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.

应用信息论、测度论和理论计算机科学的原理，我们介绍了一种具有广泛应用于编码理论的单变量信号去卷积方法，特别是在零知识单向通信渠道中，例如从未知生成源解码没有先验知识和无法发送返回消息的信息。我们的多维空间重建方法从任意接收到的信号中得出的结果不依赖于编码解码方案、计算模型、编程语言、形式理论、计算（或半计算）的算法复杂性近似方法以及任意选择的（计算）事件概率测度。该方法源于一种实现人工通用智能的原理，能够构建一个独立于任意先验概率分布的模型，用于解码非随机数据，可应用于信号处理、因果去卷积、拓扑和几何特性编码、密码学以及生物和技术签名检测。