LAB10

Shannon's entropy factors in probability while Hartley does not. Shannon's entropy is basically asking how many yes or no questions does it take to get to the root of the question. It factors in probability unlike Hartley's, though through some research on wikipedia I found that the two are very closely related and have joint theorems (entitled the Shannon-Hartley theorem) which ironically is what I've been studying in my Physics of Sound course, as it relates to the Nyquist theorem, which states how much information could travel through a telegram.

What I Learned...

There wasn't a whole lot that I learned about linear regression. I have always been a math sort of guy. The first time I played around with linear regression was my freshmen year of high school (algebra 2/trig). This is where I learned that linear regression is a method of seeing "how correlated" the data between the x and y data points, or the dependent and independent variables. There is a function on any TI-83 calculator which will run linear regression for you, including the r and r^2 values, which is how I originally performed the task. When graphing data points the data will rarely line up perfectly linearly. So linear regression tell you the best fit line that runs through the data (aka the slope, y-intercept, and the r value which is basically a percentage fit). R comes out say as .94 which means that the data points match a line with 94% accuracy or fit to the line.
What I did learn from this lab was a lot of about excel . I had never used excel before at least to this extent. It is extremely useful for doing many calculations at once, and I'm sure I'll being using it more in the future solely for convenience. It's very interesting seeing the relationship between two variables, and then having a relatively simple tool and set of equations that will tell you how likely it is that the data relates to each other, because the charts are basically scatter plots, but if the data relates then it will form a positive or negative relationship. It doesn't have to be linear either, there is of course every type of regression such as quadratic regression, exponential regression (probably the most useful out of any of them, in my experiences), and linear regression. Another very useful tool aside from showing there is a relationship is predicting future behavior because of it. If linear regression shows strong correlation you can predict fairly accurately population growths or bacteria growths (probably exponential regression though). For the example in the lab with shoe size in relation to height, you could fairly accurately predict how big the foot of someone who is, say, 7 feet tall.
However, as with any inductive modeling you can never be for certain, past events do not always relate to future events with perfect accuracy, which is why science is full of theories and not facts.

Screen Shot of Cars Traveled

Task3

Second Truth Table

A B O1 O2

0 0 1 1

0 1 1 1

1 0 1 1

1 1 0 0

De Morgan’s law is proved true because both the end results are the same, which is true when both are false or when both are true.

Task2

First Truth Table

A B OUTPUT

0 0 1

0 1 0

1 0 0

1 1 1

Conclusion: The circuit always outputs a "true" or positive result (or a "1") when A and B are both the same (0 or 1).

Binary Conversions

First, to convert the decimal number 529 to decimal you continually divide the number by 2, and if there is a remainder after each division it represents a "1". The numbers which divide evenly will thus represent a "0" in the binary representation. This is better explained just by showing it, so here is the conversion for 529. (R=remainder). Notice how the right side forms the binary code which is read from bottom up.
529
529/2 = 264 R 1
264/2 = 132 R 0
132/2 = 66 R 0
66/2 = 33 R 0
33/2 = 16 R 1
16/2 = 8 R 0
8/2 = 4 R 0
4/2 = 2 R 0
2/2 = 1 R 0
1/2 = 0 R 1

Thus, the binary code for 529 is 1000010001.


To convert from decimal to binary is a bit more complicated in my opinion, but not very difficult at all once the concept is understood. The binary 110010101 is represented in decimal by multiplying (from right to left) the binary number times 2 to the power of the n-1. For example, there are 9 numbers in this sequence. The numbers are then added together to get the final decimal number. So the first number (the "1" all the way to the right) is multiplied by 2^0. (n=1, so n-1=0). Likewise, the last number "1" (all the way to the left) is multiplied by 2^8 (n=9, so n-1=8), or 258. Below is the long-hand math done for the binary of 110010101.

1*2^0 = 1
0*2^1 = 0
1*2^2 = 4
0*2^3 = 0
1*2^4 = 16
0*2^5 = 0
0*2^6 = 0
1*2^7 = 128
1*2^8 = 258

So, the decimal of 110010101 is (1+0+4+0+16+0+0+128+258) = 405



The difference between a positional and non-positional number system is relatively simple. Positional numbers are numbers which contain a base, normally 10 in the English number system. Which means that there is a specific set of symbols (in our case, 0-9) which, depending on the placement, makes the value greater or lower. For example, we have "places" for each number, "5" alone represents a much lower number than "500" simply because the 5 is in the "hundreds" place in this case.
Non-positional numbers are complex in that you have to understand what each symbol represents in relation to the numbers next to it. There is not a constant multiplier and the position does give the value an increase or decrease. The Roman Numeral System works on this concept. For example XIII is equal to 13. The position the X is in does not give it a specific value other than what it represents which is a 10. The "I's" following only represent 1s.

Unix MSDos

I actually remember typing in commands as a kid to get to the games I wanted to play. I would have to type a very exact code in order for the game to come up; obviously before the day of icons. Some important Unix codes I learned that also have Windows/DOS commands.

1) cd is an important command which changes the directory you are currently in and can also move the directory to a folder is /directory is added after it. The MS-DOS command is the same for this particular commadn
2)rm (filename) is used to delete files on the Unix system, the MS-DOS counterpart is listed as del(file name)
3) "who" is used to check who is on the same system you are. Since Unix is an open network of computers which can share and transfer files, one of the first forms of the "internet." who allows me to actually see who is on the same unix system that I am. When I checked this another completely random person appeared, who was not even near the workstation I was at.
4)cal is another useful little feature in which you can view any calender for any year by typing cal (month in #) (year). The MS-DOS version is the same for this I believe, except that you can type out the month such as February instead of "2"

Global Swarming

When I read this article I was immediately reminded of many of the things I had learned last semester in a telecommunications course entitled "Living in the Information Age", in which we studied terms such as data smog, which that we are living in an age where we have such an enormous access to information that it's nearly impossible to get a clear source of information. For example, when you search nearly any topic on Google you will get millions of hits or possibilities of sites for your searching. Andy Clark's article deals with this towards the end of the chapter, the sorting and collecting of this data into some coherent form. While any effort towards organizing the internet is enormous, the best solution (in my opinion) would be to sort out the sites before even typing anything in on Google. If the websites were sorted before you even searched it would allow for more relevant or reliable results. I remember when I use to play computers games like Simcity and the Sims, I never realized that it actually benefited in my understanding of the complex systems that we all use in multiple forms every single day.
In dealing with the first part of the article, I noticed many references to Google and its ability to sort out what you are searching and then record this data for others to use if relevant.Google does solely do this for our benefit however. Google is a master of targeting audiences. First, Google allows users to customize a front page called MyGoogle. This directly allows Google and other businesses to know what any person's interests are, and advertise accordingly. Google grosses eight billion a year on advertisements alone, so they must be doing something right...or wrong.
I greatly enjoy Andy Clark's writing style, it is descriptive but relatively to the point. The analogy of the slug was my particular favorite. It was very logical and allowed me to grasp the concept of data tracking well. Amazon.com is a key example of this data tracking. Nearly everything we do on this earth is now somehow recorded, especially when shopping online. Online companies such as Amazon and Ebay keep track of everything I buy on their websites. Because of this, they are able to compose a seller profile. This profile works by collecting data from everything a consumer buys, then putting together more possible things they would like to buy based on their previous purchases. We can easily note this through the "People who bought this also bought..." an obvious sign of the data tracking of Amazon and other companies.
This relates to the snails in that we follow other people's tracks in order to make our own decision. I think this is one of the main dangers that is only touched on in Chapter 6. We are no longer independent in nearly anything online. We need others opinions, just as the reference to the "worn thumbed pages" in Clark's article. It seems rather ridiculous that we would need a concept like this online. If we are always looking for those worn thumbed pages we may never have original ideas, instead we will in some form plagiarize others writing. I enjoy the internet because everything is left open for my interpretation, if I don't like what something says I can easily find another source that contradicts it. I've also heard of the interactive paper, which is could be a useful tool...but also does not seem very necessary in the days of laptop computers and palm pilots.

Modeling the World

This section of the lecture was particularly interesting to me, but at the same time a little bit confusing. It was one of those things where I perfectly understood the example of how to model a plant based on how the individual sections grow, but when asked to give my own model of something I wouldn't know where to start. The basis of the idea is just the scientific process where you theorize, test, conclude, and repeat. It's kind of like that Thomas Edison quote that goes something like, "I did not fail 10,000 times, I just found 10,000 ways not to make a light bulb." (or something along those lines). Modeling the world is much the same way, it's very much a guess and check sort of approach to science.
I remember in high school learning about the Fibonacci sequence. I actually used it to figure out the probability of the Plinko game in the Price Is Right. I was fascinated by the usefulness of such a universal and simple set of numbers. I also remember learning about another number or ratio called "the Golden Ratio" that, when worked out, equals around 1.618 which then can be applied to a multitude of things in nature, such as the length of a segment of an appendage in comparison to the next. Many Renaissance artists used this ratio in their art because it was aesthetically pleasing just as the Fibonacci sequence is to the ear.

Technologies to Bond With

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Andy Clark's article was the most interesting read thus far, and definitely kept my attention through his descriptive language and immense knowledge on the subject of the possible repercussions of technology.

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In chapter 4 of his book, he discusses the difference between opaque and transparent technologies. Below are my summarized definitions of each.

Opaque Technology

1. Technology in which we are constantly aware of whether through such things as the immobility of the item or the complexity in trying to use the item.
   • For example, computers are often considered opaque due to the constant updates, crashing, and unease of most operations.

Transparent Technology

1. Transparent technology is when we become so accustomed to having the technology that it is completely unnoticeable that we ever are using it. It has become part of us in some way.
   
   
   • An example of this would be, as Andy Clark describes, a wristwatch. If someone asks what time it is we do not say, "No, but I can find out." Instead, we say, "Yes," and then look down to find what the time is.

Note: Certain things can switch from opaque to transparent depending the user. For example, a guitar is opaque to the beginner but often considered transparent to the experienced musician.

Infoport Link

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Library of Babel Response

While a rather confusing read, there are some central points that I was able to get from this reading. This library is of near infinite size, and because of that, there is information on everything on earth. But because of this, there is a mix of falsehood and truth. Which for some reason means that there is a book which summarizes all truth in this world, so the one who reads this may be akin to God. I read a brief summary on Wikipedia about the article, which helped clarify an aspect of the story through the dactolygraphic monkey theorem (a much simpler theorem than it sounds). It was an interesting read, but I don't believe I really gained all that much from it, though I'm sure I'm missing out on some point. I enjoy reading about the concepts of infinity and through further reading I came about a "sequel" where the author satirically notes the internet will straighten the confusion through a simple search. What I was really confused about was all the exact numbers for the number of shelves, rooms, et cetera. I was wondering if this had significance or symbolism.

http://infoport.blogspot.com/

Jibberish

This is the first post of my blog just to see if I am doing this correctly; it does not mean anything and is pointless to read.