hello
maybe this ? NB it does not need conv but requires Statistics and Machine Learning Toolbox
% dummy data
t= 0:0.25:100;
y = sin(2*pi*t./max(t))+rand(size(t));
%% main code (you choose the amount of samples and overlap)
buffer_size = 50; % in samples
overlap = 25; % overlap expressed in samples
[new_time,data_pc] = my_fn(t,y,buffer_size,overlap,90); %90th percentile of buffered signal
figure(1),
plot(t,y,new_time,data_pc,'*-r');
%%%%%%%%%% function %%%%%%%%%%%%%%
function [new_time,data_out] = my_fn(t,y,buffer_size,overlap,p)
% NB : buffer size and overlap are integer numbers (samples)
% data (in , out) are 1D arrays (vectors)
shift = buffer_size-overlap; % nb of samples between 2 contiguous buffers
samples = numel(y);
nb_of_loops = fix((samples-buffer_size)/shift +1);
for k=1:nb_of_loops
start_index = 1+(k-1)*shift;
stop_index = min(start_index+ buffer_size-1,samples);
x_index(k) = round((start_index+stop_index)/2);
data_out(k) = prctile(y(start_index:stop_index),p); %
end
new_time = t(x_index); % time values are computed at the center of the buffer
end
