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research on Ground Clutter Polarization Suppression Algorithm Based on Prior information

Research on Ground Clutter Polarization Suppression Algorith...

引用

IET International Radar Conference 2023, IRC 2023

作者： Liu, Sheng Cai, Jiong Zhang, Jichuan Wang, Rui Hu, Cheng The Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing100081 China The Advanced Technology Research Institute Beijing Institute of Technology Jinan250300 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing100081 China

ISBN: (纸本)9781839539954

Low-altitude unmanned aerial vehicle (UAV) targets are challenging to detect by radar due to their low flight altitude, which exposes them to strong interferences from complex ground clutter. To address this, this paper proposes a ground clutter polarization suppression method based on prior information, facilitating the effective detection of low-altitude UAV targets. Initially, the method establishes a clutter prior information database and a clutter map according to the actual radar application environment, classifying different types of clutter. This serves as the prior information for clutter, supporting detection in the context of complex ground clutter backgrounds. Subsequently, a linear polarization filtering algorithm optimized based on maximizing the signal-to-clutter ratio (SCR) is proposed for specific clutter environments. A strong clutter point is selected from the clutter map, and the effectiveness of the algorithm proposed in this paper is analyzed and verified using real-world radar data collected from field measurements. The results indicate that the proposed algorithm improves the average signal-to-clutter ratio by 15.39 dB compared to single polarization channels. This method provides a reference for complex ground clutter polarization suppression and polarization detection. © The Institution of Engineering & Technology 2023.

关键词： Unmanned aerial vehicles (UAV)

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Common Targets Polarimetric Calibration Technique for Long Blind Range Radar

Common Targets Polarimetric Calibration Technique for Long B...

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IET International Radar Conference 2023, IRC 2023

作者： Tan, Lijia Li, Weidong Wang, Rui Wang, Jiangtao Li, Muyang Hu, Cheng The Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing100081 China The Advanced Technology Research Institute Beijing Institute of Technology Jinan250300 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing100081 China

ISBN: (纸本)9781839539954

Many research indicates the importance of polarimetric information in radar systems for purposes such as detection, tracking, and parameter estimation. The calibration process is essential for obtaining precise polarimetric information. However, conducting calibration experiments for long blind range radars can be challenging in fields where ground clutter is prominent. For multi-pulse width switching radar, this paper introduces a polarimetric calibration algorithm by measuring common targets of short- and long-range waveforms. Firstly, we switch between various waveforms to measure the strong echo common targets in the overlapping detection range. The scattering matrices of common targets can be obtained by the short-range waveform calibrated by the conventional method. Then, the polarization error model is built based on the measured scattering matrices of long-range waveform, and the true values of the scattering matrices of common targets are used to estimate the polarization errors of long-range waveform. The effectiveness of the algorithm is validated through simulation results. © The Institution of Engineering & Technology 2023.

关键词： Polarimeters

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The Optimal Partition Number Estimation Algorithm Based on Variational Inference

The Optimal Partition Number Estimation Algorithm Based on V...

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IET International Radar Conference 2023, IRC 2023

作者： Zhang, Jichuan Jiang, Qi Xu, Liang Wang, Rui Hu, Cheng The Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing100081 China The Advanced Technology Research Institute Beijing Institute of Technology Jinan250300 China The Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing100081 China

ISBN: (纸本)9781839539954

The partition of individual targets into proper clusters is crucial in tracking group targets with complex spatial structures. However, most of the current clustering methods suffer from heavy computation burdens and unfitting a priori knowledge, resulting in the degradation of robustness. This paper proposes an optimal estimate of partition number based on the variational inference for group targets. By maximizing the variational evidence lower bound, which serves as the model-selected criterion, the optimal partition number can be estimated. The K-means++ algorithm is used to initialize the variation iteration. The proposed algorithm does not require a priori information and has low computation complexity, which benefits from the deterministic approximation character of variational inference. The effectiveness of the proposed method is verified by simulation results. © The Institution of Engineering & Technology 2023.

关键词： Consensus algorithm

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Leaf image recognition using Fourier transform based on ordered sequence

Leaf image recognition using Fourier transform based on orde...

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8th International Conference on intelligent computing Technology, ICIC 2012

作者： Yang, Li-Wei Wang, Xiao-Feng Department of Automation University of Science and Technology of China Hefei Anhui 230027 China Intelligent Computing Laboratory Hefei Institute of Intelligent Machines Chinese Academy of Sciences P.O. Box 1130 Hefei Anhui 230031 China Key Lab. of Network and Intelligent Information Processing Hefei University Hefei Anhui 230601 China

ISBN: (纸本)9783642315879

There are a number of leaf recognition methods, but most of them are based on Euclidean space. In this paper, we will introduce a new description of feature for the leaf image recognition, which represents the leaf contour with the ordered sequence. For a leaf image, points on the contour represent the most important information of the leaf. Thus, by extracting serial points of the leaf contour, the unique corresponding ordered sequence can be obtained for a contour. Then, we can compute the amplitude-frequency feature by performing the Discrete Fourier transform on the ordered sequence. Since the low-frequency part of the Fourier transform represents the global information and the high-frequency part the local details, we can adopt the amplitude-frequency feature for leaf image recognition. Experimental results on the famous Swedish library and ICL library show that the proposed feature is effective for leaf image recognition. © 2012 Springer-Verlag.

关键词： Image recognition

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Downstream Task-agnostic Transferable Attacks on Language-Image Pre-training Models

Downstream Task-agnostic Transferable Attacks on Language-Im...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yiqiang Lv Jingjing Chen Zhipeng Wei Kai Chen Zuxuan Wu Yu-Gang Jiang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai Collaborative Innovation Center of Intelligent Visual Computing

Vision-language pre-trained models (e.g., CLIP) trained on large-scale datasets via self-supervised learning, are drawing increasing research attention since they can achieve superior performances on multi-modal downstream tasks. Nevertheless, we find that the adversarial perturbations crafted on vision-language pre-trained models can be used to attack different corresponding downstream task models. Specifically, to investigate such adversarial transferability, we introduce a task-agnostic method named Global and Local Augmentation (GLA) attack to generate highly transferable adversarial examples on CLIP, to attack black-box downstream task models. GLA adopts random crop and resize at both global and local patch levels, to create more diversity and make adversarial noises robust. Then GLA generates the adversarial perturbations by minimizing the cosine similarity between intermediate features from augmented adversarial and benign examples. Extensive experiments on three CLIP image encoders with different backbones and three different downstream tasks demonstrate the superiority of our method compared with other strong baselines. The code is available at https://***/yqlvcoding/GLAattack.

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Motion Guided Region Message Passing for Video Captioning

Motion Guided Region Message Passing for Video Captioning

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International Conference on Computer Vision (ICCV)

作者： Shaoxiang Chen Yu-Gang Jiang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai Collaborative Innovation Center on Intelligent Visual Computing

ISBN: (纸本)9781665428132

Video captioning is an important vision task and has been intensively studied in the computer vision community. Existing methods that utilize the fine-grained spatial information have achieved significant improvements, however, they either rely on costly external object detectors or do not sufficiently model the spatial/temporal relations. In this paper, we aim at designing a spatial information extraction and aggregation method for video captioning without the need of external object detectors. For this purpose, we propose a Recurrent Region Attention module to better extract diverse spatial features, and by employing Motion-Guided Cross-frame Message Passing, our model is aware of the temporal structure and able to establish high-order relations among the diverse regions across frames. They jointly encourage information communication and produce compact and powerful video representations. Furthermore, an Adjusted Temporal Graph Decoder is proposed to flexibly update video features and model high-order temporal relations during decoding. Experimental results on three benchmark datasets: MSVD, MSR-VTT, and VATEX demonstrate that our proposed method can outperform state-of-the-art methods.

关键词： Location awareness Computer vision Visualization Message passing Computational modeling Detectors Feature extraction

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Joint speech signal enhancement based on spectral subtraction and SVD filter 6

Joint speech signal enhancement based on spectral subtractio...

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6th International Conference on Spoken Language processing, ICSLP 2000

作者： Lu, Wenkai Zhang, Xuegong Li, Yanda Liqin, Shen Weibin, Zhu Institute of Information Processing Department of Automation Key State Lab of Intelligent Technology and System Tsinghua University Beijing100084 China Speech Group IBM China Research Lab. No 26 6th Street Shangdi Haidian District Beijing100085 China

ISBN: (纸本)7801501144

A joint speech signal enhancement based on singular value decomposition filter after spectral subtraction (SSVD) is proposed in this paper. The residual noise after spectral subtraction, which results for audible musical noise, is reduced further by SVD filter. The matrix size in spectral domain can be reduced half, and larger step-length adopted by SVD filter in spectral domain leads to lower cost, which make sure that the system can work in real-time. A novel speech/pause detector based on entropy(ESPD) is proposed too. The new detector improves the performance of the whole noise suppression system significantly.

关键词： Singular value decomposition

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TOP: Towards Better Toxicity Prediction by Deep Molecular Representation Learning

TOP: Towards Better Toxicity Prediction by Deep Molecular Re...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yuzhong Peng Ziqiao Zhang Qizhi Jiang Jihong Guan Shuigeng Zhou Key Lab of Scientific Computing Intelligent Information Processing in Universities of Guangxi Nanning Normal University Nanning China Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Tongji University Shanghai China

At the early stages of the drug discovery, molecule toxicity prediction is crucial to excluding drug candidates that are likely to fail in clinical trials. In this paper, we presented a novel molecular representation method and developed a corresponding deep learning-based framework called TOP (the abbreviation of TOxicity Prediction). TOP integrated a serial special data processing methods, a bidirectional gated recurrent unit-based RNN (BiGRU) and a fully connected neural network for end-to-end molecular representation learning and chemical toxicity prediction. TOP can automatically learn a mixed molecular representation from not only SMILES contextual information that describes the molecule structure, but also physiochemical properties. Therefore, TOP can overcome the drawbacks of existing methods that use either of them, thus greatly promotes toxicity prediction. We conducted extensive experiments over 14 classic toxicity prediction tasks on three different benchmark datasets, including balanced and imbalanced ones. The results show that, with the help of the novel molecular representation method, TOP significantly outperforms not only three baseline machine learning methods, but also five state-of-the-art methods.

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The signal reconstruction of speech by KPCA 6

The signal reconstruction of speech by KPCA

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6th International Conference on Spoken Language processing, ICSLP 2000

作者： Yan, Hui Zhang, Xuegong Li, Yanda Shen, Liqin Zhu, Weibin Institute of Information Processing Department of Automation Key State Lab of Intelligent Technology and System Tsinghua University Beijing100084 China Speech Group IBM China Research Lab. No 26 6th Street Shangdi Haidian District Beijing100085 China

ISBN: (纸本)7801501144

A new method for speech signal reconstruction is proposed by performing a nonlinear Kernel Principal Component Analysis (KPCA). By the use of kernel functions, one can efficiently compute principal components in high-dimensional feature spaces, and reconstruct vectors mapping from input space by those dominant principal components. As the reconstructed vectors is expressed in high dimensional feature space and they could not exist pre-image in input space. For finding pre-image, we use iteration method to approximate the pre-image. The experimental results using KPCA in data reconstruction and denoising in speech signal show that it had many potential advantages comparing with PCA.

关键词： Iterative methods

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Algebra and Calculus of the Resource Space Model

Algebra and Calculus of the Resource Space Model

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2007年第三届语义和知识网格国际会议(Third International Conference on Semantics,Knowledge,and Grid)(SKG 2007)

作者： Yunpeng Xing Hai Zhuge China Knowledge Grid Research Group Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 China

Resource Space Model (RSM) is a semantic model to manage resources in the future interconnection environment. The query capability is an important aspect of RSM as a semantic resource management model. This paper reports the research result on the query capability of RSM from two perspectives: resource space algebra and resource space calculus. The equivalence of the resource space algebra and the resource space calculus has been discussed.

关键词： Algebra Calculus Resource management Space technology Environmental management Ontologies Database languages information processing Computers Conference management

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