Engineering Commons^™

Formally Analyzed M-Coupon Protocol With Confirmation Code (Mcwcc), Keri̇m Yildirim, Gökhan Dalkiliç, Nevci̇han Duru

Turkish Journal of Electrical Engineering and Computer Sciences

There are many marketing methods used to attract customers' attention and customers search for special discounts and conduct research to get products cheaper. Using discount coupons is one of the widely used methods for obtaining discounts. With the development of technology, classical paper-based discount coupons become e-coupons and then turn into mobile coupons (m-coupons). It is inevitable that retailers will use m-coupon technology to attract customers while mobile devices are used in daily life. As a result, m-coupon technology is a promising technology. One of the significant problems with using m-coupons is security. Here it is necessary to ensure the …

Detection Of Hemorrhage In Retinal Images Using Linear Classifiers And Iterative Thresholding Approaches Based On Firefly And Particle Swarm Optimization Algorithms, Kemal Adem, Mahmut Heki̇m, Seli̇m Demi̇r

Turkish Journal of Electrical Engineering and Computer Sciences

We propose a novel iterative thresholding approach based on firefly and particle swarm optimization to be used for the detection of hemorrhages, one of the signs of diabetic retinopathy disease. This approach consists of the enhancement of the image using basic preprocessing methods, the segmentation of vessels with the help of Gabor and Top-hat transformation for the removal of the vessels from the image, the determination of the number of regions with hemorrhages and pixel counts in these regions using firefly algorithm (FFA) and particle swarm optimization algorithm (PSOA)-based iterative thresholding, and the detection of hemorrhages with the help of …

Local Directional-Structural Pattern For Person-Independent Facial Expression Recognition, Farkhod Makhmudkhujaev, Md Tauhid Bin Iqbal, Byungyong Ryu, Oksam Chae

Turkish Journal of Electrical Engineering and Computer Sciences

Existing popular descriptors for facial expression recognition often suffer from inconsistent feature description, experiencing poor accuracies. We present a new local descriptor, local directional-structural pattern (LDSP), in this work to address this issue. Unlike the existing local descriptors using only the texture or edge information to represent the local structure of a pixel, the proposed LDSP utilizes the positional relationship of the top edge responses of the target pixel to extract more detailed structural information of the local texture. We further exploit such information to characterize expression-affiliated crucial textures while discarding the random noisy patterns. Moreover, we introduce a globally …

Accurate And Compact Stochastic Computations By Exploiting Correlation, Hamdan Abdellatef, Mohamed Khalil Hani, Nasir Shaikh-Husin

Turkish Journal of Electrical Engineering and Computer Sciences

Recent studies have shown, contrary to what was previously believed, that by exploiting correlation in stochastic computing (SC) designs, more accurate SC circuits with low area cost can be realized. However, if these basic SC circuits or blocks are cascaded in series to form a large complex system, correlation between stochastic numbers (SNs) from one block to the next would be lost; thus, inaccuracies are introduced. In this study, we propose correlating circuits to be used in building complex correlated SC systems. One of the circuits is the correlator that restores lost correlations between two SNs due to previous processing. …

A Multiseed-Based Svm Classification Technique For Training Sample Reduction, Imran Sharif, Debasis Chaudhuri

Turkish Journal of Electrical Engineering and Computer Sciences

A support vector machine (SVM) is not a popular method for a very large dataset classification because the training and testing time for such data are computationally expensive. Many researchers try to reduce the training time of SVMs by applying sample reduction methods. Many methods reduced the training samples by using a clustering technique. To reduce its high computational complexity, several data reduction methods were proposed in previous studies. However, such methods are not effective to extract informative patterns. This paper demonstrates a new supervised classification method, multiseed-based SVM (MSB-SVM), which is particularly intended to deal with very large datasets …

Optimal Set Of Eeg Features In Infant Sleep Stage Classification, Maja Cic, Mario Milicevic, Igor Mazic

Turkish Journal of Electrical Engineering and Computer Sciences

This paper evaluates six classification algorithms to assess the importance of individual EEG rhythms in the context of automatic classification of infant sleep. EEG features were obtained by Fourier transform and by a novel technique based on the empirical mode decomposition and generalized zero crossing method. Of six evaluated classification algorithms, the best classification results were obtained with the support vector machine for the combination of all presented features from four EEG channels. Three methods of attribute ranking were assessed: relief, principal component analysis, and wrapper-based optimized attribute weights. The outcomes revealed that the optimal selection of features requires one …

Improvement Of Quantized Adaptive Switching Median Filter For Impulse Noise Reduction In Gray-Scale Digital Images, Haidi Ibrahim, Ahmed Khaldoon Abdalameer

Turkish Journal of Electrical Engineering and Computer Sciences

Digital images may suffer from fixed value impulse noise due to several causes. The noise significantly degrades the quality of the image, which may affect the subsequence image processing. Therefore, a noise reduction technique is required to restore the image. In this paper, a new method, which is called improvement of quantized adaptive switching median filter (IQASMF), has been proposed to reduce the fixed value impulse noise from gray-scale digital images. The implementation of IQASMF has five processing blocks. The first processing block is the noise detection block, where the noise pixel candidates are detected based on the intensity value. …

Boltzmann Analysis Of Electron Swarm Parameters In Chf3+Cf4 Mixtures, Hidir Düzkaya, Süleyman Sungur Tezcan

Turkish Journal of Electrical Engineering and Computer Sciences

The electron drift velocity, mean energy, ionization, attachment, effective ionization coefficient, limit electrical field, and synergism of pure CHF3 (fluoroform), pure CF4 (tetrafluoromethane), and CHF3+CF4 gas mixtures are calculated by Boltzmann equation analysis in a wide range of density normalized electrical fields (E/N). The finite difference method is used to solve the two-term approximation of the Boltzmann equation under steady-state Townsend conditions. To our knowledge, no previous electron swarm parameters of these mixtures have been published. At constant E/N values, the electron mean energies and drift velocities increase with decreasing CHF3 content. The addition of CF4 into the mixture increases …

Comparative Analysis Of A Novel Topology For Single-Phase Z-Source Inverter With Reduced Number Of Switches, Himanshu Sharma, Rintu Khanna, Neelu Jain

Turkish Journal of Electrical Engineering and Computer Sciences

Z-source inverter has recently been introduced to overcome the limitations of conventional voltage source inverter. This paper deals with a novel topology of single-phase Z-source inverter (ZSI). This topology reduced the number of passive elements and active switches in order to make the inverter cheaper and smaller in size compared to traditional ZSI. Detailed analysis of the proposed topology is presented in this paper which includes calculations of boost factor, total harmonic disorder, magnitude of output voltage etc. Modulation technique used to control the switching of the proposed inverter is explained in detail. This paper also compares the proposed topology …

On The Stability Of Inverse Dynamics Control Of Flexible-Joint Parallel Manipulators In The Presence Of Modeling Error And Disturbances, Sitki Kemal İder, Ozan Korkmaz, Mustafa Semi̇h Deni̇zli̇

Turkish Journal of Electrical Engineering and Computer Sciences

Inverse dynamics control is considered for flexible-joint parallel manipulators in order to obtain a good trajectory tracking performance in the case of modeling error and disturbances. It is known that, in the absence of modeling error and disturbance, inverse dynamics control leads to linear fourth-order error dynamics, which is asymptotically stable if the feedback gains are chosen to make the real part of the eigenvalues of the system negative. However, when there are modeling errors and disturbances, a linear time-varying error dynamics is obtained whose stability is not assured only by keeping the real parts of the frozen-time eigenvalues of …

Neural Network Controller For Nanopositioning Of A Smooth Impact Drive Mechanism, Xiaohui Lu, Dong Chen, Tinghai Cheng, Zhe Li

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, neural network theory is used to improve the positioning accuracy of smooth impact drive mechanisms (SIDMs), by designing a displacement controller that consists of a neural network identification (NNI) and a neural network controller (NNC). The dynamics of the SIDM are described by the NNI, which consists of an input layer, hidden layer, and output layer. The parameters of the NNI are adjusted using back propagation. The NNC is designed as a proportional-derivative (PD) controller, which is used to accurately control the displacement of the SIDM. The PD parameters are adjusted with an adaptive adjustment algorithm. A …

Synchronization And Antisynchronization Protocol Design Of Chaotic Nonlinear Gyros: An Adaptive Integral Sliding Mode Approach, Fazal Ur Rahman, Qudrat Khan, Rini Akmeliawati

Turkish Journal of Electrical Engineering and Computer Sciences

A novel control protocol design, via integral sliding mode control with parameter update laws, for synchronization and desynchronization of a chaotic nonlinear gyro with unknown parameters is the focus of this work. The error dynamics of the actual system are substructured into nominal and uncertain parts to employ adaptive integral sliding mode (AISM) control. The uncertain parameters are estimated via devised adaptive laws. Then the disagreement dynamics are guided to origin via AISM control. The stabilizing controller is also designed in terms of nominal control along with a compensating component. The control and the parameter update laws are constructed to …

An Evaluation Of The Information Security Awareness Of University Students, Alan Pike

Dissertations

Between January 2017 and March 2018, it is estimated that more than 1.9 billion personal and sensitive data records were compromised online. The average cost of a data breach in 2018 was reported to be in the region of US$3.62 million. These figures alone highlight the need for computer users to have a high level of information security awareness (ISA). This research was conducted to establish the ISA of students in a university. There were three aspects to this piece of research. The first was to review and analyse the security habits of students in terms of their own personal …

Sdn Testbed For Evaluation Of Large Exo-Atmospheric Emp Attacks, Diogo Oliveira, Nasir Ghani, Majeed M. Hayat, Jorge Crichigno, Elias Bou-Harb

Electrical and Computer Engineering Faculty Research and Publications

Large-scale nuclear electromagnetic pulse (EMP) attacks and natural disasters can cause extensive network failures across wide geographic regions. Although operational networks are designed to handle most single or dual faults, recent efforts have also focused on more capable multi-failure disaster recovery schemes. Concurrently, advances in software-defined networking (SDN) technologies have delivered highly-adaptable frameworks for implementing new and improved service provisioning and recovery paradigms in real-world settings. Hence this study leverages these new innovations to develop a robust disaster recovery (counter-EMP) framework for large backbone networks. Detailed findings from an experimental testbed study are also presented.

Microscale Direct Measurement Of Localized Photothermal Heating In Tissue-Mimetic Hydrogels, Benyamin Davaji, James E. Richie, Chung-Hoon Lee

Electrical and Computer Engineering Faculty Research and Publications

Photothermal hyperthermia is proven to be an effective diagnostic tool for cancer therapy. The efficacy of this method directly relies on understanding the localization of the photothermal effect in the targeted region. Realizing the safe and effective concentration of nano-particles and the irradiation intensity and time requires spatiotemporal temperature monitoring during and after laser irradiation. Due to uniformities of the nanoparticle distribution and the complexities of the microenvironment, a direct temperature measurement in micro-scale is crucial for achieving precise thermal dose control. In this study, a 50 nm thin film nickel resistive temperature sensor was fabricated on a 300 nm …

Predicting Cascading Failures In Power Grids Using Machine Learning Algorithms, Rezoan Ahmed Shuvro, Pankaz Das, Majeed M. Hayat, Mitun Talukder

Electrical and Computer Engineering Faculty Research and Publications

Although there has been notable progress in modeling cascading failures in power grids, few works included using machine learning algorithms. In this paper, cascading failures that lead to massive blackouts in power grids are predicted and classified into no, small, and large cascades using machine learning algorithms. Cascading-failure data is generated using a cascading failure simulator framework developed earlier. The data set includes the power grid operating parameters such as loading level, level of load shedding, the capacity of the failed lines, and the topological parameters such as edge betweenness centrality and the average shortest distance for numerous combinations of …

Age Grading An. Gambiae And An. Arabiensis Using Near Infrared Spectra And Artificial Neural Networks, Masabho Peter Milali, Maggy T. Sikulu-Lord, Samson S. Kiware, Floyd E. Dowell, George F. Corliss, Richard J. Povinelli

Electrical and Computer Engineering Faculty Research and Publications

Background

Near infrared spectroscopy (NIRS) is currently complementing techniques to age-grade mosquitoes. NIRS classifies lab-reared and semi-field raised mosquitoes into < or ≥ 7 days old with an average accuracy of 80%, achieved by training a regression model using partial least squares (PLS) and interpreted as a binary classifier.

Methods and findings

We explore whether using an artificial neural network (ANN) analysis instead of PLS regression improves the current accuracy of NIRS models for age-grading malaria transmitting mosquitoes. We also explore if directly training a binary classifier instead of training a regression model and interpreting it as a binary classifier improves the accuracy. A total of 786 and 870 NIR spectra collected from laboratory reared An. gambiae and An. arabiensis, respectively, were used and pre-processed according …

Multiscale Mathematical Modelling Of Nonlinear Nanowire Resonators For Biological Applications, Rosa Fallahpourghadikolaei

Theses and Dissertations (Comprehensive)

Nanoscale systems fabricated with low-dimensional nanostructures such as carbon nanotubes, nanowires, quantum dots, and more recently graphene sheets, have fascinated researchers from different fields due to their extraordinary and unique physical properties. For example, the remarkable mechanical properties of nanoresonators empower them to have a very high resonant frequency up to the order of giga to terahertz. The ultra-high frequency of these systems attracted the attention of researchers in the area of bio-sensing with the idea to implement them for detection of tiny bio-objects. In this thesis, we originally propose and analyze a mathematical model for nonlinear vibrations of nanowire …

Estimating The Spectrum In Computed Tomography Via Kullback–Leibler Divergence Constrained Optimization, Wooseok Ha, Emil Y. Sidky, Rina Foygel Barber, Taly Gilat Schmidt, Xiaochuan Pan

Biomedical Engineering Faculty Research and Publications

Purpose

We study the problem of spectrum estimation from transmission data of a known phantom. The goal is to reconstruct an x‐ray spectrum that can accurately model the x‐ray transmission curves and reflects a realistic shape of the typical energy spectra of the CT system.

Methods

Spectrum estimation is posed as an optimization problem with x‐ray spectrum as unknown variables, and a Kullback–Leibler (KL)‐divergence constraint is employed to incorporate prior knowledge of the spectrum and enhance numerical stability of the estimation process. The formulated constrained optimization problem is convex and can be solved efficiently by use of the exponentiated‐gradient (EG) …

Spatial And Temporal Influences On Discrimination Of Vibrotactile Stimuli On The Arm, Valay Shah, Maura Casadio, Robert A. Scheidt, Leigh A. Mrotek

Biomedical Engineering Faculty Research and Publications

Body–machine interfaces (BMIs) provide a non-invasive way to control devices. Vibrotactile stimulation has been used by BMIs to provide performance feedback to the user, thereby reducing visual demands. To advance the goal of developing a compact, multivariate vibrotactile display for BMIs, we performed two psychophysical experiments to determine the acuity of vibrotactile perception across the arm. The first experiment assessed vibration intensity discrimination of sequentially presented stimuli within four dermatomes of the arm (C5, C7, C8, and T1) and on the ulnar head. The second experiment compared vibration intensity discrimination when pairs of vibrotactile stimuli were presented simultaneously vs. sequentially …

Ct Automated Exposure Control Using A Generalized Detectability Index, P. Khobragade, Franco Rupcich, Jiahua Fan, Dominic J. Crotty, Naveen M. Kulkarni, Stacy D. O'Connor, W. Dennis Foley, Taly Gilat Schmidt

Biomedical Engineering Faculty Research and Publications

Purpose

Identifying an appropriate tube current setting can be challenging when using iterative reconstruction due to the varying relationship between spatial resolution, contrast, noise, and dose across different algorithms. This study developed and investigated the application of a generalized detectability index (d'_gen) to determine the noise parameter to input to existing automated exposure control (AEC) systems to provide consistent image quality (IQ) across different reconstruction approaches.

Methods

This study proposes a task‐based automated exposure control (AEC) method using a generalized detectability index (d'_gen). The proposed method leverages existing AEC methods that are based on a prescribed …

Computational Characterization Of The Cellular Origins Of Electroencephalography, Shane Hesprich, Scott A. Beardsley

Biomedical Engineering Faculty Research and Publications

Despite the widespread use of Electroecephalography (EEG) as an imaging modality, neural generators of current dipoles measured by EEG at the scalp are not fully understood. Here, we use two morphologically accurate multicompartments neuron models (layer IV pyramidal cell and layer V spiny stellate cell) to characterize how spiking neurons generate current dipoles in response to synaptic input. The simulations indicate that the dipole generated by synaptic inputs required to drive a pyramidal cell to threshold is smaller than the dipole associated the action potential itself. These results suggest a greater role of spiking neural activity toward EEG signals measured …

Uas Flight Operations In Complex Terrain: Assessing The Agricultural Impact From Hurricane Maria In The Central Mountainous Region Of Puerto Rico, Kevin Adkins

Publications

Hurricane Maria struck Puerto Rico in September 2017 as a Category 4 storm causing major damage to infrastructure, agriculture and natural ecosystems, as well as the loss of many lives. Among the crops hardest hit was coffee, one of the most important crops in Puerto Rico. As a perennial system, coffee takes various production forms along a gradient from high shade/biodiversity coffee farms to low shade coffee monocultures and therefore offers an ideal means for the study of resistance and resilience of an agroecosystem to weather and climate disturbance. During the summer of 2018, 14 impacted farms across the production …

An Investigation Of The Magneto-Active Slosh Control For Cylindrical Propellant Tanks Using Floating Membranes, Manikandan Vairamani, Pedro Llanos, Balaji Sivasubramanian, Somnath Nagendra, Kevin Crosby

Publications

The phenomenon of sloshing is a substantial challenge in propellant management, particularly in reduced gravity where surface tension-driven flows result in large slosh amplitudes and relatively long decay time scales. Propellant Management Devices (PMDs) such as the rigid baffles and elastomeric membranes are often employed to counteract motion of the free surface. In the present study, we investigate an active PMD that utilizes a free-floating membrane that, under an applied static magnetic field, becomes rigid and suppresses slosh. This semi-rigid structural layer can thereby replace bulky baffle structures and reduce the overall weight of the tank. In this paper, the …

Instilling An Entrepreneurial Mindset In A New Generation Of First-Year Engineering Students Through A Graphics Course Project, Lulu Sun, Leroy Long Iii

Publications

Each year, an increasing number of engineering start-up companies emerge in the U.S. and around the world. Innovation and entrepreneurship have never been so pronounced, especially in science, technology, engineering, and mathematics (STEM) fields. How can we train engineering students to be more entrepreneurially-minded so they are well-equipped to become global innovators? Engineering educators can use entrepreneurially-minded learning activities to help students develop an entrepreneurial mindset, which is a set of beliefs, attitudes, and behaviors. At a mid-sized Southeastern private institution, we used an open-ended team project and an end-of-semester poster competition within a freshman-level engineering graphics course to encourage …

Infographic: Enhancing Engineering Students’ Communication Skills Through A Team-Based Graphics Course Project, Leroy Long Iii, Kari L. Jordan, William Wanyagah

Publications

This infographic supports the article Enhancing Engineering Students’ Communication Skills through a Team-Based Graphics Course Project which can be accessed here: https://commons.erau.edu/asee-edgd/conference70/papers-2016/5/

A Topic Modeling Approach For Code Clone Detection, Mohammed Salman Khan

UNF Graduate Theses and Dissertations

In this thesis work, the potential benefits of Latent Dirichlet Allocation (LDA) as a technique for code clone detection has been described. The objective is to propose a language-independent, effective, and scalable approach for identifying similar code fragments in relatively large software systems. The main assumption is that the latent topic structure of software artifacts gives an indication of the presence of code clones. It can be hypothesized that artifacts with similar topic distributions contain duplicated code fragments and to prove this hypothesis, an experimental investigation using multiple datasets from various application domains were conducted. In addition, CloneTM, an LDA-based …

Testing Coulwave For Use In Modeling Cross-Shore Sand Transport And Beach Profile Evolution, Patrick Michael Cooper

UNF Graduate Theses and Dissertations

Realistic, reliable, and effective modeling of cross-shore sediment transport is not present in the current literature. Building that model requires the accurate recreation of breaking wave processes in the nearshore. To develop that first step for an as-yet-to-be-designed model, multiple phase-resolving wave transformation algorithms are reviewed for in-depth investigation. The COULWAVE model is selected for robust testing. Testing of the COULWAVE model shows that, although capable of recreating realistic results, it does not adequately describe major wave characteristics in the surf zone, across a wide range of conditions, to warrant use in a future cross-shore sediment transport model.

Trapping Aco Applied To Mri Of The Heart, Shannon Lloyd Birchell

UNF Graduate Theses and Dissertations

The research presented here supports the ongoing need for automatic heart volume calculation through the identification of the left and right ventricles in MRI images. The need for automated heart volume calculation stems from the amount of time it takes to manually processes MRI images and required esoteric skill set. There are several methods for region detection such as Deep Neural Networks, Support Vector Machines and Ant Colony Optimization. In this research Ant Colony Optimization (ACO) will be the method of choice due to its efficiency and flexibility. There are many types of ACO algorithms using a variety of heuristics …

Power System Loading Margin Enhancement By Optimal Statcom Integration:A Case Study, Sasidharan Shreedharan, Tibin Joseph, Sebin Joseph, Chittesh Veni Chandran, Vishnu J., Vipin Das P

Articles

Safe and secure network operation with acceptable voltage level has become a challenging task for utilities requiring corrective measures to be implemented. Network upgrades using Flexible Alternating Current Transmission System devices are being considered to serve this purpose. To this end, static loading margin enhancement by optimal static synchronous compensator (STATCOM) allocation to enhance the power transfer capability with minimal voltage variation is presented. Maximum loadability is formulated as an optimization problem, subjected to voltage and small-signal stability constraints. Stability indices are presented and incorporated with the optimization problem to ensure secure operation under maximum loading. The scheme is executed …