Why Does Deconvolution Result in a Peak Followed by a Valley?

In summary: Sorry, but I don't know where the topic about signal analysis should go..?In summary, the conversation is about signal analysis and specifically, how to perform deconvolution for quantifying blood flow in the brain. The speaker has two known functions describing the concentration of contrast solution and is trying to find h(t) through convolution. However, they are struggling to understand why their results show a peak followed by a small valley. They are seeking help and someone suggests trying the discrete Fourier transformation and posting on the USENET newsgroup comp.dsp.
  • #1
PaulPaul
2
0
Sorry, but I don't know where the topic about signal analysis should go..?
----------------------------------------------------------------------
I'm doing research about quantifying blood flow in the brain. Basically I need to know how to perform deconvolution (I think.)

I have two functions that describe the concentration of contrast solution:

f(t) = a(t-to)^b exp[ -(t-to)/c ]
g(t) = d(t-t1)^k exp[ -(t-t1)/m ]

where a,b,c,d,k,m,to, and t1 are known parameters (found by fitting the function to measurements.)

now say f = h * g (* - convolution)
How can I find h(t)? What type of result can I expect?

I have tried using the discrete Fourier transformation. But I often find h to be a peak followed by a small valley. I don't understand why this would be the deconvolution.

Please help. I'm stuck.
(http://s153.photobucket.com/albums/s235/s1020099/)
 
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  • #2
PaulPaul said:
Sorry, but I don't know where the topic about signal analysis should go..?

try the USENET newsgroup comp.dsp .

----------------------------------------------------------------------
I'm doing research about quantifying blood flow in the brain. Basically I need to know how to perform deconvolution (I think.)

I have two functions that describe the concentration of contrast solution:

f(t) = a(t-to)^b exp[ -(t-to)/c ]
g(t) = d(t-t1)^k exp[ -(t-t1)/m ]

where a,b,c,d,k,m,to, and t1 are known parameters (found by fitting the function to measurements.)

now say f = h * g (* - convolution)
How can I find h(t)? What type of result can I expect?


if c is not the same number as m, i don't think you can do it. well, it might depend a little on what if f() and g() have unit step functions applied to them.

but the basic idea is to compute the Fourier Transform of f(t) and g(t), divide [itex]F(\omega)[/itex] by [itex]G(\omega)[/itex] to get [itex]H(\omega)[/itex] and then inverse Fourier transform that result.

I have tried using the discrete Fourier transformation. But I often find h to be a peak followed by a small valley. I don't understand why this would be the deconvolution.

Please help. I'm stuck.
(http://s153.photobucket.com/albums/s235/s1020099/)
 

Related to Why Does Deconvolution Result in a Peak Followed by a Valley?

What is signal analysis?

Signal analysis is a process of examining and interpreting data from a signal, which is a measurable and quantifiable physical quantity. It involves techniques and methods for extracting useful information from the signal, such as identifying patterns, trends, and abnormalities.

What are the main steps involved in signal analysis?

The main steps in signal analysis include signal acquisition, preprocessing, feature extraction, feature selection, and classification. Signal acquisition involves collecting raw data from the signal source. Preprocessing involves filtering, smoothing, and removing noise from the signal. Feature extraction involves identifying relevant characteristics of the signal. Feature selection involves selecting the most important features for further analysis. Classification involves using machine learning algorithms to classify the signal into different categories.

What are the applications of signal analysis?

Signal analysis has various applications in different fields, including telecommunications, biomedical engineering, finance, and environmental monitoring. In telecommunications, it is used for signal processing in wireless communications. In biomedical engineering, it is used for analyzing signals from medical devices, such as electrocardiograms and blood pressure monitors. In finance, it is used for analyzing financial market data. In environmental monitoring, it is used for analyzing signals from sensors to detect changes in the environment.

What are some common techniques used in signal analysis?

Some common techniques used in signal analysis include Fourier analysis, wavelet analysis, time-frequency analysis, and statistical analysis. Fourier analysis is used to decompose a signal into its frequency components. Wavelet analysis is used to analyze signals with non-stationary characteristics. Time-frequency analysis is used to analyze signals that vary over time. Statistical analysis is used to identify patterns and trends in the signal data.

What are the challenges in signal analysis?

Some of the challenges in signal analysis include dealing with noise in the signal, selecting relevant features for analysis, and choosing appropriate classification algorithms. Noise in the signal can make it difficult to extract useful information and can affect the accuracy of the analysis. Selecting relevant features can be challenging, as there may be a large number of features in the signal data. Choosing the right classification algorithm for a particular signal analysis problem can also be a challenge, as different algorithms may perform differently depending on the characteristics of the signal.

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