Open Access. Powered by Scholars. Published by Universities.®
Numerical Analysis and Computation Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Institution
-
- University of Nevada, Las Vegas (5)
- Claremont Colleges (2)
- East Tennessee State University (2)
- Embry-Riddle Aeronautical University (2)
- Old Dominion University (2)
-
- Singapore Management University (2)
- The University of Southern Mississippi (2)
- University of Kentucky (2)
- University of New Mexico (2)
- Brigham Young University (1)
- COBRA (1)
- Cal Poly Humboldt (1)
- California Polytechnic State University, San Luis Obispo (1)
- City University of New York (CUNY) (1)
- Clemson University (1)
- Dartmouth College (1)
- Eastern Washington University (1)
- Kennesaw State University (1)
- LSU New Orleans (1)
- Liberty University (1)
- Mississippi State University (1)
- Murray State University (1)
- Northern Illinois University (1)
- San Jose State University (1)
- Southern Methodist University (1)
- Technological University Dublin (1)
- The British University in Egypt (1)
- The University of Akron (1)
- University of Dayton (1)
- University of Louisville (1)
- Keyword
-
- Machine Learning (3)
- Algorithms (2)
- Casino floor optimization (2)
- Evolutionary computing (2)
- Non-linear data modeling (2)
-
- Simulation (2)
- Triangulation (2)
- AI Privacy (1)
- Adaptive systems (1)
- Adversarial Robustness (1)
- Algebraic multigrid (1)
- Applied sciences (1)
- Approximation theory (1)
- Arc-cutset constraint (1)
- Arithmetic Cost (1)
- Artificial Intelligence (1)
- Artificial Intelligence (AI) (1)
- Bathymetry (1)
- Behavioral economics (1)
- Benefits of AI (1)
- Bias in AI Systems (1)
- Bifurcation (1)
- Bin Packing (1)
- Bioinformatics (1)
- Biomedical signal processing (1)
- Calculus of variations (1)
- Celestial mechanics (1)
- Central configuration (1)
- Chain-of-Thought (1)
- Chaos (1)
- Publication Year
- Publication
-
- Publications (3)
- Electronic Theses and Dissertations (2)
- International Conference on Gambling & Risk Taking (2)
- Master's Theses (2)
- Mathematics & Statistics ETDs (2)
-
- Research Collection School Of Computing and Information Systems (2)
- UNLV Theses, Dissertations, Professional Papers, and Capstones (2)
- All Dissertations (1)
- Basic Science Engineering (1)
- COBRA Preprint Series (1)
- CURE Proceedings (1)
- Cal Poly Humboldt theses and projects (1)
- Calvert Undergraduate Research Awards (1)
- Computer Science Theses & Dissertations (1)
- Conference papers (1)
- Dartmouth College Ph.D Dissertations (1)
- Dissertations (1)
- Dissertations, Theses, and Capstone Projects (1)
- EWU Masters Thesis Collection (1)
- Electrical and Computer Engineering Faculty Publications (1)
- Engineering Management & Systems Engineering Faculty Publications (1)
- HMC Senior Theses (1)
- Honors College Theses (1)
- Journal of Humanistic Mathematics (1)
- Journal of Nonprofit Innovation (1)
- LSU New Orleans Theses and Dissertations (1)
- Library Philosophy and Practice (e-journal) (1)
- Mathematics Theses and Dissertations (1)
- Senior Honors Theses (1)
- Symposium of Student Scholars (1)
- Publication Type
- File Type
Articles 31 - 44 of 44
Full-Text Articles in Numerical Analysis and Computation
Signal Flow Graph Approach To Efficient Dst I-Iv Algorithms, Sirani M. Perera
Signal Flow Graph Approach To Efficient Dst I-Iv Algorithms, Sirani M. Perera
Publications
In this paper, fast and efficient discrete sine transformation (DST) algorithms are presented based on the factorization of sparse, scaled orthogonal, rotation, rotation-reflection, and butterfly matrices. These algorithms are completely recursive and solely based on DST I-IV. The presented algorithms have low arithmetic cost compared to the known fast DST algorithms. Furthermore, the language of signal flow graph representation of digital structures is used to describe these efficient and recursive DST algorithms having (n�1) points signal flow graph for DST-I and n points signal flow graphs for DST II-IV.
Simulation Of Nuclear Fusion Using A One Dimensional Particle In Cell Method, Steven T. Margell
Simulation Of Nuclear Fusion Using A One Dimensional Particle In Cell Method, Steven T. Margell
Cal Poly Humboldt theses and projects
In this thesis several novel techniques are developed to simulate fusion events in an isotropic, electrostatic three-dimensional Deuterium-Tritium plasma. These techniques allow us to accurately predict three-dimensional collision events with a one-dimensional model while simultaneously reducing compute time via a nearest neighbor algorithm. Furthermore, a fusion model based on first principles is developed that yields an average fusion reactivity which correlates well with empirical results.
Modeling Traffic At An Intersection, Kaleigh L. Mulkey, Saniita K. Fasenntao
Modeling Traffic At An Intersection, Kaleigh L. Mulkey, Saniita K. Fasenntao
Symposium of Student Scholars
The main purpose of this project is to build a mathematical model for traffic at a busy intersection. We use elements of Queueing Theory to build our model: the vehicles driving into the intersection are the “arrival process” and the stop light in the intersection is the “server.”
We collected traffic data on the number of vehicles arriving to the intersection, the duration of green and red lights, and the number of vehicles going through the intersection during a green light. We built a SAS macro code to simulate traffic based on parameters derived from the data.
In our program …
Efficient Thermal Image Segmentation Through Integration Of Nonlinear Enhancement With Unsupervised Active Contour Model, Fatema Albalooshi, Evan Krieger, Paheding Sidike, Vijayan K. Asari
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 …
Singular Value Computation And Subspace Clustering, Qiao Liang
Singular Value Computation And Subspace Clustering, Qiao Liang
Theses and Dissertations--Mathematics
In this dissertation we discuss two problems. In the first part, we consider the problem of computing a few extreme eigenvalues of a symmetric definite generalized eigenvalue problem or a few extreme singular values of a large and sparse matrix. The standard method of choice of computing a few extreme eigenvalues of a large symmetric matrix is the Lanczos or the implicitly restarted Lanczos method. These methods usually employ a shift-and-invert transformation to accelerate the speed of convergence, which is not practical for truly large problems. With this in mind, Golub and Ye proposes an inverse-free preconditioned Krylov subspace method, …
Parameters Estimation Of Material Constitutive Models Using Optimization Algorithms, Kiswendsida Jules Kere
Parameters Estimation Of Material Constitutive Models Using Optimization Algorithms, Kiswendsida Jules Kere
Williams Honors College, Honors Research Projects
Optimization Algorithms are very useful for solving engineering problems. Indeed, optimization algorithms can be used to optimize engineering designs in terms of safety and economy. Understanding the proprieties of materials in engineering designs is very important in order to make designs safe. Materials are not really perfectly homogeneous and there are heterogeneous distributions in most materials. In this paper, Self-OPTIM which is an inverse constitutive parameter identification framework will be used to identify parameters of a linear elastic material constitutive model. Data for Self-OPTIM will be obtained using ABAQUS simulation of a dog-bone uniaxial test. Optimization Algorithms will be used …
Generating Combinatorial Objects- A New Perspective, Alexander Chizoma Nwala
Generating Combinatorial Objects- A New Perspective, Alexander Chizoma Nwala
Computer Science Theses & Dissertations
Combinatorics is the science of "possibilities." This definition, while not formal is a fair statement because all too often, in order to gain insight into the solution of many counting problems, we explore the possibilities. In some cases we seek to know how many options, while in other cases we seek to enumerate or list the options. Irrespective of the scenario, combinatorics plays a vital role today. In many instances such as exploring the options for choosing a new password for a combination lock, we employ combinatorics. In considering the possible license plate permutations for a state, or to see …
A Fast Algorithm For The Inversion Of Quasiseparable Vandermonde-Like Matrices, Sirani M. Perera, Grigory Bonik, Vadim Olshevsky
A Fast Algorithm For The Inversion Of Quasiseparable Vandermonde-Like Matrices, Sirani M. Perera, Grigory Bonik, Vadim Olshevsky
Publications
The results on Vandermonde-like matrices were introduced as a generalization of polynomial Vandermonde matrices, and the displacement structure of these matrices was used to derive an inversion formula. In this paper we first present a fast Gaussian elimination algorithm for the polynomial Vandermonde-like matrices. Later we use the said algorithm to derive fast inversion algorithms for quasiseparable, semiseparable and well-free Vandermonde-like matrices having O(n2) complexity. To do so we identify structures of displacement operators in terms of generators and the recurrence relations(2-term and 3-term) between the columns of the basis transformation matrices for quasiseparable, semiseparable and well-free polynomials. Finally we …
Data Mining Based Hybridization Of Meta-Raps, Fatemah Al-Duoli, Ghaith Rabadi
Data Mining Based Hybridization Of Meta-Raps, Fatemah Al-Duoli, Ghaith Rabadi
Engineering Management & Systems Engineering Faculty Publications
Though metaheuristics have been frequently employed to improve the performance of data mining algorithms, the opposite is not true. This paper discusses the process of employing a data mining algorithm to improve the performance of a metaheuristic algorithm. The targeted algorithms to be hybridized are the Meta-heuristic for Randomized Priority Search (Meta-RaPS) and an algorithm used to create an Inductive Decision Tree. This hybridization focuses on using a decision tree to perform on-line tuning of the parameters in Meta-RaPS. The process makes use of the information collected during the iterative construction and improvement phases Meta-RaPS performs. The data mining algorithm …
Degree Constrained Triangulation, Roshan Gyawali
Degree Constrained Triangulation, Roshan Gyawali
UNLV Theses, Dissertations, Professional Papers, and Capstones
Triangulation of simple polygons or sets of points in two dimensions is a widely investigated problem in computational geometry. Some researchers have considered variations of triangulation problems that include minimum weight triangulation, de-launay triangulation and triangulation refinement. In this thesis we consider a constrained version of the triangulation problem that asks for triangulating a given domain (polygon or point sites) so that the resulting triangulation has an increased number of even degree vertices. This problem is called Degree Constrained Triangulation (DCT). We propose four algorithms to solve DCT problems. We also present experimental results based on the implementation of the …
Random Number Generation: Types And Techniques, David F. Dicarlo
Random Number Generation: Types And Techniques, David F. Dicarlo
Senior Honors Theses
What does it mean to have random numbers? Without understanding where a group of numbers came from, it is impossible to know if they were randomly generated. However, common sense claims that if the process to generate these numbers is truly understood, then the numbers could not be random. Methods that are able to let their internal workings be known without sacrificing random results are what this paper sets out to describe. Beginning with a study of what it really means for something to be random, this paper dives into the topic of random number generators and summarizes the key …
Improved Algorithms For Ear-Clipping Triangulation, Bartosz Kajak
Improved Algorithms For Ear-Clipping Triangulation, Bartosz Kajak
UNLV Theses, Dissertations, Professional Papers, and Capstones
We consider the problem of improving ear-slicing algorithm for triangulating a simple polygon. We propose two variations of ear-slicing technique for generating “good-quality” triangulation. The first approach is based on searching for the best triangle along the boundary. The second approach considers polygon partitioning on a pre-process before applying the ear-slicing. Experimental investigation reveals that both approaches yield better quality triangulation than the standard ear-slicing method.
Dynamic Decision Making And Race Games, Shipra De
Dynamic Decision Making And Race Games, Shipra De
Calvert Undergraduate Research Awards
Frequent criticism in dynamic decision making research pertains to the overly complex nature of the decision tasks used in experimentation. To address such concerns we study dynamic decision making with respect to the simple race game Hog, which has a computable optimal decision strategy. In the two-player game of Hog, individuals compete to be the first to reach a designated threshold of points. Players alternate rolling a desired quantity of dice. If the number one appears on any of the dice, the player receives no points for his turn; otherwise, the sum of the numbers appearing on the dice is …
Tight Lower Bound For The Sparse Travelling Salesman Problem, Fredrick Mtenzi
Tight Lower Bound For The Sparse Travelling Salesman Problem, Fredrick Mtenzi
Conference papers
The Sparse Travelling Salesman Problem (Sparse TSP) which is a variant of the classical Travelling Salesman Problem (TSP) is the problem of finding the shortest route of the salesman when visiting cities in a region making sure that each city is visited at least once and returning home at the end. In the Sparse TSP, the distance between cities may not obey the triangle inequality; this makes the use of algorithms and formulations designed for the TSP to require modifications in order to produce near-optimal results. A lower bound for optmisation problems gives us the quality guarantee of the near-optimal …