Body: <p>I have a three datasets each containing one signal of a specific type (normal, periodically jammed, constantly jammed). I would like to experiment with this dataset to predict wether a signal is normal or if being jammed, the type. Fot this experimentation I thought about CNN but I read that they work better with 2D data and images. What would you recommend?</p>
  <p>EDit: It is a time series.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-07-25T17:08:18.950Z
Comments.Text: What is the data type, vector or time-series/sequence? If vector, use MLP; If sequence, go with RNN or 1d CNN.
Comments.UserDisplayName: Luca Anzalone
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-08-01T13:08:17.303Z
Comments.Text: Can you please elaborate more?
Comments.UserDisplayName: AnneBlue
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-08-01T17:44:16.543Z
Comments.Text: Sure,  but I at least need to know the data type (i.e., the shape, dimensionality) :) I mean what is this "signal" you referring to? It is a sequence of features of shape `(N, T, D)`? Where `N` is the number of data samples, `T` the sequence length, and `D` the dimensionality of num. features-
Comments.UserDisplayName: Luca Anzalone
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-08-02T06:17:52.087Z
Comments.Text: Thank you for the reply, https://github.com/AMHD/Jamming-Detection-in-IoT-Wireless-Networks-An-Edge-AI-Based-Approach ...in this repo, in the dataset portion you can see the signal values (they have been recorded at a constant interval of 30 minutes) are in a 1D array form. What's the simplest deep learning appraoch that will fit? I'm trying to experiment with jammer signal & normal signal identiication through DL approaches but I want to start with simple approach. Thank you for your time.
Comments.UserDisplayName: AnneBlue
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-08-04T10:06:24.707Z
Comments.Text: Since the datasets are just a list of numbers, I think the simplest approach to be classification. Basically, you concatenate the three datasets into one while associating a label to each number: you should obtain something like `(x, y=1=normal)` or `(x,y=0=jammed)` for each entry/sample. Then you feed the `x` to the neural net classifier to predict the `y`. Let me know if this works. Also, remember to shuffle the data while training the neural net
Comments.UserDisplayName: Luca Anzalone
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-08-04T12:43:36.187Z
Comments.Text: Hi Luca, I was wondering would concatenating and shuffling be a good idea? because to my understanding, the list of numbers in one file is one complete signal so if I were to shuffle the numbers, the sequence would be interrupted as the values vary in some pattern. The rest I understand, it can be a multi-class classification problem, y=0 for normal, y=1 for a randomly jammed signal and y=2 for a constantly jammed signal / splitting into testing and training bit and so on. What do you think? I'll try the classification method and definitely let you know how it went.
Comments.UserDisplayName: AnneBlue
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-08-06T09:32:14.167Z
Comments.Text: Ok if, so you can also group the single numbers into consecutive chunks of size `T`, classifying a sequence of `T` of them each time. Doing so results in sequences of shape `(B,T)` or `(B, T, 1)`. Then you can still apply MLP but also RNNs. But I suggest you to try the simplest thing first, then try with something more complex.
Comments.UserDisplayName: Luca Anzalone
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-08-10T05:52:41.843Z
Comments.Text: I see, that makes sense, I'll do the grouping approach definitely. I'll let you know how it goes or if I face another challenge
Comments.UserDisplayName: AnneBlue
Tags: <convolutional-neural-networks><model-request><signal-processing><1d-convolution><2d-convolution>
Title: What's the simplest deep learning approach in signal processing? A 1D CNN?
UserDisplayName: AnneBlue
ThreadText: # What's the simplest deep learning approach in signal processing? A 1D CNN?
  
  (Asked by: username_0 on 2024-07-24)
  
  I have a three datasets each containing one signal of a specific type (normal, periodically jammed, constantly jammed). I would like to experiment with this dataset to predict wether a signal is normal or if being jammed, the type. Fot this experimentation I thought about CNN but I read that they work better with 2D data and images. What would you recommend?
  
  EDit: It is a time series.
  
  **Tags:** convolutional-neural-networks, model-request, signal-processing, 1d-convolution, 2d-convolution
  
  > **Comments:**
  > **username_1**: What is the data type, vector or time-series/sequence? If vector, use MLP; If sequence, go with RNN or 1d CNN.  
  > **username_0**: Can you please elaborate more?  
  > **username_1**: Sure,  but I at least need to know the data type (i.e., the shape, dimensionality) :) I mean what is this "signal" you referring to? It is a sequence of features of shape `(N, T, D)`? Where `N` is the number of data samples, `T` the sequence length, and `D` the dimensionality of num. features-  
  > **username_0**: Thank you for the reply, https://github.com/AMHD/Jamming-Detection-in-IoT-Wireless-Networks-An-Edge-AI-Based-Approach ...in this repo, in the dataset portion you can see the signal values (they have been recorded at a constant interval of 30 minutes) are in a 1D array form. What's the simplest deep learning appraoch that will fit? I'm trying to experiment with jammer signal & normal signal identiication through DL approaches but I want to start with simple approach. Thank you for your time.  
  > **username_1**: Since the datasets are just a list of numbers, I think the simplest approach to be classification. Basically, you concatenate the three datasets into one while associating a label to each number: you should obtain something like `(x, y=1=normal)` or `(x,y=0=jammed)` for each entry/sample. Then you feed the `x` to the neural net classifier to predict the `y`. Let me know if this works. Also, remember to shuffle the data while training the neural net  
  > **username_0**: Hi username_1, I was wondering would concatenating and shuffling be a good idea? because to my understanding, the list of numbers in one file is one complete signal so if I were to shuffle the numbers, the sequence would be interrupted as the values vary in some pattern. The rest I understand, it can be a multi-class classification problem, y=0 for normal, y=1 for a randomly jammed signal and y=2 for a constantly jammed signal / splitting into testing and training bit and so on. What do you think? I'll try the classification method and definitely let you know how it went.  
  > **username_1**: Ok if, so you can also group the single numbers into consecutive chunks of size `T`, classifying a sequence of `T` of them each time. Doing so results in sequences of shape `(B,T)` or `(B, T, 1)`. Then you can still apply MLP but also RNNs. But I suggest you to try the simplest thing first, then try with something more complex.  
  > **username_0**: I see, that makes sense, I'll do the grouping approach definitely. I'll let you know how it goes or if I face another challenge  
  
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  ## 0 answers
  
  