Physical Sciences and Mathematics Commons^™

Deep Learning Methods For Fingerprint-Based Indoor And Outdoor Positioning, Fahad Alhomayani

Electronic Theses and Dissertations

Outdoor positioning systems based on the Global Navigation Satellite System have several shortcomings that have deemed their use for indoor positioning impractical. Location fingerprinting, which utilizes machine learning, has emerged as a viable method and solution for indoor positioning due to its simple concept and accurate performance. In the past, shallow learning algorithms were traditionally used in location fingerprinting. Recently, the research community started utilizing deep learning methods for fingerprinting after witnessing the great success and superiority these methods have over traditional/shallow machine learning algorithms. The contribution of this dissertation is fourfold:

First, a Convolutional Neural Network (CNN)-based method for …

Multi-Functional Fluorescence Microscopy Via Psf Engineering For High-Throughput Super-Resolution Imaging, Jinhan Ren

Electronic Theses and Dissertations, 2020-2023

Image-based single cell analysis is essential to study gene expression levels and subcellular functions with preserving the native spatial locations of biomolecules. However, its low throughput has prevented its wide use to fundamental biology and biomedical applications which require large cellular populations in a rapid and efficient fashion. Here, we report a 2.5D microcopy (2.5DM) that significantly improves the image acquisition rate while maintaining high-resolution and single molecule sensitivity. Unlike serial z-scanning in conventional approaches, volumetric information is simultaneously projected onto a 2D image plane in a single shot by engineering the fluorescence light using a novel phase pattern. The …

Directional Link Management Using In-Band Full-Duplex Free Space Optical Transceivers For Aerial Nodes, A F M Saniul Haq

Electronic Theses and Dissertations, 2020-2023

Free-space optical (FSO) communication has become very popular for wireless applications to complement and, in some cases, replace legacy radio-frequency for advantages like unlicensed band, spatial reuse, and enhanced security. Even though FSO can achieve very high bit-rate (tens of Gbps), range limitation due to high attenuation and weather dependency has always restricted its practical implementation to indoor application like data centers and outdoor application like Project Loon. Building-to-building communication, smart cars, and airborne drones are potential futuristic wireless communication sectors for mobile ad-hoc FSO networking. Increasing social media usage demands high-speed mobile connectivity for applications like video call and …

Quantifying The Value Of Foam-Based Flexible Floating Solar Photovoltaic Systems, Koami Soulemane Hayibo

Dissertations, Master's Theses and Master's Reports

Distributed generation with solar photovoltaic (PV) technology is economically competitive if net metered in the U.S. Yet there is evidence that net metering is misrepresenting the true value of distributed solar generation so that the value of solar (VOS) is becoming the preferred method for evaluating economics of grid-tied PV. VOS calculations are challenging and there is widespread disagreement in the literature on the methods and data needed. To overcome these limitations, this thesis reviews past VOS studies to develop a generalized model that considers realistic future avoided costs and liabilities. The approach used here is bottom-up modeling where the …

Superresolution Enhancement With Active Convolved Illumination, Anindya Ghoshroy

Dissertations, Master's Theses and Master's Reports

The first two decades of the 21st century witnessed the emergence of “metamaterials”. The prospect of unrestricted control over light-matter interactions was a major contributing factor leading to the realization of new technologies and advancement of existing ones. While the field certainly does not lack innovative applications, widespread commercial deployment may still be several decades away. Fabrication of sophisticated 3d micro and nano structures, specially for telecommunications and optical frequencies will require a significant advancement of current technologies. More importantly, the effects of absorption and scattering losses will require a robust solution since this renders any conceivable application of metamaterials …

Detection Of Amyotrophic Lateral Sclerosis Disease By Variational Modedecomposition And Convolution Neural Network Methods From Event-Relatedpotential Signals, Fatma Lati̇foğlu, Firat Orhan Bulucu, Rami̇s İleri̇

Turkish Journal of Electrical Engineering and Computer Sciences

Amyotrophic lateral sclerosis (ALS), also known as motor neuron disease, is a neurological disease that occurs as a result of damage to the nerves in the brain and restriction of muscle movements. Electroencephalography (EEG) is the most common method used in brain imaging to study neurological disorders. Diagnosis of neurological disorders such as ALS, Parkinson's, attention deficit hyperactivity disorder is important in biomedical studies. In recent years, deep learning (DL) models have been started to be applied in the literature for the diagnosis of these diseases. In this study, event-related potentials (ERPs) were obtained from EEG signals obtained as a …

On Performance Analysis Of Multioperator Ran Sharing For Mobile Networkoperators, Yekta Türk, Engi̇n Zeydan

Turkish Journal of Electrical Engineering and Computer Sciences

Enhancing the coverage and eliminating the poor performance is key to balance end-user experience andfuture network investments for mobile network operators (MNOs). Although vast amounts of infrastructure investmentsare provided by MNOs, there are still coverage and capacity planning problems at remote locations. This is because,in most cases, the population density and return-of-investments are low in those areas. In this paper, radio accessnetwork (RAN) sharing paradigm is utilized on experimental sites in Turkey to accommodate user equipment of multiplenetwork operators under the same cell sites. We first investigate characteristics, benefits, and limitations of two differentRAN sharing deployment scenarios. Then, a city-wide …

Developing A Machine Learning Framework For Upwind Surveyed Regions, Witenberg Santiago Rodrigues Souza

Graduate Research Theses & Dissertations

Every year, the Oil & Gas industry loses $2B dollars due to fugitive natural gas leaks. Identifying the source of a leak is a complex challenge, especially in suburban areas where gas leakage may be mixed with other sources. Besides the multitude of possible locations for a leak in an urban area, surveying an entire city may take considerable time and funds depending on the chosen method. This work proposes a framework based on unmanned areal vehicles (UAV) to survey a region for gas leaks. To accomplish this goal, we rely on the concept of a Upwind Survey Region (USR). …

Hydrocarbons In The Niobrara Formation, Powder River Basin, Northeast Wyoming, From Geophysical Well Logs, Jennifer Shilo James

Graduate Research Theses & Dissertations

The Powder River Basin, located in Wyoming, is a moderate producer of hydrocarbons with production hovering around 100 million barrels of oil per year. Recently the Niobrara Formation has garnered attention for hydrocarbon exploration and production using both conventional and unconventional drilling. A quick-look method for evaluating organic content, using either existing or real time data sets gathered from geophysical well logs, provides an opportunity to rapidly, accurately, and economically assess the formation’s organic content and thus hydrocarbon bearing potential. The popular Schmoker method determines organic content by relating gamma ray intensity to formation density. It has been successfully employed …

Investigation Of Spontaneously Patterned Nanoporous Organosilicate Thin Film Channels For Liquid Chromatography Applications., Ashwin Sanjay Bhaskaran

Graduate Research Theses & Dissertations

Microscale devices are attractive options for the advancement of biomedical engineering and life science. They have huge potential in the pharmaceutical and biomedical fields provided the critical factors such as device size, product purity and efficiency are not a hindrance. In most cases, traditional microelectronics fabrication processes are quite costly and complicated. By improving on them, their application in fields such as chemical sensing, detection and analysis is possible. By implementing this on a chip, we can reduce the cost and improve portability. This thesis focuses on the investigation of a novel class of spontaneously patterned nanoporous organosilicate nanoparticle-based films …

Engraved Digit Detection Using Hog-Real Adaboost And Deep Neural Network, Tuan Linh Dang, Thang Cao, Yukinobu Hoshino

Turkish Journal of Electrical Engineering and Computer Sciences

This paper proposes a framework for recognizing sequences of digits engraved on steel plates. These digits are normally blurred, dirty, not clear, tilted, and sometimes overlapped by other digits. Several digits in a string with uneven spacing and different sizes are detected at the same time. The framework consists of two main components called histogram of oriented gradient-real AdaBoost module and deep neural network module. The first component is used to detect digit windows, and the second component is employed to recognize digits inside the detected windows. Experimental results demonstrated that the proposed framework could be a potential solution to …

Software And Data Visualization Platform For Groundwater Level And Quality Monitoring System, Carlos M. Oppus, Ma. Aileen Leah G. Guzman, Maria Leonora C. Guico, Jose Claro N. Monje, Mark Glenn F. Retirado, John Chris T. Kwong, Genevieve C. Ngo, Annael J. Domingo

Electronics, Computer, and Communications Engineering Faculty Publications

Rapid urbanization and increasing population come with the increased extraction and use of groundwater resources. To track the effect of these activities on groundwater level and quantity; a system for real-time monitoring is devised. In this paper; we present a software system design that enables a locally-developed groundwater level and water quality monitoring hardware setup to gather water quality parameter data; send it to a cloud server; and present organized data for better visualization. The hardware setup consists of an Arduino microcontroller. Upon deployment; the hardware setup is linked to an Android application that connects to the web-based platform.

Improved Secure And Low Computation Authentication Protocol For Wireless Body Area Network With Ecc And 2d Hash Chain, Soohyeon Choi

Electronic Theses and Dissertations

Since technologies have been developing rapidly, Wireless Body Area Network (WBAN) has emerged as a promising technique for healthcare systems. People can monitor patients’ body condition and collect data remotely and continuously by using WBAN with small and compact wearable sensors. These sensors can be located in, on, and around the patient’s body and measure the patient’s health condition. Afterwards sensor nodes send the data via short-range wireless communication techniques to an intermediate node. The WBANs deal with critical health data, therefore, secure communication within the WBAN is important. There are important criteria in designing a security protocol for a …

Lightweight Encryption Based Security Package For Wireless Body Area Network, Sangwon Shin

Electronic Theses and Dissertations

As the demand of individual health monitoring rose, Wireless Body Area Networks (WBAN) are becoming highly distinctive within health applications. Nowadays, WBAN is much easier to access then what it used to be. However, due to WBAN’s limitation, properly sophisticated security protocols do not exist. As WBAN devices deal with sensitive data and could be used as a threat to the owner of the data or their family, securing individual devices is highly important. Despite the importance in securing data, existing WBAN security methods are focused on providing light weight security methods. This led to most security methods for WBAN …

Developing An Institutional Arrangement For A Whole-Of-Government And Whole-Of-Community Approach To Regional Adaptation To Sea Level Rise: The Hampton Roads Pilot Project, Juita-Elena (Wie) Yusuf, J. Gail Nicula, Burton St. John Iii, Meagan M. Jordan, Michelle Covi, Carol Considine, Marina Saitgalina, Joshua Behr

School of Public Service Faculty Publications

Adaptation to sea level rise (SLR) requires coordination among local, state, and federal entities and collaboration across governments, nonprofits, businesses, and residents. This coordination and collaboration are reflected in institutional arrangements associated with a whole-of-government and whole-of-community approach to regional adaptation. This study analyzes the development of an interlocal agreement (ILA), the Hampton Roads Sea Level Rise Preparedness and Resilience Intergovernmental Planning Pilot Project (the Pilot Project), as an example of such an arrangement. This study assesses how factors throughout three phases of ILA development (initiation, implementation, and execution) influence outcomes and effectiveness. Drawing upon participant observation, document analysis, survey …

Electroosmotic Flow Of Viscoelastic Fluid Through A Constriction Microchannel, Jianyu Ji, Shizhi Qian, Zhaohui Liu

Mechanical & Aerospace Engineering Faculty Publications

Electroosmotic flow (EOF) has been widely used in various biochemical microfluidic applications, many of which use viscoelastic non-Newtonian fluid. This study numerically investigates the EOF of viscoelastic fluid through a 10:1 constriction microfluidic channel connecting two reservoirs on either side. The flow is modelled by the Oldroyd-B (OB) model coupled with the Poisson–Boltzmann model. EOF of polyacrylamide (PAA) solution is studied as a function of the PAA concentration and the applied electric field. In contrast to steady EOF of Newtonian fluid, the EOF of PAA solution becomes unstable when the applied electric field (PAA concentration) exceeds a critical value for …

A Simple Background Elimination Method For Miniaturized Fiber-Optic Raman Probe, Bohong Zhang

Masters Theses

"Raman scattering is called a photonic - molecular interaction based on the kinetic model of the analytic. Due to the uniqueness of the Raman scattering technique, it can provide a unique fingerprint signal for molecular recognition. However, a serious challenge often encountered in Raman measurement comes from the requirements of fast, real-time remote sensing, background fluorescence suppression, and micro-environmental detection.

A new Miniaturized Fiber-Optic Raman Probe (MFORP) for Raman spectroscopy, used especially for eliminating background fluorescence and enhancing sampling, is presented. Its main purpose is to provide an overview of excellent research on the detection of very small substances and …

Tunable Optical Filter Using Phase Change Materials For Smart Ir Night Vision Applications, Remona Heenkenda, Keigo Hirakawa, Andrew Sarangan

Electro-Optics and Photonics Faculty Publications

In this paper we present a tunable filter using Ge2Sb2Se4Te1 (GSST) phase change material. The design principle of the filter is based on a metal-insulator-metal (MIM) cavity operating in the reflection mode. This is intended for night vision applications that utilize 850nm as the illumination source. The filter allows us to selectively reject the 850nm band in one state. This is illustrated through several daytime and nighttime imaging applications.

Dales Objects: A Large Scale Benchmark Dataset For Instance Segmentation In Aerial Lidar, Nina M. Singer, Vijayan K. Asari

Electrical and Computer Engineering Faculty Publications

We present DALES Objects, a large-scale instance segmentation benchmark dataset for aerial lidar. DALES Objects contains close to half a billion hand-labeled points, including semantic and instance segmentation labels. DALES Objects is an extension of the DALES (Varney et al., 2020) dataset, adding additional intensity and instance segmentation annotation. This paper provides an overview of the data collection, preprocessing, hand-labeling strategy, and final data format. We propose relevant evaluation metrics and provide insights into potential challenges when evaluating this benchmark dataset. Finally, we provide information about how researchers can access the dataset for their use at go.udayton.edu/dales3d.

Generation Of Excited Species In A Streamer Discharge, Shirshak K. Dhali

Electrical & Computer Engineering Faculty Publications

At or near atmospheric pressure, most transient discharges, particularly in molecular gases or gas mixture containing molecular gases, result in a space charge dominated transport called a streamer discharge. The excited species generation in such discharges forms the basis for plasma chemistry in most technological applications. In this paper, we simulate the propagation of streamers in atmospheric pressure N₂ to understand the energy partitioning in the formation of various excited species and compare the results to a uniform Townsend discharge. The model is fully two-dimensional with azimuthal symmetry. The results show a significantly larger fraction of the energy goes …

Joint Modeling Of Rnaseq And Radiomics Data For Glioma Molecular Characterization And Prediction, Zeina A. Shboul, Norou Diawara, Arastoo Vossough, James Y. Chen, Khan M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

RNA sequencing (RNAseq) is a recent technology that profiles gene expression by measuring the relative frequency of the RNAseq reads. RNAseq read counts data is increasingly used in oncologic care and while radiology features (radiomics) have also been gaining utility in radiology practice such as disease diagnosis, monitoring, and treatment planning. However, contemporary literature lacks appropriate RNA-radiomics (henceforth, radiogenomics) joint modeling where RNAseq distribution is adaptive and also preserves the nature of RNAseq read counts data for glioma grading and prediction. The Negative Binomial (NB) distribution may be useful to model RNAseq read counts data that addresses potential shortcomings. …

The Resistive Barrier Discharge: A Brief Review Of The Device And Its Biomedical Applications, Mounir Laroussi

Electrical & Computer Engineering Faculty Publications

This paper reviews the principles behind the design and operation of the resistive barrier discharge, a low temperature plasma source that operates at atmospheric pressure. One of the advantages of this plasma source is that it can be operated using either DC or AC high voltages. Plasma generated by the resistive barrier discharge has been used to efficiently inactivate pathogenic microorganisms and to destroy cancer cells. These biomedical applications of low temperature plasma are of great interest because in recent times bacteria developed increased resistance to antibiotics and because present cancer therapies often are accompanied by serious side effects. Low …

A Novel Method For Soc Estimation Of Li-Ion Batteries Using A Hybrid Machinelearning Technique, Eymen İpek, Murat Yilmaz

Turkish Journal of Electrical Engineering and Computer Sciences

The battery system is one of the key components of electric vehicles (EV) which has brought groundbreaking technologies. Since modern EVs have mostly Li-ion batteries, they need to be monitored and controlled to achieve safe and high-performance operation. Particularly, the battery management system (BMS) uses complex processing systems that perform measurements, estimation of the battery states, and protection of the system. State of charge (SOC) estimation is a major part of these processes which defines remaining capacity in the battery until the next charging operation as a proportion to the total battery capacity. Since SOC is not a parameter that …

Covid-19 Detection On Ibm Quantum Computer With Classical-Quantum Transferlearning, Erdi̇ Acar, İhsan Yilmaz

Turkish Journal of Electrical Engineering and Computer Sciences

Diagnose the infected patient as soon as possible in the coronavirus 2019 (COVID-19) outbreak which is declared as a pandemic by the world health organization (WHO) is extremely important. Experts recommend CT imaging as a diagnostic tool because of the weak points of the nucleic acid amplification test (NAAT). In this study, the detection of COVID-19 from CT images, which give the most accurate response in a short time, was investigated in the classical computer and firstly in quantum computers. Using the quantum transfer learning method, we experimentally perform COVID-19 detection in different quantum real processors (IBMQx2, IBMQ-London and IBMQ-Rome) …

Development Of Computationally Efficient Biorthogonal Wavelets, Mehmet Cemi̇l Kale

Turkish Journal of Electrical Engineering and Computer Sciences

Daubechies 5-tap/3-tap (Daub 5/3) wavelet and Kale 5-tap/3-tap (Kale 5/3) wavelet are computationally efficient wavelets which can be implemented by bitwise shifts and additions in the lifting scheme. In this work, presented is a formulation for computationally efficient wavelet prediction (P) and update (U) filters of two-channel lifting structures. Their subband decomposition scheme counterparts are also given. This research bases itself on the Daub 5/3 and Kale 5/3 wavelets and develops a formula for wavelets (which can be implemented with bitwise shifts and additions) that are derived from these two wavelets. The proposed wavelets are tried on 16 test images …

Time-Oriented Interactive Process Miner: A New Approach For Time Prediction, İsmai̇l Yürek, Derya Bi̇rant, Özlem Ece Yürek, Kökten Ulaş Bi̇rant

Turkish Journal of Electrical Engineering and Computer Sciences

Everyday information systems collect a different kind of process instances of a business flow. As time goes on, the size of the collected data builds up speedily and constitutes a huge amount of data. It is a very challenging task to obtain valuable information and features of processes from such big data. Considering in advance, the trend and different features of the ongoing process are essential. Especially, time management is crucial in designing and conducting business processes. In this article, a novel process miner algorithm is proposed for time prediction, named time-oriented İnteractive process miner (T-IPM), which predicts the remaining …

Abnormal Behavior Detection Using Sparse Representations Through Sequentialgeneralization Of K-Means, Ahlam Aldhamari, Rubita Sudirman, Nasrul Humaimi Mahmood

Turkish Journal of Electrical Engineering and Computer Sciences

The potential capability to automatically detect and classify human behavior as either normal or abnormal events is an important aspect in intelligent monitoring/surveillance systems. This study presents a new high-performance framework for detecting behavioral abnormalities in video streams by utilizing only the patterns for normal behaviors. In this paper, we used a hybrid descriptor, called a foreground optical flow energy (FGOFE), which makes use of two effective motion techniques in order to extract the most descriptive spatiotemporal features in video sequences. The FGOFE descriptor can effectively capture both weak and sudden incidents in a scene. The sequential generalization of k-means …

Characterization Of Different Crowd Behaviors Using Novel Deep Learningframework, Abdullah Jaman Alzahrani, Sultan Daud Khan

Turkish Journal of Electrical Engineering and Computer Sciences

Crowd behavior understanding is recognized as a complex problem due to unpredictable behavior of humans and complex interactions of individuals in groups. For crowd managers, it is crucial to understand the crowd dynamics to manage the crowd efficiently and effectively. Current practice of crowd management is based on manual analysis of the scene. Such manual analysis of the scene is a tedious job and usually prone to errors due to limited human capabilities. Therefore, the task of automatizing crowd analysis has received tremendous attention from the research community during the recent years. In this paper, we propose a deep model …

A New Method For Optimal Expansion Planning In Electrical Energy Distributionnetworks With Distributed Generation Resources Considering Uncertainties, Amir Masoud Mohaghegh, S Yaser Derakhshandeh, Abbas Kargar

Turkish Journal of Electrical Engineering and Computer Sciences

The present study aims to introduce a robust model for distribution network expansion planning considering system uncertainties. The proposed method determines optimal size and placement of distributed generation resources, as well as installation and reinforcement of feeders and substations. This model is designed to minimize cost and to determine the best time for the installation of equipment in the expansion planning. In the proposed expansion planning, the fuzzy logic theory is employed to model uncertainties of loads and energy price. Also, since the proposed model is a nonlinear and nonconvex optimization problem, a tri-stage algorithm is developed to solve it. …

Speed-Sensorless Predictive Torque Controlled Induction Motor Drive Withfeed-Forward Control Of Load Torque For Electric Vehicle Applications, Emrah Zerdali̇, Ridvan Demi̇r

Turkish Journal of Electrical Engineering and Computer Sciences

Nowadays, the global trend is towards reducing CO2 emissions and one solution is to replace internal combustion vehicles with electric vehicles. To this end, electric drive system, the most crucial part of an electric vehicle, has gained importance and has become a major research field. The induction motor (IM) is one of the best candidates for electric vehicle applications due to its advantages such as having simple and robust design, its low cost maintenance requirements and the ability to operate in harsh environments. However, it has a highly nonlinear model with timevarying electrical and mechanical parameters making them difficult to …