m |
m |
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| Line 51: | Line 51: | ||
==Problem 2== | ==Problem 2== | ||
| − | # Specify the size of <math>YY^ | + | # Specify the size of <math>YY^t</math> and <math>Y^tY</math>. Which matrix is smaller? |
<center> | <center> | ||
| − | <math>Y</math> is of size <math>p\times N</math>, so the size of <math>YY^ | + | <math>Y</math> is of size <math>p\times N</math>, so the size of <math>YY^t</math> is <math>p\times p</math>. |
| − | <math>Y</math> is of size <math>p\times N</math>, so the size of <math>Y^ | + | <math>Y</math> is of size <math>p\times N</math>, so the size of <math>Y^tY</math> is <math>N\times N</math>. |
| − | Obviously, the size of <math>Y^ | + | Obviously, the size of <math>Y^tY</math> is much smaller, since <math>N<<p</math>. |
</center> | </center> | ||
| − | # Prove that both <math>YY^ | + | # Prove that both <math>YY^t</math> and <math>Y^tY</math> are both symmetric and positive semi-definite matrices. |
<center> | <center> | ||
| Line 67: | Line 67: | ||
<math> | <math> | ||
| − | (YY^ | + | (YY^t)^t=YY^t |
</math> | </math> | ||
To prove it is positive semi-definite: | To prove it is positive semi-definite: | ||
| Line 74: | Line 74: | ||
<math> | <math> | ||
| − | x^ | + | x^tYY^tx=(Y^tx)^T(Y^tx)\geq 0 |
</math> | </math> | ||
| + | So the matrix <math>YY^t</math> is positive semi-definite. | ||
| + | |||
| + | The proving procedures for <math>Y^tY</math> are the same. | ||
</center> | </center> | ||
Revision as of 00:45, 7 July 2019
Communication, Networking, Signal and Image Processing (CS)
Question 5: Image Processing
August 2016 (Published on Jul 2019)
Problem 1
- Calculate an expression for $ \lambda_n^c $, the X-ray energy corrected for the dark current.
$ \lambda_n^c=\lambda_n^b-\lambda_n^d $
- Calculate an expression for $ G_n $, the X-ray attenuation due to the object's presence.
$ G_n=-\mu(x,y_0+n*\Delta d)\lambda_n $
- Calculate an expression for $ \hat{P}_n $, an estimate of the integral intensity in terms of $ \lambda_n $, $ \lambda_n^b $, and $ \lambda_b^d $.
$ \lambda_n=(\lambda_n^b-\lambda_n^d)e^{-\int_0^x \mu(t)dt} $
$ \hat{P}_n=\int_0^x \mu(t)dt=-log\frac{\lambda_n}{\lambda_n^b-\lambda_n^d} $
- For this part, assume that the object is of constant density with $ \mu(x,y)=\mu_0 $. Then sketch a plot of $ \hat{P}_n $ versus the object thickness, $ T_n $, in $ mm $, for the $ n^{th} $ detector. Label key features of the curve such as its slope and intersection.
Problem 2
- Specify the size of $ YY^t $ and $ Y^tY $. Which matrix is smaller?
$ Y $ is of size $ p\times N $, so the size of $ YY^t $ is $ p\times p $.
$ Y $ is of size $ p\times N $, so the size of $ Y^tY $ is $ N\times N $.
Obviously, the size of $ Y^tY $ is much smaller, since $ N<<p $.
- Prove that both $ YY^t $ and $ Y^tY $ are both symmetric and positive semi-definite matrices.
To prove it is symmetric:
$ (YY^t)^t=YY^t $ To prove it is positive semi-definite:
Let $ x $ be an arbitrary vector
$ x^tYY^tx=(Y^tx)^T(Y^tx)\geq 0 $ So the matrix $ YY^t $ is positive semi-definite.
The proving procedures for $ Y^tY $ are the same.
- Derive expressions for $ V $ and $ \Sigma $ in terms of $ T $, and $ D $.
- Drive expressions for $ U $ in terms of $ Y $, $ T $, and $ D $.
- Derive expressions for $ E $ in terms of $ Y $, $ T $, and $ D $.
- If the columns of $ Y $ are images from a training database, then what name do we give to the columns of $ U $?
