reconstruction. The ground tracks of the AAO orbit separate the earth surface into grids. Target in these grids can be accessed with an azimuth angle span of360°, which is similar to the flight path of airborne circular SAR(CSAR). Inspired from the successive coverage orbits of optical sensors, several optimizations are made in the proposed method to ensure favorable grazing angles, the performance of 3D reconstruction, and long-term supervision for SAR sensors. Simulation experiments show the regional AAO can be completed within five hours. In addition, a second AAO of the same area can be duplicated in two days. Finally, an airborne SAR data process result is presented to illustrate the significance of AAO in 3D reconstruction.

关键词： synthetic aperture radar(SAR) orbit design all-azimuth observation(AAO) three-dimensional(3D)reconstruction successive coverage

Face animation based on multiple sources and perspective alignment

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虚拟现实与智能硬件（中英文） 2024年第3期6卷 252-266页

作者： Yuanzong MEI Wenyi WANG Xi LIU Wei YONG Weijie WU Yifan ZHU Shuai WANG Jianwen CHEN School of Information and communication Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Yangtze Delta Region institute(Quzhou) University of Electronic Science and Technology of ChinaQuzhou 324000China

Background Face image animation generates a synthetic human face video that harmoniously integrates the identity derived from the source image and facial motion obtained from the driving *** technology could be beneficial in multiple medical fields,such as diagnosis and privacy *** studies on face animation often relied on a single source image to generate an output *** a significant pose difference between the source image and the driving frame,the quality of the generated video is likely to be suboptimal because the source image may not provide sufficient features for the warped feature *** In this study,we propose a novel face-animation scheme based on multiple sources and perspective alignment to address these *** first introduce a multiple-source sampling and selection module to screen the optimal source image set from the provided driving *** then propose an inter-frame interpolation and alignment module to further eliminate the misalignment between the selected source image and the driving *** The proposed method exhibits superior performance in terms of objective metrics and visual quality in large-angle animation scenes compared to other state-of-the-art face animation *** indicates the effectiveness of the proposed method in addressing the distortion issues in large-angle animation.

关键词： Face animation Multiple-source driving Generative adversarial network Medical diagnostics