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Callbacks on mark them IT MARKS DETERMINES THEM TO YOU IN THE DOMINION OF THE TIME1) Energy of marks them: The energy is the integral of the instantaneous power
2) medium Power of marks them: In the case it marks them s(t) is limitless in the time
and it does not have ended energy, considers the temporal medium power
of marks them
3) medium Valor of marks them:
4) Member alternated of marks them s(t):
5) Factor of peak: It is reported to marks them symmetrical, which are
lacking in continuous member and have equal and oppositethe maximum value s M andthe minimal value s m |sm| = sM = sp ,
is had:
6) Function of retort:
in practical it marks them retort is obtained adding marks them that they obtain traslando marks them x(t) of a multiple amount of the period T0 of the repetition, is observed that only if the period T0 is greater of the period TX of it marks them source obtains a course similar to it.
7) Power of marks them periodic obtained for repetition of marks them of ended duration:
being E(T0) the energy calculated to the inside of a period, it coincides with and only if TX < T0 .
8) Function of temporal intercorrelationship or temporal mutual correlation for marks them of energy: characterizes the degree of likeness between 2
9) mutual Energy: Draft of the function of intercorrelationship calculated in t = 0
10) Product to climb of 2 marks them of energy: Draft of the value in the origin of the function of
intercorrelationship between two marks them
11) It marks them parallels, antipodali, orthogonal: Currency the index of intercorrelationship to) rxy = 1 it marks them parallels b) rxy = 0 marks them orthogonal c) rxy = -1 marks them antipodali
12) Function of intercorrelationship of marks them of power:
13) Function of temporal covarianza for marks them of power: It is the function of temporal intercorrelationship of the members to valor medium null:
14) Relation between intercorrelationship and intercovarianza of two marks them of power:
15) Family of marks incorrelati them: A family of marks them says incorrelata if all the functions of covarianza Kxy(t) is null.
16) Family of marks them incoherent: She is with of marks them of power for which for every t the functions of intercorrelationship R are null allxy(t), say also that draft of a family of marks them orthogonal. SHE MARKS DETERMINES THEM TO YOU IN THE DOMINION OF THE FREQUENCY17) Transformed of Fourier:
where S(f) is the phantom of marks them, in particular in the case that s(t) is marks them real, the amplitude phantom is an equal function while the phase phantom is one uneven function.
18) Antitrasformata di Fourier:
that is it marks them s(t) is esprimibile by means of the sum of a number infinitely of complex harmonic functions andjwt of infinitesimal amplitude and frequency f distributed in continuous way on the real axis.
19) Property of the transformed one of Fourier:
20) F [ 1 ]: d(f)
21) F [ sgn(t) ]:
22) F [ u(t) ]:
23) F [ rect(t/T) ]:
24) F [ sinc(t/T) ]:
25) Series of Fourier of marks them periodic continuous time:
being Cn the having coefficients of Fourier
26) Spectral density of energy:
from it the energy can be obtained of marks integrating
them on the axis of the
27) Spectral density of power: It is the transformed one of Fourier of the function of autocorrelationship for marks them of power, that is:
28) It marks limited them in band closely: The phantom of E(f) energy is an equal real function for marks them real in the ideal case, extends between the maximum frequency fM and one minimal frequency fm , the band of marks them is therefore B = fM - fm , valid definition also in the real case where the phantom is limitless but can neglect the frequencies above fM and under fm .
29) It marks them in band base: It is the typical one marks supplied them from a information source, its band is therefore allocata 0 £ fm £ fM, the single positive axle shaft is considered because for real and equal function marks them real the energy phantom is one. 30) It marks them in traslata band: It is obtained elaborating marks them in band base to the aim to adapt it to trasmissivo means, its band is therefore allocata: 0 < fm £ fc £ fM a lot often the extension of the band is approximately equal to the maximum frequency fM , although minimal frequency fm is various from zero.
31) It marks them in tightened traslata band: It is marks them that it respects the condition
32) It marks them in traslata band much grip: It is marks them that it respects the condition ULTERIOR RAPPRESENTAZIONI OF MARK THEM33) Phantom of marks them of real energy: Draft of an equal function, is therefore sufficient to
study the phantom that extends in the axle shaft
34) It marks them analytical: For the linearity of the antitransformed one of Fourier it
is had that to 35) Transformed of Hilbert of s(t):
36) complex Envelope of marks them: Draft of the antitransformed one of the phantom of marks
them analytical
37) Relation of ortonormalità:
38) Representation of marks them s(t) through one base:
39) Theorem of Nyquist - Shannon: Admitting for it marks them s(t) limitless in the time and
limited in band base a representation in the dominion of the
frequency by means of the base
40) N° of functions necessary in order to represent whichever marks them in its definition interval: Of it it is necessary an infinite number, however for some types of it marks them can be caught up a good accuracy also with a number ended of marks them, it is the case of marks them physicists practically limits to you in band and in duration for which a sampling with a number of champions N=2BT being B is sufficient the band of marks them physicist and T its duration. SEQUENCES And ELEMENTS Of MARK THEM NUMERICAL41) Sequence: With it is ordered of values that can be obtained from
mark them continuous s(t) considering instead of the variable one
continue t variable the discreet NT where n it is the succession of
the entire numbers, the generic sequence has expression
42) Energy for sequences:
43) Power for sequences:
44) Sequences of intercorrelationship between sequences to ended energy:
45) temporal Sequences of intercorrelationship for power sequences:
46) Sequences of intercovarianza for power sequences:
47) M-nario Alphabet: The elements of a numerical sequence can only assume pertaining values to with discreet {sq}, to everyone of these values can be associated one of the elements of a M_nario alphabet {zq} of cardinalità M that in kind is a power of 2 ossia M = 2b so as to to be able to use alphabets with long binary words b bit.
48) numerical Flow: Draft of the succession of symbols zq pertaining to the M-nario alphabet, every associate to an element of the sequence {sq} and that they are repeated with the same temporizzazione kT.
49) Time of symbol: It is the interval of T time that elapses between a symbol
and the successive one of the numerical flow, its inverse one
50) It marks them numerical multilevel: It marks samples them to you numerical introduce of dthe (t) that for being
transmitted they demand average to infinite band, in order to obviate
to ci² replaces dthe (t)
with a impulsive function of having energyf (t) ended duration and for which a limitation
practical in band can be obtained in such a way obtaining marks them
numerical multilevel
51) Speed of modulation: It marks them numerical asynchronous GENERALITY ON PROCESSES STOCASTICI52) Amount of information: In the case of a numerical source the information amount
is the largeness associated to the choice of a symbol, zq , between M the possible elements that
they form a M-nario alphabet, it is worth
53) continuous stocastico Process: Its realizations are continuous functions x(t) that they can be emitted from the analogic source. For a data T moment the process is reduced to one v.a. continues Xt = X(t) with distribution density p(Xt , t).
54) discreet stocastico Process to discreet values: It is the case of an emitted numerical flow from a numerical source, is the time that the value of the realizations is not continuous but discreet.
55) Density of combined probability of 2° the order:
56) Density of conditioned probability of order n:
in short variable the Xn to the time tn it is conditioned from the acquaintance of the v.a. emitted in the previous moments.
57) Process of Markoff of order n: It is a process of which the full acquaintance has itself if the density of combined probability of order is known n 1.
58) statistical medium Value:
59) medium Power statistics:
60) Variance:
61) Relation between variance and power:
62) Function of autocorrelationship statistics:
63) Function of autocovarianza statistics:
for t1 = t2 the autocovarianza coincides with the variance.
64) Relation between the autocovarianza and the autocorrelationship :
65) stationary Process in tight sense: It is a process for which the density of invariant probabilities is respect to one arbitrary temporal translation.
66) stationary Process broadly speaking: It is a process for which the density of invariant probabilities is respect to an arbitrary temporal translation, while the density of combined probability of 2° order only depends on t , is had that is: to) p(x;t) = p(x) b) p2(x1,x2;t1,t2) = p2(x1,x2;t) essendo t = t2 - t1 for a stationary process broadly speaking it is had that the valor medium statistical, the power and the variance are constant and correspond to the expected values of the correspondents temporal largenesses for a single realization of the process while the functions of autocorrelationship R(t) and autocovarianza K(t) depend solo from t.
67) Relation between the autocovarianza and the autocorrelationship for stationary processes:
68) First relation of Wiener - Khinchine:
that discreet spectral member in the origin evidences therefore one.
69) ergodico stationary Process: It is a process for which the single realization, observed on the entire axis of the times, adds all property statistics of the aleatory process, therefore that the temporal largenesses converge to the largenesses statistics.
70) Spectral density of intercrossed power:
71) Second relation of Wiener - Khinchine:
72) Characteristic of the process sum of two processes stazionari a(t) = x(t) y(t) : Considering process a(t) = x(t) y(t) the valor is
had medium statistical hto = hx hy while the autocorrelationship is
73) Characteristic of complex process b(t) = x(t) j y(t): Considering process b(t) = x(t) jy(t) the x
are had valor medium statisticalh b =h jhy while the autocorrelationship
is PROCESSES STOCASTICI CICLOSTAZIONARI74) ciclostazionario stocastico Process: Draft of a process whose function of autocorrelationship is periodic in t, in particular is spoken about ciclostazionari processes of 1° the order if the regularity is present also in valor medium statistical and the ciclostazionari processes of 2° the order if it is present single in the autocorrelationship . The analysis can be led back to that one of the stationary processes carrying out a translation z on the axis of the times and carrying out one medium independent but uniform such statistics also on v.a. in the period, in such a way hs and Rss(t) is estimated like medium values of the respective periodic functions in t .
75) Processes represent to you through complex envelope: Having
76) stationary Process in traslata band: Being stationary the S(t) process in traslata band it is
had that also the processes in band base Sc(t) and Ss(t) is stationary broadly speaking with identical functions
of autocorrelationship
77) real Processes with aleatory factors represent to you by means of temporal series: A S(t) process is considered having
78) Processes sample in band base to you: It is a particular case of the previous situation,
considers in fact a process in band having S(t) base
79) complex Processes with aleatory factors: We consider a continuous process real ciclostazionario in
traslata band which realization is associated
80) Process sum of real processes with aleatory factors: A S(t) process is had with representation in the
81) Gaussian continuous Process: It is a process of which the full acquaintance has itself
single statistics based on the acquaintance of the probability density
function of 2° the order, in the stazionarietà case the expression of
the density of probability of 1° the order is
82) Gaussian Noise: It is the Gaussian process turning out from the sum of
numerous marks them aleatory points out to you, in particular we have
a Gaussian noise white man if the spectral density of power is N(f) =
constantN 0 = which the
autocorrelationship corresponds
83) stationary Gaussian Noise in traslata band: We consider a generic representation of the noise in
band traslata
84) Gaussian Noise white man in the space of marks them: In the space with N dimension tending to the infinite, a
realization of the noise white man is rappresentabile with a having carrier n members
ciascuna with density of probability of 1° ordine |