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Many algorithms have been derived to perform these operations, ranging from crude select-the-nearest-value approaches, to more sophisticated processing algorithms. The best decimators use sample lattice transforms* to deduce a lesser number of lines, pixels per line, and frames per second from the given sample data (i.e., the data obtained from the analog-to-digital conversion process). Sample lattice conversion produces fewer quantization artifacts than merely choosing some of the available values in the sample set on a nearest-neighbor basis. For a simple reduction in bits per sample value, the crudest way is simply to truncate, whereas the least noticeable way is to dither each value before truncating. Dithering is the process of adding noise to the values in frequency regions that the senses are less attuned to, in order to make the pixel truncation process appear to distort the samples less.



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between these and crystalline cells is that rather than using crystalline silicon, these use chemical compounds to semiconduct. The chemical compounds are deposited on top of a substrate, that is to say a base for the solar cell. There are some formulations that do not require silicon at all, such as CIS (copper indium diselenide) and cadmium telluride. However, there is also a process called amorphous silicon, where silicon is deposited on a substrate, although not in a uniform crystal structure, but as a thin film. In addition, rather than being slow to produce, thin-film solar cells can be produced using a continuous process, which makes them much cheaper. However, the disadvantage is that while they are cheaper, thin-film solar cells are less efficient than their crystalline counterparts. Some different solar photovoltaic technologies are compared in Table 10-1. Figures are given for the efficiency of the cell technology, and the average area of cells required to generate 1 kW peak power when facing in the right direction! When looking at the merits of crystalline cells and thin-film cells, we can see that crystalline cells produce the most power for a given area. However, the problem with them is that they are expensive to produce and quite inflexible (as you are limited to constructing panels from standard cell sizes and cannot change or vary their shape).





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*If a video sequence is digitally sampled, you get a three dimensional lattice of samples, of dimension pixels per line, by lines per frame, by frames in the sequence. In other words, all the pixel values can be represented as a cubic lattice, with axes representing the dimensions of the video sequence in both space and time. If you need to change the number of frames per unit time, or the number of lines per frame, for example, a sample lattice transform, which is a mathematical algorithm for creating the new sample lattice by three dimensionally interpolating the source lattice sample values, is used. The algorithm has the property of mathematically producing the new sample values in close agreement with what they would have been if the original source video sequence had been sampled according to the dimensions of the final lattice.

Using fewer bits to represent each color Reducing the number of pixels in the image Reducing the amount the value of a pixel can change compared to its nearest neighbors Reducing the number of frames per second

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SQL Server errors SQL Server constraints are preventing the change When your DiffGram is not giving you the result that you expect, you should use SQL Server Profiler to see if the DiffGram is able to generate a valid query, and then execute the query against the database. If you execute the query in Query Analyzer, you will see the error that SQL Server is returning. If there is no query generated in Profiler, you will have to determine why the system is unable to parse and process the DiffGram. Investigate the following: Is the DiffGram a well-formed XML file Is the DiffGram pointing to an XSD schema file Is the XSD schema valid Do the DiffGram and XSD files contain all required namespace references Does the XSD schema map the XML components to database objects Did you map all columns to elements and attributes

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