An Affine Combination of two LMS adaptive filters transient mean square analysis

Abstract:
The design of many adaptive filters requires a tradeoff between convergence speed and steady-state mean-square error (MSE). A faster (slower) convergence speed yields a larger (smaller) steady-state mean-square deviation (MSD) and MSE. This property is usually independent of the type of adaptive algorithm, i.e., least mean-square (LMS), normalized least
Mean-square (NLMS), recursive least squares (RLS), or affine projection (AP).
This design tradeo ....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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Grayscale Image Retrieval using DCT on Row mean,
Column mean and Combination



Abstract

Today in the age of information explosion, how to search appropriate data from huge information
pool has become vital issue. Images have giant share in this information pool. Because of easy
availability of imaging devices, millions of images are being added to image pool every day. Image
retrieval deals with searching relevant images from large image database. The paper presents novel
image retrieval techniques bas ....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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Jet impingement heat transfer – Part I: Mean and root-mean-square heat transfer and velocity distributions
Abstract
Impinging jets provide a means of achieving high heat transfer coefficients both locally and on an area averaged basis. The currentwork forms the first stage of a two part investigation of heat transfer distributions from a heated flat surface subject to an impinging airjet for Reynolds numbers from 10,000 to 30,000 and non-dimensional surface to jet exit spacing, H/D, from 0.5 to 8. In the present paper,the rel ....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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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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Hello, i need the matlab code for a least squares channel estimation algorithm? can somebody hepl me? ....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 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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