Example 1

    uses MtxExpr, Math387, MtxVec, SignalUtils, MtxVecTee, MtxVecEdit,OptimalFir;

    procedure TForm1.Button1Click(Sender: TObject);
    var b,c,num,den: Vector;
        n,i: integer;
        IirState: TIirState;
        State: TSample;
    begin
    //Alternative 1: Tone with 5 periods
    //      b := Sin(Ramp(300,0,2*Pi*5/300));

    //Alternative 2: Gaussian noise
          b := RandGauss(300);
          c.Copy(b);
          n := 10;
          State := 0;
          for i := 0 to (b.Length div n) - 1 do //streaming test 1
          begin
              b.SetSubRange(i*n,n);
              ExpAverageFilter(b,State,10); //set to 10%
          end;
          b.SetFullRange;
          DrawIt([c,b],['Unfiltered','Filtered']);

          b.Copy(c);
          ExpAverageFilter(10,num,den); //set to 10x, (1/10 = 0.1, => 10%)
          FillChar(IirState,SizeOf(IirState),0);

          IirInit(Num,Den,IirState);
          for i := 0 to (b.Length div n) - 1 do //streaming test 2
          begin
              b.SetSubRange(i*n,n);
              c.SetSubRange(i*n,n);
              IirFilter(b,c,IirState);
          end;
          IirFree(IirState);          
          b.SetFullRange;
          c.SetFullRange;
          DrawIt([b,c],['Unfiltered','Filtered']);
    end;

    #include "MtxVecCPP.h" //MtxVecCPP.cpp must be included in the project
    #include "MtxVecEdit.hpp"
    #include "MtxVecTee.hpp"
    #include "SignalUtils.hpp"
    #include "OptimalFir.hpp"
    #include <string.h>

    void __fastcall TForm1::BitBtn1Click(TObject *Sender)
    {
      Vector num,den,b,c;
      int n,i;
      TIirState IirState;
      double	State = 0;
      //Alternative 1: Tone with 5 periods
      //      b = Sin(Ramp(300,0,2*Pi*5/300));

      //Alternative 2: Gaussian noise
      b->Size(300);
      b->RandGauss();
      c = b;  //delayed deep copy
      n = 10;
      for (i = 0; i < (b->Length/n); i++) //streaming test 1
        ExpAverageFilter(b(i*n,i*n+n-1),State,10); //set to 10%

      DrawIt(OPENARRAY(TVec*,(c,b)),OPENARRAY(AnsiString,("Unfiltered","Filtered")));

      b = c;
      ExpAverageFilter(10,num,den); //set to 10x, (1/10 = 0.1, => 10%)
      memset(&IirState,0,sizeof(IirState));

      IirInit(num,den,IirState);
      for (i = 0; i < (b->Length/n); i++) //streaming test 2
        IirFilter(b(i*n,i*n+n-1),c(i*n,i*n+n-1),IirState);

      DrawIt(OPENARRAY(TVec*,(b,c)),OPENARRAY(AnsiString,("Unfiltered","Filtered")));
    }

    using Dew.Math;
    using Dew.Math.Editors;
    using Dew.Math.Units;
    using Dew.Signal;
    using Dew.Signal.Units;
    using Dew.Math.Tee;
    using Dew.Signal.Tee;


  private void button1_Click(object sender, EventArgs e)
  {
      Vector b;
      Vector c = new Vector(0);
      Vector num = new Vector(0);
      Vector den = new Vector(0);
                
      int n;
      int i;

      TIirState IirState = new TIirState();
      double state;

//Alternative 1: Tone with 5 periods
//      b := Sin(Ramp(300,0,2*Pi*5/300));

//Alternative 2: Gaussian noise
      b = MtxExpr.RandGauss(300,false);
      c.Copy(b);
      n = 10;
      state = 0;
      int bLength = b.Length;
      for (i = 0; i < (bLength / n); i ++) //streaming test 1
      {
          b.SetSubRange(i*n,n);
          SignalUtils.ExpAverageFilter(b,ref state, 10); //set to 10%
      }
      b.SetFullRange();
      TeeChart.DrawIt(new TVec[2] {c,b}, new string[2] {"Unfiltered","Filtered"},"Exponential averaging",false);

      b.Copy(c);
      SignalUtils.ExpAverageFilter(10,num,den); //set to 10x, (1/10 = 0.1, => 10%)          

      SignalUtils.IirInit(num,den,ref IirState,false);
      bLength = b.Length;
      for (i = 0; i < (bLength / n); i ++) //streaming test 2
      {
          b.SetSubRange(i*n,n);
          c.SetSubRange(i*n,n);
          SignalUtils.IirFilter(b,c,ref IirState);
      }
      SignalUtils.IirFree(ref IirState);

      b.SetFullRange();
      c.SetFullRange();
      TeeChart.DrawIt(new TVec[2] { b, c }, new string[2] { "Unfiltered", "Filtered" }, "Exponential averaging with IirFilter", false);
    }

    y[i] = 1/d * x[i] + (d-1)/d * y[i-1]

    x.. input signal
    y.. output signal
    d.. Decay factor

    In terms of percentage:

    y[i] = a * x[i] + b * y[i-1] ,   a + b = 1

    a*100 ... percent of the new data used.
    b*100 ... percent of the old average used to compute the new average.