检索结果-内蒙古大学图书馆

IEEE OPEN JOURNAL OF signal processing 2025年 6卷 184-193页

作者： Schynol, Lukas Hemsing, Moritz Pesavento, Marius Tech Univ Darmstadt D-64283 Darmstadt Germany

We consider the minimization of $\ell _{1}$-regularized least-squares problems. A recent optimization approach uses successive convex approximations with an exact line search, which is highly competitive, especially i... 详细信息

We consider the minimization of $\ell _algorithms$-regularized least-squares problems. A recent optimization approach uses successive convex approximations with an exact line search, which is highly competitive, especially in sparse problem instances. This work proposes an acceleration scheme for the successive convex approximation technique with a negligible additional computational cost. We demonstrate this scheme by devising three related accelerated algorithms with provable convergence. The first introduces an additional descent step along the past optimization trajectory in the variable update that is inspired by Nesterov's accelerated gradient method and uses a closed-form step size. The second performs a simultaneous descent step along both the best response and the past trajectory, thereby finding a two-dimensional step size, also in closed-form. The third algorithm combines the previous two approaches. All algorithms are hyperparameter-free. Empirical results confirm that the acceleration approaches improve the convergence rate compared to benchmark algorithms, and that they retain the benefits of successive convex approximation also in non-sparse instances.

关键词： signal processing algorithms Trajectory Vectors Optimization Approximation algorithms Gradient methods Convergence Computational efficiency signal processing Sparse matrices Compressed sensing FISTA LASSO L1-regularized least squares Nesterov's accelerated gradient method optimization successive convex approximation

来源：评论

学校读者我要写书评

暂无评论

HF2TNet: A Hierarchical Fusion Two-Stage Training Network for Infrared and Visible Image Fusion

引用

IEEE signal processing LETTERS 2024年 31卷 3164-3168页

作者： Lv, Ting Ji, Chuanming Jiang, Hong Liu, Yu Southwest Univ Sci & Technol Sch Informat Engn Mianyang 621010 Sichuan Peoples R China Univ Technol Sydney Fac Engn & Informat Technol Sydney 2007 Australia Hefei Univ Technol Dept Biomed Engn Hefei 230009 Peoples R China

In the field of infrared and visible image fusion, current algorithms often focus on complex feature extraction and sophisticated fusion mechanisms, ignoring the issues of information redundancy and feature imbalance. These limit effective information aggregation. To address these issues, this paper proposes a hierarchical fusion strategy with a two-stage training network, abbreviated as HF2TNet, which achieves effective information aggregation in a staged manner. In the initial training stage, a three-stream encoder-decoder architecture is proposed, seamlessly integrating CNN and transformer modules. This architecture extracts both global and local features from visible and infrared images, capturing their shared attributes before the fusion process. Moreover, a multi-shared attention module (MSAM) is proposed to profoundly reconstruct and augment the visible and infrared features, ensuring the preservation and enhancement of details across modalities. In the subsequent stage, HF2TNet utilizes the pre-integrated features as query inputs for the dual MSAMs. These modules interact with the previously reconstructed infrared and visible features to enhance complementary information and ensure a balanced feature fusion. Experimental results indicate HF2TNet's superior performance on standard datasets like MSRS and TNO, especially in complex scenes, demonstrating its potential in multimodal image fusion.

关键词： Feature extraction Training Image reconstruction Image fusion Transformers Decoding Convolution Vectors signal processing algorithms Sensor phenomena and characterization CNN deep learning image fusion transformer

来源：评论

学校读者我要写书评

暂无评论

Cross-Domain Underwater Sound Source Localization Algorithm Based on Binaural Matrix and Mutual Information Constraint Loss

引用

IEEE JOURNAL OF OCEANIC ENGINEERING 2025年第2期50卷 1419-1428页

作者： Li, Ruwei Li, Man Li, Qiuyan Li, Jiangqiao Beijing Univ Technol Sch Informat Sci & Technol Beijing 100124 Peoples R China Chinese Soc Naval Architects & Marine Engineers Beijing 100094 Peoples R China

The accuracy of existing underwater sound source localization algorithms is unsatisfactory, and most of them cannot achieve cross-domain localization. To solve these problems, a cross-domain underwater sound source localization algorithm based on a binaural matrix and mutual information constraint loss is proposed. In this algorithm, a new binaural matrix feature is first extracted based on binaural cues, which is less susceptible to environmental interference and can obtain reliable direction information from received signals. Then, a constrained loss based on mutual information is designed to constrain the proposed neural network to accurately learn the shared representations of different domains. This ensures that the high-dimensional representations used for localization have more explicit orientation directionality. Finally, a cross-domain underwater sound source localization network is constructed to achieve accurate cross-domain localization. Experimental results indicate that the algorithm proposed in this study has a higher localization accuracy than comparative algorithms, both in the same domain and in different domains.

关键词： Location awareness Direction-of-arrival estimation Feature extraction Estimation Accuracy signal processing algorithms Hydrophones Training Mutual information Deep learning Cross-domain deep learning mutual information underwater sound source localization

来源：评论

学校读者我要写书评

暂无评论

Reconstructed Variational Bayesian Kalman Filter Under Heavy-Tailed and Skewed Noises

引用

IEEE signal processing LETTERS 2024年 31卷 2405-2409页

作者： Wang, Ke Wu, Panlong Zhao, Baochen Kong, Lingqi He, Shan Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Peoples R China

In this letter, a new reconstructed variational Bayesian (RVB) Kalman filter is proposed for handling heavy-tailed and skewed noises. The Student's t distribution is used to model predicted state, and the skewed Gaussian-exponential mixture distribution is used to model measurement noises. In order to more accurately deal with process noises, we model the predicted covariance based on the inverse Wishart distribution. Subsequently, a new RVB framework is proposed to separate the iterations of process and measurement noise related parameters, which achieves decoupling of hidden variables. Under the new RVB framework, the RVB-ST-SGEM-KF algorithm is derived. The superiority of the proposed RVB framework and algorithm are validated through a maneuvering target tracking scenario.

关键词： Noise signal processing algorithms Noise measurement Heavily-tailed distribution Predictive models Adaptation models Target tracking Heavy-tailed and skewed noises Kalman Filter reconstructed variational Bayesian skewed Gaussian-exponential mixture distribution student's t distribution

来源：评论

学校读者我要写书评

暂无评论

High-Speed Energy-Efficient Fixed-Point Signed Multipliers for FPGA-Based DSP Applications

引用

IEEE EMBEDDED SYSTEMS LETTERS 2024年第4期16卷 417-420页

作者： Nagar, Mitul Sudhirkumar Mathuriya, Aditya Patel, Sohan H. Engineer, Pinalkumar J. Sardar Vallabhbhai Natl Inst Technol Dept Elect Engn Surat 395007 India

Among various platforms for computer vision algorithms, FPGA has gained popularity as a low-power solution. These algorithms involve convolution operation which are extensively performed using signed multipliers. Hence, this work proposes high-speed and energy-efficient signed fixed-point multipliers for digital signal processing (DSP) applications. This work focuses on reducing the combinational path delay (CPD) using LUT-based Booth radix-4 partial product (PP) generation with Bewick's sign extension and Dadda-based concurrent PP reduction with carry save adder (CSA) for Xilinx (now AMD) FPGA. The proposed design eliminates the requirement of a long carry chain for PP reduction. The proposed multiplier reduces CPD by 3%, 4%, and 16% compared to the state-of-the-art (SoA) multiplier for 8x8 , 16x16 , and 32x32 sizes, respectively. We have also analyzed our proposed 32x32 multiplier by pipelining, which offers CPD and EDP reduction by 12.28% and 19.47% at the cost of a 3% and 80% increase in LUTs and flip-flops, respectively, compared to the combinatorial multiplier.

关键词： Table lookup Field programmable gate arrays Adders Vegetation Standards signal processing algorithms Resource management Booth radix-4 digital signal processing (DSP) energy-efficient field-programmable gate arrays signed fixedpoint multiplier

来源：评论

学校读者我要写书评

暂无评论

Robust Elliptic Positioning via Sparse Regularization and ADMM for a Distributed MIMO Radar in the Presence of Outliers

引用

IEEE signal processing LETTERS 2024年 31卷 2110-2114页

作者： Dang, Xudong Liu, Hongwei Yan, Junkun Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China

Large-magnitude errors, known as outliers, have a significant impact on the accuracy of elliptic positioning (EP) for a distributed MIMO radar. Most of the existing EP techniques are developed in the least squares sense, susceptible to outliers. In this letter, we propose to leverage a sparse argument to model the outliers and decompose the measurement noise into two components, namely, inliers and outliers. By doing so, the robust EP is formulated as a & ell;(0) -norm minimization problem with non-convex constraints. By relaxing the objective & ell;(0) -norm with the convex & ell;(1) -norm, an alternating direction method of multipliers based algorithm is derived to efficiently solve the relaxed optimization problem. Numerical results illustrate the improved performance of the proposed method compared to the existing methods.

关键词： Noise Pollution measurement Noise measurement MIMO radar Optimization Convex functions signal processing algorithms Alternating direction method of multipliers (ADMM) distributed MIMO radar elliptic positioning outliers sparse regularization

来源：评论

学校读者我要写书评

暂无评论

Ball Bearing Fault Diagnosis Based on Hybrid Adversarial Learning

引用

IEEE ACCESS 2025年 13卷 74163-74174页

作者： Bai, Xiaofeng Ul Haq, Qazi Mazhar Khan, Aftab Alam Li, Chaohui Alsharif, Hind Anwar, Muhammad Shahid Li, Chengwei Harbin Inst Technol Sch Instrumentat Sci & Engn Harbin 150001 Peoples R China Yuan Ze Univ Dept Comp Sci & Engn Taoyuan 320315 Taiwan Umm Al Qura Univ Coll Comp Comp Sci & Artificial Intelligence Dept Mecca 21955 Saudi Arabia King Fahd Univ Petr & Minerals IRC Finance & Digital Econ Dhahran 31261 Saudi Arabia

Ball bearings are prone to faults in their inner and outer rings and rolling elements. Timely detection of these faults is crucial, especially when adversarial perturbations are present, as deep learning-based fault diagnosis models may misclassify these faults. To address this issue, this study proposes a hybrid adversarial learning method that combines convolutional neural networks with a generative adversarial network framework. In this method, the generator introduces perturbations and adaptively adjusts them based on their magnitude and gradient information. The discriminator was used to verify the effectiveness of adversarial perturbations. The goal of this hybrid adversarial learning method is to improve the fault recognition accuracy of a model when subjected to perturbation attacks. The experimental results show that under adversarial perturbation attacks, the proposed method outperforms other deep learning models and defence methods, demonstrating the effectiveness of this approach.

关键词： Ball bearings convolutional neural networks continuous wavelet transforms deep learning data visualization fault diagnosis generative adversarial networks machine learning robustness signal processing algorithms Ball bearings convolutional neural networks continuous wavelet transforms deep learning data visualization fault diagnosis generative adversarial networks machine learning robustness signal processing algorithms

来源：评论

学校读者我要写书评

暂无评论

Interpretable Transfer Unrolling Network for Intelligent Cross-Domain Condition Monitoring of Rotating Machinery

引用

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Chen, Kai Ding, Chuancang Wang, Baoxiang Wang, Jun Huang, Weiguo Zhu, Zhongkui Soochow Univ Sch Rail Transportat Suzhou 215131 Jiangsu Peoples R China Soochow Univ Intelligent Urban Rail Engn Res Ctr Jiangsu Prov Suzhou 215131 Jiangsu Peoples R China Suzhou Univ Sci & Technol Sch Mech Engn Suzhou 215009 Jiangsu Peoples R China

In the domain of mechanical condition monitoring, deep neural networks (DNNs) have proven their excellent performance. Meanwhile, some interpretable networks have been proposed to improve interpretability and have shown promising results in interpretable intelligent condition monitoring. However, when the working condition becomes variable and the tasks of transfer learning need to be carried out, most existing interpretable networks no longer work effectively. Therefore, this article proposes an interpretable transfer-unrolling network (ITUN) to fulfill interpretable transfer tasks in the domain of condition monitoring. In ITUN, to realize the interpretability of feature extraction, a multilayer sparse coding model is established, and its iterative solving algorithm is deduced. Meanwhile, the equivalent form of iterative solving algorithm is attained by unrolling fast iterative soft thresholding algorithm (FISTA), which serves as the feature extractor of ITUN. Moreover, to facilitate cross-domain fault diagnosis, a double-dictionary is embedded into the sparse coding model to extract the shared and private features from source and target domains, respectively, and the loss function of ITUN for interpretable transfer diagnosis is designed. The effectiveness of the proposed ITUN is verified through two bearing datasets. In each transfer task of two bearing datasets, the diagnosis accuracy of the proposed ITUN is above 98% and its robustness is obviously superior to other comparison methods, which prove that the proposed ITUN can accomplish the task of interpretable transfer learning outstandingly in the domain of mechanical condition monitoring.

关键词： Feature extraction Fault diagnosis Condition monitoring signal processing algorithms Transfer learning Encoding Artificial neural networks Ad hoc networks Accuracy Training Algorithm unrolling double-dictionary sparse coding model intelligent fault diagnosis interpretable neural network transfer learning

来源：评论

学校读者我要写书评

暂无评论

Adaptive Pattern-Coupled Sparse Bayesian Learning for Channel Estimation in OTFS Systems

引用

IEEE signal processing LETTERS 2024年 31卷 2895-2899页

作者： Chen, Zhuo Niu, Xiaoming Ding, Jian Wu, Hong Liu, Zhiyang Nankai Univ Coll Elect Informat & Opt Engn Tianjin 300350 Peoples R China Nankai Univ Tianjin Key Lab Optoelect Sensor & Sensing Network Tianjin 300350 Peoples R China China Res Inst Radiowave Propagat Natl Key Lab Electromagnet Environm Qingdao 266107 Peoples R China Nankai Univ Minist Educ Engn Res Ctr Thin Film Optoelect Technol Tianjin 300350 Peoples R China

The orthogonal time frequency space (OTFS) has emerged as a promising modulation waveform for high-mobility wireless communications owing to its robust advantages of resisting Doppler effects. However, due to the limit of the frame duration, the fractional Doppler shift appears, which is a challenge for channel estimation in OTFS systems. In this letter, we formulate the channel estimation problem as a block sparse signal recovery issue and propose an adaptive pattern-coupled sparse Bayesian learning (APCSBL) method. To be specific, we introduce a pattern-coupled hierarchical Gaussian prior model to characterize the dependencies among adjacent channel coefficients. On this basis, an adaptive hyperparameter strategy is presented, in which we appropriately utilize various coupling parameters further to characterize the strength of the correlation between adjacent elements. Then we exploit the expectation maximization (EM) algorithm to update the hidden variables and the channel vector. Simulation results demonstrate that the proposed algorithm outperforms existing methods and works for various environments.

关键词： Channel estimation Symbols Bayes methods Delays signal processing algorithms Indexes Doppler shift Vectors Estimation Adaptive systems Adaptive pattern-coupled sparse Bayesian learning block sparse signal recovery channel estimation OTFS

来源：评论

学校读者我要写书评

暂无评论

MIMO-Based Indoor Localisation With Hybrid Neural Networks: Leveraging Synthetic Images From Tidy Data for Enhanced Deep Learning

引用

IEEE JOURNAL OF SELECTED TOPICS IN signal processing 2025年第3期19卷 559-571页

作者： Castillo-Cara, Manuel Martinez-Gomez, Jesus Ballesteros-Jerez, Javier Garcia-Varea, Ismael Garcia-Castro, Raul Orozco-Barbosa, Luis Univ Nacl Educ Distancia UNED Madrid 28040 Spain Univ Castilla La Mancha Albacete 02071 Spain Univ Politecn Madrid Madrid 28040 Spain

Indoor localization determines an object's position within enclosed spaces, with applications in navigation, asset tracking, robotics, and context-aware computing. Technologies range from WiFi and Bluetooth to advanced systems like Massive Multiple Input-Multiple Output (MIMO). MIMO, initially designed to enhance wireless communication, is now key in indoor positioning due to its spatial diversity and multipath propagation. This study integrates MIMO-based indoor localization with Hybrid Neural Networks (HyNN), converting structured datasets into synthetic images using TINTO. This research marks the first application of HyNNs using synthetic images for MIMO-based indoor localization. Our key contributions include: (i) adapting TINTO for regression problems;(ii) using synthetic images as input data for our model;(iii) designing a novel HyNN with a Convolutional Neural Network branch for synthetic images and an MultiLayer Percetron branch for tidy data;and (iv) demonstrating improved results and metrics compared to prior literature. These advancements highlight the potential of HyNNs in enhancing the accuracy and efficiency of indoor localization systems.

关键词： Location awareness Accuracy Neural networks Measurement Deep learning Complexity theory Antennas Antenna measurements Base stations signal processing algorithms Massive MIMO deep learning hybrid neural network synthetic images positioning indoor localisation

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：