CHS: Small: Embedding Discreet Digital Data in Physical Artifacts

近几十年来，人们对数字信息的编码和传输技术进行了深入的研究，主要是针对数字通信信道。与此同时，在增材制造、网络物理系统、增强现实和物联网等领域，数字和物理之间的界限变得越来越模糊。这种趋势提供了一个机会，探索新的编码问题的通道体现的物理文物（如质量，声波和光波）。例如，我们如何将信息编码到加工零件中，以便机器人系统可以读取信息进行自动操作？当一个物理人工制品纯粹为人类消费而设计时，它参与数字世界的能力是有限的。 但是，如果我们可以在创建物理工件的过程中对数字信息（或标签）进行编码，那么物理标签将在许多应用中具有广泛的用途，其中物理工件与数字系统交互，包括装配，销售，机器人导航和操纵以及增强和混合现实。该项目的目标是理解和解决这些新出现的问题，并提出一个系统的方法，使人类和机器消费的物理工件。该方法将结合联合收割机的计算算法和物理编码方案的设计，主要来自计算机图形学，计算机视觉，信号处理，多媒体，优化和仿真领域。新工具将在众多应用领域中发挥作用，而在本研究期间接受培训的学生将了解不同领域的设计问题，并从多个学科开发解决方案。这项工作将产生额外的广泛影响，因为研究人员将邀请不同背景（计算机科学，工程，艺术）和不同层次（本科，硕士和博士）的学生参与。该项目将探索和应用通过利用人类感知的限制，在物理工件合成过程中谨慎和内在编码数字有效载荷的想法。在本研究的上下文中，术语“物理人工制品”是指人类可感知的物理世界的任何方面（例如，质量、声音、光等）;术语“数字有效载荷”指的是旨在由机器消费的数据，而“谨慎”意味着有效载荷的存在不应改变人对物理人工制品的感知，而“内在”意味着有效载荷在物理人工制品的生产过程中直接编码，而不是在生产之后的单独步骤中编码。从计算机科学的角度来看，该方法将结合联合收割机的计算算法和物理编码方案的设计。这项工作将集中在三个具体的情况下，信息编码在1）增材制造，2）声波，和3）光波。通过这些案例研究，该研究将推进一种系统的方法，用于在广泛的物理工件中编码信息。该方法将在多个方面形成，包括理解物理伪影的相关感知特性，识别用于数据嵌入的物理伪影的不可感知修改，分析信息容量，理解和提高编码鲁棒性，和实施一个易于-该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响进行评估，被认为值得支持审查标准。

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.