scholarly journals Power Sum Polynomials in a Discrete Tomography Perspective

2021 ◽  
pp. 325-337
Author(s):  
Silvia M. C. Pagani ◽  
Silvia Pianta
Keyword(s):  
Discrete Tomography ◽  
Power Sum

scholarly journals ON THE GROWTH OF LINEAR RECURRENCES IN FUNCTION FIELDS

2020 ◽  
pp. 1-10
Author(s):  
CLEMENS FUCHS ◽  
SEBASTIAN HEINTZE
Keyword(s):  
Number Field ◽  
Function Field ◽  
Function Fields ◽  
Linear Recurrence ◽  
Recurrence Sequence ◽  
Linear Recurrences ◽  
Field Case ◽  
Power Sum ◽  
Linear Recurrence Sequence

Abstract Let $ (G_n)_{n=0}^{\infty } $ be a nondegenerate linear recurrence sequence whose power sum representation is given by $ G_n = a_1(n) \alpha _1^n + \cdots + a_t(n) \alpha _t^n $ . We prove a function field analogue of the well-known result in the number field case that, under some nonrestrictive conditions, $ |{G_n}| \geq ( \max _{j=1,\ldots ,t} |{\alpha _j}| )^{n(1-\varepsilon )} $ for $ n $ large enough.

Reconstructing binary and colored matrices in discrete tomography

4OR ◽  
2014 ◽  
Vol 12 (3) ◽  
pp. 297-298
Author(s):  
Ghassen Tlig
Keyword(s):  
Discrete Tomography

Boundary Ghosts for Discrete Tomography

2021 ◽  
Author(s):  
Matthew Ceko ◽  
Timothy Petersen ◽  
Imants Svalbe ◽  
Rob Tijdeman
Keyword(s):  
Discrete Tomography

scholarly journals Generic iterative subset algorithms for discrete tomography

2009 ◽  
Vol 157 (3) ◽  
pp. 438-451 ◽  
Author(s):  
K.J. Batenburg ◽  
J. Sijbers
Keyword(s):  
Discrete Tomography

scholarly journals Multivalued Discrete Tomography Using Dynamical System That Describes Competition

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Takeshi Kojima ◽  
Tetsushi Ueta ◽  
Tetsuya Yoshinaga
Keyword(s):  
Dynamical System ◽  
Theoretical Analysis ◽  
Continuous Time ◽  
Numerical Experiments ◽  
Nonlinear Dynamical System ◽  
Reconstructed Image ◽  
Optimization Approach ◽  
Discrete Tomography ◽  
Nonlinear Dynamical ◽  
Time Optimization

Multivalued discrete tomography involves reconstructing images composed of three or more gray levels from projections. We propose a method based on the continuous-time optimization approach with a nonlinear dynamical system that effectively utilizes competition dynamics to solve the problem of multivalued discrete tomography. We perform theoretical analysis to understand how the system obtains the desired multivalued reconstructed image. Numerical experiments illustrate that the proposed method also works well when the number of pixels is comparatively high even if the exact labels are unknown.

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