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High-Precision DOA Estimation Method Based on Synthetic Aperture Technique

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Journal of Beijing institute of Technology 2024年第2期33卷 111-118页

作者： Yongjia Dou Guangcai Sun Yuqi Wang Mengdao Xing National Key Laboratory of Radar Signal processing Xidian UniversityXi’an 710071China School of communication and information Engineering Xi’an University of Science and TechnologyXi’an 710054China

The existing direction-of-arrival(DOA)estimation methods only utilize the current received signals,which are susceptible to *** this paper,a method for DOA estimation based on a motion platform is proposed to achieve high-precision DOA estimation by utilizing past and present *** concept of synthetic aperture is introduced to construct a linear DOA estima-tion model.A DOA fine-tuning method based on the linear model is proposed to eliminate the lin-ear DOA variation,achieving a non-coherent accumulation of DOA ***,the baseband modulation and the phase modulation caused by the range history are compensated to achieve the coherent accumulation of all the DOA *** results show that the proposed method can significantly improve the DOA estimated accuracy at low signal-to-noise ratios(SNR).

关键词： synthetic aperture direction-of-arrival(DOA)estimation coherent accumulation

MEMS reservoir computing system with stiffness modulation for multi-scene data processing at the edge

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Microsystems & Nanoengineering 2024年第3期10卷 421-432页

作者： Xiaowei Guo Wuhao Yang Xingyin Xiong Zheng Wang Xudong Zou The State Key Laboratory of Transducer Technology Aerospace Information Research InstituteChinese Academy of SciencesBeijingChina School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of SciencesBeijingChina QILU Aerospace information Research institute JinanChina

Reservoir computing(RC)is a bio-inspired neural network structure which can be implemented in hardware with *** has been applied across various fields such as memristors,and electrochemical reactions,among which the microelectro-mechanical systems(MEMS)is supposed to be the closest to sensing and computing *** previous MEMS RCs have demonstrated their potential as reservoirs,the amplitude modulation mode was found to be inadequate for computing directly upon *** achieve this objective,this paper introduces a novel MEMS reservoir computing system based on stiffness modulation,where natural signals directly influence the system stiffness as *** this innovative concept,information can be processed locally without the need for advanced data collection and *** present an integrated RC system characterized by small volume and low power consumption,eliminating complicated setups in traditional MEMS RC for data discretization and *** simulation and experiment were conducted on our *** performed nonlinearity tuning for the resonator and optimized the post-processing algorithm by introducing a digital mask ***,our MEMS RC is capable of both classification and forecasting,surpassing the capabilities of our previous non-delay-based *** method successfully processed word classification,with a 99.8%accuracy,and chaos forecasting,with a 0.0305 normalized mean square error(NMSE),demonstrating its adaptability for multi-scene data *** work is essential as it presents a novel MEMS RC with stiffness modulation,offering a simplified,efficient approach to integrate sensing and *** approach has initiated edge computing,enabling emergent applications in MEMS for local computations.

关键词： computing hardware stiffness

RGB-guided hyperspectral image super-resolution with deep progressive learning

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CAAI Transactions on Intelligence Technology 2024年第3期9卷 679-694页

作者： Tao Zhang Ying Fu Liwei Huang Siyuan Li Shaodi You Chenggang Yan School of Computer Science and Technology Beijing Institute of TechnologyBeijingChina Satellite information Intelligent processing and Application Research Laboratory Beijing Institute of Remote SensingBeijingChina Key Laboratory of Spectral Imaging Technology Xi'an Institute of Optics and Precision Mechanics of Chinese Academy of SciencesXi'anChina institute of Informatics University of AmsterdamAmsterdamNetherlands School of communication Engineering Hangzhou Dianzi UniversityHangzhouChina

Due to hardware limitations,existing hyperspectral(HS)camera often suffer from low spatial/temporal ***,it has been prevalent to super-resolve a low reso-lution(LR)HS image into a high resolution(HR)HS image with a HR RGB(or mul-tispectral)image *** approaches for this guided super-resolution task often model the intrinsic characteristic of the desired HR HS image using hand-crafted ***,researchers pay more attention to deep learning methods with direct supervised or unsupervised learning,which exploit deep prior only from training dataset or testing *** this article,an efficient convolutional neural network-based method is presented to progressively super-resolve HS image with RGB image ***-ically,a progressive HS image super-resolution network is proposed,which progressively super-resolve the LR HS image with pixel shuffled HR RGB image ***,the super-resolution network is progressively trained with supervised pre-training and un-supervised adaption,where supervised pre-training learns the general prior on training data and unsupervised adaptation generalises the general prior to specific prior for variant testing *** proposed method can effectively exploit prior from training dataset and testing HS and RGB images with spectral-spatial *** has a good general-isation capability,especially for blind HS image *** experimental results show that the proposed deep progressive learning method out-performs the existing state-of-the-art methods for HS image super-resolution in non-blind and blind cases.

关键词： computer vision deep neural networks image processing image resolution unsupervised learning

On-chip source-device-independent quantum random number generator

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Photonics Research 2024年第7期12卷 1379-1394页

作者： LANG LI MINGLU CAI TAO WANG ZICONG TAN PENG HUANG KAN WU GUIHUA ZENG State Key Laboratory of Advanced Optical communication Systems and Networks Shanghai Jiao Tong UniversityShanghai 200240China institute of Quantum Sensing and information processing School of Sensing Science and EngineeringShanghai Jiao Tong UniversityShanghai 200240China Shanghai Research Center for Quantum Sciences Hefei National LaboratoryShanghai 201315China Shanghai Xun Tai Quantech Co. Ltd.Shanghai 200241China

Quantum resources offer intrinsic randomness that is valuable for applications such as cryptography, scientific simulation, and computing. Silicon-based photonics chips present an excellent platform for the cost-effective deployment of next-generation quantum systems on a large scale, even at room temperature. Nevertheless,the potential susceptibility of these chips to hacker control poses a challenge in ensuring security for on-chip quantum random number generation, which is crucial for enabling extensive utilization of quantum resources.

关键词： temperature quantum random

Diffraction deep neural network-based classification for vector vortex beams

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Chinese Physics B 2024年第3期33卷 387-392页

作者：彭怡翔陈兵王乐赵生妹 institute of Signal processing and Transmission Nanjing University of Posts and Telecommunications (NJUPT)Nanjing 210003China Key Laboratory of Broadband Wireless communication and Sensor Network Technology Ministry of EducationNanjing 210003China National Laboratory of Solid State Microstructures Nanjing UniversityNanjing 210093China

The vector vortex beam(VVB)has attracted significant attention due to its intrinsic diversity of information and has found great applications in both classical and quantum ***,a VVB is unavoidably affected by atmospheric turbulence(AT)when it propagates through the free-space optical communication environment,which results in detection errors at the *** this paper,we propose a VVB classification scheme to detect VVBs with continuously changing polarization states under AT,where a diffractive deep neural network(DDNN)is designed and trained to classify the intensity distribution of the input distorted VVBs,and the horizontal direction of polarization of the input distorted beam is adopted as the feature for the classification through the *** numerical simulations and experimental results demonstrate that the proposed scheme has high accuracy in classification *** energy distribution percentage remains above 95%from weak to medium AT,and the classification accuracy can remain above 95%for various strengths of *** has a faster convergence and better accuracy than that based on a convolutional neural network.

关键词： vector vortex beam diffractive deep neural network classification atmospheric turbulence

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)

Generation of orbital angular momentum hologram using a modified U-net

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Chinese Physics B 2024年第3期33卷 401-407页

作者：郑志刚韩菲菲王乐赵生妹 institute of Signal processing and Transmission Nanjing University of Posts and TelecommunicationsNanjing 210003China Key Laboratory of Broadband Wireless communication and Sensor Network Technology (Ministry of Education) Nanjing University of Posts and TelecommunicationsNanjing 210003China National Laboratory of Solid State Microstructures Nanjing UniversityNanjing 210093China

Orbital angular momentum(OAM)holography has become a promising technique in information encryption,data storage and opto-electronic computing,owing to the infinite topological charge of one single OAM mode and the orthogonality of different OAM *** this paper,we propose a novel OAM hologram generation method based on a densely connected U-net(DCU),where the densely connected convolution blocks(DCB)replace the convolution blocks of the ***,the reconstruction process of the OAM hologram is integrated into DCU as its output layer,so as to eliminate the requirement to prepare training data for the OAM hologram,which is required by conventional neural networks through an iterative *** experimental and simulation results show that the OAM hologram can rapidly be generated with the well-trained DCU,and the reconstructed image's quality from the generated OAM hologram is significantly improved in comparison with those from the Gerchberg-Saxton generation method,the Gerchberg-Saxton based generation method and the U-net *** addition,a 10-bit OAM multiplexing hologram scheme is numerically demonstrated to have a high capacity with OAM hologram.

关键词： orbital angular momentum(OAM) holography OAM holography deep learning

Segment-based traffic smoothing algorithm for VBR video stream

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2006年第4期7卷 543-548页

作者： LIU Yun-qiang YU Song-yu WANG Xiang-wen ZHOU Jun institute of linage communication and information processing Shanghai Jiao Tong University Shanghai 200030 China

Transmission of variable bit rate (VBR) video, because of the burstiness of VBR video traffic, has high fluctuation in bandwidth requirement. Traffic smoothing algorithm is very efficient in reducing burstiness of the VBR video stream by trans- mitting data in a series of fixed rates. We propose in this paper a novel segment-based bandwidth allocation algorithm which dynamically adjusts the segmentation boundary and changes the transmission rate at the latest possible point so that the video segment will be extended as long as possible and the number of rate changes can be as small as possible while keeping the peak rate low. Simulation results showed that our approach has small bandwidth requirement, high bandwidth utilization and low computation cost.

关键词： Segmentation Traffic smoothing Variable bit rate (VBR) video

High performance receiving and processing technology in satellite beam hopping communication

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Journal of Systems Engineering and Electronics 2024年第4期35卷 815-828页

作者： ZHAI Shenghua HUI Tengfei GONG Xianfeng ZHANG Zehui GAO Xiaozheng YANG Kai School of communication Engineering Xi’Dian UniversityXian 710126China CAST(China Academy of Space Technology)-Xi’an institute of Space Radio Technology Xi’an 710010China School of Cyberspace Science and Technology Beijing Institute of TechnologyBeijing 100081China School of information and Electronics Beijing Institute of TechnologyBeijing 100081China

Beam-hopping technology has become one of the major research hotspots for satellite communication in order to enhance their communication capacity and ***,beam hopping causes the traditional continuous time-division multiplexing signal in the forward downlink to become a burst signal,satellite terminal receivers need to solve multiple key issues such as burst signal rapid synchronization and high-per-formance ***,this paper analyzes the key issues of burst communication for traffic signals in beam hopping sys-tems,and then compares and studies typical carrier synchro-nization algorithms for burst ***,combining the requirements of beam-hopping communication systems for effi-cient burst and low signal-to-noise ratio reception of downlink signals in forward links,a decoding assisted bidirectional vari-able parameter iterative carrier synchronization technique is pro-posed,which introduces the idea of iterative processing into car-rier *** at the technical characteristics of communication signal carrier synchronization,a new technical approach of bidirectional variable parameter iteration is adopted,breaking through the traditional understanding that loop struc-tures cannot adapt to low signal-to-noise ratio burst ***,combining the DVB-S2X standard physical layer frame format used in high throughput satellite communication systems,the research and performance simulation are *** results show that the new technology proposed in this paper can significantly shorten the carrier synchronization time of burst signals,achieve fast synchronization of low signal-to-noise ratio burst signals,and have the unique advantage of flexible and adjustable parameters.

关键词： beam-hopping high throughput satellite high per-formance reception processing