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Coming to Grips with the Frequency Domain

Xilinx Employee
Xilinx Employee
0 0 49.4K

 

By Adam P. Taylor, Chief Engineer, e2v

 

(Excerpted from the latest issue of Xcell Journal)

 

 

For many engineers, working in the frequency domain does not come as naturally as working within the time domain, probably because the frequency domain is associated with complicated mathematics. However, to unlock the true potential of Xilinx FPGA-based solutions, you need to feel comfortable working within both of these domains.

 

Depending upon the type of signal—repetitive or nonrepetitive, discrete or nondiscrete—there are a number of methods you can use to convert between time and frequency domains, including Fourier series, Fourier transforms and Z transforms. Within electronic signal processing and FPGA applications in particular, you will most often be interested in one transform: the discrete Fourier transform (DFT), which is a subset of the Fourier transform. Engineers use the DFT to analyze signals that are periodic and discrete—that is, they consist of a number of n bit samples evenly spaced at a sampling frequency that in many applications is supplied by an ADC within the system.

 

From telecommunications to image processing, radar and sonar, it is hard to think of a more powerful and adaptable analysis technique to implement within an FPGA than the Fourier transform. Indeed, the DFT forms the foundation for one of the most commonly used FPGA-based applications: It is the basis for generating the coefficients of the finite input response (FIR) filter (see Xcell Journal issue 78, “Ins and Outs of Digital Filter Design and Implementation”).

However, its use is not just limited to filtering. The DFT and IDFT are used in telecommunications processing to perform channelization and recombination of the telecommunication channels. In spectral-monitoring applications, they are used to determine what frequencies are present within the monitored bandwidth, while in image processing the DFT and IDFT are used to handle convolution of images with a filter kernel to perform, for example, image pattern recognition. All of these applications are typically implemented using a more efficient algorithm to calculate the DFT than the one shown above.

 

All told, the ability to understand and implement a DFT within your FPGA is a skill that every FPGA developer should have.

 

Note: This blog post is a short excerpt from a much larger technical article that appeared in the most recent issue of Xcell Journal. To read Adam Taylor’s full article with pages of technical details on implementing FFTs in FPGAs, see “Coming to Grips with the Frequency Domain.”

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