diff --git a/ifcs2018_proceeding.tex b/ifcs2018_proceeding.tex
index 09ffff9..2b87cfd 100644
--- a/ifcs2018_proceeding.tex
+++ b/ifcs2018_proceeding.tex
@@ -96,9 +96,18 @@ the first and last are equal to zero. But if we have too sample size for few coe
 \begin{figure}[h!tb]
 \includegraphics[width=\linewidth]{images/float-vs-integer.pdf}
 \caption{Impact of the quantization resolution of the coefficients}
+%\label{float_vs_int}
+\end{figure}
+
+\begin{figure}[h!tb]
+\includegraphics[width=\linewidth]{images/demo_filtre}
+\caption{Impact of the quantization resolution of the coefficients: the quantization is
+set to 6~bits, setting the 30~first and 30~last coefficients out of the initial 128~band-pass
+filter coefficients to 0.}
 \label{float_vs_int}
 \end{figure}
 
+
 \section{Filter optimization}
 
 A basic approach for implementing the FIR filter is to compute the transfer function of
diff --git a/images/demo_filtre.eps b/images/demo_filtre.eps
new file mode 100644
index 0000000..aac35ab
--- /dev/null
+++ b/images/demo_filtre.eps
@@ -0,0 +1,1433 @@
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diff --git a/images/demo_filtre.m b/images/demo_filtre.m
new file mode 100644
index 0000000..3adae6d
--- /dev/null
+++ b/images/demo_filtre.m
@@ -0,0 +1,22 @@
+pkg load signal
+close all
+clear all
+
+d=firls(128,[0 .2 .3 .4 .5 1],[0 0 1 1 0 0]);
+d=d/max(d);
+plot(d)
+N=6;
+k=find(abs(d)<2^(-N))
+hold on
+plot(k,0,'r.')
+line([1 128],[2^(-N) 2^(-N)])
+line([1 128],[-2^(-N) -2^(-N)])
+legend('filter coefficients',[num2str(N),' bit quantization'])
+kk=find(diff(k)>1);
+k(kk(1))
+k(kk(end))
+xlabel('cofficient num');ylabel('normalized coef. value')
+line([0 k(kk(1))],[0.1 0.1])
+line([k(kk(end)) 128],[0.1 0.1])
+text(10,0.15,'30 coefs at 0')
+text(110,0.15,'30 coefs at 0')
diff --git a/images/demo_filtre.pdf b/images/demo_filtre.pdf
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