From Compressed Sensing to Collective Sensing: a Complex Network Approach

ECCS-0968730李欣，西弗吉尼亚大学从压缩感知到集体感知：一种复杂网络方法摘要知识价值：基于约简主义的分治方法已经在从传感器和手机到机器人和计算机的许多工程系统的设计中取得了丰硕的成果。相比之下，自然界中的复杂系统，如蚁群和人脑，是由大量简单单元相互作用以及环境相互作用的集体动力学驱动的。这些单位如何以自组织的方式形成全球复杂的行为仍然是科学中的一个大谜团。解决这个难题，即使是部分解决，也可能对集成和混合系统的设计和优化产生深远的影响，从而更好地服务于各种工程应用。该项目的重点是对传感和处理的集成的集体方法。与压缩传感不同，压缩传感的成功是基于数学规范或基函数的巧妙构建，该项目利用了物理世界中感官信号的基本组织原理。在成功完成该项目后，将更好地理解复杂网络的集体动态如何与传感组件相互作用，以支持运动感知和模式识别等复杂任务。拟议的研究活动将通过培养研究生纳入教育（包括科学研究伦理学）、促进网络科学相关学习、开发网络空间教材以及扩大代表性不足群体的参与。所有收集的数据、产生的代码和实验结果都将向公众发布，这促进了可重复研究的原则。对工程的科学观点预计将有利于工程专业的学生在课堂内外。网络科学和传感器技术之间的联系可能会影响到电子成像行业，并对集成传感器系统的设计产生潜在的影响。

ECCS-0968730Xin Li, West Virginia UniversityFrom Compressed Sensing to Collective Sensing: a Complex Network ApproachABSTRACTIntellectual Merit: Reductionism-based divide-and-conquer approach has been fruitful to the design of many engineering systems from sensors and cell-phones to robots and computers. By contrast, complex systems in nature such as ant colony and human brain are driven by the collective dynamics involving a large number of simple units interacting with each other as well as the environment. How those units form a globally complex behavior in a self-organizing fashion remains a big mystery in science. Solving this puzzle, even partially, may have profound implications into the design and optimization of integrative and hybrid systems that could better serve various engineering applications. This project focuses on a collective approach toward the integration of sensing and processing. Unlike compressed sensing whose success is based on ingenious construction of mathematical norms or basis functions, this project exploits the underlying organizational principle of sensory signals in the physical world. Upon the successful completion of this project, there will be an improved understanding of how the collective dynamics of complex networks interacts with the sensing component to support complex tasks such as motion perception and pattern recognition.Broader Impacts: The proposed research activities will be integrated into the education through training graduate students (including ethnics of scientific research), promoting network-science related learning, developing cyber-space teaching material, and broadening the participation of underrepresented groups. All collected data, produced codes and experimental results will be released to the public, which promotes the principle of reproducible research. The scientific perspective toward engineering is expected to benefit engineering students both inside and outside the classroom. The connection between network science and sensor technology could leverage into the industry of electronic imaging and have potential impact on the design of integrated sensor systems.