Question about deconvolution of signals (digital signal processing)

In summary, the conversation discusses the use of Gaussian smoothing filters as a low-pass filter on a set of discrete data. The problem arises when using the findpeaks() function in Matlab, as it takes over the desired peak. The solution is to use the local mins of the derivative, as there appears to be an inflection point around x=35.
  • #1
mwhar
2
0
I have a set of discrete data that I have performed multiple Gaussian smoothing filters on to act as a low-pass filter. What I have come up with is something like this:

http://i51.tinypic.com/152kieg.jpg

I'm using findpeaks() in matlab, and a peak that I want is being taken over by the adjacent Gaussian. The one I'm trying to get is around x=35. Does anyone know of a method where I can get the x value of that point? Thanks!

-Michael
 
Physics news on Phys.org
  • #2
Nevermind, I figured it out. I ended up using the local mins of the derivative.
 
  • #3
FWIW, I was thinking that there is an inflection point around 35.
 

FAQ: Question about deconvolution of signals (digital signal processing)

What is deconvolution of signals in digital signal processing?

Deconvolution of signals is a process used in digital signal processing to separate and extract specific information from a combined signal. It involves reversing the effects of convolution, which is a mathematical operation that combines two signals to produce a third signal. Deconvolution is commonly used to remove noise, blur, and other distortions from signals, allowing for a clearer and more accurate representation of the original signal.

What are the applications of deconvolution in digital signal processing?

Deconvolution has various applications in digital signal processing, including image restoration, noise removal, and channel equalization. It is also used in fields such as medical imaging, audio processing, and communication systems to enhance the quality of signals and improve the accuracy of data analysis.

How does deconvolution differ from convolution in digital signal processing?

Deconvolution and convolution are inverse operations. While convolution combines two signals to produce a third signal, deconvolution separates the combined signal into its original components. In digital signal processing, deconvolution is used to reverse the effects of convolution and extract specific information from a signal, whereas convolution is used to combine signals for various purposes.

What are the common techniques used for deconvolution in digital signal processing?

There are several techniques used for deconvolution in digital signal processing, including Wiener deconvolution, maximum entropy deconvolution, and Richardson-Lucy deconvolution. These techniques use different mathematical algorithms to remove noise and distortions from signals and produce a clearer representation of the original signal.

Are there any limitations or challenges associated with deconvolution in digital signal processing?

Yes, there are some limitations and challenges associated with deconvolution in digital signal processing. One of the major challenges is finding the appropriate deconvolution technique for a specific signal or application. In addition, deconvolution can also introduce artifacts and false features if not applied correctly, and it may require significant computational resources for complex signals. It is important to carefully analyze and understand the signal before applying deconvolution techniques to avoid these limitations.

Similar threads

Engineering Signal Conditioning and Processing
Replies
3
Views
2K
Engineering How to find RMS, and Peak Voltages of this signal?
Replies
1
Views
1K
Problem understanding a parameter in a digital filter
Replies
1
Views
2K
How to use the window functions on a signal in MATLAB?
Replies
2
Views
2K
MATLAB Deconvolving two signals with different sampling rates
Replies
4
Views
2K
Signals Description (Digital, Analog, Discrete-time, etc)
Replies
9
Views
2K
Digital signal processing - Pseudo Inverse Method
Replies
1
Views
2K
I Making Sense of Notation Confusion in Statistical Digital Signal Processing
Replies
6
Views
2K
Digital Signal Processing - signals, reconstruction, filters
Replies
12
Views
6K
Help with Signal Processing Homework
Replies
1
Views
2K

Hot Threads

Recent Insights

Back
Top