i think its b sorry if wrong
Answer:
Explanation: What is that word you typed?
Answer:
Please check the explanation.
Explanation:
The US education department mentioned that in 1900 there were only 10% of students enrolled in the schools, however by the end of 1992, the percentage increased by 85% and it became 95%. In 1930 1 million students were enrolled in university. And by 2012, it became 21.6 million. The teachers began to follow a new type of teaching, and the academics began to follow a special method for communique, education as well as for helping the students understand the concepts.
Though, after the 1980s the personal computer came into being in schools and colleges. Since then numerous versions of PC have originated in the marketplace as well as mobiles tracked by Smartphones, and now nearly 100% of people in the US have smartphones. Virtual reality, augmented reality, AI, Machine learning. etc. has cemented the way for the virtual classrooms. Also, each subject is now up with fruits and not just-food. The consequence is such a delightful setup of the Virtual schoolrooms in the entire US, and all over the ecosphere. The projectors, VR devices, AI applications for education, online classroom facility, Electronic version of chalkboards, and in fact everything is no sophisticated, and it is making not only teaching easy but learning as well. And the result is, students are ending up with better results, and teachers seem to be happier and more relaxed. And that is making school management satisfied as well.
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.
Answer:
The complete program is as follows:
def convert_distance(miles):
km = miles * 1.6 # approximately 1.6 km in 1 mile
return km
my_trip_miles = 55
# 2) Convert my_trip_miles to kilometers by calling the function above
my_trip_km =convert_distance(my_trip_miles) #3) Fill in the blank to print the result of the conversion
# 4) Calculate the round-trip in kilometers by doubling the result,
print("The distance in kilometers is " +str(my_trip_km))
# and fill in the blank to print the result
print("The round-trip in kilometers is " + str(my_trip_km * 2))
Explanation:
<em>The program is self-explanatory because I used the same comments in the original question.</em>
