\begin{xpl}
-Consider for instance that $\mathsf{N}=13$, $\mathcal{P}=\{1,2,11\}$ (so $\mathsf{p}=3$), and that
+Consider for instance that $\mathsf{N}=13$, $\mathcal{P}=\{1,2,11\}$ (so $\mathsf{p}=2$), and that
$s=\left\{
\begin{array}{l}
u=\underline{6,} ~ \underline{11,5}, ...\\
The \textsc{Figure~\ref{graphe1}} shows what happens when
displaying each iteration result.
On the contrary, the \textsc{Figure~\ref{graphe2}} explicits the behaviors
-when always applying 2 or 3 modification and next outputing results.
+when always applying either 2 or 3 modifications before generating results.
Notice that here, orientations of arcs are not necessary
since the function $f_0$ is equal to its inverse $f_0^{-1}$.
\end{xpl}
In this context, $\mathcal{P}$ is the singleton $\{b\}$.
If $b$ is even, any vertex $e$ of $\Gamma_{\{b\}}(f_0)$ cannot reach
its neighborhood and thus $\Gamma_{\{b\}}(f_0)$ is not strongly connected.
- If $b$ is even, any vertex $e$ of $\Gamma_{\{b\}}(f_0)$ cannot reach itself
+ If $b$ is odd, any vertex $e$ of $\Gamma_{\{b\}}(f_0)$ cannot reach itself
and thus $\Gamma_{\{b\}}(f_0)$ is not strongly connected.
\end{proof}
such that $\Gamma_{\{b\}}$ is strongly connected.
-
\ No newline at end of file
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: "main"
+%%% ispell-dictionary: "american"
+%%% mode: flyspell
+%%% End:
investigated too, while other modifications of the hypercube will
be regarded in order to enlarge the set of known chaotic
and random iterations.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: "main"
+%%% ispell-dictionary: "american"
+%%% mode: flyspell
+%%% End:
The next section presents how to build balanced Hamiltonian cycles in the
$\mathsf{N}$-cube with the objective to embed them into the
pseudorandom number generator.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: "main"
+%%% ispell-dictionary: "american"
+%%% mode: flyspell
+%%% End:
Obviously, the number of iterations $b$ has to be sufficiently large
to provide a uniform output distribution.
To reduce the number of iterations, the provided Gray code
-should ideally possess the both balanced and locally balanced properties.
+should ideally possess both balanced and locally balanced properties.
However, none of the two algorithms is compatible with the second one:
balanced Gray codes that are generated by state of the art works~\cite{ZanSup04,DBLP:journals/combinatorics/BhatS96} are not locally balanced. Conversely,
locally balanced Gray codes yielded by Igor Bykov approach~\cite{Bykov2016}
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: "main"
+%%% ispell-dictionary: "american"
+%%% mode: flyspell
+%%% End:
The exploitation of chaotic systems to generate pseudorandom sequences
-is an hot topic~\cite{915396,915385,5376454}. Such systems are
+is a hot topic~\cite{915396,915385,5376454}. Such systems are
fundamentally chosen due to their unpredictable character and their
sensitiveness to initial conditions. In most cases, these generators
simply consist in iterating a chaotic function like the logistic
against the NIST suite. This research work ends by a conclusion
section, where the contribution is summarized and intended future work
is outlined.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: "main"
+%%% ispell-dictionary: "american"
+%%% mode: flyspell
+%%% End:
\bibliography{biblio}
\end{document}
+
+%%% Local Variables:
+%%% mode: latex
+%%% ispell-dictionary: "american"
+%%% mode: flyspell
+%%% End:
This is the aims of the next section.
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: "main"
+%%% ispell-dictionary: "american"
+%%% mode: flyspell
+%%% End:
For each number $\mathsf{N}=4,5,6,7,8$ of bits, we have generated
the functions according to the method
-given in Sect.~\ref{sec:SCCfunc}.
+given in Sect.~\ref{sec:SCCfunc}.
+% MENTION FILTRAGE POSSIBLE LORS DE CONSTRUCTION... (SCV)
For each $\mathsf{N}$, we have then restricted this evaluation to the function
whose Markov Matrix (issued from Eq.~(\ref{eq:Markov:rairo}))
has the smallest practical mixing time.
\end{table}
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: "main"
+%%% ispell-dictionary: "american"
+%%% mode: flyspell
+%%% End:
A specific random walk in this modified hypercube is first
introduced (See section~\ref{sub:stop:formal}). We further
-theoretical study this random walk to
-provide a upper bound of fair sequences
+ study this random walk in a theoretical way to
+provide an upper bound of fair sequences
(See section~\ref{sub:stop:bound}).
We finally complete these study with experimental
-results that reduce this bound (Sec.~\ref{sub:stop:stop}).
+results that reduce this bound (Sec.~\ref{sub:stop:exp}).
Notice that for a general references on Markov chains
see~\cite{LevinPeresWilmer2006},
and particularly Chapter~5 on stopping times.
$$
\caption{Average Stopping Time}\label{table:stopping:moy}
\end{table}
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: "main"
+%%% ispell-dictionary: "american"
+%%% mode: flyspell
+%%% End: