Browsing by Keywords "Convolution"

Now showing items 1-6 of 6

    • Convexity in source separation: Models, geometry, and algorithms 

      McCoy, M. B.; Cevher, V.; Dinh, Q. T.; Asaei, A.; Baldassarre, L. (Institute of Electrical and Electronics Engineers Inc.IEEE, 2014)
      Source separation, or demixing, is the process of extracting multiple components entangled within a signal. Contemporary signal processing presents a host of difficult source separation problems, from interference cancellation ...

    • Fast algorithms for digital computation of linear canonical transforms 

      Koç, A.; Oktem, F. S.; Özaktaş, Haldun M.; Kutay, M. A. (Springer, New York, 2016)
      Fast and accurate algorithms for digital computation of linear canonical transforms (LCTs) are discussed. Direct numerical integration takes O.N2/ time, where N is the number of samples. Designing fast and accurate ...

    • Fractional free space, fractional lenses, and fractional imaging systems 

      Sümbül, U.; Ozaktas, H. M. (OSA - The Optical Society, 2003)
      Continuum extensions of common dual pairs of operators are presented and consolidated, based on the fractional Fourier transform. In particular, the fractional chirp multiplication, fractional chirp convolution, and ...

    • On possible deterioration of smoothness under the operation of convolution 

      Uludağ, A. Muhammed (Bilkent University, 1996)
      We show that the convolution of two probability densities which are restrictions to R of entire functions can possess infinite essential supremuin on each interval. We also present several sufficient conditions of ...

    • Optimal filtering in fractional Fourier domains 

      Kutay, M. A.; Ozaktas, H. M.; Arıkan, O. (Institute of Electrical and Electronics Engineers, 1997-05)
      For time-invariant degradation models and stationary signals and noise, the classical Fourier domain Wiener filter, which can be implemented in O(N log N) time, gives the minimum mean-square-error estimate of the original ...

    • Structured least squares problems and robust estimators 

      Pilanci, M.; Arikan, O.; Pinar, M. C. (IEEE, 2010-10-22)
      A novel approach is proposed to provide robust and accurate estimates for linear regression problems when both the measurement vector and the coefficient matrix are structured and subject to errors or uncertainty. A new ...