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You may be wondering whether you could just forget about those pesky benchmark and peer group comparisons we discussed earlier. Why not simply invest in funds that received the top four- or ve-star billing from Morningstar (To nd a fund s Morningstar Rating , go to its Quicktake report on Morningstar.com and click on Ratings.) You certainly can do that. A ve-star top overall rating from Morningstar is nothing to sneeze at. For a fund to have a Morningstar Rating it must be in existence for over three years. Then Morningstar runs monthly analyses on all funds on a three-, ve-, and ten-year trailing period and assigns a rating to each fund. The top 10 percent of a category get the enviable ve stars, the next 22.5 percent get the four stars, the middle 35 percent get three stars, the next 22.5 percent get two stars, and the bottom 10 percent get one. Most of the funds I would use would have four or ve stars. But don t pick a fund solely on that basis. There could be some very good funds that are not rated at all because they lack the three-year track record required by Morningstar, even though the manager him- or herself is very experienced. Other funds may be awed in some area that prevents them from garnering top billing but be worth your consideration anyway. What you want to do is build on the fund information you glean from the Morningstar Rating system. Morningstar Rating , which debuted in 1985, is best used as a

Many photographers are beginning to use online storage options for backing up image files. This can be a great solution too. But if you use online storage, make sure it s not your only backup. We all know what happened to many Internet companies a few years ago when the economy took a dive.

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Dreamweaver also appends the file:///C| prefix (or file:///Macintosh HD on a Mac) if you haven t yet saved the document in which you are inserting the image. However, when you save the document, Dreamweaver automatically updates the image addresses to be document-relative.

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1. Display a layout and double-click the desired viewport to enter model space from that layout. 2. Open the Layer Properties Manager. You now see four new columns VP Color, VP Linetype, VP Lineweight, and VP Plot Style as shown in Figure 17.7.

To create a new normal layer, click the New Layer icon at the bottom of the Layers palette, as shown in Figure 1.7.

Figure 5-9: DMA Channel assignments shown in the Computer Properties window of the Windows Device Manager.

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1. Identify the language you want to use for calculations. You can dynamically switch between the two scripting languages in the Script Editor and make a choice for the language you want to use when you decide to write a script. However, it s always a good idea to set up some form properties when you begin to design a form. Among the properties you can assign to a new design is the default scripting language. 2. Select File Form Properties. The language you identify as your default scripting language is handled in the Defaults tab in the Form Properties dialog box. When the Form Properties dialog opens, click the Defaults tab. From the Default Language pull-down menu, select FormCalc as shown in Figure 33.33. For forms returned to you via e-mail, be certain the Default Run At pulldown menu selection is Client.

is described. Based on the minimization of mutual information, we present then a natural gradient algorithm for the causal minimum phase nite impulse response (FIR) multichannel lter. Using information back-propagation, we also discuss an e cient implementation of the learning algorithm for the non-causal FIR lters. Computer simulations are presented to illustrate the validity and good learning performance of the proposed algorithms. Existing adaptive algorithms for blind deconvolution and equalization can generally be classi ed into two categories: The mutual information minimization/entropy maximization and the cumulant-based algorithms [307, 556, 546, 503, 1203]. The Bussgang algorithms [479, 96, 83] and the natural gradient algorithm [38, 111, 393] are two typical examples of the rst category. The Bussgang techniques are iterative equalization schemes that employ stochastic gradient descent procedures to minimize non-convex cost functions depending on the equalizer output signals. The Bussgang algorithms are simple and easy to implement, but, they may converge to wrong solutions resulting in poor performance of the equalizer. In the Cumulant Fitting Procedure (CFP) [1190, 1191, 1201], the channel identi cation process directly employs the minimization of higher-order cumulant-based nonlinear cost functions. The underlying cost functions in the CFP are multi-modal, as in the case of Bussgang algorithms. The natural gradient approach was developed by Amari et al. to overcome the drawback of the Bussgang algorithm [38, 39]. It has been shown that the natural gradient can considerably improve e ciency in blind separation and blind deconvolution [111, 405]. The main objective of this chapter is to review and extend existing adaptive natural gradient algorithms for various multichannel blind deconvolution models.

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