hG              !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmno p q r s t u v w x y z { | } ~ !"""""""####$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$%&&&&&&&&'()))**+,-.//////00012223333333444444566777788888888888889999999:;;;;;<<< < = > > >?????????@@ABBBBCC D!D"E#E$E%E&F'F(G)H*I+I,I-I.I/J0K1K2K3K4K5K6K7K8K9K:K;K<L=M>N?N@NANBNCNDNENFNNportable experimentalm.p.donadio@ieee.org GHe^x  ln (1+x), 0 <= x <= 1 cos x sin x  atan x, -1 < x < 1 cosh x sinh x atanh x portable experimentalm.p.donadio@ieee.orgRadix-2 Decimation in Time FFT x[n] N  FFT function X[k] portable experimentalm.p.donadio@ieee.org Goertzel's algorithm for complex inputs x[n] k X[k] Power via Goertzel's algorithm for complex inputs x[n] k |X[k]|^2 Goertzel's algorithm for real inputs x[n] k X[k] Power via Goertzel's algorithm for real inputs x[n] k |X[k]|^2    portable experimentalm.p.donadio@ieee.org Rader'%s Algorithm using direct convolution x[n] N X[k] Rader'"s Algorithm using FFT convolution x[n] N  FFT function X[k] I   portable experimentalm.p.donadio@ieee.org$Prime Factor Algorithm doing row FFT's then column FFT's x[n] nrows ncols  FFT function X[k] JKLMNOportable experimentalm.p.donadio@ieee.org Length 2 FFT x[n] X[k]  Length 3 FFT x[n] X[k]  Length 4 FFT x[n] X[k] portable experimentalm.p.donadio@ieee.orgx[n] X[k] Pportable experimentalm.p.donadio@ieee.org$Cooley-Tukey algorithm doing row FFT's then column FFT's x[n] nrows ncols  FFT function X[k] 'Cooley-Tukey algorithm doing column FFT's then row FFT's x[n] nrows ncols  fft function X[k] QRSTUV portable experimentalm.p.donadio@ieee.orgCompute the median of a list 5Compute the center of the list in a more lazy manner $and thus halves memory requirement.  portable experimentalm.p.donadio@ieee.orgnoise  white noise  portable experimentalm.p.donadio@ieee.orgnoise  purple noise  portable experimentalm.p.donadio@ieee.orgnoise  brown noise  portable experimentalm.p.donadio@ieee.orgWXYZ[\]^_`abcdefgportable experimentalm.p.donadio@ieee.org 32 bits in [0,1] X U 32 bits in [0,1) X U 32 bits in (0,1] X U !32 bits in (0,1) X U "53 bits in [0,1], ie 64-bit IEEE 754 in [0,1] X U #.53 bits in [0,1), ie 64-bit IEEE 754 in [0,1) X U $53 bits in (0,1] X U %53 bits in (0,1) X U &transforms uniform [0,1] to [a,b] a b U U'  !"#$%&  !"#$%&  !"#$%&portable experimentalm.p.donadio@ieee.org';Generates a list of geometric random variables from a list  of uniforms p U X '''portable experimentalm.p.donadio@ieee.org(7Generates a list of gamma random variables from a list 3 of uniforms via the inverse transformation method n lambda U X (((portable experimentalm.p.donadio@ieee.org)=Generates a list of exponential random variables from a list 3 of uniforms via the inverse transformation method   F(x) = 1 - exp(-lambda*x)  F^-1(x) = -log(1 - x) / lambdalambda U X )))portable experimentalm.p.donadio@ieee.org*:Generates a list of binomial random variables from a list  of uniforms n p U X ***portable experimentalm.p.donadio@ieee.org+*Calculates the Chebyshev approximation to f(x) over [a,b] f(x) a b N c_n ,)Evaluates the Chebyshev approximation to f(x) over [a,b] at x c_n a b x f(x) +,+,+,portable experimentalm.p.donadio@ieee.org-*Type for results of the simplex algorithm ./0hijkl1)The simplex algorithm for standard form: min c'x where Ax = b, x >= 0  a!(0,0) = -z  a!(0,j) = c'  a!(i,0) = b a!(i,j) = A_ij stating tableau  solution mno2 The two-phase simplex algorithm stating tableau  solution pq-./012-0/.12-0/../012portable experimentalm.p.donadio@ieee.org3AThis is the simple phase unwrapping algorithm from Oppenheim and  Schafer. epsilon ARG arg 333portable experimentalm.p.donadio@ieee.org44? creates a sine wave with normalized frequency wn (numerically A controlled oscillator, or NCO) using the recurrence relation y[n] =  2cos(wn)*y[n-1] - y[n-2],. Eventually, cumulative errors will creep G into the data. This is unavoidable since performing AGC on this type F of real data is hard. The good news is that the error is small with  floating point data. w phi y 55: mixes (multiplies) x by a real sine wave with normalized G frequency wn. This is usually called an NCOM: Numerically Controlled  Oscillator and Modulator. w phi x y 6778 returns an infinite list representing a complex phasor F with a phase step of wn radians, ie a quadrature nco with normalized  frequency wn radians/-sample. Since Haskell uses lazy evaluation, F rotate will only be computed once, so this NCO uses only one sin and F one cos for the entire list, at the expense of 4 mults, 1 add, and 1  subtract per point. w phi y 885 mixes the complex input x with a quardatue nco with  normalized frequency wn radians/ sample using complex multiplies $ (perform a complex spectral shift) w phi x y r996 mixes the complex input x with a quadrature nco with  normalized frequency wn radians/sample in quadrature (I/Q modulation) w phi x y 456789457896456789portable experimentalm.p.donadio@ieee.org:Cascade of functions, eg cascade [ f1, f2, f3 ] x == (f3 . f2 . f1) x f_n(x) x[n] y[n] ; Gain node y[n] = a * x[n]a x[n] y[n] < Bias node y[n] = x[n] + aa x[n] y[n] = Adder node z[n] = x[n] + y[n]x[n] y[n] z[n] :;<=:;<=:;<= >?@ABCDEFGHI >?@ABCDEFGHI >?@ABCDEFGHI >?@ABCDEFGHIportable experimentalm.p.donadio@ieee.orgJLU decomposition via Crout' s Algorithm A LU(A) K&Solution to Ax=b via LU decomposition LU(A) b x L0Improve a solution to Ax=b via LU decomposition A LU(A) b x x' M&Matrix inversion via LU decomposition A A^-1 N-Determinant of a matrix via LU decomposition LU(A) det(A) O LU solver using original matrix A b x P"determinant using original matrix A det(A) JKLMNOPJKLMNOPJKLMNOPportable experimentalm.p.donadio@ieee.orgQ(Matrix-matrix multiplication: A x B = C A B C R(Matrix-vector multiplication: A x b = c A b c STranspose of a matrix A A^T T6Hermitian transpose (conjugate transpose) of a matrix A A^H QRSTQRSTQRSTportable experimentalm.p.donadio@ieee.orgUImplementation of Prony' s method p q g[n] (b,a) UUUportable experimentalm.p.donadio@ieee.org stuvwVLowpass filter wc M h[n] WHighpass filter wc M h[n] XBandpass filter wl wu M h[n] YBandstop filter wl wu M h[n] ZMultiband filter  mags  w M h[n] [Raised-cosine filter ws beta M h[n] VWXYZ[VWXYZ[VWXYZ[portable experimentalm.p.donadio@ieee.org\9Implements the following function, which is a FIR filter  y[n] = sum(k=0,M) h[k]*x[n-k]GWe implement the fir function with five helper functions, depending on B the type of the filter. In the following functions, we use the O&S E convention that m is the order of the filter, which is equal to the  number of taps minus one. h[n] x[n] y[n] xyz{|}~]\]\]\]portable experimentalm.p.donadio@ieee.org^=This is an integrator when a==1, and a leaky integrator when 0 < a < 1. y[n] = a * y[n-1] + x[n]a x[n] y[n] _First order section, DF1  v[n] = b0 * x[n] + b1 * x[n-1] y[n] = v[n] - a1 * y[n-1]a_1 b_0 b_1 x[n] y[n] `First order section, DF2  w[n] = -a1 * w[n-1] + x[n] y[n] = b0 * w[n] + b1 * w[n-1]a_1 b_0 b_1 x[n] y[n] aFirst order section, DF2T  v0[n] = b0 * x[n] + v1[n-1] y[n] = v0[n] v1[n] = -a1 * y[n] + b1 * x[n]a_1 b_0 b_1 x[n] y[n] b)Direct Form I for a second order section  v[n] = b0 * x[n] + b1 * x[n-1] + b2 * x[n-2] y[n] = v[n] - a1 * y[n-1] - a2 * y[n-2]a_1 a_2 b_0 b_1 b_2 x[n] y[n] c3Direct Form II for a second order section (biquad)  w[n] = -a1 * w[n-1] - a2 * w[n-2] + x[n] y[n] = b0 * w[n] + b1 * w[n-1] + b2 * w[n-2]a_1 a_2 b_0 b_1 b_2 x[n] y[n] d5Transposed Direct Form II for a second order section  v0[n] = b0 * x[n] + v1[n-1] y[n] = v0[n] v1[n] = -a1 * y[n] + b1 * x[n] + v2[n-1] v2[n] = -a2 * y[n] + b2 * x[n]a_1 a_2 b_0 b_1 b_2 x[n] y[n] eDirect Form I IIR  v[n] = sum(k=0..M) b_k*x[n-k] y[n] = v[n] - sum(k=1..N) a_k*y[n-k]v[n] is calculated with \ (b,a) x[n] y[n] fDirect Form II IIR  w[n] = x[n] - sum(k=1..N) a_k*w[n-k] y[n] = sum(k=0..M) b_k*w[n-k](b,a) x[n] y[n] ghijklm^_`abcdefghijklm^_`abcdefghijklm^_`abcdefghijklmportable experimentalm.p.donadio@ieee.orgnPolyphase interpolator L h[n] x[n] y[n] nnn portable experimentalm.p.donadio@ieee.orgo"Evaluate a polynomial using Horner' s method. pAdd two polynomials qrSubtract two polynomials sScale a polynomial tMultiply two polynomials uvDivide two polynomials w3Modulus of two polynomials (remainder of division) x3Raise a polynomial to a non-negative integer power y'Polynomial substitution y(n) = x(w(n)) z{Polynomial substitution y(n) = x(w(n)) where the coefficients of x are also polynomials. |Polynomial derivative }Polynomial integration ~Convert roots to a polynomial opqrstuvwxyz{|}~opqrstuvwxyz{|}~opqrstuvwxyz{|}~!portable experimentalm.p.donadio@ieee.org generates Chebyshev polynomials N T_N(x) "portable experimentalm.p.donadio@ieee.org Lowpass to lowpass: s --> s/wc wc (b,a) (b',a') Lowpass to highpass: s --> wc/s wc (b,a) (b',a') Lowpass to bandpass: s --> (s^2 + wl*wu) / (s(wu-wl)) wl wu (b,a) (b',a') Lowpass to bandstop: s --> (s(wu-wl)) / (s^2 + wl*wu) wl wu (b,a) (b',a') #portable experimentalm.p.donadio@ieee.orgLowpass to lowpass: z^-1 --> (z^-1 - a)/ (1 - a*z^-1) theta_p omega_p (b,a) (b',a') Lowpass to Highpass: z^-1 --> -(z^-1 + a)/ (1 + a*z^-1) theta_p omega_p (b,a) (b',a') Lowpass to Bandpass: z^-1 --> theta_p  omega_p1  omega_p2 (b,a) (b',a') Lowpass to Bandstop: z^-1 --> theta_p  omega_p1  omega_p2 (b,a) (b',a') $portable experimentalm.p.donadio@ieee.org,< takes the continuous impluse response function (one of the  functions below, f-) and number of points in the interpolation, p, time  shifts it by x:, samples it, and creates an array with the interpolation / coeficients that can be used as a FIR filter. f p x h[n] ,,,%portable experimentalm.p.donadio@ieee.orgdesigns smooth FIR filters p q h[n] &portable experimentalm.p.donadio@ieee.org Performs the bilinear transform T_s (b,a) (b',a') "Function for frequency prewarping w_c T_s W_c 'portable experimentalm.p.donadio@ieee.orgRoot finder using Laguerre' s method epsilon iteration limit the polynomial  the roots (portable experimentalm.p.donadio@ieee.org!Performs the matched-z transform T_s (b,a) (b',a') )portable experimentalm.p.donadio@ieee.org'Generates Butterworth filter prototype N (b,a) %Generates Chebyshev filter prototype epsilon N (b,a) -Generates Inverse Chebyshev filter prototype epsilon N (b,a) *portable experimentalm.p.donadio@ieee.orgComplex test data Real test data +portable experimentalm.p.donadio@ieee.orgA b x ,portable experimentalm.p.donadio@ieee.org=levinson takes an array, r, of autocorrelation values, and a D model order, p, and returns an array, a, of the model estimate and  rho, the noise power. r p (a,rho) -portable experimentalm.p.donadio@ieee.orgThe Quinn-Fernandes algorithm y initial w estimate w .portable experimentalm.p.donadio@ieee.org)Discrete frequency periodigram maximizer X[k] k w /portable experimentalm.p.donadio@ieee.orgCompute the mean of a list  Mean(X) = 1/N sum(i=1..N) x_iCompute the variance of a list  Var(X) = sigma^2  = 1/N-1 sum(i=1..N) (x_i-mu)^2)Compute the standard deviation of a list " StdDev(X) = sigma = sqrt (Var(X))(Compute the average deviation of a list  AvgDev(X) = 1/N sum(i=1..N) |x_i-mu|Compute the skew of a list  Skew(X) = 1/N sum(i=1..N) ((x_i-mu)/sigma)^3Compute the kurtosis of a list  Kurt(X) = ( 1/N sum(i=1..N) ((x_i-mu)/sigma)^4 ) - 30portable experimentalm.p.donadio@ieee.orgFGenerates a list of poisson random variables from a list of uniforms. 4lambda - expectation value, should be non-negative. 3uniformly distributed values from the interval [0,1] Poisson distributed outputs 8Split after every element that satisfies the predicate. "A candidate for a Utility module. 1portable experimentalm.p.donadio@ieee.org2portable experimentalm.p.donadio@ieee.orgX1 X2 t X1 X2 t X1 X2 t 3portable experimentalm.p.donadio@ieee.org raw cross-correllation x y k R_xy[k] biased cross-correllation x y k R_xy[k] / N unbiased cross-correllation x y k R_xy[k] / (N-k) raw auto-correllation x k R_xx[k] biased auto-correllation x k R_xx[k] / N unbiased auto-correllation x k R_xx[k] / (N-k) 4portable experimentalm.p.donadio@ieee.orgraw cross-covariance .We define covariance in terms of correlation. Cxy(X,Y) = E[(X - E[X] )(Y - E[Y])]  = E[XY] - E[X]E[Y]  = Rxy(X,Y) - E[X]E[Y] x y k C_xy[k] raw auto-covariance Cxx(X,X) = E[(X - E[X] )(X - E[X])]  = E[XX] - E[X]E[X]  = Rxy(X,X) - E[X]^2 x k C_xx[k] biased cross-covariance x y k C_xy[k] / N unbiased cross-covariance x y k C_xy[k] / (N-k) biased auto-covariance x k C_xx[k] / N unbiased auto-covariance x k C_xx[k] / (N-k) 5portable experimentalm.p.donadio@ieee.orgTHIS DOES NOT WORK 6portable experimentalm.p.donadio@ieee.orgconv convolves two finite sequences 7portable experimentalm.p.donadio@ieee.org all zeros single impulse  unit step ramp 8portable experimentalm.p.donadio@ieee.org ! is the unit delay function, eg, delay1 [ 1, 2, 3 ] == [ 0, 1, 2, 3 ]% is the n sample delay function, eg, delay 3 [ 1, 2, 3 ] == [ 0, 0, 0, 1, 2, 3 ] downsample throws away every n'th sample, eg,  downsample 2 [ 1, 2, 3, 4, 5, 6 ] == [ 1, 3, 5 ]upsample, inserts n-1 zeros between each sample, eg, upsample 2 [ 1, 2, 3 ] == [ 1, 0, 2, 0, 3, 0 ]upsampleAndHold% replicates each sample n times, eg, upsampleAndHold 3 [ 1, 2, 3 ] == [ 1, 1, 1, 2, 2, 2, 3, 3, 3 ]Cmerges elements from two lists into one list in an alternating way interleave [0,1,2,3] [10,11,12,13] == [0,10,1,11,2,12,3,13]2split a list into two lists in an alternating way  uninterleave [1,2,3,4,5,6] == ([1,3,5],[2,4,6])It's a special case of "Numeric.Random.Spectrum.Pink.split. &pad a sequence with zeros to length n pad [ 1, 2, 3 ] 6 == [ 1, 2, 3, 0, 0, 0 ] generates a  if the given condition holds 8Computes the square of the Euclidean norm of a 2D point  Power with fixed exponent type. 5 This eliminates warnings about using default types. 9portable experimentalm.p.donadio@ieee.orgQuinn's First Estimator (FCI1) X[k] k w Quinn's Second Estimator (FCI2) X[k] k w Quinn's Third Estimator (FCI3) X[k] k w  Eric Jacobsen' s Estimator X[k] k w MacLeod's Three Point Estimator X[k] k w MacLeod's Three Point Estimator X[k] k w Rife and Vincent' s Estimator X[k] k w :portable experimentalm.p.donadio@ieee.org Pisarenko's method for a single sinusoid x w ;portable experimentalm.p.donadio@ieee.org>The weighted linear predictor form of the frequency estimator window z w  WLP using Lank, Reed, and Pollon' s window z w  WLP using kay' s window z w WLP using Lovell and Williamson' s window z w )WLP using Clarkson, Kootsookos, and Quinn' s window z rho sigma w <portable experimentalm.p.donadio@ieee.orgEComputes an AR(p) model estimate from x using the Yule-Walker method x p (a,rho) DComputes an AR(p) model estimate from x using the covariance method x p (a,rho) MComputes an AR(p) model estimate from x using the modified covariance method x p (a,rho)  6Computes an AR(p) model estimate from x using the Burg' method x p (a,rho)    =portable experimentalm.p.donadio@ieee.org 8Computes an MA(q) model estimate from x using the Durbin' s method > where l is the order of the AR process used in the algorithm x q l (a,rho)    >portable experimentalm.p.donadio@ieee.org %Butterworth filter response function N w_c w  |H_c(w)|^2  #Chebyshev filter response function N epsilon w_c w  |H_c(w)|^2  +Inverse Chebyshev filter response function  Note that w_c/ is a property of the stopband for this filter N epsilon w_c w  |H_c(w)|^2          ?portable experimentalm.p.donadio@ieee.orgApplys a window, w, to a sequence x w[n] x[n] w[n] * x[n] rectangular window M w[n] Bartlett window M w[n] Hanning window M w[n] Hamming window M w[n] Blackman window M w[n] Generalized Hamming window alpha M w[n] rectangular window beta M w[n] rectangular window M w[n]    @portable experimentalm.p.donadio@ieee.org Designs a lowpass Kaiser filter wp ws dp ds h[n] !Designs a highpass Kaiser filter wp ws dp ds h[n] Aportable experimentalm.p.donadio@ieee.orgFilter shaprening routine h[n] .function that implements the sharpened filter Bportable experimentalm.p.donadio@ieee.org*Generates lowpass Butterworth IIR filters (wp,dp) (ws,ds) (b,a) (Generates lowpass Chebyshev IIR filters (wp,dp) (ws,ds) (b,a) 0Generates lowpass Inverse Chebyshev IIR filters (wp,dp) (ws,ds) (b,a) Cportable experimentalm.p.donadio@ieee.orgCIC interpolator R M N x[n] y[n] CIC interpolator R M N x[n] y[n] Dportable experimentalm.p.donadio@ieee.org Halfband interpolator h[n] x[n] y[n] !Halfband decimator h[n] x[n] y[n]  ! ! !Eportable experimentalm.p.donadio@ieee.org"DNormal random variables via the Central Limit Theorm (not explicity  given, but see Ross) BIf mu=0 and sigma=1, then this will generate numbers in the range  [-n2,n2] Number of uniforms to sum  (mu,sigma) U X #CNormal random variables via the Box-Mueller Polar Method (Ross, pp  450--452) BIf mu=0 and sigma=1, then this will generate numbers in the range  [-8.57,8.57]4 assuing that the uniform RNG is really giving full  precision for doubles.  (mu,sigma) U X $0Acceptance-Rejection Method (Ross, pp 448--450) BIf mu=0 and sigma=1, then this will generate numbers in the range  [-36.74,36.74]5 assuming that the uniform RNG is really giving full  precision for doubles.  (mu,sigma) U X %?Ratio Method (Kinderman-Monahan) (Knuth, v2, 2ed, pp 125--127) BIf mu=0 and sigma=1, then this will generate numbers in the range  [-1e15,1e15]9 (?) assuming that the uniform RNG is really giving full  precision for doubles.  (mu,sigma) U X "#$%"#$%"#$%Fportable experimentalm.p.donadio@ieee.org&Kellet' s filter noise  pinked noise 'Voss' s algorithm FUNTESTED, but the algorithm looks like it is working based on my hand  tests. number of octaves to sum noise  pinked noise &'&'&'Gportable experimentalm.p.donadio@ieee.org($Radix-2 Decimation in Frequency FFT x[n] N  FFT function X[k] (((Hportable experimentalm.p.donadio@ieee.org)$Radix-4 Decimation in Frequency FFT x[n] N  FFT function X[k] )))Iportable experimentalm.p.donadio@ieee.org*.This is the driver routine for calculating FFT's. All of the = recursion in the various algorithms are defined in terms of *. x[n] X[k] +;Inverse FFT, including scaling factor, defined in terms of * X[k] x[n] ,FThis is the algorithm for computing 2N-point real FFT with an N-point " complex FFT, defined in terms of * x[n] X[k] -HThis is the algorithm for computing a 2N-point real inverse FFT with an * N-point complex FFT, defined in terms of + X[k] x[n] . Algorithm for 2 N-point real FFT' s computed with N-point complex  FFT, defined in terms of * x1[n] x2[n] (X1[k],X2[k]) *+,-.*+,-.*+,-.Jportable experimentalm.p.donadio@ieee.org/ fast_conv8 convolves two finite sequences using DFT relationships ///Kportable experimentalm.p.donadio@ieee.org0123456789:; 0123456789:; 0123456789:; 0123456789:;Lportable experimentalm.p.donadio@ieee.org< Sliding FFT N x[n]  X[k] <<<Mportable experimentalm.p.donadio@ieee.org=(Split-Radix Decimation in Frequency FFT x[n] N  FFT function X[k] ===Nportable experimentalm.p.donadio@ieee.org >?@ABCDEF >?@ABCDEF >?@ABCDEF >?@ABCDEFOPQRSTUVWXYZ[\]^_`abcd e f g h i j k lmnopqrstuvwxyz{|}~l !"""""""####$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$%&&&& & & & & '()))**+,-.//////00l0 1!2"2#2$3%3&3'3(3)3*3l4+4,4-4.4/4051626l737475768788898:8;8<8=8>8?8@8A8B8C9D9E9F9G9H9I9J:K;L;M;N;O;P<Q<R<S<T=U>V>W>X?Y?Z?[?\?]?^?_?`?a@b@cAdBeBfBgBhCiCjDkDlEmEnEoEpFqFrGsHtIuIvIwIxIyJzK{K|K}K~KKKKKKKKLMNNNNNNNNN                 ""%%%%'-...03336669:;;??????????@@@@@@CCCDEE F F F I IIKKKKKKKLL dsp-0.2.1Polynomial.MaclaurinNumeric.Transform.Fourier.R2DIT"Numeric.Transform.Fourier.GoertzelNumeric.Transform.Fourier.RaderNumeric.Transform.Fourier.PFA!Numeric.Transform.Fourier.FFTHardNumeric.Transform.Fourier.DFTNumeric.Transform.Fourier.CTNumeric.Statistics.MedianNumeric.Random.Spectrum.WhiteNumeric.Random.Spectrum.PurpleNumeric.Random.Spectrum.Brown Numeric.Random.Generator.MT19937#Numeric.Random.Distribution.Uniform%Numeric.Random.Distribution.Geometric!Numeric.Random.Distribution.Gamma'Numeric.Random.Distribution.Exponential$Numeric.Random.Distribution.BinomialNumeric.Approximation.ChebyshevMatrix.Simplex DSP.UnwrapDSP.Source.Oscillator DSP.FlowgraphNumeric.Special.Trigonometric Matrix.LU Matrix.MatrixDSP.Filter.IIR.PronyDSP.Filter.FIR.TapsDSP.Filter.FIR.FIRDSP.Filter.IIR.IIRDSP.Multirate.PolyphasePolynomial.BasicPolynomial.ChebyshevDSP.Filter.Analog.TransformDSP.Filter.IIR.TransformDSP.Filter.FIR.PolyInterpDSP.Filter.FIR.SmoothDSP.Filter.IIR.BilinearPolynomial.RootsDSP.Filter.IIR.MatchedzDSP.Filter.Analog.PrototypeDSP.Estimation.Spectral.KayDataMatrix.CholeskyMatrix.Levinson'DSP.Estimation.Frequency.QuinnFernandesDSP.Estimation.Frequency.PerMaxNumeric.Statistics.Moment#Numeric.Random.Distribution.PoissonNumeric.Statistics.CovarianceNumeric.Statistics.TTestDSP.CorrelationDSP.CovarianceDSP.Estimation.Spectral.ARMADSP.ConvolutionDSP.Source.Basic 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