Which of the following is the best conceptual definition of one dimensional convolution?1 point“Inverting” of a shape, where the inversion matches a feature.“Distortion” of one signal, according to the feature shape“Intertwining” of two signals, where one wraps around the other to form a feature.“Sliding” of two signals, where a matched feature gives a high value of convolution

The best conceptual definition of one-dimensional convolution is "Sliding" of two signals, where a matched feature gives a high value of convolution.

Here's a step-by-step explanation:

1. Convolution is a mathematical operation that is a special kind of linear operation.

2. In one-dimensional convolution, we have two signals. One is the input signal and the other one is the filter or kernel which we slide over the input signal.

3. The kernel is flipped and then slid over the input signal. For every position, we calculate the element-wise multiplication of the kernel and the part of the input it currently covers and sum them up.

4. This sum is the output of the convolution for the current position of the kernel on the input.

5. The kernel is then slid over the entire input signal, calculating the sum for each position.

6. The result is a new signal which is typically of different size than the input signal.

7. The output signal will have high values at places where the input signal and the kernel matched well. This is why the definition says "where a matched feature gives a high value of convolution".