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The computation of the { v ( r , p ) } has several names in the literature. In some cases, it is referred to simply as the preprocessor or weighting processor. From the DFT-based filter bank interpretation of the transmultiplexer, in which the filter pulse function h ( k ) is viewed as a spectral window function, the operation is called windowing and folding. Some of the first researchers in the area [link] termed it polyphase filtering . Even though the reasons for this name are fairly obscure, it is commonly used.

Figure three is a complex flow chart with three major rows of  objects. The first row is a series of eleven squares containing the label z^-1. The squares are labeled Sampled FDM Input x(k). The squares are grouped in three sections. The first is a series of five squares, with arrows in between them, pointing to the right. To the right of this section are three black dots in line with the arrows. This is followed by a series of three more squares with similar arrows, titled above as x(k-N). To the right of this section are three more black dots in line with the arrows. This is followed by another three squares with similar arrows with the title x(k-QN+1). Below this is a dashed line labeled Decimate by M. Below this, and below the first series of squares, are six arrows pointing down. The next row is labeled Pulse Response Coefficients {h(k)}. This row is a series of circles labeled h(0) through h(5), followed by a black dot, followed by three circles labeled h(N-1), h(N), and h(N+1), followed by two black dots, followed by four more circles labeled h(QN-4), h(QN-3), h(QN-2), h)QN-1). Arrows below these circles point down to a fourth row of objects. This row is centered in the figure with four circles labeled inside with a large plus sign. On the two left circles, there are four black dots along the right side of the circle, and on the two right circles, there are four black dots on top of the circles. In between these two sections of circles are two black dots. The circles point down with arrows to a large rectangle containing an equation. The arrows are labeled v(r, 0), v(r, 1), ..., v(r, N-1). Inside the rectangle, the equation reads y_n(r) = e (-2πrMn)/N N-1 Σ p=0 e^(2πnp/N) v(r, p). Below the rectangle is one more arrow labeled Tuner Output y_n(r) pointing to the right. To the right of the rectangle is a large equation that reads v(r, p) = Q-1 Σ q=0 h(qN + p) × x(rM - qN - p)
Signal Flow to the Output of the Single-Channel Digital Tuner

Once the input data has been preprocessed, windowed and folded, or polyphase filtered, as you will, the resulting N values of v ( r , p ) are Fourier-transformed to produce y n ( r ) . Notice that all of this computation must be repeated for each value of r .

It will be useful later to know how much computation is required to implement this simplified tuner. Assume for this calculation that the input data x ( k ) is complex-valued and that the pulse response h ( k ) is real-valued. If so, then 2 Q N multiply-add operations are needed for each computation of the { v ( r , p ) } and 4 N multiply-adds (approximately) are needed for the computation of the single point of the DFT, all of this at the decimated sampling rate of f s M . A conventional tuner using a real-valued, L-point pulse response and complex input data requires 4 f s multiply-adds for the mixer and 2 f s L M multiply-adds for the filtering. Comparing the two shows that the filtering/weighting is exactly the same for the two, while the tuning vs. DFT comparison depends on the relative values of M and N . Using the example of the basic transmux , where N = M , we find that the two are equal. When M < N , the simplified equations actually require slightly more computation. Why then do we go to this trouble?

Generalization to the fft-based digital transmultiplexer

What if we desire to tune a second channel, say one that has a center frequency of f l = m · Δ f ? Following through the derivation done before, we find that y m ( r ) is given by the same equations except that n is replaced with m . Examining the situation more closely we notice that the { v ( r , p ) } need not be recomputed to obtain the second tuner output. In fact, the only operation required to obtain the second tuner output is to recompute the inverse DFT, but this time evaluated for the index m instead of n . The conventional tuning approach must be completely repeated to obtain the output for another channel. It is usually the case that the computation of the { v ( r , p ) } is much larger than the computation required for the DFT. The fact that it need not be repeated quickly makes the preprocessor/DFT scheme significantly more efficient than the conventional digital tuner approach as the number of channels to be tuned grows. If we use the number of multiply-adds as an indication of computational complexity, and if we denote the number of channels to be tuned by the integer C , we can quantify this comparison by noting that

G converntional = C [ 4 f s + 2 f s Q N M ] multiply - adds

are needed for C conventional decimated digital tuners while

Questions & Answers

Application of nanotechnology in medicine
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industrial application...? mmm I think on the medical side as drug carrier, but you should go deeper on your research, I may be wrong
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scanning tunneling microscope
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Bob Reply
write examples of Nano molecule?
Bob
The nanotechnology is as new science, to scale nanometric
brayan
nanotechnology is the study, desing, synthesis, manipulation and application of materials and functional systems through control of matter at nanoscale
Damian
Is there any normative that regulates the use of silver nanoparticles?
Damian Reply
what king of growth are you checking .?
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What fields keep nano created devices from performing or assimulating ? Magnetic fields ? Are do they assimilate ?
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Introduction about quantum dots in nanotechnology
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Source:  OpenStax, An introduction to the fdm-tdm digital transmultiplexer. OpenStax CNX. Nov 16, 2010 Download for free at http://cnx.org/content/col11165/1.2
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