Engineering Commons^™

Modeling Energy Consumption Of High-Performance Applications On Heterogeneous Computing Platforms, Gary D. Lawson Jr.

Computational Modeling & Simulation Engineering Theses & Dissertations

Achieving Exascale computing is one of the current leading challenges in High Performance Computing (HPC). Obtaining this next level of performance will allow more complex simulations to be run on larger datasets and offer researchers better tools for data processing and analysis. In the dawn of Big Data, the need for supercomputers will only increase. However, these systems are costly to maintain because power is expensive. Thus, a better understanding of power and energy consumption is required such that future hardware can benefit.

Available power models accurately capture the relationship to the number of cores and clock-rate, however the relationship …

Gridiron-Gurus Final Report: Fantasy Football Performance Prediction, Kyle Tanemura, Michael Li, Erica Dorn, Ryan Mckinney

Computer Science and Software Engineering

Gridiron Gurus is a desktop application that allows for the creation of custom AI profiles to help advise and compete against in a Fantasy Football setting. Our AI are capable of performing statistical prediction of players on both a season long and week to week basis giving them the ability to both draft and manage a fantasy football team throughout a season.

Fdetect Webserver: Fast Predictor Of Propensity For Protein Production, Purification, And Crystallization, Fanchi Meng, Chen Wang, Lukasz Kurgan

Computer Science Publications

Background: Development of predictors of propensity of protein sequences for successful crystallization has been actively pursued for over a decade. A few novel methods that expanded the scope of these predictions to address additional steps of protein production and structure determination pipelines were released in recent years. The predictive performance of the current methods is modest. This is because the only input that they use is the protein sequence and since the experimental annotations of these data might be inconsistent given that they were collected across many laboratories and centers. However, even these modest levels of predictive quality are still …

Accelerating The Solving Of Nonlinear Equations Using The Homotopy Method: Application On Finding The Operating Point Of Complex Circuits, Fathi Dhiabi, Mohamed Boumehraz

Turkish Journal of Electrical Engineering and Computer Sciences

Analog circuits with nonlinear elements (e.g., diode, BJT, and CMOS) and integrated circuits are very complex systems. As a result, they are very difficult to analyze because of the need to generate a nonlinear equation system solution in order to do so. Solving nonlinear equations is still a challenging problem. Iterative methods, however, are frequently used to solve them. This paper describes a method that can be used to both accelerate the solving of nonlinear equations and find the operating point in various integrated circuits by construction of the global homotopy equation of the analog circuit. This is done by …

Respiratory Prediction And Image Quality Improvement Of 4d Cone Beam Ct And Mri For Lung Tumor Treatments, Seonyeong Park

Theses and Dissertations

Identification of accurate tumor location and shape is highly important in lung cancer radiotherapy, to improve the treatment quality by reducing dose delivery errors. Because a lung tumor moves with the patient's respiration, breathing motion should be correctly analyzed and predicted during the treatment for prevention of tumor miss or undesirable treatment toxicity. Besides, in Image-Guided Radiation Therapy (IGRT), the tumor motion causes difficulties not only in delivering accurate dose, but also in assuring superior quality of imaging techniques such as four-dimensional (4D) Cone Beam Computed Tomography (CBCT) and 4D Magnetic Resonance Imaging (MRI). Specifically, 4D CBCT used in CBCT …

E-Mfdbscan: An Evolutionary Clustering Algorithm For Gene Expression Time Series, Atakan Erdem, Taflan İmre Gündem

Turkish Journal of Electrical Engineering and Computer Sciences

DNA microarray experiments are frequently used because they have various advantages. However, gene expression data from DNA microarray experiments are noisy, and, consequently, the computations that are based on such noisy data may lack accuracy. In this paper, an evolutionary uncertain data-clustering algorithm, E-MFDBSCAN, and a prediction model using E-MFDBSCAN for uncertain data are proposed. The proposed methodology may be successfully applied to noisy gene expression data. In this methodology, global patterns of time series data can be extracted using our evolutionary clustering approach. These patterns are used to infer future projections. In the proposed methodology, an autoregressive time series …