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For real stationary processes, the one-sided density on $[0,\pi ]$ is twice the usual two-sided density. Its integral is the variance and ${\gamma }_k={\int }_0^{\pi }f(\omega )\cos (k\omega )d\omega$. When covariances are absolutely summable, $f(\omega )={\pi }^{-1}({\gamma }_0+2{\sum }_{k\ge 1}{\gamma }_k\cos (k\omega ))$.

An optical photodetector detector pixel is a spatially sampled photosensitive element. Counts from distinct detector pixels have independent photon shot noise in the ideal model, but charge transfer, electrical coupling and resampling can produce covariance.

An orthogonal basis of an inner-product vector space consists of nonzero mutually orthogonal vectors that span the space. The orthogonal projection of $v$ onto a finite-dimensional span is ${\sum }_i⟨v,u_i⟩u_i/⟨u_i,u_i⟩$. Dividing each vector by its norm produces an orthonormal basis. For centered random variables, the inner product is covariance; an orthogonal basis therefore consists of uncorrelated variables, without requiring unit variance.

The stationary squared process has the positive series $X_t^2={\alpha }_0{\sum }_{k\ge 0}{\alpha }_2^k{\prod }_{j=0}^k{\epsilon }_{t-2j}^2$. Even and odd observations therefore use disjoint families of independent random variables. This explains zero lag-one covariance of the squares despite dependence at lag two.