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== Max Woesner ==
== Max Woesner ==
[[Max Woesner|Back to my Home Page]]


=== Homework #2 - Something interesting from class ===
<br>
The <b>Linear Time Invariant System Game</b> can be used to help us understand the impulse response of a linear time invariant system.
<table border=1>
<tr>
  <td><b>Input<math>-----\longrightarrow\,\!</math></b></td>
  <td><b>Linear Time Invariant System</b></td>
  <td><b><math>\longrightarrow\,\!</math>Output</b></td>
  <td><b>Reason</b></td>
</tr>
<tr>
  <td><math>\delta (t) \!</math></td>
  <td><math>-------\longrightarrow\,\!</math></td>
  <td><math>h(t)\!</math></td>
  <td>Given</td>
</tr>
<tr>
  <td><math>\delta (t-t_0)\!</math></td>
  <td><math>-------\longrightarrow\,\!</math></td>
  <td><math>h(t-t_0)\!</math></td>
  <td>Time Invariance</td>
</tr>
<tr>
  <td><math>x(t_0)\delta (t-t_0)\!</math></td>
  <td><math>-------\longrightarrow\,\!</math></td>
  <td><math>x(t_0)h(t-t_0)\!</math></td>
  <td>Proportionality</td>
<tr>
  <td><math>\int_{-\infty}^{\infty} x(t_0)\delta\ (t-t_0)dt_0\!</math></td>
  <td><math>-------\longrightarrow\,\!</math></td>
  <td><math>\int_{-\infty}^{\infty} x(t_0)h(t-t_0)dt_0\!</math></td>
  <td>Superposition</td>
</tr>
</table>
<br>
where <math>\int_{-\infty}^{\infty} x(t_0)\delta\ (t-t_0)dt_0 = x(t)\!</math> <b> for any <math> x(t)\!</math></b> and <math>\int_{-\infty}^{\infty} x(t_0)h(t-t_0)dt_0\!</math> is the <b> convolution integral.</b><br>
We can expand the game further.


=== Homework #2 - Something interesting from class ===
<table border=1>
<tr>
  <td><b>Input<math>-----\longrightarrow\,\!</math></b></td>
  <td><b>Linear Time Invariant System</b></td>
  <td><b><math>\longrightarrow\,\!</math>Output</b></td>
  <td><b>Reason</b></td>
</tr>
<tr>
  <td><math>\delta (t) \!</math></td>
  <td><math>-------\longrightarrow\,\!</math></td>
  <td><math>h(t)\!</math></td>
  <td>Given</td>
</tr>
<tr>
  <td><math>\delta (t-t_0)\!</math></td>
  <td><math>-------\longrightarrow\,\!</math></td>
  <td><math>h(t-t_0)\!</math></td>
  <td>Time Invariance</td>
</tr>
<tr>
  <td><math>x(t_0)\delta\ (t-t_0)\!</math></td>
  <td><math>-------\longrightarrow\,\!</math></td>
  <td><math>x(t_0)h(t-t_0)\!</math></td>
  <td>Proportionality</td>
<tr>
  <td><math>x(t)\!</math></td>
  <td><math>-------\longrightarrow\,\!</math></td>
  <td><math>\int_{-\infty}^{\infty} x(t_0)h(t-t_0)dt_0\!</math></td>
  <td>Superposition</td>
</tr>
<tr>
  <td><math>e^{j2\pi ft}\!</math></td>
  <td><math>-------\longrightarrow\,\!</math></td>
  <td><math>\int_{-\infty}^{\infty} e^{j2\pi ft_0}h(t-t_0)dt_0\!</math></td>
  <td>Superposition</td>
</tr>
 
</table>
<br>


This page is under development
Let <math>\lambda\ = t-t_0</math>, so <math>t_0 = t-\lambda\ </math> and <math>dt_0 = -d\lambda\ </math><br>
Therefore <math>\int_{-\infty}^{\infty} e^{j2\pi ft_0}h(t-t_0)dt_0 = \int_{+\infty}^{-\infty} h(\lambda)e^{j2\pi f(t-\lambda)}(-d\lambda) = e^{j2\pi ft}\int_{-\infty}^{\infty} h(\lambda)e^{-j2\pi f\lambda}d\lambda\!</math><br>
This tells us that <math>e^{j2\pi ft}\!</math> is the eigenfunction and <math>\int_{-\infty}^{\infty} h(\lambda)e^{-j2\pi f\lambda}d\lambda\!</math> is the eigenvalue of <b>all linear time invariant systems.</b><br>
This amazing conclusion makes solving linear time invariant systems (the only systems we are really able to solve) so much simpler that we usually approximate real-world nonlinear problems as linear systems so we can solve them.<br>

Latest revision as of 08:46, 8 October 2009

Max Woesner

Back to my Home Page

Homework #2 - Something interesting from class


The Linear Time Invariant System Game can be used to help us understand the impulse response of a linear time invariant system.

Input Linear Time Invariant System Output Reason
δ(t) h(t) Given
δ(tt0) h(tt0) Time Invariance
x(t0)δ(tt0) x(t0)h(tt0) Proportionality
x(t0)δ(tt0)dt0 x(t0)h(tt0)dt0 Superposition


where x(t0)δ(tt0)dt0=x(t) for any x(t) and x(t0)h(tt0)dt0 is the convolution integral.
We can expand the game further.

Input Linear Time Invariant System Output Reason
δ(t) h(t) Given
δ(tt0) h(tt0) Time Invariance
x(t0)δ(tt0) x(t0)h(tt0) Proportionality
x(t) x(t0)h(tt0)dt0 Superposition
ej2πft ej2πft0h(tt0)dt0 Superposition


Let λ=tt0, so t0=tλ and dt0=dλ
Therefore ej2πft0h(tt0)dt0=+h(λ)ej2πf(tλ)(dλ)=ej2πfth(λ)ej2πfλdλ
This tells us that ej2πft is the eigenfunction and h(λ)ej2πfλdλ is the eigenvalue of all linear time invariant systems.
This amazing conclusion makes solving linear time invariant systems (the only systems we are really able to solve) so much simpler that we usually approximate real-world nonlinear problems as linear systems so we can solve them.