Practice Causality - 6.1.4.1 | Module 6: Time Domain Analysis of Discrete-Time Systems | Signals and Systems
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6.1.4.1 - Causality

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

Define causality in the context of DT-LTI systems.

πŸ’‘ Hint: Think about the influence of future inputs.

Question 2

Easy

Is h[n] = Ξ΄[n] + 0.5Ξ΄[nβˆ’1] causal?

πŸ’‘ Hint: Check the values of h[n] for n<0.

Practice 4 more questions and get performance evaluation

Interactive Quizzes

Engage in quick quizzes to reinforce what you've learned and check your comprehension.

Question 1

What defines a causal system?

  • It can predict future inputs
  • It depends on current and past inputs
  • It has no impulse response

πŸ’‘ Hint: Remember the definition of causality.

Question 2

True or False: A non-causal system's output can depend on future input samples.

  • True
  • False

πŸ’‘ Hint: Consider the nature of causality in systems.

Solve and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Propose a scenario where a non-causal system can be beneficial and explain why. Then, contrast it with a real-time application where causality is crucial.

πŸ’‘ Hint: Consider where anticipatory data can be implemented.

Question 2

Examine the impulse response h[n] = 3Ξ΄[n] + 2Ξ΄[nβˆ’2] - Ξ΄[n+1] and determine its causality status, providing justification.

πŸ’‘ Hint: Evaluate h[n] for all negative indices.

Challenge and get performance evaluation