'Linear transformation' diaporamas de présentation

Eigenvalues and Eigenvectors

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Engineering Analysis

Engineering Analysis. Chapter 4 Linear Transformations. Linear mappings from one vector space to another play an important role in mathematics. This chapter provides an introduction to the theory of such mappings. 4.1 Definition and Examples.

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Temporal Probabilistic Models Pt 2

Temporal Probabilistic Models Pt 2. Agenda. Kalman filtering Dynamic Bayesian Networks Particle filtering. Kalman Filtering. In a nutshell Efficient filtering in continuous state spaces Gaussian transition and observation models

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Raymond J. Carroll Texas A&M University stat.tamu/~carroll carroll@stat.tamu

Non/Semiparametric Regression and Clustered/Longitudinal Data. Raymond J. Carroll Texas A&M University http://stat.tamu.edu/~carroll carroll@stat.tamu.edu Postdoctoral Training Program: http://stat.tamu.edu/B3NC. Palo Duro Canyon, the Grand Canyon of Texas. West Texas ?. East Texas ?.

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MCB- Signal Transduction Lecture 1

MCB- Signal Transduction Lecture 1. General Concepts of Signal Transduction Cell Communication Types of Receptors Molecular Signaling Receptor Binding Scatchard Analysis Competitive Binding Second Messengers. Signaling throughout Evolution. Bacteria Sense nutrients

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Advanced Topics in Standard Setting

Advanced Topics in Standard Setting. Advanced Topics in Standard Setting. Methodology Implementation Validity of standard setting. What is Standard Setting?.

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Introduction to Modern Cryptography Lecture 3

Introduction to Modern Cryptography Lecture 3 (1) Finite Groups, Rings and Fields (2) AES - Advanced Encryption Standard. + ,0, and -a are only notations!. Review - Groups.

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Tutorial: Breakthroughs in 3-D Reconstruction and Motion Analysis

Tutorial: Breakthroughs in 3-D Reconstruction and Motion Analysis. Jana Kosecka (CS, GMU) Yi Ma (ECE, UIUC) Shankar Sastry (EECS, Berkeley) Stefano Soatto (CS, UCLA) Rene Vidal (Berkeley, John Hopkins) Omid Shakernia (EECS, Berkeley). PRIMARY REFERENCE.

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Signal , Weight Vector Spaces and Linear Transformations

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The P C P starting point

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State Estimation and Kalman Filtering

State Estimation and Kalman Filtering. Sensors and Uncertainty. Real world sensors are noisy and suffer from missing data (e.g., occlusions, GPS blackouts) Use sensor models to estimate ground truth, unobserved variables, make forecasts. How does this relate to motion?. A robot must:

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The Biquaternions

The Biquaternions . Renee Russell Kim Kesting Caitlin Hult SPWM 2011. Sir William Rowan Hamilton (1805-1865). Physicist, Astronomer and Mathematician. Contributions to Science and Mathematics:. Optics Classical and Quantum Mechanics Electromagnetism Algebra:

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Hash-Aided Motion Estimation

Hash-Aided Motion Estimation. Alwin Anbu and Argyrios Zymnis. Outline. Motivation Approach to Problem Results Conclusion. Motivation. ME computationally intensive Usually performed at Encoder to reduce Decoder complexity Distributed Video Coding, aimed at reducing encoder complexity

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click to start

click to start. S. PHONE BOOTH. S. PHONE BOOTH. S. S. S. S. S. S. S. S. S. S. PHONE BOOTH. S. TRANSFORMATION. S. Example: A LINEAR TRANSFORMATION. If ( x,y ) is a point on y = x + 2 then. Add the vector . to every vector in the null space and you get

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REFLECTION

REFLECTION. REFLECTION. Find a matrix M such that M =. The reflection of through y = mx. v. v. v. v. w. w. The reflection of through. Reflection is a linear transformation. ?. sin = m. m. ?. 1. cos = 1. y = mx. ?.

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Introduction

Introduction. PCA (Principal Component Analysis) An effective statistical method for reducing dataset dimensions while keeping spatial characteristics as much as possible Applications Image compression Pattern recognition Characteristics: Designed for unlabeled data

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Chapter 7

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PROJECTION

PROJECTION. v. v. u. u. Projection is a linear transformation. The . projection . of onto . The . projection . of onto . is a multiple of. v. u. u. v. The norm of . a vector one unit long that lies on . v. u. v. ?. The length of. u. The . projection .

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Changing the unit of measurement

Changing the unit of measurement . AP Statistics. Linear transformation . A linear transformation changes the original variable x into a new variable given by an equation of the form Adding the constant a shifts all values of x upward or downward by the same amount

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Linear transformation rule

Linear transformation rule. When adding a constant to a random variable, the mean changes but not the standard deviation . When multiplying a constant to a random variable, the mean and the standard deviation changes.

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