2 boyutlu Schur algoritması
2-D Schur algorithm
- Tez No: 46616
- Danışmanlar: PROF.DR. AHMET H. KAYRAN
- Tez Türü: Yüksek Lisans
- Konular: Elektrik ve Elektronik Mühendisliği, Electrical and Electronics Engineering
- Anahtar Kelimeler: Belirtilmemiş.
- Yıl: 1995
- Dil: Türkçe
- Üniversite: İstanbul Teknik Üniversitesi
- Enstitü: Fen Bilimleri Enstitüsü
- Ana Bilim Dalı: Belirtilmemiş.
- Bilim Dalı: Belirtilmemiş.
- Sayfa Sayısı: 90
Özet
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Özet (Çeviri)
As the Schur structures form orthogonal bases, linear adaptive algorithms such as least mean-square (LMS) and recursive least-squares (RLS) can be applied to solve for 2-D system paramaters. It is anticipated that the orthogonality property of the structure can be utilized to derive 2-D schur autoregressive-moving average (ARMA) models, and to solve the 2-D joint-process estimation problem.In the Schur order-update recursions, one can find general expressions for reflection coefficients forward and backward gap functions and error powers in more compact form. For p = 1,2,..., m and n = 1,2,...,p lattice coefficients can be written as: ?p(n) _ _p-n. -p(«) _ LP- Mn-1) ' LP Ep-n A(»-D and the minimum mean-square errors are given by ET = E^m~l) (l -I^“0 f^0) and tn ' m \ o m J The general form of the orthogonal 2-D schur equations are given by » 8Z.W Sp (W i tpn) Sp-n (*l>*2) Sp («^»«y p=l,2,...,m; n=l,2,...,pand starting withg^0)(^) = ğ? (kxJQ = R^k^-p) for p= 0,1,..., m algorithm starts from the 0-th order and continues up to the m-th order. The prediction error powers can be written as Ep-n EP i 1 _f(”)2 P“lit 1 p-n Ep-n p(H-l) In this thesis, an example has been given in order to explain the outline of the theory by means of a first-quadrant support second order quarter-plane model. The proposed 2-D Schur structures are amenable to systolic implementations. This is quite significant as the processing of the 2-D data fields such as images in real time require high data rates. The simplicity of the algorithm is the main attractive feature and the only requirement is to select an ordering schema with two types of shifts (vertical and horizontal) in the prediction support region as a result of this, the first stages are 1-D gap functions. -vin-As the Schur structures form orthogonal bases, linear adaptive algorithms such as least mean-square (LMS) and recursive least-squares (RLS) can be applied to solve for 2-D system paramaters. It is anticipated that the orthogonality property of the structure can be utilized to derive 2-D schur autoregressive-moving average (ARMA) models, and to solve the 2-D joint-process estimation problem. -IX-In the Schur order-update recursions, one can find general expressions for reflection coefficients forward and backward gap functions and error powers in more compact form. For p = 1,2,..., m and n = 1,2,...,p lattice coefficients can be written as: ?p(n) _ _p-n. -p(«) _ LP- Mn-1) ' LP Ep-n A(»-D and the minimum mean-square errors are given by ET = E^m~l) (l -I^”0 f^0) and tn ' m \ o m J The general form of the orthogonal 2-D schur equations are given by » 8Z.W Sp (W i tpn) Sp-n (*l>*2) Sp («^»«y p=l,2,...,m; n=l,2,...,pand starting withg^0)(^) = ğ? (kxJQ = R^k^-p) for p= 0,1,..., m algorithm starts from the 0-th order and continues up to the m-th order. The prediction error powers can be written as Ep-n EP i 1 _f(“)2 P ”lit 1 p-n Ep-n p(H-l) In this thesis, an example has been given in order to explain the outline of the theory by means of a first-quadrant support second order quarter-plane model. The proposed 2-D Schur structures are amenable to systolic implementations. This is quite significant as the processing of the 2-D data fields such as images in real time require high data rates. The simplicity of the algorithm is the main attractive feature and the only requirement is to select an ordering schema with two types of shifts (vertical and horizontal) in the prediction support region as a result of this, the first stages are 1-D gap functions. -vin-As the Schur structures form orthogonal bases, linear adaptive algorithms such as least mean-square (LMS) and recursive least-squares (RLS) can be applied to solve for 2-D system paramaters. It is anticipated that the orthogonality property of the structure can be utilized to derive 2-D schur autoregressive-moving average (ARMA) models, and to solve the 2-D joint-process estimation problem.
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