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## The Gradient Function

The Gradient Function I am trying to find a formula that will work out the gradient of any line (the gradient function) I am going to start with the simplest cases, e.g. g=c², g=c, g=c3 etc. as they are probably going to be the easiest equations to solve as they are likely to be less complex and hopefully the formulas to the more complex equations will be easier to discover by looking at the previous formulas. I am going to look at the line g=c² first. g=c² c 1 2 3

## The Gradient Function

The Gradient Function Introduction The gradient of any line is the steepness at which it slopes; on straight lines it can be worked out by drawing a right angled triangle using the line itself as the hypotenuse to find out the âˆ†y, and âˆ†x. The gradient of a line can then be worked out by dividing âˆ†y by âˆ†x. The following graphic shows an example: [IMAGE] However, with a curved graph, the gradient is different at different points. To work out the gradient at a point of a curved

## Gradient Function

Gradient Function For this investigation, I have to find the relationship between a point of any non-linear graph and the gradient of the tangent, which is the gradient function. First of all, I have to define the word, 'Gradient'. Gradient means the slope of a line or a tangent at any point on a curve. A tangent is basically a line, curve, or surface that touches another curve but does not cross or intersect it. To find a gradient, observe the graph below: [IMAGE][IMAGE] All you

## Gradient Function Investigation

Gradient Function Investigation Gradient Function In this investigation I am going to investigate the gradients of the graphs Y=AXN Where A and N are constants. I shall then use the information to find a formula for all curved graphs. To start the investigation I will draw the graphs where A=1 and N= a positive integer. Y=X2 X Height Width Gradient 1 1 0.5 2 2 4 1 4 3 9 1.5 6 4 16 2 8 Looking at the results above I can

## Discovering the Gradient Function

Discovering the Gradient Function The aim of this investigation is to discover the gradient function for the graphs y = ax where a and n are constants. I will do this by beginning with the simplest cases, as I believe that these will be the most simple equations to solve. I am doing this in the hope that discovering the equations for these simple cases will aid me in discovering the more complex formulas. Firstly I will construct the graphs of: y=x, y=2x, y=3x, y=4x. And attempt to

## Gradient Functions

Gradient Functions In the following coursework, will investigate the gradient functions using the formula y=ax^n, where a is a constant and n is a number. a n Y=ax^n 1 1 x 2 1 2x 3 1 3x 4 1 4x 5 1 5x a n Y=ax^n 1 2 x 2 2 4x 3 2 6x 4 2 8x 5 2 10x a n Y=ax^n 1 3 3x^2 2 3 6x^2 3 3 9x^2 4 3 12x^2 5 3 15x^2 a n Y=ax^n 1 4

## Aerodynamic characteristics estimation

in the center of mass (if there are not in the center of mass they have to be calculated [ ] for the center of mass) (1) where: , , and If the aerodynamics coefficients are linear functions only of the aerodynamic parameters (angle of attack, side slip angle and the angular velocities) We suppose that the gradient of the aerodynamic coefficients are constants. It means that the velocity is small (e.g. less than 100 m/s) or that the change of velocity in the time interval ( ) is small enough to

## Corner Detection Are Useful for Computer Vision Applications

other in terms of precision localization, accuracy, speed, and information they provide. Model classification and orientation are the most interest information needed in process of edge tracking. For some of these approaches, the CRF (Corner-Response-Function) can be shown to be invariant in scale, rotation or even afﬁne transformations. Here we review the literature to place our contribution in context. The attempt to simultaneous realization of corner detection and description of its properties is proved

## An Investigation into the Effect of Lipase Concentration on the Hydrolysis Of Fats

Hydrolysis Of Fats Using the data loggers a recording of the pH was taken every 5 seconds and for each experiment the data loggers produced graphs of the change in pH. From each of these graphs a gradient was calculated which showed the rate of pH change per second. Firstly I calculated the gradients by choosing the steepest section of the graph and dividing the change in pH of this section by the time. However this method proved to be quite inaccurate giving very varied results, for example

## The Gradient and Directional Derivative

Introduction: Gradient: In vector calculus, the gradient is considered as vector field in a function.It points to the points in the route of the maximum rate of increase of the scalar field. Its magnitude is the maximum rate of modify. Directional derivative: Directional derivative represents the instantaneous rate of modification of the function. It generalizes the view of a partial derivative. Gradient: The gradient is defined for the function f(x,y) is as gradf(x,y)= [gradf(x,y)]