Answer:
public class ANot {
public static void main(String[] args) {
int numberOfSides = 20;
boolean isQuadrilateral;
if(numberOfSides==4){
isQuadrilateral = true;
System.out.println("The triangle is quadrilateral");
}
else{
isQuadrilateral=false;
System.out.println("The triangle is not quadrilateral");
}
}
}
Explanation:
- Create and Initilize the int variable numberOfSides (You can also receive this from a user using the scanner class for example).
- create a boolean variable isQuadrilateral
- Use if and else statement to check if numberOfSides ==4 and assign true to isQuadrilateral else assign false
When we focus on stereotypes we may <span>unconsciously look for information to support our generalizations .</span>
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.
FTP (File Transfer Protocol) can be used for a variety of tasks. For instance, webmasters using FTP for pushing updates/files to their websites can provide easy and straightforward changes to their services without the need to physically transfer files onto the host server. FTP should be used when you must update a file or files on a host server for a variety of reasons and you do not have access to the host server physically. However, FTP also has some inherent security risks which is why some webmasters/hosts chose to opt out of pushing updates through FTP in favour of physical file transfer.
It can help you more be aware of whats going on and what you need to do.
