Stochastic sampling techniques, in particular Poisson and fittered sampling, are developed and analyzed. These approaches allow the construction of alias-free approximations to continuous functions using discrete calculations. Stochastic sampling scatters high frequency information into broadband noise rather than generating the false patterne produced by regular sampling. The type of randomness used in the sampling process controls the spectral character of the noise. The average sampling rate and the function being sampled determine the amount of noise that is produced. Stochastic sampling is applied adaptively so that a greater number of samples are taken where the function varies most. An estimate is used to determine how many samples to take over a given region. Noise reducing filters are used to increase the efficacy of a given sampling rate. The filter width is adaptively controlled to further improve performance. Stochastic sampling can be applied spatiotemporally as well as to other aspects of scene simulation. Ray tracing is one example of an image synthesis approach that can be antialiased by stochastic sampling.

In some signal processing applications, it is desirable to build very high performance fast Fourier transform (FFT) processors. To meet the performance requirements, these processors are typically highly pipelined. Until the advent of VLSI, it was not possible to build a single chip which could be used to construct pipeline FFT processors of a reasonable size. However, VLSI implementations have constraints which differ from those of discrete implementations, requiring another look at some of the typical FFT'algorithms in the light of these constraints.