Answer:
Recursive solutions can be less efficient than their iterative counterparts
Explanation:
Recursion can be defined or described as a method of solving a problem where the solution depends on solutions to smaller instances of the same problem.
It entails using iteration to ensure that smaller parts of a solution are satisfied towards solving thw overall problem.
Ita major disadvantage seems to be that it seem to be less efficient than their iterative counterparts. This is as a result of concentrating on trying to solve a smaller instances.
A, B , and E sorry if I’m wrong
Answer:
PER= 0.824
Explanation:
Total bytes = 1024+6
=1030 bytes
=1030*8 bits
= 8240 bits
BER = 0.0001
Packet Level Error (PER) = 8240*0.0001
= 0.824
Answer:
Check the explanation
Explanation:
#!usr/bin/python
#FileName: sieve_once_again.py
#Python Version: 2.6.2
#Author: Rahul Raj
#Sat May 15 11:41:21 2010 IST
fi=0 #flag index for scaling with big numbers..
n=input('Prime Number(>2) Upto:')
s=range(3,n,2)
def next_non_zero():
"To find the first non zero element of the list s"
global fi,s
while True:
if s[fi]:return s[fi]
fi+=1
def sieve():
primelist=[2]
limit=(s[-1]-3)/2
largest=s[-1]
while True:
m=next_non_zero()
fi=s.index(m)
if m**2>largest:
primelist+=[prime for prime in s if prime] #appending rest of the non zero numbers
break
ind=(m*(m-1)/2)+s.index(m)
primelist.append(m)
while ind<=limit:
s[ind]=0
ind+=m
s[s.index(m)]=0
#print primelist
print 'Number of Primes upto %d: %d'%(n,len(primelist))
if __name__=='__main__':
sieve()
Answer:
Distributed Computing is used to solve common problem from multiple locations at different locations.
Explanation:
It is the architecture of processor, that is used to combine the different resources of computer from different domain to solve the common problem.
It allows us resource sharing such as hardware and software that are connected to the computer.
Examples
Following are the examples of Distributed computing.
- Telecommunication Network
- sensor network
- banking network