Beyond a Usage Threshold, NO Form of Energy is Sustainable or Green We are Running Out of “Garbage Dump Space ” To Dissipate “Used ” Energy Into

To date, almost all of the research on green/sustainable energy has been concerned with procurement of ever increasing amounts of energy for human consumption. This singular focus only on the supply-side of the problem completely overlooks what happens to the energy after we use it; thereby implicitly making the dangerously wrong assumption that the earth has unlimited capacity to dissipate energy. In this position paper, we remind the reader that the earth can dissipate only a finite amount of even the greenest of the green forms of energy, while still maintaining thermal equilibria that have evolved over eons. Any long term sustainable energy solution therefore must include a curbing/limiting/controlling our demand for (and consequently, our consumption of) energy. Otherwise, even if and even after all the green-house-effects are fully eliminated, the earth still might eventually experience unnaturally large temperature increase because the amount of energy dissipated is too large

Development of a computational image sensor with applications in integrated sensing and processing

The objective of this research was to build a reprogrammable computational imager utilizing on-chip analog computations for the purpose of studying the capabilities of integrated sensing and processing. Unlike conventional imaging systems, which acquire image data and perform calculations on it, this system tightly integrates the computation and sensing into one process. This allows the exploration of intelligent and efficient sensory and processing. The IC architecture and circuit designs have focused on wide dynamic range signals. The fundamental computation performed is a separable two-dimensional transform. This allows various operations, including block transformations and separable convolutions. The operations are reprogramable and utilize analog memory and processing along with digital control. The random access to both the image plane and the computational operations allows for intraframe transform variations creating a hardware foundation for dynamic sampling and computation. One can also capture scenes with non-uniform resolution. Advantages, including utilization of feedback from processing to sensing and extensions of the technology including support for wavelets and larger transforms are also explored.Ph.D

chip error compensation, light adaptation, and image enhancement with a cmos transform image sensor

ACKNOWLEDGEMENTS I want to thank all the members of my group, my advisor, and my committee for all their help and time