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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jiani Liu Ce Zhu Yang Chen Xiaolin Huang Yipeng Liu School of Information and Communication Engineering University of Electronic Science and Technology of China (UESTC) Chengdu China Department of Automation The MOE Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China

Phase retrieval seeks to reconstruct a series of image sequences from measurements that only capture their magnitudes. Current approaches either flatten and stack the image sequences, disregarding their multidimensional structural information, or fail to account for errors within the sensing vectors/tensors. To address these two issues simultaneously, we propose a unified framework for the phase retrieval problem, namely tensor total least squares (TTLS). Specifically, we set up a tensor representation for image sequences and the corresponding measurement model, and for the first time employ the advanced tensor ring network to effectively explore the inherent multidimensional structure for more accurate estimation. Moreover, in addition to the additive noise, the multiplicative errors within the sensing tensor can be also well-corrected, leading to a more robust estimation. Experimental results on both simulated data and real videos demonstrate the superiority of the proposed method.

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Mitigating Receiver Impact on Radio Frequency Fingerprint Identification via Domain Adaptation

arXiv

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arXiv 2024年

作者： Yang, Liu Li, Qiang Ren, Xiaoyang Fang, Yi Wang, Shafei School of Information and Communication Engineering University of Electronic Science and Technology of China P. Chengdu611731 China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing100083 China

Radio Frequency Fingerprint Identification (RFFI), which exploits non-ideal hardware-induced unique distortion resident in the transmit signals to identify an emitter, is emerging as a means to enhance the security of communication systems. Recently, machine learning has achieved great success in developing state-of-the-art RFFI models. However, few works consider cross-receiver RFFI problems, where the RFFI model is trained and deployed on different receivers. Due to altered receiver characteristics, direct deployment of RFFI model on a new receiver leads to significant performance degradation. To address this issue, we formulate the cross-receiver RFFI as a model adaptation problem, which adapts the trained model to unlabeled signals from a new receiver. We first develop a theoretical generalization error bound for the adaptation model. Motivated by the bound, we propose a novel method to solve the cross-receiver RFFI problem, which includes domain alignment and adaptive pseudo-labeling. The former aims at finding a feature space where both domains exhibit similar distributions, effectively reducing the domain discrepancy. Meanwhile, the latter employs a dynamic pseudo-labeling scheme to implicitly transfer the label information from the labeled receiver to the new receiver. Experimental results indicate that the proposed method can effectively mitigate the receiver impact and improve the cross-receiver RFFI performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Palmprint recognition

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Stretchable and self-healable spoof plasmonic meta-waveguide for wearable wireless communication system

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Light(science & Applications) 2022年第11期11卷 2725-2737页

作者： Bu-Yun Yu De-Wei Yue Ke-Xin Hou Lu JU Hao Chen Cong Ding Zhen-Guo Liu Yun-Qian Dai Hari Krishna Bisoyi Ying-Shi Guan Wei-Bing Lu Cheng-Hui Li Quan Li State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast UniversityNanjing 210096China Center for Flexible RF Technology Frontiers Science Center for Mobile Information Communication and SecuritySoutheast UniversityNanjing 210096China Purple Mountain Laboratories Nanjing 21111China State Key Laboratory of Coordination Chemistry School of Chemistry and Chemical EngineeringNanjing UniversityNanjing 210023China Institute of Advanced Materials and School of Chemistry and Chemical Engineering Southeast UniversityNanjing 211189China Advanced Materials and Liquid Crystal Institute and Chemical Physics Interdisciplinary Program Kent State UniversityKentOH 44242USA

Microwave transmission lines in wearable systems are easily damaged after frequent mechanical deformation,posing a severe threat to wireless ***,we report a new strategy to achieve stretchable microwave transmission lines with superior reliability and durability by integrating a self-healable elastomer with serpentine-geometry plasmonic meta-waveguide to support the spoof surface plasmon polariton(SSPP).After mechanical damage,the self-healable elastomer can autonomously repair itself to maintain the electromagnetic performance and mechanical ***,the specially designed SSPP structure exhibits excellent stability and damage *** if the self-healing process has not been completed or the eventual repair effect is not ideal,the spoof plasmonic meta-waveguide Can still maintain reliable ***-healing material enhances strength and durability,while the SSPP improves stability and gives more tolerance to the self-healing *** design coordinates the structural design with material synthesis to maximize the advantages of the SSPP and self-healing material,signifcantly improving the relability and durability of stretchable microwave transmission *** also perform communication quality experiments to demonstrate the potential of the proposed meta-waveguide as interconnects in future body area network systems.

关键词： process durability maintain

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Constant modulus waveform design for radar with Doppler tolerance based on local ambiguity function template matching 5

Constant modulus waveform design for radar with Doppler tole...

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5th IEEE International Conference on Electronic information and communication technology, ICEICT 2022

作者： Zhang, Qian Du, Xiaolin Cui, Guolong Zheng, Jibin Dong, Jun School of Computer and Control Engineering Yantai University Yantai China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Xidian University National Laboratory of Radar Signal Processing Xi'an China

ISBN: (数字)9781665472111

ISBN: (纸本)9781665472111

To tackle the problem of Doppler sensitivity of phase-coded waveform in radar systems, a method for designing transmit waveform with Doppler tolerance under constant modulus (CM) constraint is considered. In this method, the variance between the ideal local ambiguity function (LAF) and the real one is used as the objective function to establish an optimization problem. To solve the non-convex quartic problem, a template matching sequence optimization (TMSO) algorithm is proposed. By introducing an auxiliary variable and problem transformation, the solution to the original problem can finally be obtained, involving two alternately iterative subproblems with closed-form solutions. Numerical experiments show that, compared with the reference algorithms, the proposed algorithm can not only obtain a transmit waveform with good Doppler tolerance, but also has a significant advantage in the execution efficiency. © 2022 IEEE.

关键词： Template matching

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Hyperspectral Image Cross Scene Classification Combined Domain Adaptation with XGBoost

Hyperspectral Image Cross Scene Classification Combined Doma...

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2024 International Conference on Measurement, communication, and Virtual Reality, MCVR 2024

作者： Liu, Chengyang Wu, Haibin Li, Jiarong Liu, Xinyu Yan, Siqi Wang, Aili Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application College of Measurement and Control Technology and Communication Engineering Harbin University of Science and Technology Harbin150080 China School of Electronics and Information Engineering Harbin Institute of Technology Harbin150001 China

ISBN: (纸本)9781510691773

To address the issues of domain discrepancies and limited labeled samples in hyperspectral image classification of cross scene, this paper proposes an ensemble learning framework that integrates eXtreme Gradient Boosting (XGBoost) with confidence domain adaptation strategies to improve classification performance. First, the Houston dataset from different scenes is input separately into a deep embedding model. The hyperspectral images are embedded into a low-dimensional space, ensuring that similar spectra are represented more closely in this space, thereby capturing the intrinsic structure and distribution of the hyperspectral image dataset. Subsequently, these samples are mapped into a discriminator model, where domain adaptation techniques are employed to reduce differences in feature distributions across different scenes. Additionally, confidence-based learning is used to identify and remove noisy samples in the Houston dataset, thereby enhancing the alignment of feature distributions among various scenes. Finally, through ensemble learning, a weighted voting strategy is employed to combine multiple base classifiers into a strong classification model. This approach better captures the complex features within the data, thereby reducing the bias of individual classifiers and enhancing the overall generalization ability and robustness of the model. Experimental results on the Houston dataset demonstrate that the proposed method outperforms popular domain adaptation learning models and traditional methods in terms of classification accuracy and robustness, showcasing its potential and advantages in practical applications. This result confirms the effectiveness of combining XGBoost and confidence domain adaptation strategies in handling hyperspectral image classification. © 2025 SPIE.

关键词： Photointerpretation

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Coastal Wind Speed Retrieval From HY-2C Scatterometer Based On Light Gradient Boosting Model

Coastal Wind Speed Retrieval From HY-2C Scatterometer Based ...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Biao Zhang Jingyi Liu Lanjie Zhang Xuehua Li Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing China Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing Information Science and Technology University Beijing China Key Laboratory of Microwave Remote Sensing National Space Science Center Chinese Academy of Sciences Beijing China

In this paper, the sea surface wind speed (SSWS) retrieval accuracy for HaiYang-2C (HY-2C) scatterometer in the coastal regions is evaluated. To improve coastal SSWS accuracy, we use the European Centre for Medium-range Weather Forecast Reanalysis 5th Generation (ECMWF ERA5) data as the reference output to establish the coastal multi-interval SSWS retrieval model based on the light gradient boosting model (LGBM). Compared with the ERA5 wind speed, the root mean square error (RMSE) of the LGBM-based model in the low, medium, and high wind speed ranges is 36.36%, 33.33%, and 34.52% lower than that of the HY-2C wind product, respectively. This paper also compares the SSWS retrieval results with the National Data Buoy Center buoy measurements. Compared to the HY-2C L2B wind product, the RMSE is reduced by 21.05%. This paper provides a dependable reference to coastal SSWS retrieval of HY-2C scatterometer observations.

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Study on Fluctuation Characteristics of Acoustic Signals under Density Layered Fluid Disturbance

Study on Fluctuation Characteristics of Acoustic Signals und...

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IEEE/OES China Ocean Acoustics (COA)

作者： Jintao Yong Yunfei Chen Zhenshan Wang Bing Jia Rui Wang Shiqi Mo Science and Technology on Underwater Test and Control Laboratory Dalian China College of Underwater Acoustic Engineering Harbin Engineering University Harbin China College of Information and Communication Engineering Dalian University of Technology Dalian China

ISBN: (数字)9798350340297

ISBN: (纸本)9798350340303

This study investigates the fluctuation characteristics of acoustic signals in the presence of flow field perturbations within density-stratified fluids through laboratory simulation experiments. A density stratification tank was used to replicate the ocean's stratified environment. The SUBOFF scaled model was employed to mimic the underwater vehicle's navigation process in towing mode. controlled density stratification simulation experiments were conducted to explore the acoustic signal fluctuation characteristics within the wake flow field of the submerged model. Data on the undulation properties of acoustic signals that propagated through the wake flow field of the underwater vehicle were collected. The undulation characteristics of the acoustic signal at two towing speeds and various spatial positions were analyzed. In alignment with the typical marine environment characteristics, this study designed the stratification conditions, the scale of the scaled model, and the towing speed based on the Froude number (Fri) similarity criterion and the geometric similarity law. The analysis of the experimental results indicates that the fluctuating frequency of the acoustic signal amplitude diminishes over time as the wake dissipates post-target passage. Concurrently, the intensity of the high-frequency fluctuation component of the signal also decreases with the dissipation of the wake.

关键词： Time-frequency analysis Fluctuations Fluids Navigation Perturbation methods Oceans Attenuation Acoustics Underwater vehicles

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Coastal SEA Surface Salinity Retrieval Analysis from SMAP Mission using Machine Learning

Coastal SEA Surface Salinity Retrieval Analysis from SMAP Mi...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： YanFang Lv YiFan Zhang JingYi Liu LanJie Zhang Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing China Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing Information Science and Technology University Beijing China Key Laboratory of Microwave Remote Sensing National Space Science Center Chinese Academy of Sciences Beijing China

Sea surface salinity (SSS) plays an essential role in the study of global climate change. Especially, high accuracy coastal SSS is of great significance for marine aquaculture and other aspects. SMAP is a high resolution satellite equipped with radars and radiometers for measuring soil moisture, which can also achieve observations of SSS. Based on SMAP L2B V5.0 data, to improve the coastal SSS retrieval accuracy, this paper proposes a method for retrieving SSS in coastal regions using deep neural networks (DNN) and analyzes the impact of different variables on retrieving SSS. By comparing the retrieval results of DNN with HYCOM SSS and in situ SSS, the root mean square errors (RMSE) of DNN SSS were 0.36 psu and 0.45 psu, respectively. Compared to SMAP SSS products from physical retrieval algorithm, the RMSE of DNN SSS decreased by 1.38 psu and 1.23 psu, respectively. The results indicate that using DNN to retrieve SSS in coastal regions can achieve high accuracy.

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An Enhanced Neuron Attribution-Based Attack Via Pixel Dropping

An Enhanced Neuron Attribution-Based Attack Via Pixel Droppi...

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IEEE International Conference on Image Processing

作者： Zhi Lin Anjie Peng Hui Zeng Kaijun Wu Wenxin Yu Southwest University of Science and Technology Sichuan MY China Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science & Technology Jiangsu NJ China Science and Technology on Communication Security Laboratory Sichuan CD China

Convolutional neural networks (CNNs) are vulnerable to adversarial examples (AEs). Existing feature-level attacks explore the neuron importance to distort the intrinsic object-aware features which are shareable among different CNNs, thus achieving great performance in transferability. In this work, we propose an enhanced neuron attribution-based attack via pixel dropping (ENAA) and try to increase the number of positive neurons to distort the object-aware features more fully than NAA. Specifically, when computing neuron attribution, we use a pixel dropping scheme to expand the regions where the source model pays attention to the image. Our ENAA can make the target model shift the attention regions of AEs far away from those of clean images. Experimental results validate that the proposed method outperforms the state-of-the-art feature-level attacks both in white-box and black-box settings.

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An Adaptive Power Allocation Scheme for Mini-UAV Tracking with Distributed Radar in Dense Urban Environment 10

An Adaptive Power Allocation Scheme for Mini-UAV Tracking wi...

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10th International Conference on control, Automation and information sciences, ICCAIS 2021

作者： Cao, Ziyi Yuan, Ye Yi, Wei Chen, Linxiu University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9781665440295

In this paper, a new power allocation scheme of the distributed radar system is proposed for mini-UAV tracking tasks in urban environments, considering the influence of the building occlusion on the probability of detection. Firstly, the probability of detection of each radar node is derived according to the estimated position of the target and the visibility of each radar node. Then, according to the probability of detection, the adaptive cost function concerning the transmit power and tracking accuracy is designed and used to the formulation of the optimization problem. Simulation results show that the proposed power allocation scheme can improve the target tracking accuracy in urban environments compared to the conventional method. © 2021 IEEE.

关键词： Cost functions

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