We show an improved lower bound for the Fp estimation problem in a data stream setting for p > 2. A data stream is a sequence of items from the domain [n] with possible repetitions. The frequency vector x is an n-dimensional non-negative integer vector x such that x(i) is the number of occurrences of i in the sequence. Given an accuracy parameter Ω(n) < ǫ < 1, the problem of estimating the pth ...

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This paper presents the design of an Iterative Learning Observer (ILO) for the purpose of estimating system states while simultaneously identifying timevarying parameters. The proposed ILO uses a novel updating mechanism to identify time-varying parameters instead of using integrators which are commonly used in classical adaptive observers to identify constant parameters while estimating system...

In this paper we look at the problem where we have two mixed sets of data on the same scatter plot. We want to calculate linear regression lines for each of the two sets, but first of all we need to decide which data belong to which set. We propose one possible solution to this problem involving data classification and parameters estimation.

Many correlation filters have been designed to be invariant to certain parameters within a set of input images. For example, the construction of rotation and scale invariant filters is well documented. However, an estimation of a generic varying parameter is not available by these methods. This paper presents a two-filter method that estimates the value of a varying parameter in the input image.