Computer Engineering Commons^™

Advanced Hyperthermia Treatment: Optimizing Microwave Energy Focus For Breast Cancer Therapy, Burak Acar, Tuba Yilmaz Abdolsaheb, Ali Yapar

Turkish Journal of Electrical Engineering and Computer Sciences

This paper presents a fast antenna phase optimization scheme to enable microwave power focusing for breast cancer hyperthermia. The power focusing is achieved through the maximization of the deposited electric field on the target malignant tumor tissue. To do so, a malignant breast tumor, the surrounding breast medium, and the skin of the breast are modeled as a cylindrical structure composed of eccentric cylinders, and electric field distribution is computed analytically in terms of cylindrical harmonics. This approach minimized the computational cost and simplified the breast medium model. To ensure applicability across various breast types, the dielectric properties (DPs) of …

Consensus-Based Virtual Leader Tracking Algorithm For Flight Formation Control Of Swarm Uavs, Berat Yıldız, Akif Durdu, Ahmet Kayabaşi

Turkish Journal of Electrical Engineering and Computer Sciences

Technological developments in industrial areas also impact unmanned aerial vehicles (UAVs). Recent improvements in both software and hardware have significantly increased the use of many UAVs in social and military fields. In particular, the widespread use of these vehicles in social areas such as entertainment, shipping, transportation, and delivery and military areas such as surveillance, tracking, and offensive measures has accelerated the research on swarm systems. This study examined the previous investigations on swarm UAVs and aimed to create a more efficient algorithm. The effectiveness of the proposed algorithm was compared with other leader-based applications. A swarm consisting of 5 …

Cascade Controller Design Via Controller Synthesis For Load Frequency Control Of Electrical Power Systems, Yavuz Güler, Mustafa Nalbantoğlu, Ibrahim Kaya

Turkish Journal of Electrical Engineering and Computer Sciences

The regulation of tie-line electricity flow and frequency of electrical power systems (EPS) is crucial for ensuring their robustness to parameter changes and efficient management of disturbances. To this end, a novel cascade control design approach utilizing a serial Proportional-Integral-Derivative controller with a filter (PIDF) is proposed in this paper. The parameters of the controllers are derived analytically, and it is employed in both loops of the cascade control system to regulate the Load Frequency Control (LFC) of EPS. The implementation of PIDF controllers in both loops is utilized in the cascade control scheme for various power systems featuring different …

Atomic Comagnetometer Gyroscopes For Inertial Navigation Systems: A Review, Murat Salim Karabinaoglu, Bekir Çakir, Mustafa Engin Başoğlu

Turkish Journal of Electrical Engineering and Computer Sciences

In recent years, developments in quantum sensing, laser, and atomic sensor technologies have also enabled advancement in the field of quantum navigation. Atomic-based gyroscopes have emerged as one of the most critical atomic sensors in this respect. In this review, a brief technology statement of spin exchange relaxation free (SERF) and nuclear magnetic resonance (NMR) type atomic comagnetometer gyroscope (CG) is presented. Related studies in the literature have been gathered, and the fundamental compositions of CGs with technical basics are presented. A comparison of SERF and NMR CGs is provided. A basic simulation of SERF CG was carried out because …

Uncovering And Mitigating Spurious Features In Domain Generalization, Saeed Karimi, Hamdi̇ Di̇bekli̇oğlu

Turkish Journal of Electrical Engineering and Computer Sciences

Domain generalization (DG) techniques strive to attain the ability to generalize to an unfamiliar target domain solely based on training data originating from the source domains. Despite the increasing attention given to learning from multiple training domains through the application of various forms of invariance across those domains, the enhancements observed in comparison to ERM are nearly insignificant under specified evaluation rules. In this paper, we demonstrate that the disentanglement of spurious and invariant features is a challenging task in conventional training since ERM simply minimizes the loss and does not exploit invariance among domains. To address this issue, we …

Lower Data Attacks On Advanced Encryption Standard, Orhun Kara

Turkish Journal of Electrical Engineering and Computer Sciences

The Advanced Encryption Standard (AES) is one of the most commonly used and analyzed encryption algorithms. In this work, we present new combinations of some prominent attacks on AES, achieving new records in data requirements among attacks, utilizing only 2 4 and 2 16 chosen plaintexts (CP) for 6-round and 7-round AES 192/256, respectively. One of our attacks is a combination of a meet-in-the-middle (MiTM) attack with a square attack mounted on 6-round AES-192/256 while another attack combines an MiTM attack and an integral attack, utilizing key space partitioning technique, on 7-round AES-192/256. Moreover, we illustrate that impossible differential (ID) …

Spatiotemporal Reaction Dynamics Control In Two-Photon Polymerization For Enhancing Writing Characteristics, Aofei Mao, Sarah Fess, Nada Kraiem, P. Li, Zhipeng P. Wu, Qiuchi Zhu, Xi Huang, Peixun Fan, Bai Cui, Jean-Francois Silvain, Suxing Hu, Mitchell Anthamatten, Sean P. Regan, David Harding, Yongfeng Lu

Department of Electrical and Computer Engineering: Faculty Publications

Since 2001, 3D microfabrication based on two-photon polymerization (TPP) has drawn extensive attention and interest in biology, optics, photonics, material science, and high-energy physics. The in-volume fabrication capability due to the threshold behavior of two-photon absorption enables TPP higher flexibility compared with other nanofabrication techniques. However, as determined by the in-volume fabrication feature as well as various reaction dynamics, the writing characteristics of TPP, such as throughput, accuracy, surface quality, and fabrication capability, are still limited. Herein, a comprehensive study is performed on the spatiotemporal behavior of reaction dynamics during TPP fabrication, mainly focusing on spatiotemporal characteristics of radical diffusion, …

A Deep Learning Convolutional Neural Network For Antenna Near-Field Prediction And Surrogate Modeling, Md Rayhan Khan, Constantinos L. Zekios, Shubhendu Bhardwaj, Stavros V. Georgakopoulos

Department of Electrical and Computer Engineering: Faculty Publications

This study investigates the use of deep learning techniques for building a generalized surrogate model that can accurately and very efficiently predict antenna performance parameters. Notably, we focus on applications where a substantial amount of simulation time is required and prior data is available for deep learning use. Specifically, for these applications, we introduce deep learning models that efficiently and reliably model the near-field of the antenna. These models, in turn, accurately predict far-field properties and essential antenna metrics, such as the reflection coefficient. To demonstrate the efficiency of our method, the widely used rectangular patch antenna is considered, encompassing …

Predicting The Water Situation In Jordan Using Auto Regressive Integrated Moving Average (Arima) Model, Shahed Al-Khateeb

Jerash for Research and Studies Journal مجلة جرش للبحوث والدراسات

Countries' water security is inextricably related to their economic position. Jordan is one of the world's five poorest countries regarding water resources. Climate change and water scarcity are threatening Jordan's economic growth and food security.

The objectives of the study are to use a statistical artificial intelligence model, which is called the Autoregressive Integrated Moving Average model to predict water productivity in Jordan and the world for the year 2021-2026, based on a real dataset from World Development Indicators from the World Bank. The study also aims to predict the total per capita share of fresh water based on the …

Performance Analysis Of Mobile Edge Computing Deployment Models In 5g Networks, Safaa Alali, Abdulkaim Assalem

Jerash for Research and Studies Journal مجلة جرش للبحوث والدراسات

5G networks and Mobile Edge Computing (MEC) are two main pillars of the next technological revolution. The first pillar provides ultra-reliable, high-bandwidth connectivity with ultra-low latency, while the second pillar gives mobile networks cloud computing capabilities at the edge of the network, enabling compute-dense, context-aware services for mobile users. So the next generation of mobile networks will see close integration between computing and communication. Therefore, it was necessary to study the different deployment options for edge hosts in 5G network, to know the effect of those options on the overall performance of the network. In this research, (Omnetpp 6.0) network …

Requirements For Employing Artificial Intelligence Applications In Higher Education And Its Challenges, Nuha Musa Otoom

Jerash for Research and Studies Journal مجلة جرش للبحوث والدراسات

The study aimed to determine the requirements for applications of artificial intelligence in the field of higher education, and its challenges. The descriptive survey method (content analysis) was used, where the researcher collected information and documents about artificial intelligence and the requirements for employing its applications and challenges, by referring to many reliable sources and references that contributed to Reaching the results that the research seeks to achieve, The results showed that there are a set of requirements for employing artificial intelligence applications in higher education, the most prominent of which is spreading a culture that supports artificial intelligence in …

Data-Driven Approaches For Enhancing Power Grid Reliability, Behrouz Sohrabi

Electronic Theses and Dissertations

This thesis explores the transformative potential of data-driven approaches in addressing key operational and reliability issues in power systems. The first part of this thesis addresses a prevalent problem in power distribution networks: the accurate identification of load phases. This study develops a data-driven model leveraging consumption measurements from smart meters and corresponding substation data to reconstruct topology information in low-voltage distribution networks. The proposed model is extensively tested using a dataset with more than 5,000 real load profiles, demonstrating satisfactory performance for large-scale networks. The second part of the thesis pivots to a crucial safety concern: the risk and …

Empathy, Education, And Awareness: A Vr Hackathon’S Approach To Tackling Climate Change, Noor Alqallaf, Dalia W. Elnagar, Sherif G. Aly, Khalil I. Elkhodary, Rami Ghannam

Faculty Journal Articles

Climate change education is crucial for fostering informed and engaged future generations. However, traditional pedagogies often fail to engage learners fully and provide real-world, experiential learning. This paper presents a novel approach to climate change education through a three-day virtual reality (VR) hackathon. The hackathon focused on four United Nations (UN) Sustainable Development Goals (SDGs)—Quality Education, Affordable and Clean Energy, Sustainable Cities and Communities, and Climate Action. Using VR technology and game design software, engineering students worked in teams. They competed against each other in designing immersive environments that demonstrated their understanding of these SDGs and climate change. Our goal …

A Study Of Random Partitions Vs. Patient-Based Partitions In Breast Cancer Tumor Detection Using Convolutional Neural Networks, Joshua N. Ramos

Master's Theses

Breast cancer is one of the deadliest cancers for women. In the US, 1 in 8 women will be diagnosed with breast cancer within their lifetimes. Detection and diagnosis play an important role in saving lives. To this end, many classifiers with varying structures have been designed to classify breast cancer histopathological images. However, randomly partitioning data, like many previous works have done, can lead to artificially inflated accuracies and classifiers that do not generalize. Data leakage occurs when researchers assume that every image in a dataset is independent of each other, which is often not the case for medical …

Designing An Optical Router Based On A Multimode-Interference Silicon-On-Insulator Coupler With Tunable Power Transmittance, Dana S. Akil, Muhammad A. Othman, Sherif M. Sherif, Mohamed A. Swillam

Faculty Journal Articles

The demand on fast and high-bandwidth data transmission is in continuous increase. These demands are highly dependent on optical signal manipulation, including switching, modulation, and routing. We demonstrate a two-port silicon optical router based on the multimode interferometer (MMI) configuration. The same MMI structure was used for both inward and backward waveguiding to reduce the total length of the device. A phase shifter consisting of two ring-like waveguides made of silicon p-n junctions was used to introduce the phase shift needed for optical routing upon voltage application. Two designs for the MMI optical router were studied: Firstly, a conventional MMI …

Uncovering The Critical Drivers Of Blockchain Sustainability In Higher Education Using A Deep Learning-Based Hybrid Sem-Ann Approach, Mohammed Alshamsi, Mostafa Al-Emran, Tugrul Daim, Mohammed A. Al-Sharafi, Gulin Idil Sonmezturk Bolatan, Khaled Shaalan

Engineering and Technology Management Faculty Publications and Presentations

The increasing popularity of Blockchain technology has led to its adoption in various sectors, including higher education. However, the sustainability of Blockchain in higher education is yet to be fully understood. Therefore, this research examines the determinants affecting Blockchain sustainability by developing a theoretical model that integrates the protection motivation theory (PMT) and expectation confirmation model (ECM). Based on 374 valid responses collected from university students, the proposed model is evaluated through a deep learning-based hybrid structural equation modeling (SEM) and artificial neural network (ANN) approach. The PLS-SEM results confirmed most of the hypotheses in the proposed model. The sensitivity …

A Machine Learning Approach For Multipath Characterization And Mitigation Using Chipshape Observations, Sean A. L. Quiterio

Theses and Dissertations

Multipath continues to be a significant error source in satellite navigation. Recent solutions with Neural Networks (NN) model the effects of multipath on the autocorrelation function to predict errors in the Delay Lock Loop (DLL). Chipshape correlation provides a detailed look into the spreading code transitions in the time domain. It is useful in applications such as Signal Quality Monitoring (SQM) and is much more sensitive to multipath than autocorrelation. This research proposes NN models that each predict pseudorange or carrier range errors due to multipath by monitoring the chipshape correlation output. For a simulation with 50 MHz precorrelation bandwidth …

Weakly Supervised Attention-Based Recognition Under Spectral, Turbulence, And Resource Variations, Kshitij Naresh Nikhal

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

While supervised optimization paradigms are ubiquitous across diverse recognition systems, the risk of over-fitting and increasing bias have limited their applicability.

This dissertation focuses on unsupervised learning—learning without precisely curated data—and argues that unsupervised learning methods can enable both discriminability and generalizability. Through the use of attention-based machine learning and advanced clustering, unsupervised methods are able to focus on fine-grained information in images without any explicit supervision. The dissertation introduces a domain-bridging framework for tasks like cross-spectrum matching and long-range recognition, utilizing intra-domain clustering and inter-domain matching to generate pseudo-labels. Additionally, a hash-based network is proposed to accelerate the search …

Insights Into Cellular Evolution: Temporal Deep Learning Models And Analysis For Cell Image Classification, Xinran Zhao

Master's Theses

Understanding the temporal evolution of cells poses a significant challenge in developmental biology. This study embarks on a comparative analysis of various machine-learning techniques to classify cell colony images across different timestamps, thereby aiming to capture dynamic transitions of cellular states. By performing Transfer Learning with state-of-the-art classification networks, we achieve high accuracy in categorizing single-timestamp images. Furthermore, this research introduces the integration of temporal models, notably LSTM (Long Short Term Memory Network), R-Transformer (Recurrent Neural Network enhanced Transformer) and ViViT (Video Vision Transformer), to undertake this classification task to verify the effectiveness of incorporating temporal features into the classification …

Hardware-In-The-Loop Autonomous Trajectory Generations For On-Orbit Rendezvous And Proximity Operations, Connor Slattery

Theses and Dissertations

Utilizing the C++ version of GPOPS within a robotic operating system to assess efficacy on flight-ready hardware is a vital interest of the space industry today. The findings in this paper show the ability of employing GPOPS in C++ on flight-ready hardware, leveraging optimal control as the main driver to creating a control pipleline to allow a robotic platform to generate and follow an optimized trajectory with minimal user input.

Performance Analysis Of Intel Total Memory Encryption - Multi-Key, Charles W. Rockett

Theses and Dissertations

Secure cloud computing is an evolution in data management and security. As cloud-based services, ranging from storage solutions accessible from mobile devices to complex applications, become integral to daily operations, securing data throughout the life cycle of its use is essential. In response, hardware companies are advancing encryption and security technologies to protect data in use to thwart unauthorized access while mitigating the performance impacts of security and encryption features. This thesis addresses TME-MK performance when enabled and disabled on a server using all host threads and with three virtualized environments assigned separate unique threads each. The primary aim is …

A Novel Data Fusion Fr Amework To Enhance Contextual Awareness Of The Autonomous Vehicles For Accurate Decision Making, Henry Alexander Ignatious

Dissertations

Autonomous driving has the potential to bring significant changes and benefits to various aspects of transportation. Autonomous vehicles (AVs) use a combination of advanced sensors, cameras, radar, lidar, GPS, maps, and AI algorithms to perceive their environment, make decisions, and control their movements. Though there is a significant increase in the AVs utility, there are several challenges associated with the AVs among which ensuring safety and security for a reliable drive is still an existing challenge. The majority of accidents involving the AVs result from faulty decision-making resulting in fatal incidents. Multiple elements contribute to the flawed decision-making in autonomous …

Can Neural Networks Reach Human Vision Levels On Object Recognition Tasks?, Luke D. Baumel, Mikayla Cutler, Matt Hyatt, Joseph Tocco, William Friebel, Nicholas Baker Dr., George K. Thiruvathukal Dr.

Psychology: Faculty Publications and Other Works

Object recognition is a crucial function of biological vision; it allows us to draw conclusions about a visual scene that transcends the image formed by the retina. However, the task of object recognition quickly becomes a challenge when hindrances such as viewing angle, object distance from observer, illuminant qualities, and potential occlusions become active variables. Additionally, the diversity of visual features within the same category of object, coupled with the numerous contexts in which an object may be observed is demonstrative of the formidable task that is object recognition. Previous research showed a significant texture bias in Convolutional Neural Networks’ …

Ai For Dummies, Jacob Mazurkiewicz

D.U.Quark

No abstract provided.

Text Summarization, Varun Gottam, Anusha Vunnam, Purna Sarovar Puvvada

Symposium of Student Scholars

The current era is known as the information era. Every day, millions of gigabytes of data are being transferred from one point to another. As the creation of data became easy, it became hard to keep track of the important points and the gist of data especially in areas such as research and news. To solve this conundrum, text summarization is introduced. This is a process of summarizing text from across different documents or large datasets such that it can be read and understood easily by both humans and machines.

Yieldnet: Intelligent Fruit Yield Estimation For Selected Orchards Using Deep Learning Based Semantic Segmentation, Maheswari P

Theses and Dissertations

Agriculture contributes more resources for developing sustainable economic growth of the nation. Precision agriculture employs advanced techniques (machine learning and deep learning) for developing the intelligent systems of various agricultural applications. Among various agricultural tasks, yield estimation of crops plays a vital role in decision-making such as harvesting, marketing, cultivation practices, etc. Traditionally yield estimation is performed manually which has major drawbacks i.e., needs experts opinion, time-consuming and it is a challenging task for big orchards. To overcome these issues, an intelligent yield estimation model using neural network-based systems is required.

Some of the literature works have been explored for …

Steminism: Analyzing Factors That Improve Retention Of Women In Stem, Kira Carter, Jane Kelley, Jason Vasser-Elong, Rc Patterson

Dissertations

Our co-authored research ‘Steminism: Analyzing Factors That Improve Retention for Women as STEM Majors’ analyzed factors that contributed to the retention of women in science, technology, engineering, and mathematics (STEM) programs at Missouri University of Science & Technology (Missouri S&T). Women make up half of the US population, and while careers in (STEM) are an integral part of the US economy, women are underrepresented in these career fields. The purpose of our dissertation is to address the underrepresentation of women in STEM majors. Our methodology included homogeneous sampling to collect qualitative data. More specifically, we consulted with academic advisors and …

An Efficient Regression Testing Suite Optimization System With Iso Quality Factors, Prakash V

Theses and Dissertations

Regression testing is a black-box testing technique. It is utilized to validate an alteration in code in the software to ensure whether it has affected the present performance of the product. It has also been used to assess the adjusted variants of the product. Moreover, software testing is the most efficient process in Software Development Life Cycle (SLDC).

The study introduces Green cloud computing, incorporating computer resources such as foundations, PCs, application administrations, and information stockpiling. Notably, the research imbibes reliability, dependability, and maintainability as quality meters in the validation process. The goal of the proposed system is to implement …

Detecting Substance Use Disorder Using Social Media Data And Dark Web: Time And Knowledge Aware Study, Usha Lokala, Orchid Chetia Phukan, Triyasha Ghosh Dastidar, Francois Lamy, Raminta Daniulaityte, Amit Sheth

Faculty Publications

Opioid and substance misuse is rampant in the United States today, with the phenomenon known as the "opioid crisis". The relationship between substance use and mental health has been extensively studied, with one possible relationship being: substance misuse causes poor mental health. However, the lack of evidence on the relationship has resulted in opioids being largely inaccessible through legal means. This study analyzes the substance use posts on social media with opioids being sold through crypto market listings. We use the Drug Abuse Ontology, state-of-the-art deep learning, and knowledge-aware BERT-based models to generate sentiment and emotion for the social media …

Adaptive Inter-Vehicular Collaboration For Level 5 Autonomous Driving, Sumbal Malik

Thesis/ Dissertation Defenses

The evolution of sensor technologies, mobile networks, and artificial intelligence has significantly expanded the horizons of various sectors, notably in autonomous driving. According to the World Health Organization, over 1.3 million people succumb to traffic accidents annually, predominantly due to human negligence. Complex urban environments, characterized by mixed traffic flow involving Human Driven Vehicles and Connected and Autonomous Vehicles, intricate traffic patterns, frequent lane changes, and slow speeds, face escalating challenges related to traffic accidents and congestion. These challenges lead to wasted time, increased fuel consumption, and heightened pollution emission levels. Hence, the development of a safe and robust version …