標(biāo)題: Titlebook: Compressed Sensing and its Applications; MATHEON Workshop 201 Holger Boche,Robert Calderbank,Jan Vybíral Book 2015 Springer International P [打印本頁] 作者: formation 時間: 2025-3-21 19:24
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作者: lactic 時間: 2025-3-21 21:48 作者: 孵卵器 時間: 2025-3-22 01:25
https://doi.org/10.1007/978-3-319-16042-9Acoustic Imaging; Compressed Sensing; Convex Optimization; Machine Learning; Quantization; Structured Spa作者: Gratulate 時間: 2025-3-22 05:33 作者: VEST 時間: 2025-3-22 10:14
The Quest for Optimal Sampling: Computationally Efficient, Structure-Exploiting Measurements for Coe object to recover, but also on its structure. This chapter is about understanding this phenomenon, and demonstrating how it can be fruitfully exploited by the design of suitable sampling strategies in order to outperform more standard compressed sensing techniques based on random matrices.作者: FLAG 時間: 2025-3-22 13:00
Eigene Führungsrolle in China verstehenin a suitable basis or dictionary. Due to its solid mathematical backgrounds, it quickly attracted the attention of mathematicians from several different areas, so that the most important aspects of the theory are nowadays very well understood. In recent years, its applications started to spread out作者: FLAG 時間: 2025-3-22 19:46 作者: NAIVE 時間: 2025-3-23 00:21
Eigene Führungsrolle in China verstehenage inversion, provided that the image is sparse in an apriori known dictionary. For imaging problems in spectrum analysis (estimating complex exponential modes), and passive and active radar/sonar (estimating Doppler and angle of arrival), this dictionary is usually taken to be a DFT basis (or fram作者: 慷慨援助 時間: 2025-3-23 03:32
https://doi.org/10.1007/978-3-8349-6646-9ges, gene expression data from a DNA microarray, social network data, etc.), yet is such that its desired properties lie in some low dimensional structure (sparsity, low-rankness, clusters, etc.). In the modern viewpoint, the goal is to come up with efficient algorithms to reveal these structures an作者: 處理 時間: 2025-3-23 07:57
Eigene Führungsrolle in China verstehene object to recover, but also on its structure. This chapter is about understanding this phenomenon, and demonstrating how it can be fruitfully exploited by the design of suitable sampling strategies in order to outperform more standard compressed sensing techniques based on random matrices.作者: 血統(tǒng) 時間: 2025-3-23 10:33 作者: assent 時間: 2025-3-23 16:49
Strategisches Talentmanagement in Chinalores the interaction of quantization and compressive sensing and examines practical quantization strategies for compressive acquisition systems. Specifically, we first provide a brief overview of quantization and examine fundamental performance bounds applicable to any quantization approach. Next, 作者: 裁決 時間: 2025-3-23 21:06
Die Ableitung von Technologiestrategien,d at estimating the parameters of a density mixture on training data in a compressive manner by computing a low-dimensional . of the data. The sketch represents empirical moments of the underlying probability distribution. Instantiating the framework on the case where the densities are isotropic Gau作者: 殺子女者 時間: 2025-3-23 22:49 作者: 沉積物 時間: 2025-3-24 02:27
,Schlu?betrachtung und Ausblick,near and non-adaptive estimation problems. It is therefore an advisable strategy for noncoherent information retrieval in, for example, sporadic blind and semi-blind communication and sampling problems. But, the conventional model is not practical here since the compressible signals have to be estim作者: 水汽 時間: 2025-3-24 07:34
Strategisches Technologiemanagementve an overview of the analysis sparsity model and present theoretical conditions that guarantee successful nonuniform and uniform recovery of signals from noisy measurements. We derive a bound on the number of Gaussian and subgaussian measurements by examining the provided theoretical guarantees und作者: Feedback 時間: 2025-3-24 13:24 作者: jealousy 時間: 2025-3-24 15:15 作者: Mettle 時間: 2025-3-24 20:22
https://doi.org/10.1007/978-3-8350-9173-3low rank from incomplete information. Here we consider a further extension to the reconstruction of tensors of low multi-linear rank in recently introduced hierarchical tensor formats from a small number of measurements. Hierarchical tensors are a flexible generalization of the well-known Tucker rep作者: 公豬 時間: 2025-3-25 01:53 作者: 出汗 時間: 2025-3-25 06:04
Compressed Sensing and its Applications978-3-319-16042-9Series ISSN 2296-5009 Series E-ISSN 2296-5017 作者: 不遵守 時間: 2025-3-25 10:48
Eigene Führungsrolle in China verstehene object to recover, but also on its structure. This chapter is about understanding this phenomenon, and demonstrating how it can be fruitfully exploited by the design of suitable sampling strategies in order to outperform more standard compressed sensing techniques based on random matrices.作者: 撤退 時間: 2025-3-25 15:12 作者: Range-Of-Motion 時間: 2025-3-25 17:16
Temporal Compressive Sensing for Video,ough modern imagers are capable of both simultaneous spatial and temporal resolutions at micrometer and microsecond scales, the power required to sample at these rates is undesirable. The field of compressive sensing (CS) has recently suggested a solution to this design challenge. By exploiting phys作者: 結(jié)果 時間: 2025-3-25 22:44 作者: Confidential 時間: 2025-3-26 01:48
Recovering Structured Signals in Noise: Least-Squares Meets Compressed Sensing,ges, gene expression data from a DNA microarray, social network data, etc.), yet is such that its desired properties lie in some low dimensional structure (sparsity, low-rankness, clusters, etc.). In the modern viewpoint, the goal is to come up with efficient algorithms to reveal these structures an作者: Dysplasia 時間: 2025-3-26 04:22
The Quest for Optimal Sampling: Computationally Efficient, Structure-Exploiting Measurements for Coe object to recover, but also on its structure. This chapter is about understanding this phenomenon, and demonstrating how it can be fruitfully exploited by the design of suitable sampling strategies in order to outperform more standard compressed sensing techniques based on random matrices.作者: Landlocked 時間: 2025-3-26 08:47 作者: Osteoarthritis 時間: 2025-3-26 16:12
Quantization and Compressive Sensing,lores the interaction of quantization and compressive sensing and examines practical quantization strategies for compressive acquisition systems. Specifically, we first provide a brief overview of quantization and examine fundamental performance bounds applicable to any quantization approach. Next, 作者: 遍及 時間: 2025-3-26 18:18
Compressive Gaussian Mixture Estimation,d at estimating the parameters of a density mixture on training data in a compressive manner by computing a low-dimensional . of the data. The sketch represents empirical moments of the underlying probability distribution. Instantiating the framework on the case where the densities are isotropic Gau作者: hypnogram 時間: 2025-3-26 21:39
Two Algorithms for Compressed Sensing of Sparse Tensors, a sparse signal from relatively few linear measurements via a suitable nonlinear minimization process. Conventional CS theory relies on vectorial data representation, which results in good compression ratios at the expense of increased computational complexity. In applications involving color image作者: Indebted 時間: 2025-3-27 03:00 作者: 煩躁的女人 時間: 2025-3-27 05:27 作者: inundate 時間: 2025-3-27 12:24
Structured Sparsity: Discrete and Convex Approaches,learning, and optimization. In fact, most natural data can be . represented, i.e., a small set of coefficients is sufficient to describe the data using an appropriate basis. Sparsity is also used to enhance interpretability in real-life applications, where the relevant information therein typically 作者: 沒有準(zhǔn)備 時間: 2025-3-27 13:56
Explicit Matrices with the Restricted Isometry Property: Breaking the Square-Root Bottleneck,ucted using random processes, while explicit constructions are notorious for performing at the “square-root bottleneck,” i.e., they only accept sparsity levels on the order of the square root of the number of measurements. The only known explicit matrix which surpasses this bottleneck was constructe作者: 江湖郎中 時間: 2025-3-27 17:48 作者: 裁決 時間: 2025-3-28 01:17 作者: Aids209 時間: 2025-3-28 02:48 作者: 正式演說 時間: 2025-3-28 07:52 作者: 臭名昭著 時間: 2025-3-28 11:57 作者: 異端 時間: 2025-3-28 18:07 作者: GIBE 時間: 2025-3-28 22:08 作者: 不法行為 時間: 2025-3-28 23:01
Sparse Model Uncertainties in Compressed Sensing with Application to Convolutions and Sporadic Commstable low-dimensional embeddings of the uncalibrated receive signals whereby addressing also efficient communication-oriented methods like . random demodulation. Exemplarily, we investigate in more detail sparse convolutions serving as a basic communication channel model. In using some recent resul作者: 褪色 時間: 2025-3-29 07:00 作者: 尾隨 時間: 2025-3-29 08:14 作者: 詞匯表 時間: 2025-3-29 13:56 作者: 不能仁慈 時間: 2025-3-29 16:48 作者: 人工制品 時間: 2025-3-29 21:15
https://doi.org/10.1007/978-3-8349-6646-9orm, mixed ../.. norm, etc.). While the LASSO algorithm has been around for 20 years and has enjoyed great success in practice, there has been relatively little analysis?of its performance. In this chapter, we will provide a full performance analysis and compute, in closed form, the mean-square-erro作者: Institution 時間: 2025-3-30 01:10
Führungskontext in China versteheny. Beyond NAH, this chapter shows how compressive sensing is being applied to other acoustic scenarios such as active sonar, sampling of the plenacoustic function, medical ultrasound imaging, localization of directive sources, and interpolation of plate vibration response.作者: Tonometry 時間: 2025-3-30 04:22
Strategisches Technologiemanagementamework for compressed sensing of higher-order tensors which preserves the intrinsic structure of tensorial data with reduced computational complexity at reconstruction. We demonstrate that GTCS offers an efficient means for representation of multidimensional data by providing simultaneous acquisiti作者: 言行自由 時間: 2025-3-30 08:59 作者: GUISE 時間: 2025-3-30 13:41 作者: 巨碩 時間: 2025-3-30 19:56
https://doi.org/10.1007/978-3-8350-9173-3heoretical difficulties in designing and analyzing algorithms for low rank tensor recovery. For instance, a canonical analogue of the tensor nuclear norm is NP-hard to compute in general, which is in stark contrast to the matrix case. In this book chapter we consider versions of iterative hard thres作者: 推崇 時間: 2025-3-30 23:47
