locate_peaks matlab function
October 6, 2014
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function indices = locate_peaks(ip)
%function to find peaks
%a peak is any sample which is greater than its two nearest neighbours
index = 1;
num = 2;
indices = [];
for k = 1 + num : length(ip) - num
seg = ip(k-num:k+num);
[val, max_index] = max(seg);
if max_index == num + 1
indices(index) = k;
index = index + 1;
end;
end;
Categories: matlab code
Linear Phase Filters – why they are used
October 1, 2014
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%% Linear phase filters - preserve shape of a filtered signal
% This is the code used during a youtube video presentation dealing with linear phase filters
% Search for linear phase at http://youtube.com/ddorran
%
% Code available from https://dadorran.wordpress.com
%
close all ; clear all; clc
fs = 100;
T = 1/fs; %sampling interval
N = 2000; %length of signal being synthesised
n = 0:N-1; %samples of the signal
t = n*T;
plot_range = [N/2-100:N/2+100];
%% synthesise a signal
x = cos(2*pi*10*t) + 0.5*cos(2*pi*20*t + 1.4);
subplot(2,1,1);
plot(t(plot_range),x(plot_range))
xlabel('Time (seconds)');
ylabel('Amplitude')
title('Synthesised Signals')
axis tight
% Add some noise
ns = randn(1,length(x)+100)*2;
%filter the noise to synthesise band limited noise
[b a] = butter(5, [0.28 0.33],'bandpass');
ns_filtered = filter(b,a,ns);
%add noise to clean signal
x_ns = x +ns_filtered(end-length(x)+1:end);
hold on
noisy_x = plot(t(plot_range), x_ns(plot_range),'r');
legend('clean signal', 'noisy signal')
%% Plot frequency Content of Noisy Signal
subplot(2,1,2)
X_ns = fft(x_ns);
fax = [0:N-1]/(N/2); % normalised frequency axis
plot(fax(1:N/2), abs(X_ns(1:N/2))/(N/2)) ; %plot first half of spectrum
xlabel('frequency ( x \pi rads/sample)')
ylabel('Magnitude')
title('Magnitude Spectrum of Noisy Signal')
%% Filter out the noise using an IIR filter (non-linear phase)
[b_iir a_iir] = cheby1(10, 0.5, [0.27 0.34], 'stop');
y_iir = filter(b_iir,a_iir, x_ns);
[H_iir w] = freqz(b_iir,a_iir); %determine frequency response
subplot(2,1,2);
hold on
plot(w/pi, abs(H_iir),'r')
legend('|X(\omega)|','|H(\omega)|')
pause
Y_iir = fft(y_iir);
plot(fax(1:N/2), abs(Y_iir(1:N/2))/(N/2),'g') ; %plot first half of spectrum
legend('|X(\omega)|','|H(\omega)|','|Y(\omega)|')
pause
subplot(2,1,1)
non_linear_y = plot(t(plot_range),y_iir(plot_range),'g')
legend('clean signal', 'noisy signal','filtered signal')
pause
set(noisy_x,'visible', 'off')
%% Examine the magnitude and phase response of the IIR filter
figure(2)
subplot(2,1,1)
plot(w/pi,abs(H_iir))
xlabel('frequency ( x \pi rads/sample)')
ylabel('Magnitude')
title('Magnitude Response of filter')
subplot(2,1,2)
plot(w/pi,angle(H_iir))
xlabel('frequency ( x \pi rads/sample)')
ylabel('Phase Shift')
title('Phase Response of filter')
%% Now filter using an FIR filter (with linear phase)
b_fir = fir1(100, [0.27 0.34],'stop');
a_fir = 1;
y_fir = filter(b_fir,a_fir, x_ns);
figure(1)
subplot(2,1,1)
plot(t(plot_range),y_fir(plot_range),'k')
legend('clean signal', 'noisy signal','filtered signal (non-linear)','filtered signal (linear)')
[H_fir, w ]= freqz(b_fir,a_fir);
subplot(2,1,2)
plot(w/pi, abs(H_fir),'k')
legend('|X(\omega)|','|H(\omega) Non-linear|','|Y(\omega)|','|H(\omega)| linear')
%% Compare the frequency responses of the two filter design approaches
figure(2)
subplot(2,1,1)
hold on
plot(w/pi,abs(H_fir),'g')
legend('non-linear filter','linear filter')
subplot(2,1,2)
hold on
plot(w/pi,angle(H_fir),'g')
legend('non-linear filter','linear filter')
pause
%% Why does linear phase preserve the shape??
close all
clear all; clc;
fs = 1000;
t = 0:1/fs:2;
x1 = cos(2*pi*3*t-pi/2);
x2 = cos(2*pi*5*t-(pi/2)/3*5);
pause
subplot(3,1,1)
plot(t,x1)
subplot(3,1,2)
plot(t,x2)
subplot(3,1,3)
plot(t,x1+x2,'g')
hold on
Categories: matlab code, youtube demo code
pitch/period tracking using autocorrelation
September 24, 2014
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%% Using Autocorrelation to track the local period of a signal
% This code is used as part of a youtube video demonstration
% See http://youtube.com/ddorran
%
% Code available at https://dadorran.wordpress.com
%
% The following wav file can be downloaded from
% https://www.dropbox.com/s/3y25abf1xuqpizj/speech_demo.wav
%% speech analysis example
[ip fs] = wavread('speech_demo.wav');
max_expected_period = round(1/50*fs);
min_expected_period = round(1/200*fs);
frame_len = 2*max_expected_period;
for k = 1 : length(ip)/frame_len -1;
range = (k-1)*frame_len + 1:k*frame_len;
frame = ip(range);
%show the input in blue and the selected frame in red
plot(ip);
set(gca, 'xtick',[],'position',[ 0.05 0.82 0.91 0.13])
hold on;
temp_sig = ones(size(ip))*NaN;
temp_sig(range) = frame;
plot(temp_sig,'r');
hold off
%use xcorr to determine the local period of the frame
[rxx lag] = xcorr(frame, frame);
subplot(3,1,3)
plot(lag, rxx,'r')
rxx(find(rxx < 0)) = 0; %set any negative correlation values to zero
center_peak_width = find(rxx(frame_len:end) == 0 ,1); %find first zero after center
%center of rxx is located at length(frame)+1
rxx(frame_len-center_peak_width : frame_len+center_peak_width ) = min(rxx);
% hold on
% plot(lag, rxx,'g');
% hold off
[max_val loc] = max(rxx);
period = abs(loc - length(frame)+1);
title(['Period estimate = ' num2str(period) 'samples (' num2str(fs/period) 'Hz)']);
set(gca, 'position', [ 0.05 0.07 0.91 0.25])
[max_val max_loc] = max(frame);
num_cycles_in_frame = ceil(frame_len/period);
test_start_positions = max_loc-(period*[-num_cycles_in_frame:num_cycles_in_frame]);
index = find(test_start_positions > 0,1, 'last');
start_position = test_start_positions(index);
colours = 'rg';
subplot(3,1,2)
plot(frame);
set(gca, 'position',[ 0.05 0.47 0.91 0.33])
pause
for g = 1 : num_cycles_in_frame
if(start_position+period*(g) <= frame_len && period > min_expected_period)
cycle_seg = ones(1, frame_len)*NaN;
cycle_seg(start_position+period*(g-1):start_position+period*(g)) =...
frame(start_position+period*(g-1):start_position+period*(g));
hold on
plot(cycle_seg,colours(mod(g, length(colours))+1)) %plot one of the available colors
hold off
end
end
pause
end
%% synthesise a periodic signal to use as a basic demo
fs = 500;
T = 1/fs;
N = 250; % desired length of signal
t = [0:N-1]*T; %time vector
f1 = 8; f2=f1*2;
x = sin(2*pi*f1*t-pi/2) + sin(2*pi*f2*t);
plot(t, x)
ylabel('Amplitude')
xlabel('Time (seconds)')
title('Synthesised Signal');
%% Determine the autocorrelation function
[rxx lags] = xcorr(x,x);
figure
plot(lags, rxx)
xlabel('Lag')
ylabel('Correlation Measure')
title('Auto-correlation Function')
%% Illustrate the auto correlation process
%function available from https://dadorran.wordpress.com
illustrate_xcorr(x,x)
%% Identify most prominent peaks
% Most prominent peak will be at the center of the correlation function
first_peak_loc = length(x) + 1;
% Lots of possible ways to identify second prominent peak. Am going to use a crude approach
% relying on some assumed prior knowledge of the signal. Am going to assume
% that the signal has a minimum possible period of .06 seconds = 30 samples;
min_period_in_samples = 30;
half_min = min_period_in_samples/2 ;
seq = rxx;
seq(first_peak_loc-half_min: first_peak_loc+half_min) = min(seq);
plot(rxx,'rx');
hold on
plot(seq)
[max_val second_peak_loc] = max(seq);
period_in_samples = abs(second_peak_loc -first_peak_loc)
period = period_in_samples*T
fundamental_frequency = 1/period
%% Autocorrelation of a noisy signal
x2 = x + randn(1, length(x))*0.2;
plot(x2)
ylabel('Amplitude')
xlabel('Time (seconds)')
title('Noisy Synthesised Signal');
[rxx2 lags] = xcorr(x2,x2);
figure
plot(lags, rxx2)
xlabel('Lag')
ylabel('Correlation Measure')
title('Auto-correlation Function')
%% Autocorrelation technique can be problematic!
% Consider the following signal
f1 = 8; f2=f1*2;
x3 = sin(2*pi*f1*t) + 5*sin(2*pi*f2*t);
plot(t, x3)
ylabel('Amplitude')
xlabel('Time (seconds)')
title('Synthesised Signal');
[rxx3 lags] = xcorr(x3,x3,'unbiased');
figure
plot(lags, rxx3)
xlabel('Lag')
ylabel('Correlation Measure')
title('Auto-correlation Function')
seq = rxx3;
seq(first_peak_loc-half_min: first_peak_loc+half_min) = min(seq);
plot(seq)
[max_val second_peak_loc] = max(seq);
period_in_samples = abs(second_peak_loc -first_peak_loc)
Categories: matlab code, youtube demo code
illustrate_xcorr – code for cross correlation demos
September 24, 2014
1 comment
% This function illustrates the cross correlation process in action
%
% Usage:
% fs = 1000;
% T = 1/fs;
% N = 500; % desired length of signal
% t = [0:N-1]*T; %time vector
% f1 = 8; f2=f1*2;
% x = sin(2*pi*f1*t) + sin(2*pi*f2*t);
%
% % To step though each sample use the following:
% illustrate_xcorr(x,x)
%
% % to step through using 50 steps use:
% illustrate_xcorr(x,x, 50)
%
function illustrate_xcorr(x, y, varargin)
if(length(x) > length(y))
y(end+1:length(x)) = 0; %zero pad so the signals are the same length
else
x(end+1:length(y)) = 0; %zero pad so the signals are the same length
end
num_steps = 2*length(x)-1;
if(nargin ==3)
arg = varargin{1};
if(isnumeric(arg))
num_steps = ceil(abs(arg));
end
end
if(nargin > 3)
error('See help on this function to see how to use it properly')
end
[rxy lags] = xcorr(x,y); %cross correlate signals
disp('The signal being autocorrelated is shown in blue (two instances)')
disp('As you hit the space bar the lower plot will move into different lag positions')
disp('The correlation function shown in red is updated for each lag position');
disp('keep pressing the space bar to step through the illustration ...');
figure
plot_width = 0.3; plot_height = 0.25;
top_ax_h = subplot(3,1,1);
plot(x)
axis tight
set(top_ax_h, 'visible','off', 'units', 'normalized')
set(top_ax_h,'position', [0.5-plot_width/2 5/6-plot_height/2 plot_width plot_height])
mid_ax_h = subplot(3,1,2);
plot(y)
axis tight
set(mid_ax_h, 'visible','off', 'units', 'normalized')
set(mid_ax_h,'position', [0.5-plot_width/2-plot_width 5/6-3*plot_height/2-0.01 plot_width plot_height])
bottom_ax_h = subplot(3,1,3);
corr_h = plot(lags,rxy,'r');
axis tight
set(bottom_ax_h,'units', 'normalized','Ytick',[])
set(bottom_ax_h,'position', [0.5-plot_width*3/2 0.2-plot_height/2 plot_width*3 plot_height])
set(corr_h, 'Ydata', ones(1, length(rxy))*NaN); %clear the correlation funciton plot once its set up
normalised_shift_size = 2*plot_width/(num_steps-1);
corr_seg_len = length(rxy)/num_steps;
for k = 1 : num_steps
if(k > 1)
new_pos = get(mid_ax_h,'position') + [normalised_shift_size 0 0 0];
set(mid_ax_h,'position', new_pos);
end
set(corr_h, 'Ydata', [rxy(1:round(corr_seg_len*k)) ones(1,length(rxy)-round(corr_seg_len*k))*NaN])
pause
end
Categories: matlab code, youtube demo code
requantise
September 17, 2014
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% this function takes a signal ip and modifies it so that
% occupies 2^(num_bits) quantisation levels
%
% ns = rand(1, 1000);
% op = requantise(ns, 2);
% plot(ns)
% hold on
% plot(op,'r') % youshould be able to clearly see the 4 possible levels the
% new signal occupies
function op = requantise(ip, num_bits)
num_levels = 2^num_bits;
quantization_diff = (max(ip)-min(ip))/num_levels;
quantization_levels = min(ip)+quantization_diff/2:quantization_diff:max(ip)-quantization_diff/2;
op = zeros(1,length(ip));
for k = 1: length(ip)
[min_diff closest_level_index] = min(abs(quantization_levels- ip(k)));
op(k) = quantization_levels(closest_level_index);
end
Categories: matlab code
Audio Time Scale Modification – Phase Vocoder Implementation in Matlab
June 2, 2014
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function synthSignal = pl_phaseVocoder_variable_analysis_hop(signal, tsm_factor, winSamps)
% A phase locked vocoder time-scale modification algorithm based upon Jean
% Laroche's 1999 work. The synthesis hop is fixed at one quarter the analysis window (hanning) while the analysis hop is scaled by the time scale factor; this results in more FFT computations than the bonada 2000 approach for slowing down - but provides smoother transitions frame to frame of the magnitude spectrums and, in general, a better quality of output. This code started
% out as the code provided by Tae Hong Park, but has changed significantly
% over the years
%
% David Dorran, Audio Research Group, Dublin Institute of Technology
% david.dorran@dit.ie
% http://eleceng.dit.ie/dorran
% http://eleceng.dit.ie/arg
%
% make sure input is mono and transpose if necessary
[r, c] = size(signal);
if r &gt; 1
signal = signal';
end;
[r, c] = size(signal);
if r &gt; 1
disp('Note :only works on mono signals');
synthSignal = [];
return
end;
% add a few zeros to stop the algorithm failing
zpad = zeros(1, 44100/4);
signal = [signal, zpad];
if nargin &lt; 3
winSamps = 2048;
end
winSampsPow2 = winSamps;
synHopSamps = winSampsPow2/4;
anHopSamps = round(synHopSamps/tsm_factor);
win = hanning(winSampsPow2);
X = specgram(signal, winSampsPow2, 100,win, winSampsPow2 - anHopSamps);
moduli = abs(X);
phases = angle(X);
[numBins, numFrames ] = size(phases);
syn_phases = zeros(numBins, numFrames); % a holder for synthesis phases
twoPi = 2*pi;
omega = twoPi * anHopSamps * [0:numBins-1]'/numBins/2; %the expected phase hop per frame
syn_phases(:,1) = phases(:,1) .* ( synHopSamps/ anHopSamps);
for idx = 2: numFrames
ddx = idx - 1;
deltaPhi = princarg(phases(:,idx) - phases(:,ddx) -omega); %calculate priciple determination of the hetrodyned phase increment
phaseInc = (omega+deltaPhi)/anHopSamps; % phase increment per sample
%locate the peaks
pk_indices = [];
pk_indices = locate_peaks(moduli(:,idx));
if(~length(pk_indices))
pk_indices = [1 10 12]; % just in case an odd situation is encountered e.g. a sequence of zeros
end
%update phase of each peak channel using the phase propagation formula
syn_phases(pk_indices,idx) = syn_phases(pk_indices,ddx)+synHopSamps*phaseInc(pk_indices); %synthesis phase
%update phase of channels in region of influence
% first calculate angle of rotation
rotation_angles = syn_phases(pk_indices,idx) - phases(pk_indices,idx);
start_point = 1; %initialize the starting point of the region of influence
for k = 1: length(pk_indices) -1
peak = pk_indices(k);
angle_rotation = rotation_angles(k);
next_peak = pk_indices(k+1);
end_point = round((peak + next_peak)/2);
ri_indices = [start_point : peak-1, (peak+1) : end_point]; %indices of the region of influence
syn_phases(ri_indices,idx) = angle_rotation + phases(ri_indices, idx);
start_point = end_point + 1;
end;
end;
%Make sure that the LHS and RHS of the DFT's of the synthesis frames are a
%complex conjuget of each other
Z = moduli.*exp(i*syn_phases);
Z = Z(1:(numBins),:);
conj_Z = conj(flipud(Z(2:size(Z,1) -1,:)));
Z = [Z;conj_Z];
synthSignal = zeros(round(length(signal)*tsm_factor+length(win)), 1);
curStart = 1;
for idx = 1:numFrames-1
curEnd = curStart + length(win) - 1;
rIFFT = real(ifft(Z(:,idx)));
synthSignal([curStart:curEnd]) = synthSignal([curStart:curEnd]) + rIFFT.*win;
curStart = curStart + synHopSamps;
end
%--------------------------------------------------------------------------
function indices = locate_peaks(ip)
%function to find peaks
%a peak is any sample which is greater than its two nearest neighbours
index = 1;
num = 2;
indices = [];
for k = 1 + num : length(ip) - num
seg = ip(k-num:k+num);
[val, max_index] = max(seg);
if max_index == num + 1
indices(index) = k;
index = index + 1;
end;
end;
%--------------------------------------------------------------------------
function Phase = princarg(Phasein)
two_pi = 2*pi;
a = Phasein/two_pi;
k = round(a);
Phase = Phasein-k*two_pi;
Categories: audio processing, matlab code
Audio time-scale modification – VSOLA algorithm in matlab
June 2, 2014
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function op = vsola(ip, tsm_factor, P)
% Implementation of the variable parameter synchronised overlap add VSOLA algorithm
% This implementation makes use of the standard SOLA algorithm (Rocus and Wilgus, ICASSP 1986) and some efficient paramter settings for the SOLA algorithm (Dorran, Lawlor and Coyle, ICASSP 2003) and (Dorran, Lawlor and Coyle, DAFX 2003)
% Given an input, ip, the longest likely pitch period, P, and and a time scale modification factor, tsm_factor, return an output, op, that is a time scaled version of the input. The synthesis_overlap_size is the length, in samples of the lowest likely fundametal period of the input.
% for speech synthesis_overlap_size is set to (16/1000)*fs samples and for music synthesis_overlap_size is typically set to (20/1000)*fs samples
%
% David Dorran, Audio Research Group, Dublin Institute of Technology
% david.dorran@dit.ie
% http://eleceng.dit.ie/dorran
% http://eleceng.dit.ie/arg
%
% make sure input is mono and transpose if necessary
[r, c] = size(ip);
if r > 1
ip = ip';
end;
[r, c] = size(ip);
if r > 1
disp('Note :only works on mono signals');
op = [];
return
end;
% initialize the values of analysis_frame_length, analysis_window_offset, synthesis_frame_offset and length_of_overlap
desired_tsm_len = round(length(ip)*tsm_factor);
P = round(P); %longest likely pitch period in samples
Lmax = round(P * 1.5);% found this to a reasonable value for the Lmax- Lmax is the duration over which the correlation function is applied
stationary_length = (P * 1.5); % found this to a reasonable value for the stationary length - This is the max duration that could be discarded/repeated
analysis_window_offset = round((stationary_length - P)/abs(tsm_factor - 1)); % this equation was derived.
synthesis_window_offset = round(analysis_window_offset * tsm_factor);
analysis_frame_length = round(Lmax + synthesis_window_offset);
number_of_analysis_frames = floor((length(ip)- analysis_frame_length)/analysis_window_offset);
if number_of_analysis_frames < 2 %not much time-scaling being done just return the input
op = ip;
return;
end;
%the next two lines just ensure that the last frame finishes at the very end of the signal (not essential)
zpad = zeros(1, (number_of_analysis_frames*analysis_window_offset) + analysis_frame_length - length(ip));
ip = [ip zpad];
%initialize the output
op = zeros(1, desired_tsm_len);
%initialize the first output frame
op(1 : analysis_frame_length) = ip(1 : analysis_frame_length);
min_overlap = round(Lmax - P); %ensure that there is some minimum overlap
count = 0;
% Loop for the 2nd analysis frame to the number_of_analysis_frames
for m = 1 : number_of_analysis_frames
%grab the mth input frame
ip_frame = ip(analysis_window_offset * m : (analysis_window_offset * m) + analysis_frame_length - 1);
%grab the maximum overlapping segments from the inout frame and the current output
seg_1 = op(round(synthesis_window_offset*(m-1))+analysis_frame_length - Lmax : round(synthesis_window_offset*(m-1))+analysis_frame_length -1);
seg_2 = ip_frame(1: Lmax);
%compute the correlation of these segments
correlation = xcorr(seg_2, seg_1,'coeff');
%Find the best point to overlap (opt_overlap_length) making sure not to exceed the maximum or go below the minimum overlap.
correlation(length(correlation) - Lmax -1: length(correlation)) = -100;
correlation(1: min_overlap) = -100;
[max_correlation, opt_overlap_length] = max(correlation);
if(max_correlation == 0)
opt_overlap_length = Lmax;
end;
% offset = Lmax - opt_overlap_length;
% if ((offset + analysis_window_offset - synthesis_window_offset) >= 0 & (offset + analysis_window_offset - synthesis_window_offset) <= P)
% count = count +1;
% end;
% append mth analysis frame to the current synthesised output using a linear cross fade
ov_seg = linear_cross_fade(seg_1, ip_frame, opt_overlap_length);
ov_len =(round(synthesis_window_offset*m)+analysis_frame_length) - (round(synthesis_window_offset*(m-1))+analysis_frame_length - Lmax) + 1;
ov_seg = ov_seg(1:ov_len);
op(round(synthesis_window_offset*(m-1))+analysis_frame_length - Lmax: round(synthesis_window_offset*m)+analysis_frame_length) = ov_seg;
end; % end of for loop
% linear cross fade the first segment with the second segment given a certain amount of overlap
% |----------seg1----------|
% |---------------seg2-----------------------|
% |--overlap-|
function op = linear_cross_fade(seg_1, seg_2, overlap_length, cross_fade_duration)
error(nargchk(3,4,nargin));
if nargin < 4
cross_fade_duration = overlap_length;
end
if cross_fade_duration > overlap_length
cross_fade_duration = overlap_length;
end
% overlap the end of seg_1 with the start of seg_2 using a linear cross-fade
if (length(seg_1) < overlap_length),
seg_2 = seg_2(overlap_length - length(seg_1) + 1: length(seg_2));
overlap_length = length(seg_1);
end; % end of if statement
if (length(seg_2) < overlap_length),
seg_1 = seg_1(length(seg_1) - (overlap_length - overlap_length): length(seg_1));
overlap_length = length(seg_2);
end; % end of if statement
overlapping_region = zeros(1, cross_fade_duration); % initialize the overlapping region
seg_1 = seg_1(1: length(seg_1) - (overlap_length - cross_fade_duration));
op = zeros(1, length(seg_1) + length(seg_2) - cross_fade_duration);
if(overlap_length ~= 1)
linear_ramp1 = (cross_fade_duration-1:-1:0) ./(cross_fade_duration-1);
linear_ramp2 = (0:1:cross_fade_duration-1) ./(cross_fade_duration-1);
overlapping_region = (seg_1(length(seg_1)-cross_fade_duration+1:length(seg_1)).*linear_ramp1) + (seg_2(1: cross_fade_duration).*linear_ramp2);
end;
op = [seg_1(1:length(seg_1)-cross_fade_duration) ,overlapping_region , seg_2(cross_fade_duration+1:length(seg_2))];
% END of linear_cross_fade function
Categories: audio processing, matlab code, Uncategorized
pic18f4620 xc8 compiler bundle
May 22, 2014
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Here’s a small (5.2 MB) bundle that I use to compile and program c code on my pic18f4620 using a pickit2 on my PC. I’ve only tested this on XP.
It doesn’t have all the features of a full blown IDE like MPLABX (no stepping through code I’m afraid) but is a nice concise package that encourages the use of command line scripting.
Getting Started
- Download the zip file on to your PC or, preferably, a USB key
- Extract the files to a folder on your PC or USB key
The extracted folder contains the xc8 compiler files (modified to work with the pic18f4620 only), the pk2cmd software tool which is used to communicate with the PICkit2, and Notepad++, which can be used to write and modify code.
To create a new project do the following:
- First create a new folder inside the “robosumo_files” folder and copy the build.bat file into that folder.
- Run the Notepad++ editor and paste the example code (flashing LED) given below. Save the code in the new folder as a file called “main.c” (don’t include quotes in the filename).
- Make sure the PICkit2 programmer is correctly connected to the PIC18f4620 and finally double click the build.bat file.
- If there are no errors in the code and the hardware is set up correctly then the PIC18f4620 will be programmed.
Example Code – Flashing LED
This is a pared back example program for the PIC18F4620. It blinks an LED connected to pin 19 (RD0). See this circuit
//
// PIC18F4620 example program
// Written by Ted Burke (ted.burke@dit.ie)
// Last update 28-2-2013
//
#include <xc.h>
#pragma config OSC=INTIO67,MCLRE=OFF,WDT=OFF,LVP=OFF,BOREN=OFF
int main(void)
{
// Make RD0 a digital output
TRISD = 0b11111110;
while(1)
{
LATDbits.LATD0 = 1; // Set pin RD0 high
_delay(125000); // 0.5 second delay
LATDbits.LATD0 = 0; // Set pin RD0 low
_delay(125000); // 0.5 second delay
}
}
Categories: pic18f, Uncategorized
pic18f4620 assembly bundle
May 22, 2014
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Here’s a small (3.6 MB) bundle that I use to program my pic18f4620 using a pickit2 with assembly code on my PC. I’ve only tested this on XP.
It doesn’t have all the features of a full blown IDE like MPLABX (no stepping through code I’m afraid) but is a nice concise package that encourages the use of command line scripting.
The bundle also includes some example code.
Getting Started
Download the bundle from here and extract the files.
The extracted folder contains the mpasmx assembler files, the pk2cmd software tool which is used to communicate with the PICkit2, and Notepad++ text editor, which can be used to write and modify code.
The bundle includes 6 example projects.
To create a new project do the following:
- First create a new folder inside the “pic18f4620_asm” folder and copy the build.bat file into that folder.
- Run the Notepad++ editor and paste the example code (flashing LED) given below. Save the code in the new folder as a file called “main.asm” (don’t include quotes in the filename).
- Make sure the PICkit2 programmer is correctly connected to the PIC18f4620 and finally double click the build.bat file.
If there are no errors in the code and the hardware is set up correctly then the PIC18f4620 will be programmed.
Example Code to work with this circuit
This code will flash the LED connected to pin 19 (RD0)
;configure the assembler directive 'list' so as to set processor to 18f4620 and set the radix used for data expressions to decimal (can be HEX|DEC|OCT)
list p=18f4620, r=DEC
#include <p18f4620.inc>
; configure the micro so that the watchdog timer is off, low-voltage programming is off, master clear is off and the clock works off the internal oscillator
config WDT=OFF, LVP=OFF, MCLRE=OFF, OSC=INTIO67
;The org directive tells the compiler where to position the code in memory
org 0x0000 ;The following code will be programmed in reset address location i.e. This is where the micro jumps to on reset
goto Main ;Jump to Main immediately after a reset
;--------------------------------------------------------------------------
; Main Program
;--------------------------------------------------------------------------
org 0x0100
Main
clrf TRISD ; All PORT D IO pins are configured as outputs
; Set up timer 0 so it can be used to control flash rate
bcf T0CON, T0CS; use internal instruction cycle clock
bcf T0CON, T08BIT; use 16 bit mode
bcf INTCON, TMR0IF; reset timer overflow flag
bsf T0CON, TMR0ON; turn on timer 0
loop
; check if the timer0 overflow flag has been set
btfss INTCON, TMR0IF
goto loop
bcf INTCON, TMR0IF; reset timer overflow flag
;invert LATD0
movlw 0x1
XORWF LATD,F
goto loop
END
Categories: pic18f, Uncategorized
Heartrate (BPM) Example Matlab Code
May 22, 2014
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This is the code I used in my youtube video at http://youtu.be/3tdumuwHgxc
% program to determine the BPM of an ECG signal
% count the dominant peaks in the signal (these correspond to heart beats)
% - peaks are defined to be sampels greater than their two nearest neighbours and greater than 1
beat_count = 0;
for k = 2 : length(sig)-1
if(sig(k) > sig(k-1) & sig(k) > sig(k+1) & sig(k) > 1)
%k
%disp('Prominant peak found');
beat_count = beat_count + 1;
end
end
% Divide the beats counted by the signal duration (in minutes)
fs = 100;
N = length(sig);
duration_in_seconds = N/fs;
duration_in_minutes = duration_in_seconds/60;
BPM_avg = beat_count/duration_in_minutes;
Categories: matlab code, Uncategorized, youtube demo code
dadorran 