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检索条件"机构=National Key Laboratory of Science and Technology on Bind Signal Processing"

共 283 条记录，以下是91-100 订阅

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A modified multi-objective whale optimization algorithm with dynamic leader selection mechanism 12

A modified multi-objective whale optimization algorithm with...

引用

12th International Conference on Measuring technology and Mechatronics Automation, ICMTMA 2020

作者： Feng, Wentao Deng, Bing National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu Sichuan610041 China

ISBN: (纸本)9781728170817

The whale optimization algorithm (WOA) has less control parameters and it is easier to implement. The multi-objective whale optimization algorithm (MOWOA) also shows good exploration and exploitation capability. A modified multi-objective whale optimization algorithm with dynamic leader selection mechanism (MMOWOA-DLS) is proposed. First, the opposition-based learning (OBL) is employed to accelerate the convergence speed. Second, a dynamic leader selection mechanism is utilized to improve the solution accuracy. Third, a modified archive grid controller is proposed to delete redundant solutions in external archive. The simulation results show that the performance of MMOWOA-DLS outperforms other algorithms. © 2020 IEEE.

关键词： Dynamics

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A self-adaptive modified whale optimization algorithm for optimizing the linear antenna array 12

A self-adaptive modified whale optimization algorithm for op...

引用

12th International Conference on Measuring technology and Mechatronics Automation, ICMTMA 2020

作者： Feng, Wentao Song, Kekang National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu Sichuan610041 China

ISBN: (纸本)9781728170817

The whale optimization algorithm (WOA) is an effective algorithm for solving complex optimization problem. Its unique search mechanism results in better exploitation than exploration capability which means that it's easy to get trapped in the local optima. A self-adaptive modified whale optimization algorithm named of SAMWOA is proposed. A self-adaptive quasi-opposition method and novel population updating mechanism is integrated into the whale optimization algorithm to improve the solution accuracy. Finally, the proposed algorithm is utilized in optimizing the radiation pattern of a 16-element linear antenna array. The experimental results show that SAMWOA algorithm can reduce the sidelobe level obviously. © 2020 IEEE.

关键词： Optimization

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SAR Image Feature Selection and Change Detection Based on Sparse Coefficient Correlation

SAR Image Feature Selection and Change Detection Based on Sp...

引用

International Conference on Control, Automation, Robotics and Vision (ICARCV)

作者： Wanying Song Huan Quan Yuang Chen Peng Zhang Xi'an Key Laboratory of Heterogeneous Network Convergence Communication Xi'an University of Science and Technology Xian China National Laboratory of Radar Signal Processing Xidian University Xian China

ISBN: (纸本)9781665476881

High-dimensional features extraction and selection is of great significance for synthetic aperture radar (SAR) image change detection. In this paper, a feature selection based on sparse coefficient correlation, abbreviated as SR-PCC, is proposed to realize the local reconstruction of known samples, so as to improve the accuracy of change detection. Firstly, high-dimensional texture features are extracted from real SAR images and then fused by stacking. Secondly, for the known samples, the sparse representation is performed and then the sparse coefficients are obtained. Then, the Pearson correlation coefficient method is used to select sparse coefficients related to the image itself, thus realizing local optimal reconstruction. Finally, the selected features are inputted into the support vector machine (SVM) to realize change detection. Experiments on real SAR images demonstrate the effectiveness of the proposed SR-PCC in high-dimensional feature selection and illustrate that it can provide better change detection maps.

关键词： Support vector machines Correlation coefficient Correlation Stacking Feature extraction Radar polarimetry Image reconstruction

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Polarization-Guided Strategy for Ship Detection in Single-Polarization SAR Images

Polarization-Guided Strategy for Ship Detection in Single-Po...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Jian Guan Haotian Yuan Pengming Feng Guangjun He Shichao Jin Group of Intelligent Signal Processing College of Computer Science and Technology Harbin Engineering University Harbin China Modeling and Emulation in E-Government National Engineering Laboratory Harbin Engineering University Harbin China State Key Laboratory of Space-Ground Integrated Information Technology Beijing China

High-performance target detection algorithms have been proposed for ship detection in Synthetic Aperture Radar (SAR) images in recent years. However, most of them are applied in the situation of single-polarization SAR images and rarely consider the important polarization information in SAR images. In this paper, a polarization-guided strategy is presented to improve the detection performance in single polarization SAR images by predicting polarization type. In which, an extra polarization-guided head is employed following the backbone network to guide the feature maps from the backbone to explore polarization information. Then, the feature map with the polarization information is fused with those from backbone network to enhance feature extraction in feature pyramid networks (FPN), and yields better detection performance. Experiments conducted demonstrate the effectiveness of the proposed method, which can be easily merged with different detectors.

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Speech Topic Classification Based on Pre-trained and Graph Networks

Speech Topic Classification Based on Pre-trained and Graph N...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Fangjing Niu Tengfei Cao Ying Hu Hao Huang Liang He School of Information Science and Engineering Xinjiang University Urumqi China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi China Department of Electronic Engineering Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology Beijing China

Speech Topic Classification (STC) automatically classifies audio clips into predefined categories, which is widely used in short video, personalized recommendation and other fields. At present, the common system is composed of two parts: first, the speech is converted into text by automatic speech recognition (ASR), and then the text topic is classified by natural language processing (NLP). Most of them have problems such as error propagation and lack of global structure. So in this paper, we propose a new end-to-end framework based on a pre-trained model and graph network. The pre-trained model is used to extract the semantic features with sequential structure instead of acoustic features, and the combination with the global features of conversational context constructed by graph network has achieved good results on the Fisher dataset.

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A fast deep-learning signal detection architecture for the short-wave band 6

A fast deep-learning signal detection architecture for the s...

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6th Annual International Conference on Network and Information Systems for Computers, ICNISC 2020

作者： Wang, Keren Li, Weihao National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu China Information Engineering University Zhengzhou China

Wideband signal detection plays an important role in wireless communication systems. In recent years, deep learning (DL) has been introduced, and many trial efforts have been posed. In this paper, a fast deep-learning signal detection architecture is proposed for the short-wave band, which consists of the GPU-based Down Digital Converter (DDC), the DL-based signal detector, and the signal data viewer & recorder. Wideband signal data are taken directly as the input, and detected signals are automatically recorded as the output of the architecture. Experimental results suggest that our architecture is capable of detecting signals in the whole short-wave band in quasi-real-time, and specifically, with single GPU device. © 2020 Journal of Physics: Conference Series.

关键词： signal detection

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DOA Estimation for RIS Assisted Multi-User Wireless Communication System with Squared-Sine Error Criterion and Lawson Norm Constraint

DOA Estimation for RIS Assisted Multi-User Wireless Communic...

引用

IEEE International Conference on Electronic Information and Communication technology (ICEICT)

作者： Canping Yu Li Li Yingsong Li Dazhi Sun Liping Li Zhixiang Huang Tian Hong Loh Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China Aircraft Airworthiness Institute China Academy of Civil Aviation Science and Technology (Center of Aviation Safety Technology CAST) Beijing China Electromagnetic and Eletrochemical Technologies Department National Physical Laboratory Teddington Middlesex UK

Reconfigurable intelligent surface (RIS) can control the phase of reflected signals, and has become a hotspot in the field of wireless communication. In this paper, we propose a direction-of-arrival (DOA) estimation algorithm for RIS assisted multi-user wireless communication systems with squared-sine error criterion and Lawson norm constraint. This algorithm utilizes the sparsity of the signal and can achieve fast and accurate DOA estimation under the interference of impulsive noises. To suppress the line-of-sight (LOS) interference and thus to maximize the received signal-to-interference-plus-noise ratio (SINR), we also propose a random optimal phase selection (ROPS) technique to design the control matrix of RIS elements. The simulation results show that the proposed algorithm performs better than the existing methods.

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Calibration and Estimation for FDA-MIMO Radar with Random Amplitude and Phase Errors

Calibration and Estimation for FDA-MIMO Radar with Random Am...

引用

IEEE International Conference on Radar

作者： Feilong Liu Shengqi Zhu Jingwei Xu Ximin Li Yanhong Xu Wenwen Si National Key Laboratory of Radar Signal Processing Xidian University Xi’an Shannxi China the School of Communication and Information Engineering Xi’an University of Science and Technology Xi’an Shannxi China Beijing Institute of Space Long March Vehicle Xi’an Beijing China

The combination of frequency diverse array (FDA) with range-angle-time dependent beampattern and waveform diversity in multiple input multiple output (MIMO) radar, namely, FDA-MIMO radar, offers additional degrees-of-freedom to enhance the overall system performance. The existing parameter estimation algorithms for FDA-MIMO radar are difficult to achieve effective target estimation when there is amplitude-phase error in the array antenna. In this paper, the proposed method can achieve the calibration and estimation for FDA-MIMO radar with random amplitude and phase errors. First, the signal model for FDA-MIMO radar signal model with random amplitude and phase errors is established. Second, the angle and range estimation is obtained by the spectral peak search method with reduced dimension. Then, the amplitude and phase error of each array element are estimated by the singular value decomposition and eigenvalue decomposition. Finally, the multiple simulation experiments show that in the presence of amplitude and phase errors, the existing algorithms have completely failed, but the proposed algorithm still has the ability to estimate the target with high precision.

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MSCA-UNet: Multi-Scale Channel Adaptive Sensing Module for Medical Image Segmentation

MSCA-UNet: Multi-Scale Channel Adaptive Sensing Module for M...

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New Trends in Computational Intelligence (NTCI), International Conference on

作者： Liangjin Diao Longfeng Shen Jiacong Chen Fangzhen Ge Mengwen Li Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior (ICACB) College of Computer Science and Technology Huaibei Normal University Huaibei Anhui China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei Anhui China

ISBN: (数字)9798331517021

ISBN: (纸本)9798331517038

Recent advancements in CNNs for medical image segmentation have focused on the combination of CNN and Transformer architectures to capture both local and global features. However, challenges remain, such as effectively utilizing fused features at the encoder-decoder bottleneck and addressing blurred object edges in datasets. To tackle these issues, we introduce a multi-scale channel adaptive sensing module, enhancing the model's representational and perceptual capabilities by integrating multi-scale and channel-specific information. Additionally, we propose a cross-perception module and multi-scale segmentation head to improve global coordination and multi-scale information gathering. Experiments on the ACDC and Synapse datasets demonstrate our model's superiority.

关键词： Image segmentation Adaptation models Head Image edge detection Computer architecture Transformers Market research Sensors Synapses Biomedical imaging

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Continuous Distributed processing of Software Defined Radar

Continuous Distributed Processing of Software Defined Radar

引用

2021 CIE International Conference on Radar, Radar 2021

作者： Li, Bing Qiu, Qiang Gong, Shiqi Liu, Yongjun Lei, Yu Institute of Computing Technology CAS Key Lab of Network Data Science and Technology Chinese Academy of Sciences Beijing China State Key Laboratory of Internet of Things for Smart City University of Macau Macau China Xidian University National Laboratory of Radar Signal Processing Xi'an China Golaxy Data Technology Co. Ltd. Beijing China

ISBN: (纸本)9781665498142

Software-defined radar has been an active research field for more than ten years. However, the low performance and low scalability of the traditional processing techniques of SDR make it hard to deal with complex radar tasks. The stream processing engine is a software that supports low latency and high scalability distributed data processing. It is widely used in log, image, and video processing scenarios but rarely used in radar applications. This paper reviews processing software-defined radar data by using the stream processing engine, and we illustrate the differences between processing log data and radar data. Furthermore, we discuss some optimizations for stream processing engines to get better performance in the radar data processing. © 2021 IEEE.

关键词： distributed processing Software-defined radar stream processing engine

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