Answer:
see explaination
Explanation:
#include<stdio.h>
/* Your solution goes here */
//Impllementation of SwapArrayEnds method
void SwapArrayEnds(int sortArray[],int SORT_ARR_SIZE){
//Declare tempVariable as integer type
int tempVariable;
if(SORT_ARR_SIZE > 1){
tempVariable = sortArray[0];
sortArray[0] = sortArray[SORT_ARR_SIZE-1];
sortArray[SORT_ARR_SIZE-1] = tempVariable;
}
}
int main(void) {
const int SORT_ARR_SIZE = 4;
int sortArray[SORT_ARR_SIZE];
int i = 0;
sortArray[0] = 10;
sortArray[1] = 20;
sortArray[2] = 30;
sortArray[3] = 40;
SwapArrayEnds(sortArray, SORT_ARR_SIZE);
for (i = 0; i < SORT_ARR_SIZE; ++i) {
printf("%d ", sortArray[i]);
}
printf("\n");
return 0;
}
Please go to attachment for the program screenshot and output
Answer:
A and C
Explanation:
Option A:
In IPv6 there is a rule to reduce an IPv6 address when there are two or more consecutive segments of zeros just one time. This rule says that you can change the consecutive zeros for “::”
Here is an example
How to reduce the following IPv6 address?
ff02:0000:0000:0000:0000:0000:0000:d500
Ans: ff02::d500
Example 2:
2001:ed02:0000:0000:cf14:0000:0000:de95
Incorrect Answer -> 2001:ed02::cf14::de95
Since the rule says that you can apply “::” just one time, you need to do it for a per of zero segments, so the correct answer is:
Correct Answer -> 2001:ed02::cf14:0:0:de95
Or
2001:ed02:0:0:cf14::de95
Option C:
Since in IPv6 there are 
available addresses which means 340.282.366.920.938.463.463.374.607.431.768.211.456 (too many addresses), there is no need of NAT solution, so each device can have its own IP address by the same interface to have access through the internet if needed. If not, you can block the access through internet by the firewall.
Answer:
A. simple to construct and easy to repair
Explanation:
A dynamic microphone works on the principle of electromagnetic principle. A diaphragm is attached to a coil of wire which helps in producing sound. Responding to the sound waves, the coil of the wire is vibrated by the diaphragm. A magnetic field is created by the magnet which is present inside the coil of wire. The electrical signal is generated in response to the motion of the coil. The speed of the motion produces the amount of the current.
Since the construction of a dynamic microphone is easier as compared to any other microphone, John is likely to opt to build one.
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.
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believes that the average individual does not need a smart phone
<span>firivolous mean not serious, light, unimportant.</span>
