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In recent years, the traditional feature engineering process for training machine learning models is being automated by the feature extraction layers integrated in deep learning archi-tectures. In wireless networks, many studies were conducted in automatic learning of feature representations for domain-related challenges. However, most of the exist...
Contexts in source publication
Context 1
... we examined the best clusters developed with the baseline and the CNN-based approach using histograms of samples accompanied with the average cluster spectrograms in Figure 5 and Figure 6, respectively. ...
Context 2
... the best baseline approach containing 3 clusters, Fig- ure 5 shows that the cluster 0 contains mostly the samples from sub-bands between 1 and 7, the cluster 1 contains the samples from the left-most sub-band and the cluster 2 contains almost all of the samples from the right-most sub-band. Sub-bands refer to sections of frequency-wise segmentation as shown in Figure 2. ...
Context 3
... weaker signal seems to be a consequence of the nonuniform sensing capability of the sensor. Looking back to Figure 5, the left-most and the right-most samples have gradually vanishing brightness towards the edges. Plotting the average of the assigned spectrograms from each of the clusters supports this observation. ...
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