Let the message be M : 1001 0001 and the generator function is G : 1001
Solution :
CRC sender
<u> </u>
1001 | 1001 0001 000
<u> 1001 </u>
<u> 0000 0001 </u>
1000
<u> 1001 </u>
0001 000
<u> 1 001 </u>
0001
Here the generator is 4 bit - 1, so we have to take three 0's which will be replaced by reminder before sending to received--
eg 1001 0001 001
Now CRC receiver
<u> </u>
1001 | 1001 0001 001
<u> 1001 </u>
0000 0001
<u> 1001 </u>
1000
<u> 1001 </u>
0001 001
<u> 0001 001 </u>
0000
No error
Answer:
A varying infrastructure.
Explanation:
The competitive pressure it creates for multinational companies in the industry is one of difference in infrastructure because different technical standards are found in different parts of the world.
For instance, A technical standard known as Global Systems for Mobile (GSM) is common in Europe, and an alternative standard, Code Division Multiple Access (CDMA), is more common in the United States and parts of Asia.
Consequently, equipment designed for GSM will not work on a CDMA network and vice versa as a result of varying infrastructure, so companies would be innovative in order to improve on their products.
Answer: True
Explanation:
Subset sum problem and Knapsack problem can be solved using dynamic programming.
In case of Knapsack problem there is a set of weights associative with objects and a set of profits associated with each object and a total capacity of knapsack let say C. With the help of dynamic programming we try to include object's weight such that total profit is maximized without fragmenting any weight of objects and without exceeding the capacity of knapsack, it is also called as 0/1 knapsack problem.
Similar to knapsack problem, in subset sum problem there is set of items and a set of weights associated with the items and a capacity let say C, task is to choose the subset of items such that total sum of weights associated with items of subset is maximized without exceeding the total capacity.
On the basis of above statements we can say that subset sum problem is generalization of knapsack problem.
