搜索结果: 1-12 共查到“信息与通信工程 sparse”相关记录12条 . 查询时间(0.078 秒)
Academy of Mathematics and Systems Science, CAS Colloquia & Seminars：Geometry and recovery of spectral-sparse signals
频谱稀疏信号 几何 恢复
2023/4/26
Spectral-sparse signals are those sparse in the Fourier domain and are very common in wireless communications, radar, sonar, medical imaging and other applications. Their recovery from noisy, limited ...
清华大学殷柳国教授来中北大学信息与通信工程学院作“Generalized Sparse Codes for Non-Gaussian Channels: Code Design,Algorithms, and Applications”专题报告（图）
清华大学 殷柳国 中北大学信息与通信工程学院 广义随机编码 抗干扰 通信编码
2022/11/24
2022年6月17日下午，国家级人才称号获得者，清华大学殷柳国教授来中北大学信息与通信工程学院作“Generalized Sparse Codes for Non-Gaussian Channels: Code Design,Algorithms, and Applications”专题报告。本次专题报告是南邮80周年校庆之际通信与信息工程学院举办的第10次“通院学术大讲堂”校庆系列报告会。本次报...
Smooth compression, Gallager bound and Nonlinear sparse-graph codes
Data compression data smoothness coding
2015/8/21
A data compression scheme is defined to be smooth if its image (the codeword) depends gracefully on the source (the data). Smoothness is a desirable property in many practical contexts, and widely use...
Stable Recovery of Sparse Overcomplete Representations in the Presence of Noise
Sparse representation Overcomplete Representation
2015/8/21
Overcomplete representations are attracting interest in signal processing theory, particularly due
to their potential to generate sparse representations of signals. However, in general, the problem o...
Templates for Convex Cone Problems with Applications to Sparse Signal Recovery
Optimal first-order methods Nesterov’s accelerated descent algorithms proximal algorithms conic duality smoothing by conjugation the Dantzig selector the LASSO nuclearnorm minimization
2015/6/17
This paper develops a general framework for solving a variety of convex cone problems that frequently arise in signal processing, machine learning, statistics, and other fields. The approach works as ...
Global Testing under Sparse Alternatives: ANOVA, Multiple Comparisons and the Higher Criticism
Detecting a sparse signal analysis of variance higher criticism minimax detection incoherence random matrices suprema of Gaussian processes compressive sensing
2015/6/17
Testing for the significance of a subset of regression coefficients in a linear model, a staple of statistical analysis, goes back at least to the work of Fisher who introduced the analysis of varianc...
The estimation of a sparse vector in the linear model is a fundamental problem in signal processing, statistics, and compressive sensing. This paper establishes a lower bound on the mean-squared error...
A Non-Uniform Sampler for Wideband Spectrally-Sparse Environments
Non-uniform sampler compressed sensing wideband ADC indium-phosphide HBT sample-and-hold
2015/6/17
We present a wide bandwidth, compressed sensing based non-uniform sampling (NUS) system with a custom sampleand-hold chip designed to take advantage of a low average sampling rate. By sampling signals...
Low-Rank and Sparse Matrix Decomposition for Accelerated Dynamic MRI with Separation of Background and Dynamic Components
compressed sensing low-rank matrix completion sparsity dynamic MRI
2015/6/17
Purpose: To apply the low-rank plus sparse (L+S) matrix decomposition model to reconstruct undersampled dynamic MRI as a superposition of background and dynamic components in various problems of clini...
Super-resolution via Transform-invariant Group-sparse Regularization
Super-resolution Transform-invariant Group-sparse
2015/6/17
We present a framework to super-resolve planar regions found in urban scenes and other man-made environments by taking into account their 3D geometry. Such regions have highly structured straight edge...
NESTA: A FAST AND ACCURATE FIRST-ORDER METHOD FOR SPARSE RECOVERY
Nesterov’s method smooth approximations of nonsmooth functions `1 minimization duality in convex optimization continuation methods compressed sensing total-variation minimization
2015/6/17
Accurate signal recovery or image reconstruction from indirect and possibly undersampled data is a topic of considerable interest; for example, the literature in the recent field of compressed sensing...
Exploring Statistical Properties for Semantic Annotation: Sparse Distributed and Convergent Assumptions for Keywords
Semantic Annotation Sparse Distributed Convergent Assumptions
2010/12/21
Does there exist a compact set of visual topics in form of keyword clusters capable to represent all images visual content within an acceptable error? In this paper, we answer this question by analyzi...