ELECTRICAL ENGINEERING DEPARTMENT EE210A: ADAPTATION AND LEARNING (A. H. SAYED)

EE210A: Adaptation and Learning Professor Ali H. Sayed

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MAIN RESULT FROM LAST LECTURE

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LECTURE #06

CONSTRAINED LINEAR ESTIMATION Sections in order: 6.1-6.3

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A MATRIX RESULT

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A MATRIX RESULT

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MOTIVATION

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EXAMPLE

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UNKOWN CONSTANT

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MINIMUM-VARIANCE UNBIASED EST.

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MINIMUM-VARIANCE UNBIASED EST.

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MVUE PROBLEM FORMULATION

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MVUE OR BLUE PROBLEM

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INTERPRETATION

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INTUITION

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GAUSS-MARKOV THEOREM

Carl F. Gauss (1777-1855)

Andrey A. Markov (1856-1922)

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ARGUMENT

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CONSTRAINED OPTIMIZATION

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EXAMPLE

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EXAMPLE

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CHANNEL AND NOISE ESTIMATION

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COLLECTING DATA

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COMPARING ESTIMATORS

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NOISE POWER ESTIMATION

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DECISION-FEEDBACK EQUALIZATION

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CHANNEL AND EQUALIZER MODELS

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DFEs

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EQUALIZER DESIGN

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EQUALIZER DESIGN

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EQUALIZER DESIGN

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EQUALIZER DESIGN

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ASSUMPTIONS

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PROBLEM FORMULATION

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FORWARD FILTER

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BACKWARD FILTER

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BACKWARD FILTER

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CHANNEL MODEL

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CHANNEL MODEL

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CHANNEL MODEL

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CHANNEL MODEL

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CHANNEL MODEL

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CHANNEL MODEL

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EXAMPLE

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EXAMPLE

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EXAMPLE

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COMPUTER PROJECT

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COMPUTER PROJECT

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COMPUTER PROJECT

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COMPUTER PROJECT

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COMPUTER PROJECT

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COMPUTER PROJECT

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COMPUTER PROJECT

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COMPUTER PROJECT

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COMPUTER PROJECT

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COMPUTER PROJECT

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COMPUTER PROJECT

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COMPUTER PROJECT

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COMPUTER PROJECT

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COMPUTER PROJECT

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COMPUTER PROJECT