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marked with a + sign and out of the terminal marked with the - sign. Although this gives the appearance that the source is consuming power, note that +/- markings are just that and the voltage across the source wi ll depend on the entire circuit. Look carefully at I I and 12 in Fig. 8.50b. They are connected exactly as shown in Fig. 8.50a. Setting Dependent Source Gains . We will use the gain of the CCCS as an example. Comparing Figs. 8.50a and b, it appears that the direction of Ix is reversed. This was done to simplify the wiring between the CCCS and the branch where Ix is defined. By setting the gain of the CCCS to lIegative 2, the circuits become identical. Now consider the VCVS, which is wired in agreement with Fig. 8.50a. Here the gain is entered as positive 2. To set the gain of a dependent source, simply double-click on it to open the attribute box shown in Fig. 8.53 and enter the gain. Accessing the OUTPUT FilE. When you simulate the circuit, the simulation engine in PSPICE will open and the window in Fig. 8.54 will appear. When the simulation is complete, close this window to return to Schematics where you can access the output file through Examine Output File option in the ANALYSIS menu. When the OUTPUT FILE opens, scroll to the bottom to find I . You might notice that the output file window is actuall y the NOTEPAD text editor common to Windows- based PCs. So, yo u can copy and paste the simulation results directly into other software. When this circuit is simulated, the results in the output file are

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Copying a screenshot to the Clipboard works with any program or at the operating system level when capturing desktop or folder views, accessories, or virtually any view you see on your computer monitor. Once data are on the Clipboard, you can open Acrobat and convert the Clipboard data to a PDF document.

21 3458 16068 22 3913 18165 23 4375 20295 24 4840 22440 25 5304 24582 26 5763 26703 27 6213 28785 28 6650 30810 29 7070 32760 30

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Additionally, banking and financial insiders who were drug users began passing inside techniques and tips in exchange for meth. Initially, investigation of meth-related identity theft did not appeal to law enforcement either, as they were considered paper crimes versus more dangerous robberies, burglaries, or narcotics cases. It spread through loosely knit criminal networks, leaving law enforcement without central figures to target or to garner media notoriety. Meth was still not yet perceived as a major component, as there were other organized gangs, particularly Nigerian Criminal Enterprises, perpetrating similar scams in the big cities of the East and West. One telling indicator of how methamphetamine and identity theft transformed the criminal landscape in San Diego was the near disappearance of postal armed robberies in the area. After a series of hijack robberies of letter carriers in 1992 and a brief series of Post Office robberies in 1995 (both series were meth-related), San Diego experienced almost no robberies of postal personnel or facilities for many years. Fraudsters realized that committing economic crime was far safer than perpetrating violent crime. Yet just 100 miles north, in Los Angeles, Postal Inspectors formed a major crimes team to deal with the rash of armed robberies they were experiencing. THOUGHT LEADER IN IDENTITY THEFT PREVENTION

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In addition to the standard tabs, Excel 2010 also includes contextual tabs like the other Of ce applications. Whenever an object (such as a chart, a table, or a SmartArt diagram) is selected, speci c tools for working with that object are made available in the Ribbon. Figure 12-4 shows the contextual tab that appears when a chart is selected. In this case, it has three contextual tabs: Design, Layout, and Format. These tabs offer the commands for working with charts in Excel, so you are likely to use them often. Notice that the contextual tabs contain a description (Chart Tools) in Excel s title bar. When contextual tabs appear, you can, of course, continue to use all the other tabs.

Output 5.12A shortcut method to compute the cosine of the angles between column vectors of a matrix. Note that this agrees with output 5.7. For problem 5.9.

The above algorithm is computationally e cient because the matrix D do not need to be inverted at each iteration step, but it does not dramatically improve the convergence speed in comparison to the standard gradient descent method because the modi ed search direction is not exactly the natural gradient one [288]. Remark 11.2 The above algorithm uses the vector of activation functions f (y). The optimal choice of the activation function is given by equation (11.17) with qi (yi ) = ri (yi ), if we can adaptively estimate the true source probability distribution ri (yi ). Another solution is to use a score function according to the statistics of source signals. Typically, if a source signal yi is a super-Gaussian, one can choose fi (yi ) = tanh(yi ), and if it is a sub-Gaussian, one 3 can choose fi (yi ) = yi [29, 30, 402]. A question can be raised whether the learning algorithm will converge to a true solution if the approximated activation functions are used. The theory of the semi-parametric model for blind separation/deconvolution [28, 40, 41, 1356, 1358] shows that even if a misspeci ed pdf is used in the learning algorithm, it can still converge to the true solution if certain stability conditions are satis ed (see 10) [29].

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