in this paper, an interference management for multi-beam satellite network to enhance spectral efficiency is studied. first, a two-satellite for future generation satellite network using cognitive radio technology is provided.this network is composed of a secondary satellite and aprimary satellite where the primary satellite system does not suffer from severe destructive interferences coming fr...

Recovery of functions of many variables from sample values usually suffers the curse of dimensionality: The number of required samples scales exponentially with the spatial dimension. In order to avoid this severe bottleneck, one needs to impose further structural properties of the function to be recovered apart from smoothness. Here, we build on ideas from compressive sensing and introduce a f...

The paper analyses the possibility to recover different biomedical signals if limited number of samples is available. Having in mind that monitoring of health condition is done by measuring and observing key parameters such as heart activity through electrocardiogram or anatomy and body processes through magnetic resonance imaging, it is important to keep the quality of the reconstructed signal...

The goal of statistical compressive sensing is to efficiently acquire and reconstruct high-dimensional signals with much fewer measurements, given access to a finite set of training signals from the underlying domain being sensed. We present a novel algorithmic framework based on autoencoders that jointly learns the acquisition (a.k.a. encoding) and recovery (a.k.a. decoding) functions while im...

Many biological phenomena about quorum sensing in bacteria are driven by experimentally and mathematically in the last few decades. Symbiosis, virulence etc. are the major area in which these theoretical models work well. But no specific origin was established so far which can explain quorum sensing itself. This weakly nonlocal fluid dynamical model of bacterial communication system introduces ...

In this paper, we consider the problem of recovering the s largest elements of an arbitrary vector from noisy measurements. Inspired by previous work, we develop an homotopy algorithm which solves the l1-regularized least square problem for a sequence of decreasing values of the regularization parameter. Compared to the previous method, our algorithm is more efficient in the sense it only updat...

Where you can find the sparse representations and compressive sensing for imaging vision easily? Is it in the book store? On-line book store? are you sure? Keep in mind that you will find the book in this site. This book is very referred for you because it gives not only the experience but also lesson. The lessons are very valuable to serve for you, that's not about who are reading this sparse ...

Compressive sensing is a method for recording a k-sparse signal x ∈ R with (possibly noisy) linear measurements of the form y = Ax, where A ∈ Rm×n describes the measurement process. Seminal results in compressive sensing show that it is possible to recover the signal x from m = O(k log n k ) measurements and that this is tight. The model-based compressive sensing framework overcomes this lower ...