Sridhar Gollamudi Shirish Nagaraj Yih-Fang Huang Laboratory for Image and Signal Analysis Department of Electrical Engineering University of Notre Dame Notre Dame, IN 46556 E-mail: [email protected] Abstract | This paper presents the concept of SetMembership Filtering (SMF), an extension of SetMembership Identi cation (SMI) theory to the general ltering problem. A toolbox of adaptive solutions cal...

In this work a constrained adaptive filtering strategy based on conjugate gradient (CG) and set-membership (SM) techniques is presented for adaptive beamforming. A constraint on the magnitude of the array output is imposed to derive an adaptive algorithm that performs data-selective updates when calculating the beamformer’s parameters. We consider a linearly constrained minimum variance (LCMV) ...

In this paper, diagnosis for hybrid systems using a parity space approach that considers model uncertainty is proposed. The hybrid diagnoser is composed of modules which carry out the mode recognition and diagnosis tasks interacting each other, since the diagnosis module adapts accordingly to the current hybrid system mode. Moreover, the methodology takes into account the unknown but bounded un...

A wide variety of powerful cryptographic tools have been built using RSA, Diffie-Hellman, and other similar assumptions as their basis. Computational security has been achieved relative to complexity assumptions about the computational difficulty of a variety of number theoretic problems. However, these problems are closely related, and it is likely that if any one of them turns out to be effic...

This paper deals with offline nonlinear state estimation where measurements are available only when some given equality conditions are satisfied. For this type of problems, which are often met in robot localization when sonar or radar are involved, the data are qualified as fleeting because the measurements are available only at some given unknown dates. In this paper, the first approach able t...

We consider the bit-probe complexity of the set membership problem, where a set S of size at most n from a universe of size m is to be represented as a short bit vector in order to answer membership queries of the form “Is x in S?” by adaptively probing the bit vector at t places. Let s(m,n, t) be the minimum number of bits of storage needed for such a scheme. Several recent works investigate s...

Interval methods have been shown to be efficient, robust and reliable to solve difficult set-membership localization problems. However they are unsuitable in a probabilistic context, where the approximation of an unbounded probability density function by a set cannot be accepted. This paper proposes a new probabilistic approach which makes possible to use classical set-membership localization m...

In this paper we propose a method, based on a Set Membership approach, for the estimation of nonlinear regressions models. At the contrary of most of the existing identi...cation approaches, the method presented in this paper does not need any assumption about the functional form of the model to be identi...ed, but uses only some prior information on its regularity and on the size of noise corr...

Assertions of set membership, such as Amy is an artist, should not be confused with those of set inclusion, such as All artists are bohemians. Membership is not a transitive relation, whereas inclusion is. Cognitive scientists have neglected the topic, and so we developed a theory of inferences yielding conclusions about membership, e.g., Amy is a bohemian, and about non-membership, Abbie is no...