检索结果-内蒙古大学图书馆

Coarse to Fine Two-Stage Approach to Robust Tensor Completion of Visual Data

IEEE TRANSACTIONS ON CYBERNETICS 2024年第1期54卷 136-149页

作者： He, Yicong Atia, George K. Univ Cent Florida Dept Elect & Comp Engn Orlando FL 32816 USA Univ Cent Florida Dept Comp Sci Orlando FL 32816 USA

Tensor completion is the problem of estimating the missing values of high-order data from partially observed entries. Data corruption due to prevailing outliers poses major challenges to traditional tensor completion algorithms, which catalyzed the development of robust algorithms that alleviate the effect of outliers. However, existing robust methods largely presume that the corruption is sparse, which may not hold in practice. In this article, we develop a two-stage robust tensor completion approach to deal with tensor completion of visual data with a large amount of gross corruption. A novel coarse-to-fine framework is proposed which uses a global coarse completion result to guide a local patch refinement process. To efficiently mitigate the effect of a large number of outliers on tensor recovery, we develop a new M-estimator-based robust tensor ring recovery method which can adaptively identify the outliers and alleviate their negative effect in the optimization. The experimental results demonstrate the superior performance of the proposed approach over state-of-the-art robust algorithms for tensor completion.

关键词： Tensors Matrix decomposition Visualization Task analysis signal processing algorithms Sparse matrices Optimization Half-quadratic (HQ) robust method tensor completion

来源：评论

学校读者我要写书评

暂无评论

Bias-Compensated Normalized Iterative Wiener Filter Algorithm With Noisy Input

引用

IEEE signal processing LETTERS 2025年 32卷 1445-1449页

作者： Yuan, Hai Lu, Lu Zhu, Guangya Chen, Badong Sichuan Univ Coll Elect & Informat Engn Chengdu 610065 Peoples R China Sichuan Univ Coll Elect Engn Chengdu 610065 Peoples R China Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Peoples R China

The iterative Wiener filter (IWF) algorithm can achieve a fast convergence rate. However, its performance may degrade when it encounters noisy input scenarios. To tackle this problem, a novel IWF algorithm incorporating bias-compensation (BC-IWF) is proposed, which can enhance the performance of the algorithm by estimating the input noise variance. The BC-IWF algorithm optimizes the step size for each iteration and updates along the direction of the gradient. To further reduce the steady-state error, a normalized IWF by making use of the bias-compensation scheme (BC-NIWF) algorithm is proposed. Moreover, the steady-state performance of the BC-NIWF algorithm is analyzed. Simulation results demonstrate the validity of the theoretical analysis and the BC-NIWF algorithm achieves improved misadjustment compared with the state-of-the-art algorithms.

关键词： signal processing algorithms Approximation algorithms Noise measurement Steady-state Vectors Wiener filters Convergence Artificial intelligence Training Simulation Bias compensation iterative Wiener filter noisy input model steady-state analysis

来源：评论

学校读者我要写书评

暂无评论

Extreme Point Pursuit-Part II: Further Error Bound Analysis and Applications

引用

IEEE TRANSACTIONS ON signal processing 2024年 72卷 4557-4572页

作者： Liu, Junbin Liu, Ya Ma, Wing-Kin Shao, Mingjie So, Anthony Man-Cho Chinese Univ Hong Kong Dept Elect Engn Hong Kong 999077 Peoples R China Shandong Univ Sch Informat Sci & Engn Qingdao 266510 Peoples R China Chinese Univ Hong Kong Dept Syst Engn & Engn Management Hong Kong 999077 Peoples R China

In the first part of this study, a convex-constrained penalized formulation was studied for a class of constant modulus (CM) problems. In particular, the error bound techniques were shown to play a vital role in providing exact penalization results. In this second part of the study, we continue our error bound analysis for the cases of partial permutation matrices, size-constrained assignment matrices and non-negative semi-orthogonal matrices. We develop new error bounds and penalized formulations for these three cases, and the new formulations possess good structures for building computationally efficient algorithms. Moreover, we provide numerical results to demonstrate our framework in a variety of applications such as the densest k-subgraph problem, graph matching, size-constrained clustering, non-negative orthogonal matrix factorization and sparse fair principal component analysis.

关键词： Gradient methods Complexity theory Optimization Computational efficiency signal processing algorithms Minimization Principal component analysis Constant modulus optimization non-convex optimization error bound densest subgraph problem PCA graph matching clustering ONMF

来源：评论

学校读者我要写书评

暂无评论

Samplet Basis Pursuit: Multiresolution Scattered Data Approximation With Sparsity Constraints

引用

IEEE TRANSACTIONS ON signal processing 2024年 72卷 1813-1823页

作者： Baroli, Davide Harbrecht, Helmut Multerer, Michael Univ Svizzera Italiana Fac Informat CH-6962 Lugano Switzerland Univ Basel Dept Math & Comp Sci CH-4051 Basel Switzerland

We consider scattered data approximation in samplet coordinates with l(1)-regularization. The application of an l(1)-regularization term enforces sparsity of the coefficients with respect to the samplet basis. Samplets are wavelet-type signed measures, which are tailored to scattered data. Therefore, samplets enable the use of well-established multiresolution techniques on general scattered data sets. They provide similar properties as wavelets in terms of localization, multiresolution analysis, and data compression. By using the Riesz isometry, we embed samplets into reproducing kernel Hilbert spaces and discuss the properties of the resulting functions. We argue that the class of signals that are sparse with respect to the embedded samplet basis is considerably larger than the class of signals that are sparse with respect to the basis of kernel translates. Vice versa, every signal that is a linear combination of only a few kernel translates is sparse in samplet coordinates. We propose the rapid solution of the problem under consideration by combining soft-shrinkage with the semi-smooth Newton method. Leveraging on the sparse representation of kernel matrices in samplet coordinates, this approach converges faster than the fast iterative shrinkage thresholding algorithm and is feasible for large-scale data. Numerical benchmarks are presented and demonstrate the superiority of the multiresolution approach over the single-scale approach. As large-scale applications, the surface reconstruction from scattered data and the reconstruction of scattered temperature data using a dictionary of multiple kernels are considered.

关键词： Kernel signal resolution Sparse matrices Multiresolution analysis Hilbert space signal processing algorithms Dictionaries Basis pursuit multiresolution analysis samplets scattered data

来源：评论

学校读者我要写书评

暂无评论

Sparse Array Design for Optimum Beamforming Using Deep Learning

引用

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2024年第1期60卷 133-144页

作者： Hamza, Syed A. Amin, Moeness G. Widener Univ Sch Engn Chester PA 19013 USA Villanova Univ Coll Engn Ctr Adv Commun CAC Villanova PA 19085 USA

This article considers sparse array design for receive beamforming achieving maximum signal-to-interference plus noise ratio (MaxSINR). We develop a design approach based on supervised neural network where class labels are generated using an efficient sparse beamformer spectral analysis (SBSA) approach. SBSA uses explicit information of the unknown narrowband interference environment for training the network and bears close performance to training using exhaustive search by enumerations which is computationally prohibitive for large arrays. The employed deep neural network (DNN) effectively approximates the unknown mapping from the input received data spatial correlations to the output of sparse configuration with effective interference mitigation capability. The problem is posed as a classification problem where the sparse array configuration achieving MaxSINR is one-hot encoded, and indicated by the output layer of DNN. We evaluate the performance of the DNN in terms of the sparse array classification accuracy as well as in terms of the ability of the classified sparse array to mitigate interference and maximize signal power. It is shown that the DNN effectively learns the optimal sparse configuration which has desirable SINR characteristics, hence paving the way for efficient real-time implementation.

关键词： Sensor arrays Interference Sensors Array signal processing signal to noise ratio Training signal processing algorithms Deep neural network (DNN) maximum signal-to-interference plus noise ratio (MaxSINR) sparse arrays

来源：评论

学校读者我要写书评

暂无评论

Artificial Intelligence and Machine Learning Tools for Improving Early Warning Systems of Volcanic Eruptions: The Case of Stromboli

引用

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2024年第12期46卷 7973-7982页

作者： Longo, Roberto Lacanna, Giorgio Innocenti, Lorenzo Ripepe, Maurizio IMEC B-3001 Leuven Belgium Le Mans Univ Lab Acoust Univ Mans LAUM Inst Acoust Grad Sch IA GS CNRSUMR6613 F-72085 Le Mans France Univ Firenze Dipartimento Sci Terra I-50121 Florence Italy Ist Nazl Geofis & Vulcanol Osservatorio Etneo I-50121 Catania Italy

Explosive volcanic blasts can occur suddenly and without any clear precursors. Many volcanoes have erupted in the last years with no evident change in the eruptive parameters and with dramatic consequences for the population living nearby the volcano and the tourists visiting the active areas. In recent years, a big effort has been made to develop Early Warning systems to issue timely alerts to the population. At Stromboli volcano, the development of sensitive instruments to measure the deformation (tilt) of the ground has revealed that the volcano edifice is inflating tens of minutes before the explosion following a recurrent exponential ramp-like pattern. This scale-invariant of ground deformation has allowed the development of a quasi-deterministic Early Warning system which is operative since 2019. In this article we show how Artificial Intelligence and Machine Learning can be successfully applied to improve the efficiency and the sensitivity of Early Warning systems, provided the availability of a comprehensive experimental data set on past explosive events. The approach presented here for the Stromboli case demonstrates promising results also in forecasting the intensity of explosive events, offering valuable insights and new perspectives into the potential risks associated with volcanic activities.

关键词： Deformation Explosives Volcanoes Alarm systems signal processing algorithms Pattern recognition Monitoring Early warning gate recurrent units logistic regression pattern recognition random forest classifier Stromboli volcano time series classification

来源：评论

学校读者我要写书评

暂无评论

ISAC-NET: Model-Driven Deep Learning for Integrated Passive Sensing and Communication

引用

IEEE TRANSACTIONS ON COMMUNICATIONS 2024年第8期72卷 4692-4707页

作者： Jiang, Wangjun Ma, Dingyou Wei, Zhiqing Feng, Zhiyong Zhang, Ping Peng, Jinlin Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Key Lab Universal Wireless Commun Minist Educ Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Natl Innovat Inst Def Technol Artificial Intelligence Res Ctr Beijing 102206 Peoples R China

Wireless communication with the enormous demands of sensing ability have given rise to the integrated passive sensing and communication (IPSAC) technology. The main challenge of IPSAC is how to achieve high sensing and communication performance by integrating the passive sensing and communication demodulation. In this paper, we propose an integrated sensing and communication (ISAC) signal processing optimization scheme by jointly processing the pilot and data signals. To solve the optimization problem, we propose an ISAC signal processing algorithm based on iterative optimization, which alternates the passive sensing and channel reconstruction to realize target sensing. However, the hyper-parameter configuration of the iterative optimization algorithm influences the performance of target detection and communication demodulation. Recognizing this fact, we propose a model-driven ISAC network (ISAC-NET) that adopts the block-by-block signal processing method to improve the communication and sensing performance. The proposed ISAC-NET obtains suitable hyper-parameters by deep learning to guarantee the performance and convergence of communication and sensing signal processing. From the simulation results, ISAC-NET obtains better communication performance than the traditional signal demodulation algorithm, which is close to OAMP-Net2. Compared to the 2D-DFT algorithm, ISAC-NET demonstrates significantly enhanced sensing performance. In summary, ISAC-NET is a promising tool for the IPSAC systems.

关键词： Sensors signal processing algorithms Receivers signal processing Optimization Demodulation Transmitters Deep learning (DL) integrated sensing and communication (ISAC) passive sensing

来源：评论

学校读者我要写书评

暂无评论

Variance Reduced Random Relaxed Projection Method for Constrained Finite-Sum Minimization Problems

引用

IEEE TRANSACTIONS ON signal processing 2024年 72卷 2188-2203页

作者： Yang, Zhichun Xia, Fu-quan Tu, Kai Yue, Man-Chung Sichuan Normal Univ Sch Math Sci Chengdu 610068 Peoples R China Shenzhen Univ Sch Math Sci Shenzhen 518060 Peoples R China Univ Hong Kong Musketeers Fdn Inst Data Sci Hong Kong Peoples R China Univ Hong Kong Dept Ind & Mfg Syst Engn Hong Kong Peoples R China

For many applications in signal processing and machine learning, we are tasked with minimizing a large sum of convex functions subject to a large number of convex constraints. In this paper, we devise a new random projection method (RPM) to efficiently solve this problem. Compared with existing RPMs, our proposed algorithm features two useful algorithmic ideas. First, at each iteration, instead of projecting onto the set defined by one of the constraints, our algorithm only requires projecting onto a half-space approximation of the set, which significantly reduces the computational cost as it admits a closed-form formula. Second, to exploit the structure that the objective is a sum, variance reduction is incorporated into our algorithm to further improve the performance. As theoretical contributions, under a novel error bound condition and other standard assumptions, we prove that the proposed RPM converges to an optimal solution and that both optimality and feasibility gaps vanish at a sublinear rate. In particular, via a new analysis framework, we show that our RPM attains a faster convergence rate in optimality gap than existing RPMs when the objective function has a Lipschitz continuous gradient, capitalizing the benefit of the variance reduction. We also provide sufficient conditions for the error bound condition to hold. Experiments on a beamforming problem and a robust classification problem are presented to demonstrate the superiority of our RPM over existing ones.

关键词： signal processing algorithms Convergence Approximation algorithms Optimization Convex functions Indexes Standards Constrained optimization finite-sum minimization random projection method relaxed projection variance reduction convergence rate analysis

来源：评论

学校读者我要写书评

暂无评论

Percentile Optimization in Wireless Networks-Part II: Beamforming for Cell-Edge Throughput Maximization

引用

IEEE TRANSACTIONS ON signal processing 2024年 72卷 1533-1547页

作者： Khan, Ahmad Ali Adve, Raviraj S. Ericsson R&D Ottawa ON K2K 2V6 Canada Univ Toronto Edward S Rogers Sr Dept Elect & Comp Engn Toronto ON M5S 3G4 Canada

Part I of this two-part paper focused on the formulation of percentile problems, complexity analysis, and development of power control algorithms via the quadratic fractional transform (QFT) and logarithmic fractional transform (LFT) for sum-least-q(th)-percentile (SLqP) rate maximization problems. In this second part, we first tackle the significantly more challenging problems of optimizing SLqP rate via beamforming in a multiuser, multiple-input multiple-output (MU-MIMO) network to maximize cell-edge throughput. To this end, we first propose an adaptation of the QFT algorithm presented in Part I that enables optimization of the complex-valued multidimensional beamforming weights for the SLqP rate utility function. We also introduce a new class of problems which we term as sum-greatest-q(th)-percentile weighted mean squared error (SGqP-WMSE) minimization. We show that this class subsumes the well-known sum-weighted mean squared error (WMMSE) minimization and max-WMSE minimization problems. We demonstrate an equivalence between this class of problems and the SLqP rate maximization problems, and show that this correspondence can be exploited to obtain stationary-point solutions for the aforementioned beamforming problem. Next, we develop extensions for the QFT and LFT algorithms from Part I to optimize ergodic long-term average or ergodic SLqP utility. Finally, we also consider related problems which can be solved using the proposed techniques, including hybrid utility functions targeting optimization at specific subsets of users within cellular networks.

关键词： Optimization Array signal processing Throughput Minimax techniques Power control Minimization signal processing algorithms Percentile optimization cell-edge beamforming cyclic maximization WMMSE

来源：评论

学校读者我要写书评

暂无评论

Decentralized Generalized Approximate Message-Passing for Tree-Structured Networks

引用

IEEE TRANSACTIONS ON INFORMATION THEORY 2024年第10期70卷 7385-7409页

作者： Takeuchi, Keigo Toyohashi Univ Technol Dept Elect & Elect Informat Engn Toyohashi 4418580 Japan

Decentralized generalized approximate message-passing (GAMP) is proposed for compressed sensing from distributed generalized linear measurements in a tree-structured network. Consensus propagation is used to realize average consensus required in GAMP via local communications between adjacent nodes. Decentralized GAMP is applicable to all tree-structured networks that do not necessarily have central nodes connected to all other nodes. State evolution is used to analyze the asymptotic dynamics of decentralized GAMP for zero-mean independent and identically distributed Gaussian sensing matrices. The state evolution recursion for decentralized GAMP is proved to have the same fixed points as that for centralized GAMP when homogeneous measurements with an identical dimension in all nodes are considered. Furthermore, existing long-memory proof strategy is used to prove that the state evolution recursion for decentralized GAMP with the Bayes-optimal denoisers converges to a fixed point. These results imply that the state evolution recursion for decentralized GAMP with the Bayes-optimal denoisers converges to the Bayes-optimal fixed point for the homogeneous measurements when the fixed point is unique. Numerical results for decentralized GAMP are presented in the cases of linear measurements and clipping. As examples of tree-structured networks, a one-dimensional chain and a tree with no central nodes are considered.

关键词： Bayes methods Distributed algorithms Ad hoc networks Convergence Sensors Symmetric matrices signal processing algorithms Compressed sensing generalized approximate message-passing decentralized algorithms consensus propagation tree-structured networks state evolution

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：