Engineering Commons^™

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 …

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 …

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 …

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 …

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 …

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 …

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 …

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’ …

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 …

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 …

A Systematic Survey On 5g And 6g Security Considerations, Challenges, Trends, And Research Areas, Paul Scalise, Matthew Boeding, Hamid Sharif, Joseph Delloiacovo, John Reed

Department of Electrical and Computer Engineering: Faculty Publications

With the rapid rollout and growing adoption of 3GPP 5thGeneration (5G) cellular services, including in critical infrastructure sectors, it is important to review security mechanisms, risks, and potential vulnerabilities within this vital technology. Numerous security capabilities need to work together to ensure and maintain a sufficiently secure 5G environment that places user privacy and security at the forefront. Confidentiality, integrity, and availability are all pillars of a privacy and security framework that define major aspects of 5G operations. They are incorporated and considered in the design of the 5G standard by the 3rd Generation Partnership Project (3GPP) with the goal …

Efficiency Optimization Method For Data Sampling In Power Grid Topology Scheduling Simulation, Yingying Zhao, Pusen Dong, Tianchen Zhu, Fan Li, Yun Su, Zhenying Tai, Qingyun Sun, Hang Fan

Journal of System Simulation

Abstract: To address the large simulation computational workload and low simulation speed caused by the scale and complexity of the new power system, a simulation acceleration method for topology scheduling based on the distributed and quantization mechanisms is proposed. The parallelization of topology scheduling models is used to increase the scale of data simulation sampling in unit time. The introduced quantization operators accelerate the computation speed of the topology scheduling model operators, reduces the time cost of the every single simulation. Case studies confirm the effectiveness of the topology simulation acceleration, in which the available transfer capacity of the simulated …

Runoff Intelligent Prediction Method Based On Broad-Deep Fusion Time-Frequency Analysis, Ying Han, Lehao Wang, Shumei Wang, Xiang Zhang, Xingxing Luo

Journal of System Simulation

Abstract: Broad learning system(BLS) is introduced to tackle the existed disadvantage that LSTM-based runoff prediction model is easy to fall into local optimization. To reduce the influence of noise on the prediction results, the variational mode decomposition (VMD) is adopted to transform the onedimensional time-domain runoff signal to the two-dimensional time-frequency plane. The runoff prediction model based on VMD-LSTM-BLS is proposed. The simulation results demonstrate that the prediction accuracy of the new model is more significantly improved compared with the baseline model and the existing LSTM-based runoff prediction model.

Simulation Platform Of Agv System Scheduling Algorithms In Uncertain Environment, Zhihao Shi, Haihui Shen

Journal of System Simulation

Abstract: As a complete automated guided vehicle(AGV) system scheduling algorithm, it must include conflict-handling strategy, together with common dispatching strategy and routing algorithm. However, due to the uncertainty, characteristics of such scheduling algorithm are difficult to be analyzed theoretically, and the relevant study is lacking. An AGV system simulation platform based on the discreteevent simulation technique is designed and developed, which can flexibly set the scheduling problem, choose the dispatching strategy, routing algorithm, and conflict-handling strategy for the scheduling algorithm, to run the simulation. The platform has a visual interface, from which the running status of AGVs and the performance …

Research On Motion Planning Of Hexapod Robot Based On Drl And Free Gait, Xinpeng Wang, Huiqiao Fu, Guizhou Deng, Kaiqiang Tang, Chunlin Chen, Canghao Liu

Journal of System Simulation

Abstract: To improve the passability and the motion performance of the hexapod robot in the unstructured environment, a multi-contact motion planning algorithm based on DRL and free gait planner is proposed. Firstly, the free gait planner obtains the reachable footholds under the target state and outputs the optimal gait sequence. The center of mass motion policy of the hexapod robot in the randomly generated plum blossom pile environment is obtained by using deep reinforcement learning training. To ensure the reachability between adjacent states of the robot in motion, the state transition feasibility model is used to judge the state transition …

Robust Predictive Control Of Nonplanar Fully-Actuated Uavs, Yun Ma, Yuan Wang, Meng Li, Peng Wang, Yanling Tang

Journal of System Simulation

Abstract: Targeting the problem that nonplanar fully-actuated unmanned aerial vehicles (UAVs) are susceptible to external winds and unmodeled dynamics, the predictive control system with good robustness is designed. A nonlinear motion model with six degrees of freedom is established through the Newton-Euler approach. A linear extended state observer is designed to estimate the state variables by transforming the system affected by matched and unmatched disturbances into an equivalent system only affected by the matched disturbances. A predictive controller is designed for the equivalent system to reduce the output oscillation and input surging and a disturbance compensator is also designed to …

Reconnaissance Mission Planning Method For Air-Ground Heterogeneous Unmanned Systems, Guohui Zhang, Ya'nan Zhang, Ang Gao, Aoyu Xu

Journal of System Simulation

Abstract: Compared with the air-based homogeneous unmanned system, the motion capabilities, resource payloads, and combat scenes in the air-ground heterogeneous unmanned system increase the number of constraint conditions and significantly increase the computational complexity of the solution model. The modeling of collaborative combat missions and the efficient solution of large-scale problems are the key issues. With the time, path cost, and reconnaissance benefit as the objective functions, considering the constraints such as the endurance of unmanned platforms, a multi-objective programming model for the reconnaissance missions of an air-ground heterogeneous unmanned system is constructed. Aiming at the urban combat environments with …

A Simplification Method Of Large-Scale Unit Commitment Model Based On Boundary Method, Yanping Xu, Mingxin Zhao, Xiaohui Qin, Keyou He, Xiaohan Wu, Pei Zhang

Journal of System Simulation

Abstract: As the scale of power grid expands, in the market environment, the variables and constraints in the security-constrained unit commitment (SCUC) model considering power grid security constraints increase significantly and the solvability of the model reduces. When the model scale is too large, even the existing commercial solvers cannot solve it. Aiming at the model rapid solving, from the perspective of reducing the number of model constraints, a linear constraint simplification method based on the boundary method is proposed. The proposed method can effectively reduce the model's size by eliminating the redundant linear constraints. The IEEE-39, WECC 179 and …

Optimal Scheduling Strategy Of Virtual Power Plant With Carbon Emission And Carbon Penalty Considering Uncertainty Of Wind Power And Photovoltaic Power, Jijun Shui, Daogang Peng, Yankan Song, Qiang Zhou

Journal of System Simulation

Abstract: To better meet the development needs of China's new power system, an optimal scheduling strategy of virtual power plant(VPP) with carbon emission and carbon penalty considering the uncertainty of wind power and photovoltaic power is proposed. The mathematical description of photovoltaic(PV), wind turbine(WT), combined heat and power(CHP) unit and energy storage system (ESS) is carried out, and a wind-solar output model considering the uncertainty is established. The scenario generation and reduction method is used to generate the typical scenario. To maximize the overall operation benefit of VPP, considering carbon emission cost and carbon penalty, an optimal scheduling model of …