Computer Engineering Commons^™

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 …

Change Detection Without Difference Image Computation Based On Multiobjective Cost Function Optimization, Turgay Çeli̇k, Zeki̇ Yetgi̇n

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, we propose a novel method for unsupervised change detection in multi-temporal satellite images by using multiobjective cost function optimization via genetic algorithm (GA). The spatial image grid of the input multi-temporal satellite images is divided into two distinct regions, representing ``changed'' and ``unchanged'' regions between input images, via the intermediate change detection mask produced by the GA. The dissimilarity of pixels of ``changed'' regions and similarity of pixels of ``unchanged'' regions between input multi-temporal images are measured using image quality metrics which consider correlation, spectral distortion, radiometric distortion, and contrast distortion. The contextual information of each pixel …

Decentralized Coordination Of Multiple Autonomous Vehicles, Yongcan Cao

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

This dissertation focuses on the study of decentralized coordination algorithms of multiple autonomous vehicles. Here, the term decentralized coordination is used to refer to the behavior that a group of vehicles reaches the desired group behavior via local interaction. Research is conducted towards designing and analyzing distributed coordination algorithms to achieve desired group behavior in the presence of none, one, and multiple group reference states.

Decentralized coordination in the absence of any group reference state is a very active research topic in the systems and controls society. We first focus on studying decentralized coordination problems for both single-integrator kinematics and …

Random Approach To Optimization Of Overlay Public-Resource Computing Systems, Grzegorz Chmaj, Krzysztof Walkowiak

Electrical & Computer Engineering Faculty Research

The growing need for computationally demanding systems triggers the development of various network-oriented computing systems organized in a distributed manner. In this work we concentrate on one kind of such systems, i.e. public-resource computing systems. The considered system works on the top of an overlay network and uses personal computers and other relatively simple electronic equipment instead of supercomputers. We assume that two kinds of network flows are used to distribute the data in the public-resource computing systems: unicast and peer-to-peer. We formulate an optimization model of the system. After that we propose random algorithms that optimize jointly the allocation …

An Approach Based On Particle Swarm Computation To Study The Nanoscale Dg Mosfet-Based Circuits, Fayacl Djeffal, Toufik Bendib, Redha Benzid, Abdelhamid Benhaya

Turkish Journal of Electrical Engineering and Computer Sciences

The analytical modeling of nanoscale Double-Gate MOSFETs (DG) requires generally several necessary simplifying assumptions to lead to compact expressions of current-voltage characteristics for nanoscale CMOS circuits design. Further, progress in the development, design and optimization of nanoscale devices necessarily require new theory and modeling tools in order to improve the accuracy and the computational time of circuits' simulators. In this paper, we propose a new particle swarm strategy to study the nanoscale CMOS circuits. The latter is based on the 2-D numerical Non-Equilibrium Green's Function (NEGF) simulation and a new extended long channel DG MOSFET compact model. Good agreement between …

Softcomputing Identification Techniques Of Asynchronous Machine Parameters: Evolutionary Strategy And Chemotaxis Algorithm, Nouri Benaïdja

Turkish Journal of Electrical Engineering and Computer Sciences

Softcomputing techniques are receiving attention as optimisation techniques for many industrial applications. Although these techniques eliminate the need for derivatives computation, they require much work to adjust their parameters at the stage of research and development. Issues such as speed, stability, and parameters convergence remain much to be investigated. This paper discusses the application of the method of reference model to determine parameters of asynchronous machines using two optimisation techniques. Softcomputing techniques used in this paper are evolutionary strategy and the chemotaxis algorithm. Identification results using the two techniques are presented and compared with respect to the conventional simplex technique …

Parameter Identification Of A Separately Excited Dc Motor Via Inverse Problem Methodology, Mounir Hadef, Mohamed Rachid Mekideche

Turkish Journal of Electrical Engineering and Computer Sciences

Identification is considered to be among the main applications of inverse theory and its objective for a given physical system is to use data which is easily observable, to infer some of the geometric parameters which are not directly observable. In this paper, a parameter identification method using inverse problem methodology is proposed. The minimisation of the objective function with respect to the desired vector of design parameters is the most important procedure in solving the inverse problem. The conjugate gradient method is used to determine the unknown parameters, and Tikhonov's regularization method is then used to replace the original …

Evolutionary Methodology For Optimization Of Image Transforms Subject To Quantization Noise, Michael Ray Peterson

Browse all Theses and Dissertations

Lossy image compression algorithms sacrifice perfect imagereconstruction in favor of decreased storage requirements. Modelossy compression schemes, such as JPEG2000, rely upon the discrete wavelet transform (DWT) to achieve high levels of compression while minimizing the loss of information for image reconstruction. Some compression applications require higher levels of compression than those achieved through application of the DWT and entropy coding. In such lossy systems, quantization provides high compression rates at the cost of increased distortion. Unfortunately, as the amount of quantization increases, the performance of the DWT for accurate image reconstruction deteriorates. Previous research demonstrates that a genetic algorithm can …

Analysis And Optimization Of Mobile Phone Antenna Radiation Performance In The Presence Of Head And Hand Phantoms, Erdem Ofli, Chung-Huan Li, Nicolas Chavannes, Niels Kuster

Turkish Journal of Electrical Engineering and Computer Sciences

A commercial clam shell phone CAD model is used to numerically investigate the effect of a hand phantom on mobile phone antenna radiation performance. The simulation results show that the grip of the hand phantom is the most important parameter to antenna performance. The antenna is converted into a parameterized form, then optimized to achieve the targeted multi-band performance in real-usage conditions.

M Solutions Good, M-1 Solutions Better, Luc Longpre, William Gasarch, G. W. Walster, Vladik Kreinovich

Departmental Technical Reports (CS)

One of the main objectives of theoretical research in computational complexity and feasibility is to explain experimentally observed difference in complexity.

Empirical evidence shows that the more solutions a system of equations has, the more difficult it is to solve it. Similarly, the more global maxima a continuous function has, the more difficult it is to locate them. Until now, these empirical facts have been only partially formalized: namely, it has been shown that problems with two or more solutions are more difficult to solve than problems with exactly one solution. In this paper, we extend this result and show …

Post Register Allocation Spill Code Optimization, Christopher Lupo, Kent Wilken

Computer Science and Software Engineering

A highly optimized register allocator should provide an efficient placement of save/restore code for procedures that contain calls. This paper presents a new approach to placing callee-saved save and restore instructions that generalizes Chow's shrink-wrapping technique (Chow 1988). An efficient, profile-guided, hierarchical spill code placement algorithm is used to analyze the structure of a procedure to calculate the minimum dynamic execution count locations to place callee-saved save and restore code. The algorithm is implemented in the Gnu Compiler Collection and has been tested on the SPEC CPU2000 Integer Benchmark suite. Results show that the technique reduces the number of dynamic …

H-Infinity Estimation For Fuzzy Membership Function Optimization, Daniel J. Simon

Electrical and Computer Engineering Faculty Publications

Given a fuzzy logic system, how can we determine the membership functions that will result in the best performance? If we constrain the membership functions to a specific shape (e.g., triangles or trapezoids) then each membership function can be parameterized by a few variables and the membership optimization problem can be reduced to a parameter optimization problem. The parameter optimization problem can then be formulated as a nonlinear filtering problem. In this paper we solve the nonlinear filtering problem using H_∞ state estimation theory. However, the membership functions that result from this approach are not (in general) sum normal. …

A Simple And Global Optimization Algorithm For Engineering Problems: Differential Evolution Algorithm, Dervi̇ş Karaboğa, Selçuk Ökdem

Turkish Journal of Electrical Engineering and Computer Sciences

Differential Evolution (DE) algorithm is a new heuristic approach mainly having three advantages; finding the true global minimum regardless of the initial parameter values, fast convergence, and using few control parameters. DE algorithm is a population based algorithm like genetic algorithms using similar operators; crossover, mutation and selection. In this work, we have compared the performance of DE algorithm to that of some other well known versions of genetic algorithms: PGA, Grefensstette, Eshelman. In simulation studies, De Jong's test functions have been used. From the simulation results, it was observed that the convergence speed of DE is significantly better than …

Optimization Of Receptive Fields For Mlp Networks With Ensemble Encoding, Deena Osama Hassan

Archived Theses and Dissertations

Ensemble encoding is a biologically-motivated, distributed data representation scheme for MLP networks. Multiple overlapping receptive fields are used to enhance locality of representation. The number, form, and placement of receptive fields have a great impact on performance. This thesis presents four heuristics, two based on descriptive statistics, and two based on clustering, for optimizing receptive field configuration, and compares their performance on four benchmark data sets. The two statistical approaches are based on the mean and median properties of the data set. The two clustering methods are the c-means and fuzzy c-means clustering. The four data sets used are well-known …

Synthesis And Optimization Of Digital Systems For Low Power At Logic Level Of Abstraction, Ihab Mostafa Amin Amer

Archived Theses and Dissertations

In this thesis we tackle one of the most important fields of research, which is reducing power consumption in digital systems. The importance of this field comes from the fact that nowadays, several digital devices are intensively used in our daily life. Thus, minimizing their power consumption is a common demand from the technological as well as the economical point of view. Some basic definitions in the low power design field are introduced. Besides, a preview of different efforts that were exerted in the field of reducing power consumption of digital systems at various levels of abstraction is presented. A …

Probabilities, Intervals, What Next? Optimization Problems Related To Extension Interval Computations To Situations With Partial Information About Probabilities, Vladik Kreinovich

Departmental Technical Reports (CS)

When we have only interval ranges [xi-,xi+] of sample values x1,...,xn, what is the interval [V-,V+] of possible values for the variance V of these values? We prove that the problem of computing the upper bound V+ is NP-hard. We provide a feasible (quadratic time) algorithm for computing the exact lower bound V- on the variance of interval data. We also provide feasible algorithms that computes V+ under reasonable easily verifiable conditions, in particular, in case interval uncertainty is introduced to maintain privacy in a statistical database.

We also extend the main formulas of interval arithmetic for different arithmetic operations …

Training Radial Basis Neural Networks With The Extended Kalman Filter, Daniel J. Simon

Electrical and Computer Engineering Faculty Publications

Radial basis function (RBF) neural networks provide attractive possibilities for solving signal processing and pattern classification problems. Several algorithms have been proposed for choosing the RBF prototypes and training the network. The selection of the RBF prototypes and the network weights can be viewed as a system identification problem. As such, this paper proposes the use of the extended Kalman filter for the learning procedure. After the user chooses how many prototypes to include in the network, the Kalman filter simultaneously solves for the prototype vectors and the weight matrix. A decoupled extended Kalman filter is then proposed in order …

Optimal Elimination Of Inconsistency In Expert Knowledge: Formulation Of The Problem, Fast Algorithms, Timothy J. Ross, Berlin Wu, Vladik Kreinovich

Departmental Technical Reports (CS)

Expert knowledge is sometimes inconsistent. In this paper, we describe the problem of eliminating this inconsistency as an optimization problem, and present fast algorithms for solving this problem.

A Comparative Analysis Of Networks Of Workstations And Massively Parallel Processors For Signal Processing, David C. Gindhart

Theses and Dissertations

The traditional approach to parallel processing has been to use Massively Parallel Processors (MPPs). An alternative design is commercial-off-the-shelf (COTS) workstations connected to high-speed networks. These networks of workstations (NOWs) typically have faster processors, heterogeneous environments, and most importantly, offer a lower per node cost. This thesis compares the performance of MPPs and NOWs for the two-dimensional fast Fourier transform (2-D FFT). Three original, high-performance, portable 2-D FFTs have been implemented: the vector-radix, row-column and pipeline. The performance of these algorithms was measured on the Intel Paragon, IBM SP2 and the AFIT NOW, which consists of 6 Sun Ultra workstations …

The Application Of Neural Networks To Optimal Robot Trajectory Planning, Daniel J. Simon

Electrical and Computer Engineering Faculty Publications

Interpolation of minimum jerk robot joint trajectories through an arbitrary number of knots is realized using a hardwired neural network. Minimum jerk joint trajectories are desirable for their similarity to human joint movements and their amenability to accurate tracking. The resultant trajectories are numerical rather than analytic functions of time. This application formulates the interpolation problem as a constrained quadratic minimization problem over a continuous joint angle domain and a discrete time domain. Time is discretized according to the robot controller rate. The neuron outputs define the joint angles (one neuron for each discrete value of time) and the Lagrange …