2002Unpublished venueRequires access

An algorithm for uniform orthogonalization

Pedro L. D. Peres, A. Lopes, Ivanil Sebastião Bonatti

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Abstract

Orthogonal functions for signal representation are perhaps the most important tool for signal processing and analysis. These functions are used because the lead to an easier method of calculating the coefficients of a signal representation. In addition, if the signal set can be expressed by the displacements of a basic function, the signal reconstruction can be achieved by storing only this basic function at the receiver to represent the entire set. This paper presents a procedure named uniform orthogonalization for the generation of uniform and orthogonal sets of functions, starting from linearly independent non-orthogonal ones.

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What this paper is about

Orthogonal functions for signal representation are perhaps the most important tool for signal processing and analysis. These functions are used because the lead to an easier method of calculating the coefficients of a signal representation. In addition, if the signal set can be expressed by the displacements of a basic function, the signal reconstruction can be achieved by storing only this basic function at the receiver to represent the entire set. This paper presents a procedure named uniform orthogonalization for the generation of uniform and orthogonal sets of functions, starting from linearly independent non-orthogonal ones.

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Available abstract

Orthogonal functions for signal representation are perhaps the most important tool for signal processing and analysis. These functions are used because the lead to an easier method of calculating the coefficients of a signal representation. In addition, if the signal set can be expressed by the displacements of a basic function, the signal reconstruction can be achieved by storing only this basic function at the receiver to represent the entire set. This paper presents a procedure named uniform orthogonalization for the generation of uniform and orthogonal sets of functions, starting from linearly independent non-orthogonal ones.

Key concepts: Orthogonalization, Orthogonal functions, Representation (politics), Algorithm, SIGNAL (programming language), Signal processing, Set (abstract data type), Function (biology)

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