Higher Order Aberrations (HOAs) are complex refractive errors in the human eye that cannot be corrected by regular lens systems. Researchers have developed numerous approaches to analyze the effect of these refractive errors; the most popular among these approaches use Zernike polynomial approximation to describe the shape of the wavefront of light exiting the pupil after it has been altered by the refractive errors. We use this wavefront shape to create a linear imaging system that simulates how the eye perceives source images at the retina. With phase information from this system, we create a second linear imaging system to modify source images so that they would be perceived by the retina without distortion. By modifying source images, the visual process cascades two optical systems before the light reaches the retina, a technique that counteracts the effect of the refractive errors. While our method effectively compensates for distortions induced by HOAs, it also introduces blurring and loss of contrast; a problem that we address with Total Variation Regularization. With this technique, we optimize source images so that they are perceived at the retina as close as possible to the original source image. To measure the effectiveness of our methods, we compute the Euclidean error between the source images and the images perceived at the retina. When comparing our results with existing corrective methods that use deconvolution and total variation regularization, we achieve an average of 50% reduction in error with lower computational costs.

Utilization of machine learning algorithms in time-series data analysis is crucial to effective decision making in today's dynamic and competitive environment. One data type of growing interest is the electricity consumer load profile (LP) data. Owing to advances in the smart grid, immense amount of LP data became available to policymakers as potential to improving the electricity sector. Due to the growing size and volatile nature of LP data, development and evaluation of clustering approaches has been of high demand in recent energy research, whereas the classification techniques receive less attention. This study is the first to address the effect of LP time-series data temporal dimension on the classification performance using the most popular classification algorithms in machine learning including decision trees, support vector machines (SVM), discriminant analysis, and ensemble methods. Results indicate a decline in the classification accuracy as the temporal dimension increases. Accordingly, this study proposes a fuzzy classification heuristic-based method inspired by the genetic algorithm (GA) which proves to maintain robustness against high temporal dimensions. The results are assessed using real data from industrial consumers with 420 daily LPs and 93 weekly LPs.

The technology exists for the migration of healthcare data from its archaic paper-based system to an electronic one, and, once in digital form, to be transported anywhere in the world in a matter of seconds. The advent of universally accessible healthcare data has benefited all participants, but one of the outstanding problems that must be addressed is how the creation of a standardized nationwide electronic healthcare record system in the United States would uniquely identify and match a composite of an individual's recorded healthcare information to an identified individual patients out of approximately 300 million people to a 1:1 match. To date, a few solutions to this problem have been proposed that are limited in their effectiveness. We propose the use of biometric technology within our fingerprint, iris, retina scan, and DNA (FIRD) framework, which is a multiphase system whose primary phase is a multilayer consisting of these four types of biometric identifiers: 1) fingerprint; 2) iris; 3) retina scan; and 4) DNA. In addition, it also consists of additional phases of integration, consolidation, and data discrepancy functions to solve the unique association of a patient to their medical data distinctively. This would allow a patient to have real-time access to all of their recorded healthcare information electronically whenever it is necessary, securely with minimal effort, greater effectiveness, and ease.