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Filtering by: Committee Member Coop, Leonard B. Advisor Kim, Jinsub

Foreground signature extraction for a non-linear hyperspectral mixing model

Creator:

Hollis, Jarrod

Abstract:

Hyperspectral images consist of intensity measurements associated with a large number of EM wavelengths per pixel. When a pixel contains a single material, the measurements of the pixel match the spectral signature of the material. When multiple materials are present in a single pixel, spectral mixing occurs, and signatures of...

Resource Type:

Masters Thesis

Full Text:

acknowledge Dr. Xiao Fu and Dr. Leonard Coop for serving on my defense committee. Finally, I would like to
Identifiability-Aware Joint Sparse Error Correction For Robust State Estimation

Creator:

Kibria, Sharmin

Abstract:

We study joint nonlinear state estimation with multi-period measurement vectors that are potentially corrupted by sparse gross errors. The identifiability-aware approach is proposed to leverage common characteristics of fundamentally identifiable gross errors to enhance error correction performance. First, we derive a necessary rank condition that the sparsity pattern of any...

Resource Type:

Dissertation

Full Text:

Identifiability Condition . . . . . . . . . 52 B Convergence Analysis Of Total Error Estimation Algorithm
Power Network Parameter Correction via Sparse Unsupervised Regression

Creator:

Senaratne, Galpottage Dilan S.

Abstract:

Accurate information of power network parameters is essential for performing various power system monitoring and control tasks including state estimation, economic dispatch, and contingency analysis. In this paper, we present a novel approach of power network parameter correction wherein we exploit the sparse nature of parameter errors. Parameter error correction...

Resource Type:

Masters Thesis

Full Text:

Raich, Prof. Eduardo Cotilla-Sanchez, Prof. Xiao Fu and GCR Prof. Leonard Coop for their time to be