We introduce a general-purpose univariate signal deconvolution method based
on the principles of an approach to Artificial General Intelligence. This
approach is based on a generative model that combines information theory and
algorithmic probability that required a large calculation of an estimation of a
`universal distribution' to build a general-purpose model of models independent
of probability distributions. This was used to investigate how non-random data
may encode information about the physical properties such as dimension and
length scales in which a signal or message may have been originally encoded,
embedded, or generated. This multidimensional space reconstruction method is
based on information theory and algorithmic probability, and it is agnostic,
but not independent, with respect to the chosen computable or semi-computable
approximation method or encoding-decoding scheme. The results presented in this
paper are useful for applications in coding theory, particularly in
zero-knowledge one-way communication channels, such as in deciphering messages
sent by generating sources of unknown nature for which no prior knowledge is
available. We argue that this can have strong potential for cryptography,
signal processing, causal deconvolution, life, and techno signature detection.Comment: 35 page