Subspace Methods
Mostrando 13-16 de 16 artigos, teses e dissertações.
-
13. PARAMETRIC IDENTIFICATION OF MECHANICAL SYSTEMS USING SUBSPACE ALGORITHMS / IDENTIFICAÇÃO PARAMÉTRICA DE SISTEMAS MECÂNICOS USANDO ALGORITMOS DE SUBESPAÇO
Parametric identification of mechanical systems is one of the main applications of the system identification techniques in Mechanical Engineering, specifically for the identification of modal parameters of flexible structures. One of the main problems in the identification is the presence of noise in the measurements. This work presents an analysis in the pr
Publicado em: 2003
-
14. Descrição unificada de metodos de estimação DOA em arranjo de sensores
This work is concerned with the direction-of-arrival estimation of plane waves impinging on a sensors array. This problem arises from applications such as radar, sonar and, more recently, in wireless communications. Among the usual parameter estimation methods, the parametric ones are more accurate than the spectral ones. In this work the parametric methods
Publicado em: 2000
-
15. AVALIAÇÃO DE MÉTODOS DE ESTIMAÇÃO DA DIREÇÃO DE CHEGADA DE SINAIS EM SISTEMAS DE COMUNICAÇÕES CELULARES / EVALUATION OF ESTIMATION METHODS OF DIRECTION OF ARRIVAL OF SIGNALS IN CELLULAR COMMUNICATIONS SYSTEMS
This work intends to contribute for the employment of the Adaptive Antennas in Wireless Communications. In this way, a review of methods for direction of arrival estimation and beamforming is presented. The performance of some subspace fitting direction of arrival estimation methods is evaluated by simulation considering four propagation scenarios: The first
Publicado em: 1998
-
16. Multi-class cancer classification by total principal component regression (TPCR) using microarray gene expression data
DNA microarray technology provides a promising approach to the diagnosis and prognosis of tumors on a genome-wide scale by monitoring the expression levels of thousands of genes simultaneously. One problem arising from the use of microarray data is the difficulty to analyze the high-dimensional gene expression data, typically with thousands of variables (gen
Oxford University Press.