检索结果-内蒙古大学图书馆

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： T. Dahl N. Christophersen D. Gesbert Department of Informatics University of Oslo Blindern Norway

Identification of the channel matrix is of main concern in wireless MIMO (Multiple Input Multiple Output) systems. Here, we present an SVO-based approach for blind identification of the main independent parallel channels. The right and left singular vectors are estimated directly (no channel matrix estimation is necessary) and continuously updated during normal transmission. The approach is related to the iterative Power Method (8), as well as the time reversal approach ([4]).

关键词： MIMO Signal processing algorithms Noise measurement Bit error rate Lead Wire iterative algorithm

来源：评论

学校读者我要写书评

暂无评论

TTSL iterative localization algorithm in wireless sensor networks

TTSL iterative localization algorithm in wireless sensor net...

引用

Chinese Control Conference (CCC)

作者： Xu Luo Li Chai Jun Yang Engineering Research Center of Metallurgical Automation and Measurement Technology Ministry of Education Wuhan University of Science and Technology 430081 China

iterative Multilateration is widely used in multi-hop wireless sensor networks, where few anchor nodes are deployed in a large area. To compensate for the lackness of robustness in iterative localization when there are sufficient numbers of reference nodes around some blind node, a new estimation algorithm called TTSL is proposed in this paper. Firstly, we obtain all samples using trilateration from every combination of three reference nodes. Then the location of the blind node is estimated. Simulation experiments show that the proposed TTSL algorithm can reduce the position error sufficiently.

关键词： iterative algorithm Wireless sensor networks Estimation Electronic mail TV Robustness Ultrasonic imaging

来源：评论

学校读者我要写书评

暂无评论

An iterative Weighing algorithm for Image Reconstruction in Compressive Sensing

An Iterative Weighing Algorithm for Image Reconstruction in ...

引用

International Conference on Pervasive Computing Signal Processing and Applications (PCSPA)

作者： Hao Chen Xiaoyang Ma Ye Zhang Wenyang Tang School of Electronics and Information Engineering Harbin Institute of Technology Harbin China School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China

Compressive sensing (CS), is a framework which points us a promising way of not measuring N-dimensional signals directly, but rather a set of related measurements, which a linear combination of the original underlying N-dimensional signal. However, the traditional CS reconstruction methods use l1-norm optimization which usually gives poor performance on 2D signal or only suit for specific natural image. In this paper, an iterative weighing algorithm for image reconstruction in CS is proposed. According to the sparsity of last iteration, the algorithm iteratively refines the weighting coefficients to enhance the sparsity of the reconstruction results until the convergence is reached. The experiments for natural image and remote sensing image demonstrate that the proposed method can outperforms the traditional CS framework in image reconstruction in the sense that the PSNR of reconstruction image improve over 2dB in the average.

关键词： Image reconstruction Compressed sensing Sparse matrices PSNR iterative algorithm Signal processing algorithms iterative methods

来源：评论

学校读者我要写书评

暂无评论

A SVC iterative LEARNING algorithm BASED ON SAMPLE SELECTION FOR LARGE SAMPLES

A SVC ITERATIVE LEARNING ALGORITHM BASED ON SAMPLE SELECTION...

引用

2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

作者： ZI-JIE CHEN BO LIU XU-PENG HE Medicine Business School Guangdong Pharmaceutical University Guangzhou 510006 China College of Computer Science and Engineering South China University of Technology Guangzhou 510640 School of Mathematics Science South China University of Technology Guangzhou 510640 China

This paper focuses on an effective and efficient Support Vector Machine classification training algorithm for large *** method is called 'SVC iterative learning algorithm based on sample selection (short for SVCI)'.Initially, a sample selection strategy based on fuzzy c-means clustering is performed to select partial samples as the first training set, so that common decomposition algorithms are competent and efficient in the small-scale ***, iterative training is applied to improve the rough learning machine to guarantee *** a new training, another sample selection strategy is carried out to define the new training set The final optimal classifier is approximate to the one of the original *** on several large-scale UCI data sets show that, this iterative algorithm can converge quickly, double training speed and cut down the number of support vectors by a half with losing quite little accuracy.

关键词： Support vector machine Large samples Sample selection Fuzzy c-means iterative algorithm

来源：评论

学校读者我要写书评

暂无评论

iterative algorithms based on weight splitting to solve Riccati matrix equation XDX - XC - BX plus A=0

引用

COMPUTATIONAL & APPLIED MATHEMATICS 2025年第1期44卷 1-13页

作者： Erfanifar, Raziyeh Hajarian, Masoud Shahid Beheshti Univ Fac Math Sci Dept Appl Math Tehran Iran

Algebraic Riccati matrix equations arise naturally in various situations and their role and application in systems, filtering, stochastic process, and control theory, in particular, have been well established in recent years. This study presents iterative algorithms to solve the algebraic Riccati matrix equation (ARME) R( X) = XDX - XC - BX + A = 0, based on the weight splitting (WS). We demonstrate that the iterative algorithms converge to non-positive and non-negative solutions of the ARME in special situations. To compare the newalgorithms with the previously existing algorithm, we present some numerical examples.

关键词： Algebraic Riccati matrix equations iterative algorithm Splitting iteration method

来源：评论

学校读者我要写书评

暂无评论

Estimating object and field phase through in-line intensity measurements using a twinning algorithm

引用

OPTICS COMMUNICATIONS 2025年 579卷

作者： Tillo, Dakshin Ivan, J. Solomon Indian Inst Space Sci & Technol Dept Phys Appl & Adapt Opt Lab Valiamala PO Thiruvananthapuram 695547 Kerala India

A method to estimate the object phase as well as the field phase through in-line multiple transverse plane intensity measurements is demonstrated, with no requirement fora reference field. A twinning mechanism to estimate the object phase while simultaneously estimating the phase of the light field (field phase) at any plane before and after the object is outlined. The twinning mechanism is illustrated through its implementation on a Gerchberg-Saxton (GS) type algorithm. A forward propagating GS-type algorithm is twinned with a backward propagating GS-type algorithm, through an intersection occurring at the object plane. The efficacy of the algorithm is demonstrated through comparison with a standard interferometric method on numerically generated intensities corresponding to random as well as dislocated phase objects. It is seen that for low noise conditions, the fidelity of the retrieved object phase, is comparable to that obtained through the interferometric method. Estimation of the object, as well as the field phase, is experimentally demonstrated through the twinned GS-type algorithm on both random as well as dislocated phase objects.

关键词： Object phase retrieval Non-Interferometric iterative algorithm In-line intensity measurements

来源：评论

学校读者我要写书评

暂无评论

I2HGNN: iterative Interpretable HyperGraph Neural Network for semi-supervised classification

引用

NEURAL NETWORKS 2025年 183卷 106929页

作者： Zhang, Hongwei Wang, Saizhuo Hu, Zixin Qi, Yuan Huang, Zengfeng Guo, Jian Fudan Univ Shanghai Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China Int Digital Econ Acad Shenzhen Peoples R China

Learning on hypergraphs has garnered significant attention recently due to their ability to effectively represent complex higher-order interactions among multiple entities compared to conventional graphs. Nevertheless, the majority of existing methods are direct extensions of graph neural networks, and they exhibit noteworthy limitations. Specifically, most of these approaches primarily rely on either the Laplacian matrix with information distortion or heuristic message passing techniques. The former tends to escalate algorithmic complexity, while the latter lacks a solid theoretical foundation. To address these limitations, we propose a novel hypergraph neural network named I2HGNN, which is grounded in an energy minimization function formulated for hypergraphs. Our analysis reveals that propagation layers align well with the message-passing paradigm in the context of hypergraphs. I2HGNN achieves a favorable trade-off between performance and interpretability. Furthermore, it effectively balances the significance of node features and hypergraph topology across a diverse range of datasets. We conducted extensive experiments on 15 datasets, and the results highlight the superior performance of I2HGNN in the task of hypergraph node classification across nearly all benchmarking datasets.

关键词： iterative algorithm Hypergraph Optimization Classification

来源：评论

学校读者我要写书评

暂无评论

Discriminative Anchor Learning for Efficient Multi-View Clustering

引用

IEEE TRANSACTIONS ON MULTIMEDIA 2025年 27卷 1386-1396页

作者： Qin, Yalan Pu, Nan Wu, Hanzhou Sebe, Nicu Shanghai Univ Sch Commun & Informat Engn Shanghai 200444 Peoples R China Univ Trento Dept Informat Engn & Comp Sci I-38100 Trento Italy

Multi-view clustering aims to study the complementary information across views and discover the underlying structure. For solving the relatively high computational cost for the existing approaches, works based on anchor have been presented recently. Even with acceptable clustering performance, these methods tend to map the original representation from multiple views into a fixed shared graph based on the original dataset. However, most studies ignore the discriminative property of the learned anchors, which ruin the representation capability of the built model. Moreover, the complementary information among anchors across views is neglected to be ensured by simply learning the shared anchor graph without considering the quality of view-specific anchors. In this paper, we propose discriminative anchor learning for multi-view clustering (DALMC) for handling the above issues. We learn discriminative view-specific feature representations according to the original dataset and build anchors from different views based on these representations, which increase the quality of the shared anchor graph. The discriminative feature learning and consensus anchor graph construction are integrated into a unified framework to improve each other for realizing the refinement. The optimal anchors from multiple views and the consensus anchor graph are learned with the orthogonal constraints. We give an iterative algorithm to deal with the formulated problem. Extensive experiments on different datasets show the effectiveness and efficiency of our method compared with other methods.

关键词： Representation learning Kernel Optimization Laplace equations Vectors Time complexity Matrix decomposition Feature extraction Data visualization Costs Multi-view clustering discriminative anchor learning shared anchor graph orthogonal constraints iterative algorithm effectiveness and efficiency

来源：评论

学校读者我要写书评

暂无评论

New test algorithms for system of absolute value equations

引用

ENGINEERING COMPUTATIONS 2025年第3期42卷 1225-1244页

作者： Wang, Yiwei Chu, Xue Wang, Haijun Yancheng Inst Technol Sch Automot Engn Yancheng Peoples R China China Univ Min & Technol Xuzhou Peoples R China

PurposeThis study presents an interval test numerical iteration method specifically designed to solve a system of absolute value equations (SAVE). The proposed method is valuable and efficient in solving a system of absolute value equations. Several numerical examples are used to demonstrate the accuracy and efficiency of the proposed ***/methodology/approachWe investigated the NP-hard system of absolute value equations (SAVE). A new algorithm is designed to compute the unknown solution of SAVE on a digital computer. The new test algorithm procedure consists of two parts. In the first part, a multidimensional interval centered on an approximate solution of the problem is guaranteed to contain an exact solution. In the second part, the new iterative process is guaranteed to converge to the exact solution of SAVE for all initial points in a given multidimensional interval. Numerical results illustrate that the new algorithm is effective and *** study provides empirical insights into how to solve a system of absolute value ***/valueThis paper fulfills an identified need to study absolute value equations.

关键词： System of absolute value equations Linear complementarity problem Existence of solutions iterative algorithm

来源：评论

学校读者我要写书评

暂无评论

Material parameter inversion using cross-entropy optimization based on Lamb Wave dispersion spectra

引用

NDT & E INTERNATIONAL 2025年 153卷

作者： Burger, Ruben Heilig, Markus Grosse, Christian U. Wu, Datong Munich Univ Appl Sci HM Lothstr 34 D-80335 Munich Bavaria Germany Tech Univ Munich Chair Nondestruct Testing Lichtenbergstr 2 D-85748 Garching Bavaria Germany Polytec GmbH Polytec Pl 1-7 D-76337 waldbronn Baden Wurttembe Germany

For plate-like structures, non-destructive testing using ultrasonic Lamb waves has found many applications as oscillation modes and dispersion properties depend directly on the elastic material parameters. However, the dispersive nature of these modes complicates the analysis in the temporal-spatial domain. Instead, the frequency-wavenumber domain has proven advantageous because it allows the separation of simultaneously excited modes. Inversion of dispersion information to determine material properties is still a matter of research. This paper proposes an algorithm based on the cross-entropy method which has been proven successful for many challenging optimization problems. This algorithm is used to determine the elastic properties (specific Lam & eacute;parameters) and the layer thickness of isotropic samples directly from the dispersion data of Lamb waves. This allows a full characterization of the elastic properties of the material in the case of known density. A cost function is developed that works directly on the raw dispersion data, requiring no thresholding or mode detection. The convergence of this method is shown to be wide with parameter search ranges of 300% or more. The properties of the cost function were investigated by parameter study. The algorithm is evaluated through finite element simulations of Lamb wave propagation in three different isotropic materials. The findings indicate an average error of less than 1%. Measurement data for four samples (two steel plates;fused silica and lithium niobate wafers) show a strong correlation with literature values for the elastic parameters. The estimated thicknesses align with the measured values within the 5 mu m and are in agreement with literature values.

关键词： Ultrasound Lamb waves iterative algorithm Material characterization Laser doppler vibrometry Optical excitation

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：