نتایج جستجو برای: compressive sensing
تعداد نتایج: 145295 فیلتر نتایج به سال:
Sequential Compressive Sensing, which may be widely used in sensing devices, is a popular topic of recent research. This paper proposes an online recovery algorithm for sparse approximation of sequential compressive sensing. Several techniques including warm start, fast iteration, and variable step size are adopted in the proposed algorithm to improve its online performance. Finally, numerical ...
In this chapter, we discuss how noise radar systems are suitable for realizing practically the promises of compressive sensing in radar imaging, in general, and in urban-sensing applications, in particular. Noise radar refers to radio frequency imaging systems that employ transmit signals that are generated to resemble random noise waveforms. Noise radar has recently been successfully applied t...
با پیشرفت های صورت گرفته در حوز? پردازش سیگنال گسسته و سخت افزارهای مرتبط با آن و نیاز روزافزون به سیستم های حس گر با نرخ های نمونه برداری هرچه بیشتر و از طرف دیگر محدودیت های فیزیکی جهت پیاده سازی چنین سیستم هایی، ارائ? تکنیک هایی جهت کاهش و بهینه سازی نرخ نمونه برداری، بدون از دست دادن کیفیت سیگنال ضروری می نماید. در سال های اخیر حوزه ای جدید در پردازش سیگنال گسسته پدید آمده که قادر است با نر...
This paper proposes perceptual compressive sensing. The network is composed of a fully convolutional measurement and reconstruction network. For the following contributions, the proposed framework is a breakthrough work. Firstly, the fully-convolutional network measures the full image which preserves structure information of the image and removes the block effect. Secondly, with the employment ...
Compressive sensing is a signal acquisition framework based on the revelation that a small collection of linear projections of a sparse signal contains enough information for stable recovery. In this paper we introduce a new theory for distributed compressive sensing (DCS) that enables new distributed coding algorithms for multi-signal ensembles that exploit both intraand inter-signal correlati...
Compressive sensing microarrays (CSMs) are DNA-based sensors that operate using group testing and compressive sensing (CS) principles. In contrast to conventional DNA microarrays, in which each genetic sensor is designed to respond to a single target, in a CSM, each sensor responds to a set of targets. We study the problem of designing CSMs that simultaneously account for both the constraints f...
This paper proposes an extension of compressive sensing that allows to express the sparsity prior in a dictionary of bases. This enables the use of a fixed set of non-adaptive linear measurements and an adaptive recovery process. This reconstruction optimizes the basis to the structure of the sensed signal. An iterative thresholding algorithm is used to perform both the recovery and the estimat...
Parametric images provide insight into the spatial distribution of physiological parameters, but they are often extremely noisy, due to low SNR of tomographic data. Direct estimation from projections allows accurate noise modeling, improving the results of post-reconstruction fitting. We propose a method, which we name kinetic compressive sensing (KCS), based on a hierarchical Bayesian model an...
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