**Aim**: To develop a convolutional neural network that resolves mirror motion from video.
**Input** : Previous simulated video of beam spot motion in pitch by applying 4 sine waves of frquencies **0.2, 0.4, 0.1, 0.3 Hz** and amplitude ratios to frame size to be **0.1, 0.04, 0.05, 0.08 **where** random uniform noise ranging 0.05 **has been added to amplitudes and frequencies**. **This is divided into **train (0.4)**, **validation (0.1)** and **test (0.5)**.
__Model topology__:
- Number of filters = 2
- Kernel size = 2
- Size of pooling windows = 2
- -----> Dense layer of 4 nodes ----> Output layer of 1 node
__Activation__: selu linear
__Batch size__ = 32, __Number of epochs__ = 128, __loss function__ = mean squared error
**Optimizer: Nadam ( learning rate = 0.00001, beta_1 = 0.8, beta_2 = 0.85)**
Plots of CNN output & applied signal given in Attachment 1. The variation in loss value with epochs given in Attachment 2.
This needs to be further analysed with increasing random uniform noise over the pixels and by training CNN on simulated data of varying ampltides and frequencies for sine waves. |