Smoothing a signal or data set approximates the data to reveal patterns and exclude noise, fine-scale structure and rapid changing phenomina.
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1answer
63 views
How to apply Hamming Window?
I am new in matlab and signal processing. The time series that have been used are obtained from accelerometer in a building. As far as I understand both the time series' length and window function ...
1
vote
1answer
43 views
Is there a need of Point interpolation before proceeding for gaussian smoothing of an incomplete distribution?
Suppose there is a distribution that has values sampled on the interval 1-25 with corresponding sample values that have to be smoothed. For example:
...
2
votes
2answers
148 views
Smoothing data by using Kalman filter
I would like to ask about smoothing data by using Kalman filter. Due to quantization, I have data that is not smooth. How can I smooth this data by using Kalman Filter. For your information, the data ...
0
votes
1answer
55 views
How i can smooth a sinusoidal signal using it's local maximum
I have a signal that it is like a Sinusoidal signal with many local maximum, I want to smooth this signal with almost connecting it's local maximum to each other.
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1answer
63 views
Estimating number for iterations for gaussian smoothing
I have some data sets on which I applied Gaussian smoothing using [1 4 6 4 1] kernel. In my program I iterated this kernel 50 times on the data sets. But only a few ...
1
vote
2answers
62 views
When should the sum of all elements of a gaussian kernel be zero?
I found an approximation of a 5x5 2D convolution kernel like this :
Here, the sum of the elements is zero and this one was used for Laplacian of Gaussian!
Another one here :
This one has all ...
5
votes
2answers
147 views
Fitting piecewise splines to noisy data
I have a system that gives me a noisy data set similar to the one generated by this matlab/octave code. The y-axis represents the signal intensity and the x-axis represents spatial distance.
...
1
vote
2answers
91 views
filter for reducing background noise in image
I have images where there is a lot of "black" background (few shades of black). (In many images at least half the pixels are background). I need to get interest points from the image, but because the ...
1
vote
1answer
100 views
Help me in understanding smoothing
Follow up to the question here
This is a screen shot of an intermediate step in the middle of my calculation
The dotted line called MY is supposed to be a smoothed version of db pow y
db pow y is ...
4
votes
1answer
168 views
how does this equation correspond to smoothing?
Please help me understand smoothing of data. This is a follow up to my previous question posted here. Especially the top answer by Junuxx where he says a way of smoothing a function $f(x)$ is:
$$
...
6
votes
3answers
183 views
Solving optimization problem used for high quality denoising
The highest voted answer to this question suggests that to denoise a signal while preserving sharp transitions one should
minimize the objective function:
$$ |x-y|^2 + b|f(y)| $$
where ...
4
votes
1answer
275 views
How can I smoothly interpolate between 2 position?
I've got a 1D signal (position of a servo motor over time) and I've extracted 'peaks'/'key' positions picking running average "local extrema" points.
Below is are 2 plots from 2 servos and the white ...
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votes
4answers
500 views
Bag of tricks for denoising signals while maintaining sharp transitions?
I know this is signal dependent, but when facing a new noisy signal what is your bag of tricks for trying to denoise a signal while maintaining sharp transitions (e.g. so any sort of simple averaging, ...
8
votes
1answer
286 views
Directly compare subpixel shifts between two spectra — and get believable errors
I have two spectra of the same astronomical object. The essential question is this: How can I calculate the relative shift between these spectra and get an accurate error on that shift?
Some more ...
3
votes
2answers
808 views
How to remove the boundary effects arising due to zero padding in scipy/numpy fft?
I have made a python code to smoothen a given signal using the Weierstrass transform, which is basically the convolution of a normalised gaussian with a signal.
The code is as follows:
...
5
votes
2answers
211 views
What are the characteristics of a “good” smoothing convolution kernel?
At work we were smoothing a signal by convolving with either
f1=[0.2000 0.2000 0.2000 0.2000 0.2000]
or
...
7
votes
1answer
460 views
How do I use a Savitzky Golay filter to find local maxima (in between samples) in a discretely sampled 1D signal?
I have a seismic signal y(i):
Here I have found one maximum: i=152.54, y=222.29 manually and plotted it in red.
I want to find all maxima automatically.
I read that the Savitzky Golay Filter (SGF) ...
6
votes
2answers
424 views
Finding local peaks in-between samples
I have n discrete samples of a seismic signal y[n]:
I want to find local maxima in the signal.
A naive test for if y[n] is a maximum would be:
y[n]: maxima if y[n] > y[n-1] and y[n] > y[n+1].
...
7
votes
3answers
300 views
Savitzky-Golay smoothing filter for not equally spaced data
I have a signal that is measured at 100Hz and I need to apply the Savitzky-Golay smoothing filter on this signal. However, on closer inspection my signal is not measured at perfectly constant rate, ...
8
votes
1answer
231 views
Calculating smoothed derivative of a signal by using difference with larger step=convolving with rectangular window
I have a signal sampled at $\Delta t: fi(ti=i\Delta t)$ where i = 0..n-1. I want to find the first derivative of the signal: f'(t).
My first thought was to estimate this by a central difference:
...
6
votes
2answers
439 views
How to find smoothed estimates of the derivative and second derivative of a signal?
I have a signal sampled at $\Delta t$: $f_i(t_i=i\Delta t)$ where i = 0..n-1.
I want to find the first and second derivative of the signal: f'(t) and f''(t).
My first thought was to estimate the ...
7
votes
1answer
162 views
“Ensemble averaging … cannot track dynamic changes”?
A book claims this as a motivation for introducing exponential averaging:
A disadvantage of ensemble averaging is that the resulting estimate cannot track dynamic changes occurring in the observed ...
5
votes
1answer
582 views
How to decide whether to use AR or MA for smoothing data?
Imagine I've got some offline data that I want to smooth. I could use an auto-regressive or moving-average filter of some appropriate order for conducting the smoothing. On which criteria should I ...
