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Self-adapting RF Stealth Signal Design Method in RATR

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Transactions of Nanjing University of Aeronautics and Astronautics 2021年第6期38卷 1020-1027页

作者： XIAO Yongsheng HUANG Lizhen HE Fengshou ZHOU Jianjiang Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong UniversityNanchang 330063P.R.China College of Electronic and Information Engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106P.R.China Leihua Electronic Technology Research Institute Aviation Industry Corporation of ChinaLtd.Wuxi 214063P.R.China

Combining the mutual information theory and the sequential hypothesis testing(SHT)method,a selfadapting radio frequency(RF)stealth signal design method is proposed. The channel information is gained through the radar echo and feeds back to the radar system,and then the radar system adaptively designs the transmission waveform. So the close-loop system is formed. The correlations between these transmission waveforms are decreased because of the adaptive change of these transmission waveforms,and the number of illuminations is reduced for adopting the SHT,which lowers the transmission power of the radar system. The radar system using the new method possesses the RF stealth performance. Aiming at the application of radar automatic target recognition(RATR),experimental simulations show the effectiveness and feasibility of the proposed method.

关键词： radio frequency stealth sequential hypothesis testing mutual information close-loop system

Combining Feature Extraction and Hidden Markov Modelling for Automatic Speech recognition

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Combining Feature Extraction and Hidden Markov Modelling for...

International Conference on Communication Technology(ICCT’94)

作者： WU Jianxiong CHEN Peifang CHAN Chorkin DENG Li Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Department of Computer Science University of Hong Kong Dcpartment of Electrical and Computer Engineering University of WaterlooWaterlooOntarioCanada N2L 3G1.

＜正＞Feature extraction is very important for the classifier design and the overall performance of *** recognition ***,due to the lack of theoretical guidances,feature extraction and classifier design are usually treated separately in current speech recognition *** *** proposes an approach to combine linear feature extraction with continuous density hidden Markov modeling（HMM） which is currently the most successful speech pattern classifier.A maximumlikelihood based algorithm is derived to iteratively train HMM parameters as well as the parameters of the feature *** algorithm is an exteusion of the Baum-Welcli parameter re-estimation algorithm for conventional HMMs and thus has a nice property of guara, nteed convergence.

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One-shot distributed algorithm for generalized eigenvalue problem

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arXiv 2020年

作者： Lyu, Kexin He, Fan Huang, Xiaolin Yang, Jie Chen, Liming Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University MOE Key Laboratory of System Control and Information Processing 800 Dongchuan Road Shanghai200240 China Institute of Medical Robotics Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China Ecole Centrale de Lyon France

Nowadays, more and more datasets are stored in a distributed way for the sake of memory storage or data privacy. The generalized eigenvalue problem (GEP) plays a vital role in a large family of high-dimensional statistical models. However, the existing distributed method for eigenvalue decomposition cannot be applied in GEP for the divergence of the empirical covariance matrix. Here we propose a general distributed GEP framework with one-shot communication for GEP. If the symmetric data covariance has repeated eigenvalues, e.g., in canonical component analysis, we further modify the method for better convergence. The theoretical analysis on approximation error is conducted and the relation to the divergence of the data covariance, the eigenvalues of the empirical data covariance, and the number of local servers is analyzed. Numerical experiments also show the effectiveness of the proposed algorithms. Copyright © 2020, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

CDFI: Cross Domain Feature Interaction for Robust Bronchi Lumen Detection

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CDFI: Cross Domain Feature Interaction for Robust Bronchi Lu...

IEEE International Conference on Robotics and Automation (ICRA)

作者： Jiasheng Xu Tianyi Zhang Yangqian Wu Jie Yang Guang–Zhong Yang Yun Gu Institute of Medical Robotics Shanghai Jiao Tong University Shanghai CHINA Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai CHINA MOE Key Laboratory of System Control and Information Processing Shanghai P.R. China Shanghai Center for Brain Science and Brain-Inspired Technology Shanghai China

Endobronchial intervention is increasingly used as a minimally invasive means for the treatment of pulmonary diseases. In order to reduce the difficulty of manipulation in complex airway networks, robust lumen detection is essential for intraoperative guidance. However, these methods are sensitive to visual artifacts which are inevitable during the surgery. In this work, a cross domain feature interaction (CDFI) network is proposed to extract the structural features of lumens, as well as to provide artifact cues to characterize the visual features. To effectively extract the structural and artifact features, the Quadruple Feature Constraints (QFC) module is designed to constrain the intrinsic connections of samples with various imaging-quality. Furthermore, we design a Guided Feature Fusion (GFF) module to supervise the model for adaptive feature fusion based on different types of artifacts. Results show that the features extracted by the proposed method can preserve the structural information of lumen in the presence of large visual variations, bringing much-improved lumen detection accuracy.

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Matching confidence analysis method based on tests of hypotheses

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Matching confidence analysis method based on tests of hypoth...

IEEE International Conference on Systems, Man and Cybernetics

作者： Nong Sang Ruolin Wang Tianxu Zhang Institute for Pattern Recognition and Artificial Intelligence State E ducation Commission Laboratory for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan Hubei China Department of Civil Engineering Wuhan University of Hydraulic and Electrical Engineering Hubei China

ISBN: (纸本)0780340531

Matching confidence is an important element for evaluating image matching quality. A method is presented which uses the technique of tests of hypotheses to determine image matching confidence under a certain testing level. The authors use similarity measurement values to be the statistics. Experimental results with large real images prove the effectiveness of the method to determine image matching confidence.

关键词： Testing image matching Stereo image processing image reconstruction Statistics Navigation Remotely operated vehicles pattern matching Layout Optimized production technology

Mixed one-bit compressive sensing with application to overexposure correction for CT reconstruction

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arXiv 2017年

作者： Huang, Xiaolin Xia, Yan Shi, Lei Huang, Yixing Yan, Ming Hornegger, Joachim Maier, Andreas Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Department of Radiology Stanford University CA United States School of Mathematical Sciences Fudan University Shanghai China Pattern Recognition Lab of Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Department of Computational Mathematics Science and Engineering Michigan State University MI United States

When a measurement falls outside the quantization or measurable range, it becomes saturated and cannot be used in classical reconstruction methods. For example, in C-arm angiography systems, which provide projection radiography, fluoroscopy, digital subtraction angiography, and are widely used for medical diagnoses and interventions, the limited dynamic range of C-arm flat detectors leads to overexposure in some projections during an acquisition, such as imaging relatively thin body parts (e.g., the knee). Aiming at overexposure correction for computed tomography (CT) reconstruction, we in this paper propose a mixed one-bit compressive sensing (M1bitCS) to acquire information from both regular and saturated measurements. This method is inspired by the recent progress on one-bit compressive sensing, which deals with only sign observations. Its successful applications imply that information carried by saturated measurements is useful to improve recovery quality. For the proposed M1bit-CS model, alternating direction methods of multipliers is developed and an iterative saturation detection scheme is established. Then we evaluate M1bit-CS on one-dimensional signal recovery tasks. In some experiments, the performance of the proposed algorithms on mixed measurements is almost the same as recovery on unsaturated ones with the same amount of measurements. Finally, we apply the proposed method to overexposure correction for CT reconstruction on a phantom and a simulated clinical image. The results are promising, as the typical streaking artifacts and capping artifacts introduced by saturated projection data are effectively reduced, yielding significant error reduction compared with existing algorithms based on extrapolation. Copyright © 2017, The Authors. All rights reserved.

关键词： Compressed sensing

Learn What You Need in Personalized Federated Learning

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arXiv 2024年

作者： Lv, Kexin Ye, Rui Huang, Xiaolin Yang, Jie Chen, Siheng Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China Shanghai Jiao Tong University Shanghai200240 China Shanghai Jiao Tong University Shanghai200240 China Shanghai AI Laboratory Shanghai200232 China

Personalized federated learning aims to address data heterogeneity across local clients in federated learning. However, current methods blindly incorporate either full model parameters or predefined partial parameters in personalized federated learning. They fail to customize the collaboration manner according to each local client's data characteristics, causing unpleasant aggregation results. To address this essential issue, we propose Learn2pFed, a novel algorithm-unrolling-based personalized federated learning framework, enabling each client to adaptively select which part of its local model parameters should participate in collaborative training. The key novelty of the proposed Learn2pFed is to optimize each local model parameter's degree of participant in collaboration as learnable parameters via algorithm unrolling methods. This approach brings two benefits: 1) mathmatically determining the participation degree of local model parameters in the federated collaboration, and 2) obtaining more stable and improved solutions. Extensive experiments on various tasks, including regression, forecasting, and image classification, demonstrate that Learn2pFed significantly outperforms previous personalized federated learning methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Parameter estimation

AirwayNet: A Voxel-Connectivity Aware Approach for Accurate Airway Segmentation Using Convolutional Neural Networks

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arXiv 2019年

作者： Qin, Yulei Chen, Mingjian Zheng, Hao Gu, Yun Shen, Mali Yang, Jie Huang, Xiaolin Zhu, Yue-Min Yang, Guang-Zhong Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Insa Lyon Lyon France Hamlyn Centre for Robotic Surgery Imperial College London London United Kingdom

Airway segmentation on CT scans is critical for pulmonary disease diagnosis and endobronchial navigation. Manual extraction of airway requires strenuous efforts due to the complicated structure and various appearance of airway. For automatic airway extraction, convolutional neural networks (CNNs) based methods have recently become the stateof-the-art approach. However, there still remains a challenge for CNNs to perceive the tree-like pattern and comprehend the connectivity of airway. To address this, we propose a voxel-connectivity aware approach named AirwayNet for accurate airway segmentation. By connectivity modeling, conventional binary segmentation task is transformed into 26 tasks of connectivity prediction. Thus, our AirwayNet learns both airway structure and relationship between neighboring voxels. To take advantage of context knowledge, lung distance map and voxel coordinates are fed into AirwayNet as additional semantic information. Compared to existing approaches, AirwayNet achieved superior performance, demonstrating the effectiveness of the network's awareness of voxel connectivity. Copyright © 2019, The Authors. All rights reserved.

关键词： Computerized tomography

Robot Debater: Debate-styled Text Auto-generation System Based on Large Foundation Language Models

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Robot Debater: Debate-styled Text Auto-generation System Bas...

pattern recognition and Machine Learning (PRML), IEEE International Conference on

作者： Yu Zhu Yijun Ling Xufeng Ling Jie Yang Shanghai Library (Institute of Scientific and Technical Information of Shanghai) Shanghai China Shanghai Xiangming High School Shanghai China School of Artificial Intelligence Shanghai Normal University Tianhua College Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China

We use a large foundation language model, which is fine-tuned with debate corpora, to develop a robot debater application. To address the limitations of requiring immense computational power in large base language models, this study takes advantage of the Low Rank Adaption characteristic prevalent in domain expert knowledge. By applying Low Rank Adaption technology and fine-tuning with a dedicated dataset, the computational load is reduced to just one-thousandth of what is needed for a large language model, greatly expanding the application scenarios of robot debaters using large language models. In view of the characteristics of debate competitions, this model can preset a variety of debate scenarios and supports personalized debate processes. It employs intelligent voice recognition technology combined with a multi-channel voice input method, allowing for precise localization of different human debaters and improving the accuracy of voice input recognition. The system can support multiple large-scale language generation models and utilize various different voice broadcasting systems, including male and female voice styles, as well as a range of voice emotions. This model can be applied to debate competitions held in universities, high schools, and various industries. It can support human-machine debates as well as machine-to-machine debates.

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