Choose a correct option stating difference between convolution and multiplication.Group of answer choicesConvolution leads to a superposition of signals while multiplication does not consider all the signalsConvolution is a multiplication of added signals.Convolution leads to addition and multiplication leads to the multiplicationConvolution is multiplication but of signals
Question
Choose a correct option stating difference between convolution and multiplication.Group of answer choicesConvolution leads to a superposition of signals while multiplication does not consider all the signalsConvolution is a multiplication of added signals.Convolution leads to addition and multiplication leads to the multiplicationConvolution is multiplication but of signals
Solution
The correct option is: Convolution leads to a superposition of signals while multiplication does not consider all the signals.
Here's why:
Convolution is a mathematical operation that is a special way to combine two signals to form a third signal. It is the integral of the product of the two functions, after one is reversed and shifted. The integral is evaluated for all values of shift, producing the convolution function. Convolution is used in digital signal processing to find the output of a linear time-invariant system, given the input and the impulse response.
On the other hand, multiplication is a basic arithmetic operation that simply multiplies two numbers together. In the context of signals, multiplication does not consider the superposition of signals, it simply multiplies the values of the two signals together at each point in time.
Similar Questions
1×1 convolutions are the same as multiplying by a single number. True/False?
What is Convolution
Weighted superposition of time-shifted impulse responses is termed as _______ for discrete-time signals.Group of answer choicesConvolution IntegralNoneConvolution SumConvolution Multiple
Which is false?Group of answer choicesOutput of a layer in the residual network is the normal output (i.e, what’s produced after applying a filter and an activation function) + the layers input.Output of a layer in the dense network is the normal output (i.e, what’s produced after applying a filter and an activation function) + the layers input.Output of layer 𝑙 in the dense network will be one of the inputs for layers 𝑖∈(𝑙+1,𝐿) where 𝐿 is the total number of layers.A problem of convolution network is that some features may get extracted earlier in the network, but could be useful later on. However, it is hard to keep track of previous non-modified outputs.
As per the convolution property of z-transformGroup of answer choicesthe z-transform of convolution of two sequences is the addition of their respective z-transforms.the z-transform of convolution of two sequences is the product of their respective z-transforms.the z-transform of convolution of two sequences is the convolution of their respective z-transforms.none
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