Could you list a number of methods to recognize patterns or profiles in a 1-dimensional signal?

Could you please list a number of ways to recognize a pre-trained pattern or profile in a 1-dimensional signal?

Here's one: If you know the representation of the signal before it's been exposed to noise you can used matched filtering to optimally determine if the signal is present in a noisy version. Simply convolve the known signal with the noisy version and in the output peaks will show on top of the noise indicating the signal was detected. The "backwards signal" shown below will be handled by the convolution's flip and shift.