Figure 2.

Representation of a temporal convolution over a single sensor channel in a three-layer convolutional neural network (CNN). Layer $(l-1)$ defines the sensor data at the input. The next layer (l) is composed of two feature maps ($a_1^l\left(\tau \right)$ and $a_2^l\left(\tau \right)$) extracted by two different kernels ($K_{11}^{(l-1)}$ and $K_{21}^{(l-1)}$). The deepest layer (layer $(l+1)$) is composed by a single feature map, resulting from temporal convolution in layer l of a two-dimensional kernel $K_1^l$. The time axis (which is convolved over) is horizontal.