Video summary
Amplitude Scaling of Continuous-Time Signals
Main summary
Key takeaways
Main ideas and concepts
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Two types of signal scaling are discussed:
- Time scaling (covered in a previous lecture).
- Amplitude scaling (current lecture).
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Amplitude scaling of a continuous-time signal:
- Start with an original signal (X_T).
- Multiply its amplitude by a real constant (\beta).
- The new (scaled) signal is (Y_T), given by: [ Y_T = \beta \, X_T ]
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The behavior depends on the value of (\beta), specifically on (|\beta|).
Methodology / instructions (amplitude scaling procedure)
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Given a continuous-time signal (X_T) defined piecewise (in the example):
- (X_T = 0) for (T < 0)
- (X_T = 2) for (0 \le T \le 2)
- (X_T = 0) for (T > 2)
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Amplitude scaling rule:
- Choose a real (\beta).
- Compute the scaled signal: [ Y_T = \beta X_T ]
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Evaluate two key cases:
Case 1: Amplification
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Condition:
- (|\beta| > 1)
- Range of (\beta): ((-\infty, -1) \cup (1, \infty))
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Example:
- (\beta = 2) (so (|\beta| = 2 > 1))
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Since (X_T = 2) on ([0,2]), then: [ Y_T = 2 \cdot 2 = 4 \quad \text{for } 0 \le T \le 2 ]
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For (T < 0) and (T > 2), (X_T = 0 \Rightarrow Y_T = 0).
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Key lesson:
- Amplitude increases (amplification).
- No time expansion or compression occurs—the time axis stays the same.
Case 2: Reduction
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Condition:
- (|\beta| < 1)
- Range of (\beta): ((-1, 0) \cup (0, 1))
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Example:
- (\beta = 0.5) (so (|\beta| = 0.5 < 1))
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On ([0,2]), where (X_T = 2): [ Y_T = 0.5 \cdot 2 = 1 \quad \text{for } 0 \le T \le 2 ]
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Outside ([0,2]), (X_T = 0 \Rightarrow Y_T = 0).
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Key lesson:
- Amplitude decreases (reduction).
- Time remains unchanged (still no expansion/compression).
Discrete-time note
- The same approach (multiplying by (\beta)) can be used for discrete-time amplitude scaling.
- Therefore, the lecture does not cover discrete-time separately.
Speakers / sources featured
- No named speakers or external sources are mentioned in the provided subtitles (the instructor is referenced only implicitly).