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Assessing the performance of scheduling heuristics through simulation requires one to generate synthetic instances of tasks and machines with well-identified properties. Carefully controlling these properties is mandatory to avoid any bias. We consider the scheduling problem consisting of allocating independent sequential tasks on unrelated machines while minimizing the maximum execution time. In...
Linear and nonlinear filtering for state estimation (e.g. position estimation or sensor fusion) for indoor positioning and navigation applications is a challenging task. Sensor fusion becomes more important with cost-effective sensors being readily available. However, state estimation with recursive Bayesian filters for sensor fusion and filtering are difficult to apply. We present an overview for...
The Kalman filter is a versatile tool used in control and signal processing systems to predict statistically significant data from noisy measurements. In many practical control systems, not all the system states are directly controllable and observable. From noisy measurements of a limited subset of the observable system states, the Kalman filter predicts the mean values and covariances of the complete...
In this paper, we propose an order statistics-based unbiased homomorphic system to reduce multiplicative noise. The design of such a system is based on the probability density function (PDF) of the noise. First, we generalize the order statistics-based nonlinear filter called the sampled function weighted order (SFWO) filter proposed in [1] to reduce additive noise, to the case when the additive noise...
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