Python Operating Environment 1.2.1

Visral OE (Operating Environment) lets users efficiently access all the power of Python by means of sentient editors, separate input output portals, selective execution, and much more; simplifying and improving the Python experience.

Python Operating Environment 1.2.1

Visral OE (Operating Environment) lets users efficiently access all the power of Python by means of sentient editors, separate input output portals, selective execution, and much more; simplifying and improving the Python experience.

Python Operating Environment 1.2.1

Visral OE (Operating Environment) lets users efficiently access all the power of Python by means of sentient editors, separate input output portals, selective execution, and much more; simplifying and improving the Python experience.

Python Operating Environment 1.2.1

Visral OE (Operating Environment) lets users efficiently access all the power of Python by means of sentient editors, separate input output portals, selective execution, and much more; simplifying and improving the Python experience.

Match n Freq 6.10

Pulse shaping filter program that finds the pole-zero locations of a transfer function, H(s), for a matched filter. H(s) equals a -desired- signal (Yout) divided by a given input signal (Yin). Both Yout and Yin are functions of frequency. Group delay may also be calculated to compliment a given data set, thus, providing a flat group delay. Another improved productivity example do to using Calculus (level) programming.

Match n Freq 6.10

Pulse shaping filter program that finds the pole-zero locations of a transfer function, H(s), for a matched filter. H(s) equals a -desired- signal (Yout) divided by a given input signal (Yin). Both Yout and Yin are functions of frequency. Group delay may also be calculated to compliment a given data set, thus, providing a flat group delay. Another improved productivity example do to using Calculus (level) programming.

Match n Freq 6.10

Pulse shaping filter program that finds the pole-zero locations of a transfer function, H(s), for a matched filter. H(s) equals a -desired- signal (Yout) divided by a given input signal (Yin). Both Yout and Yin are functions of frequency. Group delay may also be calculated to compliment a given data set, thus, providing a flat group delay. Another improved productivity example do to using Calculus (level) programming.

Match n Freq 6.10

Pulse shaping filter program that finds the pole-zero locations of a transfer function, H(s), for a matched filter. H(s) equals a -desired- signal (Yout) divided by a given input signal (Yin). Both Yout and Yin are functions of frequency. Group delay may also be calculated to compliment a given data set, thus, providing a flat group delay. Another improved productivity example do to using Calculus (level) programming.

Match n Freq 6.10

Pulse shaping filter program that finds the pole-zero locations of a transfer function, H(s), for a matched filter. H(s) equals a -desired- signal (Yout) divided by a given input signal (Yin). Both Yout and Yin are functions of frequency. Group delay may also be calculated to compliment a given data set, thus, providing a flat group delay. Another improved productivity example do to using Calculus (level) programming.

Match n Freq 6.10

Pulse shaping filter program that finds the pole-zero locations of a transfer function, H(s), for a matched filter. H(s) equals a -desired- signal (Yout) divided by a given input signal (Yin). Both Yout and Yin are functions of frequency. Group delay may also be calculated to compliment a given data set, thus, providing a flat group delay. Another improved productivity example do to using Calculus (level) programming.

Match n Freq 6.10

Pulse shaping filter program that finds the pole-zero locations of a transfer function, H(s), for a matched filter. H(s) equals a -desired- signal (Yout) divided by a given input signal (Yin). Both Yout and Yin are functions of frequency. Group delay may also be calculated to compliment a given data set, thus, providing a flat group delay. Another improved productivity example do to using Calculus (level) programming.

Match n Freq 6.10

Pulse shaping filter program that finds the pole-zero locations of a transfer function, H(s), for a matched filter. H(s) equals a -desired- signal (Yout) divided by a given input signal (Yin). Both Yout and Yin are functions of frequency. Group delay may also be calculated to compliment a given data set, thus, providing a flat group delay. Another improved productivity example do to using Calculus (level) programming.

Match n Freq 6.10

Pulse shaping filter program that finds the pole-zero locations of a transfer function, H(s), for a matched filter. H(s) equals a -desired- signal (Yout) divided by a given input signal (Yin). Both Yout and Yin are functions of frequency. Group delay may also be calculated to compliment a given data set, thus, providing a flat group delay. Another improved productivity example do to using Calculus (level) programming.

ChemMaths 15.5

ChemMaths is a engineering,mathematical and chemistry program. Software suitable for Engineering,Chemistry/Science Professionals and educational use. Contains information on 3000+ chemical compounds,allows predition of chemical compound properties,critical constants, thermodynamic properties etc,periodic table, solves 400+ chemical/electrical/mechcanical engineering, phyics, and mathematical equations. Contains 200+ unit conversions & more.

SpectrumSolvers 6.10

Find best Spectral Estimation Method for a Power Spectral Density plot. A menu of 10+ spectral estimators from Steve Kay's textbook 'Modern Spectral Estimation' 1988 is available to choose from. The results differ dramatically from one estimator to another. Plus, varying input parameters and/or number of points may provide discrepancies. Observe how zero padding effects your results. See 50+ dB weak signal detection.

MEPX 2015.05.22

MEPX - a complex data analysis software running on Windows, Mac OSX and Linux Ubuntu. It is used for solving regression and classification problems. MEPX is based on Multi Expression Programming which is actually an evolutionary algorithm capable of generating computer programs in an automatic way. Models for regression and binary classification problems can be easily obtained.

SRS1 Cubic Spline for Excel 2.51

This is free software that adds several spline and linear interpolation functions to Microsoft Excel. It is simple to use because the new functions work just like all other existing Excel functions. The new functions can be used for data analysis, forecasting, and many other applications. See http://www.SRS1Software.com for demos and more information.

Regression Analysis and Forecasting 3.0

The Multiple Regression Analysis and Forecasting model provides confidence in identifying value drivers and forecasting business plan and scientific data. While it utilizes a range of commonly employed statistical measures to test the validity of the analysis, results are summarized in text for ease of use. Once relationships have been identified, forecasting can be accomplished based on a range of available methodologies.

Regression Analysis and Forecasting 3.0

The Multiple Regression Analysis and Forecasting model provides confidence in identifying value drivers and forecasting business plan and scientific data. While it utilizes a range of commonly employed statistical measures to test the validity of the analysis, results are summarized in text for ease of use. Once relationships have been identified, forecasting can be accomplished based on a range of available methodologies.

Regression Analysis and Forecasting 3.0

The Multiple Regression Analysis and Forecasting model provides confidence in identifying value drivers and forecasting business plan and scientific data. While it utilizes a range of commonly employed statistical measures to test the validity of the analysis, results are summarized in text for ease of use. Once relationships have been identified, forecasting can be accomplished based on a range of available methodologies.

Regression Analysis and Forecasting 3.0

The Multiple Regression Analysis and Forecasting model provides confidence in identifying value drivers and forecasting business plan and scientific data. While it utilizes a range of commonly employed statistical measures to test the validity of the analysis, results are summarized in text for ease of use. Once relationships have been identified, forecasting can be accomplished based on a range of available methodologies.

Regression Analysis and Forecasting 3.0

The Multiple Regression Analysis and Forecasting model provides confidence in identifying value drivers and forecasting business plan and scientific data. While it utilizes a range of commonly employed statistical measures to test the validity of the analysis, results are summarized in text for ease of use. Once relationships have been identified, forecasting can be accomplished based on a range of available methodologies.

Regression Analysis and Forecasting 3.0

The Multiple Regression Analysis and Forecasting model provides confidence in identifying value drivers and forecasting business plan and scientific data. While it utilizes a range of commonly employed statistical measures to test the validity of the analysis, results are summarized in text for ease of use. Once relationships have been identified, forecasting can be accomplished based on a range of available methodologies.

Regression Analysis and Forecasting 3.0

The Multiple Regression Analysis and Forecasting model provides confidence in identifying value drivers and forecasting business plan and scientific data. While it utilizes a range of commonly employed statistical measures to test the validity of the analysis, results are summarized in text for ease of use. Once relationships have been identified, forecasting can be accomplished based on a range of available methodologies.

Regression Analysis and Forecasting 3.0

The Multiple Regression Analysis and Forecasting model provides confidence in identifying value drivers and forecasting business plan and scientific data. While it utilizes a range of commonly employed statistical measures to test the validity of the analysis, results are summarized in text for ease of use. Once relationships have been identified, forecasting can be accomplished based on a range of available methodologies.

Page 6 of 21       1 2 3 4 57 8 9 10 11 12 13 14 15 16 17 18 19 20