can you provide clear explanation for the formulas used in adaptive fuzzy k mean clustering algorithm for image segmentation... n how the calculations are done??? ....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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What is a Project?

a project is development of new product or the launch of a new service or a marketing campaign . A project is not something that is a part of our normal business operations. It is typically created once, it is temporary, and it is specific. As one expert notes, It has a beginning and an end. A project consumes resources (whether people, cash, materials, or time), and it has funding limits.

Processes

Traditionally, project management includes a number of elements: four to five process groups, and a co ....etc

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can you please provide me the code for absolute mean brightness error.. ....etc

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Abstract—Mean-Shift (MS) is a powerful nonparametric clustering method. Although good accuracy can be achieved, its computational cost is particularly expensive even on moderate data sets. In this paper, for the purpose of algorithmic speedup, we develop an agglomerative MS clustering method along with its performance analysis. Our method, namely Agglo-MS, is built upon an iterative query set compression mechanism which is motivated by the quadratic bounding optimization nature of MS algorithm. The whole framework can be efficiently implement ....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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presented by:
K.PRATHYUSHA
T.RAJ KUMAR
D.PAPARAO
G.SHIRISHA


Bit plane
A Bit plane consists of the bits corresponding to the same significant level in all the elements
For example, the most significant bit plane is formed by considering the most significant bit(MSB) of each elements
The elements of a gray scale image have a maximum value of 255 and hence can be represented in binary domain using 8 bits
Higher order Bit planes of an image carry a significant amount of visually relevant deta ....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 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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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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