Browsing by Author "Dr. Yahya Fathi, Committee Member"

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An Application of Linear Optimization in Anaglyph Stereo Image Rendering
(2006-04-26) Zhang, Zhe; Dr. David F. McAllister, Committee Chair; Dr. Yahya Fathi, Committee Member; Dr. S. Purushothaman Iyer, Committee Member
We evaluate a new method for computing color anaglyphs based on uniform approximation in CIE color space. The method depends on the spectral distribution properties of the primaries of the monitor and the transmission functions of the filters in the viewing glasses. We will compare the result of this method with several other methods that have been proposed for computing anaglyphs. To compute the color at a given pixel in the anaglyph image requires solving a linear program. We exploit computational properties of the simplex algorithm to reduce computation time by 72 to 89 percent for the images tested. After computing the color at a pixel, a depth-first search is performed to collect all neighboring pixels with similar color so that a simple matrix-vector multiplication can be applied. We also parallelize the algorithm and implement it on a cluster environment. We discuss the effects of different data dividing schemes.
A Comparison of Factor Screening Methods for Simulation Models
(2007-01-01) Yaesoubi, Reza; Dr. James R. Wilson, Committee Member; Dr. Stephen D. Roberts, Committee Chair; Dr. Charles E. Smith, Committee Member; Dr. Yahya Fathi, Committee Member
Computer simulation models that represent a real-world system consist of a large number of input variables which are generally referred to as factors in Design of Experiments (DOE). The large number of involved factors makes certain analyses which are usually conducted on the simulation models prohibitive or impractical. These analyses may include building predictive metamodels, finding the optimum factor settings for the simulated system, and etc. Factor Screening experiments are intended to examine all or some of the involved factors to identify those with significant effect on a selected response (output). The identified important factors can then be used in subsequent analyses. This thesis is focused on factor screening methods with promising performance on simulation models from the medical decision making community with a relatively large number of factors. Two groups of factor screening methods are addressed: classical designs which are generally used for physical systems, and recent designs which have been exclusively developed for simulation models. Among the classic designs, 2k Fractional Factorial (FF) Designs and Central Composite Designs are investigated in depth, because of their superior performance on the simulation models. Among the recent methods developed for simulation, Sequential Bifurcation (SB), folded-over SB (SB-X), Cheng's method, Controlled Sequential Bifurcation (CSB), folded-over CSB (CSB-X), Latin Hypercube Designs (LHD), and Nearly Orthogonal Latin Hypercube (NOLH) designs are addressed. In addition, two methods based on Cheng's method are developed in this thesis: the Modified Cheng's method, and the folded-over Modified Cheng's method (MCh-X). MCh-X is shown in this research that has superior performance compared with FF designs, Cheng's method, and CSB-X for situations where the response has high homogeneous variance. Next, several criteria are considered for evaluating the factor screening methods, and the screening methods are compared based on the proposed criteria. Furthermore, the factor screening experiments are conducted on two available deterministic and stochastic simulation models. For the deterministic medical decision model, 2k Fractional Factorial Designs, folded-over SB (SB-X), and Nearly Orthogonal Latin Hypercube (NOLH) designs are used; and for the stochastic medical decision model, 2k Fractional Factorial Designs, folded-over Modified Cheng's (MChe-X), and folded-over CSB (CSB-X) were applied. Finally, based on quantitative measures, the performance of each method used for the available simulation models is evaluated in terms of its efficiency (requiring minimum number of runs), effectiveness (accuracy), and cost-effectiveness (achieving the highest accuracy with the least number of runs). Cost-effectiveness, which to the best of our knowledge has never been used as a criterion for evaluating factor screening methods, is introduced as a new measure encompassing both the concept of accuracy and efficiency. The research revealed that for the deterministic model, SB-X and for the stochastic model, MCh-X are the most cost-effective methods.
An Industrial Application of the 2-Dimensional Finite Bin Packing Problem
(2003-03-02) Nagarajan, Rajesh P; Dr. Robert B Handfield, Committee Chair; Dr. Ralph Smith, Committee Member; Dr. Yahya Fathi, Committee Member
The 2-Dimensional Finite Bin Packing Problem is a NP hard problem with it being observed in many of the process industries like woodcutting, textile manufacturing and steel industry. Here the scenario is a fiberglass mesh manufacturing industry, whose prime objective is to reduce material wastages due to cutting. We evaluate a few exact methods and a few heuristic techniques, which have been proposed to solve this problem. We further select Finite Best-Strip algorithm for implementation. We make modification to that technique to handle guillotine constraints. An after effect of this technique is observed in order tracking, which is reduced using k-way graph partitioning technique. The problem is tested for its performance by using datasets provided by the company. The results obtained are compared with the actual scenario. The experimental results show that this technique performs much better than the currently used technique and also a much better upper bound on the solution is observed when compared to previously known upper bounds for problems of these types. We also discuss on the future scope and enhancements possible for this technique to obtain better results.
Nano-scale Molecular Docking and Assembly Simulator (NanoDAS) with Haptic Force-Torque Rendering and Energy Minimization for Computer-Aided Molecular Design (CAMD)
(2006-06-30) Lai-Yuen, Susana Karina; Dr. Yahya Fathi, Committee Member; Dr. Christopher G. Healey, Committee Member; Dr. Yuan-Shin Lee, Committee Chair; Dr. Donald W. Brenner, Committee Member; Dr. Ezat T. Sanii, Committee Member
The objective of this research is to investigate and develop computational and haptic interface techniques to facilitate the design of molecular docking and molecular assembly for computer-aided molecular design (CAMD). Nano-scale molecular docking and molecular assembly are vital for the discovery and development of medicines, nano-scale devices, and new materials. In this paper, a new method called NanoDAS (Nano-scale Docking and Assembly Simulator) is presented to determine the feasibility of a ligand molecule reaching the binding site of a receptor molecule. To improve the design of molecular docking process, effective user intervention is necessary and is introduced through the use of a 5-DOF (degrees of freedom) force-torque feedback Haptic device developed at our research lab. Through the force-torque feedback haptic interface, a user is able to feel the forces exerted on the ligand by the receptor and find a feasible path using the proposed NanoDAS. The user is also able to determine whether the ligand can actually dock into the receptor by considering its conformational changes using a proposed energy minimization algorithm. The developed techniques can be used in Computer-Aided Molecular Design (CAMD) and Computer-Aided Drug Design (CADD) applications. Computer implementations and practical examples of the proposed methods are also presented.
Nonlinear Programming and Optimal Control Approach To the study of Social Network
(2007-06-20) Hong, Chung-Chien; Dr. N.G. Medhin, Committee Chair; Dr. H.T. Banks, Committee Member; Dr. H.T. Tran, Committee Member; Dr. Yahya Fathi, Committee Member
This research is a study of social network analysis and we approach it using nonlinear programming, statistics, dynamical systems and differential games theory. The ideas and techniques developed can be adapted to formulate public policy for social intervention, understanding cultural and social groups, marketing strategies by businesses, international relations etc. The study of social networks deals with the mathematical study of the formation and evolution of friendship links between members of a given social group. Each member of a social group has a set of preferred values and attributes and forms links with other members of the social group on the basis of shared values and attributes. This is precisely the basis of the nonlinear programming approach. That is, one seeks to construct an appropriate nonlinear programming on the basis of identified values and attributes of a social group. The solution of the nonlinear programming problem is used to decide whether or not a link is likely to exist between any two members of the social group. A friendship network can be conveniently presented by using a matrix called a social matrix. A type of social matrix that is commonly used is one where each entry of the matrix is either one or zero corresponding to the presence or absence of friendship respectively. Each member of a group, in general, acts on the basis of self interest, for example, to get as many links as possible with controlled time varying strategic compromises on personal preferences and attributes resulting in a time evolving social network. To capture the essence of the time evolution of the friendship network a differential games approach is appropriate. In this dissertation the study of social networks is initially approached using nonlinear programming. Then, dynamic models are considered for time evolving social networks. The solutions of these models are then analyzed for their qualitative and long time behavior. The dynamic models are then used to formulate differential games models for social networks. Illustrative examples, numerical computations, and analyses are presented to illustrate how one uses these twin approaches for the study of social networks.
Stochastic Modeling for Optimal Batch Scheduling of Prophylactic Dispensing Centers
(2010-04-09) Moomaw, Lindsay M.; Dr. Brian T. Denton, Committee Chair; Dr. Julie S. Ivy, Committee Member; Dr. Yahya Fathi, Committee Member
We study stochastic optimization models for scheduling batch arrivals to a Point of Dispensing (POD) in response to a biological emergency in which mass vaccination or dispensing of antiviral medication is implemented. The objective of our model is to minimize the total expected waiting time for customers and idle time of servers while considering the stochasticity of service times and customer flow through the POD. We begin with a simplified design of a POD which includes three function-defined servers and the basic queuing elements that would be utilized in a realistic POD, including splitting and merging queues. We create two-stage stochastic programming formulations to model two cases. In the first case of splitting queues, numerical results suggest an optimal constant batch interarrival time can produce total expected waiting and idle time costs near those of the optimal solution to the stochastic program. Stochastic programming formulations for the second case of split and merged queues prove to be more difficult. We study information and integer relaxations which provide approximations that are easier to solve. However, our results indicate relatively wide gaps on the optimal solution value. We expand the POD design to include eight stations to better represent a realistic facility. Using discrete event simulation, we test two batch interarrival time heuristics and report the similarity in their best solutions with respect to lowest expected costs of waiting and total operating time.