Hector Zenil , Luan Carlos de Sena Monteiro Ozelim
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引用次数: 0
Abstract
In communication systems, the circumstances and capabilities of senders and receivers cannot be known/assumed beforehand so as to design optimal semantic transference strategies. Regardless of the recipient (plants, insects, or even life forms unknown on Earth), the spatio-temporal scale of a message could be inappropriate and may never be decoded due to incompatibilities at both ends. We devise a new method to encode messages that is agnostic vis-a-vis space and time scales. We propose the use of fractal functions as self-executable carriers for sending messages, given their properties of structural self-similarity and scale invariance. We call this ‘fractal messaging’. Starting from a spatial embedding, we introduce a framework for a space-time scale-free messaging approach. In creating a space and time agnostic framework for message transmission, encoding a message that could be decoded at several spatio-temporal scales is the objective. Our core idea is to encode a binary message as waves along infinitely many frequencies (in power-like distributions) and amplitudes, transmit such a message, and then decode and reproduce it. To do so, the components/cycles of the Weierstrass function, a known fractal, are used as carriers of the message. Each component will have its amplitude modulated to embed the binary stream, allowing for a space-time agnostic approach to messaging.
期刊介绍:
Informatics and Computer Science Intelligent Systems Applications is an esteemed international journal that focuses on publishing original and creative research findings in the field of information sciences. We also feature a limited number of timely tutorial and surveying contributions.
Our journal aims to cater to a diverse audience, including researchers, developers, managers, strategic planners, graduate students, and anyone interested in staying up-to-date with cutting-edge research in information science, knowledge engineering, and intelligent systems. While readers are expected to share a common interest in information science, they come from varying backgrounds such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioral sciences, and biochemistry.