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Indoor navigation using particle filter and sensor fusion

Köping L., Mühsam T., Ofenberg C., Czech B., Bernard M., Schmera J., Deinzer F.

Annual of Navigation

2012

No. 19, part 2

31--40

In this paper we present an indoor localization system based on particle filter and multiple sensor data like acceleration, angular velocity and compass data. With this approach we tackle the problem of documentation on large building yards during the construction phase. Due to the circumstances of such an environment we cannot rely on any data from GPS, Wi-Fi or RFID. Moreover this work should serve us as a first step towards an all-in-one navigation system for mobile devices. Our experimental results show that we can achieve high accuracy in position estimation.

Hardened Bloom filters, with an application to unobservability

Bernard M., Leprévost F.

Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica

2012

Vol. 12, no. 4

11--22

Classical Bloom filters may be used to elegantly check if an element e belongs to a set S, and, if not, to add e to S. They do not store any data and only provide boolean answers regarding the membership of a given element in the set, with some probability of false positive answers. Bloom filters are often used in caching system to check that some requested data actually exist before doing a costly lookup to retrieve them. However, security issues may arise for some other applications where an active attacker is able to inject data crafted to degrade the filters’ algorithmic properties, resulting for instance in a Denial of Service (DoS) situation. This leads us to the concept of hardened Bloom filters, combining classical Bloom filters with cryptographic hash functions and secret nonces. We show how this approach is successfully used in the TrueNyms unobservability system and protects it against replay attacks.

Detecting Irrelevant Subtrees to Improve Probabilistic Learning from Tree-structured Data

Habrard A., Bernard M., Sebban M.

Fundamenta Informaticae

2005

Vol. 66, nr 1,2

103--130

In front of the large increase of the available amount of structured data (such as XML documents), many algorithms have emerged for dealing with tree-structured data. In this article, we present a probabilistic approach which aims at a priori pruning noisy or irrelevant subtrees in a set of trees. The originality of this approach, in comparison with classic data reduction techniques, comes from the fact that only a part of a tree (i.e. a subtree) can be deleted, rather than the whole tree itself. Our method is based on the use of confidence intervals, on a partition of subtrees, computed according to a given probability distribution. We propose an original approach to assess these intervals on tree-structured data and we experimentally show its interest in the presence of noise.

Azoles. Part 46 [1]. Uncommon Products from the Reactions of Some Nitroazoles with Tertiary Carbanions

Bernard M.

Polish Journal of Chemistry

2001

Vol. 75, nr 10

1465-1474

Some unusual products, like dimers, rotamers and ketone, were isolated from the reaction mixtures of vicarious nucleophilic substitution, involving nitroazoles and tertiary carbanions. Possible pathways of their formation are discussed.