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19th IEEE International Multi-Conference on Systems, signals and Devices, SSD 2022

作者： Zamoum, Yasmine Baiche, Karim Benkada, Khaled Mohammed Rahou, Mohammed Boushaki, Prof. Razika Kouzou, Prof. Abdellah Systems and Telecommunications Engineering University of Mhamed Bougara Boumerdes Algeria Institute of Electrical and Electronic Engineering University of Mhamed Bougara Boumerdes Algeria Applied Automatics and Signal Processing Laboratory Institute of Electrical and Electronic Engineering University of Mhamed Bougara Boumerdes Algeria Applied Automation and Industrial Diagnostics Laboratory Laadi Faculty of Science and Technology University of Djelfa Algeria

ISBN: (数字)9781665471084

ISBN: (纸本)9781665471084

The research Unmanned Aerial Vehicles (UAV) has been considerably advanced. This work is interested principally in dynamic modeling of the four rotor mini aircraft named as a quadrotor. The dynamical model is obtained by Newton-Euler methodology. The robust controller is designed and simulated using a linear and nonlinear control techniques. Proportional Derivative Control and the Backstepping algorithm methods have been implemented in MATLAB/SIMULINK. © 2022 IEEE.

关键词： Controllers

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Spectral Masked Autoencoder for Few-Shot Hyperspectral Image Classification

Spectral Masked Autoencoder for Few-Shot Hyperspectral Image...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Pengming Feng Kaihan Wang Jian Guan Guangjun He Shichao Jin State Key Laboratory of Space-Ground Integrated Information Technology Space Star Technology CO. Ltd. Beijing China Group of Intelligent Signal Processing (GISP) College of Computer Science and Technology Harbin Engineering University Harbin China

Though deep learning methods have achieved the state-of-the-art performance for hyperspectral image (HSI) classification, they often highly rely on large amount of samples for training, and introduce few-shot challenge due to the lack of labeled samples. In this paper, a self-supervised method is presented for HSI classification in the few-shot scenario, where masked autoencoder is employed to reconstruct the masked bands in spectral domain for model pretraining with limited labeled sample, namely Spectral-MAE. The proposed method not only avoids the overfitting via the pretraining, but also provides the model’s ability for effective feature extraction while avoiding the high spatial redundancy. Experiments conducted verify the effectiveness of the proposed method for HSI classification in few-shot situation as compared with other methods.

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A Sparse Recovery Method on EPC-MIMO Radar System

A Sparse Recovery Method on EPC-MIMO Radar System

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IEEE International Conference on Information Communication and signal processing (ICICSP)

作者： Kun Liu Xiongpeng He Guisheng Liao Jingwei Xu Shengqi Zhu Yifan Guo National Laboratory of Radar Signal Processing Xidian University Xi'an China School of Marine Science and technology Northwestern Polytechnical University Xi'an China

Multiple-Input Multiple-Output Radar with Element-Pulse Coding (EPC) is a novel way to address the performance degradation caused by range ambiguity in space-time adaptive processing. In this paper, we use the sparse recovery method to solve the problem that EPC-MIMO has a large demand for independent and identically distributed (IID) samples. On the one hand, we use the Sparse Bayesian Learning (SBL) to achieve space-time spectral estimation under the small sample condition, and on the other hand, the use of prior knowledge, reduce the redundancy of the sparse recovery dictionary and improve the computational efficiency of the algorithm. The simulation results demonstrate the effectiveness of the proposed method.

关键词： Degradation Dictionaries Spaceborne radar Simulation Redundancy signal processing algorithms Estimation

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A Cross-Task Transfer SAR Target Recognition Method Based on AMFFNet and IACGAN

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IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Wan, Xunyang Du, Xiaolin Cui, Guolong Chen, Xiaolong Zheng, Jibin Yantai University School of Computer and Control Engineering Yantai264005 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Naval Aviation University Yantai264001 China Xidian University National Key Laboratory of Radar Signal Processing Xi'an710071 China

Convolutional neural networks (CNN) have shown excellent capability in synthetic aperture radar (SAR) target recognition. However, obtaining sufficient training samples for improving network recognition is difficult due to the deficiency of labeled SAR image data. To tackle the problem, a cross-task transfer method for SAR target recognition is proposed in this paper, which is based on a designed attention multi-scale feature fusion network (AMFFNet) and an improved auxiliary classifier generative adversarial network (IACGAN). In this method, firstly, a data augmentation method based on you only cut twice (YOCT) is devised for expanding the training samples. Secondly, the partial weight parameters of AMFFNet are initialized by the pre-trained IACGAN discriminator. Finally, AMFFNet is fine-tuned using the training samples. Additionally, on the network structure of AMFFNet, multi-scale features are extracted by adopting a multi-scale depth separable convolution module, which allows the model to acquire more informative features. Attention mechanisms are employed in this network to enhance its ability to capture important features while reducing the effects of noise. A multi-level feature fusion structure is used to further raise the representational capability of the model. Experimental results on multiple open-source real SAR datasets show that the proposed method can achieve better recognition performance under different percentages of training data, and the effectiveness and superiority of the method are confirmed by its robustness to noise. © 1965-2011 IEEE.

关键词： Convolutional neural networks

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ASTF: Visual Abstractions of Time-Varying Patterns in Radio signals

arXiv

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arXiv 2022年

作者： Zhao, Ying Ge, Luhao Xie, Huixuan Bai, Genghuai Zhang, Zhao Wei, Qiang Lin, Yun Liu, Yuchao Zhou, Fangfang School of Computer Science and Engineering Central South University Changsha China China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu China College of Information and Communication Engineering Harbin Engineering University Harbin China China Research Institute of Radiowave Propagation Qingdao China

A time-frequency diagram is a commonly used visualization for observing the time-frequency distribution of radio signals and analyzing their time-varying patterns of communication states in radio monitoring and management. While it excels when performing short-term signal analyses, it becomes inadaptable for long-term signal analyses because it cannot adequately depict signal time-varying patterns in a large time span on a space-limited screen. This research thus presents an abstract signal time-frequency (ASTF) diagram to address this problem. In the diagram design, a visual abstraction method is proposed to visually encode signal communication state changes in time slices. A time segmentation algorithm is proposed to divide a large time span into time slices. Three new quantified metrics and a loss function are defined to ensure the preservation of important time-varying information in the time segmentation. An algorithm performance experiment and a user study are conducted to evaluate the effectiveness of the diagram for long-term signal analyses. Copyright © 2022, The Authors. All rights reserved.

关键词： Binary sequences

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New Structure of CCR with an AOANN Threshold

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Journal of Optical Communications 2021年第1期42卷 103-109页

作者： Rabehi, Abdelhalim Djebbari, Ali Hafaifa, Ahmed Souahlia, Abdelkerim Taleb-Ahmed, Abdelmalik Telecommunications and Digital Signal Processing Laboratory Faculty of Electrical Engineering University of Djillali Liabes Sidi Bel Abbes22000 Algeria Applied Automation and Industrial Diagnostics Laboratory Faculty of Science and Technology University of Ziane Achour Djelfa17000 Algeria Faculty of Science and Technology University of Ziane Achour Djelfa17000 Algeria LAMIH Laboratory UMR C.N.R.S 8530 University of Valenciennes and Hainaut-Cambrésis. Mont Houy Valenciennes59313 France

In this paper, artificial neural network-based adaptive optimal threshold estimation for a two-dimensional optical code division multiple access conventional correlation receiver is proposed. A multilayer perceptron neural network with back-propagation learning algorithm is considered. This estimator uses the weight (w) and the length (F) of the code word, the number of active users (Ν) and the signal to noise ratio as inputs to estimate the required optimal threshold. We have evaluated the proposed approach on a data set of 46,200 samples. We have found that it gives accurate results: 0.029 for the root mean square error, 0.37% for the relative root mean square error and 99.984% for the correlation coefficient (R), which reflects the efficiency of the proposed optimal threshold estimator. © 2018 Walter de Gruyter GmbH, Berlin/Boston 2018.

关键词： signal to noise ratio

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Consensus Disturbance Rejection of Nonlinear Multi-Agent Systems Over Cooperation-Competition Networks

Consensus Disturbance Rejection of Nonlinear Multi-Agent Sys...

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2021 China Automation Congress, CAC 2021

作者： Wang, Qiang He, Wangli Tan, Dayu Zhong, Weimin Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China

ISBN: (纸本)9781665426473

This study addresses the consensus problem for nonlinear multi-agent systems (MASs) under the exogenous disturbances over cooperation-competition networks. Firstly, for judging the disturbances produced by the exogenous environment, a disturbance observer-based approach is developed and incorporated into the controller. Secondly, two novel nonlinear control protocols depended on state feedback and output feedback control are constructed to guarantee consensus for MASs over the signed directed graph, respectively. Thirdly, theoretical guarantees on the effectiveness of the proposed controller in steering the system to a bipartite leader-following consensus with exogenous disturbances are derived. Finally, numerical simulations inspired by a real-world physical MAS are provided to illustrate the effectiveness of the proposed controllers. © 2021 IEEE

关键词： Feedback control

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A Deep Learning-based Super-resolution Model for Bistatic SAR Image

A Deep Learning-based Super-resolution Model for Bistatic SA...

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2021 International Conference on Electronics, Circuits and Information Engineering, ECIE 2021

作者： Cen, Xi Song, Xuan Li, Yachao Wu, Chunfeng Xidian University School of Life Science and Technology Xi'An China Xidian University Key Laboratory for Radar Signal Processing Xi'An China China Sanjiang Space Group Wu Han China

ISBN: (纸本)9781665418690

Target detection and recognition of bistatic SAR image has been widely studied in recently. However, how to accurately detect and recognize targets with low-resolution and small size in the image is still a problem. Image super-resolution reconstruction technology can increase image resolution and expand target size to improve detection and recognition performance. Thus, we in this paper bring deep learning onto the topic of bistatic SAR image super-resolution reconstruction and propose a novel super-resolution reconstruction model (named FSRCNN) for bistatic SAR images. The proposed model is characterized by a feature extractor with different structures, a feedback feature enhancement block, and a feature fusion module. During the experiments of bistatic SAR measured data, our method is proved to perform clearer visual effects than other image super-resolution reconstruction method. Moreover, our model achieves the best value on peak signal to noise ratio and structural similarity, which indicates that our model can effectively super-resolution reconstruct bistatic SAR images. © 2021 IEEE.

关键词： Synthetic aperture radar

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Receiver distortion nornalization method for specific emitter identification 9

Receiver distortion nornalization method for specific emitte...

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9th IEEE Joint International Information technology and Artificial Intelligence Conference, ITAIC 2020

作者： Shi, Mentkai Huang, Yuanling Wang, Guiliang University School of Information System Engineering PLA Strategic Support Force Information Engineering University Zhengzhou China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu China

Specific emitter identification (SEI) is an identification technique that identify the transmitting device with the hardware imperfection. However, the identification features are influenced by the receiver distortions and can be different when the receivers are changed. Previous SEI studies rarely concern the method to normalize the distortions among different receivers. To solve this problem, a SEI receiver normalization method is proposed. In the proposed method, a frequency-domain adaptive equalization system is contained to normalize the distortions in receivers. According to the experiment, this method can effectively eliminate the SEI feature difference caused by receivers. Then the robust of the proposed method is discussed. © 2020 IEEE.

关键词： Equalizers

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A Tor Anonymity Attack Experiment Platform Driven by Raspberry Pi

A Tor Anonymity Attack Experiment Platform Driven by Raspber...

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Prognostics and System Health Management (PHM-Jinan), International Conference on

作者： Qi Wang Weiquan Cao National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu China

ISBN: (数字)9781728151816

ISBN: (纸本)9781728151823

Private Tor network is essential for analyzing security issues both for Tor project as well as for numerous researchers. However, current research work mainly focus on improving scalability and efficiency by simulating traffic discretely with single node and restoration level by deploying a complete private network with high cost. In this paper, we propose a novel methodology constructing real private tor network by editing and controlling Raspberry Pis, called Tor anonymity attack experiment platform. Compared with other platforms of the same type, the platform has the advantages of low cost, easy to scale, high quality of real-time data transmission, high degree of network protocol restoration as well as convenient configuration. In order to demonstrate the platform can be used as a private and complete Tor anonymous network experiment environment, we conduct a source tracing attack as a study case. Experimental results and theoretical analysis both show that the platform can support researchers to carry out numerous Tor network security research with high degree of restoration efficiently and cheaply.

关键词： Servers Economic indicators Protocols Software Emulation Operating systems Virtual machining

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