Digital signal processing, linear time invariant system,

In summary: for summarizing the conversation, but x[n+1] is a future value and not a past value, so the system is not causal.
  • #1
Asma
12
0
I really confused, I found in a book that the following system,
y[n]= x[n+1]-x[n], is not causal!
But from the definition of causality that the output y[n0] depends only on the input samples x[n] for n<=n0,,,
So I think that this system is causal...

If you agree with me please tell me that it's correct...

Thanks in advance...
 
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  • #2
x[n+1] is not a past or present sample of the input it is a future sample so that is why it is not causal.

It's actually like this, x[n-k] where k>=0
 
  • #3
But x[n+1] means that the sample is shifted to the left, (less than 0 or means it is positioned at -1) so it is a past value then the system is causal.!
 
  • #4
It's true that x[n+1] would be x[n] shifted to the left by one, but x[n+1] is a future value. Think about the graphs - if we have x[n] = {1,2,3,4..} where 1 has the zero place, x[n+1] would be, as you say, {2,3,4,5..} with 2 having the zero place. Do you see how now the first sample in x[n+1] (which is 2) is the FUTURE value of x[n] at the ones place?

You're right that the graph shifts, this is visual meaning of future values. The graph does shift left.
 
  • #5
FOIWATER said:
It's true that x[n+1] would be x[n] shifted to the left by one, but x[n+1] is a future value. Think about the graphs - if we have x[n] = {1,2,3,4..} where 1 has the zero place, x[n+1] would be, as you say, {2,3,4,5..} with 2 having the zero place. Do you see how now the first sample in x[n+1] (which is 2) is the FUTURE value of x[n] at the ones place?

You're right that the graph shifts, this is visual meaning of future values. The graph does shift left.
Thanks for your answer... It is clear now.. Thanks
 

1. What is digital signal processing?

Digital signal processing (DSP) is the use of mathematical algorithms to manipulate digital signals, such as audio or video, in order to improve their quality or extract useful information from them.

2. How does linear time invariant system differ from other systems?

A linear time invariant (LTI) system is a system whose output is only dependent on its input and does not change over time. This means that the output of an LTI system will always be the same for a given input, regardless of when it is applied. Other systems, such as nonlinear or time-varying systems, may have outputs that are dependent on previous inputs or change over time.

3. What are some common applications of DSP?

DSP has a wide range of applications, including audio and video processing, telecommunications, medical imaging, and control systems. It is also used in areas such as speech recognition, image recognition, and data compression.

4. How is DSP used in real-world scenarios?

DSP is used in many different real-world scenarios, such as noise cancellation in headphones, image enhancement in digital cameras, and signal processing in telecommunications to improve the quality of phone calls and data transmission. It is also used in medical devices, such as MRI machines, to process and analyze medical images.

5. What are some key concepts to understand in DSP?

Some key concepts in DSP include sampling, which is the process of converting analog signals into digital signals; filtering, which is the process of removing unwanted noise or frequencies from a signal; and convolution, which is a mathematical operation used to combine two signals to produce a third signal. It is also important to understand the properties of LTI systems, such as linearity and time-invariance, in order to analyze and design DSP systems effectively.

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