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Algebraically Approximate and Noisy Realization of Discrete-Time Systems and Digital Images [electronic resource] /by Yasumichi Hasegawa.

by Hasegawa, Yasumichi [author.]; SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Lecture Notes in Electrical Engineering: 50Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2009.Description: online resource.ISBN: 9783642032172.Subject(s): Engineering | Computer vision | Physics | Engineering | Signal, Image and Speech Processing | Complexity | Control, Robotics, Mechatronics | Image Processing and Computer VisionDDC classification: 621.382 Online resources: Click here to access online
Contents:
Algebraically Approximate and Noisy Realization of Discrete-Time Dynamical Systems -- Input/Output Map and Additive Noises -- Algebraically Approximate and Noisy Realization of Linear Systems -- Algebraically Approximate and Noisy Realization of So-called Linear Systems -- Algebraically Approximate and Noisy Realization of Almost Linear Systems -- Algebraically Approximate and Noisy Realization of Pseudo Linear Systems -- Algebraically Approximate and Noisy Realization of Affine Dynamical Systems -- Algebraically Approximate and Noisy Realization of Linear Representation Systems -- Algebraically Approximate and Noisy Realization of Digital Images -- Algebraically Approximate and Noisy Realization of Two-Dimensional Images.
In: Springer eBooksSummary: This monograph deals with approximation and noise cancellation of dynamical systems which include linear and nonlinear input/output relationships. It also deal with approximation and noise cancellation of two dimensional arrays. It will be of special interest to researchers, engineers and graduate students who have specialized in filtering theory and system theory and digital images. This monograph is composed of two parts. Part I and Part II will deal with approximation and noise cancellation of dynamical systems or digital images respectively. From noiseless or noisy data, reduction will be made. A method which reduces model information or noise was proposed in the reference vol. 376 in LNCIS [Hasegawa, 2008]. Using this method will allow model description to be treated as noise reduction or model reduction without having to bother, for example, with solving many partial differential equations. This monograph will propose a new and easy method which produces the same results as the method treated in the reference. As proof of its advantageous effect, this monograph provides a new law in the sense of numerical experiments. The new and easy method is executed using the algebraic calculations without solving partial differential equations. For our purpose, many actual examples of model information and noise reduction will also be provided.
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Algebraically Approximate and Noisy Realization of Discrete-Time Dynamical Systems -- Input/Output Map and Additive Noises -- Algebraically Approximate and Noisy Realization of Linear Systems -- Algebraically Approximate and Noisy Realization of So-called Linear Systems -- Algebraically Approximate and Noisy Realization of Almost Linear Systems -- Algebraically Approximate and Noisy Realization of Pseudo Linear Systems -- Algebraically Approximate and Noisy Realization of Affine Dynamical Systems -- Algebraically Approximate and Noisy Realization of Linear Representation Systems -- Algebraically Approximate and Noisy Realization of Digital Images -- Algebraically Approximate and Noisy Realization of Two-Dimensional Images.

This monograph deals with approximation and noise cancellation of dynamical systems which include linear and nonlinear input/output relationships. It also deal with approximation and noise cancellation of two dimensional arrays. It will be of special interest to researchers, engineers and graduate students who have specialized in filtering theory and system theory and digital images. This monograph is composed of two parts. Part I and Part II will deal with approximation and noise cancellation of dynamical systems or digital images respectively. From noiseless or noisy data, reduction will be made. A method which reduces model information or noise was proposed in the reference vol. 376 in LNCIS [Hasegawa, 2008]. Using this method will allow model description to be treated as noise reduction or model reduction without having to bother, for example, with solving many partial differential equations. This monograph will propose a new and easy method which produces the same results as the method treated in the reference. As proof of its advantageous effect, this monograph provides a new law in the sense of numerical experiments. The new and easy method is executed using the algebraic calculations without solving partial differential equations. For our purpose, many actual examples of model information and noise reduction will also be provided.

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