The seminal work on set membership state estimation [1] F. C. Schweppe. Recursive state estimation: unknown but bounded errors and system inputs. [2] D. P. Bertsekas and I. B. Rhodes. Recursive state estimation for a set-membership description of uncertainty.

| A novel ltering methodology, recently introduced as a viable tool for adaptive signal processing , is presented for applications to interference suppression in communication systems. This technique is based on bounding the worst-case lter error by a designer-speciied value. I. Introduction This paper presents some recent work on alternative method-ologies for ltering based on a deterministic ...

We consider the non-adaptive 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 non-adaptively probing the bit vector at t places. Let sN (m,n, t) be the minimum number of bits of storage needed for such a scheme. Buhrman, Milter...

This paper proposes a system enforcing content-based message filtering conceived as a key service for On-line Social Networks (OSNs). The system allows OSN users to have a direct control on the messages posted on their walls. This is achieved trough a flexible rule-based system, that allows a user to customize the filtering criteria to be applied to their walls, and a Machine Learning based sof...

This paper concerns multicast applications that are interested in the evolution of their membership over time. It covers optimal on-line estimation algorithms for determining the membership of a multicast group. The paper briefly reviews the related work and our own contributions to the field. Using a probabilistic acknowledgement scheme – its period and probability can be set to achieve high q...

Measurement results contain different kinds of uncertainty. Besides systematic errors andrandom errors individual measurement results are also subject to another type of uncertainty,so-called emph{fuzziness}. It turns out that special fuzzy subsets of the set of real numbers $RR$are useful to model fuzziness of measurement results. These fuzzy subsets $x^*$ are called emph{fuzzy numbers}. The m...

The objective of this chapter is to provide a semantic framework for fuzzy sets in the theory of rough sets. Rough membership functions are viewed as a special type of fuzzy membership functions interpretable using conditional probabilities. The relationships between fuzzy membership functions and rough membership functions, between core and support of fuzzy set theory and lower and upper appro...

In this article, we continue the development of the theory of fuzzy sets [23], started with [14] with the future aim to provide the formalization of fuzzy numbers [8] in terms reflecting the current state of the Mizar Mathematical Library. Note that in order to have more usable approach in [14], we revised that article as well; some of the ideas were described in [12]. As we can actually unders...

Two-dimensional (TD) adaptive filtering is a technique that can be applied to many image, and signal processing applications. This paper extends the one-dimensional adaptive filter algorithms to TD structures and the novel TD adaptive filters are established. Based on this extension, the TD variable step-size normalized least mean squares (TD-VSS-NLMS), the TD-VSS affine projection algorithms (...

This paper presents a fuzzy set theory based approach to Chinese sentence-level sentiment classification. Compared with traditional topic-based text classification techniques, the fuzzy set theory provides a straightforward way to model the intrinsic fuzziness between sentiment polarity classes. To approach fuzzy sentiment classification, we first propose a fine-to-coarse strategy to estimate s...