Lab 10 Response

The main difference between Shannon's and Hartley's measures of information is that Shannon takes probability into consideration, while Hartley does not look at the probability at all. Hartley measures the uncertainty by the amount of information needed to get rid of the uncertainty. Hartley's measure also uses the logarithms of a base two. Shannon's measure of uncertainty is measured by the amount of average information needed to remove the uncertainty. Shannon's measure also involves weighted alternatives. Shannon's measure also involves the logarithms with a base of two, but in the Shannon equation he incorporates a sum value.

Lab 10: Excel Flowchart

Lab 9: Excel Response

During this lab I learned a lot about Excel and its functions. I thought that this lab was very helpful for further times when I might need to use Excel. I liked working with the chart wizard to make the charts a lot easier to make and edit.

I felt that the equations on m, b, and r were very confusing from what they said on the lab instructions. I felt that the equation for the m value was easy enough, but the b and r ones werent as familiar to me. I felt that the data analysis tool was very easy to use and helpful as a whole. After using the data analysis tool I didnt understand why we had to calculate the m,b, and r values.

After doing this I felt like using the chart wizard was very familiar to me. I felt like after I used the data analysis tool I didnt really understand what all the values stood for, except for the m, b, and r values. After completing this lab I feel that I am pretty comfortable with modeling the data that was imported from the text file, or with any type of data given.

Microsoft Excel Histogram

Lab 7: Logic Gates

For this simple logic gate everytime you would put in a "0" for both values "A" and "B" you would always get a 0 for the output. As long as both of the input values were both 1 or 0, you would get a 0 for the output. When the two inputs are different values, you get a 1 for the output.

A	B	Output 1	Output 2
0	0	1	1
0	1	1	1
1	0	1	1
1	1	0	0

In the second example for the table to the left, it shows that even though both of the two input switches went through different logic gates, they both had the exact same outputs. This table also shows that De Morgan's Law is true based on the values given and the way the logic gates worked in the above screenshot.

Binary to Decimal and Decimal to Binary, and the difference between positional and non-positional

Binary to Decimal:

1. First write out the binary number and then put the power of 2's under that number in ascending order from left to right. For example: the binary number 1001001 on top and have the power of 2's under it like: 2^1, 2^2, ... and so on.
   

2. Then under every 1 that is in the number calculate what that power of two equals. For example: a 1 has 2^3 under it so it equals 8. Do not do anything to the zeros because they don't matter here.

3. Once you get all of the numbers calculated under the 1's spots add up those numbers to get that total, which equals that number in decimal.

So for the number given in class: 110010101 by doing this process you get 405 in decimal.

Decimal to Binary:

1. First write down the number in decimal. Next, find a power of 2 that does not exceed the number given in class, 529. For example the value of 2^9= 512 so it fits into your original number without exceeding it.
   
2. Once you do that you will get a remainder and you write that down up next to the original number to keep track of it. Next, you want to do the same thing that you did with your original number, find a power of 2 that fits into the remainder that you just got and write that power of 2 under the remainder.
   
3. You do this process until you get a remainder of 1, which will make the only power of 2 that will fit into it 2^0, which is 1.
   So the first number in the binary number is 1.
4. You then skip the power 2^4 since isn't a 1. So you go over past the second, third, and fourth spot. In the fifth place you put a 1. So the number so far would be 10001.
5. You continue like this for the last power of 2, which is 2^9. And then you go over the same amount of spaces left to right and the final number in binary is 100010001.

Positional Compared to Non-Positional Number Systems:

A non-positional number system is that which does not rely on the position of a digit in a number, but it uses symbols to tell what the values are. For example, if a square stood for ten and a circle stood for one-hundred. If there was a square in front of a circle it would be 110, if the two symbols were switched around and the circle was in front of the square, it would still equal the same number of 110, even though they were switched around.

A positional number system is the same number system that we use everyday. It means that the position of the digits tell the value of the number shown. For example if you had 345, it means three-hundred and forty five. If you were to switch the numbers around to make it look like 543, then the number would be five-hundred and forty three instead, because the position of the digits determines the value of the whole number unlike the symbols in the non-positional number system.

Global Swarming Response

In the article on Global Swarming, the beginning was very interesting on how he talked about how Amazon's website has the "viewers who bought this item also liked these items". It is interesting how Amazon uses its "collaborative filtering" to show buyers other items that are somewhat related to the item they are looking at. This technique helps the buyer find new or related music for example that they have never thought of buying. Another interesting aspect of the article was the idea of "swarm intelligence". It makes sense and is a very intersting idea to look at. It really has a lot to do with the collaborative filtering as talked about in the article earlier. I did not realize that the internet search engines used this collaborative filtering to come up with web pages. The chapter was very easy to understand and made a lot of good examples towards the subject of swarm intelligence. It made you think a lot of how the internet search engines work and get their information to the user on the web.