Adaptive estimation of linear functionals over a collection of parameter spaces is considered. A between-class modulus of continuity, a geometric quantity, is shown to be instrumental in characterizing the degree of adaptability over two parameter spaces in the same way that the usual modulus of continuity captures the minimax difficulty of estimation over a single parameter space. A general co...

The Weibull distribution is widely employed in several areas of engineering because it is an extremely flexible distribution with different shapes. Moreover, it can include characteristics of several other distributions. However, successful usage of Weibull distribution depends on estimation accuracy for three parameters of scale, shape and location. This issue shifts the attentions to the requ...

In this note we consider the question of whether an adaptive controller can converge to a non-adaptive stabilizing controller. Speciically, we show for a class of back-stepping controllers with adaptive tuning functions that the set of initial conditions in state and estimation parameter for which the estimation parameter converges to a parameter which produces a destabilizing controller can ha...

of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science OBSERVER-SIDE PARAMETER ESTIMATION FOR ADAPTIVE CONTROL By Jason Nezvadovitz August 2017 Chair: Warren Dixon Major: Mechanical Engineering In adaptive control, a controller is precisely designed for a certain model of the system, but that model’s...

We describe a compressive imager that adapts the measurement basis based on past measurements within a sequential Bayesian estimation framework. Simulation study shows a 7% improvement in reconstruction performance compared to a static measurement basis. © 2011 Optical Society of America OCIS codes: 110.1758,110.1085,100.3190.

This paper considers the problem of distributed adaptive linear parameter estimation in multiagent inference networks. Local sensing model information is only partially available at the agents, and interagent communication is assumed to be unpredictable. The paper develops a generic mixed time-scale stochastic procedure consisting of simultaneous distributed learning and estimation, in which th...

We present a measurement scheme capable of achieving the quantum limit of parameter estimation using an adaptive strategy that minimizes the parameter’s variance at each step. The adaptive rule we propose makes the scheme robust against errors, in particular imperfect readouts, a critical requirement to extend adaptive schemes from quantum optics to solid-state sensors. Thanks to recent advance...

Removing noise from images is a challenging problem in digital image processing. This paper presents an image denoising method based on a maximum a posteriori (MAP) density function estimator, which is implemented in the wavelet domain because of its energy compaction property. The performance of the MAP estimator depends on the proposed model for noise-free wavelet coefficients. Thus in the wa...