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ECHO MODELING FOR ASTEROIDS WITH ROUGH SURFACE IN GOUND-BASED SAR IMAGING

ECHO MODELING FOR ASTEROIDS WITH ROUGH SURFACE IN GOUND-BASE...

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IET International Radar Conference 2023, IRC 2023

作者： Zhou, Ziyi Zhu, Kaiwen Wei, Yangkai Ding, Zegang School of Information and Electronics Beijing Institute of Technology Beijing China BeijingKey Laboratory of Embedded Real-Time Information Processing Technology Beijing China Chongqing Innovation Center Beijing Institute of Technology Chongqing China

ISBN: (纸本)9781839539954

With the development of radar imaging technology, asteroid imaging has become one of the focal points in astronomical radar research. However, in simulation and verification experiments for asteroids observation, the traditional point target approximation method is often inadequate in capturing geometric features and surface topography of asteroid. To address this issue, this paper proposes an improved method for generating asteroid echo specifically tailored for asteroid imaging tasks. Firstly, the relative motion between the radar and the asteroid is calculated to determine the slant range phase. Simultaneously, an amplitude factor that varies with the observation angle is introduced, correcting the isotropic scattering of traditional point target. Subsequently, considering the rough characteristic of asteroid surfaces, an echo generation process is presented based on the Monte Carlo method. Finally, computer simulation experiments are conducted to complete the echo generation and radar imaging of asteroid and its typical surface topography features. The experiments validate the correctness and effectiveness of the proposed method. © The Institution of Engineering & technology 2023.

关键词： Radar imaging

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The Dual Path Multi-Feature Fusion Model combining CNN and Transformer for Age Estimation of Caenorhabditis elegans

The Dual Path Multi-Feature Fusion Model combining CNN and T...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Ding, Mengqian Liu, Jun Luo, Yang Wuhan University of Science and Technology School of Computer Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China

ISBN: (纸本)9798350386226

The relatively simple genome, short life cycle, and easy observation of Caenorhabditis elegans (C. elegans) make it an excellent model organism for aging research. Age-stage prediction of C. elegans can help to assess the effectiveness of anti-aging interventions, better explore the role of individual differences in the aging process, and thus promote the development of medicine. However, there are still some problems in accurately predicting the age stages of C. elegans. On the one hand, there may be individual differences among C. elegans due to natural variation and environmental factors. On the other hand, nematode data sets are scarce and image information is complex. In this paper, we perform the age prediction task by studying the body contour morphology, biomarkers, and internal organ changes of C. elegans. Specifically, to address the effects of individual differences and datasets, this paper combines the body contour features, protein aggregation features, and pharyngeal terminal bulb features of C. elegans. In addition, considering the complex information of C. elegans images, this paper proposes a hybrid CNN and Transformer feature extraction method (DFCT), which can fully extract the global and local features of the image. A Dual Path Adaptive Feature Fusion Module (DAFM) is used to fuse the features. A series of experiments are conducted to verify the effectiveness of the proposed method. The experimental results show that the method proposed in this paper outperforms other methods in terms of accuracy and precision, especially in dividing 25 age stages into 3 categories, with an accuracy of 85.93%. © 2024 IEEE.

关键词： age estimation biological image analysis Caenorhabditis elegans CNN and Transformer image classification

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ORB image feature extraction algorithm based on fuzzy control 3

ORB image feature extraction algorithm based on fuzzy contro...

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3rd International Conference on Machine Vision, Automatic Identification, and Detection, MVAID 2024

作者： Li, Ruilong College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China

ISBN: (纸本)9781510681880

With the increasing demands for real-time accuracy in visual SLAM (Simultaneous Localization and Mapping), the critical front-end component of SLAM systems has become the extraction and matching of image features to estimate camera motion pose. Existing feature extraction methods are susceptible to local feature point density and even overlap due to uncertainties in lighting intensity and surrounding environments, leading to uneven distribution of overall feature points. This, in turn, affects the accuracy of feature matching, thereby impacting the performance of the SLAM process. To address these challenges, this paper proposes a novel ORB image feature extraction algorithm based on fuzzy control. Initially, a Gaussian pyramid of the image is constructed, and the Intensity Centroid is calculated to ensure scale and rotation invariance. Subsequently, candidate feature points are extracted using ORB at each pyramid level, then the candidate feature points are selectively filtered through fuzzy control based on the number of candidate feature points and the magnitude of their response values. Finally, a BRIEF descriptor is established for feature matching. The proposed Fuzzy-Controlled ORB Feature Extraction (OFFC) algorithm was experimentally validated on open-source TUM and Mikolajczyk datasets. Results indicate that OFFC successfully avoids excessive density and overlap of extracted feature points, reduces mismatches in image feature points, and enhances the accuracy of feature matching. Notably, OFFC demonstrates adaptability and maintains robust performance in three challenging scenarios: image blurring, varying brightness, and dense similarity of image blocks, highlighting its significant practical value for visual SLAM. © 2024 SPIE.

关键词： Fuzzy control

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ROBUST JOINT ASSOCIATION AND REGISTRATION UNDER LARGE SENSOR BIAS

ROBUST JOINT ASSOCIATION AND REGISTRATION UNDER LARGE SENSOR...

引用

IET International Radar Conference 2023, IRC 2023

作者： Ni, Na Jiang, Qi Mao, Huafeng Zhang, Jichuan Wang, Rui Hu, Cheng Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing China Advanced Technology Research Institute Beijing Institute of Technology Shandong Jinan China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing China

ISBN: (纸本)9781839539954

Radar observation of group targets has recently received much attention in the flight mechanism research and many other applications. Group targets are usually closely spaced with lots of missed detections and false alarms, making it difficult for spatial reconstruction. Multisensor systems make use of data from multiple radars to provide more accurate measurements and robust tracks than a single radar, playing an important role in the group targets observation. In the data fusion processes, multisensor measurements are associated after transformed into a global coordinate system. However, large sensor bias makes it difficult to associate measurements of closely spaced targets given outliers, and nonideal association further increase the sensor bias estimation error. In this paper, we proposed a robust joint association and registration method to simultaneously acquire sensor bias estimates and association results. A nonlinear least median of squares estimator is used to achieve better accuracy and robustness of sensor bias estimation. Simulation results show the better bias estimation and association performance of the proposed method compared to other algorithms. © The Institution of Engineering & technology 2023.

关键词： Radar measurement

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An Adaptive OS-CFAR detector for Dynamic group targets

An Adaptive OS-CFAR detector for Dynamic group targets

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IET International Radar Conference 2023, IRC 2023

作者： Shi, Mengxin Jiao, Longxiang Jiang, Qi Wang, Rui Hu, Cheng Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing China Advanced Technology Research Institute Beijing Institute of Technology Shandong Jinan China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing China

ISBN: (纸本)9781839539954

Effective detection of group targets is of great significance to people's livelihood and national security. Flocks of birds, UAV swarms and other group targets are closely spaced in the radar beam and have high dynamics, which can easily lead to the mismatch of the traditional detector parameters and missed detection. Therefore, in this paper, to solve this problem, we established the detection model of the corresponding scene, derived the probability density function expression of the threshold, and proposed the OS-CFAR detector parameter correction method with the expectation of the maximum number of detected targets as the optimization criterion. Taking the OSPA distance as the detection performance index, the detection performance under different detection parameters is compared to verify the effectiveness of this algorithm, and the experiments show that the algorithm proposed in this paper can effectively reduce the target missed detection in the group targets scene. © The Institution of Engineering & technology 2023.

关键词： Target tracking

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Performance Analysis of Sub-object Partition Method for Linear Formation Targets

Performance Analysis of Sub-object Partition Method for Line...

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IET International Radar Conference 2023, IRC 2023

作者： Xu, Li'ang Jiang, Qi Zhang, Jichuan Wang, Rui Hu, Cheng Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing China Advanced Technology Research Institute Beijing Institute of Technology Shandong Jinan China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing China

ISBN: (纸本)9781839539954

The structure of bird formation is complex and variable. Traditional sub-object partition methods cannot match different types of linear formations, leading to a degradation of performance. Recently, a new sub-object partition method applicable to different formations has been proposed. However, the performance of the method has not been analyzed and verified thoroughly. This paper analyses the effectiveness of the proposed method. The statistical rules are proposed to evaluate the performance of the sub-object partition. A series of simulations are conducted using the method of controlling variates, with several important parameters of the method and formation being chosen and discussed separately. And the quantitative results of partition accuracy are demonstrated and the impact of each parameter on partition performance is discussed. Based on the conclusion above, an empirical method for parameter initialization is proposed, which guides the practical applications. © The Institution of Engineering & technology 2023.

关键词： CLUSTERING FORMATION TARGET GROUP TARGET SUB-OBJECT PARTITION

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Analysis of Formation UAV Targets' Angular Glint Characteristics Using Phase Gradient Method

Analysis of Formation UAV Targets' Angular Glint Characteris...

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IET International Radar Conference 2023, IRC 2023

作者： Dou, Libin Jiang, Qi Zhang, Wenyuan Wang, Rui Hu, Cheng Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing China Advanced Technology Research Institute Beijing Institute of Technology Shandong Jinan China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing China

ISBN: (纸本)9781839539954

Detection of group targets is of great significance to social and national security. Due to the large individual number, obvious fluctuations, and dense distribution of group targets, angular glint phenomena may occur during radar detection of group targets. Angular glint is an inherent error generated by the superposition of individual echoes of group targets, which is an important factor affecting the tracking accuracy of group targets. This paper analyzes the mechanism of angular glint of group targets, calculates the angular glint of a typical UAV group targets using the phase gradient method. The amplitude-phase fluctuation characteristics of an individual UAV target is modelled. The statistical characteristics of UAV angular glint is analysed using the Q-Q plot. The simulation results show that the phase gradient method is capable of analyzing the angular glint characteristics of group targets. © The Institution of Engineering & technology 2023.

关键词： Target tracking

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Cross-Modal Ship Grounding: Towards Large Model for Enhanced Few-Shot Learning 27th

Cross-Modal Ship Grounding: Towards Large Model for Enhanced...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Hu, Quan Chen, Li Feng, Zhida Chen, Yaojie School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9783031781124

A growing body of research indicates that employing large models for adaptation to downstream tasks often yields remarkable performance. However, in the domain of ship detection, the potential of these large models is frequently underutilized due to domain shift issues. This paper introduces the Cross-Modal Ship Grounding (CSG) model, which leverages an efficient Cross-Modal Adapter (CMA) technology to transfer the general detection capabilities of large models to ship images, addressing domain shift with minimal training costs. To mitigate the challenges posed by complex and variable background interference, the Water-Land Separation (WLS) module is proposed to focus specifically on the water area. This module effectively addresses the issue of background target interference, thereby enhancing the model’s accuracy in complex scenes. Empirical evaluations on both private and public datasets demonstrate that the CSG model surpasses all state-of-the-art models in performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Zero-shot learning

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Lightweight Single Image Super-Resolution Network Integrating CNN and Transformer 27th

Lightweight Single Image Super-Resolution Network Integratin...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhu, Kai Chen, Li School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9783031781247

Recently, remarkable progress has been achieved in single image super-resolution using methods based on CNN and Transformer architectures. However, existing approaches often construct a substantial number of network layers, leading to a significant increase in performance requirement and memory consumption, thereby limiting the practical deployment and usability of the models. To address this issue, we propose an Alternating CNN Transformer Block and an Integrative CNN Efficient Transformer for single image super-resolution. We enhance feature extraction efficiency by combining CNN within and between Transformer modules. In addition, we propose two novel structures: Multi-branch Gated CNN and Parallel Channel Attention, aiming to efficiently extract local spatial information and global channel information from images. Extensive experiments demonstrate that our model achieves high performance while maintaining low model complexity. The proposed model attains PSNR values of 32.32 and 30.78 for the Set5 and Manga109 benchmark datasets, respectively, at a scale factor of ×4. Compared to other lightweight super-resolution models, our proposed model outperforms them at lower computational costs. The source codes are available at https://***/kylechuuuuu/ICTSRN. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： HTTP

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Remote Sensing Object Detection Based on Lightweight YOLO-V4 7

Remote Sensing Object Detection Based on Lightweight YOLO-V4

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7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering

作者： Li, Keng Cao, Yunfei Chen, He Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology 5 South Zhongguancun Street Haidian District Beijing China

ISBN: (纸本)9781510656437

The remote sensing object detection algorithms based on deep learning have high detection performances, but the network structures are too complexity to meet the real-time processing requirements in on-board remote sensing object detection. In order to solve this problem, we proposed a lightweight YOLO-v4 network, which is 76% smaller than the original YOLO-v4. As for the decrease of lightweight network's accuracy, we adopted the general instance distillation algorithm, which used the original YOLO-v4 network as the teacher network and whose detection accuracy achieved 2.1% mAP gain. © 2022 SPIE. All rights reserved.

关键词： Remote sensing

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