Questions tagged [signal-processing]
For questions about processing signals of any physical quantity, with the help of Machine Learning or Artificially Intelligent algorithms.
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What are some of the main high level approaches to applying ML on kinematic sensor data?
I've just started a project which will involve having to detect certain events in a stream of kinematic sensor data. By searching through the literature, I've found a lot of highly specific papers, ...
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Determine Frequency from Noisy Signal With Neural Networks (With Adeline Model)
I'm trying to determine the frequency from a signal with NN. I'm using the Adeline model for my project and I'm taking a few samples in each 0.1-volt step in a true signal and a noisy one.
First ...
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186
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How can I detect fast and slow motion in videos?
I'm trying to detect if a given video shot is fast or slow motion. Basically, I need to calculate a "video motion" score in a given video sequence, meaning how fast or slow motion the video is. For ...
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Which neural network/ML framework to use for a partial amount of a fixed-time amount of sensor data?
Assume I've run a set of initial experiments. For each experiment, I have a set of input signals $I_e(i, t)$, and output signals $O_e(j, t)$ for $i \approx 10$, $j \approx 10$, and $0\leq t\leq t_{max}...
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How can I learn to transform one input signal (time series) into another?
I'm posting this question here because I've been trying in vain to solve a problem for weeks and I hope some of you might have some useful suggestions.
Basically, the problem is as follows. I have 7 ...
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How does using complex weights in a neural network affect performance?
If you switch a neural network from real weights to complex weights, you're roughly doubling the size of the network, and increasing the computational load by a factor of 2 to 4. My question is, in ...
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How to detect the sine wave signal with different frequency using neural networks?
I'm wondering if there is a way to use a neural network that can detect the noisy sine wave, where the frequency is not constant. In other words, I'm not looking for a solution that would detect the ...
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106
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Can I call any function a signal?
While reading the Notation of the paper titled Geometric Deep Learning Grids, Groups, Graphs, Geodesics, and Gauges, I came across the following notations.
$$
\...
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Feature extraction for exponentially damped signals
I am looking into exponentially damped signals where it is a stationary signal (after implementing the Adfuller statistical test) and I would like to look into how can I extract meaningful features ...
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39
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What's the best method to predict/generate signal from one sensor (source) to signal from another another (target)?
I was wondering what is the best method out there to find relationship between two 1D signals so that I can predict/generate one (source) from the other (target). For example, let's say that in ...
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How does a zero-order hold kernel in a Convolutional Neural Network look like?
Several papers co-authored by Hitoshi Kiya propose to use a fixed convolutional layer with a zero-order hold kernel to avoid checkerboard artifacts in CNNs. [1, 2, 3]
While there is plenty of ...
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Is my 1D signal using CNN & RNN regression reasonable?
I want to know if my impact-echo signals are proper with CNN or RNN regression model.
I got some simulated signal, as following shows.
In previous research, people mostly consider frequency or even ...
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22
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How can I transform a signal into another using supervised learning?
I'm trying to transform a signal into another using supervised learning. My main goal is to create a model capable to transform a raw signal (Blue Line) into something similar to the "ideal" ...
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Given the high resolution signal and the low pass filter (kaiser filter), is there a way to reconstruct the low resolution signal?
When we upsampling a discrete 1d signal by 2x, we first interleave the signal by 0, then pass through a low pass filter.
low resolution signal [x1, x2, x3, x4] -> interleave 0 -> [x1, 0, x2, 0, ...
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170
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Is it effective to use deep learning method to produce a 1D signal as output from a 2D image as input?
I have a 1D signal that will produce a 2D image after some image processing algorithm. Would it be possible and effective to use deep learning method to reproduce the 1D signal if I have the 2D image ...
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Deep Learning based image restoration using multiple frames
Suppose we have a sequence of still images each of which has been contaminated by some particles(ex, dust/sand/smoke) making the images very poor in certain areas.
What architecture would be best to ...