Physical Sciences and Mathematics Commons^™

The Bracelet: An American Sign Language (Asl) Interpreting Wearable Device, Samuel Aba, Ahmadre Darrisaw, Pei Lin, Thomas Leonard

Chancellor’s Honors Program Projects

No abstract provided.

Design & Analysis Of Mixed-Mode Integrated Circuit For Pulse-Shape Discrimination, Bryan Orabutt

McKelvey School of Engineering Theses & Dissertations

In nuclear science experiments it is usually necessary to determine the type of radiation, its energy and direction with considerable accuracy. The detection of neutrons and discriminating them from gamma rays is particularly difficult. A popular method of doing so is to measure characteristics intrinsic to the pulse shape of each radiation type in order to perform pulse-shape discrimination (PSD).

Historically, PSD capable systems have been designed with two approaches in mind: specialized analog circuitry, or digital signal processing (DSP). In this work we propose a PSD capable circuit topology using techniques from both the analog and DSP domains. We …

A Hybrid Acoustic-Rf Communication Framework For Networked Control Of Autonomous Underwater Vehicles: Design And Cosimulation, Saeed Nourizadeh Azar, Oytun Erdemi̇r, Mehrullah Soomro, Özgür Gürbüz Ünlüyurt, Ahmet Onat

Turkish Journal of Electrical Engineering and Computer Sciences

Underwater control applications, especially ones using autonomous underwater vehicles (AUVs) have become very popular for industrial and military underwater exploration missions. This has led to the requirement of establishing a high data rate communication link between base stations and AUVs, while underwater systems mostly rely on acoustic communications. However, limited data rate and considerable propagation delay are the major challenges for employing acoustic communication in missions requiring high control gains. In this paper, we propose a hybrid acoustic and RF communication framework for establishing a networked control system, in which, for long distance communication and control the acoustic link is …

Framework Of Hardware Trojan Detection Leveraging Structural Checking Tool, Rafael Dacanay Del Carmen

Graduate Theses and Dissertations

Since there is a significant demand for obtaining third-party soft Intellectual Property (IP) by first-party integrated circuit (IC) vendors, it is becoming easier for adversaries to insert malicious logic known as hardware Trojans into designs. Due to this, vendors need to find ways to screen the third-party IPs for possible security threats and then mitigate them. The development of the Structural Checking (SC) tool provides a solution to this issue. This tool analyzes the structure of an unknown soft IP design and creates a network of all the signals within the design and how they are connected to each other. …

A Novel Data Lineage Model For Critical Infrastructure And A Solution To A Special Case Of The Temporal Graph Reachability Problem, Ian Moncur

Graduate Theses and Dissertations

Rapid and accurate damage assessment is crucial to minimize downtime in critical infrastructure. Dependency on modern technology requires fast and consistent techniques to prevent damage from spreading while also minimizing the impact of damage on system users. One technique to assist in assessment is data lineage, which involves tracing a history of dependencies for data items. The goal of this thesis is to present one novel model and an algorithm that uses data lineage with the goal of being fast and accurate. In function this model operates as a directed graph, with the vertices being data items and edges representing …

Radiomic Features To Predict Overall Survival Time For Patients With Glioblastoma Brain Tumors Based On Machine Learning And Deep Learning Methods, Lina Chato

UNLV Theses, Dissertations, Professional Papers, and Capstones

Machine Learning (ML) methods including Deep Learning (DL) Methods have been employed in the medical field to improve diagnosis process and patient’s prognosis outcomes. Glioblastoma multiforme is an extremely aggressive Glioma brain tumor that has a poor survival rate. Understanding the behavior of the Glioblastoma brain tumor is still uncertain and some factors are still unrecognized. In fact, the tumor behavior is important to decide a proper treatment plan and to improve a patient’s health. The aim of this dissertation is to develop a Computer-Aided-Diagnosis system (CADiag) based on ML/DL methods to automatically estimate the Overall Survival Time (OST) for …

Learning Target Class Eigen Subspace (Ltc-Es) Via Eigen Knowledge Grid, Sanjay Kumar Sonbhadra, Sonali Agarwal, P. Nagabhushan

Turkish Journal of Electrical Engineering and Computer Sciences

In one-class classification (OCC) tasks, only the target class (class-of-interest (CoI)) samples are well defined during training, whereas the other class samples are totally absent. In OCC algorithms, the high dimensional data adds computational overhead apart from its intrinsic property of curse of dimensionality. For target class learning, conventional dimensionality reduction (DR) techniques are not suitable due to negligence of the unique statistical properties of CoI samples. In this context, the present research proposes a novel target class guided DR technique to extract the eigen knowledge grid that contains the most promising eigenvectors of variance-covariance matrix of CoI samples. In …

A New Effective Denoising Filter For High Density Impulse Noise Reduction, Iman Elawady, Caner Özcan

Turkish Journal of Electrical Engineering and Computer Sciences

Today, thanks to the rapid development of technology, the importance of digital images is increasing. However, sensor errors that may occur during the acquisition, interruptions in the transmission of images and errors in storage cause noise that degrades data quality. Salt and pepper noise, a common impulse noise, is one of the most well-known types of noise in digital images. This noise negatively affects the detailed analysis of the image. It is very important that pixels affected by noise are restored without loss of image fine details, especially at high level of noise density. Although many filtering algorithms have been …

Blmdp: A New Bi-Level Markov Decision Process Approach To Joint Bidding Andtask-Scheduling In Cloud Spot Market, Mona Naghdehforoushha, Mehdi Dehghan Takht Fooladi, Mohammad Hossein Rezvani, Mohammad Mehdi Gilanian Sadeghi

Turkish Journal of Electrical Engineering and Computer Sciences

In the cloud computing market (CCM), computing services are traded between cloud providers and consumers in the form of the computing capacity of virtual machines (VMs). The Amazon spot market is one of the most well-known markets in which the surplus capacity of data centers is auctioned off in the form of VMs at relatively low prices. For each submitted task, the user can offer a price that is higher than the current price. However, uncertainty in the market environment confronts the user with challenges such as the variable price of VMs and the variable number of users. An appropriate …

Priority Enabled Content Based Forwarding In Fog Computing Via Sdn, Yasi̇n İnağ, Metehan Güzel, Feyza Yildirim Okay, Mehmet Demi̇rci̇, Suat Özdemi̇r

Turkish Journal of Electrical Engineering and Computer Sciences

As the number of Internet of Things (IoT) applications increases, an efficient transmitting of the data generated by these applications to a centralized cloud server can be a challenging issue. This paper aims to facilitate transmission by utilizing fog computing (FC) and software defined networking (SDN) technologies. To this end, it proposes two novel content based forwarding (CBF) models for IoT networks. The first model takes advantage of FC to reduce transmission and computational delay. Based on the first model, the second model makes use of the prioritization concept to address the timely delivery of critical data while ensuring the …

Estimation Of Mode Shape In Power Systems Under Ambient Conditions Using Advanced Signal Processing Approach, Rahul S, Sunitha R

Turkish Journal of Electrical Engineering and Computer Sciences

This paper presents a dynamic approach for the monitoring and estimation of electromechanical oscillatory modes in the power system in real time with less computational burden. Extensive implementation of phasor measurement units (PMU) and the utilization of advanced signal processing techniques help in identifying the dynamic behaviors of oscillatory modes. Conventional nonstationary analysis techniques are computationally weak to handle a larger quantity of data in real-time. This research utilizes the variational mode decomposition (VMD) for signal decomposition, which is highly tolerant to noise and computationally more robust. The predefined parameters of the VMD process are assigned using FFT analysis of …

Evaluating The Role Of Carbon Quantum Dots Covered Silica Nanofillers On The Partial Discharge Performance Of Transformer Insulation, Kasi Viswanathan Palanisamy, Chandrasekar Subramaniam, Balaji Sakthivel

Turkish Journal of Electrical Engineering and Computer Sciences

The article presents the experimental results on the role of carbon quantum dots (CQD) covered silica nanofillers on the partial discharge (PD) properties of transformer oil insulation. The improvement in PD performance of nanofiller blend oil is tested with increased voltage gradient and nanofiller concentration. PD of nanoblend oils for various concentrations of modified silica ranging from 0 to 0.1%wt was measured. PD activity of the test samples is simulated in the laboratory with needle, rod and plane electrode geometry combinations. The facets of PD signals such as PD magnitude, PD inception and time duration of PD extracted from phase-resolved …

A Novel Deep Reinforcement Learning Based Stock Price Prediction Using Knowledge Graph And Community Aware Sentiments, Anil Berk Altuner, Zeynep Hi̇lal Ki̇li̇mci̇

Turkish Journal of Electrical Engineering and Computer Sciences

Stock market prediction has been an important topic for investors, researchers, and analysts. Because it is affected by too many factors, stock market prediction is a difficult task to handle. In this study, we propose a novel method that is based on deep reinforcement learning methodologies for the prediction of stock prices using sentiments of community and knowledge graph. For this purpose, we firstly construct a social knowledge graph of users by analyzing relations between connections. After that, time series analysis of related stock and sentiment analysis is blended with deep reinforcement methodology. Turkish version of Bidirectional Encoder Representations from …

A New Speed Planning Method Based On Predictive Curvature Calculation For Autonomous Driving, Beki̇r Öztürk, Volkan Sezer

Turkish Journal of Electrical Engineering and Computer Sciences

As the number of vehicles in traffic is increasing day by day, the accident rates and driving effort are significantly raising. For this reason, ensuring safety and driving comfort is becoming more and more important. Driver assistance systems are the most common systems that are adopted for this purpose. With the development of technology, lane tracking support and adaptive cruise control systems are now being sold as standard equipment. More advanced research is being done for fully autonomous driving. One of the most critical parts of autonomous driving is speed profile planning. In this paper, a curvature-based predictive speed planner …

Lightweight Distributed Computing Framework For Orchestrating High Performance Computing And Big Data, Muhammed Numan İnce, Meli̇h Günay, Joseph Ledet

Turkish Journal of Electrical Engineering and Computer Sciences

In recent years, the need for the ability to work remotely and subsequently the need for the availability of remote computer-based systems has increased substantially. This trend has seen a dramatic increase with the onset of the 2020 pandemic. Often local data is produced, stored, and processed in the cloud to remedy this flood of computation and storage needs. Historically, HPC (high performance computing) and the concept of big data have been utilized for the storage and processing of large data. However, both HPC and Hadoop can be utilized as solutions for analytical work, though the differences between these may …

Strategic Integration Of Battery Energy Storage And Photovoltaic At Low Voltage Level Considering Multiobjective Cost-Benefit, Samarjit Patnaik, Manas Nayak, Meera Viswavandya

Turkish Journal of Electrical Engineering and Computer Sciences

Renewable energy sources, such as solar photovoltaic (PV) systems and battery energy storage systems (BESS), help reduce greenhouse gas emissions while fulfilling the world?s growing energy demand. The inclusion of BESS reduces the peak hour demand, and control of charging and discharging of BESS can be economical for distributors facing time-based energy pricing. This paper discusses a novel multiobjective Horse herd optimisation algorithm (MOHHOA) approach, which is inspired by the social behaviour of horses in herds for PV and BESS optimal allocation in the radial distribution system. The proposed algorithm combines multiple benefits like benefits from economic gain per day, …

Offline Tuning Mechanism Of Joint Angular Controller For Lower-Limb Exoskeleton With Adaptive Biogeographical-Based Optimization, Mohammad Soleimani Amiri, Rizauddin Ramli

Turkish Journal of Electrical Engineering and Computer Sciences

Designing an accurate controller to overcome the nonlinearity of dynamic systems is a technical matter in control engineering, particularly for tuning the parameters of the controller precisely. In this paper, a tuning mechanism for a proportional-integral-derivative (PID) controller of lower limb exoskeleton (LLE) joints by adaptive biogeographical based-optimization (ABBO) is presented. The tuning of the controller is defined as an optimization problem and solved by ABBO, which is an iterative algorithm inspired by a blending crossover operator (BLX-?). The parameters of the migration change proportionally to the growth of iteration that conveys the error to rapid convergence by narrowing the …

Software Security Management In Critical Infrastructures: A Systematic Literature Review, Gülsüm Ece Ekşi̇, Bedi̇r Teki̇nerdoğan, Cagatay Catal

Turkish Journal of Electrical Engineering and Computer Sciences

Critical infrastructure (CI) is an integrated set of systems and assets that are essential to ensure the functioning of a nation, including its economy, the public's health and/or safety. Hence, protecting critical infrastructures (CI) is vital because of the potential severe consequences that may emerge at the national level. Many CIs are now controlled by software, and likewise, software is often the major source of many security problems in critical infrastructures. Software security management in CIs has been addressed in the literature and several useful approaches have been provided. Yet, these approaches are fragmented over multiple different studies, often do …

A Survey On Organizational Choices For Microservice-Based Software Architectures, Hüseyi̇n Ünlü, Burak Bi̇lgi̇n, Onur Demi̇rörs

Turkish Journal of Electrical Engineering and Computer Sciences

During the last decade, the demand for more flexible, responsive, and reliable software applications increased exponentially. The availability of internet infrastructure and new software technologies to respond to this demand led to a new generation of applications. As a result, cloud-based, distributed, independently deployable web applications working together in a microservice-based software architecture style have gained popularity. The style has been a common practice in the industry and successfully utilized by companies. Adopting this style demands software organizations to transform their culture. However, there is a lack of research studies that explores common practices for microservices. Thus, we performed a …

An Efficient End-To-End Deep Neural Network For Interstitial Lung Disease Recognition And Classification, Masum Shah Junayed, Afsana Ahsan Jeny, Md Baharul Islam, Ikhtiar Ahmed, Afm Shahen Shah

Turkish Journal of Electrical Engineering and Computer Sciences

The automated Interstitial Lung Diseases (ILDs) classification technique is essential for assisting clinicians during the diagnosis process. Detecting and classifying ILDs patterns is a challenging problem. This paper introduces an end-to-end deep convolution neural network (CNN) for classifying ILDs patterns. The proposed model comprises four convolutional layers with different kernel sizes and Rectified Linear Unit (ReLU) activation function, followed by batch normalization and max-pooling with a size equal to the final feature map size well as four dense layers. We used the ADAM optimizer to minimize categorical cross-entropy. A dataset consisting of 21328 image patches of 128 CT scans with …

An Adaptive Search Equation-Based Artificial Bee Colony Algorithm For Transportation Energy Demand Forecasting, Durmuş Özdemi̇r, Safa Dörterler

Turkish Journal of Electrical Engineering and Computer Sciences

This study aimed to develop a new adaptive artificial bee colony (A-ABC) algorithm that can adaptively select an appropriate search equation to more accurately estimate transport energy demand (TED). Also, A-ABC and canonical artificial bee colony (C-ABC) algorithms were compared in terms of efficiency and performance. The input parameters used in the proposed TED model were the official economic indicators of Turkey, including gross domestic product (GDP), population, and total vehicle kilometer per year (TKM). Three mathematical models, linear (A-ABCL), exponential (A-ABCE), and quadratic (A-ABCQ) were developed and tested. Also, economic variables were generated using the "curve fitting" technique to …

Dual-Polarized Elliptic-H Slot-Coupled Patch Antenna For 5g Applications, Emre Alp Mi̇ran, Mehmet Çi̇ydem

Turkish Journal of Electrical Engineering and Computer Sciences

A dual-polarized, slot-coupled dielectric patch antenna design is presented in sub-6 GHz for 5G base stations. Proposed antenna is implemented using two dielectric patch layers (main patch and parasitic patch) above a feeding line layer. Excitation is realized by crossed elliptic-H slots in order to create orthogonal ± 45$^{\circ}$ polarizations. Through the use of patches at proper heights together with elliptic-H slots, significant improvement in impedance bandwidth, matching level, isolation and front-to-back ratio is acquired. Prototype antenna has an impedance bandwidth of 18.5\% (3.23--3.85 GHz) for $ S_{11} $, $ S_{22} $ $

Missing Samples Reconstruction Using An Efficient And Robust Instantaneous Frequency Estimation Algorithm, Sadiq Ali, Nabeel Ali Khan

Turkish Journal of Electrical Engineering and Computer Sciences

In order to recover missing samples in a nonstationary signal, this paper employs a time-signal analysis and filtering method. The instantaneous frequency of a multicomponent signal is first estimated by employing a robust and computationally efficient method. Then the time-frequency filtering is performed using a dechirping operation to recover missing samples. These steps are repeated until convergence. The proposed method achieves better performance than the state of art methods both in terms of the accuracy of the recovered signal and computational efficiency.

Classification And Phenological Staging Of Crops From In Situ Image Sequences By Deep Learning, Uluğ Bayazit, Deni̇z Turgay Altilar, Ni̇lgün Güler Bayazit

Turkish Journal of Electrical Engineering and Computer Sciences

Accurate knowledge of crop type information is not only valuable for verifying the declaration of farmers to obtain subsidy or insurance for the grown crop, but also for generating crop type maps that serve a variety of purposes in land monitoring and policy. On the other hand, accurate knowledge of crop phenological stage can help farm personnel apply fertilization and irrigation regimes on a timely basis. Although deep learning based networks have been applied in the past to classify the type and predict the phenological stage of crops from in situ images of fields, more advanced deep learning based networks, …

Effects Of Distributed Energy Resources On The Bulk Electric System, Stryder Loveday

UNLV Theses, Dissertations, Professional Papers, and Capstones

The traditional power grid was designed with a centralized resource distribution in mind, with a relatively small number of large power generation facilities supplying the vast majority of load. With recent efforts to further diversify the power grid as well as an increased interest in renewable energy sources, there has been an unprecedented amount of new DistributedEnergy Resources (DERs) added to the grid within the last decade. This often heavily influences the local demand where these resources are installed, often causing power to flow in ways not anticipated by the original design of the grid. This thesis reviews the potential …

Machine Learning Classification Of Digitally Modulated Signals, James A. Latshaw

Electrical & Computer Engineering Theses & Dissertations

Automatic classification of digitally modulated signals is a challenging problem that has traditionally been approached using signal processing tools such as log-likelihood algorithms for signal classification or cyclostationary signal analysis. These approaches are computationally intensive and cumbersome in general, and in recent years alternative approaches that use machine learning have been presented in the literature for automatic classification of digitally modulated signals. This thesis studies deep learning approaches for classifying digitally modulated signals that use deep artificial neural networks in conjunction with the canonical representation of digitally modulated signals in terms of in-phase and quadrature components. Specifically, capsule networks are …

Chen-Fliess Series For Linear Distributed Systems, Natalie T. Pham

Electrical & Computer Engineering Theses & Dissertations

Distributed systems like fluid flow and heat transfer are modeled by partial differential equations (PDEs). In control theory, distributed systems are generally reformulated in terms of a linear state space realization, where the state space is an infinite dimensional Banach space or Hilbert space. In the finite dimension case, the input-output map can always be written in terms of a Chen-Fliess functional series, that is, a weighted sum of iterated integrals of the components of the input function. The Chen-Fliess functional series has been used to describe interconnected nonlinear systems, to solve system inversion and tracking problems, and to design …

Development Of High Conductivity Copper Coatings For Srf Cavity, Himal Pokhrel

Physics Theses & Dissertations

The development of metallic coatings with high purity and high thermal conductivity at cryogenic temperature could be very important for application to the superconducting radiofrequency (SRF) cavity technology. The deposition of such bulk coatings on the outer surface of a niobium cavity could result in higher heat conductance and mechanical stiffness, both of which are crucial for enhancing the cavity performance at a reduced cost.

Cold spray technology was used to deposit bulk coatings of pure copper and copper-tungsten alloys on the niobium substrate and the samples of size 2 mm × 2 mm cross section were cut and subjected …

Electromagnetic Modeling Of A Wind Tunnel Magnetic Suspension And Balance System, Desiree Driver

Mechanical & Aerospace Engineering Theses & Dissertations

Wind tunnels are used to study forces and moments acting on an aerodynamic body. While most results involve some interference from the mechanical supports used to hold the model, a Magnetic Suspension and Balance System (MSBS) is void of these interferences and presents an ideal test scenario. To further investigate the feasibility of dynamic stability testing at supersonic speeds using a MSBS, a preliminary design idea is currently being developed using an existing MSBS in a subsonic wind tunnel. This review focuses on the development of a mathematical model to more accurately portray the capabilities of the 6 inch Massachusetts …

Unconventional Computation Including Quantum Computation, Bruce J. Maclennan

Faculty Publications and Other Works -- EECS

Unconventional computation (or non-standard computation) refers to the use of non-traditional technologies and computing paradigms. As we approach the limits of Moore’s Law, progress in computation will depend on going beyond binary electronics and on exploring new paradigms and technologies for information processing and control. This book surveys some topics relevant to unconventional computation, including the definition of unconventional computations, the physics of computation, quantum computation, DNA and molecular computation, and analog computation. This book is the content of a course taught at UTK.