The objective of this research is to develop and to apply advances in circuit integration and Compressive Sensing theory to the design of next generation of energy-harvesting image sensors. Compressive Sensing is a new theoretical framework for signal acquisition. In the proposed project, Compressive Sensing provides a solution for the trade-off between image quality and harvested energy. Specifically, these image sensors will have reconfigurable pixels that can work as light sensors or as tiny solar cells. Depending on the environmental conditions more or less pixels can be reconfigured to either modality.
The proposed research introduces a new sensing paradigm especially suited to sensor networks and energy-constrained sensing applications. It also introduces a new approach based on fractal geometry to increase the efficiency of on-chip solar cells. The project will advance the field of Compressive Sensing by providing hardware solutions and practical encoding techniques. Analog-to-digital converters that can jointly perform the tasks of quantization and entropy encoding are also included in this project.
The proposed research will result in low-cost image sensors suitable for surveillance, space exploration, and monitoring applications. These sensors can harvest ambient energy and, thus, can operate unattended for extended periods of time. Moreover, the proposed fractal photodiodes can be employed to improve solar cell design. The proposed educational activities will enhance the curricula of courses taught at University of Missouri-Kansas City. A design competition and a workshop will be implemented to increase the participation of underrepresented students in research activities and to enrich outreach efforts.
