Shannon Channel Capacity

In 1944 Claude Shannon introduce a formula to determine the theoretical highest data rate for a a channel

C=B log 2(1+S/n)

In this formula, B is the bandwidth of the channel. S/N is the signal to noice ratio and C is the capacity (Shannon Capacity) in BPS.

We can calculate the theoretical highest bit rate of regular telephone.

A Telephone line normally has a bandwidth of 3000Hz. The signal to noise ratio is usually 3162-(35db) for this channel capacity is

       C=B log ₂ (1+S/N)

       C=3000 log₂ ⁽(1+3162)

       C=3000 log₂ (3163)

       C=3000*1162

       C=34860 BPS

TCP/IP or OSI - Which one came first

The TCP/IP model, which is realistically the Internet Model, came into existence about 10 years before the OSI model.

History of TCP

       From 1973 to 1974, Cerf's networking research group at Stanford worked out details of the idea, resulting in the first TCP specification. A significant technical influence was the early networking work at Xerox PARC, which produced the PARC Universal Packet protocol suite, much of which existed around that time.

        In March 1982, the US Department of Defense declared TCP/IP as the standard for all military computer networking. In 1985, the Internet Advisory Board (later renamed the Internet Architecture Board) held a three-day workshop on TCP/IP for the computer industry, attended by 250 vendor representatives, promoting the protocol and leading to its increasing commercial use.

        In 1985, the first Interop conference focused on network interoperability by broader adoption of TCP/IP. The conference was founded by Dan Lynch, an early Internet activist. From the beginning, large corporations, such as IBM and DEC, attended the meeting. Interoperability conferences have been held every year since then. Every year from 1985 through 1993, the number of attendees tripled

Lempel–Ziv–Welch Compression

Lempel–Ziv–Welch (LZW) is a universal lossless data compression algorithm created by Abraham Lempel, Jacob Ziv, and Terry Welch. The algorithm is simple to implement, and has the potential for very high throughput in hardware implementations. It was the algorithm of the widely used Unix file compression utility compress, and is used in the GIF image format.Its works like index backside of our notebook.

• I am taking string pattern for elaboration to show compression.
• Make your choice that how much character you want to take, I am taking 4 character maximum for dictionary entry
  

Algorithms

Upper and Lower bound of a function

Upper Bound : Proving an upper bound means you have proven that the algorithm will use no more than some limit on a resource.

Lower Bound : Proving a lower bound means you have proven that the algorithm will use no less than some limit on a resource.


Upper and lower bounds have to do with the minimum and maximum "complexity" of an algorithm (I use that word advisedly since it has a very specific meaning in complexity analysis).

Take, for example, our old friend, the bubble sort. In an ideal case where all the data are already sorted, the time taken is f(n), a function dependent on n, the number of items in the list. That's because you only have to make one pass of the data set (with zero swaps) to ensure your list is sorted.

In a particularly bad case where the data are sorted in the opposite to the order you want, the time taken becomes f(n2). This is because each pass moves one element to the right position and you need npasses to do all elements.