algorithm - Signal processing (in Java) -

algorithm - Signal processing (in Java) -

i have sensor reading from, code in java, dont think issue language specific, more approach related.

the sensor produces signal high , low pulses, heartbeat. however, "high" pulse not same level, nor "low". interested in relative difference. however, lone not enough, within single "session" high , low values may alter (think curved mid point)

ive included image of 4 "types" of signals able handle. top left "ideal" i'm sure can handle that, other 3 sadly more mutual , less easy handle.

my current approach has been average of data, , see how many times point crossed, tell me how many high , low pulses there were.

i'd know if there simple way of detecting high , low pulse, without using averaging method.

when stated wanted extract frequency of wave first thing thought of fourier transform; converts signal time domain frequency domain. given next sample wave:

this sine way have added noise , trend. underlying sine wave has frequency of 1.5hz

you fourier transform

here can see big response @ 0hz, linear trend , can ignore in case. after can see 1 peak in response @ 1.5hz, frequency of our input signal. in other words; 1 time have fourier transform result info point largest value (after remove low frequency results)

java code

apachi commons has fast fourier transform class used create transformation. takes input sampled info wave , outputs complex number, modulus of complex number (the square root of real part squared plus imaginary part squared) equal energy @ frequency. each entry i in outputted array referes frequency @ i*samplingfrequency/noofsamples.

however java code below largly deals these issues you. issue fast fourier transform number of input entries must powerfulness of 2.

import org.apache.commons.math3.complex.complex; import org.apache.commons.math3.transform.dftnormalization; import org.apache.commons.math3.transform.fastfouriertransformer; import org.apache.commons.math3.transform.transformtype; public class fouriertest { public static void main(string[] args) { double samplingfrequency=10; //hz, know info , need set here double[] frequencydomain = new double[input.length]; fastfouriertransformer transformer = new fastfouriertransformer(dftnormalization.standard); seek { complex[] complex = transformer.transform(input, transformtype.forward); (int = 0; < complex.length; i++) { double real = (complex[i].getreal()); double imaginary = (complex[i].getimaginary()); frequencydomain[i] = math.sqrt((real * real) + (imaginary * imaginary)); } } grab (illegalargumentexception e) { system.out.println(e); } //only frequencydomain.length/2 since sec half mirror image or first half for(int i=0;i<frequencydomain.length/2;i++){ double frequency=samplingfrequency*i/frequencydomain.length; system.out.println("frequency: " + frequency + "\t\tenergycomponent: " + frequencydomain[i]); } } static double[] input = new double[]{ 0.017077407 , //sample @ 0 seconds 1.611895528 , //sample @ 0.1 seconds 2.063967663 , //sample @ 0.2 seconds 1.598492541 , //etc 0.184678933 , 0.02654732 , 0.165869218 , 1.026139745 , 1.914179294 , 2.523684208 , 1.71795312 , 0.932131202 , 1.097366772 , 1.107912105 , 2.843777623 , 2.503608192 , 2.540595787 , 2.048111122 , 1.515498608 , 1.828077941 , 2.400006658 , 3.562953532 , 3.34333491 , 2.620231348 , 2.769874641 , 2.423059324 , 2.11147835 , 3.473525478 , 4.504105599 , 4.325642774 , 3.963498242 , 2.842688545 , 2.573038184 , 3.434226007 , 4.924115479 , 4.876122332 , 4.553580015 , 3.92554604 , 3.804585546 , 3.476610932 , 4.535171252 , 5.398007229 , 5.729933758 , 5.573444511 , 4.487695977 , 4.133046459 , 4.796637209 , 5.091399617 , 6.420441446 , 6.473462022 , 5.663322311 , 4.866446009 , 4.840966187 , 5.329697081 , 6.746910181 , 6.580067494 , 7.140083322 , 6.243532245 , 4.960520462 , 5.100901901 , 6.794495306 , 6.959324497 , 7.194674358 , 7.035874424 }; }

java algorithm signals