Answer:
Difference between laptops and desktop computer are:
- Desktop computers contain wide variety of components, but laptops are realtively expensive than desktop computers because laptop has high speed and better graphics designs which increased the cost of whole system.
- Desktop computer processors are larger in size and it is more efficient and powerful as compared to laptops because laptop processor still has some limitations.
- Laptop uses less power as compared to desktop computer because laptop contain small components and that is why it needs less power.
- Desktop computers are easy to upgrade and in laptops hard drive and memory, these two components needs to be upgraded.
Hubs are very simple devices that connect network components, sending a packet of data to all other connected devices.
Hubs are relatively basic network connectors that send a packet of data to every other connected device. Compared to a hub, a switch is more intelligent and has the ability to filter and forward data to a specific location. Within various networks, switches are utilized. Nodes of the network are any computers or printers connected to it. A network workstation is a personal computer that is linked to a network (note that this is different form the usage of the term workstation as a high-end microcomputer). Nodes of the network are any computers or printers connected to it. A network workstation is a personal computer that is linked to a network.
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Answer:
Which sentence best matches the context of the word reticent as it is used in the following example?
We could talk about anything for hours. However, the moment I brought up dating, he was extremely reticent about his personal life.
Explanation:
Which sentence best matches the context of the word reticent as it is used in the following example?
We could talk about anything for hours. However, the moment I brought up dating, he was extremely reticent about his personal life.
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.
