Thus, the use of regular and modular computational structures as cycle convolution and circular correlation having local and regular data communications can be very useful in obtaining a good VLSI implementation for DCT, IDCT [10-18], DST and IDST [20-23], discrete Fourier transform (DFT)  or discrete Hartley transform (DHT) [24-29] using both the systolic array architectural paradigm or distributed arithmetic [30-34].
Chiper, "Radix-2 Fast Algorithm for Computing Discrete Hartley Transform of Type III," IEEE Transactions on Circuits and SystemsII, vol.
Chiper, "Fast Radix-2 Algorithm for the Discrete Hartley Transform of Type II," IEEE Signal Processing Letters, vol.
Senhadji, "Fast Radix-3 Algorithm for the Generalized Discrete Hartley Transform of Type II", IEEE Signal Processing Letters.
Senhadji, "A Novel SplitRadix Fast Algorithm for 2-D Discrete Hartley Transform," IEEE Transactions on Circuits and Systems I: Regular Papers, vol.
In this paper we propose to measure energy of specific motor imageries in the brain signal using Fast Hartley transform along with the Chebyshev filter and selecting the ideal channels for the classification problem using the proposed support vector machine.
Hartley transform compared to Fourier transforms is a real function.
A very important property of Hartley Transform is its symmetry
Another important feature is that the transform pairs are both real which provides good computational advantages for Hartley Transform (HT) over the Fourier transform (FT).
The properties of the DHT are similar to those of the discrete Fourier transform (DFT) and Fast Hartley transform (FHT)  which is similar to the familiar Fast Fourier Transform (FFT).
The energy from the preprocessed EEG epoch was extracted using a combination of Fast Hartley transform and Chebyshev filter.