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- Arezou Keshavarz, Yang Wang, Stephen P. Boyd
- 2011 IEEE International Symposium on Intelligent…
- 2011

We consider an optimizing process (or parametric optimization problem), i.e., an optimization problem that depends on some parameters. We present a method for imputing or estimating the objective function, based on observations of optimal or nearly optimal choices of the variable for several values of the parameter, and prior knowledge (or assumptions)… (More)

In this paper we develop a distributed vision-based smart home care system aimed for monitoring elderly persons remotely. The cameras are triggered by a broadcast from the user badge when accelerometers on the badge detect a signal indicating an accidental fall. Tracking the approximate position of the user by the network allows for triggering cameras with… (More)

We consider the use of quadratic approximate value functions for stochastic control problems with inputaffine dynamics and convex stage cost and constraints. Evaluating the approximate dynamic programming policy in such cases requires the solution of an explicit convex optimization problem, such as a quadratic program, which can be carried out efficiently.… (More)

This paper considers moving horizon estimation (MHE) approach to solution of staged quadratic programming (QP) problems. Using an insight into the constrained solution structure for the growing horizon, we develop a very accurate iterative update of the arrival cost in the MHE solution. The update uses a quadratic approximation of the arrival cost and… (More)

- Ali Maleki Tabar, Arezou Keshavarz, Hamid Aghajan
- VSSN '06
- 2006

A wireless sensor network employing multiple sensing and event detection modalities and distributed processing is proposed for smart home monitoring applications. Image sensing and vision-based reasoning are employed to verify and further analyze events reported by other sensors. The system has been developed to address the growing application domain in… (More)

- Parham Aarabi, Jerry Chi-Ling Lam, Arezou Keshavarz
- 2007 10th International Conference on Information…
- 2007

The fundamental point of this paper is that the fusion of several simple, somewhat unreliable, and somewhat inefficient frontal face detectors results in an efficient and reliable frontal face detector which, without any training, performs similarly to a state-of-the-art neural network based face detector trained on 60,000 images. The simple detectors used… (More)

- Arezou Keshavarz, Parham Aarabi
- ISM
- 2006

Generalized additive models are an effective regression tool, popular in the statistics literature, that provides an automatic extension of traditional linear models to nonlinear systems. We present a distributed algorithm for fitting generalized additive models, based on the alternating direction method of multipliers (ADMM). In our algorithm the component… (More)

- J. Papari, H. R. Dalili Oskouei, A. Keshavarz
- The 2nd International Conference on Control…
- 2011

Adaptive beamforming algorithms that control smart antennas is known to have resolution and interference rejection capability when coefficients of the array factor (AF) is known. However, the performance of adaptive beamforming techniques may degrade severely in the presence of mismatches between the presumed and actual signal steering vectors. This problem… (More)

We develop an algorithm called bubble clustering which attempts to cover a given set of points with the union of k ellipses of minimum total volume. This algorithm operates by splitting and merging ellipses according to an annealing schedule and does not assume any prior distribution on the data. We compare our algorithm with k-means and the EM algorithm… (More)

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