### Signal Metrics

The *mean* of a signal stored in a matlab row- or column-vector
`x` can be computed in matlab as

mu = sum(x)/Nor by using the built-in function

`mean()`. If

`x`is a 2D matrix containing

`N`elements, then we need

`mu = sum(sum(x))/N`or

`mu = mean(mean(x))`, since

`sum`computes a sum along ``dimension 1'' (which is along columns for matrices), and

`mean`is implemented in terms of

`sum`. For 3D matrices,

`mu = mean(mean(mean(x)))`, etc. For a higher dimensional matrices

`x`, ``flattening'' it into a long column-vector

`x(:)`is the more concise form:

N = prod(size(x)) mu = sum(x(:))/Nor

mu = x(:).' * ones(N,1)/NThe above constructs work whether

`x`is a row-vector, column-vector, or matrix, because

`x(:)`returns a concatenation of all columns of

`x`into one long column-vector. Note the use of

`.'`to obtain non-conjugating vector transposition in the second form.

`N = prod(size(x))`is the number of elements of

`x`. If

`x`is a row- or column-vector, then

`length(x)`gives the number of elements. For matrices,

`length()`returns the greater of the number of rows or columns.

^{I.1}

#### Signal Energy and Power

In a similar way, we can compute the *signal energy*
(sum of squared moduli) using any of the following constructs:

Ex = x(:)' * x(:) Ex = sum(conj(x(:)) .* x(:)) Ex = sum(abs(x(:)).^2)The average power (energy per sample) is similarly

`Px = Ex/N`. The norm is similarly

`xL2 = sqrt(Ex)`(same result as

`xL2 = norm(x)`). The norm is given by

`xL1 = sum(abs(x))`or by

`xL1 = norm(x,1)`. The infinity-norm (Chebyshev norm) is computed as

`xLInf = max(abs(x))`or

`xLInf = norm(x,Inf)`. In general, norm is computed by

`norm(x,p)`, with

`p=2`being the default case.

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Inner Product

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Vector Interpretation of Complex Numbers