Zobrazeno 1 - 10
of 55
pro vyhledávání: '"Anatoli Torokhti"'
Autor:
Anatoli Torokhti, Phil Howlett
Publikováno v:
Symmetry, Integrability and Geometry: Methods and Applications, Vol 2, p 039 (2006)
We propose and justify a new approach to constructing optimal nonlinear transforms of random vectors. We show that the proposed transform improves such characteristics of {rank-reduced} transforms as compression ratio, accuracy of decompression and r
Externí odkaz:
https://doaj.org/article/30ec060c6d6d457f989911c3537d9692
Autor:
Anatoli Torokhti, Phil Howlett
In this book, we study theoretical and practical aspects of computing methods for mathematical modelling of nonlinear systems. A number of computing techniques are considered, such as methods of operator approximation with any given accuracy; operato
Publikováno v:
IEEE Transactions on Signal Processing. 70:5148-5163
Refereed/Peer-reviewed Many recent applications involve distributed signal processing where a source signal is observed by, say, local receiver-transmitters and then transmitted to a reconstruction center for the signal estimation. An optimal determi
Autor:
Anatoli Torokhti, Pablo Soto-Quiros
Publikováno v:
Signal Processing. 154:272-279
Our work addresses an improvement in accuracy for dimensionality reduction and reconstruction of random signals. The proposed transform targets noisy signals. This is because in the case of highly noisy signals, the known optimal methods might produc
Autor:
Phil Howlett, Anatoli Torokhti
Let $\boldsymbol{f}$ be a square-integrable, zero-mean, random vector with observable realizations in a Hilbert space H, and let $\boldsymbol{g}$ be an associated square-integrable, zero-mean, random vector with realizations which are not observable
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bcd0bc7e39796abcabb1acf80c6cf91a
https://hdl.handle.net/11541.2/144452
https://hdl.handle.net/11541.2/144452
Autor:
Pablo Soto-Quiros, Anatoli Torokhti
Publikováno v:
Signal Processing. 132:183-196
We propose and justify new transforms of random vectors which provide, under a certain condition, better associated accuracy than that of the optimal transforms, the generic Karhunen-Loève transform and the transform considered by Brillinger. It is
Autor:
Anatoli Torokhti, Pablo Soto-Quiros
Our work addresses an improvement in accuracy for dimensionality reduction and reconstruction of random signals. The proposed transform targets noisy signals. This is because in the case of highly noisy signals, the known optimal methods might produc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bcf3d0f31e24f8d2316d84728cee2a2c
https://hdl.handle.net/11541.2/134572
https://hdl.handle.net/11541.2/134572
Autor:
Anatoli Torokhti, Pablo Soto-Quiros
Publikováno v:
IWOBI
New multi-objective operators of random signals are presented in this paper. The new operators improve, under a unrestrictive condition, the performance of known techniques: the generalized Karhunen-Loéve transform, the transform considered by Brill
Publikováno v:
Signal Processing. 111:199-209
A new method for the estimation of a large set of stochastic signals is proposed and justified. A specific restriction is that a priori information on the set of input-output signal pairs can only be obtained, in the form of covariance matrices (or t