## Hello world!

Aha!  This is where the Blog goes.

Maybe I’ll make a blog about building a universal interface board for the low cost FPGA app boards from Xilinx, Alters (Intel, I’ll never get used to that) and Actel (Microsemi, I’ll never get used to that either.)

on second thought, I’ll put that in the ‘Projects’ section.

here’s a Matlab signal processing Demo for now.

### Signal Processing

 MATLAB is most popular for its Signal Processing capabilities, here in this section we are going to discuss various signal processing techniques and signal processing problems solved with the help of MATLAB. First of all, we will learn how to generate a signal in MATLAB such as sinusoidal and then we will add the random noise to it and analyze the effects of noise to the signal. Most MATLAB functions require you to begin with a vector representing a time base. Consider generating data with a 1000 Hz sample frequency, for example. An appropriate time vector is generated as follow: fSampling = 1000;        This is the Sampling Frequency With the help of this Sampling Frequency, one can generate the appropriate time vector as follow t = [0 : 1/fSampling : 1] ‘ ; Given t, you can create a sample signal y consisting of sinusoid s, 50 Hz or anything you want as follow: y = sin(2*pi*50*t); Now we hope that you understand this, otherwise goto the MATLAB page and see the basic example of generating the Sinusoidal Waveform. Now Lets advance our discussion and add some extra parameter to our signal, the script file for this is as follow: clc; clear; fSampling = 1000; t = [0:0.001:1]; y = sin(2*pi*50*t) + 2*sin(2*pi*120*t); subplot(2,1,1) plot(t(1:50),y(1:50),'r','LineWidth',1.5); grid on; title('\bf\itSinusoidal Signal Without Noise'); xlabel('\bfTime\rightarrow'); ylabel('\bfAmplitude\rightarrow'); ylabel('\bf\itAmplitude\rightarrow'); subplot(2,1,2) randn('state',0); yn = y + 0.5*randn(size(t)); plot(t(1:50),yn(1:50),'LineWidth',1.5,'Color',[.9 .7 .2]); grid on; ylabel('\bf\itAmplitude\rightarrow'); title('\bf\itSinusoidal Signal With Normally Distributed Noise'); xlabel('\bfTime\rightarrow');  This script file is named as Signal_Corrupted.m, now run this script file and view the output which is as follow:- The above figure clearly shows the partial difference between the original signal and the Noise Corrupted Signal, to view the disastrous effect of noise on the signal view the figure and code below:  clc; clear; fSampling = 1000; t = [0:0.001:1]; y = sin(2*pi*50*t) + 2*sin(2*pi*120*t); subplot(2,1,1) plot(t(1:50),y(1:50),'r','LineWidth',1.5); grid on; title('\bf\itSinusoidal Signal Without Noise'); xlabel('\bfTime\rightarrow'); ylabel('\bfAmplitude\rightarrow'); ylabel('\bf\itAmplitude\rightarrow'); subplot(2,1,2) randn('state',0); yn = y + 1.5*randn(size(t)); plot(t(1:50),yn(1:50),'LineWidth',1.5,'Color',[.9 .7 .2]); grid on; ylabel('\bf\itAmplitude\rightarrow'); title('\bf\itSinusoidal Signal With Normally Distributed Noise'); xlabel('\bfTime\rightarrow');  Now save the image as "Signal_Corrupted_Heavily.m", now run the script file and view the output as follow:- The Signal gets corrupted heavily by the noise, this is clearly shown in the figure.