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Full-Text Articles in Physical Sciences and Mathematics
Modeling Wireless Networks For Rate Control, David C. Ripplinger
Modeling Wireless Networks For Rate Control, David C. Ripplinger
Theses and Dissertations
Congestion control algorithms for wireless networks are often designed based on a model of the wireless network and its corresponding network utility maximization (NUM) problem. The NUM problem is important to researchers and industry because the wireless medium is a scarce resource, and currently operating protocols such as 802.11 often result in extremely unfair allocation of data rates. The NUM approach offers a systematic framework to build rate control protocols that guarantee fair, optimal rates. However, classical models used with the NUM approach do not incorporate partial carrier sensing and interference, which can lead to significantly suboptimal performance when actually …
Adaptive Balancing Of Exploitation With Exploration To Improve Protein Structure Prediction, Tj Brunette
Adaptive Balancing Of Exploitation With Exploration To Improve Protein Structure Prediction, Tj Brunette
Open Access Dissertations
The most significant impediment for protein structure prediction is the inadequacy of conformation space search. Conformation space is too large and the energy landscape too rugged for existing search methods to consistently find near-optimal minima. Conformation space search methods thus have to focus exploration on a small fraction of the search space. The ability to choose appropriate regions, i.e. regions that are highly likely to contain the native state, critically impacts the effectiveness of search. To make the choice of where to explore requires information, with higher quality information resulting in better choices. Most current search methods are designed to …
Automated, Parallel Optimization Algorithms For Stochastic Functions, Dheeraj Chahal
Automated, Parallel Optimization Algorithms For Stochastic Functions, Dheeraj Chahal
All Dissertations
The optimization algorithms for stochastic functions are desired specifically for real-world and simulation applications where results are obtained from sampling, and contain experimental error or random noise. We have developed a series of stochastic optimization algorithms based on the well-known classical down hill simplex algorithm. Our parallel implementation of these optimization algorithms, using a framework called MW, is based on a master-worker architecture where each worker runs a massively parallel program. This parallel implementation allows the sampling to proceed independently on many processors as demonstrated by scaling up to more than 100 vertices and 300 cores.
This framework is highly …
Characterization Of A Boron Carbide Heterojunction Neutron Detector, James E. Bevins
Characterization Of A Boron Carbide Heterojunction Neutron Detector, James E. Bevins
Theses and Dissertations
New methods for neutron detection have become an important area of research in support of national security objectives. In support of this effort, p-type B5C on n-type Si heterojunction diodes have been built and tested. This research sought to optimize the boron carbide (BC) diode by coupling the nuclear physics modeling capability of GEANT4 and TRIM with the semiconductor device simulation tools. Through an iterative modeling process of controllable parameters, optimal device construction was determined such detection efficiency and charge collection were optimized. This allows an estimation of expected charge collection and efficiency given a set of operating …
Optimal Summer Camp Layout, Anthony Bonifonte
Optimal Summer Camp Layout, Anthony Bonifonte
Honors Papers
Convex optimization is an important branch of operations research. It generalizes linear programming and offers powerful tools for modelling problems and discovering optimal solutions to real world problems. Mathematically it is an interesting topic because it ties together many branches: linear algebra, multivariable calculus, and numerical analysis, to name a few. Modelling a problem as a convex optimization problem can be challenging but offers many benefits. Algorithm design is critically important to ensure precision of solutions that solve with minimal computation power. From an engineering perspective it is also incredibly useful, since many more situations can be modeled than with …
Algorithms For Training Large-Scale Linear Programming Support Vector Regression And Classification, Pablo Rivas Perea
Algorithms For Training Large-Scale Linear Programming Support Vector Regression And Classification, Pablo Rivas Perea
Open Access Theses & Dissertations
The main contribution of this dissertation is the development of a method to train a Support Vector Regression (SVR) model for the large-scale case where the number of training samples supersedes the computational resources. The proposed scheme consists of posing the SVR problem entirely as a Linear Programming (LP) problem and on the development of a sequential optimization method based on variables decomposition, constraints decomposition, and the use of primal-dual interior point methods. Experimental results demonstrate that the proposed approach has comparable performance with other SV-based classifiers. Particularly, experiments demonstrate that as the problem size increases, the sparser the solution …
On Constrained Optimization Schemes For Joint Inversion Of Geophysical Datasets, Uram Anibal Sosa Aguirre
On Constrained Optimization Schemes For Joint Inversion Of Geophysical Datasets, Uram Anibal Sosa Aguirre
Open Access Theses & Dissertations
In the area of geological sciences, there exist several experimental techniques used to advance in the understanding of the Earth. We implement a joint inversion least-squares (LSQ) algorithm to characterize one dimensional Earth's structure by using seismic shear wave velocities as a model parameter. We use two geophysical datasets sensitive to shear velocities, namely Receiver Function and Surface Wave dispersion velocity observations, with a choice of an optimization method: Truncated Singular Value Decomposition (TSVD) or Primal-Dual Interior-Point (PDIP). The TSVD and the PDIP methods solve a regularized unconstrained and a constrained minimization problem, respectively. Both techniques include bounds into the …
Modeling Direct Runoff Hydrographs With The Surge Function, Denis Voytenko
Modeling Direct Runoff Hydrographs With The Surge Function, Denis Voytenko
USF Tampa Graduate Theses and Dissertations
A surge function is a mathematical function of the form f(x)=axpe-bx. We simplify the surge function by holding p constant at 1 and investigate the simplified form as a potential model to represent the full peak of a stream discharge hydrograph. The previously studied Weibull and gamma distributions are included for comparison. We develop an analysis algorithm which produces the best-fit parameters for every peak for each model function, and we process the data with a MATLAB script that uses spectral analysis to filter year-long, 15-minute, stream-discharge data sets. The filtering is necessary to locate the …