Now showing items 1-20 of 27

    • Average error in recovery of sparse signals and discrete fourier transform 

      Özçelikkale, Ayça; Yüksel, S.; Özaktaş Haldun M. (IEEE, 2012-04)
      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 ...
    • Coded scenes for fast system calibration in magnetic particle imaging 

      Ilbey, S.; Top, C. B.; Güngör, A.; Saritas, E. U.; Güven, E. (Institute of Electrical and Electronics Engineers, 2018)
      Magnetic nanoparticle (MNP) agents have a wide range of clinical application areas for both imaging and therapy. MNP distribution inside the body can be imaged using Magnetic Particle Imaging (MPI). For MPI image reconstruction ...
    • A compression method based on compressive sampling for 3-D laser range scans of indoor environments 

      Dobrucali O.; Barshan, B. (2010)
      When 3-D models of environments need to be transmitted or stored, they should be compressed efficiently to increase the capacity of the communication channel or the storage medium. We propose a novel compression technique ...
    • A compression method for 3-D laser range scans of indoor environments based on compressive sensing 

      Dobrucali O.; Barshan, B. (2011)
      Modeling and representing 3-D 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 digital receiver: first hardware implementation results 

      Korucu, A. B.; Çakar, O.; Alp, Y. K.; Gök, G.; Arikan, O. (Institute of Electrical and Electronics Engineers, 2018)
      In this work, first real hardware implementation results of CDR (Compressive Digital Receiver) are detailed. CDR is a digital receiver technology, which estimates frequency, amplitude, pulse-width etc. parameters of the ...
    • Compressive sensing based flame detection in infrared videos 

      Günay O.; Enis Çetin, A. (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 delay-doppler radars 

      Teke O.; Arikan, O.; Gürbüz, A.C. (2013)
      Compressive Sensing theory shows that, a sparse signal can be reconstructed from its sub-Nyquist 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 

      Ozkan V.A.; Takan T.; Kakenov N.; Kocabas C.; Altan H. (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. © ...
    • Compressive sensing-based robust off-the-grid stretch processing 

      Ilhan, I.; Gurbuz, A. C.; Arikan, O. (Institution of Engineering and Technology, 2017)
      Classical stretch processing (SP) obtains high range resolution by compressing large bandwidth signals with narrowband receivers using lower rate analogue-to-digital converters. SP achieves the resolution of the large ...
    • Detection of sparse targets with structurally perturbed echo dictionaries 

      Guldogan, M. B.; Arikan, O. (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 ...
    • Expectation maximization based matching pursuit 

      Gurbuz, A.C.; Pilanci, M.; Arikan, O. (2012)
      A novel expectation maximization based matching pursuit (EMMP) algorithm is presented. The method uses the measurements as the incomplete data and obtain the complete data which corresponds to the sparse solution using an ...
    • Image feature extraction using compressive sensing 

      Eleyan, A.; Kose, K.; Cetin, A.E. (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 ...
    • Learning-based compressive MRI 

      Gözcü, B.; Mahabadi, R. K.; Li, Y. H.; Ilıcak, E.; Çukur, T.; Scarlett, J.; Cevher, V. (Institute of Electrical and Electronics Engineers, 2018)
      In the area of magnetic resonance imaging (MRI), an extensive range of non-linear reconstruction algorithms has been proposed which can be used with general Fourier subsampling patterns. However, the design of these ...
    • A new OMP technique for sparse recovery 

      Teke O.; Gürbüz, A.C.; Arikan, O. (2012)
      Compressive Sensing (CS) theory details how a sparsely represented signal in a known basis can be reconstructed using less number of measurements. However in reality there is a mismatch between the assumed and the actual ...
    • Online Calibration of Modulated Wideband Converter 

      Alp, Y. K.; Korucu, A. B.; Karabacak, A. T.; Gürbüz, A. C.; Arikan, O. (Institute of Electrical and Electronics Engineers Inc., 2016)
      In this work, we propose a new method for online calibration of recently proposed Modulated Wideband Converter (MWC), which digitizes wideband sparse signals below the Nyquist limit without loss of information by using ...
    • Perturbed orthogonal matching pursuit 

      Teke, O.; Gurbuz, A. C.; Arikan, O. (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 ...
    • Projections onto convex sets (POCS) based optimization by lifting 

      Cetin, A.E.; Bozkurt, A.; Gunay O.; Habiboglu, Y.H.; Kose, K.; Onaran I.; Tofighi, M.; Sevimli, R.A. (2013)
      A new optimization technique based on the projections onto convex space (POCS) framework for solving convex and some non-convex optimization problems are presented. The dimension of the minimization problem is lifted by ...
    • Range-Doppler radar target detection using compressive sensing 

      Sevimli, R.A.; Tofighi, M.; Cetin, A.E. (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 ...
    • Range-doppler radar target detection using denoising within the compressive sensing framework 

      Akin Sevimli, R.; Tofighi, M.; Cetin, A.E. (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 ...
    • A recursive approach to reconstruction of sparse signals 

      Teke O.; Arikan, O.; Gurbuz, A.C. (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 ...