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Its main distinction from the DFT is that it transforms real inputs to real outputs, with no intrinsic involvement of [[complex number]]s. Just as the DFT is the discrete analogue of the continuous [[Fourier transform]] (FT), the DHT is the discrete analogue of the continuous [[Hartley transform]] (HT), introduced by [[Ralph V. L. Hartley]] in 1942.<ref name="Hartley_1942"/> Because there are fast algorithms for the DHT analogous to the [[fast Fourier transform]] (FFT), the DHT was originally proposed by [[Ronald N. Bracewell]] in 1983<ref name="Bracewell_1983"/> as a more efficient computational tool in the common case where the data are purely real. It was subsequently argued, however, that specialized FFT algorithms for real inputs or outputs can ordinarily be found with slightly fewer operations than any corresponding algorithm for the DHT. == Definition == Formally, the discrete Hartley transform is a linear, invertible [[function (mathematics)|function]] ''H'': '''R'''<sup>''n''</sup> → '''R'''<sup>''n''</sup> (where '''R''' denotes the set of [[real number]]s). The ''N'' real numbers ''x''<sub>0</sub>, ..., ''x''<sub>''N''−1</sub> are transformed into the ''N'' real numbers ''H''<sub>0</sub>, ..., ''H''<sub>''N''−1</sub> according to the formula :<math>H_k = \sum_{n=0}^{N-1} x_n \operatorname{cas} \left( \frac{2 \pi}{N} n k \right) = \sum_{n=0}^{N-1} x_n \left[ \cos \left( \frac{2 \pi}{N} n k \right) + \sin \left( \frac{2 \pi}{N} n k \right) \right] \quad \quad k = 0, \dots, N-1 .</math> The combination <math>\cos(z) + \sin(z)</math> <math>= \sqrt{2} \cos\left(z-\frac\pi 4\right)</math> is sometimes denoted '''{{math|cas(''z'')}}''', and should not be confused with {{math|1=[[cis (mathematics)|cis(''z'')]] = ''e<sup>iz</sup>'' = cos(''z'') + ''i''&thinsp;sin(''z'')}}, or {{math|1=''e''<sup>−''iz''</sup> = cis(−''z'')}} which appears in the DFT definition (where ''i'' is the [[imaginary unit]]). As with the DFT, the overall scale factor in front of the transform and the sign of the sine term are a matter of convention. Although these conventions occasionally vary between authors, they do not affect the essential properties of the transform. == Properties == The transform can be interpreted as the multiplication of the vector (''x''<sub>0</sub>, ...., ''x''<sub>''N''−1</sub>) by an ''N''-by-''N'' [[matrix (math)|matrix]]; therefore, the discrete Hartley transform is a [[linear operator]]. The matrix is invertible; the inverse transformation, which allows one to recover the ''x<sub>n</sub>'' from the ''H<sub>k</sub>'', is simply the DHT of ''H<sub>k</sub>'' multiplied by 1/''N''. That is, the DHT is its own inverse ([[Involution (mathematics)|involutory]]), up to an overall scale factor. The DHT can be used to compute the DFT, and vice versa. For real inputs ''x<sub>n</sub>'', the DFT output ''X''<sub>''k''</sub> has a real part (''H''<sub>''k''</sub> + ''H''<sub>''N−k''</sub>)/2 and an imaginary part (''H''<sub>''N−k''</sub> − ''H<sub>k</sub>'')/2. Conversely, the DHT is equivalent to computing the DFT of ''x<sub>n</sub>'' multiplied by 1&thinsp;+&thinsp;''i'', then taking the real part of the result. As with the DFT, a cyclic [[convolution]] '''z''' = '''x'''∗'''y''' of two vectors '''x''' = (''x<sub>n</sub>'') and '''y''' = (''y<sub>n</sub>'') to produce a vector '''z''' = (''z<sub>n</sub>''), all of length ''N'', becomes a simple operation after the DHT. In particular, suppose that the vectors '''X''', '''Y''', and '''Z''' denote the DHT of '''x''', '''y''', and '''z''' respectively. Then the elements of '''Z''' are given by: :<math> \begin{matrix} Z_k & = & \left[ X_k \left( Y_k + Y_{N-k} \right) + X_{N-k} \left( Y_k - Y_{N-k} \right) \right] / 2 \\ Z_{N-k} & = & \left[ X_{N-k} \left( Y_k + Y_{N-k} \right) - X_k \left( Y_k - Y_{N-k} \right) \right] / 2 \end{matrix} </math> where we take all of the vectors to be periodic in ''N'' (''X<sub>N</sub>'' = ''X''<sub>0</sub>, et cetera). Thus, just as the DFT transforms a convolution into a pointwise multiplication of complex numbers (''pairs'' of real and imaginary parts), the DHT transforms a convolution into a simple combination of ''pairs'' of real frequency components. The inverse DHT then yields the desired vector '''z'''. In this way, a fast algorithm for the DHT (see below) yields a fast algorithm for convolution. (This is slightly more expensive than the corresponding procedure for the DFT, not including the costs of the transforms below, because the pairwise operation above requires 8 real-arithmetic operations compared to the 6 of a complex multiplication. This count doesn't include the division by 2, which can be absorbed e.g. into the 1/''N'' normalization of the inverse DHT.) == Fast algorithms == Just as for the DFT, evaluating the DHT definition directly would require O(''N''<sup>2</sup>) arithmetical operations (see [[Big O notation]]). There are fast algorithms similar to the FFT, however, that compute the same result in only O(''N'' log ''N'') operations. Nearly every FFT algorithm, from [[Cooley–Tukey FFT algorithm|Cooley–Tukey]] to [[Prime-factor FFT algorithm|prime-factor]] to Winograd (1985)<ref name="Sorensen_1985"/> to [[Bruun's FFT algorithm|Bruun's]] (1993),<ref name="Bini-Bozzo_1993"/> has a direct analogue for the discrete Hartley transform. (However, a few of the more exotic FFT algorithms, such as the QFT, have not yet been investigated in the context of the DHT.) In particular, the DHT analogue of the Cooley–Tukey algorithm is commonly known as the '''fast Hartley transform''' (FHT) algorithm, and was first described by Bracewell in 1984.<ref name="Bracewell_1984"/> This FHT algorithm, at least when applied to [[power of two|power-of-two]] sizes ''N'', is the subject of the United States [[software patent|patent]] number 4,646,256, issued in 1987 to [[Stanford University]]. Stanford placed this patent in the public domain in 1994 (Bracewell, 1995).<ref name="Bracewell_1995"/> As mentioned above, DHT algorithms are typically slightly less efficient (in terms of the number of [[floating-point]] operations) than the corresponding DFT algorithm (FFT) specialized for real inputs (or outputs). This was first argued by Sorensen et al. (1987)<ref name="Sorensen_1987"/> and Duhamel & [[Martin Vetterli|Vetterli]] (1987).<ref name="Duhamel-Vetterli_1987"/> The latter authors obtained what appears to be the lowest published operation count for the DHT of power-of-two sizes, employing a split-radix algorithm (similar to the [[split-radix FFT algorithm|split-radix FFT]]) that breaks a DHT of length ''N'' into a DHT of length ''N''/2 and two real-input DFTs (''not'' DHTs) of length ''N''/4. In this way, they argued that a DHT of power-of-two length can be computed with, at best, 2 more additions than the corresponding number of arithmetic operations for the real-input DFT. On present-day computers, performance is determined more by [[CPU cache|cache]] and [[CPU pipeline]] considerations than by strict operation counts, and a slight difference in arithmetic cost is unlikely to be significant. Since FHT and real-input FFT algorithms have similar computational structures, neither appears to have a substantial ''a priori'' speed advantage ({{ill|Miodrag Popović (engineer){{!}}Popović|sr|Миодраг Поповић (електротехничар)}} and Šević, 1994).<ref name="Popović-Šević_1994"/> As a practical matter, highly optimized real-input FFT libraries are available from many sources (e.g. from CPU vendors such as [[Intel]]), whereas highly optimized DHT libraries are less common. On the other hand, the redundant computations in FFTs due to real inputs are more difficult to eliminate for large [[prime number|prime]] ''N'', despite the existence of O(''N'' log ''N'') complex-data algorithms for such cases, because the redundancies are hidden behind intricate permutations and/or phase rotations in those algorithms. In contrast, a standard prime-size FFT algorithm, [[Rader's FFT algorithm|Rader's algorithm]], can be directly applied to the DHT of real data for roughly a factor of two less computation than that of the equivalent complex FFT (Frigo and Johnson, 2005).<ref name="Frigo-Johnson_2005"/> On the other hand, a non-DHT-based adaptation of Rader's algorithm for real-input DFTs is also possible (Chu & [[Charles Sidney Burrus|Burrus]], 1982).<ref name="Chu-Burrus_1982"/> == Multi-Dimensional Discrete Hartley Transform (MD-DHT) == The rD-DHT (MD-DHT with "r" dimensions) is given by <math>X(k_1,k_2,...,k_r)=\sum_{n_1=0}^{N_1-1} \sum_{n_2=0}^{N_2-1} \dots \sum_{n_r=0}^{N_r-1} x(n_1,n_2,...,n_r){\rm cas}(\frac{2\pi n_1 k_1}{N_1}+\dots +\frac{2\pi n_r k_r}{N_r}),</math> with <math>k_i = 0,1,\ldots, N_i-1</math> and where <math>{\rm cas}(x)=\cos(x)+\sin(x).</math> Similar to the 1-D case, as a real and symmetric transform, the MD-DHT is simpler than the MD-DFT. For one, the inverse DHT is identical to the forward transform, with the addition of a scaling factor; [[File:Img DHT prop2.png|centre|frameless|400x400px]] and second, since the kernel is real, it avoids the computational complexity of [[complex number]]s. Additionally, the DFT is directly obtainable from the DHT by a simple additive operation (Bracewell, 1983).<ref name="Bracewell_1983"/> The MD-DHT is widely used in areas like image and optical signal processing. Specific applications include computer vision, high-definition television, and teleconferencing, areas that process or analyze motion images (Zeng, 2000).<ref name="Zeng-Bi-Leyman_2000"/> === Fast algorithms for the MD-DHT === As computing speed keeps increasing, bigger multidimensional problems become computationally feasible, requiring the need for fast multidimensional algorithms. Three such algorithms follow. In pursuit of separability for efficiency, we consider the following transform (Bracewell, 1983),<ref name="Bracewell_1983"/> <math>\hat{X}(k_1,k_2,...,k_r)=\sum_{n_1=0}^{N_1-1} \sum_{n_2=0}^{N_2-1} \dots \sum_{n_r=0}^{N_r-1} x(n_1,n_2,...,n_r){\rm cas}(\frac{2\pi n_1 k_1}{N_1}) \dots {\rm cas}(\frac{2\pi n_r k_r}{N_r}).</math> It was shown in Bortfeld (1995),<ref name="Bortfeld-Dinter_1995"/> that the two can be related by a few additions. For example, in 3-D, <math>X(k_1,k_2,k_3) = \frac{1}{2} [\hat{X}(k_1,k_2,-k_3)+\hat{X}(k_1,-k_2,k_3)+\hat{X}(-k_1,k_2,k_3)-\hat{X}(-k_1,-k_2,-k_3)].</math> For <math>\hat{X}</math>, row-column algorithms can then be implemented. This technique is commonly used due to the simplicity of such R-C algorithms, but they are not optimized for general M-D spaces. Other fast algorithms have been developed, such as radix-2, radix-4, and split radix. For example, Boussakta (2000)<ref name="Boussakta_2000"/> developed the 3-D vector radix, <math>X(k_1,k_2,...,k_r)=\sum_{n_1=0}^{N-1} \sum_{n_2=0}^{N-1}\sum_{n_r=0}^{N-1} x(n_1,n_2,n_3){\rm cas}(\frac{2\pi}{N}(n_1 k_1+n_2 k_2 +n_3 k_3))</math> <math>=\sum_{n_1:even}\sum_{n_2:even}\sum_{n_3:even}+\sum_{n_1:even}\sum_{n_2:even}\sum_{n_3:odd}+\sum_{n_1:even}\sum_{n_2:odd}\sum_{n_3:even}</math> <math>+\sum_{n_1:even}\sum_{n_2:odd}\sum_{n_3:odd}+\sum_{n_1:odd}\sum_{n_2:even}\sum_{n_3:even}+\sum_{n_1:odd}\sum_{n_2:even}\sum_{n_3:odd}</math> <math>+\sum_{n_1:odd}\sum_{n_2:odd}\sum_{n_3:even}+\sum_{n_1:odd}\sum_{n_2:odd}\sum_{n_3:odd}.</math> It was also presented in Boussakta (2000)<ref name="Boussakta_2000"/> that this 3D-vector radix algorithm takes <math>(\frac{7}{4})N^3 \log_2 N</math> multiplications and <math>(\frac{31}{8})N^3 \log_2 N</math> additions compared to <math>3N^3 \log_2 N</math> multiplications and <math>(\frac{9}{2})N^3 \log_2 N+3N^2</math> additions from the row-column approach. The drawback is that the implementation of these radix-type of algorithms is hard to generalize for signals of arbitrary dimensions. Number theoretic transforms have also been used for solving the MD-DHT, since they perform extremely fast convolutions. In Boussakta (1988),<ref name="Boussakta_1988"/> it was shown how to decompose the MD-DHT transform into a form consisting of convolutions: For the 2-D case (the 3-D case is also covered in the stated reference), <math>X(k,l)=\sum_{n=0}^{N-1} \sum_{m=0}^{M-1}x(n,m){\rm cas}(\frac{2\pi nk}{N}+\frac{2\pi ml}{M}),\;</math> <math>k=0,1,\ldots ,N-1</math> , <math>l=0,1,\ldots,M-1</math> can be decomposed into 1-D and 2-D circular convolutions as follows, <math>X(k,l)=\begin{cases} X_1(k,0) \\ X_2(0,l) \\ X_3(k,l)\end{cases}</math> where <math>X_1(k,0)=\sum_{n=0}^{N-1} (\sum_{m=0}^{M-1}x(n,m)){\rm cas}(\frac{2\pi nk}{N}),\;</math> <math>k=0,1,\ldots,N-1</math> <math>X_2(0,l)=\sum_{m=0}^{M-1} (\sum_{n=0}^{N-1}x(n,m)){\rm cas}(\frac{2\pi ml}{M}),\;</math> <math>l=1,2,\dots, M-1</math> <math>X_3(k,l)=\sum_{n=0}^{N-1} \sum_{m=0}^{M-1}x(n,m){\rm cas}(\frac{2\pi nk}{N}+\frac{2\pi ml}{M})\;,</math> <math>k=1,2,\ldots, N-1</math> <math>l=1,2,\ldots,M-1.</math> Developing <math>X_3</math> further, <math>X_3(k,l)=\sum_{n=0}^{N-1}x(n,0){\rm cas}(\frac{2\pi nk}{N})+\sum_{m=1}^{M-1} x(0,m){\rm cas}(\frac{2\pi ml}{M})</math> <math>+\sum_{n=1}^{N-1} \sum_{m=1}^{M-1}x(n,m){\rm cas}(\frac{2\pi nk}{N}+\frac{2\pi ml}{M}).</math> At this point we present the Fermat number transform (FNT). The t<sup>th</sup> [[Fermat number]] is given by <math>F_t=2^b+1</math>, with <math>b=2^t</math>. The well known Fermat numbers are for <math>t=0,1,2,3,4,5,6</math> (<math>F_t</math> is prime for <math>0\le t \le 4</math>), (Boussakta, 1988).<ref name="Boussakta_1988"/> The Fermat number transform is given by <math>X(k,l)=\sum_{n=0}^{N-1} \sum_{m=0}^{M-1}x(n,m)\alpha_{1}^{nk}\alpha_{2}^{ml} \mod F_t</math> with <math>k=0,\ldots, N-1, l=0,\ldots,M-1</math>. <math>\alpha_1</math> and <math>\alpha_2</math> are roots of unity of order <math>N</math> and <math>M</math> respectively <math>(\alpha_{1}^{N}=\alpha_{2}^{M}=1 \mod F_t)</math>. Going back to the decomposition, the last term for <math>X_3(k,l)</math> will be denoted as <math>X_4(k,l)</math>, then <math>X_4(k,l)=\sum_{n=1}^{N-1} \sum_{m=1}^{M-1}x(n,m){\rm cas}(\frac{2\pi nk}{N}+\frac{2\pi ml}{M}),</math> <math>k=1,2,\ldots,N-1</math> <math>l=1,2,\ldots,M-1.</math> If <math>g_1</math> and <math>g_2</math> are [[Primitive root modulo n|primitive roots]] of <math>N</math> and <math>M</math> (which are guaranteed to exist if <math>M</math> and <math>N</math> are [[Prime number|prime]]) then <math>g_1</math> and <math>g_2</math> map <math>(n,m)</math> to <math>(g_1^{n}\mod N, g_2^m\mod M).</math> So, mapping <math>n,m,k</math> and <math>l</math> to <math>g_1^{-n},g_2^{-m}, g_1^k</math> and <math>g_2^l</math>, one gets the following, <math>X_4(g_1^k,g_2^l)=\sum_{n=0}^{N-2} \sum_{m=0}^{M-2}x(g_1^{-n},g_2^{-m}){\rm cas}(\frac{2\pi g_1^{ (-n+k)}}{N}+\frac{2\pi g_2^{(-m+l)}}{M}),</math> <math>k=0,1,\ldots,N-2</math> <math>l=0,1,\ldots,M-2</math>. Which is now a [[circular convolution]]. With <math>Y(k,l)=X_4(g_1^k,g_2^l)</math>, <math>y(n,m)=x(g_1^{-n},g_2^{-m})</math>, and <math>h(n,m)={\rm cas}(\frac{2\pi g_1^n}{N}+\frac{2\pi g_2^m}{M})</math>, one has <math>Y(k,l)=\sum_{n=0}^{N-2} \sum_{m=0}^{M-2}y(n,m)h(<k-n>_N,<l-m>_M)</math> <math>Y(k,l)=FNT^{-1} \{FNT[y(n,m)]\otimes FNT[h(n,m)]</math> where <math>\otimes</math> denotes term by term multiplication. It was also stated in (Boussakta, 1988)<ref name="Boussakta_1988"/> that this algorithm reduces the number of multiplications by a factor of 8–20 over other DHT algorithms at a cost of a slight increase in the number of shift and add operations, which are assumed to be simpler than multiplications. The drawback of this algorithm is the constraint that each dimension of the transform has a [[Primitive root modulo n|primitive root]]. ==References== {{reflist|refs= <ref name="Hartley_1942">{{cite journal |author-link=Ralph Hartley |author-last=Hartley |author-first=Ralph V. L. |title=A More Symmetrical Fourier Analysis Applied to Transmission Problems |journal=[[Proceedings of the IRE]] |volume=30 |issue=3 |pages=144–150 |date=March 1942 |doi=10.1109/JRPROC.1942.234333|s2cid=51644127 }}</ref> <ref name="Bracewell_1983">{{cite journal |author-link=Ronald N. Bracewell |author-last=Bracewell |author-first=Ronald N. |title=Discrete Hartley Transform |journal=[[Journal of the Optical Society of America]] |volume=73 |issue=12 |pages=1832–1835 |date=1983 |doi=10.1364/josa.73.001832|s2cid=120611904 }}</ref> <ref name="Bracewell_1984">{{cite journal |author-link=Ronald N. Bracewell |author-last=Bracewell |author-first=Ronald N. |title=The Fast Hartley Transform |journal=[[Proceedings of the IEEE]] |volume=72 |issue=8 |pages=1010–1018 |date=1984 |doi=10.1109/proc.1984.12968|s2cid=21988816 }}</ref> <ref name="Bracewell_1995">{{cite journal |author-link=Ronald N. Bracewell |author-last=Bracewell |author-first=Ronald N. |title=Computing with the Hartley Transform |journal=Computers in Physics |volume=9 |issue=4 |pages=373–379 |date=1995 |doi=10.1063/1.168534|bibcode=1995ComPh...9..373B |doi-access=free }}</ref> <ref name="Sorensen_1985">{{cite journal |author-first1=Henrik V. |author-last1=Sorensen |author-first2=Douglas L. |author-last2=Jones |author-link2=Douglas L. Jones |author-first3=Charles Sidney |author-last3=Burrus |author-link3=Charles Sidney Burrus |author-first4=Michael T. |author-last4=Heideman |title=On computing the discrete Hartley transform |journal=IEEE Transactions on Acoustics, Speech, and Signal Processing |volume=ASSP-33 |issue=4 |pages=1231–1238 |date=1985}}</ref> <ref name="Bini-Bozzo_1993">{{cite journal |author-first1=Dario Andrea |author-last1=Bini |author-first2=Enrico |author-last2=Bozzo |title=Fast discrete transform by means of eigenpolynomials |journal=[[Computers & Mathematics with Applications]] |volume=26 |issue=9 |pages=35–52 |date=1993 |doi=10.1016/0898-1221(93)90004-f|doi-access=free }}</ref> <ref name="Sorensen_1987">{{cite journal |author-first1=Henrik V. |author-last1=Sorensen |author-first2=Douglas L. |author-last2=Jones |author-link2=Douglas L. Jones |author-first3=Michael T. |author-last3=Heideman |author-first4=Charles Sidney |author-last4=Burrus |author-link4=Charles Sidney Burrus |title=Real-valued fast Fourier transform algorithms |journal=IEEE Transactions on Acoustics, Speech, and Signal Processing |volume=ASSP-35 |issue=6 |pages=849–863 |date=1987}}</ref> <ref name="Duhamel-Vetterli_1987">{{cite journal |author-first1=Pierre |author-last1=Duhamel |author-first2=Martin |author-last2=Vetterli |author-link2=Martin Vetterli |title=Improved Fourier and Hartley transform algorithms: application to cyclic convolution of real data |journal=IEEE Transactions on Acoustics, Speech, and Signal Processing |volume=ASSP-35 |pages=818–824 |date=1987}}</ref> <ref name="Popović-Šević_1994">{{cite journal |author-first1=Миодраг [Miodrag] |author-last1=Поповић [Popović] |author-link1=:sr:Миодраг Поповић (електротехничар) |author-first2=Dragutin |author-last2=Šević |title=A new look at the comparison of the fast Hartley and Fourier transforms |journal=[[IEEE Transactions on Signal Processing]] |volume=42 |issue=8 |pages=2178–2182 |date=1994 |doi=10.1109/78.301854|bibcode=1994ITSP...42.2178P }}</ref> <ref name="Frigo-Johnson_2005">{{cite journal |author-first1=Matteo |author-last1=Frigo |author-first2=Steven G. |author-last2=Johnson |title=The Design and Implementation of FFTW3 |url=http://fftw.org/fftw-paper-ieee.pdf |journal=[[Proceedings of the IEEE]] |volume=93 |issue=2 |pages=216–231 |date=2005 |doi=10.1109/jproc.2004.840301 |citeseerx=10.1.1.66.3097|s2cid=6644892 }}}</ref> <ref name="Chu-Burrus_1982">{{cite journal |author-first1=Shuni |author-last1=Chu |author-first2=Charles Sidney |author-last2=Burrus |author-link2=Charles Sidney Burrus |title=A prime factor FTT <nowiki>[</nowiki>''sic''<nowiki>]</nowiki> algorithm using distributed arithmetic |journal=IEEE Transactions on Acoustics, Speech, and Signal Processing |volume=30 |issue=2 |pages=217–227 |date=1982 |doi=10.1109/tassp.1982.1163875}}</ref> <ref name="Bortfeld-Dinter_1995">{{cite journal |author-first1=Thomas |author-last1=Bortfeld |author-first2=Wolfgang |author-last2=Dinter |title=Calculation of Multidimensional Hartley Transforms Using One-Dimensional Fourier Transforms |journal=[[IEEE Transactions on Signal Processing]] |volume=43 |issue=5 |pages=1306–1310 |date=1995 |doi=10.1109/78.382424|bibcode=1995ITSP...43.1306B }}</ref> <ref name="Boussakta_1988">{{cite journal |author-first1=Said |author-last1=Boussakta |author-first2=Alan G. J. |author-last2=Holt |title=Fast Multidimensional Discrete Hartley Transform using Fermat Number Transform |journal= IEE Proceedings G - Electronic Circuits and Systems |volume=135 |issue=6 |pages=235–237 |date=1988 |doi=10.1049/ip-g-1.1988.0036}}</ref> <ref name="Boussakta_2000">{{cite journal |author-first1=Said |author-last1=Boussakta |author-first2=Osama |author-last2=Alshibami |title=Fast Algorithm for the 3-D Discrete Hartley Transform |journal=[[International Conference on Acoustics, Speech, and Signal Processing|International Conference on Acoustics, Speech, and Signal Processing '00]]|number=4 |pages=2302–2305 |date=2000}}</ref> <ref name="Zeng-Bi-Leyman_2000">{{cite journal |author-first1=Yonghang |author-last1=Zeng |author-first2=Guoan |author-last2=Bi |author-first3=Abdul Rahim |author-last3=Leyman |title=Polynomial Transform Algorithms for Multidimensional Discrete Hartley Transform |journal=[[IEEE International Symposium on Circuits and Systems]] |number=V |pages=517–520 |date=2000}}</ref> }} == Further reading == * {{cite book |author-link=Ronald N. Bracewell |author-last=Bracewell |author-first=Ronald N. |title=The Hartley Transform |publisher=[[Oxford University Press]] |date=1986 |edition=1 |isbn=978-0-19503969-6}} * {{cite journal |author-first1=Said |author-last1=Boussakta |author-first2=Alan G. J. |author-last2=Holt |title=Fast Multidimensional Discrete Hartley Transform using Fermat Number Transform |journal= IEE Proceedings G - Electronic Circuits and Systems |volume=135 |issue=6 |pages=235–237 |date=1988 |doi=10.1049/ip-g-1.1988.0036}} * {{cite journal |author-first1=Jonathan |author-last1=Hong |author-first2=Martin |author-last2=Vetterli |author-link2=Martin Vetterli |author-first3=Pierre |author-last3=Duhamel |title=Basefield transforms with the convolution property |journal=[[Proceedings of the IEEE]] |volume=82 |issue=3 |pages=400–412 |date=1994 |doi=10.1109/5.272145|url=https://infoscience.epfl.ch/record/33890/files/HongVD94.pdf }} * {{cite journal |author-first=Mark A. |author-last=O'Neill |author-link=Mark A. O'Neill |title=Faster than Fast Fourier |journal=[[BYTE]] |volume=13 |issue=4 |pages=293–300 |date=1988}} * {{cite journal |first1=Kraig J. |last1=Olnejniczak |first2=Gerald T. |last2=Heydt |title=Scanning the Special Section on the Hartley transform |journal=[[Proceedings of the IEEE]] |volume=82 |pages=372–380 |date=March 1994}} (NB. Contains extensive bibliography.) 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