Signaly - Revision history

Balogh at 13:26, 18 June 2008

2008-06-18T13:26:49Z

Balogh at 13:26, 18 June 2008

2008-06-18T13:26:25Z

Palo at 12:30, 18 June 2008

2008-06-18T12:30:44Z

Balogh: /* Plávajúci priemer */

2008-06-18T12:29:44Z

‎Plávajúci priemer

Balogh: /* Plávajúci priemer */

2008-06-18T12:28:42Z

‎Plávajúci priemer

Balogh at 12:27, 18 June 2008

2008-06-18T12:27:24Z

Balogh at 12:25, 18 June 2008

2008-06-18T12:25:17Z

Balogh at 12:14, 18 June 2008

2008-06-18T12:14:14Z

Balogh at 12:13, 18 June 2008

2008-06-18T12:13:31Z

New page

== Signály ==

% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %
% %
% Takto sa kresli spektrum signalu typu Sinus s f 440 Hz. %
% %
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %

f_sample = 8000; % [Hz] - pre telefony OK
duration = 1; % [s] - jedna sekunda
freq = 440; % [Hz] - komorne A4 = 440 Hz
A = 0.9; % [V] - amplituda signalu z <-1,1>

time = 0 : (1/f_sample) : duration;
SinusSignal = A * sin( 2*pi*freq*time + 0 );

T_sample = 1/f_sample;
N = length(SinusSignal);

maxtime = (N-1) * T_sample; % Doba trvania signalu

time = (0:N-1) * T_sample; % Casova osa
freq = (0:N-1) / maxtime; % Kmitoctova osa

% Fourierova analyza:

S = fft(SinusSignal); % Vypocet komplexneho spektra
Mod = 2/N * abs(S); % Spravne amplitudove spektrum
Pha = 180/pi * angle(S); % Fazove spektrum v stupnoch

N1 = round(N/2); % Kreslime len relevantu cast po f_sample/2

subplot(2,1,1);
plot(freq(1:N1),Mod(1:N1)); % Aplitudova frekvencna charakteristika

subplot(2,1,2);
plot(freq(1:N1),Pha(1:N1)); % Fazova frekvencna charakteristika

@@ Line 1: / Line 1: @@
 == Spektrá ==
-<source lang="matlab">
+<source lang="octave">
   % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %
   %                                                             %

@@ Line 1: / Line 1: @@
 == Spektrá ==
+<source lang="matlab">
   % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %
   %                                                             %
@@ Line 39: / Line 39: @@
   plot(freq(1:N1),Pha(1:N1));       % Fazova frekvencna charakteristika
+</source>
 [[Image:spektrum.m]]

@@ Line 69: / Line 69: @@
-[[Image:movingaverage.m]]
+[[Media:movingaverage.m|movingaverage.m]]
 === Príklad použitia ===
@@ Line 114: / Line 114: @@
-[[Media:MAexample.m]]
+[[Media:MAexample.m|MAexample.m]]
-[[Media:data.m]]
+[[Media:data.m|data.m]]

@@ Line 71: / Line 71: @@
 [[Image:movingaverage.m]]
+=== Príklad použitia ===
   % Priklad na pouzivanie MA filtra:
-−
   SignalSinus=sin(linspace(1,6*pi,300))
-−
-−
   % Najprv sa pozrime, ako poskodi MA filter povodny harmonicky
   % signal pri roznych velkostiach okienka:
-−
   plot(SignalSinus);  hold on;
   plot(movingaverage(SignalSinus,5));
   plot(movingaverage(SignalSinus,25));
   plot(movingaverage(SignalSinus,100));
-−
-−
   % Teraz uz naozaj ideme filtrovat. Vidno, ze ak jed MA filter
   % normalny cisty signal mierne tlmi, tuto sa to neprejavi, pretoze
   % sum niektore hodnoty rozhodi aj smerom nahor, aj nadol. Takze
   % vyfiltrovany sinus je niekedy aj vacsi ako povodny...
-−
   clg;
-−
   noise    = 0 + 0.1 * randn(size(SignalSinus)); % \mu = 0, \sigma = 0.1
-−
   plot(SignalSinus+noise); hold on;
   plot(movingaverage(SignalSinus+noise,10),"r");
   plot(SignalSinus,"g");
-−
-−
-−
   % Teraz ideme filtrovat realne data z teplomera:
-−
   clg;
   data; % nacita vektor temperature s teplotami:
-−
-−
   t1 = movingaverage(temperature(:,2)',10);  % skus rozlicne M = 10, 30, 100
   plot(t1);
   hold on;
   plot(temperature(:,2),"g");
-−
-−
 [[Media:MAexample.m]]
 [[Media:data.m]]

@@ Line 49: / Line 49: @@
   % Verzia 1.0 na prvy pokus 5. 6. 2008 R.B.
+  %
-−
   function y = movingaverage (x, M)
-−
   %  x je vstupny vektor, ktory filtrujeme
   %  M je rad filtra, kolko clenov priemerujeme
-−
   y=[zeros(1,M-1) x];     % predlzime vektor o M-1 nul na zaciatku
                           % aby sa aj prva hodnota zaratala do priemeru
@@ Line 60: / Line 60: @@
    y(i) = sum( y( i : (i+M-1) ) );       % spocitavame M-tice
   endfor
   y=y(1:length(x));                      % a vektor zasa skratime o ten
                                          % pridavok, lenze na konci
-−
   endfunction
 [[Image:movingaverage.m]]

← Older revision		Revision as of 12:27, 18 June 2008
Line 114:		Line 114:


−	[[~~Image~~:MAexample.m]]	+	[[Media:MAexample.m]]
		+	[[Media:data.m]]

← Older revision		Revision as of 12:25, 18 June 2008
Line 1:		Line 1:
−	== ~~Signály~~ ==	+	== Spektrá ==


Line 42:		Line 42:

	[[Image:spektrum.m]]		[[Image:spektrum.m]]
		+
		+	== Plávajúci priemer ==
		+
		+	% Toto je moj vlastny plavajuci priemer
		+	%
		+	% Verzia 1.0 na prvy pokus 5. 6. 2008 R.B.
		+	%
		+
		+	function y = movingaverage (x, M)
		+
		+	% x je vstupny vektor, ktory filtrujeme
		+	% M je rad filtra, kolko clenov priemerujeme
		+
		+	y=[zeros(1,M-1) x]; % predlzime vektor o M-1 nul na zaciatku
		+	% aby sa aj prva hodnota zaratala do priemeru
		+	for i=1 : length(x)
		+	y(i) = sum( y( i : (i+M-1) ) ); % spocitavame M-tice
		+	endfor
		+
		+	y=y/M; % napokon to vydelime
		+
		+	y=y(1:length(x)); % a vektor zasa skratime o ten
		+	% pridavok, lenze na konci
		+
		+	endfunction
		+
		+	[[Image:movingaverage.m]]
		+
		+
		+	Príklad použitia:
		+
		+	% Priklad na pouzivanie MA filtra:
		+
		+	SignalSinus=sin(linspace(1,6*pi,300))
		+
		+
		+	% Najprv sa pozrime, ako poskodi MA filter povodny harmonicky
		+	% signal pri roznych velkostiach okienka:
		+
		+	plot(SignalSinus); hold on;
		+	plot(movingaverage(SignalSinus,5));
		+	plot(movingaverage(SignalSinus,25));
		+	plot(movingaverage(SignalSinus,100));
		+
		+
		+	% Teraz uz naozaj ideme filtrovat. Vidno, ze ak jed MA filter
		+	% normalny cisty signal mierne tlmi, tuto sa to neprejavi, pretoze
		+	% sum niektore hodnoty rozhodi aj smerom nahor, aj nadol. Takze
		+	% vyfiltrovany sinus je niekedy aj vacsi ako povodny...
		+
		+	clg;
		+
		+	noise = 0 + 0.1 * randn(size(SignalSinus)); % \mu = 0, \sigma = 0.1
		+
		+	plot(SignalSinus+noise); hold on;
		+	plot(movingaverage(SignalSinus+noise,10),"r");
		+	plot(SignalSinus,"g");
		+
		+
		+
		+	% Teraz ideme filtrovat realne data z teplomera:
		+
		+	clg;
		+	data; % nacita vektor temperature s teplotami:
		+
		+
		+	t1 = movingaverage(temperature(:,2)',10); % skus rozlicne M = 10, 30, 100
		+	plot(t1);
		+	hold on;
		+	plot(temperature(:,2),"g");
		+
		+
		+	[[Image:MAexample.m]]

@@ Line 1: / Line 1: @@
 == Signály ==
@@ Line 39: / Line 38: @@
   subplot(2,1,2);
   plot(freq(1:N1),Pha(1:N1));       % Fazova frekvencna charakteristika