https://orcid.org/0000-0001-6256-8898
jjmg@edu.es
jjmg@um.es
jjmg@um.edu
Prof. Asis. Sr. Juan José Molina González
University of Murcia

Quantum spectral method

Considerer an yn_as python/script/Std/Fft in an discrete interval [a,b +C computed some the integer
Std/Fft_rot_y=x(e*x) integer_N-length{Gaussian N/3}Markov_
dat/Fft{discrete transform of Fourier) by a Gaussian an considerer the Y{Math Z-transform2^(-n) n^2/N-lentgh the root the occasiones can be computed some an Markov by the drunk conic interval (a,b).With the z-transform, the s-plane represents a set of signals (complex exponentials (Section 1.8)). For any given LTI (Section 2.1) system, some of these signals may cause the output of the system to converge, while others cause the output to diverge ("blow up"). The set of signals that cause the system's output to converge lie in the region of convergence (ROC). This module will discuss how to find this region of convergence for any discrete-time, LTI system.The region of convergence, known as the ROC, is important to understand because it defines the region where the z-transform exists. The z-transform of a sequence is defined a X(z)=∑n=−∞∞INTEGERe^x[n]z−/integer for any 3I(N-length){Gaussian N3}Using the demonstration, learn about the region of convergence for the Laplace Transform.Clearly, in order to craft a system that is actually useful by virtue of being causal and BIBO stable, we must ensure that it is within the Region of Convergence, which can be ascertained by looking at the pole zero plot. The Region of Convergence is the area in the pole/zero plot of the transfer function in which the function exists. For purposes of useful filter design, we prefer to work with rational functions, which can be described by two polynomials, one each for determining the poles and the zeros, respectively.

By any ROC period of Fft for an X(z)=∑n=−∞∞INTEGERe^x[n]z−/Hk/integer for any 3I({Gaussian N3}N-length) the series can produced are modular forms of G(z/N)&Z-transform over Laplace tramsform or Markov.

Proton-positron in SRH(K)/SRH(knh) for plank non constant decay in l- l+ muon and tau hadron for e+- an run 1 and0 to switchet for the circuit throuth the markovian ver grafic of hadron by the S+ and S- positron previously proton by the colisioner. The Markovian took the form of Schörodinger eq. For charge (+-) in hadron S+- by the lepton-proton by heavy metals radiactives for Sr to higuer velocity and relativity mass follow beauty decays quarks that appear in its natural behaviour in cosmic rays and produced in the colision proton.___-s+ AND S- AND PLANK NON CONSTANT OVER THIS EQUATIONS THAT DESCRIBING THE GAUSSIAN FOR MATHWORDS IN MATHLAB., WHEN ITS NATURAL LANGUAGE EACH TO FACE IS LINUX L TO MILLIONS OF BITS THAT IN THE LENGUAGE OF L* RECEIVE EL NOuN DE BAR SOME THE GAUSSIAN FOR SRH(K) OVER SRH(K*nH) Note: that 1 bar represented the unity of sets by the couple (i,j) in Qubits some portadora of information in quantum by the duet charge pronton-positron and took each unity of bar 4 random path possibles over 2 couples. That are some are describing the e+- muoon and tau by the possibles charges and are duone extensive for proton_____S+ and S- hadron experiment.

Lepton and quarks resultants of the colision can be received in an input image that has been degraded by constant power additive noise. wiener2 uses a pixelwise adaptive Wiener method based on statistics estimated from a local neighborhood of each pixel.
[J,noise_out] = wiener2(I,[m n]) returns the estimates of the additive noise power wiener2 calculates before doing the ltering is the adaptative linked for return to signals of the colision and degree in The syntax wiener2(I,[m n],[mblock nblock],noise) has been removed. Use the wiener2(I,[m
n],noise) syntax instead. J = wiener2(I,[m n],noise) [J,noise_out] = wiener2(I,[m n])
lepton-proton.odt