Nearly Orthogonal (+1,-1) Matrices in Higher
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Nearly Orthogonal (+1,-1) Matrices in Higher Dimensions: An Application to Signal Processing. Jennifer Seberry and Tianbing Xia University of Wollongong. JSPS-DST Asian Academic Seminar 2013, Discrete Mathematics and its Applications November 3-10, 2013 University of Tokyo. 1.

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Nearly Orthogonal (+1,-1) Matrices in Higher Dimensions: An Application to Signal Processing

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Nearly orthogonal 1 1 matrices in higher dimensions an application to signal processing

Nearly Orthogonal (+1,-1) Matrices in Higher Dimensions: An Application to Signal Processing

Jennifer Seberry and Tianbing Xia

University of Wollongong

JSPS-DST Asian Academic Seminar 2013,

Discrete Mathematics and its Applications

November 3-10, 2013

University of Tokyo

1


Nearly orthogonal 1 1 matrices in higher dimensions an application to signal processing

Why Study Higher Dimensional?

  • Shlichta, 1979, said: “proper higher dimensional matrices may find application in error-correcting codes, where their hierarchy of orthogonalities permit a variety of checking procedures. Other types of Hadamard matrices may be of use in security codes on the basis of their resemblance to random binary matrices”.

  • Communication encoding uses frequency, phase, time, amplitude, polarity (light) to increase bandwidth; believe we can use higher dimensions to augment these

  • Applications in spectroscopy (see Horadam)

2


Nearly orthogonal 1 1 matrices in higher dimensions an application to signal processing

Examples of propriety

  • Shlichta’s , de Launey’s, Yang’s and Hammer&Seberry’s higher dimensional Hadamard matrices can be proper: that is all the 2-dimensional subspaces are orthogonal i.e. Hadamard matrices

  • Higher n-dimensional Hadamard matrices made from Walsh functions are not proper in any dimension smaller than n

3


Nearly orthogonal 1 1 matrices in higher dimensions an application to signal processing

Examples of 2^3 Hadamard matrices

  • Shlichta gave a construction for 2^n

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Nearly orthogonal 1 1 matrices in higher dimensions an application to signal processing

Shlichta 1979

  • In his beautiful paper on “Higher dimensional matrices” Shlichta constructed higher dimensional matrices of side 2 which are orthogonal…but what does orthogonal mean?

  • Proper: every two dimensional subspace is an Hadamard matrix…this works for complex, jacket, Butson, weighing matrices, and orthogonal designs

  • Propriety: when the k dimensional subspace in direction m is orthogonal that is the k-1 dimensional subspaces are super imposed

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Nearly orthogonal 1 1 matrices in higher dimensions an application to signal processing

Example from Hammer and Seberry

  • From IEEE

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Nearly orthogonal 1 1 matrices in higher dimensions an application to signal processing

Statement of de Launey and Yang’s construction

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Nearly orthogonal 1 1 matrices in higher dimensions an application to signal processing

Example of de Launey’s and Yang’s construction in higher dimensions

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Nearly orthogonal 1 1 matrices in higher dimensions an application to signal processing

Higher Dimensional Version Example

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Nearly orthogonal 1 1 matrices in higher dimensions an application to signal processing

Higher Dimensional Version Example

10


Nearly orthogonal 1 1 matrices in higher dimensions an application to signal processing

Example of Hammer & Seberry’s difference set developed construction

  • This only applies to Menon difference sets including Ming-yuan Xia’s splendid work

  • Kathy Horadam & C Lin extended this construction to any group developed Hadamard matrices and all co-cyclic Hadamard matrices: they extended perfect binary arrays

11


Nearly orthogonal 1 1 matrices in higher dimensions an application to signal processing

Example of Seberry & Xia’s difference set developed construction in higher dimensions

12


Nearly orthogonal 1 1 matrices in higher dimensions an application to signal processing

Hammer & Seberry: Other Examples

13


Nearly orthogonal 1 1 matrices in higher dimensions an application to signal processing

Comparison of efficiency of construction

  • Higher dimensional matrices via group development will be faster to construct than the de Launey-Yang construction but

  • The group developed higher dimensional Hadamard matrices depend on difference sets and Menon difference sets have been hard to find: but we can use them for near orthogonal higher dimensional matrices

r

r

14


Nearly orthogonal 1 1 matrices in higher dimensions an application to signal processing

What if we change orthogonal to near orthogonal?

  • de Launey and Levin were looking at this problem in 2009

  • Windpassinger, Pertsch, Cioppa have more recently looked at near orthogonal higher dimensional matrices for codes, diffraction and experimental design

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Nearly orthogonal 1 1 matrices in higher dimensions an application to signal processing

Near orthogonal and higher dimensional: I

  • Take any proper higher dimensional Hadamard matrix and remove one coordinate from each direction. The resulting matrix will have had either (1,1) or (1, -1) or (-1,1) or (-1,-1) removed from the inner product of any two rows in a 2-dimensional plane. Thus we will have near orthogonality for any such pair

  • However this may be very time consuming or even difficult to construct, repair or analyse…

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Nearly orthogonal 1 1 matrices in higher dimensions an application to signal processing

Near orthogonal and higher dimensional: II

  • The group developed construction from difference sets other than Menon difference sets

  • Use Paley, Stanton-Sprott and Singer difference sets with parameters 2-(4t-1,2t-1,t-1). These can be group developed for 4t-1 any odd prime power to get near orthogonal higher dimensional matrices

  • Using group developed 2 x 2 matrices we can use the Hammer-Seberry construction to extend to any number of dimensions; we note that in some pairs of dimensions there will be n^2 correlation but other pairs are fine

17


Nearly orthogonal 1 1 matrices in higher dimensions an application to signal processing

Example using difference sets

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Nearly orthogonal 1 1 matrices in higher dimensions an application to signal processing

Near orthogonal and higher

dimensional: III

  • Use the Hammer and Seberry construction to make Williamson Hadamard into high dimensions.

  • With appropriate definitions this can also be extended to Williamson type, Ito’s, Goethal-Seidel type, Wallis-Whiteman type, Xia type and others to form higher dimensional matrices…then use deletion of any coordinate as above

  • It is easy to see how this can be used for Hadamard error codes with 8t codewords of length 4t, distance 2t and dimension 8t (we have yet to relate this to Reed-Solomon codes)

19


Nearly orthogonal 1 1 matrices in higher dimensions an application to signal processing

Example from Hammer & Seberry

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Nearly orthogonal 1 1 matrices in higher dimensions an application to signal processing

Near orthogonal and higher dimensional: IV

  • Use the ideas of the Hammer & Seberry for the Williamson construction extended to high dimensions but with

    • sequences with small correlation

    • two supplementary difference sets extended to a group generated form

    • Complex sequences

    • Higher dimensional quaternion based codes

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Nearly orthogonal 1 1 matrices in higher dimensions an application to signal processing

Applications to signal processing

  • The high dimensional orthogonal matrix can be used to encode the whole of a large file. For example, a database or a video with error-correction for single errors and burst errors by choice of the encoding matrix

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Nearly orthogonal 1 1 matrices in higher dimensions an application to signal processing

More uses and applications

  • Any thing that previously used zero autocorrelation can be adapted to near zero autocorrelation

  • Applications in spectroscopy, acoustics, communication, security(?)....

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Nearly orthogonal 1 1 matrices in higher dimensions an application to signal processing

References and Acknowledgements

  • We acknowledge Kathy Horadam’s magnificent, pioneering and insightful research and book:

    • K.J. Horadam, Hadamard Matrices and their Applications, Princeton University Press, Princeton, 2006

  • Y.X. Yang, Theory and Applications of Higher-Dimensional Hadamard Matrices, Kluwer, Dordrecht, 2001.

24


Nearly orthogonal 1 1 matrices in higher dimensions an application to signal processing

References

  • Warwick de Launey and Daniel M Gordon: On the density of the set of known Hadamard orders. Cryptography and Communications 2(2): 233-246 (2010)

  • Warwick de Launey and David A Levin, : (1, -1)-Matrices with Near-Extremal Properties. SIAM J. Discrete Math. 23(3): 1422-1440 (2009)

  • Warwick de Launey: On the asymptotic existence of Hadamard matrices. J. Comb. Theory, Ser. A 116(4): 1002-1008 (2009)

  • Warwick de Launey, Kathy J. Horadam: A Weak Difference Set Construction for Higher Dimensional Designs. Des. Codes Cryptography 3(1): 75-87 (1993)

  • Warwick de Launey: A note on N -dimensional Hadamard matrices of order 2t and Reed-Muller codes. IEEE Transactions on Information Theory 37(3): 664- (1991)

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Nearly orthogonal 1 1 matrices in higher dimensions an application to signal processing

References

  • Kathy J. Horadam: Hadamard matrices and their applications: Progress 2007-2010. Cryptography and Communications 2(2): 129-154 (2010)

  • ChristophWindpassinger and Robert F. H. Fischer: Low-Complexity Near-Maximum-Likelihood Detection and Precoding for MIMO Systems using Lattice Reduction , ITW2003, Paris, France, March 31 – April 4, 2003

  • Thomas Pertsch, Ulf Peschel, Falk Lederer, Jonas Burghoff, Matthias Will, Stefan Nolte, and Andreas Tünnermann: Discrete diffraction in two-dimensional arrays of coupled waveguides in silica, Optics Letters, Vol. 29, Issue 5, pp. 468-470 (2004)  

  • Thomas M Cioppa, Efficient near orthogonal and space-filling experimental designs for high dimensional complex designs, Dissertation, Naval Postgraduate School, Monterey, CA, (2002)

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Nearly orthogonal 1 1 matrices in higher dimensions an application to signal processing

References +

  • Paul J Shlichta, Higher dimensional Hadamard matrices, IEEE Trans Inf Th 25, 5 (1979) 566-572

  • J Hammer and Jennifer Seberry, Higher dimensional orthogonal designs and applications, IEEE Trans Inf Th 27 (1981) 772-779

  • Yixian Yang, The proofs of some conjectures on higher dimensional Hadamard matrices, Kexue Tongbao (translated from Chinese) 31, 24 (1986) 1662-1667

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