  Based on the principles of information theory, measure theory, and
theoretical computer science, we introduce a univariate signal deconvolution
method with a wide range of applications to coding theory, particularly in
zero-knowledge one-way communication channels, such as in deciphering messages
from unknown generating sources about which no prior knowledge is available and
to which no return message can be sent. Our multidimensional space
reconstruction method from an arbitrary received signal is proven to be
agnostic vis-a-vis the encoding-decoding scheme, computation model, programming
language, formal theory, the computable (or semi-computable) method of
approximation to algorithmic complexity, and any arbitrarily chosen
(computable) probability measure of the events. The method derives from the
principles of an approach to Artificial General Intelligence capable of
building a general-purpose model of models independent of any arbitrarily
assumed prior probability distribution. We argue that this optimal and
universal method of decoding non-random data has applications to signal
processing, causal deconvolution, topological and geometric properties
encoding, cryptography, and bio- and technosignature detection.
