diff --git a/ifcs2018_proceeding.tex b/ifcs2018_proceeding.tex
index deb6bb6..ba377ce 100644
--- a/ifcs2018_proceeding.tex
+++ b/ifcs2018_proceeding.tex
@@ -171,11 +171,12 @@ computational resources: optimizing some criteria within finite, limited
 resources indeed matches the definition of a classical optimization problem.
 
 Specifically the degrees of freedom when addressing the problem of replacing the single monolithic
-FIR with a cascade of optimized filters are the number of coefficients $N_i$ of each filter $i$ and
-the number of bits $C_i$ representing the coefficients. Because each FIR in the chain is fed the output of the previous stage,
+FIR with a cascade of optimized filters are the number of coefficients $N_i$ of each filter $i$,
+the number of bits $C_i$ representing the coefficients and the number of bits $D_i$ representing
+-the data fed to the filter. Because each FIR in the chain is fed the output of the previous stage,
 the optimization of the complete processing chain within a constrained resource environment is not
 trivial. The resource occupation of a FIR filter is considered as $C_i \times N_i$ which aims
-at approximating the number of bits needed in a worst case condition to represent the output of the 
+at approximating the number of bits needed in a worst case condition to represent the output of the
 FIR. Indeed, the number of bits generated by the FIR is $(C_i+D_i)\times\log_2(N_i)$ with $D_i$
 the number of bits needed to represent the data $x_k$ generated by the previous stage, but the
 $\log$ function is avoided for its incompatibility with a linear programming description, and
@@ -231,9 +232,9 @@ rejection capability. Weighing these two criteria allows designing the linear pr
 \label{noise-rejection}
 \end{figure}
 
-The objective function maximizes the noise rejection ($\max(\Delta_{i_{\max}})$) while keeping resource 
-occupation below a user-defined threshold, or as will be discussed here, aims at minimizing the area 
-needed to reach a given rejection ($\min(S_q)$ in the forthcoming discussion, Eqs. \ref{cstr_size} 
+The objective function maximizes the noise rejection ($\max(\Delta_{i_{\max}})$) while keeping resource
+occupation below a user-defined threshold, or as will be discussed here, aims at minimizing the area
+needed to reach a given rejection ($\min(S_q)$ in the forthcoming discussion, Eqs. \ref{cstr_size}
 and \ref{cstr_rejection}). The MILP solver is allowed to choose the number of successive
 filters, within an upper bound. The last problem is to model the noise rejection. Since filter
 noise rejection capability is not modeled with linear equations, a look-up-table is generated