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Beat Notes

Rick Lyons ● March 13, 2013 ● Coded in Matlab 
%     Filename:  Beat_Frequency.m
%
%     [Richard Lyons, Feb. 2013]
      
clear, clc
 
Fs = 8192; % Sample rate of dig. samples
N = 8192;  % Number of time samples
n = 0:N-1;
 
Wave_1 = sin(2*pi*210*n/Fs);  % First tone, 210 Hz
Wave_2 = sin(2*pi*200*n/Fs);  % Second tone, 200 Hz
 
        % Plot the two tones 
        figure(1)
        plot(n/Fs, Wave_1, '-b')
        ylabel('200 Hz'); xlabel('Time (sec.)'); 
        hold on
        plot(n/Fs, Wave_2, '-r')
        axis([0, 0.05, -1.2, 1.5]); 
        ylabel('Input tones'); xlabel('Time (sec.)'); 
        title('red = 200 Hz tone,  blue = 210 Hz tone'); 
        grid on, zoom on
        hold off
 
Product = Wave_1.*Wave_2;
Sum = Wave_1 + Wave_2;

        % Plot the tones' product and sum 
        figure(2)
        subplot(2,1,1)
        plot(n/Fs, Product, '-b'), 
        ylabel('Product'); xlabel('Time (sec.)'); 
        grid on, zoom on
        hold on
        Red_Curve = 0.5*cos(2*pi*10*n/Fs) + 0.5;  % Used for plotting only
        plot(n/Fs, Red_Curve, '-r')
        axis([0, 0.3, -1.25, 1.5]);
        hold off
        grid on, zoom on
 
        subplot(2,1,2)
        plot(n/Fs, Sum, '-b')
        hold on
        Red_Curve = 2*cos(2*pi*5*n/Fs);  % Used for plotting only
        plot(n/Fs, Red_Curve, '-r')
        axis([0, 0.3, -2.4, 3]);
        hold off
        ylabel('Sum'); xlabel('Time (sec.)');
        grid on, zoom on
 
% Play all the signals
    sound(Wave_1, Fs)
    pause(1.2)
    sound(Wave_2, Fs)
    pause(1.2)
    sound(Product, Fs)
    pause(1.2)
    sound(Sum, Fs)
 
% Spec analysis of the "Sum" signal
Spec = fft(Sum);
Spec_Mag = abs(Spec);
Freq = n*Fs/N; % Freq vector in Hz
 
        figure (3)  % Plot positive-freq spec amg
        plot(Freq(1:N/16), Spec_Mag(1:N/16))
        title('Spec Mag of "Sum" signal')
        ylabel('Mag.'), xlabel('Hz')
        grid on, zoom on

Computing the Nth Roots of a Number

Rick Lyons ● January 9, 2013●1 comment ● Coded in Matlab 
function [nth_Roots] = roots_nth(Num, n, Plot)
% ROOTS_NTH computes the nth roots of Num.
%
% Inputs:
%   Num is the number(s) for which the nth roots will be computed.
%     Num can be real-valued or complex-valued.
%     If Num has more than one element, then Num must be a row vector.
%   n must be a positive integer.
%   Plot is a string equal to 'Y' if a complex plane plot of 
%        the n complex roots of each element in Num is desired. 
%        Principle root is plotted as a blue square.  Remaining roots 
%        are red dots.  If input Plot string does not exist, then no 
%        plot is generated.
%
% Output:
%   nth_Roots is a column vector (with n rows) giving the nth roots of Num.
%
% Example:
%     clear, clc               % clear all variables
%     Num = [2+j*11, 3*exp(j*pi)]; % Two numbers
%     n = 3; % find the 3rd roots
%     [nth_Roots] = roots_nth(Num, n, 'Y')
%
%   Results are:
%       nth_Roots =
%
%          2.0000 + 1.0000i   0.7211 + 1.2490i
%         -1.8660 + 1.2321i  -1.4422 + 0.0000i
%         -0.1340 - 2.2321i   0.7211 - 1.2490i
%
%   [R. Lyons, Jan. 2013]
 
% Input argument check
if (nargin<2),
       disp('Not enough input arguments!!')
       return
    end;
    if (nargin==2)
        Plot == 'N'
    end
   
Num_of_Columns = max(size(Num)); % How many elements are in Num?
 
for Column_Loop = 1:Num_of_Columns;
    Mag(Column_Loop) = abs(Num(Column_Loop));
    Angle_Degrees(Column_Loop) = angle(Num(Column_Loop))*180/pi;
    for k = 1:n
        Root_Mag = Mag(Column_Loop)^(1/n);
        Root_Angle_Deg(k) = Angle_Degrees(Column_Loop)/n + (k-1)*360/n;
        nth_Roots(k,Column_Loop) = Root_Mag*exp(j*Root_Angle_Deg(k)*pi/180);
    end
    
    % Plot roots, on complex plane(s), if necessary
    if Plot == 'Y'
        figure
            % Plot unit circle
            Num_Pts = 200;     % Number of circle points
            Index = 0 : Num_Pts-1;
            Alpha = 1/Num_Pts;
            Points = Root_Mag*exp(j*2*pi*Alpha*Index);
            plot(Points, ':k','markersize',5)
            grid on
        hold on
 
        % Plot principle root (square blue dot) and list results in plot
            axis([-1.1*Root_Mag, 1.1*Root_Mag, -1.1*Root_Mag, 1.1*Root_Mag]);
            plot(Root_Mag*cos(Root_Angle_Deg(1)*pi/180),  ...
                Root_Mag*sin(Root_Angle_Deg(1)*pi/180), 'bs');
            text(-Root_Mag/2, 3*Root_Mag/4, ['Magnitudes = ',num2str(Root_Mag)]);
            text(-Root_Mag/2, 3*Root_Mag/4-Root_Mag/10, ...
                ['Principle Angle (deg.) = ',num2str(Root_Angle_Deg(1))]);
    
        % Plot remaining roots as red dots
            for k = 2:n
                plot(Root_Mag*cos(Root_Angle_Deg(k)*pi/180),  ...
                    Root_Mag*sin(Root_Angle_Deg(k)*pi/180), 'ro');
                text(-Root_Mag/2, 3*Root_Mag/4-k*Root_Mag/10, ...
                    ['             Angle ',num2str(k),' (deg.) = ', ...
                    num2str(Root_Angle_Deg(k))]);
            end % End k loop
            xlabel('Real'); ylabel('Imag.');
        hold off
    end % End 'if Plot == 'Y''
 
end % End Column_Loop

Computing CIC Filter Register Pruning Using Matlab [Updated]

Rick Lyons ● February 15, 2012●8 comments ● Coded in Matlab 
%  Filename: CIC_Word_Truncation.m
%
%  Computes CIC decimation filter accumulator register  
%  truncation in each filter stage based on Hogenauer's  
%  'accumulator register pruning' technique.
%
%   Inputs:
%     N = number of decimation CIC filter stages (filter order).
%     R = CIC filter rate change factor (decimation factor).
%     M = differential delay.
%     Bin = number of bits in an input data word.
%     Bout = number of bits in the filter's final output data word.
%   Outputs:
%     Stage number (ranges from 1 -to- 2*N+1).
%     Bj = number of least significant bits that can be truncated
%       at the input of a filter stage.
%     Accumulator widths = number of a stage's necessary accumulator
%       bits accounting for truncation.
 
%  Richard Lyons Feb., 2012
 
clear, clc
 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%  Define CIC filter parameters
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%N = 4;  R = 25;  M = 1;  Bin = 16;  Bout = 16; % Hogenauer paper, pp. 159
%N = 3;  R = 32;  M = 2;  Bin = 8;  Bout = 10; % Meyer Baese book, pp. 268
%N = 3;  R = 16;  M = 1;  Bin = 16;  Bout = 16; % Thorwartl's PDF file

N = 3; R = 8; M = 1; Bin = 12; Bout = 12; % Lyons' blog Figure 2 example
 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Find h_sub_j and "F_sub_j" values for (N-1) cascaded integrators
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
    disp(' ')
    for j = N-1:-1:1
        h_sub_j = [];
        h_sub_j((R*M-1)*N + j -1 + 1) = 0;
        for k = 0:(R*M-1)*N + j -1
            for L = 0:floor(k/(R*M)) % Use uppercase "L" for loop variable
                Change_to_Result = (-1)^L*nchoosek(N, L)...
                                  *nchoosek(N-j+k-R*M*L,k-R*M*L);
                h_sub_j(k+1) =  h_sub_j(k+1) + Change_to_Result;
            end % End "L" loop
        end % End "k" loop
        F_sub_j(j) = sqrt(sum(h_sub_j.^2));
    end % End "j" loop
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
    
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Define "F_sub_j" values for up to seven cascaded combs
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
F_sub_j_for_many_combs = sqrt([2, 6, 20, 70, 252, 924, 3432]);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%  Compute F_sub_j for last integrator stage
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
F_sub_j(N) = F_sub_j_for_many_combs(N-1)*sqrt(R*M);  % Last integrator   
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%  Compute F_sub_j for N cascaded filter's comb stages
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
for j = 2*N:-1:N+1
    F_sub_j(j) = F_sub_j_for_many_combs(2*N -j + 1);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Define "F_sub_j" values for the final output register truncation
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
F_sub_j(2*N+1) = 1; % Final output register truncation
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Compute column vector of minus log base 2 of "F_sub_j" values
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Minus_log2_of_F_sub_j = -log2(F_sub_j)';
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Compute total "Output_Truncation_Noise_Variance" terms
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
CIC_Filter_Gain = (R*M)^N;
Num_of_Bits_Growth = ceil(log2(CIC_Filter_Gain));
% The following is from Hogenauer's Eq. (11)
    %Num_Output_Bits_With_No_Truncation = Num_of_Bits_Growth +Bin -1;
    Num_Output_Bits_With_No_Truncation = Num_of_Bits_Growth +Bin;
Num_of_Output_Bits_Truncated = Num_Output_Bits_With_No_Truncation -Bout;
Output_Truncation_Noise_Variance = (2^Num_of_Output_Bits_Truncated)^2/12;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Compute log base 2 of "Output_Truncation_Noise_Standard_Deviation" terms
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Output_Truncation_Noise_Standard_Deviation = ...
    sqrt(Output_Truncation_Noise_Variance);
Log_base2_of_Output_Truncation_Noise_Standard_Deviation = ...
    log2(Output_Truncation_Noise_Standard_Deviation);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Compute column vector of "half log base 2 of 6/N" terms
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
    Half_Log_Base2_of_6_over_N = 0.5*log2(6/N);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Compute desired "B_sub_j" vector
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
B_sub_j = floor(Minus_log2_of_F_sub_j ...
          + Log_base2_of_Output_Truncation_Noise_Standard_Deviation ...
          + Half_Log_Base2_of_6_over_N);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
disp(' '), disp(' ')
disp(['N = ',num2str(N),',   R = ',num2str(R),',   M = ',num2str(M),...
        ',   Bin = ', num2str(Bin),',   Bout = ',num2str(Bout)])
disp(['Num of Bits Growth Due To CIC Filter Gain = ', ...
    num2str(Num_of_Bits_Growth)])
disp(['Num of Accumulator Bits With No Truncation = ', ...
    num2str(Num_Output_Bits_With_No_Truncation)])
% disp(['Output Truncation Noise Variance = ', ...
%     num2str(Output_Truncation_Noise_Variance)])
% disp(['Log Base2 of Output Truncation Noise Standard Deviation = ',...
%         num2str(Log_base2_of_Output_Truncation_Noise_Standard_Deviation)])
% disp(['Half Log Base2 of 6/N = ', num2str(Half_Log_Base2_of_6_over_N)])
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Create and display "Results" matrix
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
for Stage = 1:2*N
    Results(Stage,1) = Stage;
    Results(Stage,2) = F_sub_j(Stage);
    Results(Stage,3) = Minus_log2_of_F_sub_j(Stage);
    Results(Stage,4) = B_sub_j(Stage);
    Results(Stage,5) = Num_Output_Bits_With_No_Truncation -B_sub_j(Stage);
end
% Include final output stage truncation in "Results" matrix
    Results(2*N+1,1) = 2*N+1;  % Output stage number
    Results(2*N+1,2) = 1;
    Results(2*N+1,4) = Num_of_Output_Bits_Truncated;
    Results(2*N+1,5) = Bout;
    %Results % Display "Results" matrix in raw float-pt.form
 
% % Display "F_sub_j" values if you wish
% disp(' ')
% disp(' Stage        Fj        -log2(Fj)    Bj   Accum width')
% for Stage = 1:2*N+1
%   disp(['  ',sprintf('%2.2g',Results(Stage,1)),sprintf('\t'),sprintf('%12.3g',Results(Stage,2)),...
%         sprintf('\t'),sprintf('%7.5g',Results(Stage,3)),sprintf('\t'),...
%         sprintf('%7.5g',Results(Stage,4)),sprintf('\t'),sprintf('%7.5g',Results(Stage,5))])
% end
 
% Display Stage number, # of truncated input bits, & Accumulator word widths
disp(' ')
disp(' Stage(j)   Bj   Accum (adder) width')
for Stage = 1:2*N
    disp(['  ',sprintf('%2.0f',Results(Stage,1)),...
        sprintf('\t'),...
        sprintf('%5.5g',Results(Stage,4)),sprintf('\t'),...
        sprintf('%7.5g',Results(Stage,5))])
end
    disp(['  ',sprintf('%2.0f',Results(2*N+1,1)),...
        sprintf('\t'),...
        sprintf('%5.5g',Results(2*N+1,4)),sprintf('\t'),...
        sprintf('%7.5g',Results(2*N+1,5)),' (final truncation)'])
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

Adding a Controlled Amount of Noise to a Noise-Free Signal

Rick Lyons ● January 3, 2012●21 comments ● Coded in Matlab 
function [Noisy_Signal] = SNR_Set(Signal, Desired_SNR_dB)
%
%   SNR_Set(x, Desired_SNR_dB) returns the real-valued 
%   input 'Signal' contaminated with normally-distributed, 
%   zero-mean, random noise.  The signal-to-noise ratio   
%   (SNR in dB) of the output 'Noisy_Signal' signal is  
%   controlled by the input 'Desired_SNR_dB' argument measured 
%   in dB.

%   Example:
% 	Npts = 128;                  % Number of time samples
% 	n = 0:Npts-1;                % Time-domain index
% 	Signal = 3*sin(2*pi*3*n/Npts); % Real-valued signal
% 	Desired_SNR_dB = 3;      % Set SNR of output 'Noisy_Signal' to +3 dB
% 	[Noisy_Signal] = SNR_Set(Signal, Desired_SNR_dB);
%
%   Author: Richard Lyons [December 2011]
%******************************************

Npts = length(Signal); % Number of input time samples
Noise = randn(1,Npts); % Generate initial noise; mean zero, variance one

Signal_Power = sum(abs(Signal).*abs(Signal))/Npts;
Noise_Power = sum(abs(Noise).*abs(Noise))/Npts;
	%Initial_SNR = 10*(log10(Signal_Power/Noise_Power));

K = (Signal_Power/Noise_Power)*10^(-Desired_SNR_dB/10);  % Scale factor

New_Noise = sqrt(K)*Noise; % Change Noise level
	%New_Noise_Power = sum(abs(New_Noise).*abs(New_Noise))/Npts
	%New_SNR = 10*(log10(Signal_Power/New_Noise_Power))

Noisy_Signal = Signal + New_Noise;

'SNR_Set()' Function Test Code:
%  Filename SNR_Set_test.m
%
%  Tests the 'SNR_Set()" function.  Adds a predefined 
%  amount of random noise to a noise-free signal such that  
%  the noisy signal has a desired signal-to-noise ratio (SNR).
%
%  Author: Richard Lyons [December 2011]

clear, clc

% Create a noise-free signal
Npts = 128; % Number of time samples
n = 0:Npts-1; % Time-domain index
Cycles = 5;  % Integer number of cycles in noise-free sinwave signal
Signal = 3*sin(2*pi*Cycles*n/Npts); % Real-valued noise-free signal

Desired_SNR_dB = 3 % Set desired SNR in dB

[Noisy_Signal] = SNR_Set(Signal, Desired_SNR_dB); % Generate noisy signal

% Plot original and 'noisy' signals
    figure(1)
    subplot(2,1,1)
    plot(n, Signal, '-bo', 'markersize', 2)
    title('Original Signal')
    grid on, zoom on
    subplot(2,1,2)
    plot(n, Noisy_Signal, '-bo', 'markersize', 2)
    title('Noisy Signal')
    xlabel('Time-samples')
    grid on, zoom on

% Measure SNR in freq domain
Spec = fft(Noisy_Signal);
Spec_Mag = abs(Spec); % Spectral magnitude
    figure(2)
    plot(Spec_Mag, '-bo', 'markersize', 2)
    title('Spec Mag of Noisy Signal')
    xlabel('Freq-samples'), ylabel('Linear')
    grid on, zoom on
Signal_Power = Spec_Mag(Cycles+1)^2 + Spec_Mag(Npts-Cycles+1)^2;
Noise_Power = sum(Spec_Mag.^2) -Signal_Power;
Measured_SNR = 10*log10(Signal_Power/Noise_Power)

Testing the Flat-Top Windowing Function

Rick Lyons ● December 14, 2011 ● Coded in Matlab 
%    Code for testing the 'Wind_Flattop(Spec)' function in 
%    reducing 'scalloping loss' errors in time signal amplitude 
%    estimation.
%
%    Generates a time-domain sinusoid, computes its FFT, and passes 
%    that FFT sequence to the 'Wind_Flattop(Spec)' function.
%
%    The maximum output sample of the 'Wind_Flattop(Spec)' function
%    is used to estimate the peak amplitude of the original 
%    sinusoidal time-domain test signal.
%
%    The user controls the values for the test sinusoid's  
%    'Test_Freq' and peak amplitude 'Peak_Amp' near lines 23 & 24. 
%
%    Richard Lyons [December, 2011]

clear all, clc

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%  Define test parameters
Test_Freq = 7.22; % Test tone's freq. Must be less that N/2
Peak_Amp = 5;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

N = 64; % Number of time samples
Index = (0:N-1);

X = Peak_Amp*cos(2*pi*(Test_Freq)*Index/N + pi/3);

figure(1), clf
subplot(2,1,1)
plot(X,':ko', 'markersize', 4)
title('Original time signal'), grid on, zoom on

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%   FFT the input 'X' sequence and call 'Wind_Flattop()' function
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Spec = fft(X);
[Windowed_Spec] = Wind_Flattop(Spec);

subplot(2,1,2), plot(abs(Spec),'ko', 'markersize', 3)
title('SpecMag of unwindowed time signal'), grid on, zoom on

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%   Display results accuracy (error)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
disp(' ')
disp(['Test Freq = ',num2str(Test_Freq),...
        ',  True Peak Amplitude = ',num2str(Peak_Amp)])

Mag_peak_unwindowed = max(abs(Spec));
Unwindowed_Amp_Estimate = 2*Mag_peak_unwindowed/N;
Unwindowed_Amp_Estimate_Error_in_dB = ...
    20*log10(Unwindowed_Amp_Estimate/Peak_Amp);
disp(' ')
disp(['Unwindowed Peak Amplitude Estimate = ',...
        num2str(Unwindowed_Amp_Estimate)])
disp(['Unwindowed Estimate Error in dB = ',...
        num2str(Unwindowed_Amp_Estimate_Error_in_dB),' dB'])

M_peak_windowed = max(abs(Windowed_Spec));
Windowed_Amp_Estimated = 2*M_peak_windowed/N;
Windowed_Amp_Estimation_Error_in_dB = ...
    20*log10(Windowed_Amp_Estimated/Peak_Amp);
disp(' ')
disp(['Windowed Peak Amplitude Estimate = ',...
        num2str(Windowed_Amp_Estimated)])
disp(['Windowed Estimate Error in dB = ',...
        num2str(Windowed_Amp_Estimation_Error_in_dB),' dB'])

Flat-Top Windowing Function for the Accurate Measurement of a Sinusoid's Peak Amplitude Based on FFT Data

Rick Lyons ● December 14, 2011 ● Coded in Matlab 
function [Windowed_Spec] = Wind_Flattop(Spec)

%  Given an input spectral sequence 'Spec', that is the 
%  FFT of some time sequence 'x', Wind_Flattop(Spec) 
%  returns a spectral sequence that is equivalent
%  to the FFT of a flat-top windowed version of time 
%  sequence 'x'.  The peak magnitude values of output 
%  sequence 'Windowed_Spec' can be used to accurately 
%  estimate the peak amplitudes of sinusoidal components 
%  in time sequence 'x'.

%   Input: 'Spec' (a sequence of complex FFT sample values)
%
%   Output: 'Windowed_Spec' (a sequence of complex flat-top  
%                            windowed FFT sample values)
%
%   Based on Lyons': "Reducing FFT Scalloping Loss Errors 
%   Without Multiplication", IEEE Signal Processing Magazine, 
%   DSP Tips & Tricks column, March, 2011, pp. 112-116.
%
%   Richard Lyons [December, 2011]

N = length(Spec);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%   Perform freq-domain convolution
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
g_Coeffs = [1, -0.94247, 0.44247];

% Compute first two convolved spec samples using spectral 'wrap-around'
Windowed_Spec(1) = g_Coeffs(3)*Spec(N-1) ...
    +g_Coeffs(2)*Spec(N) + Spec(1) ...
    +g_Coeffs(2)*Spec(2) + g_Coeffs(3)*Spec(3);

Windowed_Spec(2) = g_Coeffs(3)*Spec(N) ...
    +g_Coeffs(2)*Spec(1) + Spec(2) ...
    +g_Coeffs(2)*Spec(3) + g_Coeffs(3)*Spec(4);

% Compute last two convolved spec samples using spectral 'wrap-around'
Windowed_Spec(N-1) = g_Coeffs(3)*Spec(N-3) ...
    +g_Coeffs(2)*Spec(N-2) + Spec(N-1) ...
    +g_Coeffs(2)*Spec(N) + g_Coeffs(3)*Spec(1);

Windowed_Spec(N) = g_Coeffs(3)*Spec(N-2) ...
    +g_Coeffs(2)*Spec(N-1) + Spec(N) ...
    +g_Coeffs(2)*Spec(1) + g_Coeffs(3)*Spec(2);

% Compute convolved spec samples for the middle of the spectrum
for K = 3:N-2
	Windowed_Spec(K) = g_Coeffs(3)*Spec(K-2) ...
        +g_Coeffs(2)*Spec(K-1) + Spec(K) ...
        +g_Coeffs(2)*Spec(K+1) + g_Coeffs(3)*Spec(K+2);
end % % End of 'Wind_Flattop(Spec)' function

Computing FFT Twiddle Factors

Rick Lyons ● November 28, 2011●10 comments ● Coded in Matlab 
% Filename:  FFT_Twiddles_Find_DSPrelated.m

%  Computes 'Decimation in Frequency' or 'Decimation 
%  in Time' Butterfly twiddle factors, for radix-2 FFTs 
%  with in-order input indices and scrambled output indices.
%  
%  To use, do two things: (1) define FFT size 'N'; and 
%  (2) define the desired 'Structure', near line 17-18, 
%  as 'Dec_in_Time' or 'Dec_in_Freq'.
%
%  Author: Richard Lyons, November, 2011
%******************************************

clear, clc

% Define input parameters
N = 8; % FFT size (Must be an integer power of 2)
Structure = 'Dec_in_Time';  % Choose Dec-in-time butterflies
%Structure = 'Dec_in_Freq';  % Choose Dec-in-frequency butterflies

% Start of processing
Num_Stages = log2(N); % Number of stages
StageStart = 1; % First stage to compute
StageStop = Num_Stages; % Last stage to compute
ButterStart = 1; %First butterfly to compute
ButterStop = N/2; %Last butterfly to compute
Pointer = 0; %Init 'results' row pointer
for Stage_Num = StageStart:StageStop
if Structure == 'Dec_in_Time'
	  for Butter_Num = ButterStart:ButterStop
	    Twid = floor((2^Stage_Num*(Butter_Num-1))/N);
	    % Compute bit reversal of Twid
	    Twid_Bit_Rev = 0; 
	    for I = Num_Stages-2:-1:0
		if Twid>=2^I
		  Twid_Bit_Rev = Twid_Bit_Rev + 2^(Num_Stages-I-2);
		  Twid = Twid -2^I;
		else, end
	    end %End bit reversal 'I' loop
	    A1 = Twid_Bit_Rev; %Angle A1
	    A2 = Twid_Bit_Rev + N/2; %Angle A2
	    Pointer = Pointer +1;
	    Results(Pointer,:) = [Stage_Num,Butter_Num,A1,A2];
	  end
     else
	  for Twiddle_Num = 1:N/2^Stage_Num
	    Twid = (2^Stage_Num*(Twiddle_Num-1))/2; %Compute integer
	    Pointer = Pointer +1;
	    Results(Pointer,:) = [Stage_Num,Twiddle_Num,Twid];
	  end 
    end % End 'if'
end % End Stage_Num loop

Results(:,1:3), disp(' Stage#  Twid#   A'), disp(' ')

Computing Acceptable Bandpass Sample Rates

Rick Lyons ● November 25, 2011 ● Coded in Matlab 
% Filename: Bandpass_Sample_Rate_Calc.m
%
%  Calculates acceptable Bandpass Sampling rate ranges 
%  based on an analog (continuous) signal's bandwidth 
%  and center freq.
%
%  Merely define bandwidth "Bw" and center frequency "Fc", in 
%  Hz, near line 22, for the analog bandpass signal and run the 
%  program.  [Example: Bw = 5, Fc = 20.]  Acceptable Fs sample  
%  rate ranges are shown in Figure 1 and displayed in Command window.
%
%  If the User defines a value for the BP sample rate Fs, near 
%  near line 28, then Figure 2 will show the locations of the 
%  positive and negative-freq spectral components after 
%  bandpass sampling.
%
%   Richard Lyons [November 2011]
%******************************************

clear, clc

Bw = 5; % Analog signal bandwidth in Hz
Fc = 20; % Analog signal center freq in Hz

% ##############################################
% Define an Fs sample rate value below

Fs = 11; % Selected Fs sample rate in Hz
% ##############################################

disp(' '), disp(['Analog Center Freq = ',num2str(Fc),' Hz.'])
disp(['Analog Bandwidth = ',num2str(Bw),' Hz.']), disp(' ')

% *****************************************************
%  Compute % display the acceptable ranges of BP sample rate 
% *****************************************************
disp('----------------------------------')
disp('Acceptable Fs ranges in Hz:')
No_aliasing = 0;  % Init a warning flag
M = 1; % Initialize a counter

while (2*Fc + Bw)/(M+1) >= 2*Bw
   FsMin = (2*Fc + Bw)/(M+1);
   FsMax = (2*Fc - Bw)/M;
   Fs_ranges(M,1) = FsMin;
   Fs_ranges(M,2) = FsMax;
   M = M + 1;
   disp([num2str(FsMin),' -to- ',num2str(FsMax)])
end
disp('----------------------------------')

% *****************************************************
%  Plot the acceptable ranges of BP sample rate 
% *****************************************************
figure(1), clf
title('Acceptable Ranges of Bandpass Sampling Rate')
xlabel('Freq (Hz)')
ylabel('This axis has no meaning')

for K = 1:M-1
   hold on
   plot([Fs_ranges(K,1),Fs_ranges(K,1)],[0.5,1.2],':g');
   plot([Fs_ranges(K,1),Fs_ranges(K,2)],[1,1],'-r','linewidth',4);
   axis([0.8*(2*Bw),1.1*max(Fs_ranges(1,2)), 0.8, 1.55])
end

plot([2*Bw,2*Bw],[0.5,1.2],'-b','linewidth',2);
text(2*Bw,1.45,'Bold red lines show acceptable Fs ranges')
text(2*Bw,1.35,'Blue line = Twice analog signal Bandwidth (2 x Bw)')
text(2*Bw,1.25,'(You can zoom in, if you wish.)')
hold off, grid on, zoom on

% **************************************************************
%  If Fs has been defined, plot spectrum of the sampled signal.  
% **************************************************************
% 
% Check if "Fs" has been defined
disp(' ')
if isempty(strmatch('Fs',who,'exact')) == 1; 
    disp('Fs sampling rate has NOT been defined.')
    % Fs does NOT exist, do nothing further.
else 
	% Fs is defined, plot the spectrum of the sampled signal.
        disp(['Fs defined as ',num2str(Fs),' Hz'])
	
    % To determine intermediate frequency (IF), check integer 
    %      part of "2Fc/Fs" for odd or even
	Temp = floor(2*Fc/Fs);
    if (Temp == 2*floor(Temp/2))
       disp(' '), disp('Pos-freq sampled spectra is not inverted.')
       Freq_IF = Fc -Fs*floor(Fc/Fs); % Computed IF frequency
	else
       disp(' '), disp('Pos-freq sampled spectra is inverted.')
       Freq_IF = Fs*(1 + floor(Fc/Fs)) -Fc; % Computed IF frequency
	end
    disp(' '), disp(['Center of pos-freq sampled spectra = ',num2str(Freq_IF),' Hz.'])

    % Prepare to plot sampled spectral range in Figure 2
    IF_MinFreq = Freq_IF-Bw/2;
    IF_MaxFreq = Freq_IF+Bw/2;
    
	figure(2), clf
	hold on
	plot([IF_MinFreq,IF_MaxFreq],[0.95, 1],'-r','linewidth',4);
	plot([Fs-IF_MaxFreq,Fs-IF_MinFreq],[1, 0.95],'-r','linewidth',4);
	plot([Fs,Fs],[0.5, 1.02],'-b','linewidth',2);
	plot([Fs/2,Fs/2],[0.5, 1.02],':b','linewidth',2);
    plot([IF_MinFreq,IF_MinFreq],[0.5, 0.95],':r');
    plot([IF_MaxFreq,IF_MaxFreq],[0.5, 1],':r');
    plot([Fs-IF_MaxFreq,Fs-IF_MaxFreq],[0.5, 1],':r');
    plot([Fs-IF_MinFreq,Fs-IF_MinFreq],[0.5, 0.95],':r');
    text(0.9*Fs,1.03,['Fs = ',num2str(Fs),' Hz'])
    text(0.8*Fs/2, 1.03,['Fs/2 = ',num2str(Fs/2),' Hz'])
    text(Fs/10,1.07,'(You can zoom in, if you wish.)')
    axis([0,1.2*Fs, 0.8, 1.1])
	
	hold off
	title('Red lines show spectral range of sampled signal')
    xlabel('Freq (Hz)')
	ylabel('This axis has no meaning')
	grid on, zoom on
    
    % ################################################################
    % Check if Fs is NOT in an acceptable freq range (aliasing occurs)
    Aliasing_Flag = 1; % Initialize a flag
    for K = 1:M-1 % Check each individual acceptable Fs range
        if Fs_ranges(K,1)<=Fs & Fs<=Fs_ranges(K,2)% & Fs<=Fs_ranges(K,2)
            % No aliasing will occur
            Aliasing_Flag = Aliasing_Flag -1;
        else, end
    end % End K loop
    if Aliasing_Flag == 1;  % Aliasing will occur
        text(Fs/10, 0.91, 'WARNING! WARNING!')
        text(Fs/10, 0.89, 'Aliasing will occur!')
    else, end
    zoom on
    % ################################################################
end

Computing Optimum Two-Stage Decimation Factors

Rick Lyons ● November 21, 2011 ● Coded in Matlab 
function [D1,D2] = Dec_OptimTwoStage_Factors(D_Total,Passband_width,Fstop)
%
%    When breaking a single large decimation factor (D_Total) into 
%    two stages of decimation (to minimize the orders of the 
%    necessary lowpass digital filtering), an optimum first 
%    stage of decimation factor (D1) can be found.  The second 
%    stage's decimation factor is then D_Total/D1.
%
%    Inputs:
%         D_Total = original single-stage decimation factor.
%         Passband_width = desired passband width of original 
%                          single-stage lowpass filter (Hz).
%         Fstop = desired beginning of stopband freq of original 
%                          single-stage lowpass filter (Hz).
%         
%    Outputs:
%         D1 = optimum first-stage decimation factor.
%         D2 = optimum second-stage decimation factor. 
%
%    Example: Assume you want to decimate a sequence by D_Total=100,
%             your original single-stage lowpass filter's passband 
%             width and stopband frequency are 1800 and 2200 Hz
%             respectively.  We use:
%             
%              [D1,D2] = Dec_OptimTwoStage_Factors(100, 1800, 2200)
%
%             giving us the desired D1 = 25, and D2 = 4. 
%             (That is, D1*D2 = 25*4 = 100 = D_Total.)
%
%    Author: Richard Lyons, November, 2011
	
% Start of processing
    DeltaF = (Fstop -Passband_width)/Fstop;
    
	Radical = (D_Total*DeltaF)/(2 -DeltaF); % Terms under sqrt sign.
	Numer = 2*D_Total*(1 -sqrt(Radical));  % Numerator.
	Denom = 2 -DeltaF*(D_Total + 1);  % Denominator.
	D1_estimated = Numer/Denom;  %Optimum D1 factor, but not 
                                  %                  an integer.

 % Find all factors of 'D_Total' 
	Factors_of_D_Total = Find_Factors_of_D_Total(D_Total);
 
 % Find the one factor of 'D_Total' that's closest to 'D1_estimated' 
    Temp = abs(Factors_of_D_Total -D1_estimated);
    Index_of_Min = find(Temp == min(Temp)); % Index of optim. D1
    D1 = Factors_of_D_Total(Index_of_Min); % Optimum first 
                                           %    decimation factor
    D2 = D_Total/D1;  % Second decimation factor.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [allfactors] = Find_Factors_of_D_Total(X)
% Finds all integer factors of intger X.
% Filename was originally 'listfactors.m', written 
% by Jeff Miller.  Downloaded from Matlab Central.
	[b,m,n] = unique(factor(X));
%b is all prime factors
	occurences = [m(1) diff(m)];
	current_factors = [b(1).^(0:occurences(1))]';
	for index = 2:length(b)
        currentprime = b(index).^(0:occurences(index));
        current_factors = current_factors*currentprime;
        current_factors = current_factors(:);
	end
	allfactors = sort(current_factors);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%