From db81f7ad9d5b1f457661973583951a2555a77203 Mon Sep 17 00:00:00 2001 From: jmfriedt Date: Fri, 2 Aug 2019 12:04:13 +0200 Subject: [PATCH] captions figures --- ifcs2018_journal.tex | 60 +++++++++++++++++++++++++++++++++++----------------- 1 file changed, 41 insertions(+), 19 deletions(-) diff --git a/ifcs2018_journal.tex b/ifcs2018_journal.tex index 5b20d7f..a34b5d6 100644 --- a/ifcs2018_journal.tex +++ b/ifcs2018_journal.tex @@ -312,7 +312,8 @@ criterion, we meet the expected rejection capability of low pass filters as show \begin{figure} \centering \includegraphics[width=\linewidth]{images/colored_custom_criterion} -\caption{Custom criterion (maximum rejection in the stopband minus the mean of the absolute value of the passband rejection) +\caption{Custom criterion (maximum rejection in the stopband minus the {\color{red} sum of the +absolute values of the passband rejection normalized to the bandwidth}) comparison between monolithic filter and cascaded filters} \label{fig:custom_criterion} \end{figure} @@ -328,7 +329,9 @@ the rejection. Hence the best coefficient set are on the vertex of the pyramid. \begin{figure} \centering \includegraphics[width=\linewidth]{images/rejection_pyramid} -\caption{Rejection as a function of number of coefficients and number of bits} +\caption{{\color{red}{Filter}} rejection as a function of number of coefficients and number of bits +{\color{red}: this lookup table will be used to identify which filter parameters -- number of bits +representing coefficients and number of coefficients -- best match the targeted transfer function.}} \label{fig:rejection_pyramid} \end{figure} @@ -343,7 +346,7 @@ Hence when summing the transfer functions, the resulting rejection shown as the with respect to a basic sum of the rejection criteria shown as a the dotted yellow line. % r2.9 Thus, estimating the rejection of filter cascades is more complex than taking the sum of all the rejection -criteria of each filter. However since the this sum underestimates the rejection capability of the cascade, +criteria of each filter. However since the {\color{red}individual filter rejection} sum underestimates the rejection capability of the cascade, % r2.10 this upper bound is considered as a conservative and acceptable criterion for deciding on the suitability of the filter cascade to meet design criteria. @@ -351,7 +354,11 @@ of the filter cascade to meet design criteria. \begin{figure} \centering \includegraphics[width=\linewidth]{images/cascaded_criterion} -\caption{Rejection of two cascaded filters} +\caption{{\color{red}Transfer function of individual filters and after cascading} the two filters, +{\color{red}demonstrating that the selected criterion of maximum rejection in the bandstop (horizontal +lines) is met. Notice that the cascaded filter has better rejection than summing the bandstop +maximum of each individual filter.} +} \label{fig:sum_rejection} \end{figure} @@ -521,7 +528,8 @@ in the computation of the results. \draw[->] (Deploy) edge node [left] { (5) } (Postproc) ; \draw[->] (Postproc) -- (Results) ; \end{tikzpicture} - \caption{Design workflow from the input parameters to the results} + \caption{Design workflow from the input parameters to the results {\color{red} allowing for +a fully automated optimal solution search.}} \label{fig:workflow} \end{figure} @@ -699,22 +707,28 @@ Figure~\ref{fig:max_1500_result} shows the rejection of the different configurat \centering \begin{subfigure}{\linewidth} \includegraphics[width=\linewidth]{images/max_500} - \caption{Signal spectrum for MAX/500} + \caption{\color{red}Filter transfer functions for varying number of cascaded filters solving +the MAX/500 problem of maximizing rejection for a given resource allocation (500~arbitrary units).} \label{fig:max_500_result} \end{subfigure} \begin{subfigure}{\linewidth} \includegraphics[width=\linewidth]{images/max_1000} - \caption{Signal spectrum for MAX/1000} + \caption{\color{red}Filter transfer functions for varying number of cascaded filters solving +the MAX/1000 problem of maximizing rejection for a given resource allocation (1000~arbitrary units).} \label{fig:max_1000_result} \end{subfigure} \begin{subfigure}{\linewidth} \includegraphics[width=\linewidth]{images/max_1500} - \caption{Signal spectrum for MAX/1500} + \caption{\color{red}Filter transfer functions for varying number of cascaded filters solving +the MAX/1500 problem of maximizing rejection for a given resource allocation (1500~arbitrary units).} \label{fig:max_1500_result} \end{subfigure} - \caption{Signal spectrum of each experimental configurations MAX/500, MAX/1000 and MAX/1500} + \caption{\color{red}Solutions for the MAX/500, MAX/1000 and MAX/1500 problems of maximizing +rejection for a given resource allocation. +The filter shape constraint (bandpass and bandstop) is shown as thick +horizontal lines on each chart.} \end{figure} In all cases, we observe that the actual rejection is close to the rejection computed by the solver. @@ -736,7 +750,8 @@ the FIR filters and remove additional processing blocks including FIFO and Progr Logic (PL -- FPGA) to Processing System (PS -- general purpose processor) communication. \begin{table}[h!tb] - \caption{Resource occupation. The last column refers to available resources on a Zynq-7010 as found on the Redpitaya.} + \caption{Resource occupation {\color{red}following synthesis of the solutions found for +the problem of maximizing rejection for a given resource allocation}. The last column refers to available resources on a Zynq-7010 as found on the Redpitaya.} \label{tbl:resources_usage} \centering \begin{tabular}{|c|c|ccc|c|} @@ -953,29 +968,36 @@ Figure~\ref{fig:min_100} shows the rejection of the different configurations in \begin{figure} \centering \begin{subfigure}{\linewidth} - \includegraphics[width=\linewidth]{images/min_40} - \caption{Signal spectrum for MIN/40} + \includegraphics[width=.91\linewidth]{images/min_40} + \caption{\color{red}Filter transfer functions for varying number of cascaded filters solving +the MIN/40 problem of minimizing resource allocation for reaching a 40~dB rejection.} \label{fig:min_40} \end{subfigure} \begin{subfigure}{\linewidth} - \includegraphics[width=\linewidth]{images/min_60} - \caption{Signal spectrum for MIN/60} + \includegraphics[width=.91\linewidth]{images/min_60} + \caption{\color{red}Filter transfer functions for varying number of cascaded filters solving +the MIN/60 problem of minimizing resource allocation for reaching a 60~dB rejection.} \label{fig:min_60} \end{subfigure} \begin{subfigure}{\linewidth} - \includegraphics[width=\linewidth]{images/min_80} - \caption{Signal spectrum for MIN/80} + \includegraphics[width=.91\linewidth]{images/min_80} + \caption{\color{red}Filter transfer functions for varying number of cascaded filters solving +the MIN/80 problem of minimizing resource allocation for reaching a 80~dB rejection.} \label{fig:min_80} \end{subfigure} \begin{subfigure}{\linewidth} - \includegraphics[width=\linewidth]{images/min_100} - \caption{Signal spectrum for MIN/100} + \includegraphics[width=.91\linewidth]{images/min_100} + \caption{\color{red}Filter transfer functions for varying number of cascaded filters solving +the MIN/100 problem of minimizing resource allocation for reaching a 100~dB rejection.} \label{fig:min_100} \end{subfigure} - \caption{Signal spectrum of each experimental configurations MIN/40, MIN/60, MIN/80 and MIN/100} + \caption{\color{red}Solutions for the MIN/40, MIN/60, MIN/80 and MIN/100 problems of reaching a +given rejection while minimizing resource allocation. The filter shape constraint (bandpass and +bandstop) is shown as thick +horizontal lines on each chart.} \end{figure} We observe that all rejections given by the quadratic solver are close to the experimentally -- 2.16.4