Engineering Commons^™

A Compact Wavelength Meter Using A Multimode Fiber, Ogbole Collins Inalegwu

Masters Theses

“Wavelength meters are very important for precision measurements of both pulses and continuous-wave optical sources. Conventional wavelength meters employ gratings, prisms, interferometers, and other wavelength-sensitive materials in their design. Here, we report a simple and compact wavelength meter based on a section of multimode fiber and a camera. The concept is to correlate the multimodal interference pattern (i.e., speckle pattern) at the end-face of a multimode fiber with the wavelength of the input lightsource. Through a series of experiments, specklegrams from the end face of a multimode fiber as captured by a charge-coupled device (CCD) camera were recorded; the images …

An Analog Cmos Particle Filter, Trevor Watson

Masters Theses

Particle filters are used in a variety of image processing and machine learning applications. Their main use in these applications is to gather information about a system of objects, by using partial or noisy observations collected from sensors. These observations are used to associate points of interest in the observations with objects and maintain this association through a series of observations.

In this paper I will investigate the performance of a particle filter implemented in 130nm analog CMOS hardware. The design goal of the particle filter is low-microwatt power consumption. Using analog hardware, rather than digital ASICs or CPUs I …

Explorations Into Machine Learning Techniques For Precipitation Nowcasting, Aditya Nagarajan

Masters Theses

Recent advances in cloud-based big-data technologies now makes data driven solutions feasible for increasing numbers of scientific computing applications. One such data driven solution approach is machine learning where patterns in large data sets are brought to the surface by finding complex mathematical relationships within the data. Nowcasting or short-term prediction of rainfall in a given region is an important problem in meteorology. In this thesis we explore the nowcasting problem through a data driven approach by formulating it as a machine learning problem.

State-of-the-art nowcasting systems today are based on numerical models which describe the physical processes leading to …