Browsing by Keywords "Signal reconstruction"
Now showing items 120 of 25

Accelerated phasecycled SSFP imaging with compressed sensing
(Institute of Electrical and Electronics Engineers Inc., 2015)Balanced steadystate free precession (SSFP) imaging suffers from irrecoverable signal losses, known as banding artifacts, in regions of large B0 field inhomogeneity. A common solution is to acquire multiple phasecycled ... 
Average error in recovery of sparse signals and discrete fourier transform
(IEEE, 201204)In compressive sensing framework it has been shown that a sparse signal can be successfully recovered from a few random measurements. The Discrete Fourier Transform (DFT) is one of the transforms that provide the best ... 
Compressed sensing on ambiguity function domain for high resolution detection
(IEEE, 2010)In this paper, by using compressed sensing techniques, a new approach to achieve robust high resolution detection in sparse multipath channels is presented. Currently used sparse reconstruction techniques are not immediately ... 
A compression method for 3D laser range scans of indoor environments based on compressive sensing
(2011)Modeling and representing 3D environments require the transmission and storage of vast amount of measurements that need to be compressed efficiently. We propose a novel compression technique based on compressive sensing ... 
Compressive sampling and adaptive multipath estimation
(IEEE, 2010)In many signal processing problems such as channel estimation and equalization, the problem reduces to a linear system of equations. In this proceeding we formulate and investigate linear equations systems with sparse ... 
Compressive sensing based flame detection in infrared videos
(IEEE, 2013)In this paper, a Compressive Sensing based feature extraction algorithm is proposed for flame detection using infrared cameras. First, bright and moving regions in videos are detected. Then the videos are divided into ... 
Compressive sensing based target detection in delaydoppler radars
(IEEE, 2013)Compressive Sensing theory shows that, a sparse signal can be reconstructed from its subNyquist rate random samples. With this property, CS approach has many applications. Radar systems, which deal with sparse signal due ... 
Compressive sensing imaging with a graphene modulator at THz frequency in transmission mode
(IEEE Computer Society, 2016)In this study we demonstrate compressive sensing imaging with a unique graphene based optoelectronic device which allows us to modulate the THz field through an array of columns or rows distributed throughout its face. © ... 
CrossTerm free based bistatic radar system using sparse least squares
(SPIE, 2015)Passive Bistatic Radar (PBR) systems use illuminators of opportunity, such as FM, TV, and DAB broadcasts. The most common illuminator of opportunity used in PBR systems is the FM radio stations. Single FM channel based PBR ... 
Detection of sparse targets with structurally perturbed echo dictionaries
(Elsevier, 2013)In this paper, a novel algorithm is proposed to achieve robust high resolution detection in sparse multipath channels. Currently used sparse reconstruction techniques are not immediately applicable in multipath channel ... 
An empirical eigenvaluethreshold test for sparsity level estimation from compressed measurements
(IEEE, 2014)Compressed sensing allows for a significant reduction of the number of measurements when the signal of interest is of a sparse nature. Most computationally efficient algorithms for signal recovery rely on some knowledge ... 
Image feature extraction using compressive sensing
(Springer Verlag, 2014)In this paper a new approach for image feature extraction is presented. We used the Compressive Sensing (CS) concept to generate the measurement matrix. The new measurement matrix is different from the measurement matrices ... 
New face representation using compressive sensing
(2011)GMM supervectors are among the most popular feature sets used in SVMbased textindependent speaker verification. Most of the studies represent speaker characteristics obtained from a long recording with a single ... 
Performance assessment of a diffraction field computation method based on source model
(2008)Efficient computation of scalar optical diffraction field due to an object is an essential issue in holographic 3D television systems. The first step in the computation process is to construct an object. As a solution for ... 
Perturbed orthogonal matching pursuit
(IEEE, 2013)Compressive Sensing theory details how a sparsely represented signal in a known basis can be reconstructed with an underdetermined linear measurement model. However, in reality there is a mismatch between the assumed and ... 
Polar compressive sampling: A novel technique using polar codes
(IEEE, 2010)Recently introduced Polar coding is the first practical coding technique that can be proven to achieve the Shannon capacity for a multitude of communication channels. Polar codes are close to ReedMuller codes except the ... 
RangeDoppler radar target detection using compressive sensing
(IEEE Computer Society, 2014)Compressive sensing (CS) idea enables the reconstruction of a sparse signal from small number of measurements. CS approach has many applications in many areas. One of the areas is radar systems. In this article, the radar ... 
Rangedoppler radar target detection using denoising within the compressive sensing framework
(European Signal Processing Conference, EUSIPCO, 2014)Compressive sensing (CS) idea enables the reconstruction of a sparse signal from a small set of measurements. CS approach has applications in many practical areas. One of the areas is radar systems. In this article, the ... 
Recovery of sparse perturbations in Least Squares problems
(IEEE, 2011)We show that the exact recovery of sparse perturbations on the coefficient matrix in overdetermined Least Squares problems is possible for a large class of perturbation structures. The well established theory of Compressed ... 
A recursive approach to reconstruction of sparse signals
(IEEE Computer Society, 2014)Compressive Sensing (CS) theory details how a sparsely represented signal in a known basis can be reconstructed using less number of measurements. In many practical systems, the observation signal has a sparse representation ...