Information Technology Reference
In-Depth Information
Fig. 2.
Organization of the memory for a process
M
p
b
0
, ..., b
m
]. Using this sequence of bytes we
is a sequence of bytes, i.e.,
(
)=[
I
p
I
p
canproduceanimage
(
) in Red-Green-Blue (RGB) coding. The image
(
)
p
i,j
. Each pixel is three contiguous bytes of
the memory image. Given the height
is a bi-dimensional array of pixels
h
of the image, each RGB pixel has the
following RGB values:
p
i,j
=[
b
3(
i
+
h·j
)
b
3(
i
+
h·j
)+1
b
3(
i
+
h·j
)+2
]
(3)
b
3(
i
+
h·j
)
is used for the red component, the second
b
3(
i
+
h·j
)+1
where the first byte
b
3(
i
+
h·j
)+2
for the blue one. Figure 3 provides
an example of two memory images for the “
cognitive task
”ofsortingavector.
Figure 3(a) is the process at the initial state and Figure 3(b) is the process that
accomplished the task. The smaller stripe on the left of each figure is the code
area. This is stable during the process execution. The bigger stripe on the right is
the vector. At the beginning of the process (Fig. 3(a)), this area is homogeneous
as it shows a random vector. At the end of the process (see Fig. 3(b)), we can
see a figure suggesting we have a sorted vector.
Finally, to approximate the physical extraction of the activation images, we
use a distortion process for the images. This distortion process allows seeing
images where contiguous pixels are merged. This approximates the condition
of images captured by a physical scanning device. We cannot expect images
of the resolutions given by equation (3). This distortion is called
smoothing
or
blurring
. We use the simplest smoothing model, i.e., the rectangular uniform
filter. According to this filter, each pixel in the smoothed image
for the green one, and the third
s
i,j
is a weighted
