Answer:
It we were asked to develop a new data compression tool, it is recommended to use Huffman coding since it is easy to implement and it is widely used.
Explanation:
The pros and the cons of Huffman coding
Huffman coding is one of the most simple compressing encoding schemes and can be implemented easily and efficiently. It also has the advantage of not being patented like other methods (e.g. arithmetic codingfor example) which however are superior to Huffman coding in terms of resulting code length.
One thing not mentioned so far shall not be kept secret however: to decode our 96 bit of “brief wit” the potential receiver of the bit sequence does need the codes for all letters! In fact he doesn’t even know which letters are encoded at all! Adding this information, which is also called the “Huffman table” might use up more space than the original uncompressed sentence!
However: for longer texts the savings outweigh the added Huffman table length. One can also agree on a Huffman table to use that isn’t optimized for the exact text to be transmitted but is good in general. In the English language for example the letters “e” and “t” occur most often while “q” and “z” make up the least part of an average text and one can agree on one Huffman table to use that on average produces a good (=short) result. Once agreed upon it doesn’t have to be transmitted with every encoded text again.
One last thing to remember is that Huffman coding is not restricted to letters and text: it can be used for just any symbols, numbers or “abstract things” that can be assigned a bit sequence to. As such Huffman coding plays an important role in other compression algorithms like JPG compression for photos and MP3 for audio files.
The pros and the cons of Lempel-Ziv-Welch
The size of files usually increases to a great extent when it includes lots of repetitive data or monochrome images. LZW compression is the best technique for reducing the size of files containing more repetitive data. LZW compression is fast and simple to apply. Since this is a lossless compression technique, none of the contents in the file are lost during or after compression. The decompression algorithm always follows the compression algorithm. LZW algorithm is efficient because it does not need to pass the string table to the decompression code. The table can be recreated as it was during compression, using the input stream as data. This avoids insertion of large string translation table with the compression data.
Hey there!
Memory (RAM) is considered to be hardware since it is a physical component that makes up the computer and is accessed through the CPU rather than internal code that makes the computer run (which is software). Therefore, if you are having issues with the memory, it is likely a problem with a memory chip, making it a hardware issue.
Hope this helped you out! :-)
The answer is already given at the end of the question; solely by the magnitude or severity of expected harm
When assessing risks of harm associated with participation in a research study, the probability of harm and the risk of the severity of harm are two distinctive elements of risk that must be considered. In probability of harm, the fact that not all possible harms are equally probable should be considered. How these two elements occur is a crucial factor in determining the level of risk of harm in a study. Given the sensitivity of the information in the case scenario above, the probability that an individual subject could be identified is low while the magnitude of the possible risk of harm is high.
Answer:
a blog
Explanation:
a forum is for questions, a wiki doesn't make sense in this situation and email wouldn't be used for this either so it should be a blog