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Muller’s method & FORTRAN features

Muller’s method & FORTRAN features

Muller’s method & FORTRAN features. Muller’s method for solving non-linear equations & FORTRAN features. April 6, 2004. Muller’s method & FORTRAN features. Polynomials. Recall: A polynomial of degree n will have n roots. The roots can be real or complex, and they

By karik
(515 views)

Splash Screen

Splash Screen

Splash Screen. Five-Minute Check (over Lesson 10–1) Main Idea and Vocabulary Example 1: Graph Quadratic Functions Example 2: Graph Quadratic Functions Example 3: Graph Quadratic Functions Example 4: Graph Quadratic Functions Example 5: Real-World Example. Lesson Menu.

By perrin
(130 views)

Solving a Nonlinear System

Solving a Nonlinear System

Solving a Nonlinear System. The most important numerical algorithm to understand in Kinematics Relied upon heavily by ADAMS, used almost in all analysis modes Kinematics Dynamics Equilibrium How does one go about finding the solution?. Newton-Raphson Method.

By torsten
(122 views)

Ohm’s Law

Ohm’s Law

Ohm’s Law. Ohm’s Law. Ohm’s Law states that the voltage v across a resistor is directly proportional to the current flowing through it. Resistance. The resistance R of an element denotes its ability to resist the flow of electric current, measured in ohms ( ). 1 ohm = 1 volt / ampere.

By abdalla
(125 views)

Portable Haptic Aids for Training and Rehabilitation

Portable Haptic Aids for Training and Rehabilitation

Portable Haptic Aids for Training and Rehabilitation. Li Jiang April 4 th 2008. Emergency personnel training. Stroke Rehabilitation. Presentation Outline. Background Portable haptic aids for emergency personnel virtual reality (VR) training

By bliss
(152 views)

Biologically Inspired Intelligent Systems

Biologically Inspired Intelligent Systems

Biologically Inspired Intelligent Systems. Lecture 08 Dr. Roger S. Gaborski. Biologically Inspired Object Categorization in Cluttered Scenes. Biologically Inspired Object Categorization in Cluttered Scenes Classification System Preprocessor Feature Extraction Neural Network (FENN)

By arlene
(192 views)

Artificial Neural Networks - Introduction -

Artificial Neural Networks - Introduction -

Artificial Neural Networks - Introduction -. Yeni Herdiyeni Dept of Computer Science – IPB. Overview. Biological inspiration Artificial neurons and neural networks Learning processes Learning with artificial neural networks. Biological inspiration.

By channer
(175 views)

Automatic Speech Recognition II

Automatic Speech Recognition II

Automatic Speech Recognition II. Hidden Markov Models Neural Network. Hidden Markov Model. DTW, VQ => recognize pattern, use distance measurement. HMM: statistical method for characterizing the properties of the frame of pattern,. Discrete-time Markov Processes. Consider a system with:

By akamu
(186 views)

100m Swimming World Records

100m Swimming World Records

100m Swimming World Records. Joe Beaulac Tiesha Tyler Myisha Tyler Willow Garrett . Table of Data Points. Graph One: Plotted w/ Line of Best Fit. Linear Function for Graph One. The equation for a linear function is y= mx+b The linear function for this graph is.. y=-.07+184.55.

By thai
(128 views)

2.6 Operations and Composition

2.6 Operations and Composition

2.6 Operations and Composition. Quiz. Let f(x) = 3x – 1, g(x) = x 2 -4, then (f + g)(x) = ______. (write the function expression ). Operations on Functions. Given two functions f and g, suppose Domain of f is D f , Domain of g is D g. Operations on Functions. Evaluate: (f+g)(6);

By tien
(170 views)

4.3 Patterns and Non-Linear Functions:

4.3 Patterns and Non-Linear Functions:

Nonlinear: Is a function whose graph is not a line or part of a line. 4.3 Patterns and Non-Linear Functions:. Family of Functions: is a group of functions with common characteristics. . Parent Function: is the simplest function of a family of functions. .

By dafydd
(225 views)

4.3 Patterns and Non-Linear Functions:

4.3 Patterns and Non-Linear Functions:

Nonlinear: Is a function whose graph is not a line or part of a line. 4.3 Patterns and Non-Linear Functions:. Family of Functions: is a group of functions with common characteristics. . Parent Function: is the simplest function of a family of functions. . GOAL: .

By ponce
(399 views)

Practical aspects of GWAS

Practical aspects of GWAS

Practical aspects of GWAS. Association studies under statistical g enetics and GenABEL hands-on tutorial. Table of contents. Introduction to genetic statistical analysis in GWA Typical study designs / general idea Popular genetic models of inheritance

By bien
(382 views)

Be Rational!

Be Rational!

What is the slope of the line?. Be Rational!. Extreme Exponents and Radical Radicals. ***Please show work to receive credit. Scientific Notation. What is the equation of the line graphed below?. Slippery Slope. What is the equation of the line graphed below?. Balance those Equations.

By koto
(159 views)

PREDICTING PROTEIN SECONDARY STRUCTURE USING ARTIFICIAL NEURAL NETWORKS

PREDICTING PROTEIN SECONDARY STRUCTURE USING ARTIFICIAL NEURAL NETWORKS

PREDICTING PROTEIN SECONDARY STRUCTURE USING ARTIFICIAL NEURAL NETWORKS. Sudhakar Reddy Patrick Shih Chrissy Oriol Lydia Shih. Proteins And Secondary Structure. Sudhakar Reddy. Project Goals. To predict the secondary structure of a protein using artificial neural networks. STRUCTURES.

By cais
(134 views)

Outline

Outline

Outline. Project 1 Hash functions and its application on security Modern cryptographic hash functions and message digest MD5 SHA. GNU Privacy Guard. Yao Zhao. Introduction of GnuPG. GnuPG Stands for GNU Privacy Guard A tool for secure communication and data storage

By anoki
(111 views)

Convergence Study of Message Passing In Arbitrary Continuous Bayesian Networks SPIE 08 @ Orlando

Convergence Study of Message Passing In Arbitrary Continuous Bayesian Networks SPIE 08 @ Orlando

Convergence Study of Message Passing In Arbitrary Continuous Bayesian Networks SPIE 08 @ Orlando. Wei Sun and KC Chang George Mason University wsun@gmu.edu kchang@gmu.edu March 2008. Outline. Bayesian Network & Probabilistic Inference Message Passing Algorithm Review

By tevin
(105 views)

Nonlinear Control:

Nonlinear Control:

Nonlinear Control:.

By nasya
(275 views)

Functions Review

Functions Review

Functions Review. BAAM!.

By tuari
(74 views)

Today we will

Today we will

LECTURE 27. Today we will Put a twist on our normally linear operational amplifier circuits to make them perform nonlinear computations Make a linear circuit model for the nonlinear NMOS transistor (Preview of EE 105) Next time we will

By raisie
(126 views)

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