k-Wave Toolbox

applyFilter

Filter input with high or low pass filter

Syntax

filtered_signal = applyFilter(signal, Fs, cutoff_f, filter_type)
filtered_signal = applyFilter(signal, Fs, cutoff_f, filter_type, ...)

Description

applyFilter filters an input signal using filter. The FIR filter coefficients are based on a Kaiser window with the specified cut-off frequency and filter type ('HighPass', 'LowPass' or 'BandPass'). Both causal and zero phase filters can be applied. An example of using applyFilter is given below.

% create a time series with a single monopolar pulse
N = 100;
dt = 1e-3;
t = 0:dt:dt*(N-1);
pulse = zeros(length(t), 1);
pulse(25) = 1;
    
% filter using applyFilter
pulse_filtered_lp = applyFilter(pulse, 1/dt, 100, 'LowPass', 'Plot', true, 'ZeroPhase', true);
pulse_filtered_hp = applyFilter(pulse, 1/dt, 100, 'HighPass', 'Plot', true);
pulse_filtered_bp = applyFilter(pulse, 1/dt, [50, 250], 'BandPass', 'Plot', true, 'ZeroPhase', true);

% plot the filtered time series
figure;
[~, scale, prefix] = scaleSI(max(t));
plot(t*scale, pulse, 'k-', t*scale, pulse_filtered_lp, 'r-', t*scale, pulse_filtered_hp, 'b-', t*scale, pulse_filtered_bp, 'g-');
xlabel(['Time [' prefix 's]']);
ylabel('Signal Amplitude [au]');
legend('Original Signal', 'Zero Phase Low Pass Filter', 'Causal High Pass Filter', 'Zero Phase Band Pass Filter');

Inputs

Optional Inputs

Optional 'string', value pairs that may be used to modify the default computational settings.

Outputs

Examples

• Image Reconstruction With Bandlimited Sensors

See Also

addNoise

attenComp

© 2009-2014 Bradley Treeby and Ben Cox.