locate_peaks matlab function

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;

Linear Phase Filters – why they are used

%% 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

pitch/period tracking using autocorrelation

%% 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)

illustrate_xcorr – code for cross correlation demos

% 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

requantise

% 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

Audio Time Scale Modification – Phase Vocoder Implementation in Matlab

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 > 1
    signal = signal';
end;
[r, c] = size(signal);
if r > 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 < 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;

Audio time-scale modification – VSOLA algorithm in matlab

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

pic18f4620 xc8 compiler bundle

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:

1. First create a new folder inside the “robosumo_files” folder and copy the build.bat file into that folder.
2.  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).
3. Make sure the PICkit2 programmer is correctly connected to the PIC18f4620 and finally double click the build.bat file.
4. 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
	}
}

pic18f4620 assembly bundle

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:

1. First create a new folder inside the “pic18f4620_asm” folder and copy the build.bat file into that folder.
2. 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).
3. 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
    

Heartrate (BPM) Example Matlab Code

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;