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(i) Choosing a signal transform, such as the DFT, and choosing a block size and an order for the subband filters. The number of subband filters is related to the block size and to the type of signal transform used. (ii) Collecting blocks of input data and transforming them by the chosen signal transform. The manner by which the data blocks are processed depends on the transform. The transformed data then play the role of regression data to the subband filters. (iii) There are mainly two ways to obtain the error signals for training the subband filters: (iii.1) In one implementation, the estimates {d^(i)} are generated by signal transforming the outputs of the subband filters. (iii.2) In another implementation, the estimates {d*(i)} are generated through fullband convolution. The estimates {d^(i)} are subtracted from the original reference sequence { d ( i ) } in' order to generate the time-domain error sequence { e ( i ) } ,which is then transformed to the frequency domain and used to train the subband filters. 465

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Having defined the signal to noise ratio output of the DFE, we can utilize it in order to create an analytical approach to quantifying BER performance of the DFE. the

Figure 5-1

Notice that the polygon Shape layer has the Bevel and Emboss and Satin effects applied to create the overspray effect.

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b) The polyphase components {.&,,- ~ ( z )of the convolution structure are updated after every } block iteration, i.e., after every new estimate wn is generated. The states (i.e., initial conditions) of the subband filters within the convolution structure should of course propagate from one iteration to another. One advantage of the constrained filter of Figs. 28.9 and 28.10 over the constrained filter of Fig. 28.2 is that the delay problem associated with the convolution structure of Fig. 28.10 can be addressed by using the modifications suggested in h o b . VI.14.

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Saturation refers to the difference of the color against its own brightness in other words, how brilliant the pixel appears to the eye. Low saturation means almost gray, and high saturation is the fully brilliant color. You adjust the hue and saturation of images for two main reasons. One is to change the color tones that were affected by adverse lighting, such as a yellow cast caused by fluorescent lights. The second is to bring back the intensity of colors that is lost due to the use of filters, haze, limited lighting, or other adjustment made in Photoshop. This section discusses using the Hue/Saturation Adjustment tool to fix the color tones in images.