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Boa tarde,<br>
<br>
A tradução que fez não me parece completamente clara.<br>
<br>
Na pratica a inovação introduzida é mais no sentido contrário, de
necessitar de menos processamento para realizar a mesma tarefa ou de
sob o ponto de vista inverso encurtar o tempo necessário à operação,
permitindo mais operações por segundo para o mesmo hardware base.<br>
<br>
Creio ter sido este o ponto de vista que quis colocar na tradução,
mas não ficou muito explicito, trabalhinhos de hora de almoço ...<br>
<br>
73!<br>
<br>
On 20-01-2012 12:49, João Gonçalves Costa wrote:
<blockquote
cite="mid:5C4C49AFB7040B42B32EB395C8F7887F3B5D1C21B6@EXH100.w2k.ctt.pt"
type="cite">
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<p class="MsoNormal"><span lang="EN-US">The faster-than-fast
Fourier transform<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">MIT researchers have
found a way to increase the speed of one of the most
important algorithms in signal processing the fast Fourier
transform (FFT).<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">It’s a method for
representing an irregular signal as a combination of pure
frequencies. It’s universal in signal processing, but it can
also be used to compress image and audio files, solve
differential equations and price stock options, among other
things.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">Ever since the FFT was
proposed in the mid-1960's people have wondered whether an
even faster algorithm could be found.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">At the Association for
Computing Machinery’s Symposium on Discrete Algorithms
(SODA) this week, a group of MIT researchers will present a
new algorithm that, in a large range of practically
important cases, improves on the fast Fourier transform.
Under some circumstances, the improvement can be dramatic —
a tenfold increase in speed. The new algorithm could be
particularly useful for image compression, enabling, say,
smartphones to wirelessly transmit large video files without
draining their batteries or consuming their monthly
bandwidth allotments.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">The new algorithm —
which associate professor Katabi and professor Piotr Indyk,
both of MIT’s Computer Science and Artificial Intelligence
Laboratory (CSAIL), developed together with their students
Eric Price and Haitham Hassanieh — relies on two key ideas.
The first is to divide a signal into narrower slices of
bandwidth, sized so that a slice will generally contain only
one frequency with a heavy weight.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">In signal processing,
the basic tool for isolating particular frequencies is a
filter. But filters tend to have blurry boundaries:<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">One range of frequencies
will pass through the filter more or less intact;
frequencies just outside that range will be somewhat
attenuated; frequencies outside that range will be
attenuated still more; and so on, until you reach the
frequencies that are filtered out almost perfectly.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">If it so happens that
the one frequency with a heavy weight is at the edge of the
filter, however, it could end up so attenuated that it can’t
be identified. So the researchers’ first contribution was to
find a computationally efficient way to combine filters so
that they overlap, ensuring that no frequencies inside the
target range will be unduly attenuated, but that the
boundaries between slices of spectrum are still fairly
sharp.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">Once they’ve isolated a
slice of spectrum, however, the researchers still have to
identify the most heavily weighted frequency in that slice.
In the SODA paper, they do this by repeatedly cutting the
slice of spectrum into smaller pieces and keeping only those
in which most of the signal power is concentrated. But in
the paper 'Nearly Optimal Sparse Fourier Transform', they
describe a much more efficient technique, which borrows a
signal-processing strategy from 4G cellular networks.
Frequencies are generally represented as up-and-down
squiggles, but they can also be though of as oscillations;
by sampling the same slice of bandwidth at different times,
the researchers can determine where the dominant frequency
is in its oscillatory cycle.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">Read the full MIT press
release at<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><a
moz-do-not-send="true"
href="http://web.mit.edu/newsoffice/2012/faster-fourier-transforms-0118.html">http://web.mit.edu/newsoffice/2012/faster-fourier-transforms-0118.html</a><o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">Read the paper Nearly
Optimal Sparse Fourier Transform<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><a
moz-do-not-send="true"
href="http://arxiv.org/PS_cache/arxiv/pdf/1201/1201.2501v1.pdf">http://arxiv.org/PS_cache/arxiv/pdf/1201/1201.2501v1.pdf</a><o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
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</blockquote>
<br>
<br>
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