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This experiment uses the basic 3 3 kernel as the image lter to achieve different effects as shown in Example 15.7. The implementation of the 2-D ltering de ned in Equation (15.13) is listed in Table 15.16. In Table 15.16, the input image located at position x of row in the data array, pixel[row][x], is ltered by an n-by-m 2-D lter filter[n][m]. The middle pixel corresponds to the current input. The input and its neighboring pixels are all contributing to the ltering results. To utilize the limited DSK memory, this experiment uses three rows of input image data for the 3 3 image lter kernel. These rows are arranged as shown in Figure 15.22. The processing starts with three rows of data. The current input and output are represented by index n as shown in Figure 15.22(a). The row indexed with n 1 contains the previous row of image data and n + 1 contains the next row. The ltering process continues from the rst column to the last column for each row. The update of the data buffer is carried one row at a time. The update of the lter delay line (three rows) is actually achieved by adjusting the 2-D data array index, row. As shown in Figure 15.22(a), row n is the current center row when the ltering process starts. After the rst update, the new current row is labeled with index n + 1 (see Figure 15.22(b)). After another update, the current row is n + 2 as shown in Figure 15.22(c). This pattern is repeated. The ltering process is depicted in Figure 15.12. It computes the dot product starting from columns then rows between the 3 3 data block and the 3 3 lter kernel.

11:

Set-up menu 2 covers a variety of options, from setting the brightness of the rear LCD to choosing how to control an attached Canon Speedlite. The following list outlines each option available from this menu.

Image row: n 1 (previous) Image row: n (current)

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depending on the context - one might either feel satisfied that the system is being used to the full, or one might be worried that it's capacity is nearing saturation There are many definitions of entropy in statistical studies Let us choose Shannon's traditional informational entropy as an example [235] For the informational entropy to work usefully as a measure, we need to be selective in the type of data which are collected In ref [33], the concept of an informational entropy was used to gauge the stability of a system over time In any feedback system there is the possibility of instability: of either wild oscillation or exponential growth Stability can only be achieved if the state of the system is checked often enough to adequately detect the resolution of the changes taking place.

If the checking rate is too slow, or the response to a given problem is not strong enough to contain it, then control is lost To define an entropy we must change from dealing with a continuous measurement to a classification of ranges Instead of measuring a value exactly, we count the amount of time a value lies within a certain range and say that all of those values represent a single state Entropy is closely associated with the amount of granularity or roughness in our perception of information, since it depends upon how we group the values into classes or states Indeed, all statistical quantifiers are related to some procedure for coarse-graining information, or eliminating detail To define an entropy one needs, essentially, to distinguish between signal and noise This is done by blurring the criteria for the system to be in a certain state.

choose the type of video output signal, whether NTSC (U.S.) or PAL (most of the world).

Image row: n+2 (next) Image row: n (previous) Image row: n+1 (current) (b)

As Shannon put it, we introduce redundancy into the states so that a range of input values (rather than a unique value) triggers a particular state If we consider every single jitter of the system to be an important quantity, to be distinguished by a separate state, then nothing is defined as noise, and chaos must be embraced as the natural law However, if one decides that certain changes in the system are too insignificant to distinguish between, such that they can be lumped together and categorized as a single state, then one immediately has a distinction between useful signal and error margins for useless noise In physics, this distinction is thought of in terms of order and disorder Let us represent a single quantifier of system resources as a function of time f ( t ) .

set the rear LCD brightness, choosing from seven levels. By pressing the SET button you see the current playback image along with a vertical black-to-white step-scale.

This function could be the amount of CPU usage, or the changing capacity of system disks, or some other variable We wish to analyse the behaviour of system resources by computing the amount of entropy in the signal f ( t ) This can be done by coarse-graining the range of f ( t ) into N cells:.

Image row: n+2 (current) Image row: n+3 (next) Image row: n+1 (previous) (c)

and the constants F are the boundaries of the ranges (imagine drawing horizontal threshold lines from the tick marks of Figure 11.9, thus dividing up the graph into horizontal slices). The probability that the signal lies in the cell i, during the time interval from zero to T, is the fraction of time the function spends in each cell i:

Battery info. A very useful readout,

Image row: n+1 (next) (a)

Now, let the statistical degradation of the system then be given by the Shannon entropy [2351

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