A regularization protocol that builds on the parsimonious parameterization methods described above, and that meets reasonable user expectations, was introduced qualitatively in Part 1 of this report. The approach can be further justified and understood through consideration of the distortion and resolution loss that results from truncated SVD analysis starting with a known, sharp distribution. Figure shows the result of using truncated SVD to fit a single component spike distribution. The spike (orange) was used to generate a noiseless decay curve. Analyzing that decay curve using a geometrically incremented (256 components) amplitude-only basis set in conjunction with the truncated (k=15) SVD algorithm gives the result plotted in blue. The result appears much like the convolution of the starting spike with a sinc ( ( ) sin xx ) function. Since sinc-like distortions are frequently caused by truncation of signals in a Fourier domain, this suggests that truncated SVD processing has roughly the same effect as calculating the inverse Fourier transform of the input spike, truncating that representation sharply at a cutoff, and then performing the forward Fourier transform to regenerate the original spectral domain.