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sp_S3_mud

# sp_S3_mud - Lecture S3 Muddiest Points General Comments...

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Lecture S3 Muddiest Points General Comments Today, we derived the convolution integral (or superposition integral). This result is very important for understanding linear systems. It is used to determine the output of a system in response to an arbitrary input, when the impulse response is known. Responses to Muddiest-Part-of-the-Lecture Cards (28 cards) 1. What are examples of noncausal systems? (2 students) Suppose you want to do some signal processing on an audio signal. If you do the processing in real time (as the audio is occurring), the system you use must be causal — the output must depend on past inputs. Now suppose you make a recording of some data onto magnetic tape or a hard drive, and want to do signal processing on the data after the fact. The output of your signal processing system at any time can depend on the future, since the “future” is all stored on the tape or hard drive. So post-processing of data can use noncausal Flters. Processing of digital images can be “noncausal”, since the independent variable is position on the image, not time. You can use information from both the left (which is like the past) and from the right (which is like the future). I’ll give an example of this in class. 2. Can you help us develop a physical intuition of the convolution integral? (1) I hope so, when we get to graphical convolution. 3.

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sp_S3_mud - Lecture S3 Muddiest Points General Comments...

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