Hello, i need the matlab code for a least squares channel estimation algorithm? can somebody hepl me? ....etc

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Introduction

Complex system time scale separation
Least cost system operation
Economic dispatch first view
Generator cost characteristics
Four curves of generator performance
Constrained optimization
Linear programming
Economic dispatch completed

Long term system planning: Production cost
Decide what to build
Hourly to monthly decisions: Unit commitment
Decide which plants to have warmed up and ready to go
Different technologies have different requirements
Minutes to Hour: Economic dispatch
Decide which plants ....etc

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LEAST MEAN SQUARE ALGORITHM



Introduction
The Least Mean Square (LMS) algorithm, introduced by Widrow and Hoff in 1959 is an adaptive algorithm, which uses a gradient-based method of steepest decent . LMS algorithm uses the estimates of the gradient vector from the available data. LMS incorporates an iterative procedure that makes successive corrections to the weight vector in the direction of the negative of the gradient vector ....etc

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Least Slack Time ( LST )
scheduling is a scheduling algorithm. It assigns priority based on the slack time of a process. It is also known as Least Laxity First. Its most common use is in embedded systems, especially those with multiple processors. It imposes the simple constraint that each process on each available processor possesses the same run time, and that individual processes do not have an affinity to a certain processor. This is what lends it a suitability to embedded systems. Slack time This scheduling Slack time is defined as the te ....etc

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Abstract
Significant research has been devoted to detecting people
in images and videos. In this paper we describe a human detection
method that augments widely used edge-based features
with texture and color information, providing us with
a much richer descriptor set. This augmentation results
in an extremely high-dimensional feature space (more than
170,000 dimensions). In such high-dimensional spaces,
classical machine learning algorithms such as SVMs are
nearly intractable with respect to training. Furthermore,
the number ....etc

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Least Slack Time ( LST )
Least Slack Time (LST) scheduling is a scheduling algorithm. It assigns priority based on the slack time of a process. It is also known as Least Laxity First. Its most common use is in embedded systems, especially those with multiple processors. It imposes the simple constraint that each process on each available processor possesses the same run time, and that individual processes do not have an affinity to a certain processor. This is what lends it a suitability to embedded systems. Slack time This scheduling algorith ....etc

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1. INTRODUCTION



1.1 INTRODUCTION

Since the rise of the Internet one of the most important factors of information technology and communication has been the security of information. Cryptography was created as a technique for encrypt and decrypt data in order to keep the message secret. Unfortunately it is sometimes not enough to keep the contents of a message secret, it may also be necessary to keep the existence of the message secret. The technique used to implement this, is called steganography.

Steganography is th ....etc

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Clustering technique-based least square support vector machine for EEG signal classification





Introduction

The electroencephalogram (EEG) signal is a measure of the
summed activity of approximately 100 millions of neurons
lying in the vicinity of the recording electrodes . It may provide
insight into the functional structure and dynamics of the
brain . Basically the EEG is complex and aperiodic time
series containing information of the electrical activity g ....etc

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LEAST MEAN SQUARE ALGORITHM

Introduction


The Least Mean Square (LMS) algorithm, introduced by Widrow and Hoff in 1959 is an adaptive algorithm, which uses a gradient-based method of steepest decent . LMS algorithm uses the estimates of the gradient vector from the available data. LMS incorporates an iterative procedure that makes successive corrections to the weight vector in the direction of the negative of the gradient vector which eventually leads to the minimum mean square error. Com ....etc

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