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kakasveta [241]
1 year ago
10

Timing circuits are a crucial component of VLSI chips. Here’s a simple model of such a timing circuit. Consider a complete balan

ced binary tree with n leaves, where n is a power of two. Each edge e of the tree has an associated length le, which is a positive number. The distance from the root to a given leaf is the sum of the lengths of all the edges on the path from the root to the leaf.
The root generates a clock signal which is propagated along the edges to the leaves. We’ll assume that the time it takes for the signal to reach a given leaf is proportional to the distance from the root to the leaf.

Now, if all leaves do not have the same distance from the root, then the signal will not reach the leaves at the same time, and this is a big problem. We want the leaves to be completely synchronized, and all to receive the signal at the same time. To make this happen, we will have to increase the lengths of certain edges, so that all root-to-leaf paths have the same length (we’re not able to shrink edge lengths). If we achieve this, then the tree (with its new edge lengths) will be said to have zero skew. Our goal is to achieve zero skew in a way that keeps the sum of all the edge lengths as small as possible.

Give an algorithm that increases the lengths of certain edges so that the resulting tree has zero skew and the total edge length is as small as possible.
Computers and Technology
1 answer:
gregori [183]1 year ago
5 0

Answer:

Let L and R be the sub-trees underneath the root r.

If the height of L (where we consider height to be the maximum length of all root-leaf paths) is ∆ more than that of R, ∆ ≥ 0, then the length of the edge  (r, R) is increased by ∆.

Then, we separately perform the same cycle over L and R.

Once more, by induction, argue that this greedy algorithm is efficient –

when it does not increase the length of (r, R) edge by ∆, then prove that the solution can be improved.

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You are consulting for a trucking company that does a large amount ofbusiness shipping packages between New York and Boston. The
likoan [24]

Answer:

Answer explained with detail below

Explanation:

Consider the solution given by the greedy algorithm as a sequence of packages, here represented by indexes: 1, 2, 3, ... n. Each package i has a weight, w_i, and an assigned truck t_i. { t_i } is a non-decreasing sequence (as the k'th truck is sent out before anything is placed on the k+1'th truck). If t_n = m, that means our solution takes m trucks to send out the n packages.

If the greedy solution is non-optimal, then there exists another solution { t'_i }, with the same constraints, s.t. t'_n = m' < t_n = m.

Consider the optimal solution that matches the greedy solution as long as possible, so \for all i < k, t_i = t'_i, and t_k != t'_k.

t_k != t'_k => Either

1) t_k = 1 + t'_k

    i.e. the greedy solution switched trucks before the optimal solution.

    But the greedy solution only switches trucks when the current truck is full. Since t_i = t'_i i < k, the contents of the current truck after adding the k - 1'th package are identical for the greedy and the optimal solutions.

    So, if the greedy solution switched trucks, that means that the truck couldn't fit the k'th package, so the optimal solution must switch trucks as well.

    So this situation cannot arise.

  2) t'_k = 1 + t_k

     i.e. the optimal solution switches trucks before the greedy solution.

     Construct the sequence { t"_i } s.t.

       t"_i = t_i, i <= k

       t"_k = t'_i, i > k

     This is the same as the optimal solution, except package k has been moved from truck t'_k to truck (t'_k - 1). Truck t'_k cannot be overpacked, since it has one less packages than it did in the optimal solution, and truck (t'_k - 1)

     cannot be overpacked, since it has no more packages than it did in the greedy solution.

     So { t"_i } must be a valid solution. If k = n, then we may have decreased the number of trucks required, which is a contradiction of the optimality of { t'_i }. Otherwise, we did not increase the number of trucks, so we created an optimal solution that matches { t_i } longer than { t'_i } does, which is a contradiction of the definition of { t'_i }.

   So the greedy solution must be optimal.

6 0
2 years ago
B.) Define latency and jitter for service flow in a fixed wireless system.
yuradex [85]
The answer would be between the base station and the subscriber station.
7 0
2 years ago
Using the Sakila database, create a query that displays the film title, description, as well the actor first and last name for a
Alex787 [66]

Answer:

Select title, description , first_name, last_name from film inner join film_actor on film.film_id = film_actor.film_id inner join actor on film_actor.actor_id = actor.actor_id where title LIKE 'zo%';

Explanation:

  • The INNER JOIN keyword selects records that have matching values in both tables.
  • The WHERE clause is used to filter records.
  • The WHERE clause is used to extract only those records that fulfill a specified condition.
7 0
1 year ago
1. What should you do if your computer is shared by your entire family and you install a plugin that saves user names and passwo
Citrus2011 [14]

Explanation:

1 make sure only you know the password

2 having weak security on one browser is basically a doorway for someone to get into your network

7 0
1 year ago
Read 2 more answers
The groups_per_user function receives a dictionary, which contains group names with the list of users. Users can belong to multi
Akimi4 [234]

The groups_per_user function receives a dictionary, which contains group names with the list of users.

Explanation:

The blanks to return a dictionary with the users as keys and a list of their groups as values is shown below :

def groups_per_user(group_dictionary):

   user_groups = {}

   # Go through group_dictionary

   for group,users in group_dictionary.items():

       # Now go through the users in the group

       for user in users:

       # Now add the group to the the list of

         # groups for this user, creating the entry

         # in the dictionary if necessary

         user_groups[user] = user_groups.get(user,[]) + [group]

   return(user_groups)

print(groups_per_user({"local": ["admin", "userA"],

       "public":  ["admin", "userB"],

       "administrator": ["admin"] }))

3 0
1 year ago
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