Numerical Analysis and Computation Commons^™

Secondary Electrohydrodynamic Flow Generated By Corona And Dielectric Barrier Discharges, Mohammadreza Ghazanchaei

Electronic Thesis and Dissertation Repository

One of the main goals of applied electrostatics engineering is to discover new perspectives in a wide range of research areas. Controlling the fluid media through electrostatic forces has brought new important scientific and industrial applications. Electric field induced flows, or electrohydrodynamics (EHD), have shown promise in the field of fluid dynamics. Although numerous EHD applications have been explored and extensively studied so far, most of the works are either experimental studies, which are not capable to explain the in depth physics of the phenomena, or detailed analytical studies, which are not time effective. The focus of this study is …

Efficient Thermal Image Segmentation Through Integration Of Nonlinear Enhancement With Unsupervised Active Contour Model, Fatema Albalooshi, Evan Krieger, Paheding Sidike, Vijayan K. Asari

Electrical and Computer Engineering Faculty Publications

Thermal images are exploited in many areas of pattern recognition applications. Infrared thermal image segmentation can be used for object detection by extracting regions of abnormal temperatures. However, the lack of texture and color information, low signal-to-noise ratio, and blurring effect of thermal images make segmenting infrared heat patterns a challenging task. Furthermore, many segmentation methods that are used in visible imagery may not be suitable for segmenting thermal imagery mainly due to their dissimilar intensity distributions.

Thus, a new method is proposed to improve the performance of image segmentation in thermal imagery. The proposed scheme efficiently utilizes nonlinear intensity …

One-Bit Compressive Sensing With Partial Support Information, Phillip North

CMC Senior Theses

This work develops novel algorithms for incorporating prior-support information into the field of One-Bit Compressed Sensing. Traditionally, Compressed Sensing is used for acquiring high-dimensional signals from few linear measurements. In applications, it is often the case that we have some knowledge of the structure of our signal(s) beforehand, and thus we would like to leverage it to attain more accurate and efficient recovery. Additionally, the Compressive Sensing framework maintains relevance even when the available measurements are subject to extreme quantization. Indeed, the field of One-Bit Compressive Sensing aims to recover a signal from measurements reduced to only their sign-bit. This …