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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Name– study of Schmitt trigger inverter circuit.
Aim – to study the working of Schmitt trigger inverter as square wave generator.
Apparatus – IC 7414, circuit board, power supply +5V DC, CRO, resistors, capacitors, connecting wires, soldering iron, cutter etc.
Circuit diagram –


Procedure –
1) You are given IC 7414. Identify the pin numbers of the IC.
2) Write the expression for the period of output waveforms. Calculate the values of R1 & C1 for three diff ....etc

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I am Neha Agarwal.I want the details of seminar on Magic Square. ....etc

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I want detailed operation of square wave generator using IC 741 ....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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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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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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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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I asked u yesterday also for this topic but no response from your side ....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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