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By contrast, thin-film cells are cheap to produce, and the only factor limiting their shape is the substrate they are mounted on. This means that you can create large cells, and cells of different shapes and sizes, all of which can be useful in certain applications. We are now going to take a detailed look at making two different types of solar cell, one will be a crystalline solar cell, and the other a thin-film solar cell. Both of the experiments are designed to be illustrative, rather than to actually make a cell with a useful efficiency. The technology required to make silicon solar cells is out of the reach of the home experimenter, so we are going to illustrate the process of how a solar cell is made, using things you can find in your kitchen. For thin-film solar cells, we are going to make an actual solar cell, which responds to light with changing electrical properties; however, the efficiency of our cell will be very poor, and it will not be able to generate a useful amount of electricity.



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Compression algorithms can choose a smaller number of bits to represent a digital symbol denoting pixel values that occur frequently in an image and longer representations for those values that occur infrequently, so that on average you use fewer bits to represent a given moving image This is called entropy encoding Entropy encoding is lossless Huffman codes are an example of entropy encoding We can exploit features of photographic images, for example, that make it likely that the intensity of a pixel is similar in intensity to its neighbors Photographic images are said to be Markov sources* If you know that the next pixel will be similar in intensity to the last, you can use predictive coding schemes, so that given the first pixel, you don t need information about the second, you can just predict its value from the value of the first.





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In fact, the more you know about the likelihood of the next pixel based on first, second, and higher orders of predictors, the less in error the predicted value will be In other words, if you can accurately predict the next pixel, given the behavior of several near neighbors, the compression will be better You can break the picture up into regions and do a transform on each region to establish its frequency domain content You perform mathematics on the block of pixels in the region to obtain a transform, which gives a spectrum of frequency values that can be represented with fewer symbols than the original block of pixels Spectral components equate roughly to colors, and transforms establish what colors are present in the block, ignoring the unimportant colors.

Do the elements and attributes in the XSD schema match the elements and attributes in the DiffGram (remember that the XML parser is case sensitive) Are all target table columns referenced in the DiffGram Does the DiffGram contain a proper value for the diffgr:hasChanges attribute Does the DiffGram have a root element and the DataSetName element

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Further, the base functions of the transform take a given spectral component (a number representing the dominant color in the region analyzed) and fit a pattern to best describe all the pixels in the analysis region with the least error The compression algorithm has several mosaic tiles representing the transform base functions, which can be thought of as textures The encoding algorithm takes the actual pixels in a region under analysis and tries to fit the best texture to that block When a best fit is found, the only data needed for reconstruction of the image are a color value representing the average color in the block and the base function, or texture, which was.

In this section we are going to look at how photovoltaic cells (PV) are made. However, rather than taking a dull, textbook approach, we are going to make the whole process fun by doing some practical kitchen experiments that mimic the process that happens in solar cell factories all around the world.

*A Markov source is a source of data that has the property of allowing you to predict the next data value, given the current one, simply because some sequences of values are statistically more likely than others. In the English language, for example, h is more likely to follow t in a word, than say a q. This is because the th letter combination occurs frequently in the English language, whereas the combination tq is very rare. In photographic images, the next pixel is more likely to be close in color to the current one than it is to be a completely different color. Images are just like that. They behave as Markov sources.

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BulkLoad is a SQLXML variant of bcp (Bulk Copy Program) and the Bulk Insert statement. It is designed for fast loading of large XML documents (or XML fragments) into SQL Server 2000. It is implemented as a COM object and, therefore, can only be used programmatically. You can use any COM-compatible language, including Visual Basic, Visual C++, VBScript, and all .NET languages such as Visual Basic .NET and Visual C# .NET. Since the component is designed for programmatic access, this section immediately dives into its programmatic use.

Table 10-1

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