Integrated Product Development Group

Mathcad - Add-On Modules (Engineering Libraries and Extension Packs)

ALL Modules are now included with the purchase of a Floating or Locked Mathcad License.

3 ENGINEERING LIBRARIES
Read the Mathcad Engineering Libraries Data Sheet >>> Click Here
Mathcad Mechanical Engineering Library
Compbines 3 classic mechanical references: Roark's Formulas for Stress & Strain, Hick's formulas & an interactive introduction to the finite element method. LEARN MORE...

Mathcad Civil Engineering Library
Combines the encyclopedic Roark's Formulas for Stress and Strain with easy-to-adapt structural design templates & examples of thermal design problems. LEARN MORE...

Mathcad Electrical Engineering Library
Hundreds of standard calculation procedures, formula and reference tables used by electrical engineers. LEARN MORE...

4 EXTENSION PACKS
Read the Mathcad Extension Pack Data Sheet >>> Click Here
Mathcad Data Analysis Extension Pack
Import, manipulate & analyze data in Mathcad. LEARN MORE...

Mathcad Image Processing Extension Pack
Provides a powerful solution for iterative exploration & investigative analysis. LEARN MORE...

Mathcad Signal Processing Extension Pack
Provides new capabilities for signal filtering, spectral analysis, time-frequency analysis & spectral estimation. LEARN MORE...

Mathcad Wavelets Extension Pack
A new approach to signal & image analysis, time series analysis, statistical signal estimation, data compression analysis & special numerical methods. LEARN MORE...

The Civil Engineering Library combines the encyclopedic Roark's Formulas for Stress and Strain with easy-to-adapt structural design templates and examples of thermal design problems. Because these are delivered electronically for use within Mathcad, you can apply these resources every day with ease. Explanatory text and examples give you detailed background and guidance along with the equations. Best of all, each title comes with word search, a hyperlinked table of contents and hyperlinked indexing.

Roark's Formulas for Stress and Strain, 6th Edition
You get the complete Mathcad edition with more than 1000 separate design cases covering straight beams and bars, curved beams, plates, and shells. This includes all 37 tables of formulas in Roark's, ready to use in your Mathcad worksheets, and more than 75 detailed example problems worked out in Mathcad.

Building Structural Design: Reinforced Concrete and Structural Steel Applications
This Electronic Book, by Thomas P. Magner, P.E., is designed for practicing structural and civil engineers. It includes 33 design templates for buildings and other structures and covers topics ranging from the analysis and design of beams, columns, base plates, footings and retaining walls, to the computation of wind and seismic loads on buildings.

Sample design problems are based on commonly used civil engineering standards. Each application includes a description of the design problem, one or more sketches, and input section, a list of computed variables, and a section of relevant Mathcad calculations. Reinforced concrete applications also include a material properties section in which the user can specify the strength and unit weight of the concrete and yield strength of the reinforcement.

Building Thermal Analysis
This Electronic Book, by Professor Andreas Athienitis of the Centre for Building Studies at Concordia University, covers the major field of heat transfer in building design. It can be used as a tool for self-study, an aid for college coursework, and an online design assistant for engineers and architects. It is a compendium of relevant, "real-life" examples stressing the theoretical, while never losing sight of the practical engineering aspects. Common design problems in building thermal design are solved, such as: sizing of a window overhang for summer shading, determining thermal comfort used on air temperature, wind and humidity, and computation of heat and solar radiation transfer through various media. Because it uses Mathcad, every number and formula is "live" so you can change parameters and plots and watch Mathcad calculate answers right there in the book.

Mathcad Electrical Engineering Library
Critical Resources for Electrical Engineers Working in Mathcad

The Electrical Engineering Library provides hundreds of standard calculation procedures, formulae and reference tables used by electrical engineers. Because these are delivered electronically for use within Mathcad, you can apply these resources every day with ease. Explanatory text and examples give you detailed background and guidance on how to use the equations. Best of all, each title comes with word search, a hyperlinked table of contents and hyperlinked indexing. Once you install the new Electrical Engineering Library CD, you gain immediate access to an interactive, electrical engineering reference library at any time from your Mathcad menu bar.

Electrical & Electronic Engineering from Hicks'
Adapted from the Standard Handbook of Engineering Calculations (edited by Tyler G. Hicks), this title delivers electrical and electronics engineering formulae and procedures, supported with explanatory text, tables and figures.

Electrical Power Systems Engineering
Electrical Power Systems Engineering Fundamental concepts for modeling electrical power distribution systems, providing analysis techniques necessary to design a functioning system, and locating possible difficulties in a proposed design. Immediately apply hundreds of calculation procedures to find solutions in the design and implementation of power distribution and power conversion systems.

Topics in Mathcad: Electrical Engineering
Useful Mathcad problem-solving techniques in the context of common design calculations from several different branches of electrical engineering, such as circuit analysis or digital filter design. These applications use Mathcad's complex arithmetic, matrix operators, equation solving power, and plotting capabilities to provide a reference source of Mathcad methods and formulas.

Mathcad Data Analysis Extension Pack Functions

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Function Name (Parameters)	Function Definition
Bicubic2D (vx, vy, Z, p, q)	Interpolates between 2-D values in Z, with locations in vx and vy, at intermediate point (p, q).
Binterp (x, b)	Interpolates results b from Spline2 (b) at point x, along with the first, second, and third derivatives.
confidence (vx, vy, F, b, conf)	Returns the confidence limits on the parameters b of a fitting function F(x,b) fit to the data vx and vy.
contingtbl (M)	Returns chi-squared, degrees of freedom, probability that chi-squared or larger would occur if variables had no association, Cramer's V, and the contingency coefficient, C for a contingency table M.
DWS (b)	Returns the Durbin-Watson statistic for the result vector, b, of the Spline2 function.
filterNaN (v)	Removes the rows of the data set, v, that have NaNs.
Ftest (v1, v2)	Tests the hypothesis that v1 and v2 are drawn from distributions having the same variance. Returns the F statistic, and the probability that a value this large or larger would occur when the distributions have the same variance.
Grubbs (v, a)	Returns indices of suspected outliers, and their Grubbs test statistics for a confidence level a.
GrubbsClassic (v, a)	Returns index of the data point most likely to be an outlier, and its Grubbs test statistic for a confidence level a.
Hlookup (z, A, r,"modifier")	Looks in the first row of A for values matched by z according to the boolean modifier. Returns the value(s) in the matched column(s) in row r.
kendltau (v1, v2)	Returns Kendall's tau, number of standard deviations from 0, and the probability that a value this large or larger would occur if the samples were uncorrelated.
kendltau2 (M)	Returns Kendall's tau, number of standard deviations from 0, and the probability that a value this large or larger would occur if contingency table M were uncorrelated.
LeastSquaresFit (vx, vy, F, guess, conf, [Stdy], [LBUB],[Acc])	Returns parameters and their confidence limits for the nonlinear fitting function F for the data vx and vy, for a confidence level conf, with optional standard deviations Stdy and optional lower and upper bounds on acceptable parameter values to accuracy Acc.
loadings (nipals)	Returns the loadings of the principal components (eigenvalues) from multivariate data returned by the nipals function.
localmax (data, [w])	Returns the local maxima in data by nearest neighbor comparison, with an optional window width w of comparison points.

localmin (data, [w])	Returns the local minima in data by nearest neighbor comparison, with an optional window width w of comparison points.
Lookup (z, A, B, modifier)	Looks in the matrix A for values matched by z according to the boolean modifier. Returns the value(s) in the same position(s) in matrix B.
markNaN (data, vindex)	Changes each element in data specified by vindex to contain a NaN.
Match (z, A, "modifier")	Returns the indices of entries in A which match z according to the boolean modifier.
matchNaN (data)	Returns the index or pair of indexes of the NaN entries in data.
Nipals (Data, numPC, maxiter, ["scale"])	Returns numPC principal components (eigenvalues), loadings, scores, and accumulated variance explained by each PC from multivariate data using a maximum of maxiter iterations. TOL specifies the termination accuracy used for eigenvalue generation. Data may be optionally scaled to the standard deviation.
Nipals2 (nipals, numPC)	Returns numPC additional principal components (eigenvalues), loadings, scores, and accumulated variance explained by each PC given the results calculated by Nipals.
order (v)	Returns the index in which the entries of v occur if sorted, based on the current value of ORIGIN.
PCAeigenvals (nipals)	Returns the principal components (eigenvalues) from multivariate data returned by the nipals function.
PCAvariance (nipals)	Returns the accumulated percentage of variance explained by the calculated principal components (eigenvalues) returned by the nipals function.
percentile (v, p)	Returns the number of values in v below p percent of the total number of points.
polycoeff (vx, vy)	Returns the coefficients of the interpolating polynomial function.
polyint (vx, vy, x)	Returns interpolated value at x using a polynomial function, and the expected error.
polyiter (vx, vy, x, N, e)	Returns interpolated value at x using a polynomial function with maximum order N and maximum error, e. Also returns the calculated error, and whether the function converged.
qqplot (v1, [v2 or "distrib"])	Returns points on a probability plot. If only v1 is specified, quantiles for v1 and the normal distribution are returned. If v2 is specifed, quantiles for v1 and v2 are returned. If 'weibull' is specified, returns natural log quantiles for v1 and the weibull quantiles.

Rank (v)	Returns the averaged position at which each value in v appears in a sorted list of the data.
rationalfit (vx, vy, conf, [m, n], [resid], [Stdy], [LBUB])	Returns parameters and their confidence limits for a rational polynomial fit of order m and n on the top and bottom, or an allowable residual chi-squared, if the function should determine the optimal order. Confidence level conf is achieved, with optional standard deviations Stdy and optional lower and upper bounds on acceptable parameter values.
rationalfitnp (vx, vy, conf, [m, n], [resid], [Stdy], [LBUB])	Returns parameters and their confidence limits for a rational polynomial fit of order m and n on the top and bottom, or an allowable residual chi-squared, if the function should determine the optimal order. Confidence level conf is achieved, with optional standard deviations Stdy and optional lower and upper bounds on acceptable parameter values.
rationalint (vx, vy, x)	Returns interpolated value at x using rational functions, and the expected error.
Scale (M, min, max)	Scales all values in M between min and max.
scores (nipals)	Returns the scores of the principal components (eigenvalues) from multivariate data returned by the nipals function.
Spear (v1, v2)	Returns Spearman's rank correlation coefficient, and associated statistics.
Spline2 (vx, vy, n, [vw], [u], [level])	Returns the optimal set of order-n B-spline knots to interpolate on data vx and vy, with optional weights vw, optional desired knots u, and an optional reject level. Output is used with Binterp.
Thiele (vx, coeff, x)	Returns the interpolated y value for the real scalar x, using the data points in vx and the coefficients returned by Thielecoeff.
Thielecoeff (vx,vy)	Returns the continued fraction coefficients of the vectors vx and vy.
ThreeSigma (v)	Returns indices of points in v whose mean divided by standard deviation is greater than three (outlier test), and the value of this quantity for each point.
trim (vdata, vindex)	Trims out the entries (rows) specified by vindex.
vhlookup (z1, z2, A)	Looks in the first column and row of A for values matched by z1 and z2, respectively. Returns the value(s) in the intersection of matched rows and columns.
VHlookup (z1, z2, A, "modifier")	Looks in the first column and row of A for values matched by z1 and z2 according to the boolean modifier. Returns the value(s) in the intersection of matched rows and columns.
Vlookup (z, A, c, "modifier")	Looks in the first column of a matrix, A, for values matched by z according to the boolean modifier. Returns the value(s) in the matched row(s) in column c. When multiple values are returned, they appear in a vector.

VSmooth (v, w)	Repeatedly median smoothes v until no additional change has occurred for each window width in w.

Mathcad Image Processing Extension Pack
The Essential Tools for Image Processing, Analysis and Visualization
The Image Processing Extension Pack, coupled with Mathcad, provides a powerful solution for iterative exploration and investigative analysis. With its extensive image processing, analysis and visualization capabilities, this Extension Pack is ideal for research scientists and engineers, design engineers, system analysts and image specialists working on imaging applications across many industries, including defense, photography, medicine, manufacturing, law enforcement and multimedia. It is also a valuable tool for students studying electrical engineering or computer sciences.

This robust Mathcad add-on tool provides a total of over 140 built-in image processing functions, including over 50 new and enhanced capabilities for filtering, morphology, edge detection, segmentation and feature extraction. In addition to this added imaging power, Mathcad's regular matrix operations and numerics are on your desktop to help you fully analyze images stored in matrix form.

The Image Processing Extension Pack also offers an interactive image viewer for easy manipulation, various file formats to make it easier to work with other applications, and expanded electronic documentation with templates and application examples. And because the Image Processing Extension Pack builds upon Mathcad's superior technical design environment, you can incorporate your image processing work with publication-quality technical documents, graphs and presentations created in Mathcad.

Combined with your Mathcad desktop, this Extension Pack provides all the depth and breadth found in competing products, along with superior ease-of-use, flexibility and extensibility.

Mathcad Image Processing Extension Pack Functions

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Function Name (Parameters)	Function Definition
addnoise (M, p, n)	Returns matrix M with added noise, where the noise has probabilty p/2 to add n to a pixel, and p/2 to subtract n.
and (M, N)	Returns the boolean AND of two image matrices M and N, which must be the same size.
augment3 (X, Y, Z)	Returns a matrix formed by putting matrices (or vectors) X, Y, and Z side by side. They must all have the same number of rows.
binarize (M, thresh)	Returns a binarized version of matrix M with pixels above threshold thresh set to 1 and below to 0.
binarize_auto (M)	Returns a binarized version of matrix M, choosing the threshold automatically.
binarize2 (M, lowThresh, highThresh, inValue, outValue)	Returns a binarized version of matrix M with pixels between lowThresh and highThresh set to inValue, and pixels outside to outValue.
blend (M, N)	Returns a blend of same-size matrices M and N (pixelwise sum - [product/255]).
canny (M, sigma, low, high)	Returns a binary edge image resulting from Canny edge detection on matrix M, using standard deviation sigma and hysteresis thresholds low and high.
center (M)	Returns fourier transform image matrix M tansformed so that DC is in the center.
centsmooth (M)	Returns matrix M smoothed with a 3x3 center weighted kernel.
clip (M, Min, Max)	Returns matrix M with elements clipped to lie between Min and Max.
close (M, Melem, b)	Performs binary closing on matrix M at threshold b with structuring element Melem.
cnvxhull (M, fg)	Returns a matrix containing the convex hull of pixels of value fg in matrix M.
colgrad (M)	Returns the column gradient (difference by columns) of matrix M.
compgrad (M)	Performs edge detection by comparing the gradients of the 8 neighbors on matrix M.

concomp (M, con, fg)	Performs connected component labeling of the pixels with grayscale value fg in matrix M, considering 4-connected neighbors if con is 4 or 8-connected if con is 8.
convol2d (M, K)	Returns the convolution of matrix M with kernel K.
convolve3 (M, K)	Returns the quick convolution of matrix M with 3x3 kernel K.
convolve5 (M, K)	Returns the quick convolution of matrix M with 5x5 kernel K.
dct2d (M)	Returns the 2D discrete cosine transform (type II) of matrix M.
diacrisp (M)	Returns matrix M crisped with a 3x3 diagonally weighted kernel.
difedge (M)	Performs edge-detection by differential convolution on matrix M.
dilate (M, Melem, r_origin, c_origin, b)	Performs binary dilation on matrix M at threshold b using structuring element Melem with origin at row r_origin and column c_origin.
dilate4 (M, b)	Performs dilation on matrix M at threshold b using 4 neighbors.
dilate8 (M, b)	Performs dilation on matrix M at threshold b using 8 neighbors.
distform (M, fg)	Returns the distance transform of image M using foreground gray value fg.
equalize (M)	Returns matrix M with grayscale adjusted to form a linear cumulative histogram.
erode (M, Melem, r_origin, c_origin, b)	Performs binary erosion on matrix M at threshold b using structuring element Melem with origin at row r_origin and column c_origin.
erode4 (M, b)	Performs erosion on matrix M at threshold b using 4 neighbors.
erode8 (M, b)	Performs erosion on matrix M at threshold b using 8 neighbors.

extract (M, n)	Returns the nth (1, 2, or 3) color component of packed 3-color matrix M.
freichen (M)	Performs edge detection by Frei-Chen convolution on matrix M.
funcdeconv (M, f, e)	Deconvolution of matrix M with frequency domain function f and error e.
funconv (M, f)	Convolution of matrix M with frequency domain function f.
funmap (M, f)	Returns matrix M with function f applied to each element.
gaussconv (M, s)	Convolution of matrix M with frequency domain gaussian of half-width s.
gaussdeconv (M, s, e)	Deconvolution of matrix M with frequency domain gaussian of half-width s with error e.
getnoise (M)	Returns the difference between matrix M and median filtered M.
gray_close (M, Melem)	Performs grayscale closing on matrix M with structuring element Melem.
gray_dilate (M, Melem, r_origin, c_origin)	Performs grayscale dilation on matrix M using structuring element Melem with origin at row r_origin and column c_origin.
gray_erode (M, Melem, r_origin, c_origin)	Performs grayscale erosion on matrix M using structuring element Melem with origin at row r_origin and column c_origin.
gray_open (M, Melem)	Performs grayscale opening on matrix M with structuring element Melem.
gray_to_rgb (gray, colormap)	Returns grayscale matrix gray converted to color using color palette matrix colormap.
hist2d (M, N, n)	Returns a two-dimensional histogram with n bins on equal-sized matrices M and N.
hls_to_rgb (HLS)	Returns array HLS in HLS color representation converted to RGB color representation.

horzflip (M)	Returns the matrix M flipped horizontally.
hsv_to_rgb (HSV)	Returns array HSV in HSV color representation converted to RGB color representation.
idct2d (M)	Returns the inverse 2D discrete cosine transform (type II) of matrix M.
imhist (M, n)	Returns an n-bin histogram of M for values between 0 and 255 (ignores values outside that range).
imhist2 (M, n)	Returns an n-bin histogram of M over its range of values.
immse (M, Q)	Returns the mean-squared-error (MSE) between image matrices M and Q.
imquant (M, n)	Returns a quantized version of matrix M containing only n equally-spaced grayscale levels between 0 and 255.
imquant2 (M, v)	Returns a quantized version of matrix M containing only the grayscale levels in vector v.
imsnr (M, Q)	Returns the signal-to-noise ratio (SNR) between image matrices M and Q.
invert (M)	Returns the matrix M with each element set to 255 - element.
invert2 (M)	Returns the matrix M with each element set to max(M) - element + min(M).
iwave2d (M, n)	The n-level inverse wavelet transform of M.
kirsch (M)	Performs edge detection by kirsch convolution and comparison on matrix M.
laplace24 (M)	Returns the convolution of matrix M with a 5x5 Laplacian kernel. The kernel's center is 24.
laplace4 (M)	Returns the convolution of matrix M with a 3x3 Laplacian kernel. The kernel's center is 4.

laplace8 (M)	Returns the convolution of matrix M with a 3x3 Laplacian kernel. The kernel's center is 8.
levelmap (M, vec)	Returns matrix with values in vec assigned by matching vec's indices to elements in matrix M. vec must be such that the elements of M are between 0 and length(vec) - 1.
mask (M, N)	Returns matrix M masked by same-size matrix N (i.e. with zeros where N is zero).
matconv (M, N)	Convolution of matrix M with frequency domain mask N.
matdeconv (M, N, e)	Deconvolution of matrix M with frequency domain mask N and error e.
medfilt (M)	Returns median filtered M.
moment_invariant (M)	Returns a vector containing the seven typical moment invariants of M.
open (M, Melem, b)	Performs binary opening on matrix M at threshold b using structuring element Melem.
or (M, N)	Returns boolean OR of two image matrices M and N, which must be the same size.
orthocrisp (M)	Returns matrix M crisped with a 3x3 orthogonally weighted kernel.
orthocrisp5 (M)	Returns matrix M crisped with a 5x5 orthogonally weighted kernel.
orthosmooth (M)	Returns matrix M smoothed with a 3x3 orthogonally weighted kernel.
orthosmooth5 (M)	Returns matrix M smoothed with a 5x5 orthogonally weighted kernel.
prewitt (M)	Performs edge detection by Prewitt convolution on matrix M.
putregion (M, N, row, col)	Returns the matrix N inserted into M at row row and column col.

quantfilt (M, elem, quantile)	Performs quantile filtering on M using neighborhood matrix elem and quantile probability quantile.
READRAW (filename, rows, cols, bits, endian, skip)	Returns the contents of a raw binary image file as a matrix. The binary file is interpreted to contain a rows x cols matrix of bits (8 or 16) bits per pixel packed integers, in "Little" or "Big" endian format, and skip bytes are skipped for header at the beginning of the file.
reg_grow (M, x_gridsize, y_gridsize, num_regions)	Performs the piecewise-constant energy-based region growing segmentation of M into num_regions regions, using initial grid spaced by x_gridsize along x and y_gridsize along y.
relerror (M, Q)	Returns the relative error between matrices M and Q.
replace (M, N, n)	Returns packed image matrix M with the nth (1, 2, or 3) color component replaced by matrix N, which must have the same number of rows as M and 1/3 as many columns.
rgb_to_gray (RGB)	Returns RGB color array RGB converted to grayscale.
rgb_to_hls (RGB)	Returns array RGB in RGB color representation converted to HLS color representation.
rgb_to_hsv (M)	Returns array RGB in RGB color representation converted to HSV color representation.
rgb_to_ycbcr (RGB)	Returns array RGB in RGB color representation converted to YCbCr color representation.
rgb_to_yiq (RGB)	Returns array RGB in RGB color representation converted to YIQ color representation.
roberts (M)	Performs edge detection by Roberts convolution on matrix M.
robinson3 (M)	Performs edge detection by 3x3 Robinson convolution and comparison on matrix M.
robinson5 (M)	Performs edge detection by 5x5 Robinson convolution and comparison on matrix M.
rotate (M, angle)	Returns the matrix M rotated angle degrees counterclockwise.
rotate180 (M)	Returns the matrix M rotated 180 degrees counterclockwise.

rotate270 (M)	Returns the matrix M rotated 270 degrees counterclockwise.
rotate90 (M)	Returns the matrix M rotated 90 degrees counterclockwise.
rowgrad (M)	Returns the row gradient (difference by rows) of matrix M.
scale (M, Min, Max)	Returns matrix M with elements scaled between Min and Max.
shape_features (M)	Returns a matrix of moments and shape features for each distinct pixel value in labeled image M.
skeleton (B)	Returns binary matrix B eroded to its innermost level.
skeleton2 (M, b)	Returns the skeleton of matrix M binarized with threshold b.
sobel (M)	Performs edge detection by Sobel convolution on matrix M.
subcolor (M, ir, jr, ic, jc)	Returns the submatrix from row ir to jr, column ic to jc, of packed color matrix M.
thin (M, b)	Returns the thinned version of matrix M binarized with threshold b.
threshold (M, thresh)	Returns the matrix M with every element below thresh set to thresh. If thresh is negative, every element above -thresh is set to -thresh.
translate (M, rows, cols, pad)	Returns matrix M translated by rows rows and cols colums, padding unfilled matrix elements with pad.
unicrisp (M)	Returns matrix M crisped with a 3x3 uniformly weighted kernel.
unismooth (M)	Returns matrix M smoothed with a 3x3 uniformly weighted kernel.
unismooth5 (M)	Returns matrix M smoothed with a 5x5 uniformly weighted kernel.

vertflip (M)	Returns the matrix M flipped vertically.
warp (M, T)	Performs bilinear warping on matrix M, using tie-points stored in matrix T.
wave2d (M, n)	The n-level wavelet tranform of M.
wavescale (M, n, Min, Max)	The n-level wavelet transform of M, scaled between Min and Max.
wiener2d (M, win_h, win_w)	Perform 2D adaptive Wiener filtering on M using a local window win_w pixels wide by win_h pixels high.
WRITERAW (filename, bits, endian)	Writes a matrix M to raw binary integer image file filename, using either 8 or 16 bits per pixel, in "Little" or "Big" endian storage order. Set this function equal to the matrix M.
ycbcr_to_rgb (YCbCr)	Returns array YCbCr in YCbCr color representation converted to RGB color representation.
yiq_to_rgb (YIQ)	Returns array YIQ in YIQ color representation converted to RGB color representation.
zoom (M, hscale, vscale)	Return image matrix M resized by factor hscale horizontally and vscale vertically.

Mathcad Signal Processing Extension Pack
Provides a Powerful Design Solution for Iterative Exploration & Investigative Analysis
With its extensive signal processing, analysis and visualization capabilities, the Signal Processing Extension Pack is ideal for electrical design, DSP, audio, recording and research engineers, as well as other engineers and scientists involved in a broad range of signal processing applications, in industries such as telecommunications, test and instrumentation, manufacturing, defense, control systems, medicine and more. It is also a valuable tool for students studying electrical engineering.

This robust Mathcad add-on tool provides a total of over 70 built-in signal processing functions, including new functionality in signal filtering, spectral analysis, time-frequency analysis and spectral estimation. In addition, new Visual Basic application examples illustrate how to use Visual Basic scripting with Mathcad for signal processing applications. The Extension Pack also gives you full support for multi-channel and complex signals, and provides Window arguments for all filtering signals.

Mathcad's easy-to-use environment is ideal for iterative exploration, investigative analysis and what-if scenarios. Plus, the Signal Processing Extension Pack builds upon Mathcad's superior technical design environment, so you can incorporate your signal processing work with publication-quality technical documents, graphs and presentations created in Mathcad.

Mathcad Signal Processing Extension Pack Functions

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