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- Atiye Alaeddini, Kristi A. Morgansen
- ACC
- 2013

This work addresses the problem of state estimation for an autonomous flying vehicle using an optic flow approach motivated by the visuomotor system of insects. We investigate the minimum number of optic flow sensors and the sensor configuration necessary to have an observable system. The system dynamics linearized about a point are not observable, but a… (More)

- Atiye Alaeddini, Kristi A. Morgansen
- ACC
- 2016

Given a network, we would like to determine which subset of nodes should be measured by limited sensing facilities to maximize information about the entire network. The optimal choice corresponds to the configuration that returns the highest value of a measure of observability of the system. Here, the determinant of the inverse of the observability Gramian… (More)

Utilizing the concept of observability, in conjunction with tools from graph theory and optimization, this paper develops an algorithm for network synthesis with privacy guarantees. In particular, we propose an algorithm for the selection of optimal weights for the communication graph in order to maximize the privacy of nodes in the network, from a control… (More)

- Atiye Alaeddini, Kristi A. Morgansen
- ECC
- 2014

- Atiye Alaeddini, Kristi A. Morgansen
- ArXiv
- 2016

This paper is concerned with the design of optimal control for finite-dimensional controlaffine nonlinear dynamical systems. We introduce an optimal control problem that specifically optimizes nonlinear observability in addition to ensuring stability of the closed loop system. A recursive algorithm is then proposed to obtain an optimal state feedback… (More)

- Atiye Alaeddini, Kristi A. Morgansen
- J. Aerospace Inf. Sys.
- 2015

Epidemiological models have tremendous potential to forecast disease burden and quantify the impact of interventions. Detailed models are increasingly popular, however these models tend to be stochastic and very costly to evaluate. Fortunately, readily available high-performance cloud computing now means that these models can be evaluated many times in… (More)

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