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As you draw, Photoshop automatically adds whatever points are needed. You only need to worry about selecting a path overlap button, which determines how paths intersect and interact. See the next section to find out which button to choose when. After you create a path, you can select it or edit it by using the two tools on the flyout directly above the pen tools flyout: Path Selection tool (black arrow): This tool selects an entire path. Just click inside the path to select it. If you created subpaths, the tool selects only the one under your cursor. You also use this tool to select vector objects, as explained in 14. Direct Selection tool (white arrow): This tool permits you to click and drag points and handles to reshape a path. You can access the tool when any other path tool is active by pressing and holding Ctrl ( on the Mac). And you can Alt-click (Option-click on the Mac) inside a path to select the entire path without switching to the Path Selection (black arrow) tool.

denotes the regression vector. The smaller eigenvalues of R, contribute to slower convergence and the larger eigenvalues limit the range of allowed step-sizes and, thereby, limit the learning abilities of the filter. Best convergence and learning performance would result when al the eigenvalues of R, are equal. This situation requires the input data to be white l so that R, would have the form R, = C T for some variance 0. ~I : However, in general, the input data is colored and the eigenvalues of R, can vary significantly in value from the smallest to the largest. One strategy to improve filter performance is to attempt to pre-whiten the data prior to adaptation. If the auto-correlation of the input sequence is known, then we could use this information to construct a filter that pre-whitens the data, as we shall explain shortly. However, since the statistics of the input data are seldom known beforehand, the design of such pre-whitening filters is generally not possible. Still, there are ways to achieve this objective in an approximate manner. One such way is to transform the regressor prior to adaptation by some pre-selected unitary transformation, such as the discrete Fourier transform (DFT) or the discrete cosine transform (DCT). These transforms have useful de-correlation properties that help improve the convergence performance of LMS for correlated input data.

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Unfortunately, Bluetooth communications are not supported at all within the BlackBerry simulator, so developers need to test and debug their programs on an actual device. This is an inconvenience and definitely makes for a slower edit-compile-test cycle during code development, but that s the way it is, at least for the currently available simulators from RIM. To debug on the device, you can either sprinkle your program liberally with message boxes that will tell you what is happening inside the code, or you can actually conduct a complete debugging session with your device over USB from within the JDE debugger. In order to use this feature of the JDE, follow these steps: 1. Make sure your BlackBerry device is attached to your computer via the standard USB cable. 2. Make sure you have installed your program to the device. (The debugger will not do this for you!) 3. In the JDE choose Attach To Handheld from the Debug menu. If all goes well you will see a Debugger attaching screen on your device. 4. Set a breakpoint in your source code. 5. Go to the device and run your program. The JDE debugger follows your program s execution, just as it would if your program were running in the simulator.

state A as represented by the 3 1 transition. Eventually, the item is permanently xed and returned to the assembly line as represented by state 5. The lower half of the owgraph represents an analogous process for the less severe type B problems. This owgraph has two paths: 0 1 3 5 and 0 2 4 5. There are two rst-order loops and they do not touch each other. There is one secondorder loop, made up of the two nontouching rst-order loops. Table 2.2 gives a summary of the paths and loops along with their transmittances. For this owgraph, the MGF of the overall waiting time distribution i.e., the waiting time from 0 5, is found using (2.26): M(s) = where P1 (s) = p01 p35 M01 (s)M13 (s)M35 (s) P2 (s) = p02 p45 M02 (s)M24 (s)M45 (s) L1 (s) = p31 M13 (s)M31 (s) + p42 M24 (s)M42 (s) L2 (s) = p31 p42 M13 (s)M31 (s)M24 (s)M42 (s) L1 (s) = p42 M24 (s)M42 (s) 1 L2 (s) = p31 M13 (s)M31 (s). 1 P1 (s)[1 L1 (s)] + P2 (s)[1 L2 (s)] 1 1 , 1 L1 (s) + L2 (s) (2.28)

Making Over Your Foreground Focus (continued)

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