Open course ware

PROFESSOR: --we'll start off with a little review, so you might chance it. We're in the middle of the chapter nine on convolutional codes. Today, I intend to hand out problem set six. I believe Ashish will have it when he gets here. And we'll hand it out at the end. Last time, I introduced you to a lot of things about convolutional codes and their encoders. I stressed that a convolutional encoder had two distinct characters. One is it's a linear time-invariant system. So we can use the linearity and time-invariance and algebraic analysis of convolutional codes. Secondly, I stressed that's it's a finite state system, because it's a finite memory system where each memory element has a finite number of possible values, over F2 or indeed over any finite field. And therefore, we can use that for different kinds of analysis. We use that, for instance, in conjunction with linear property to find the minimum distance to a particular code. And we'll...

We left off last Wednesday, last week Wednesday. So it's been awhile. So I wanted just to pick up where we left off. And then cover some new things today. But where we left off was with this diagram, OK? And, you know, I kind of went through it. And I went through it sort of quickly. But I want to make sure you understand it very well. Because this is such an important kind of diagram. And we'll see it again. And you may see it again in other classes. This is a diagram of attraction and repulsion coming together in a balance to form a bond. So, you know, last Wednesday, just to remind you, we made our first bond. All right, we made the ionic bond. And I showed you how that worked. Because, in this case, the attraction, the force of attraction is the Coulomb potential, right? Remember that goes as minus a constant times-- I'll just write it down because why not. You're using it in your goody bag. All right, so the Coulomb energy here goes as minus some cons...