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.

翻译：基于信息论、测度论和理论计算机科学的基本原理，我们提出了一种适用于编码理论中广泛场景的单变量信号反卷积方法。该方法特别适用于零知识单向通信信道——例如在无需任何先验知识且无法发送返回消息的情况下，对来自未知信源的加密信息进行破译。我们证明：从任意接收信号重建多维空间的方法，对于编码-解码方案、计算模型、编程语言、形式理论、算法复杂度的可计算（或半可计算）近似方法，以及任意选择的（可计算）事件概率测度均具有无偏性。该方法源于一种通用人工智能框架，该框架能够构建独立于任何任意假设先验概率分布的通用模型之模型。我们论证这种最优且普适的非随机数据解码方法，可应用于信号处理、因果反卷积、拓扑与几何属性编码、密码学，以及生物与技术信号探测等领域。