New perspectives on computing methods for mathematical signal processing
(A Torokhti with S Friedland, V Mehrmann, I Yamada)
Major challenges in mathematical signal processing rely on complicated
matrix computation techniques. The project will provide new methods for
solution of fundamental problems in matrix computations motivated by
difficult signal processing challenges. The expected outcomes are the
development of new theory for sophisticated computational methods & cost
effective numerical algorithms using that theory. This will enhance
Australia’s already high profile in the field. Importantly, the project
involves collaboration between four well-established experts from USA, Japan
& Australia who have significant achievements in the field, and offers
substantial opportunity for excellent postgraduate training, critical for
the future of Australian research.
Funding
2007-2009: ARC Discovery Grant. Project title: "New perspectives on computing methods for mathematical signal processing", $186,000 Chief Investigator: A/Prof. A. Torokhti. Partner Investigators: Prof. Shmuel Friedland (University of Illinois at Chicago, USA), Prof. Volker Mehrmann (Technical University, Berlin, Germany), A/Prof. Isao Yamada (Tokyo Institute of Technology, Japan).
Publications
A. Torokhti and P. Howlett, Computational Methods for Modelling of Nonlinear Systems, Elsevier, 397 p., 2007.
A.P. Torokhti, P. G. Howlett and C. Pearce, New Perspectives on Optimal Transforms for Random Vectors, Optimization: Theory and Applications, Springer, 2009 (accepted).
S. Friedland and A. P. Torokhti, Generalized rank-constrained matrix approximations, SIAM J. Matrix Anal. Appl., 29, issue 2, pp. 656-659, 2007.
A. Torokhti and S. Friedland, Towards theory of generic Principal Component Analysis, J. Multivariate Analysis, (accepted).
A. Torokhti and P. Howlett, Filtering and Compression for Infinite Sets of Stochastic Signals, Signal Processing, (accepted).
P. G. Howlett, A. Torokhti and C.E.M. Pearce, Optimal Multilinear Estimation of a Random Vector under Constraints of Causality and Limited Memory, Computational Statistics and Data Analysis, 52 , Issue 2, pp. 869-878, 2007.
A. Torokhti and S. Miklavcic, Compression and Filtering of Random Signals under Constraint of Variable Memory, Proc. of World Academy of Sci., Eng. & Tech. Vol. 30, July 2008, ISSN 1307-6884, pp. 768-773.
A. Torokhti and P. Howlett, Optimal Data Compression and Filtering: the Case of Infinite Signal Sets, Proc. of World Academy of Sci., Eng. & Tech. Vol. 30, July 2008, ISSN 1307-6884, pp. 774-783.
A. Torokhti, S. Friedland and P. Howlett, Towards generic theory of data compression, CD-rom: 2007 IEEE Int. Symp. on Inform. Theory. June 24-29 2007, Nice, France. Symposium Proceedings. ISBN: 1-4244-1429-6, pp. 291-295, 2007.
A. Torokhti, P. Howlett and C. Pearce, Information estimation from partially missed data, CD-rom: 2007 IEEE Int. Symp. on Inform. Theory. June 24-29 2007, Nice, France. Symposium Proceedings. ISBN: 1-4244-1429-6, pp. 2011-2015, 2007.