From Compressed Sensing to Collective Sensing: a Complex Network Approach

点击翻译按钮获取中文摘要

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.