Quantization is an important but often ignored consideration in discussions about compressed sensing. This paper studies the design of quantizers for random measurements of sparse signals that are optimal with respect to mean-squared error of the lasso reconstruction. We utilize recent results in high-resolution functional scalar quantization and homotopy continuation to approximate the optimal...

Quantization is an important but often ignored consideration in discussions about compressed sensing. This paper studies the design of quantizers for random measurements of sparse signals that are optimal with respect to mean-squared error of the lasso reconstruction. We utilize recent results in high-resolution functional scalar quantization and homotopy continuation to approximate the optimal...

Title of Thesis: OPERATIONAL MODELS FOR EVALUATING THE IMPACT OF PROCESS CHANGES ON CLUSTER TOOL PERFORMANCE Degree candidate: Niranjan Chandrasekaran Degree and year: Master of Science, 1999 Thesis directed by: Assistant Professor Jeffrey W. Herrmann Department of Mechanical Engineering This thesis describes operational models that integrate process models to expedite process change decisions ...