BoinJou: Difference between revisions

From Psych 221 Image Systems Engineering
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= Methods =
= Methods =
=== EM algorithm ===
=== EM algorithm ===
Subjects were 5 healthy volunteers.
 


=== Localization: Clustering ===
=== Localization: Clustering ===
Data were obtained on a GE scanner. Et cetera.
 


=== Classification ===
=== Classification ===
The MR data was analyzed using [http://white.stanford.edu/newlm/index.php/MrVista mrVista] software tools.
 


= Results =
= Results =
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:<math>x_n = \frac{1}{N} \sum_{k=0}^{N-1} X_k e^{\frac{2\pi i}{N} k n} \quad \quad n = 0,\dots,N-1.</math>
:<math>x_n = \frac{1}{N} \sum_{k=0}^{N-1} X_k e^{\frac{2\pi i}{N} k n} \quad \quad n = 0,\dots,N-1.</math>


== Retinotopic models in group-averaged data projected back into native space ==
 
Some text. Some analysis. Some figures.




= Conclusions =
= Conclusions =


Here is where you say what your results mean.


= References - Resources and related work =
 
= References =


References
References
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[[File:DataFile.zip | zip file with my data]]
[[File:DataFile.zip | zip file with my data]]


= Appendix II - Work partition (if a group project) =
= Appendix II - Work partition =
Brian and Bob gave the lectures. Jon mucked around on the wiki.

Revision as of 03:00, 20 March 2013

Back to Psych 221 Projects 2013



Introduction

You can use subsections if you like. Below is an example of a retinotopic map. Or, to be precise, below will be an example of a retinotopic map once the image is uploaded. To add an image, simply put text like this inside double brackets 'MyFile.jpg | My figure caption'. When you save this text and click on the link, the wiki will ask you for the figure.
Figure 1

Below is another example of a reinotopic map in a different subject.
Figure 2

Once you upload the images, they look like this. Note that you can control many features of the images, like whether to show a thumbnail, and the display resolution.

Figure 3


Motivation

MNI is an abbreviation for Montreal Neurological Institute.

Methods

EM algorithm

Localization: Clustering

Classification

Results

Localization: Clustering

Some text. Some analysis. Some figures.

Classification

Some text. Some analysis. Some figures.

Equations

If you want to use equations, you can use the same formats that are use on wikipedia.
See wikimedia help on formulas for help.
This example of equation use is copied and pasted from wikipedia's article on the DFT.

The sequence of N complex numbers x0, ..., xN−1 is transformed into the sequence of N complex numbers X0, ..., XN−1 by the DFT according to the formula:

Xk=n=0N1xne2πiNknk=0,,N1

where i is the imaginary unit and e2πiN is a primitive N'th root of unity. (This expression can also be written in terms of a DFT matrix; when scaled appropriately it becomes a unitary matrix and the Xk can thus be viewed as coefficients of x in an orthonormal basis.)

The transform is sometimes denoted by the symbol , as in 𝐗={𝐱} or (𝐱) or 𝐱.

The inverse discrete Fourier transform (IDFT) is given by

xn=1Nk=0N1Xke2πiNknn=0,,N1.



Conclusions

References

References

Software

Appendix I - Code and Data

Code

File:CodeFile.zip

Data

zip file with my data

Appendix II - Work partition