Skip to main content

Calculus III - 3-Dimensional Space: Equations of Lines

In this section we need to take a look at the equation of a line in  R 3 R 3 . As we saw in the previous section the equation  y = m x + b y = m x + b  does not describe a line in  R 3 R 3 , instead it describes a plane. This doesn’t mean however that we can’t write down an equation for a line in 3-D space. We’re just going to need a new way of writing down the equation of a curve. So, before we get into the equations of lines we first need to briefly look at vector functions. We’re going to take a more in depth look at vector functions later. At this point all that we need to worry about is notational issues and how they can be used to give the equation of a curve. The best way to get an idea of what a vector function is and what its graph looks like is to look at an example. So, consider the following vector function. → r ( t ) = ⟨ t , 1 ⟩ r → ( t ) = ⟨ t , 1 ⟩ A vector function is a function that takes one or more variables, one in this case, and returns a...

Differential Equations - Fourier Series: Sine - ii


Example 1 Find the Fourier sine series for f(x)=x on LxL.

First note that the function we’re working with is in fact an odd function and so this is something we can do. There really isn’t much to do here other than to compute the coefficients for f(x)=x.
Here is that work and note that we’re going to leave the integration by parts details to you to verify. Don’t forget that nL, and π are constants!
Bn=2L0Lxsin(nπxL)dx=2L(Ln2π2)(Lsin(nπxL)nπxcos(nπxL))|0L=2n2π2(Lsin(nπ)nπLcos(nπ))These integrals can, on occasion, be somewhat messy especially when we use a general L for the endpoints of the interval instead of a specific number.
Now, taking advantage of the fact that n is an integer we know that sin(nπ)=0 and that cos(nπ)=(1)n. We therefore have,
Bn=2n2π2(nπL(1)n)=(1)n+12Lnπn=1,2,3The Fourier sine series is then,
x=n=1(1)n+12Lnπsin(nπxL)=2Lπn=1(1)n+1nsin(nπxL)
At this point we should probably point out that we’ll be doing most, if not all, of our work here on a general interval (LxL or 0xL) instead of intervals with specific numbers for the endpoints. There are a couple of reasons for this. First, it gives a much more general formula that will work for any interval of that form which is always nice. Secondly, when we run into this kind of work in the next chapter it will also be on general intervals so we may as well get used to them now.

Now, finding the Fourier sine series of an odd function is fine and good but what if, for some reason, we wanted to find the Fourier sine series for a function that is not odd? To see how to do this we’re going to have to make a change. The above work was done on the interval LxL. In the case of a function that is not odd we’ll be working on the interval 0xL. The reason for this will be made apparent in a bit.
So, we are now going to do is to try to find a series representation for f(x) on the interval 0xL that is in the form,
n=1Bnsin(nπxL)

or in other words,
f(x)=n=1Bnsin(nπxL)
As we did with the Fourier sine series on LxL we are going to assume that the series will in fact converge to f(x) and we’ll hold off discussing the convergence of the series for a later section.
There are two methods of generating formulas for the coefficients, Bn, although we’ll see in a bit that they really the same way, just looked at from different perspectives.
The first method is to just ignore the fact that f(x) is not odd and proceed in the same manner that we did above only this time we’ll take advantage of the fact that we proved in the previous section that {sin(nπxL)}n=1 also forms an orthogonal set on 0xL and that,
0Lsin(nπxL)sin(mπxL)dx={L2if n=m0if nm
So, if we do this then all we need to do is multiply both sides of our series by sin(mπxL), integrate from 0 to L and interchange the integral and series to get,
0Lf(x)sin(mπxL)dx=n=1Bn0Lsin(nπxL)sin(mπxL)dx
Now, plugging in for the integral we arrive at,
0Lf(x)sin(mπxL)dx=Bm(L2)
Upon solving for the coefficient we arrive at,
Bm=2L0Lf(x)sin(mπxL)dxm=1,2,3,
Note that this is identical to the second form of the coefficients that we arrived at above by assuming f(x) was odd and working on the interval LxL. The fact that we arrived at essentially the same coefficients is not actually all the surprising as we’ll see once we’ve looked the second method of generating the coefficients.
Before we look at the second method of generating the coefficients we need to take a brief look at another concept. Given a function, f(x), we define the odd extension of f(x) to be the new function,
g(x)={f(x)if 0xLf(x)if Lx0
It’s pretty easy to see that this is an odd function.
g(x)=f((x))=f(x)=g(x)for 0<x<L
and we can also know that on 0xL we have that g(x)=f(x). Also note that if f(x) is already an odd function then we in fact get g(x)=f(x) on LxL.
Let’s take a quick look at a couple of odd extensions before we proceed any further.


Example 2 Sketch the odd extension of each of the given functions.
  1. f(x)=Lx on 0xL
  2. f(x)=1+x2 on 0xL
  3. f(x)={L2if 0xL2xL2if L2xL


Not much to do with these other than to define the odd extension and then sketch it.

a) f(x)=Lx on 0xL 
Here is the odd extension of this function.
g(x)={f(x)if 0xLf(x)if Lx0={Lxif 0xLLxif Lx0Below is the graph of both the function and its odd extension. Note that we’ve put the “extension” in with a dashed line to make it clear the portion of the function that is being added to allow us to get the odd extension.

b) f(x)=1+x2 on 0xL  
First note that this is clearly an even function. That does not however mean that we can’t define the odd extension for it. The odd extension for this function is,
g(x)={f(x)if 0xLf(x)if Lx0={1+x2if 0xL1x2if Lx0The sketch of the original function and its odd extension are ,

c) f(x)={L2if 0xL2xL2if L2xL 
Let’s first write down the odd extension for this function.
g(x)={f(x)if 0xLf(x)if Lx0={xL2if L2xLL2if 0xL2L2if L2x0x+L2if LxL2The sketch of the original function and its odd extension are,
With the definition of the odd extension (and a couple of examples) out of the way we can now take a look at the second method for getting formulas for the coefficients of the Fourier sine series for a function f(x) on 0xL. First, given such a function define its odd extension as above. At this point, because g(x) is an odd function, we know that on LxL the Fourier sine series for g(x) (and NOT f(x) yet) is,
g(x)=n=1Bnsin(nπxL)Bn=2L0Lg(x)sin(nπxL)dxn=1,2,3,
However, because we know that g(x)=f(x) on 0xL we can also see that as long as we are on 0xL we have,
f(x)=n=1Bnsin(nπxL)Bn=2L0Lf(x)sin(nπxL)dxn=1,2,3,
So, exactly the same formula for the coefficients regardless of how we arrived at the formula and the second method justifies why they are the same here as they were when we derived them for the Fourier sine series for an odd function.
Now, let’s find the Fourier sine series for each of the functions that we looked at in Example 2.

Note that again we are working on general intervals here instead of specific numbers for the right endpoint to get a more general formula for any interval of this form and because again this is the kind of work we’ll be doing in the next chapter.
Also, we’ll again be leaving the actually integration details up to you to verify. In most cases it will involve some fairly simple integration by parts complicated by all the constants (nLπ, etc.) that show up in the integral.

Comments

Popular posts from this blog

Digital Signal Processing - Basic Continuous Time Signals

To test a system, generally, standard or basic signals are used. These signals are the basic building blocks for many complex signals. Hence, they play a very important role in the study of signals and systems. Unit Impulse or Delta Function A signal, which satisfies the condition,   δ ( t ) = lim ϵ → ∞ x ( t ) δ ( t ) = lim ϵ → ∞ x ( t )   is known as unit impulse signal. This signal tends to infinity when t = 0 and tends to zero when t ≠ 0 such that the area under its curve is always equals to one. The delta function has zero amplitude everywhere except at t = 0. Properties of Unit Impulse Signal δ(t) is an even signal. δ(t) is an example of neither energy nor power (NENP) signal. Area of unit impulse signal can be written as; A = ∫ ∞ − ∞ δ ( t ) d t = ∫ ∞ − ∞ lim ϵ → 0 x ( t ) d t = lim ϵ → 0 ∫ ∞ − ∞ [ x ( t ) d t ] = 1 Weight or strength of the signal can be written as; y ( t ) = A δ ( t ) y ( t ) = A δ ( t ) Area of the weighted impulse s...

Differential Equations - First Order: Bernoulli

In this section we are going to take a look at differential equations in the form, y ′ + p ( x ) y = q ( x ) y n y ′ + p ( x ) y = q ( x ) y n where  p ( x ) p ( x )  and  q ( x ) q ( x )  are continuous functions on the interval we’re working on and  n n  is a real number. Differential equations in this form are called  Bernoulli Equations . First notice that if  n = 0 n = 0  or  n = 1 n = 1  then the equation is linear and we already know how to solve it in these cases. Therefore, in this section we’re going to be looking at solutions for values of  n n  other than these two. In order to solve these we’ll first divide the differential equation by  y n y n  to get, y − n y ′ + p ( x ) y 1 − n = q ( x ) y − n y ′ + p ( x ) y 1 − n = q ( x ) We are now going to use the substitution  v = y 1 − n v = y 1 − n  to convert this into a differential equation in terms of  v v . As we’ll see th...

Differential Equations - Systems: Solutions

Now that we’ve got some of the basics out of the way for systems of differential equations it’s time to start thinking about how to solve a system of differential equations. We will start with the homogeneous system written in matrix form, → x ′ = A → x (1) (1) x → ′ = A x → where,  A A  is an  n × n n × n  matrix and  → x x →  is a vector whose components are the unknown functions in the system. Now, if we start with  n = 1 n = 1 then the system reduces to a fairly simple linear (or separable) first order differential equation. x ′ = a x x ′ = a x and this has the following solution, x ( t ) = c e a t x ( t ) = c e a t So, let’s use this as a guide and for a general  n n  let’s see if → x ( t ) = → η e r t (2) (2) x → ( t ) = η → e r t will be a solution. Note that the only real difference here is that we let the constant in front of the exponential be a vector. All we need to do then is plug this into the d...

Calculus III - 3-Dimensional Space: Equations of Lines

In this section we need to take a look at the equation of a line in  R 3 R 3 . As we saw in the previous section the equation  y = m x + b y = m x + b  does not describe a line in  R 3 R 3 , instead it describes a plane. This doesn’t mean however that we can’t write down an equation for a line in 3-D space. We’re just going to need a new way of writing down the equation of a curve. So, before we get into the equations of lines we first need to briefly look at vector functions. We’re going to take a more in depth look at vector functions later. At this point all that we need to worry about is notational issues and how they can be used to give the equation of a curve. The best way to get an idea of what a vector function is and what its graph looks like is to look at an example. So, consider the following vector function. → r ( t ) = ⟨ t , 1 ⟩ r → ( t ) = ⟨ t , 1 ⟩ A vector function is a function that takes one or more variables, one in this case, and returns a...

Differential Equations - First Order: Modeling - i

We now move into one of the main applications of differential equations both in this class and in general. Modeling is the process of writing a differential equation to describe a physical situation. Almost all of the differential equations that you will use in your job (for the engineers out there in the audience) are there because somebody, at some time, modeled a situation to come up with the differential equation that you are using. This section is not intended to completely teach you how to go about modeling all physical situations. A whole course could be devoted to the subject of modeling and still not cover everything! This section is designed to introduce you to the process of modeling and show you what is involved in modeling. We will look at three different situations in this section : Mixing Problems, Population Problems, and Falling Objects. In all of these situations we will be forced to make assumptions that do not accurately depict reality in most cases, but wi...

Digital Signal Processing - Miscellaneous Signals

There are other signals, which are a result of operation performed on them. Some common type of signals are discussed below. Conjugate Signals Signals, which satisfies the condition  x ( t ) = x ∗ ( − t ) are called conjugate signals. Let  x ( t ) = a ( t ) + j b ( t ) So,  x ( − t ) = a ( − t ) + j b ( − t ) And  x ∗ ( − t ) = a ( − t ) − j b ( − t ) By Condition,  x ( t ) = x ∗ ( − t ) If we compare both the derived equations 1 and 2, we can see that the real part is even, whereas the imaginary part is odd. This is the condition for a signal to be a conjugate type. Conjugate Anti-Symmetric Signals Signals, which satisfy the condition  x ( t ) = − x ∗ ( − t ) are called conjugate anti-symmetric signal Let  x ( t ) = a ( t ) + j b ( t ) So  x ( − t ) = a ( − t ) + j b ( − t ) And  x ∗ ( − t ) = a ( − t ) − j b ( − t ) − x ∗ ( − t ) = − a ( − t ) + j b ( − t ) By Condition  x ( t ) = − x ∗ ( − t ) ...

Differential Equations - Systems: Repeated Eigenvalues - i

This is the final case that we need to take a look at. In this section we are going to look at solutions to the system, → x ′ = A → x x → ′ = A x → where the eigenvalues are repeated eigenvalues. Since we are going to be working with systems in which  A A  is a  2 × 2 2 × 2  matrix we will make that assumption from the start. So, the system will have a double eigenvalue,  λ λ . This presents us with a problem. We want two linearly independent solutions so that we can form a general solution. However, with a double eigenvalue we will have only one, → x 1 = → η e λ t x → 1 = η → e λ t So, we need to come up with a second solution. Recall that when we looked at the double root case with the second order differential equations we ran into a similar problem. In that section we simply added a  t t  to the solution and were able to get a second solution. Let’s see if the same thing will work in this case as well. We’ll see if → x = t e...

Differential Equations - Systems: Repeated Eigenvalues - ii

Example 3  Solve the following IVP. → x ′ = ( − 1 3 2 − 1 6 − 2 ) → x → x ( 2 ) = ( 1 0 ) x → ′ = ( − 1 3 2 − 1 6 − 2 ) x → x → ( 2 ) = ( 1 0 ) First the eigenvalue for the system. det ( A − λ I ) = ∣ ∣ ∣ ∣ − 1 − λ 3 2 − 1 6 − 2 − λ ∣ ∣ ∣ ∣ = λ 2 + 3 λ + 9 4 = ( λ + 3 2 ) 2 ⇒ λ 1 , 2 = − 3 2 det ( A − λ I ) = | − 1 − λ 3 2 − 1 6 − 2 − λ | = λ 2 + 3 λ + 9 4 = ( λ + 3 2 ) 2 ⇒ λ 1 , 2 = − 3 2 Now let’s get the eigenvector. ( 1 2 3 2 − 1 6 − 1 2 ) ( η 1 η 2 ) = ( 0 0 ) ⇒ 1 2 η 1 + 3 2 η 2 = 0 η 1 = − 3 η 2 ( 1 2 3 2 − 1 6 − 1 2 ) ( η 1 η 2 ) = ( 0 0 ) ⇒ 1 2 η 1 + 3 2 η 2 = 0 η 1 = − 3 η 2 → η = ( − 3 η 2 η 2 ) η 2 ≠ 0 → η ( 1 ) = ( − 3 1 ) η 2 = 1 η → = ( − 3 η 2 η 2 ) η 2 ≠ 0 η → ( 1 ) = ( − 3 1 ) η 2 = 1 Now find  → ρ ρ → , ( 1 2 3 2 − 1 6 − 1 2 ) ( ρ 1 ρ 2 ) = ( − 3 1 ) ⇒ 1 2 ρ 1 + 3 2 ρ 2 = − 3 ρ 1 = − 6 − 3 ρ 2 ( 1 2 3 2 − 1 6 − 1 2 ) ( ρ 1 ρ 2 ) = ( − 3 1 ) ⇒ 1 2 ρ 1 + 3 2 ρ 2 = − 3 ρ 1 = − 6 − 3 ρ 2 → ρ = ( − 6 − 3 ρ 2 ρ 2 ) ⇒ → ρ = ( − 6 0 ) if  ρ 2 = 0 ρ → ...

Differential Equations - Second Order: Repeated Roots

In this section we will be looking at the last case for the constant coefficient, linear, homogeneous second order differential equations. In this case we want solutions to a y ′′ + b y ′ + c y = 0 a y ″ + b y ′ + c y = 0 where solutions to the characteristic equation a r 2 + b r + c = 0 a r 2 + b r + c = 0 are double roots  r 1 = r 2 = r r 1 = r 2 = r . This leads to a problem however. Recall that the solutions are y 1 ( t ) = e r 1 t = e r t y 2 ( t ) = e r 2 t = e r t y 1 ( t ) = e r 1 t = e r t y 2 ( t ) = e r 2 t = e r t These are the same solution and will NOT be “nice enough” to form a general solution. We do promise that we’ll define “nice enough” eventually! So, we can use the first solution, but we’re going to need a second solution. Before finding this second solution let’s take a little side trip. The reason for the side trip will be clear eventually. From the quadratic formula we know that the roots to the characteristic equation are, r 1 , 2 = ...

Differential Equations - Laplace Transforms: Table

f ( t ) = L − 1 { F ( s ) } f ( t ) = L − 1 { F ( s ) } F ( s ) = L { f ( t ) } F ( s ) = L { f ( t ) }  1 1 s 1 s e a t e a t 1 s − a 1 s − a t n , n = 1 , 2 , 3 , … t n , n = 1 , 2 , 3 , … n ! s n + 1 n ! s n + 1 t p t p ,  p > − 1 p > − 1 Γ ( p + 1 ) s p + 1 Γ ( p + 1 ) s p + 1 √ t t √ π 2 s 3 2 π 2 s 3 2 t n − 1 2 , n = 1 , 2 , 3 , … t n − 1 2 , n = 1 , 2 , 3 , … 1 ⋅ 3 ⋅ 5 ⋯ ( 2 n − 1 ) √ π 2 n s n + 1 2 1 ⋅ 3 ⋅ 5 ⋯ ( 2 n − 1 ) π 2 n s n + 1 2 sin ( a t ) sin ⁡ ( a t ) a s 2 + a 2 a s 2 + a 2 cos ( a t ) cos ⁡ ( a t ) s s 2 + a 2 s s 2 + a 2 t sin ( a t ) t sin ⁡ ( a t ) 2 a s ( s 2 + a 2 ) 2 2 a s ( s 2 + a 2 ) 2 t cos ( a t ) t cos ⁡ ( a t ) s 2 − a 2 ( s 2 + a 2 ) 2 s 2 − a 2 ( s 2 + a 2 ) 2 sin ( a t ) − a t cos ( a t ) sin ⁡ ( a t ) − a t cos ⁡ ( a t ) 2 a 3 ( s 2 + a 2 ) 2 2 a 3 ( s 2 + a 2 ) 2 sin ( a t ) + a t cos ( a t ) sin ⁡ ( a t ) + a t cos ⁡ ( a t ) 2 a s 2 ( s 2 + a 2 ) 2 2 a s 2 ( s 2 + a 2 ) 2 cos ( a t ) − a t sin ( a t ) cos ⁡ (...