检索结果-内蒙古大学图书馆

Zero-Attracting Kernel Maximum Versoria Criterion Algorithm for Nonlinear Sparse System Identification

IEEE signal processing LETTERS 2022年 29卷 1546-1550页

作者： Jain, Sandesh Majhi, Sudhan Indian Inst Sci Dept Elect Commun Engn Bangalore 560012 Karnataka India

Sparsity-induced kernel adaptive filters have emerged as a promising candidate for a nonlinear sparse system identification (SSI) problem. The existing zero-attracting kernel least mean square (ZA-KLMS) algorithm relies on minimum mean square error criterion, which considers only second order statistics of error, thereby resulting in suboptimal performance in the presence of non-Gaussian/impulsive distortions. In this letter, we propose a novel random Fourier features (RFF) based ZA kernel maximum Versoria criterion (ZA-KMVC) algorithm, and their variants, which are robust for nonlinear SSI in the presence of non-Gaussian distortions over both stationary and time-varying environments. Furthermore, the mean-square convergence analysis of the proposed RFF-ZA-KMVC algorithm is performed. It has been observed from the simulation results that the proposed algorithm delivers better convergence performance as compared to the existing state-of-art approaches.

关键词： signal processing algorithms Kernel Prediction algorithms Cost function Convergence Adaptive filters Steady-state KLMS maximum Versoria criterion non-Gaussian random Fourier features reproducing kernel Hilbert space sparsity-aware ZA-KLMS ZA-KMVC zero-attracting

来源：评论

学校读者我要写书评

暂无评论

Joint Design of Transmit Waveform and Receive Beamforming for LPI FDA-MIMO Radar

引用

IEEE signal processing LETTERS 2022年 29卷 1938-1942页

作者： Gong, Pengcheng Zhang, Zhuoyu Wu, Yuntao Wang, Wen-Qin Wuhan Inst Technol Sch Comp Sci & Engn Wuhan 430205 Peoples R China Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China

This letter considers the joint design of transmit waveform and receive beamforming for low probability of intercept (LPI) of multiple-input multiple-output with frequency diverse array radar in cluttered environments. The goal is to maximize the output signal-to-interference-plus-noise ratio while controlling the transmit power at the target region which enhances LPI capability, subject to peak-to-average power ratio and similarity constraints. To tackle the formulated non-convex optimization problem, an alternating optimization algorithm based on the alternating direction method of multipliers is proposed. Simulation results are provided to validate the effectiveness of the developed method in terms of the objective function values as well as the transmit and receive beampatterns.

关键词： signal to noise ratio Optimization signal processing algorithms Array signal processing Radar antennas Peak to average power ratio Transmitting antennas ADMM beamforming design frequency diverse array MIMO radar SINR transmit wavefrom

来源：评论

学校读者我要写书评

暂无评论

A Low-Complexity Modified ThiNet Algorithm for Pruning Convolutional Neural Networks

引用

IEEE signal processing LETTERS 2022年 29卷 1012-1016页

作者： Tofigh, Sadegh Ahmad, M. Omair Swamy, M. N. S. Concordia Univ Dept Elect & Comp Engn Montreal PQ H3G 1M8 Canada

ThiNet is a recent method for pruning convolutional neural networks. This method uses a norm of a subset of the components of the output resulting from the convolutional layer succeeding the layer from which the filters are to be removed for pruning the network. The ThiNet algorithm is very time-consuming, in view of the fact that the filters for removal are selected one by one iteratively. In this paper, we propose a modified version of ThiNet, in which the same information on the output of the same convolutional layer as used by ThiNet is employed to select all the filters together in a single step, for pruning the network. The proposed modified algorithm is shown to have a time-complexity that is only a small fraction of that of ThiNet or any other state-of-the-art algorithm and that the pruned network has almost the same reduction in its accuracy as that of the network pruned by ThiNet.

关键词： signal processing algorithms Convolution Convolutional neural networks Training Testing Tensors Mathematical models CNN model compression convolutional neural networks network pruning ThiNet algorithm

来源：评论

学校读者我要写书评

暂无评论

A Fast and Accurate Method for Parameter Estimation of Multi-Component LFM signals

引用

IEEE signal processing LETTERS 2022年 29卷 1719-1723页

作者： Moghadasian, Seyed Saman Behbahan Khatam Alanbia Univ Technol Dept Elect Engn Behbahan *** Iran

This letter presents a fast and accurate method for parameter estimation of multi-component linear frequency modulated (MC-LFM) signals. We first show that the auto-terms of signal in fractional auto-correlation (FrAc) domain are concentrated along lines passing through the origin with slopes equal to chirp rates. The connection between the FrAc and fractional Fourier transform (FrFT) is exploited to find the chirp rates. We then easily obtain central frequencies from the same FrFT coefficients which are used for the estimation of chirp rates. We finally propose our efficient algorithm to accelerate the precise estimate of the MC-LFM signal parameters. Simulation results validate performance of the approach.

关键词： Chirp Fourier transforms Maximum likelihood estimation Frequency estimation Time-frequency analysis signal processing algorithms Kernel Fractional auto-correlation fractional Fourier transform multi-component LFM signal parameter estimation

来源：评论

学校读者我要写书评

暂无评论

Multi-Aspect Streaming Tensor Ring Completion for Dynamic Incremental Data

引用

IEEE signal processing LETTERS 2022年 29卷 2657-2661页

作者： Huang, Zhenhao Qiu, Yuning Yu, Jinshi Zhou, Guoxu Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China RIKEN Ctr Adv Intelligence Project AIP Tokyo 1030027 Japan Guangdong Univ Technol Key Lab Intelligent Detect & Internet Things Mfg Minist Educ Guangzhou 510006 Peoples R China Guangzhou Univ Sch Mech & Elect Engn Guangzhou 510006 Peoples R China

As the volume of real-world data with numerous missing entries continues to grow rapidly, tensor completion has been a powerful tool to enhance such flawed data analysis. While existing methods mainly consider static data, there is a great need to deal with streaming data. In this letter, a multi-aspect streaming tensor ring completion (MASTR) method is proposed, where the low-rank tensor ring (TR) model is exploited to capture subspace information and transfer high-order correlations between multiple sub-tensors. Experimental results on synthetic data, hyperspectral data and video data demonstrate superior recovery performance compared to state-of-the-art methods.

关键词： Tensors Streaming media Data models Computational modeling signal to noise ratio Task analysis signal processing algorithms Tensor completion streaming data tensor ring sub-tensors

来源：评论

学校读者我要写书评

暂无评论

Structured OMP for IRS-Assisted Mmwave Channel Estimation by Exploiting Angular Spread

引用

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2022年第4期71卷 4444-4448页

作者： You, You Zhang, Li Yang, Minhua Huang, Yongming You, Xiaohu Zhang, Chuan Southeast Univ Natl Mobile Commun Res Lab LEADS Southeast Univ Nanjing 211189 Peoples R China Purple Mt Labs Nanjing 211100 Peoples R China Univ Leeds Sch Elect & Elect Engn Leeds LS2 9JT W Yorkshire England

An emerging technology namely the intelligent reflecting surface (IRS) can be deployed in millimeter wave (mmWave) communication to overcome the large propagation loss and huge power consumption issues. However, the large number of passive IRS elements without signal processing abilities induces high pilot overhead for channel estimation (CE). In this paper, the angular spread feature (or cluster feature) is exploited to formulate the CE problem as a structured sparse signal recovery problem. Then, a structured orthogonal matching pursuit (S-OMP) algorithm is proposed to efficiently solve the problem by utilizing the structure of the channel matrix in row and column without a priori knowledge of the angular spread. Simulation results demonstrate that S-OMP reduces about 40% to 50% pilot overhead at low signal-to-noise ratio (SNR) (SNR= 0 dB) while maintaining the same accuracy of CE compared with the existing methods.

关键词： Surface waves Simulation Matching pursuit algorithms Channel estimation signal processing algorithms signal processing Sparse matrices Intelligent reflecting surface (IRS) channel estimation angular spread orthogonal matching pursuit (OMP)

来源：评论

学校读者我要写书评

暂无评论

A Fast and Scalable Polyatomic Frank-Wolfe Algorithm for the LASSO

引用

IEEE signal processing LETTERS 2022年 29卷 637-641页

作者： Jarret, Adrian Fageot, Julien Simeoni, Matthieu Ecole Polytech Fed Lausanne Sch Comp & Commun Sci CH-1015 Lausanne Switzerland Ecole Polytech Fed Lausanne EPFL Ctr Imaging CH-1015 Lausanne Switzerland

We propose a fast and scalable polyatomic Frank-Wolfe (P-FW) algorithm for the resolution of high-dimensional LASSO regression problems. This algorithm improves upon traditional Frank-Wolfe methods by considering generalized greedy steps with polyatomic (i.e. linear combinations of multiple atoms) update directions, hence allowing for a more efficient exploration of the search space. To preserve sparsity of the intermediate iterates, we re-optimize the LASSO problem over the set of selected atoms at each iteration. For efficiency reasons, the accuracy of this re-optimization step is relatively low for early iterations and gradually increases with the iteration count. We provide convergence guarantees for our algorithm and validate it in simulated compressed sensing setups. Our experiments reveal that P-FW outperforms state-of-the-art methods in terms of runtime, both for FW methods and optimal first-order proximal gradient methods such as the Fast Iterative Soft-Thresholding Algorithm (FISTA).

关键词： signal processing algorithms Approximation algorithms Matching pursuit algorithms Convergence Optimization Search problems Minimization Conditional gradient Frank-Wolfe LASSO sparse recovery convex optimisation

来源：评论

学校读者我要写书评

暂无评论

Fast Beampattern Synthesis Algorithm for Flexible Conformal Array

引用

IEEE signal processing LETTERS 2022年 29卷 2417-2421页

作者： Tian, Xiang Chen, Hui He, Meng-Meng Wang, Wen-Qin Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China

This letter first outlines a flexible conformal array geometric model on variable airborne wings, which can undergo complete shape change according to different combat situations, whereas the deformation may result in array beampattern distorted. In order to realize fast beampattern synthesis for a flexible conformal array, we propose a real-time beampattern optimization algorithm, where we split the protoproblem into two sub-problems based on the alternating direction method of multipliers (ADMM). In the first sub-problem, we transform the quadratic inequality constrained problem into a linear equality constrained problem, and then get the iterative update expression for the variables. Unlike the traditional interior point method, the simulation results demonstrate that the proposed algorithm can efficiently solve large-scale convex optimization problems and greatly reduce the computational complexity.

关键词： Optimization Convex functions signal processing algorithms Transforms Strain Simulation Shape Flexible conformal array parameter optimization fast beampattern synthesis alternating direction method of multipliers (ADMM)

来源：评论

学校读者我要写书评

暂无评论

Improving Frame-Online Neural Speech Enhancement With Overlapped-Frame Prediction

引用

IEEE signal processing LETTERS 2022年 29卷 1422-1426页

作者： Wang, Zhong-Qiu Watanabe, Shinji Carnegie Mellon Univ Language Technol Inst Pittsburgh PA 15213 USA

Frame-online speech enhancement systems in the short-time Fourier transform (STFT) domain usually have an algorithmic latency equal to the window size due to the use of overlap-add in the inverse STFT (iSTFT). This algorithmic latency allows the enhancement models to leverage future contextual information up to a length equal to the window size. However, this information is only partially leveraged by current frame-online systems. To fully exploit it, we propose an overlapped-frame prediction technique for deep learning based frame-online speech enhancement, where at each frame our deep neural network (DNN) predicts the current and several past frames that are necessary for overlap-add, instead of only predicting the current frame. In addition, we propose a loss function to account for the scale difference between predicted and oracle target signals. Experiments on a noisy-reverberant speech enhancement task show the effectiveness of the proposed algorithms.

关键词： Prediction algorithms Speech enhancement Discrete Fourier transforms Spectrogram Predictive models signal processing algorithms Time-domain analysis Deep learning online speech enhancement

来源：评论

学校读者我要写书评

暂无评论

A Deep Reinforcement Learning Method For Multimodal Data Fusion in Action Recognition

引用

IEEE signal processing LETTERS 2022年 29卷 120-124页

作者： Guo, Jiale Liu, Qiang Chen, Enqing Zhengzhou Univ Sch Informat Engn Zhengzhou 450001 Peoples R China

At present, in the research of multimodal human action recognition, the weighted fusion method with fixed weight is widely applied in the decision level fusion of most models. In this way, the weight is usually obtained from the original experience or traversal search, which is inaccurate or has a large amount of calculation, and ignores the different representation ability of various modal data for various classes of action information. With the help of the powerful decision-making ability of deep reinforcement learning, we propose a multimodal decision-making fusion weight allocation network based on deep reinforcement learning. This letter mainly discusses the design of the model, which involves the modeling of reinforcement learning problem in action recognition, the design of neural network and the selection of problem-solving scheme. Experimental results on NTU RGB + D and HMDB51 datasets show the effectiveness of the proposed method.

关键词： Reinforcement learning Resource management Data models signal processing algorithms Task analysis Neural networks Decision making Multimodal action recognition TD3 fusion weight allocation deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：