void FFTprocessing1(uint32_t *inFFT, float32_t* outFFT, FFT_LENGTH_ FFTlength) {
if (g_ucDMAMethod == DMA_METHOD_SLOW) {
adcNode[0].g_ucDataReady = 0;
}
float32_t complexBuffer[NUM_SAMPLES ];
ComplexBufFFT(inFFT, complexBuffer, FFTlength);
arm_cfft_f32(&arm_cfft_sR_f32_len256, complexBuffer, ifftFlag,
doBitReverse); //da test voi 512 nhung chay mot luc thi gia tri khong cap nhat nua
arm_cmplx_mag_f32(complexBuffer, outFFT, FFTlength);
// ignore the DC value
outFFT[0] = 0.0f;
uint16_t n = 0;
///(50*SAMPLE_RATE)/fftLength;
// squash everything under 100Hz
for (n = 0; n < fix_minFFTIndex; n++) {
outFFT[n] = 0.0f;
}
arm_max_f32(outFFT, FFTlength / 2, &fftNode[0].maxValue,
&fftNode[0].maxIndex);
arm_mean_f32(outFFT, FFTlength, &fftNode[0].averageValue);
// calculate frequency value of peak bin
fftNode[0].hertz = fftNode[0].HerztPerBin * (float32_t) fftNode[0].maxIndex
* 2;
setAgainSampling();
}
int main(){
const int length = 5;
float input[length] = {-0.665365, -0.329988, 0.164465, 0.043962, 0.295885};
float output[length]; // array for storing Kalman processed values
float temp[length]; // array for storing subtraction results
float temp2[length];
float miscresult[2] = {0, 0}; // array for storing mean and std dev results
float holder[length]; // array for storing convolution results
int i, j;
float corr_temp[1] = {0};
/*START OF PART II*/
kalman_state kstate;
reset(&kstate);
//Kalmanfilter_C(input, output, &kstate, length);
Kalmanfilter_asm(output, input, length, &kstate);
printf("\n");
/*END OF PART II*/
/*START OF PART III*/
// subtract
printf("subtraction:\n");
subtract(temp, input, output, length);
arm_sub_f32(input, output, temp2, length);
for(j = 0; j < length; j++){
printf("My implementation: %f CMSIS: %f\n", temp[j], temp2[j]);
}
// misc
printf("\n");
//misc(miscresult, temp, length);
arm_mean_f32(temp, length, &miscresult[0]);
arm_std_f32(temp, length, &miscresult[1]);
printf("mean: %f stdev: %f\n", miscresult[0], miscresult[1]);
// correlation
//corr_temp[0] = correlation(input, output, length);
arm_correlate_f32(input, length, output, length, &corr_temp[0]);
printf("correlation: %f\n", corr_temp[0]);
// convolution
printf("\n");
for(i = 0; i < length; i++){
holder[i] = 0;
}
convolve(holder, input, output, length);
//arm_conv_f32(input, length, output, length, holder);
for(i = 0; i < length; i++){
printf("convolution %f \n", holder[i]);
}
/*END OF PART III*/
return 0;
}
int32_t main()
{
#ifndef USE_STATIC_INIT
arm_matrix_instance_f32 srcA;
arm_matrix_instance_f32 srcB;
arm_matrix_instance_f32 dstC;
/* Input and output matrices initializations */
arm_mat_init_f32(&srcA, numStudents, numSubjects, (float32_t *)testMarks_f32);
arm_mat_init_f32(&srcB, numSubjects, 1, (float32_t *)testUnity_f32);
arm_mat_init_f32(&dstC, numStudents, 1, testOutput);
#else
/* Static Initializations of Input and output matrix sizes and array */
arm_matrix_instance_f32 srcA = {NUMSTUDENTS, NUMSUBJECTS, (float32_t *)testMarks_f32};
arm_matrix_instance_f32 srcB = {NUMSUBJECTS, 1, (float32_t *)testUnity_f32};
arm_matrix_instance_f32 dstC = {NUMSTUDENTS, 1, testOutput};
#endif
/* ----------------------------------------------------------------------
*Call the Matrix multiplication process function
* ------------------------------------------------------------------- */
arm_mat_mult_f32(&srcA, &srcB, &dstC);
/* ----------------------------------------------------------------------
** Call the Max function to calculate max marks among numStudents
** ------------------------------------------------------------------- */
arm_max_f32(testOutput, numStudents, &max_marks, &student_num);
/* ----------------------------------------------------------------------
** Call the Min function to calculate min marks among numStudents
** ------------------------------------------------------------------- */
arm_min_f32(testOutput, numStudents, &min_marks, &student_num);
/* ----------------------------------------------------------------------
** Call the Mean function to calculate mean
** ------------------------------------------------------------------- */
arm_mean_f32(testOutput, numStudents, &mean);
/* ----------------------------------------------------------------------
** Call the std function to calculate standard deviation
** ------------------------------------------------------------------- */
arm_std_f32(testOutput, numStudents, &std);
/* ----------------------------------------------------------------------
** Call the var function to calculate variance
** ------------------------------------------------------------------- */
arm_var_f32(testOutput, numStudents, &var);
while(1); /* main function does not return */
}
float FloatArray::getMean(){
float result;
/// @note When built for ARM Cortex-M processor series, this method uses the optimized <a href="http://www.keil.com/pack/doc/CMSIS/General/html/index.html">CMSIS library</a>
#ifdef ARM_CORTEX
arm_mean_f32 (data, size, &result);
#else
result=0;
for(int n=0; n<size; n++){
result+=data[n];
}
result=result/size;
#endif
return result;
}
// ARM DSP Function ****************************************************************************************
int dsp_arm(float* inputArray, float* outputArray, dsp_t* analysisOut) {
// Get diffence Array
arm_sub_f32(outputArray, inputArray, analysisOut->diffArr, ARRAY_LENGTH);
// Get mean
arm_mean_f32(analysisOut->diffArr, (uint32_t) ARRAY_LENGTH, &analysisOut->meanDiff);
// Get standard deviation
arm_std_f32(analysisOut->diffArr, (uint32_t) ARRAY_LENGTH, &analysisOut->standDevDiff);
// Get correlation
arm_correlate_f32(inputArray, ARRAY_LENGTH, outputArray, ARRAY_LENGTH, analysisOut->corrArr);
// Get convolution
arm_conv_f32(inputArray, ARRAY_LENGTH, outputArray, ARRAY_LENGTH, analysisOut->convolArr);
return 0;
}
int main()
{
//initialize testing array
float testVector[] = {0.1f,0.2f,0.3f,0.4f,0.5f};
/*COMMENTED OUT LENGTH PARAM AS IT IS INCLUDED IN HEADER FILE*/
//get the size of the array
//int length = sizeof(testVector)/sizeof(float);
//initiate empty output array of size length
float outputArrayC[length];
//initialize the struct at p=r=q 0.1 and x=k=0
kalman_state currentState = {0.1f, 0.1f, 0.0f , 0.1f, 0.0f};
//call function Kalmanfilter_C
Kalmanfilter_C(measurements, outputArrayC, ¤tState, length);
//initiate empty output array of size length
float outputArrayASM[length];
//reinitialize the struct at p=r=q 0.1 and x=k=0
currentState.p = DEF_p;
currentState.r = DEF_r;
currentState.k = DEF_k;
currentState.q = DEF_q;
currentState.x = DEF_x;
//call subroutine Kalmanfilter_asm
Kalmanfilter_asm(measurements, outputArrayASM, ¤tState, length );
//Check for correctness with a error tolerance of 0.000001
float errorTolerance = 0.000001f;
float errorPercentage = 0.01;
//is_valid(outputArrayC, outputArrayASM, length, errorTolerance, "c vs asm");
//is_valid_relative(outputArrayC, outputArrayASM, length, errorTolerance, errorPercentage,"c vs asm");
int p;
//print KalmanFilter output
for ( p = 0; p < length; p++ )
{
printf("OutputASM: %f & OutputC %f\n", outputArrayASM[p], outputArrayC[p]);
}
float differenceC[length];
float differenceCMSIS[length];
//Difference
arm_sub_f32 (measurements, outputArrayC, differenceCMSIS, length);
c_sub(measurements, outputArrayC, differenceC, length);
//is_valid(differenceC, differenceCMSIS, length, errorTolerance, "Difference");
//is_valid_relative(differenceC, differenceCMSIS, length, errorTolerance, errorPercentage,"Difference");
//Print difference vector
for ( p = 0; p < length; p++ )
{
printf("DifferenceC: %f & DifferenceCMSIS %f \n", differenceC[p], differenceCMSIS[p]);
}
//Mean
float meanCMSIS;
float meanC;
arm_mean_f32 (differenceCMSIS, length , &meanCMSIS);
c_mean(differenceC,length, &meanC);
//is_valid(&meanC, &meanCMSIS, 1, errorTolerance, "mean");
//is_valid_relative(&meanC, &meanCMSIS, 1, errorTolerance, errorPercentage, "mean");
//Print mean values
printf("MeanC: %f & MeanCMSIS %f \n", meanC, meanCMSIS);
//STD
float stdC;
float stdCMSIS;
arm_std_f32 (differenceCMSIS, length, &stdCMSIS);
c_std(differenceC, length, &stdC);
//is_valid(&stdC, &stdCMSIS, 1, errorTolerance, "STD");
//is_valid_relative(&stdC, &stdCMSIS, 1, errorTolerance, errorPercentage,"STD");
//Print std values
printf("StandardDevC: %f & StandardDevCMSIS %f \n", stdC, stdCMSIS);
//correlation
float corC[2*length-1];
float corCMSIS[2*length-1];
arm_correlate_f32 (measurements, length, outputArrayC, length, corCMSIS);
c_correlate(measurements, outputArrayC, corC, length);
//is_valid(corC, corCMSIS, 2*length-1, errorTolerance, "correlation");
//is_valid_relative(corC, corCMSIS, 2*length-1, errorTolerance, errorPercentage, "correlation");
//convolution
float convC[2*length-1];
float convCMSIS[2*length-1];
arm_conv_f32 (measurements, length, outputArrayC, length, convCMSIS);
c_conv(measurements, outputArrayC, convC, length);
//is_valid(convC, convCMSIS, 2*length-1, errorTolerance, "convolution");
//is_valid_relative(convC, convCMSIS, 2*length-1, errorTolerance, errorPercentage, "convolution");
//Print correlation and convolution values
for ( p = 0; p < (2*length-1); p++ )
{
printf("ConvC: %f & ConvCMSIS: %f \n", convC[p], convCMSIS[p]);
}
for ( p = 0; p < (2*length-1); p++ )
{
printf("CorrelateC: %f & CorrelatCMSIS: %f \n", corC[p], corCMSIS[p]);
}
return 0;
}
