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A chaos-based secure cluster protocol for wireless sensor networks

Qian Fang, Ying Liu, Xiaoqun Zhao (2008)

Kybernetika

Security mechanisms for wireless sensor networks (WSN) face a great challenge due to the restriction of their small sizes and limited energy. Hence, many protocols for WSN are not designed with the consideration of security. Chaotic cryptosystems have the advantages of high security and little cost of time and space, so this paper proposes a secure cluster routing protocol based on chaotic encryption as well as a conventional symmetric encryption scheme. First, a principal-subordinate chaotic function...

A dispersion inequality in the Hankel setting

Saifallah Ghobber (2018)

Czechoslovak Mathematical Journal

The aim of this paper is to prove a quantitative version of Shapiro's uncertainty principle for orthonormal sequences in the setting of Gabor-Hankel theory.

A framework to combine vector-valued metrics into a scalar-metric: Application to data comparison

Gemma Piella (2023)

Applications of Mathematics

Distance metrics are at the core of many processing and machine learning algorithms. In many contexts, it is useful to compute the distance between data using multiple criteria. This naturally leads to consider vector-valued metrics, in which the distance is no longer a real positive number but a vector. In this paper, we propose a principled way to combine several metrics into either a scalar-valued or vector-valued metric. We illustrate our framework by reformulating the popular structural similarity...

A general class of entropy statistics

María Dolores Esteban (1997)

Applications of Mathematics

To study the asymptotic properties of entropy estimates, we use a unified expression, called the H h , v ϕ 1 , ϕ 2 -entropy. Asymptotic distributions for these statistics are given in several cases when maximum likelihood estimators are considered, so they can be used to construct confidence intervals and to test statistical hypotheses based on one or more samples. These results can also be applied to multinomial populations.

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