It is well known that the Fourier Transform (FT) has currently a key role in signal processing applications.
The FT is useful for frequency domain analysis of a signal, i.e. it transposes a signal from time domain into
frequency domain. Many application ranging from telecommunication, electric energy distribution systems, fail
prevention analysis and general signal processing use this transform as a tool for coding/decoding or spectrum
analysis of a signal. Because of the complexity of the processing algorithm of FT and its importance in signal
analysis, many people have been working on methods and application specific processor architecture for improving
the computation performance. To get that performance improvement, efforts have been done in two directions
mainly. One related to the algorithmic point of view[1,2,3], and the other based on ASIP architecture[2,4,5,6]. The
last one was pushed by VLSI technology evolution.
In this work, we present an architecture of a processor to perform FT analysis employing a datapath with
bit-serial and digit-serial integer operation. The objective of this work was get an area efficient architecture that
could be used as a co-processor with built in all resources necessary for an embedded application. Also, we guide our
design to have a simple input / output interface. The main difference between the architecture presented here and the
others rely on the usage of a datapath bit and digit serial oriented to process each butterfly highly parallel.
This paper is organized as follows. Section 2 provides a review of FT computation. Section 3 discusses the
proposed architecture, while section 4 presents some results, followed by conclusions and future work.
FT is needed for spectrum analysis, FFT is fast FT meaning it is used to obtain spectrum of a signal quickly, the FFT algorithm inherently is fast algorithm than the conventional FT algorithm
nothing to do with the computer, its the processor the determines that (u can get processor upgrades)
The Type of transfers that must a computer interconnection structure support thats a bus is the following: (1) Memory to Processor (2) Processor to Memory (3) I/O to Processor (4) Processor to I/O (5) I/O to or from Memory
The processor (obviously)
Gigahertz are a measurement of how fast a processor operates. They can be "upgraded" in the sense that you can overclock a processor to get "more" of them, or you can install a better processor for the same effect.
because they have a high speed compared to fft
FFT reduces the computation since no. of complex multiplications required in FFT are N/2(log2N). FFT is used to compute discrete Fourier transform.
To plot the frequency spectrum using the fft function in MATLAB, first compute the one-dimensional discrete Fourier transform using fft, provide the data series as an input. Then use the abs() function to get the magnitude spectrum, and plot the result using the plot() function, with the frequency domain on the x-axis and the magnitude spectrum on the y-axis.
FT is needed for spectrum analysis, FFT is fast FT meaning it is used to obtain spectrum of a signal quickly, the FFT algorithm inherently is fast algorithm than the conventional FT algorithm
There's no need for it.
Zoom FFT is a diagnostic tool for the detection of blood clots and other diseases. It is less costly than other techniques. Zoom FFT uses a low frequency to measure blood flow in order to find clots. A transmitter passes an ultrasonic wave through the blood vessels. A reflected signal is passed to a DSP processor. (This is similar to the Doppler technique.) The process is achieved with one DSP chip in order to keep the costs lower.
FFT is faster than DFT because no. of complex multiplication in DFT is N^2 while in FFT no. of complex multiplications are N/2(log2N). for example if N=8 no. of complex multiplications required in DFT are 64. while no. of complex multiplications required in FFT are 12 thus reduces computation time.
Food For Thought
Fast Fourier Transform
hi.... for DIT fft algorithm, refer to this link, it has c-code for that. http://cnx.org/content/m12016/latest/
1045
FFT is the frequency domain representation. In can be shown in Simulink with blocks. These blocks graphically show the domain or x value plotted against the frequency or y value.