作者: ARCH 時間: 2025-3-21 21:50
The Geometry of Compressed Sensing,instead of vector data, in many problems, data is more naturally expressed in matrix form (for example a video is often best represented in a pixel by time matrix). A powerful constraint on matrices are constraints on the matrix rank. For example, in low-rank matrix recovery, the goal is to reconstr作者: 有偏見 時間: 2025-3-22 00:46 作者: exhibit 時間: 2025-3-22 08:20
Nonnegative Tensor Decomposition,mension less using the so-called t-product. A demonstration on an application in facial recognition shows the potential promise of the overall approach. We discuss a number of algorithmic options for solving the resulting optimization problems, and modification of such algorithms for increasing the 作者: 培養(yǎng) 時間: 2025-3-22 11:03 作者: companion 時間: 2025-3-22 13:34
Sparse Nonlinear MIMO Filtering and Identification,acing emphasis on minimal input resources and blind identification whereby only output samples are available plus a–priori information on input characteristics. Based on this taxonomy a variety of algorithms, existing and new, are studied and evaluated by simulations.作者: companion 時間: 2025-3-22 18:08 作者: hypotension 時間: 2025-3-22 22:11
Sparsity and Compressed Sensing in Mono-Static and Multi-Static Radar Imaging,terns reflect spectral and spatial diversity trade-offs. Characterization of the expected quality of the reconstructed images in these scenarios prior to actual data collection is a problem of central interest in task planning for multi-mode radars. Compressed sensing theory argues that the mutual c作者: acolyte 時間: 2025-3-23 04:59 作者: ticlopidine 時間: 2025-3-23 08:58
Konzepte zur Erfolgs- und Risikoanalyse,d as .-regularized . regression. We show that, under standard restricted isometry property (RIP) assumptions on the design matrix, .-minimization can provide stable recovery of a sparse signal in presence of exponential-family noise, and state some sufficient conditions on the noise distribution tha作者: GILD 時間: 2025-3-23 12:18 作者: 放棄 時間: 2025-3-23 14:35
Frederik Ahlemann,Fedi El Arbi,Axel Heckhen discussed in Sect.?.. Special attention is paid to the use of Sub-Nyquist sampling and compressed sensing techniques for realizing wideband spectrum sensing. Finally, Sect.?. shows an adaptive compressed sensing approach for wideband spectrum sensing in cognitive radio networks.作者: 維持 時間: 2025-3-23 21:22
Kunal Mohan Dr.,Frederik Ahlemannacing emphasis on minimal input resources and blind identification whereby only output samples are available plus a–priori information on input characteristics. Based on this taxonomy a variety of algorithms, existing and new, are studied and evaluated by simulations.作者: 顯示 時間: 2025-3-24 00:31 作者: 偏見 時間: 2025-3-24 02:34
Strategisches Qualit?tscontrollingterns reflect spectral and spatial diversity trade-offs. Characterization of the expected quality of the reconstructed images in these scenarios prior to actual data collection is a problem of central interest in task planning for multi-mode radars. Compressed sensing theory argues that the mutual c作者: 窒息 時間: 2025-3-24 07:15
1860-4862 btaining sparse solutions using fewer observations thanconventionally needed. The book emphasizes on the role of sparsity as a machinery for promoting low complexity representations and likewise its connections978-3-662-50894-7978-3-642-38398-4Series ISSN 1860-4862 Series E-ISSN 1860-4870 作者: 行乞 時間: 2025-3-24 14:45
Introduction to Compressed Sensing and Sparse Filtering,ing and scientific domains. Presently, there is a wealth of theoretical results that extend the basic ideas of compressed sensing essentially making analogies to notions from other fields of mathematics. The objective of this chapter is to introduce the reader to the basic theory of compressed sensi作者: Phonophobia 時間: 2025-3-24 16:32
The Geometry of Compressed Sensing,ing a geometrical interpretation. This geometric point of view not only underlies many of the initial theoretical developments on which much of the theory of compressed sensing is built, but has also allowed ideas to be extended to much more general recovery problems and structures. A unifying frame作者: incontinence 時間: 2025-3-24 20:14
Sparse Signal Recovery with Exponential-Family Noise,ng literature. Typically, the signal reconstruction problem is formulated as .-regularized . regression. From a statistical point of view, this problem is equivalent to maximum a posteriori probability (MAP) parameter estimation with Laplace prior on the vector of parameters (i.e., signal) and linea作者: linguistics 時間: 2025-3-25 01:24 作者: 我悲傷 時間: 2025-3-25 05:09 作者: Genome 時間: 2025-3-25 07:56
Sub-Nyquist Sampling and Compressed Sensing in Cognitive Radio Networks,. As a key technology, spectrum sensing enables cognitive radios to find spectrum holes and improve spectral utilization efficiency. To exploit more spectral opportunities, wideband spectrum sensing approaches should be adopted to search multiple frequency bands at a time. However, wideband spectrum作者: 斗志 時間: 2025-3-25 12:32 作者: Juvenile 時間: 2025-3-25 17:07 作者: gerrymander 時間: 2025-3-25 21:32 作者: insurrection 時間: 2025-3-26 04:04 作者: STENT 時間: 2025-3-26 04:26 作者: 精美食品 時間: 2025-3-26 08:30
,Estimation of Time-Varying Sparse Signals in?Sensor Networks,ch time interval, the fusion center transmits the predicted signal estimate and its corresponding error covariance to a selected subset of sensors. The selected sensors compute quantized innovations and transmit them to the fusion center. We consider the situation where the signal is sparse, i.e., a作者: AND 時間: 2025-3-26 15:45
Sparsity and Compressed Sensing in Mono-Static and Multi-Static Radar Imaging,Rs). We provide a brief overview of how sparsity-driven imaging has recently been used in various radar imaging scenarios. We then focus on the problem of imaging from undersampled data, and point to recent work on the exploitation of compressed sensing theory in the context of radar imaging. We con作者: 具體 時間: 2025-3-26 20:44
Structured Sparse Bayesian Modelling for Audio Restoration,an example, a model to remove impulse and background noise from audio signals via their representation in time-frequency space using Gabor wavelets is presented. A number of prior models for the sparse structure of the signal in this space are introduced, including simple Bernoulli priors on each co作者: 聲明 時間: 2025-3-26 23:11 作者: Default 時間: 2025-3-27 04:57
Lebenszyklus und Umweltanalyse,ng as emanated in the first few works on the subject. The first part of this chapter is therefore a concise exposition to compressed sensing which requires no prior background. The second half of this chapter slightly extends the theory and discusses its applicability to filtering of dynamic sparse signals.作者: Assault 時間: 2025-3-27 08:48
Unternehmensführung & Controlling large fraction of its components is zero-valued. We discuss algorithms for signal estimation in the described scenario, analyze their complexity, and demonstrate their near-optimal performance even in the case where sensors transmit a single bit (i.e., the sign of innovation) to the fusion center.作者: mortgage 時間: 2025-3-27 10:11
Book 2014 popularity and to some extent revolutionised signal processing is compressed sensing. Compressed sensing builds upon the observation that many signals in nature are nearly sparse (or compressible, as they are normally referred to) in some domain, and consequently they can be reconstructed to within作者: ureter 時間: 2025-3-27 15:49
Introduction to Compressed Sensing and Sparse Filtering,ng as emanated in the first few works on the subject. The first part of this chapter is therefore a concise exposition to compressed sensing which requires no prior background. The second half of this chapter slightly extends the theory and discusses its applicability to filtering of dynamic sparse signals.作者: 加強防衛(wèi) 時間: 2025-3-27 21:22
,Estimation of Time-Varying Sparse Signals in?Sensor Networks, large fraction of its components is zero-valued. We discuss algorithms for signal estimation in the described scenario, analyze their complexity, and demonstrate their near-optimal performance even in the case where sensors transmit a single bit (i.e., the sign of innovation) to the fusion center.作者: 遣返回國 時間: 2025-3-28 01:15
Alexander Kn?ss,Markus Kre?manned for. All extensions preserve the computational efficiency of the classic algorithms, and most of the extensions are illustrated with numerical examples, which are part of an open source Kalman smoothing Matlab/Octave package.作者: forebear 時間: 2025-3-28 05:20
Alexander Kn?ss,Markus Kre?mannroblem at hand. The obtained bounds establish the relation between the complexity of the autoregressive process and the attainable estimation accuracy through the use of a novel measure of complexity. This measure is suggested herein as a substitute to the generally incomputable restricted isometric property.作者: 箴言 時間: 2025-3-28 08:33 作者: BROOK 時間: 2025-3-28 14:11 作者: 開玩笑 時間: 2025-3-28 15:58 作者: 人類學家 時間: 2025-3-28 22:10 作者: nettle 時間: 2025-3-29 02:26
Distributed Approximation and Tracking Using Selective Gossip,hat distributed particle filters employing selective gossip provide comparable results to the centralized bootstrap particle filter while decreasing the communication overhead compared to using randomized gossip to distribute the filter computations. 作者: Felicitous 時間: 2025-3-29 06:00 作者: 小卒 時間: 2025-3-29 09:55
https://doi.org/10.1007/978-3-8349-4273-9of-the-art in nuclear norm optimization algorithms as they relate to applications. We then propose a novel application of the nuclear norm to the linear model recovery problem, as well as a viable algorithm for solution of the recovery problem. Preliminary numerical results presented here motivates further investigation of the proposed idea.作者: aviator 時間: 2025-3-29 13:30
Nuclear Norm Optimization and Its Application to Observation Model Specification,of-the-art in nuclear norm optimization algorithms as they relate to applications. We then propose a novel application of the nuclear norm to the linear model recovery problem, as well as a viable algorithm for solution of the recovery problem. Preliminary numerical results presented here motivates further investigation of the proposed idea.作者: 泥沼 時間: 2025-3-29 16:30 作者: RACE 時間: 2025-3-29 23:38 作者: Conquest 時間: 2025-3-30 02:35
Avishy Y. Carmi,Lyudmila Mihaylova,Simon J. GodsilPresents fundamental concepts, methods and algorithms able to cope with undersampled data.Introduces compressive sampling, called also compressed sensing..Written by well-known experts in the field.In作者: 可忽略 時間: 2025-3-30 06:32
Signals and Communication Technologyhttp://image.papertrans.cn/c/image/231975.jpg作者: nephritis 時間: 2025-3-30 09:50
Lebenszyklus und Umweltanalyse,ing and scientific domains. Presently, there is a wealth of theoretical results that extend the basic ideas of compressed sensing essentially making analogies to notions from other fields of mathematics. The objective of this chapter is to introduce the reader to the basic theory of compressed sensi作者: 危機 時間: 2025-3-30 16:05
