CHS: Small: Embedding Discreet Digital Data in Physical Artifacts

Technologies for coding and transmitting digital information have been thoroughly studied in recent decades, mainly for digital communication channels. Meanwhile, the line between digital and physical is becoming increasingly blurred in fields such as additive manufacturing, cyber-physical systems, augmented reality, and internet of things. This trend presents an opportunity to explore new coding problems on channels embodied by physical artifacts (such as mass, sound waves, and light waves). For example, how can we encode information in a machined part so that a robotic system can read the information for automated manipulation? When a physical artifact is designed purely for human consumption, its ability to be involved in the digital world is limited. But if we could encode digital information (or tags) during the creation of physical artifacts, the physical tags would have a wide range of uses in many applications wherein physical artifacts interact with digital systems, including assembly, sales, robotic navigation and manipulation, and augmented and mixed reality. The goal of this project is to understand and address these emerging issues, and to advance a systematic methodology for making physical artifacts for both human and machine consumption. The approach will combine the design of both a computational algorithm and a physical coding scheme, drawn primarily from the fields of computer graphics, computer vision, signal processing, multimedia, optimization, and simulation. The new tools will be useful across numerous application domains, while the students trained during this research will understand design problems in diverse areas and develop solutions from multiple disciplines. The effort will have additional broad impact because the investigators will invite participation by students with diverse backgrounds (computer science, engineering, arts) and at different levels (undergraduate, masters, and doctoral).

This project will explore and apply the idea of discreetly and intrinsically encoding digital payloads during the synthesis of physical artifacts by exploiting limitations in human perception. In the context of this research, the term 'physical artifact' refers to any aspect of the physical world that is perceivable by a human (e.g., mass, sound, light, etc.); the term 'digital payload' refers to data intended for consumption by a machine, while 'discreet' means that the presence of the payload should not alter a person's perception of the physical artifact, and 'intrinsic' means that the payload is encoded directly during the production process of the physical artifact rather than during a separate step after production. The approach, from a computer science perspective, will combine the design of both a computational algorithm and a physical coding scheme. The work will focus on three concrete cases of information encoding in 1) additive manufacturing, 2) acoustic waves, and 3) optical waves. Informed by these case studies, the research will advance a systematic methodology for coding information in a wide range of physical artifacts. This methodology will be shaped at multiple aspects, including understanding relevant perceptual characteristics of the physical artifact, identification of imperceptible modifications of the physical artifact for data embedding, analysis of the information capacity, understanding and improving coding robustness, and implementation of an easy-to-use encoding and decoding program.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.