Computer Sciences Commons^™

Simultaneous Rotor And Stator Resistance Estimation Of Squirrel Cage Induction Machine With A Single Extended Kalman Filter, Eşref Emre Özsoy, Meti̇n Gökaşan, Ovsanna Seta Estrada

Turkish Journal of Electrical Engineering and Computer Sciences

Accurate knowledge of rotor and stator resistance variations in a squirrel-cage induction motor (SCIM) is crucial for the performance of sensorless control of SCIM over a wide range of speeds. This study seeks to address this issue with a single Extended Kalman Filter (EKF) based solution, which is also known to have accuracy limitations when a high number of parameters/states are estimated with a limited number of inputs. To this aim, different from the author's previous approach in operating several EKFs in an alternating manner (the so-called braided EKF), an 8^{th}-order EKF is implemented in this study to test its …

Analysis And Estimation Of Motion Transmission Errors Of A Timing Belt Drive, Ergi̇n Kiliç, Meli̇k Dölen, Ahmet Buğra Koku

Turkish Journal of Electrical Engineering and Computer Sciences

This paper focuses on viable position estimation schemes for timing belt drives where the position of the carriage (load) is to be determined via reference models receiving input from a position sensor attached to the actuator of the timing belt. A detailed analysis of the transmission error sources is presented, and a number of relevant mathematical models are developed using a priori knowledge of the process. This paper demonstrates that such schemes are very effective when the drive system is not subjected to external loads and operating conditions do not change considerably i.e. ambient temperature, belt tension.

Precise Position Control Using Shape Memory Alloy Wires, Burcu Dönmez, Bülent Özkan, Fevzi̇ Suat Kadioğlu

Turkish Journal of Electrical Engineering and Computer Sciences

Shape memory alloys (SMAs) are active metallic ``smart'' materials used as actuators and sensors in high technology smart systems [1]. The term shape memory refers to ability of certain materials to ``remember'' a shape, even after rather severe deformations: once deformed at low temperatures, these materials will stay deformed until heated, whereupon they will return to their original, pre-deformed ``learned'' shape [2]. This property can be used to generate motion and/or force in electromechanical devices and micro-machines. However, the accuracy of SMA actuators is severely limited by their highly nonlinear stimulus-response characteristics. In this work, modeling, simulation, and experimental efforts …

Design And Evaluation Of A Linear Switched Reluctance Actuator For Positioning Tasks, António Espírito Santo, Rosário Calado, Carlos Cabrita

Turkish Journal of Electrical Engineering and Computer Sciences

This paper presents the development of a new linear switched reluctance actuator and confirms its applicability to perform positioning tasks. After the explanation of the actuator working principle and the presentation of its electromechanical topology, an analysis is accomplished using a finite elements tool. Based on the theoretical results, an experimental prototype that can develop 150 N was constructed. Its behaviour is observed under two different control methods implemented with microcontrollers. Initially, position is controlled applying a single pulse-driving scheme. Meanwhile, significant improvements are obtained with the introduction of a sliding-mode controller, allowing movements with 1 mm of resolution.

Detection Of Static Eccentricity For Permanent Magnet Synchronous Motors Using The Coherence Analysis, Mehmet Akar, Sezai̇ Taşkin, Şahi̇n Serhat Şeker, İlyas Çankaya

Turkish Journal of Electrical Engineering and Computer Sciences

This paper reports on work to detect the static eccentricity faults for permanent magnet synchronous motor (PMSM) using spectral analysis methods. Measurements are carried out by collecting the stator current and voltage, torque and speed for healthy and faulty cases of the motor. Static eccentricity case is formed by changing the rotor position in the manner of sliding the shaft on a horizontal axis. As a result of the spectral analysis for the motor currents, side band effects appeared at around the fundamental frequency are determined as a most important indicator of the eccentricity. In addition to this determination, the …

Using Covariates For Improving The Minimum Redundancy Maximum Relevance Feature Selection Method, Olcay Kurşun, Cemal Okan Şakar, Oleg Favorov, Ni̇zametti̇n Aydin, Sadik Fi̇kret Gürgen

Turkish Journal of Electrical Engineering and Computer Sciences

Maximizing the joint dependency with a minimum size of variables is generally the main task of feature selection. For obtaining a minimal subset, while trying to maximize the joint dependency with the target variable, the redundancy among selected variables must be reduced to a minimum. In this paper, we propose a method based on recently popular minimum Redundancy-Maximum Relevance} (mRMR) criterion. The experimental results show that instead of feeding the features themselves into mRMR, feeding the covariates improves the feature selection capability and provides more expressive variable subsets.

Usage Of Spline Interpolation In Catheter-Based Cardiac Mapping, Bülent Yilmaz, Uğur Cunedi̇oğlu, Engi̇n Baysoy

Turkish Journal of Electrical Engineering and Computer Sciences

Due to their minimal invasiveness catheters are highly preferred in cardiac mapping techniques used in the source localization of rhythm disturbances in the heart. In cardiac mapping, standard steerable catheters and multielectrode basket catheters are the two alternatives for the characterization of the underlying tissue on the inner (endocardium) and outer (epicardium) surfaces of the heart. As with any discrete sampling technique, an important question for catheter-based cardiac mapping is how to determine values at locations from which direct measurements are not available. Interpolation is the most common approach for providing values at unmeasured sites using the available measurements. In …

Delta-Sigma Subarray Beamforming For Ultrasound Imaging, Hasan Şaki̇r Bi̇lge

Turkish Journal of Electrical Engineering and Computer Sciences

In this study, an ultrasonic digital beamformer based on subarray processing of 1-bit delta-sigma (\Delta \Sigma ) oversampled echo signals is presented. The single-bit oversampling \Delta \Sigma conversion simplifies the coherent processing in beamforming with improved timing accuracy. Subarray processing also aims to simplify the beamforming complexity, where the partial-beam sums (low-resolution beams) are acquired from small subarrays, and then these partial beams are coherently processed for producing high-resolution beams. In the \Delta \Sigma subarray beamforming, the \Delta \Sigma coded echo signals are summed over the subarray channels, and then these partial beam-sums are first \Delta \Sigma demodulated, then processed …

Design Of Optimal Sampling Times For Pharmacokinetic Trials Via Spline Approximation, Musa Hakan Asyali

Turkish Journal of Electrical Engineering and Computer Sciences

Understanding and comparison of different drug delivery formulations are based on pharmacokinetic parameters (PKP) such as area under curve, maximum concentration, and time to reach maximum concentration. Accurate estimation of PKP is of critical importance in capturing drug absorption and elimination characteristics and in reaching bioequivalence decisions. Since PKP are estimated from a limited number of samples, the timing of the samples directly influences the accuracy of estimation. Optimization of the sampling times may not only increase the accuracy of PKP estimation, but also reduce the number of samples to be drawn, which in turn lessens the inconvenience to the …

Optimal Feature Selection For 3d Facial Expression Recognition Using Coarse-To-Fine Classification, Hamit Soyel, Hasan Demirel

Turkish Journal of Electrical Engineering and Computer Sciences

Automatic facial expression recognition for novel individuals from 3D face data is a challenging task in pattern analysis. This paper describes a feature selection process for pose-invariant 3D facial expression recognition. The process provides a lower dimensional subspace representation, which is optimized to improve the classification accuracy, retrieved from geometrical localization of facial feature points to classify facial expressions. Fisher criterion-based approach is adopted to provide a basis for the optimal selection of features. Two-stage probabilistic neural network architecture is employed as a classifier to recognize the facial expressions. In the first stage, which can be regarded as the coarse …

Fast Computation Of Determination Of The Prime Implicants By A Novel Near Minimum Minimization Method, Fati̇h Başçi̇ftçi̇, Şi̇rzat Kahramanli

Turkish Journal of Electrical Engineering and Computer Sciences

In this study proposed is an off-set-based direct-cover near-minimum minimization method for single-output Boolean functions represented in a sum-of-products form. To obtain the complete set of prime implicants including given on-cube (on-minterm), the proposed method uses off-cubes (off-minterms) expanded by this On-cube. The amount of temporary results produced by this method does not exceed the size of the off-set. To make fast computation, we used logic operations instead of standard operations. Expansion off-cubes, commutative absorption operations and intersection operations are realized by logic operations for fast computation. The proposed minimization method is tested on several different kinds of problems and …

A Second Order Approximation To Reduce The Complexity Of Ldpc Decoders Based On Gallager's Approach, Aykut Kalaycioğlu, Oktay Üreten, H. Gökhan İlk

Turkish Journal of Electrical Engineering and Computer Sciences

A piece-wise second order approximation to the f (x) = -log [tanh (x/2)] function is proposed to reduce the computational complexity of LDPC decoder's utilizing Log-Likelihood Ratio Belief Propagation (LLR-BP) algorithm based on Gallager's approach. Simulation results show that the proposed low complexity approximation doesn't cause BER performance degradation.

Performance Analysis Of Swarm Optimization Approaches For The Generalized Assignment Problem In Multi-Target Tracking Applications, Ali̇ Önder Bozdoğan, Asim Egemen Yilmaz, Murat Efe

Turkish Journal of Electrical Engineering and Computer Sciences

The aim of this study is to investigate the suitability of selected swarm optimization algorithms to the generalized assignment problem as encountered in multi-target tracking applications. For this purpose, we have tested variants of particle swarm optimization and ant colony optimization algorithms to solve the 2D generalized assignment problem with simulated dense and sparse measurement/track matrices and compared their performance to that of the auction algorithm. We observed that, although with some modification swarm optimization algorithms provide improvement in terms of speed, they still fall behind the auction algorithm in finding the optimum solution to the problem. Among the investigated …

Stpso: Strengthened Particle Swarm Optimization, Ai̇şe Zülal Şevkli̇, Fati̇h Erdoğan Sevi̇lgen

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, we present a novel approach to strengthen Particle Swarm Optimization (PSO). PSO is a population-based metaheuristic that takes advantage of individual memory and social cooperation in a swarm. It has been applied to a variety of optimization problems because of its simplicity and fast convergence. However, straightforward application of PSO suffers from premature convergence and lack of intensification around the local best locations. To rectify these problems, we modify update procedure for the best particle in the swarm and propose a simple and random moving strategy. We perform a Reduced Variable Neighborhood Search (RVNS) based local search …

Labrat™: Miniature Robot For Students, Researchers, And Hobbyists, Paul Robinette, Ryan Meuth, Ryanne Dolan, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

LabRat™ is an autonomous, self-contained mobile robot kit with batteries, motors, two bumper whisker sensors, and three infrared proximity sensors that double as channels for "Rat-to-Rat" communication. the vehicle determines its position with an optical sensor that detects movement in both lateral directions. the LabRat™ design is completely open source, including software examples and libraries. LabRat™ is designed to fit inside the body of a computer mouse and has applications in the classroom, the lab and the home. the device has been successfully used in an undergraduate robotics class. © 2009 IEEE.

Exercise Power Grid Display And Web Interface, Alexander (Alex) Chernetz

Computer Engineering

The 2008-2009 expansion of the Recreation Center at Cal Poly includes three new rooms with cardiovascular fitness equipment. As part of its ongoing commitment to sustainable development, the new machines connect to the main power grid and generate power during a workout. This document explains the process of quantifying and expressing the power generated using two interfaces: an autonomous display designed for a television with a text size and amount of detail adaptable to multiple television sizes and viewing distances, and an interactive, more detailed Web interface accessible with any Java-capable computer system or browser.

Bio-Inspired Node Localization In Wireless Sensor Networks, Raghavendra V. Kulkarni, Ganesh K. Venayagamoorthy, Maggie X. Cheng

Electrical and Computer Engineering Faculty Research & Creative Works

Many applications of wireless sensor networks (WSNs) require location information of the randomly deployed nodes. a common solution to the localization problem is to deploy a few special beacon nodes having location awareness, which help the ordinary nodes to localize. in this approach, non-beacon nodes estimate their locations using noisy distance measurements from three or more non-collinear beacons they can receive signals from. in this paper, the ranging-Based localization task is formulated as a multidimensional optimization problem, and addressed using bio-inspired algorithms, exploiting their quick convergence to quality solutions. an investigation on distributed iterative localization is presented in this paper. …

Neural Network Control Of A Class Of Nonlinear Discrete Time Systems With Asymptotic Stability Guarantees, Balaje T. Thumati, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, a single and multi-layer neural network (NN) controllers are developed for a class of nonlinear discrete time systems. under a mild assumption on the system uncertainties, which include unmodeled dynamics and bounded disturbances, by using novel weight update laws and a robust term, local asymptotic stability of the closed-loop system is guaranteed in contrast with all other NN controllers where a uniform ultimate boundedness is normally shown. Simulation results are presented to show the effectiveness of the controller design. © 2009 AACC.

R-Factor: A New Parameter To Enhance Location Accuracy In Rssi Based Real-Time Location Systems, Mohammed Rana Basheer, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

The fundamental cause of localization error in an indoor environment is fading and spreading of the radio signals due to scattering, diffraction, and reflection. These effects are predominant in regions where there is no-line-of-sight (NLoS) between the transmitter and the receiver. Efficient algorithms are needed to identify the subset of receivers that provide better localization accuracy. © 2009 IEEE.

Neural Network Control Of Quadrotor Uav Formations, Travis Dierks, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, a novel framework for leader-follower formation control is developed for the control of multiple quadrotors unmanned aerial vehicles (UAVs) based on spherical coordinates. the control objective for the follower UAV is to track its leader at a desired- separation, angle of incidence, and a bearing by using an auxiliary velocity control. Then, a novel neural network (NN) control law for the dynamical system is introduced to learn the complete dynamics of the UAV including unmodeled dynamics like aerodynamic friction. Additionally, the interconnection dynamic errors between the leader and its followers are explicitly considered, and the stability of …

A Model Based Fault Detection And Accommodation Scheme For Nonlinear Discrete-Time Systems With Asymptotic Stability Guarantee, Balaje T. Thumati, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, a fault detection and accommodation (FDA) framework is developed for unknown nonlinear discrete-time systems. the changes in the system dynamics due to the faults are modeled as a nonlinear function of state and input variables while the time profile of the fault is assumed to be exponentially developing. a fault is detected by monitoring the system states and reconstructing the fault dynamics using online approximators. the online approximator output is used first for fault detection and later reconfigured for accommodation. a stable adaptation law in discrete time is developed not only to characterize the faults but also …

Adaptive Dynamic Programming-Based Optimal Control Of Unknown Affine Nonlinear Discrete-Time Systems, Travis Dierks, Balaje T. Thumati, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

Discrete time approximate dynamic programming (ADP) techniques have been widely used in the recent literature to determine the optimal or near optimal control policies for nonlinear systems. However, an inherent assumption of ADP requires at least partial knowledge of the system dynamics as well as the value of the controlled plant one step ahead. in this work, a novel approach to ADP is attempted while relaxing the need of the partial knowledge of the nonlinear system. the proposed methodology entails a two-part process: online system identification and offline optimal control training. First, in the identification process, a neural network (NN) …

Decentralized Control Of Large Scale Interconnected Systems Using Adaptive Neural Network-Based Dynamic Surface Control, Shahab Mehraeen, Sarangapani Jagannathan, Mariesa L. Crow

Electrical and Computer Engineering Faculty Research & Creative Works

A novel decentralized controller using the dynamic surface control (DSC) is proposed for a class of uncertain large scale interconnected nonlinear systems in strict feedback form while relaxing the "explosion of complexity" problem which is observed in the typical backstepping approach. the matching condition is not assumed when dealing with the interconnection terms. Neural networks (NNs) are utilized to approximate the uncertainties in both subsystem and interconnected terms. by using novel NN weight update laws, it is demonstrated using Lyapunov stability that the closed-loop signals are asymptotically stable in the presence of NN approximation errors in contrast with the uniform …

Adaptive Distributed Fair Scheduling For Multiple Channels In Wireless Sensor Networks, Maciej Jan Zawodniok, Jagannathan Sarangapani, Steve Eugene Watkins, James W. Fonda

Electrical and Computer Engineering Faculty Research & Creative Works

A novel adaptive and distributed fair scheduling (ADFS) scheme for wireless sensor networks (WSN) in the presence of multiple channels (MC-ADFS) is developed. The proposed MC-ADFS increases available network capacity and focuses on quality-of-service (QoS) issues. when nodes access a shared channel, the proposed MC-ADFS allocates the channel bandwidth proportionally to the packet's weight which indicates the priority of the packet's flow. The packets are dynamically assigned to channels based on the packet weight and current channel utilization. The dynamic assignment of channels is facilitated by use of receiver-based allocation and alternative routes. Moreover, MC-ADFS allows the dynamic allocation of …

Rapid Prototyping Augmented Skin Pathology For Medical Simulation And Training, Annette Castelino

Electrical & Computer Engineering Theses & Dissertations

The goal of this research is to study how augmented reality technology could be applied in training medical students for better clinical practice and diagnosis in the treatment of skin conditions using skin pathology prototypes. Described within this thesis is an innovative method of producing skin abscess prototypes that augment the Standardized Patient (SP) for simulation purposes.

The method adopted is a combination of the cost-effective technique of rapid prototyping (RP) — 3D inkjet printing and 3D graphics modeling. The visual and haptic realism of these developed prototypes along with the use of moulage enable the SP to exhibit realistic …

A Smart-Phone Application For Improving Communication Skills In Children With Autism, Lakshmi Padmaja Battagiri

Electrical & Computer Engineering Theses & Dissertations

The prevalence of autism, a complex neurobiological disorder, has grown at a staggering rate in the recent past. Today, one out of 150 American children is diagnosed with autism. Generally, the disorder appears during an individual's first three years of life of and persists through his/her entire life span. The individual faces various social interaction, communication and behavioral problems. Developmental disabilities, extreme withdrawal, lack of social behaviour, severe language and attention deficits, repetitive behaviours and limited interests are the characteristics of this disorder. There is no verified cure yet, but several therapies and intervention systems especially focused on the early …

Reinforcement-Learning-Based Output-Feedback Control Of Nonstrict Nonlinear Discrete-Time Systems With Application To Engine Emission Control, Peter Shih, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier

Electrical and Computer Engineering Faculty Research & Creative Works

A novel reinforcement-learning-based output adaptive neural network (NN) controller, which is also referred to as the adaptive-critic NN controller, is developed to deliver the desired tracking performance for a class of nonlinear discrete-time systems expressed in nonstrict feedback form in the presence of bounded and unknown disturbances. The adaptive-critic NN controller consists of an observer, a critic, and two action NNs. The observer estimates the states and output, and the two action NNs provide virtual and actual control inputs to the nonlinear discrete-time system. The critic approximates a certain strategic utility function, and the action NNs minimize the strategic utility …

Image Registration Using Conformal Log Polar Mapping, Bala Krishna Vadapally

Electrical & Computer Engineering Theses & Dissertations

Image Registration is the process of aligning, or overlaying two images of the same scene that were taken at different times and/or from different viewing angles and/or by sensors with different modalities or resolutions. The variations in the imaging environment induce the difference between the images of the same scene. In our situation, we have two images of the same scene taken with two sensors, one in the visible and the other in the infrared (IR) domain. The cameras are placed adjacent to each other on a stable platform, and the images are captured almost simultaneously. This means that the …

Monte Carlo Model Of Light Propagation In Tissues And The Effects Of Phase Changes On The Light Intensity, Rakesh Choula

Electrical & Computer Engineering Theses & Dissertations

Lasers, due to their unique properties, have a wide range of applications in the medical field. For accurate laser treatments that focus on bio-tissues, prior knowledge of the amount of laser power, spot size and its irradiation time are necessary. In order to predict the effects of lasers on tissues and their bio-effects, a first necessary step is the creation of a model that can predict the temperature distributions within the tissue following laser excitation. This involves modeling light propagation through the tissue with inclusion of internal scattering, and assessment of the energy deposited by the incoming photons. The next …

A Robust Method To Detect Concealed Weapons, Anand Gone

Electrical & Computer Engineering Theses & Dissertations

Concealed weapons detection is a large problem that is faced by the Police Department nowadays. There are many disasters caused by poor detection of the weapons. Since public safety is at risk there is a need to design an efficient detector that can detect the weapons hidden under the clothing. This thesis presents a novel method for detecting concealed weapons under clothing using image processing techniques. In this thesis IR imagery is used to capture an image which works on the principle of law of black body radiation. Image thresholding is performed on the captured data using Sauvola's adaptive thresholding …