检索结果-内蒙古大学图书馆

Aperture Synthesis With Digital Array Radars and Covariant Change of Wavenumber Variables

IEEE JOURNAL OF SELECTED TOPICS IN signal processing 2023年第2期17卷 370-388页

作者： Barnes, Christopher F. Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA

Attributes of digital array radars are leveraged in enhancements of wideband frequency-wavenumber (omega-k) methods to achieve 1) single-pulse, short-range imaging from a stationary array;2) single-pulse, all-range, high-density, digital beamforming-on-receive froma stationary array;3) multiple-pulse aperture synthesis for short-range imaging with sensor movement;and 4) multiple-pulse inverse aperture synthesis for long-range imaging with tracked object movement. Modifications to conventional omega-k algorithms used in synthetic aperture radar are introduced to accommodate antenna element level data, real array element spacing, large scene size and small array size (compared to scene size). Large scene sizewith k-space processing is handled by a novel Huygens-Fresnel transfer function that does not fully rely on zero-padding to resolve array and scene size mismatch. Aperture synthesis with generalized pulse-to-pulse sensor-step operations is supported. Connections between omega-k wavenumber migration and a covariant change of variables transform associated with Dirac's spectralmodels of free and scattered electromagnetic fields are established.

关键词： Apertures Radar imaging Imaging Antenna arrays Wideband Time-frequency analysis signal processing algorithms Covariance digital array radars digital beamforming digital sensor arrays k-space beamforming k-space imaging progressive resolution radar signal processing Solopulse synthetic apertures

来源：评论

学校读者我要写书评

暂无评论

Explainable Machine Learning: The importance of a system-centric perspective [Lecture Notes]

引用

IEEE signal processing MAGAZINE 2023年第2期40卷 165-172页

作者： Narwaria, Manish Indian Inst Technol Jodhpur Dept Elect Engn Jodhpur Rajasthan India

The landscape in the context of several signal processing applications and even education [1] appears to be significantly affected by the emergence of machine learning (ML) and particularly deep learning (DL). The main reason for this is the ability of DL to model complex and unknown relationships between signals and the tasks of interest. In particular, supervised DL algorithms have been fairly successful at recognizing perceptually or semantically useful signal information in different applications (e.g., identifying objects or regions of interest from image/video signals or recognizing spoken words from speech signals, such as in speech recognition). In all of these tasks, the training process uses labeled data to learn a mapping function (typically implicitly) from signals to the desired information (class label or target label). The trained DL model is then expected to correctly recognize/classify relevant information in a given test signal. Therefore, a DL-based framework is, in general, very appealing since the features and characteristics of the required mapping are learned almost exclusively from the data without resorting to explicit model/system development.

关键词： Education Deep learning Image recognition Machine learning algorithms signal processing algorithms Speech recognition signal processing

来源：评论

学校读者我要写书评

暂无评论

Autism Detection of MRI Brain Images Using Hybrid Deep CNN With DM-Resnet Classifier

引用

IEEE ACCESS 2023年 11卷 117741-117751页

作者： Jain, Sweta Tripathy, Hrudaya Kumar Mallik, Saurav Qin, Hong Shaalan, Yara Shaalan, Khaled Deemed Univ Kalinga Inst Ind Technol Sch Comp Engn Bhubaneswar 751024 Orissa India Shri Ramdeobaba Coll Engn & Management Dept Comp Sci & Engn Nagpur 440013 India Harvard T H Chan Sch Publ Hlth Dept Environm Hlth Boston MA 02115 USA Univ Tennessee Dept Comp Sci & Engn Chattanooga TN 37403 USA Misr Univ Sci & Technol Fac Med 6th October City 16878 Egypt British Univ Dubai Fac Engn & IT Dubai Int Acad City Dubai U Arab Emirates

The neurodevelopmental Autism Spectrum Disorder (ASD) causes problems in social communication. Earlier diagnosis of ASD from brain image is necessary for reducing the effect of disorder. In this paper, deep Convolutional Neural Network (CNN) with Dwarf Mongoose optimized Residual Network (DM-ResNet) is proposed for the classification of autism disorder from Magnetic Resonance Imaging (MRI) brain images. Initially, the input brain images are preprocessed to remove the non-brain tissues. The preprocessed images are segmented with hybrid Fuzzy C Means (FCM) and Gaussian Mixture Model (GMM) which partition the image into sub groups to make it easier for classification by reducing the complexity. FCM-GMM segments the volume into predefined cortical and sub cortical regions. After segmentation, the features are extracted with Visual Geometry Group (VGG)-16 networks which comprised of several tiny kernels with filters for enhancing the depth of network and permit to extract complicated and discriminative features. Region of Interest (ROI) based functional connectivity feature is extracted with VGG-16 and these features are classified with DM optimized ResNet. The hyper parameters are optimized with DM optimization algorithm which improves the accuracy of classifier. By using the proposed approach, the accuracy of autism detection is improved to 99.83%.

关键词： Feature extraction Magnetic resonance imaging Image segmentation Autism signal processing algorithms Deep learning Classification algorithms Autism detection MRI images segmentation VGG feature extraction ResNet dwarf mongoose optimization

来源：评论

学校读者我要写书评

暂无评论

A DOA Estimation Algorithm Based on Eigenvalues Ranking Problem

引用

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷 1页

作者： Chen, Feng Yang, Desen Mo, Shiqi Harbin Engn Univ Acoust Sci & Technol Lab Harbin 150001 Peoples R China Harbin Engn Univ Key Lab Marine Informat Acquisit & Secur Minist Ind & Informat Technol Harbin 150001 Peoples R China Harbin Engn Univ Coll Underwater Acoust Engn Harbin 150001 Peoples R China

Although traditional subspace direction of arrival (DOA) algorithms enable super-resolution, the algorithm performance declines sharply when the number of sources is incorrectly estimated. This article proposes a new DOA estimation method as an optimization problem similar to the conventional minimum variance distortionless response (MVDR) that fully considers the eigenvalues ranking problem. After a mathematical derivation, a concise DOA estimation expression is given based on the mapping between eigenvalues. As the algorithm considers the relative ranking of eigenvalues, the number of sources is not required for DOA estimations. Therefore, the performance of the algorithm does not degrade due to incorrect estimations in the number of sources. To enhance the algorithm performance, this article analyzes the reasons for the formation of pseudo peaks based on the eigenvalues and gives the corresponding boundary conditions. Based on the analysis of white noise, in order to overcome the influence of color noise, this article gives a way to deal with color noise. Finally, the theory of the proposed algorithm is verified through simulation experiments, and its performance is demonstrated through comparative experiments with MUSIC under white and color noise.

关键词： Eigenvalues and eigenfunctions signal processing algorithms Direction-of-arrival estimation Multiple signal classification Estimation Covariance matrices signal to noise ratio Colored noise direction of arrival (DOA) eigenvalue ranking problem minimum variance distortionless response (MVDR) MUSIC pseudo peaks

来源：评论

学校读者我要写书评

暂无评论

Deep Learning-Based Activity Detection for Grant-Free Random Access

引用

IEEE SYSTEMS JOURNAL 2023年第1期17卷 940-951页

作者： Inacio de Souza, Joao Henrique Abrao, Taufik Univ Estadual Londrina Elect Engn Dept BR-86057970 Londrina Parana Brazil

The cellular Internet of Things wireless network is a promising solution to provide massive connectivity for machine-type devices. However, designing grant-free random access (GF-RA) protocols to manage such connections is challenging, since they must operate in interference-aware scenarios with sporadic device activation patterns and a shortage of mutually orthogonal resources. Supervised machine learning models have provided efficient solutions for activity detection, noncoherent data detection, and nonorthogonal preamble design in scenarios with massive connectivity. In this article, we develop two deep learning (DL) sparse support recovery algorithms to detect active devices in massive machine-type communication random access. The DL algorithms, developed to deploy GF-RA protocols, are based on the deep multilayer perceptron and the convolutional neural network models. Unlike previous works, we investigate the impact of the type of sequences for preamble design on the activity detection accuracy. Our results reveal that preambles based on the Zadoff-Chu sequences, which present good correlation properties, achieve better activity detection accuracy with the proposed algorithms than random sequences. Besides, we demonstrate that our DL algorithms achieve activity detection accuracy comparable to state-of-the-art techniques with extremely low computational complexity.

关键词： Convolutional neural networks signal processing algorithms Internet of Things Interference Symbols Sparse matrices Prediction algorithms Convolutional neural network grant-free access Internet of Things (IoT) massive machine-type communications (mMTC) multilayer perceptron random access protocols

来源：评论

学校读者我要写书评

暂无评论

DECONET: An Unfolding Network for Analysis-Based Compressed Sensing With Generalization Error Bounds

引用

IEEE TRANSACTIONS ON signal processing 2023年 71卷 1938-1951页

作者： Kouni, Vicky Panagakis, Yannis Natl & Kapodistrian Univ Athens Dept Informat & Telecommun Athens 15772 Greece

We present a new deep unfolding network for analysis-sparsity-based Compressed Sensing. The proposed network coined Decoding Network (DECONET) jointly learns a decoder that reconstructs vectors from their incomplete, noisy measurements and a redundant sparsifying analysis operator, which is shared across the layers of DECONET. Moreover, we formulate the hypothesis class of DECONET and estimate its associated Rademacher complexity. Then, we use this estimate to deliver meaningful upper bounds for the generalization error of DECONET. Finally, the validity of our theoretical results is assessed and comparisons to state-of-the-art unfolding networks are made, on both synthetic and real-world datasets. Experimental results indicate that our proposed network outperforms the baselines, consistently for all datasets, and its behaviour complies with our theoretical findings.

关键词： signal processing algorithms Decoding Approximation algorithms Transforms Analytical models Optimization Neural networks Compressed sensing analysis sparsity unfolding network generalization error rademacher complexity

来源：评论

学校读者我要写书评

暂无评论

Laplacian Eigenvalue Allocation Through Asymmetric Weights in Acyclic Leader-Follower Networks

引用

IEEE ACCESS 2023年 11卷 126409-126419页

作者： Parlangeli, Gianfranco Univ Salento Dipartimento Ingn Innovaz I-73100 Lecce Italy

This paper tackles the eigenvalue allocation problem through an appropriate choice of the edge weights for the class of combinatorially-symmetric Laplacian matrices of acyclic graphs, namely Laplacian matrices showing symmetric zero/nonzero values in its entries according to a tree graph pattern. The mathematical setting of the problem is remarkably suited for several current multi-agent systems engineering applications, when the communication graph is bidirectional but each agent can set the weight of each incoming neighbor value. The resulting algorithm is inherently iterative and it requires a finite time execution, so that it is well fit for real-world applications as a preliminary routine. For this reason, a special focus is devoted to a distributed implementation of the main algorithm. As a final theoretical result, it is proved that, under the strict interlacing property, the solution is positive, and the algorithm can be iterated. An illustrative example closes the paper, showing how the algorithm works in practice.

关键词： Laplace equations Eigenvalues and eigenfunctions Resource management Symmetric matrices Tree graphs Synchronization signal processing algorithms Laplacian eigenvalue allocation consensus networks tree graphs spectral graph theory

来源：评论

学校读者我要写书评

暂无评论

Broadband Spatial Spectrum Estimation Based on Space-Time Minimum Variance Distortionless Response and Frequency Derivative Constraints

引用

IEEE ACCESS 2023年 11卷 27955-27962页

作者： Wang, Yang Xu, Haiyun Wang, Bin Sun, Minglei PLA Strateg Support Force Informat Engn Univ Informat Syst Engn Zhengzhou 450001 Peoples R China

The two-dimensional optimization capability of the space-time minimum variance distortionless response (STMVDR) can improve the spatial spectrum estimation performance of the broadband beamformer with limited samples, but its high sensitivity to frequency errors may lead to the suppression of some narrowband signals not at the grid point of the frequency spectrum calculation. In order to reduce the sensitivity of STMVDR to frequency errors, a broadband spatial spectrum estimation method based on STMVDR and frequency derivative constraints is proposed in this paper. First, based on the beam response of the STMVDR beamformer, an arbitrary order frequency derivative constraint is derived. Second, combining the gain constraint and frequency derivative constraint, the linear constrained minimum variance problem is established to solve the optimal weight of the beamformer. Finally, the calculation method of the broadband spatial spectrum is derived. Numerical simulations show that the proposed method not only solves the problem of frequency mismatch caused by the insufficient number of points for calculating frequency spectrum, but also does not cause the spatial spectrum peaks to deviate from the true direction due to the broadening of the main lobe of frequency spectrum. In addition, the proposed method can reduce the computational complexity of the algorithm by more than half by increasing the interval of the frequency calculation grid points, and does not significantly loss in the angular resolution capability and estimation accuracy of the algorithm.

关键词： Broadband communication Frequency estimation Spectral analysis signal processing algorithms Narrowband Delays Spatial resolution Space-time minimum variance distortionless response frequency derivative constraint broadband spatial spectrum estimation

来源：评论

学校读者我要写书评

暂无评论

A Compound Gaussian Least Squares Algorithm and Unrolled Network for Linear Inverse Problems

引用

IEEE TRANSACTIONS ON signal processing 2023年 71卷 4303-4316页

作者： Lyons, Carter Raj, Raghu G. Cheney, Margaret US Naval Res Lab Washington DC 20375 USA Colorado State Univ Ft Collins CO 80523 USA

For solving linear inverse problems, particularly of the type that appears in tomographic imaging and compressive sensing, this paper develops two new approaches. The first approach is an iterative algorithm that minimizes a regularized least squares objective function where the regularization is based on a compound Gaussian prior distribution. The compound Gaussian prior subsumes many of the commonly used priors in image reconstruction, including those of sparsity-based approaches. The developed iterative algorithm gives rise to the paper's second new approach, which is a deep neural network that corresponds to an "unrolling" or "unfolding" of the iterative algorithm. Unrolled deep neural networks have interpretable layers and outperform standard deep learning methods. This paper includes a detailed computational theory that provides insight into the construction and performance of both algorithms. The conclusion is that both algorithms outperform other state-of-the-art approaches to tomographic image formation and compressive sensing, especially in the difficult regime of low training.

关键词： Artificial neural networks Training Tomography signal reconstruction signal processing algorithms Imaging Compounds Machine learning neural networks inverse problems nonlinear programming least squares methods

来源：评论

学校读者我要写书评

暂无评论

CGMM-Based Sound Zone Generation Using Robust Pressure Matching With ATF Perturbation Constraints

引用

IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE processing 2023年 31卷 3331-3345页

作者： Zhang, Junqing Shi, Liming Christensen, Mads Graesboll Zhang, Wen Zhang, Lijun Chen, Jingdong Northwestern Polytech Univ Ctr Intelligent Acoust & Immers Commun Sch Marine Sci & Technol Xian 710072 Peoples R China Northwestern Polytech Univ Sch Marine Sci & Technol Shaanxi Prov Key Lab Artificial Intelligence Xian 710072 Peoples R China Minist Ind & Informat Technol Key Lab Ocean Acoust & Sensing Xian 710072 Peoples R China Chongqing Univ Posts & Telecommun Intelligent Speech & Audio Lab Commun & Informat Engn Chongqing 400065 Peoples R China Aalborg Univ Dept Elect Syst DK-9000 Aalborg Denmark

Personal sound zone (PSZ) refers to the technique that uses an array of loudspeakers and digital signal processing tools to achieve spatial soundfield control. To generate the target sound zones, this technique generally requires to know the acoustic transfer functions (ATFs) between the loudspeakers and the spots where soundfields are to be controlled. In practical applications, however, the true ATFs are never accessible and they have to be measured or estimated. Due to many sophisticated reasons, the measured ATFs generally deviate from the true ones, which may lead to significant degradation in performance of sound zone reproduction. In this work, a robust pressure matching (RPM) algorithm is presented for sound zone generation. It exploits a complex Gaussian mixture model (CGMM) to model the ATFs and their perturbations. The CGMM parameters are estimated using the expectation-maximization (EM) algorithm. To improve the robustness of the pressure matching method, an uncertainty constraint is applied to the ATF estimates and the pressure matching problem is then formulated as one of biconvex optimization. The coordinate descent algorithm is subsequently used to solve the optimization problem, thereby obtaining the optimal control filter. In comparison with the existing pressure matching methods without considering the effect of ATF perturbations, the presented algorithm is able to achieve lower normalized signal distortion energy and higher signal to interference ratio. Numerical simulations justify the effectiveness of the presented algorithm as well as its advantages over the traditional methods.

关键词： Loudspeakers Perturbation methods Optimization Speech processing Robustness signal processing algorithms Uncertainty Biconvex optimization complex Gaussian mixture model expectation maximization personal sound zone robustness semidefinite relaxation

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：