Lattice QCD with chiral fermions are extremely computationally expensive, but on the other hand provides an accurate tool for studying the physics of strong interactions. Since the truncated overlap variant of domain wall fermions are equivalent to overlap fermions in four dimensions at any lattice spacing, in this paper we have used domain wall fermions for our simulations. The physical information of lattice QCD theory is contained in quark propagators. In practice computing quark propagator in lattice is an inversion problem of the Dirac operator matrix representing this quarks. In order to develop fast inversion algorithms we have used overlap solvers in two dimensions. Lattice QED theory with U(1) group symmetry in two dimensional space-times dimensions has always been a testing ground for algorithms. By the other side, motivated by our previews work that the two-grid algorithm converge faster than the standard iterative methods for overlap inversion but not for all quark masses, we thought to test this idea in less dimensions such as U(1) gauge theory. Our main objective of this paper it is to implement and develop the idea of a two level algorithm in a new algorithm coded in QCDLAB. This implementation is presented in the preconditioned GMRESR algorithm, as our new contribution in QCDLAB package. The preconditioned part of our algorithm, different from the one of [18], is the approximation of the overlap operator with the truncated overlap operator with finite N3 dimension. We have tested it for 100 statistically independent configurations on 32 x 32 lattice background U(1) field at coupling constant β=1 and for different bare quark masses mq = [0.5, 0.45, 0.4, 0.35, 0.3, 0.25, 0.2, 0.15, 0.1]. We have compared the convergence history of the preconditioned GMRESR residual norm with another overlap inverter of QCDLAB as an optimal one, such as SHUMR. We have shown that our algorithm converges faster than SHUMR for different quark masses. Also, we have demonstrated that it saves more time for light quarks compared to SHUMR algorithm. Our algorithm is approximately independent from the quark mass. This is a key result in simulations with chiral fermions in lattice theories. By the other side, if we compare the results of [18] for quark mass 0.1 in SU(3), results that our chosen preconditioned saves a factor of 2 but in U(1). Our next step is to test this algorithm in SU(3) and to adopt it in parallel.
Diagnostic examinations through analog and numerical images are today the main method of diagnosing various diseases. Numerical images can be obtained with different methods depending on the technique used. Basically, any technique of obtaining medical images is nothing but the connection of a physical process of interaction of radiation with the subject / environment and with the help of the computer, these processes became visible through numerical images. Given the nature of the physical phenomena used and the lack of perfection of detection systems, the result is not perfect but is an estimate/price of “true value” which remains unattainable. Adding to this the human error during a medical examination, the patient's movements, etc., can flow very important artifacts, which must first be understood and analyzed and then using numerical methods, to correct them in the final version of the numerical image. This paper analyzes some numerical methods of numerical image correction such as interpolation and convolution, implemented in MATLAB program. In particular the interpolation technique is applied using Artificial Neural Networks (ANN), feedforward.
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