Cognitive Radio Mcqs

Q:

Temporal reasoning allows a system to reason about its operational characteristics at discrete points in time.

A) True B) False
 
Answer & Explanation Answer: B) False

Explanation: Temporal reasoning allows a system to reason about its operational characteristics during an interval of time. For example, spectrum sensing over a period can help identify intervals of spectrum underutilization within that period of time.

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Q:

Which among the following is not a challenge for case based reasoning implementation?

A) Memory B) Fixed ontology and knowledge representation
C) Pattern matching D) Accuracy of reasoning
 
Answer & Explanation Answer: B) Fixed ontology and knowledge representation

Explanation: Case based reasoning requires a large case database. It requires a large amount of computational resources for pattern matching and to modify the solution of a close match to satisfy the current problem. Case based reasoning can be implemented in any system provided it has large memory and processing power regardless of ontology and knowledge representation.

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8
Q:

Which among the following is not a challenge of employing reasoning and learning stage in the cognitive radio?

A) Computational requirement B) Quality of Service
C) Edge conditions D) Predictable behaviour
 
Answer & Explanation Answer: B) Quality of Service

Explanation: Cognitive radio should provide a large amount of computational resources to achieve the results of each operation. Edge conditions refer to the devices which are positioned in a location without regular service and hence cannot benefit from learning techniques. Predictable behaviour refers to the ability to estimate the outcome of each step of the operation but not the final outcome.

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6
Q:

Which among the following statements provides the difference between reinforcement-based learning and temporal difference technique?

A) State represented by a directed graph B) Assignment of weightage to an action on the basis of the degree of success
C) Computation of degree of success D) Priori model of the sequence of possible states
 
Answer & Explanation Answer: D) Priori model of the sequence of possible states

Explanation: The temporal difference algorithm does not a priori model of the sequence of possible states as the temporal difference algorithm constructs the state representation during execution. The states are composed as a value function and are stored on a neural network.

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6
Q:

What is the parameter of analysis in reinforcement learning?

A) Number of requests during wake cycle B) Number of processes to achieve final outcome
C) Degree of failure D) Degree of success
 
Answer & Explanation Answer: D) Degree of success

Explanation: Reinforcement based learning assigns a weightage to an action on the basis of the degree of success of its outcome. When situations requiring similar action arise, the weightage associated with an action is analyzed for compatibility. The degree of success is computed by measuring the closeness of the obtained outcome with the expected outcome.

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9
Q:

What does an arc in a decision tree represent?

A) An action B) A decision
C) Set of all possible choices D) Set of all possible plans
 
Answer & Explanation Answer: C) Set of all possible choices

Explanation: A decision tree is a directed graph with a hierarchical set of nodes and arcs. A node represents a choice or a decision. An arc from one decision node to another decision node represents all possible choices associated with that node.

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7
Q:

Which among the following is employed for associating new concepts in the learning process for ontology based systems?

A) Dissimilarities between two consequent entries B) Similarities between two consequent entries
C) Similarities to existing entries in memory D) Dissimilarities to existing entries in memory
 
Answer & Explanation Answer: C) Similarities to existing entries in memory

Explanation: Learning in ontology based system involves integrating new knowledge with existing collection of entries in memory. A classifier is used to assess the degree of similarity between a new entry and the existing entries. The classifier accomplishes this by analyzing the properties and values of a new entry.

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7
Q:

Which among the following is not used for symbolic knowledge representation?

A) Interface B) Objects
C) Semantic nets D) Frames
 
Answer & Explanation Answer: A) Interface

Explanation: In symbolic representation and reasoning systems, storage is performed using extensible data structures to capture facts, descriptions, and properties related to a concept. Semantic nets, rules, frames, and objects are some of the structures employed to represent knowledge.

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