Engineering Commons^™

Design And Implementation Of Non-Isolated High Gain Dc-Dc Converter For Photovoltaic Application, Rajesh R

Theses and Dissertations

Fossil fuel-based power plants emit 25% of greenhouse gases and 40% of carbon emissions globally. Therefore, several countries focus on deploying RES-based electricity production (such as solar and wind). As per the International Energy Agency report, solar PV might be the lowest-cost option for generating electricity in the future. Solar energy is highly emission-free, but it is still a key challenge in procuring the maximum amount of energy from solar PV due to its non–linear characteristics. Therefore, a DC-DC converter must integrate with solar PV to increase the output voltage level. Many existing topologies exist in the literature, but the …

Effects Of Rf Signal Eventization Encoding On Device Classification Performance, Michael J. Smith, Michael A. Temple, James W. Dean

Faculty Publications

The results of first-step research activity are presented for realizing an envisioned “event radio” capability that mimics neuromorphic event-based camera processing. The energy efficiency of neuromorphic processing is orders of magnitude higher than traditional von Neumann-based processing and is realized through synergistic design of brain-inspired software and hardware computing elements. Relative to event-based cameras, the development of event-based hardware devices supporting Radio Frequency (RF) applications is severely lagging and considerable interest remains in obtaining neuromorphic efficiency through event-based RF signal processing. In the Operational Technology (OT) protection arena, this includes efficient software computing capability to provide reliable device classification. A …

Robust Ai-Driven Segmentation Of Glioblastoma T1c And Flair Mri Series And The Low Variability Of The Mrimath© Smart Manual Contouring Platform, Yassine Barhoumi, Abdul Hamid Fattah, Nidhal Carla Bouaynaya, Fanny Moron, Jinsuh Kim, Hassan M. Fathallah-Shaykh, Rouba A. Chahine, Houman Sotoudeh

Henry M. Rowan College of Engineering Departmental Research

Patients diagnosed with glioblastoma multiforme (GBM) continue to face a dire prognosis. Developing accurate and efficient contouring methods is crucial, as they can significantly advance both clinical practice and research. This study evaluates the AI models developed by MRIMath© for GBM T1c and fluid attenuation inversion recovery (FLAIR) images by comparing their contours to those of three neuro-radiologists using a smart manual contouring platform. The mean overall Sørensen–Dice Similarity Coefficient metric score (DSC) for the post-contrast T1 (T1c) AI was 95%, with a 95% confidence interval (CI) of 93% to 96%, closely aligning with the radiologists’ scores. For true positive …

Coordination Of Srf-Pll And Grid Forming Inverter Control In Microgrid With Solar Pv And Energy Storage, V. Vignesh Babu, J. Preetha Roselyn, Prabha Sundaravadivel

Electrical Engineering Faculty Publications and Presentations

Recently, there has been a huge advancement in renewable energy integration in power systems. Power converters with grid-forming or grid-following topologies are typically employed to link these decentralized power sources to the grid. However, because distributed generation has less inertia than synchronous generators, their use of renewable energy sources threatens the electrical grid's reliability. Suitable control approaches for ensuring frequency and voltage stability in the grid-connected form of operation are established in this study, which offers dynamic, seamless power switching in the islanded mode of operation. In this research, effective Phase Locked Loop (PLL) techniques for grid-forming (GFM) and grid-following …

Unveiling Anomalies: A Survey On Xai-Based Anomaly Detection For Iot, Esin Eren, Feyza Yildirim Okay, Suat Özdemi̇r

Turkish Journal of Electrical Engineering and Computer Sciences

In recent years, the rapid growth of the Internet of Things (IoT) has raised concerns about the security and reliability of IoT systems. Anomaly detection is vital for recognizing potential risks and ensuring the optimal functionality of IoT networks. However, traditional anomaly detection methods often lack transparency and interpretability, hindering the understanding of their decisions. As a solution, Explainable Artificial Intelligence (XAI) techniques have emerged to provide human-understandable explanations for the decisions made by anomaly detection models. In this study, we present a comprehensive survey of XAI-based anomaly detection methods for IoT. We review and analyze various XAI techniques, including …

Text-To-Sql: A Methodical Review Of Challenges And Models, Ali Buğra Kanburoğlu, Faik Boray Tek

Turkish Journal of Electrical Engineering and Computer Sciences

This survey focuses on Text-to-SQL, automated translation of natural language queries into SQL queries. Initially, we describe the problem and its main challenges. Then, by following the PRISMA systematic review methodology, we survey the existing Text-to-SQL review papers in the literature. We apply the same method to extract proposed Text-to-SQL models and classify them with respect to used evaluation metrics and benchmarks. We highlight the accuracies achieved by various models on Text-to-SQL datasets and discuss execution-guided evaluation strategies. We present insights into model training times and implementations of different models. We also explore the availability of Text-to-SQL datasets in non-English …

Security Fusion Method Of Physical Fitness Training Data Based On The Internet Of Things, Bin Zhou

Turkish Journal of Electrical Engineering and Computer Sciences

Physical fitness training, an important way to improve physical fitness, is the basic guarantee for forming combat effectiveness. At present, the evaluation types of physical fitness training are mostly conducted manually. It has problems such as low efficiency, high consumption of human and material resources, and subjective factors affecting the evaluation results. ”Internet+” has greatly expanded the traditional network from the perspective of technological convergence and network coverage objects. It has expedited and promoted the rapid development of Internet of Things (IoT) technology and its applications. The IoT with many sensor nodes shows the characteristics of acquisition information redundancy, node …

Dpafy-Gcaps: Denoising Patch-And-Amplify Gabor Capsule Network For The Recognition Of Gastrointestinal Diseases, Henrietta Adjei Pokuaa, Adeboya Felix Adekoya, Benjamin Asubam Weyori, Owusu Nyarko-Boateng

Turkish Journal of Electrical Engineering and Computer Sciences

Deep learning (DL) models have performed tremendously well in image classification. This good performance can be attributed to the availability of massive data in most domains. However, some domains are known to have few datasets, especially the health sector. This makes it difficult to develop domain-specific high-performing DL algorithms for these fields. The field of health is critical and requires accurate detection of diseases. In the United States Gastrointestinal diseases are prevalent and affect 60 to 70 million people. Ulcerative colitis, polyps, and esophagitis are some gastrointestinal diseases. Colorectal polyps is the third most diagnosed malignancy in the world. This …

Joint Control Of A Flying Robot And A Ground Vehicle Using Leader-Follower Paradigm, Ayşen Süheyla Bağbaşi, Ali Emre Turgut, Kutluk Bilge Arikan

Turkish Journal of Electrical Engineering and Computer Sciences

In this study, a novel control framework for the collaboration of an aerial robot and a ground vehicle that is connected via a taut tether is proposed. The framework is based on a leader-follower paradigm. The leader follows a desired trajectory while the motion of the follower is controlled by an admittance controller using an extended state observer to estimate the tether force. Additionally, a velocity estimator is also incorporated to accurately assess the leader’s velocity. An essential feature of our system is its adaptability, enabling role switching between the robots when needed. Furthermore, the synchronization performance of the robots …

Stereo-Image-Based Ground-Line Prediction And Obstacle Detection, Emre Güngör, Ahmet Özmen

Turkish Journal of Electrical Engineering and Computer Sciences

In recent years, vision systems have become essential in the development of advanced driver assistance systems or autonomous vehicles. Although deep learning methods have been the center of focus in recent years to develop fast and reliable obstacle detection solutions, they face difficulties in complex and unknown environments where objects of varying types and shapes are present. In this study, a novel non-AI approach is presented for finding the ground-line and detecting the obstacles in roads using v-disparity data. The main motivation behind the study is that the ground-line estimation errors cause greater deviations at the output. Hence, a novel …

Deep Learning-Based Breast Cancer Diagnosis With Multiview Of Mammography Screening To Reduce False Positive Recall Rate, Meryem Altın Karagöz, Özkan Ufuk Nalbantoğlu, Derviş Karaboğa, Bahriye Akay, Alper Baştürk, Halil Ulutabanca, Serap Doğan, Damla Coşkun, Osman Demi̇r

Turkish Journal of Electrical Engineering and Computer Sciences

Breast cancer is the most prevalent and crucial cancer type that should be diagnosed early to reduce mortality. Therefore, mammography is essential for early diagnosis owing to high-resolution imaging and appropriate visualization. However, the major problem of mammography screening is the high false positive recall rate for breast cancer diagnosis. High false positive recall rates psychologically affect patients, leading to anxiety, depression, and stress. Moreover, false positive recalls increase costs and create an unnecessary expert workload. Thus, this study proposes a deep learning based breast cancer diagnosis model to reduce false positive and false negative rates. The proposed model has …

Ri2ap: Robust And Interpretable 2d Anomaly Prediction In Assembly Pipelines, Chathurangi Shyalika, Kaushik Roy, Renjith Prasad, Fadi El Kalach, Yuxin Zi, Priya Mittal, Vignesh Narayanan, Ramy Harik, Amit Sheth

Publications

Predicting anomalies in manufacturing assembly lines is crucial for reducing time and labor costs and improving processes. For instance, in rocket assembly, premature part failures can lead to significant financial losses and labor inefficiencies. With the abundance of sensor data in the Industry 4.0 era, machine learning (ML) offers potential for early anomaly detection. However, current ML methods for anomaly prediction have limitations, with F1 measure scores of only 50% and 66% for prediction and detection, respectively. This is due to challenges like the rarity of anomalous events, scarcity of high-fidelity simulation data (actual data are expensive), and the complex …

Signer-Independent Sign Language Recognition With Feature Disentanglement, İnci̇ Meli̇ha Baytaş, İpek Erdoğan

Turkish Journal of Electrical Engineering and Computer Sciences

Learning a robust and invariant representation of various unwanted factors in sign language recognition (SLR) applications is essential. One of the factors that might degrade the sign recognition performance is the lack of signer diversity in the training datasets, causing a dependence on the singer’s identity during representation learning. Consequently, capturing signer-specific features hinders the generalizability of SLR systems. This study proposes a feature disentanglement framework comprising a convolutional neural network (CNN) and a long short-term memory (LSTM) network based on adversarial training to learn a signer-independent sign language representation that might enhance the recognition of signs. We aim to …

Estimation Of Useful-Stage Energy Returns On Investment For Fossil Fuels And Implications For Renewable Energy Systems, Emmanuel Aramendia, Paul E. Brockway, Peter G. Taylor, Jonathan B. Norman, Matthew K. Heun, Zeke Marshal;

University Faculty Publications and Creative Works

The net energy implications of the energy transition have so far been analysed at best at the final energy stage. Here we argue that expanding the analysis to the useful stage is crucial. We estimate fossil fuelsʼ useful-stage energy returns on investment (EROIs) over the period 1971–2020, globally and nationally, and disaggregate EROIs by end use. We find that fossil fuelsʼ useful-stage EROIs (~3.5:1) are considerably lower than at the final stage (~8.5:1), due to low final-to-useful efficiencies. Further, we estimate the final-stage EROI for which electricity-yielding renewable energy would deliver the same net useful energy as fossil fuels (EROI …

An Integrated Resource Planning Proposal For Tucson Electric Power, Valeria Bernal

Master's Projects and Capstones

An integrated resource plan (IRP) is crucial for utilities to determine the most cost-efficient energy resources while considering system reliability, environmental impact, and regulatory requirements. VitalsparK developed an IRP for Tucson Electric Power (TEP) to transition its current 70% thermal and 30% renewable energy portfolio, emitting 740 grams of CO2 per kWh, toward carbon neutrality by 2050. The proposed plan involves retiring existing infrastructure, adding 8 GW of renewable and storage capacity, and incorporating 2.5 GW of net-zero natural gas power plants. VitalsparK presented two scenarios: the Reference (REF) Case, which focuses on the least-cost portfolio to meet demand through …

Residential Ders In Service-Oriented Load Participation: Enhancing Grid Flexibility, Zhongkai Zeng

Dissertations and Theses

Amidst concerns about power consumption during peak periods and potential grid instability, the role of Distributed Energy Resource (DER) aggregation comes into consideration. Smart electric water heaters with remote capabilities and energy storage offer load reduction capabilities that can help maintain grid stability and manage residential energy consumption. DERs address challenges posed by stochastic renewable energy generation and fossil fuel power plant emissions, playing a contributing role in load shifting and enhancing grid flexibility by participating in energy management programs.

However, high unenrollment rates in demand response programs, notably programs that use direct load control, persist due to customer dissatisfaction. …

Learning Proximal Operators With Gaussian Process And Adaptive Quantization In Distributed Optimization, Aldo Duarte Vera Tudela

LSU Doctoral Dissertations

In networks consisting of agents communicating with a central coordinator and working together to solve a global optimization problem in a distributed manner, the agents are often required to solve private proximal minimization subproblems. Such a setting often requires a further decomposition method to solve the global distributed problem, resulting in extensive communication overhead. In networks where communication is expensive, it is crucial to reduce the communication overhead of the distributed optimization scheme. Integrating Gaussian processes (GP) as a learning component to the Alternating Direction Method of Multipliers (ADMM) has proven effective in learning each agent's local proximal operator to …

Band Gap Tuning Of Perovskite Solar Cells For Enhancing The Efficiency And Stability: Issues And Prospects, Md Helal Miah, Mayeen Uddin Khandaker, Md Bulu Rahman, Mohammad Nur-E-Alam, Mohammad Aminul Islam

Research outputs 2022 to 2026

The intriguing optoelectronic properties, diverse applications, and facile fabrication techniques of perovskite materials have garnered substantial research interest worldwide. Their outstanding performance in solar cell applications and excellent efficiency at the lab scale have already been proven. However, owing to their low stability, the widespread manufacturing of perovskite solar cells (PSCs) for commercialization is still far off. Several instability factors of PSCs, including the intrinsic and extrinsic instability of perovskite materials, have already been identified, and a variety of approaches have been adopted to improve the material quality, stability, and efficiency of PSCs. In this review, we have comprehensively presented …

Machine Learning For Graph Algorithms And Representations, Allison Gunby-Mann

Dartmouth College Ph.D Dissertations

This thesis explores a variety of common graph theoretic problems from a machine learning perspective. The topics covered include fundamental network problems such as distance approximation, distance sensitivity, community detection, cross-network alignment, and graph embedding dimension reduction. These projects are unified by the theme of machine learning on graphs, graph embeddings, and representations of graphs.

Aerospace Vehicle Comprising Module For Method Of Terrain, Terrain Activity And Material Classification, Matthew E. Nussbaum, Marissa S. Herron

AFIT Patents

A method of classifying terrain, terrain activity and materials through panchromatic imagery a module programmed to provide such classification and aerospace vehicles comprising such module is provided. Panchromatic images of known materials terrains and terrain activities are taken and processed to form a multiband texture cube, that due it amount of data, is stored as a computer data base. New panchromatic images of unclassified materials, terrains and/or terrain activities are processed and compared via computer with such database that allows for inexpensive, quick and efficient classification of such new images.

Scalability And Connectivity Challenges For The Future Of Digital Radio Communication, Christopher Bayliss

Honors Program: Senior Projects (Public)

This thesis explores future challenges in digital radio communication, particularly focusing on scalability and connectivity issues that could impede technological advancements. With the increasing production of digital technology and reliance on radio frequencies, there is a need for innovative solutions to overcome limitations in spectrum availability, interference management, and bandwidth constraints. This work evaluates current modulation techniques, spectrum allocation strategies, and advanced communication technologies such as cognitive radio systems and reconfigurable intelligent surfaces. Through a comprehensive literature review and analysis, this thesis identifies promising developments, including dynamic spectrum sharing and symbiotic radio systems, that could significantly enhance spectrum efficiency and …

Development Of Grid-Forming And Grid-Following Inverter Control In Microgrid Network Ensuring Grid Stability And Frequency Response, V. Vignesh Babu, J. Preetha Roselyn, C. Nithya, Prabha Sundaravadivel

Electrical Engineering Faculty Publications and Presentations

This paper proposes a control strategy for grid-following inverter control and grid-forming inverter control developed for a Solar Photovoltaic (PV)–battery-integrated microgrid network. A grid-following (GFL) inverter with real and reactive power control in a solar PV-fed system is developed; it uses a Phase Lock Loop (PLL) to track the phase angle of the voltages at the PCC and adopts a vector control strategy to adjust the active and reactive currents that are injected into the power grid. The drawback of a GFL inverter is that it lacks the capability to operate independently when the utility grid is down due to …

Influence Of Al2o3 Passivation Layer Thickness On The Thermal Stability And Quality Of Mocvd-Grown Gan On Si, S M Atiqur Rahman, Manika Tun Nafisa, Zhe Chuan Feng, Benjamin Klein, Ian T. Ferguson

Symposium of Student Scholars

This research delves into the significant impact of varying thicknesses of the Al₂O₃ passivation layer on the thermal stability and crystalline quality of GaN on Si structures, an essential aspect for the next generation of high-temperature electronic and optoelectronic devices. By adopting metal-organic chemical vapor deposition (MOCVD) for the growth process, we analyzed structures with different Al₂O₃ passivation layer thicknesses: none, 2 nm, 10 nm, and 20 nm, each built upon the GaN layer. Through Raman spectroscopy, we meticulously assessed the changes in the E₂ (High) phonon mode's peak position and full width …

Ofet Informatics: Observing The Impact Of Organic Transistor's Design Parameters On The Device Output Performance Using A Machine Learning Algorithm, Sameh O. Abdellatif, Hana Masalam, Salma Ahmed

Electrical Engineering

No abstract provided.

Regular Algorythms For Synthesis Of Optimal Control Systems For Dynamic Objects, Igamberdiyev Husan, Uktam Farkhodovich Mamirov, Xankeldiyeva Zebiniso

Technical science and innovation

The article presents algorithms for the synthesis of an optimal control system for dynamic objects. As a model, we consider a differential equation of a continuous one-dimensional system in the form of a state space, which has the properties of controllability and observability. The paper shows the need to use an observation device in order to assess how the properties of the controlled system change with the slightest change in the parameters of the control object, to assess the sensitivity of the system to these changes. When finding a solution to the equation formulated to find the parameters of the …

Toward Intuitive 3d Interactions In Virtual Reality: A Deep Learning- Based Dual-Hand Gesture Recognition Approach, Trudi Di Qi, Franceli L. Cibrian, Meghna Raswan, Tyler Kay, Hector M. Camarillo-Abad, Yuxin Wen

Engineering Faculty Articles and Research

Dual-hand gesture recognition is crucial for intuitive 3D interactions in virtual reality (VR), allowing the user to interact with virtual objects naturally through gestures using both handheld controllers. While deep learning and sensor-based technology have proven effective in recognizing single-hand gestures for 3D interactions, research on dual-hand gesture recognition for VR interactions is still underexplored. In this work, we introduce CWT-CNN-TCN, a novel deep learning model that combines a 2D Convolution Neural Network (CNN) with Continuous Wavelet Transformation (CWT) and a Temporal Convolution Network (TCN). This model can simultaneously extract features from the time-frequency domain and capture long-term dependencies using …

Effect Of Recommending Users And Opinions On The Network Connectivity And Idea Generation Process, Sriniwas Pandey, Hiroki Sayama

Northeast Journal of Complex Systems (NEJCS)

The growing reliance on online services underscores the crucial role of recommendation systems, especially on social media platforms seeking increased user engagement. This study investigates how recommendation systems influence the impact of personal behavioral traits on social network dynamics. It explores the interplay between homophily, users’ openness to novel ideas, and recommendation-driven exposure to new opinions. Additionally, the research examines the impact of recommendation systems on the diversity of newly generated ideas, shedding light on the challenges and opportunities in designing effective systems that balance the exploration of new ideas with the risk of reinforcing biases or filtering valuable, unconventional …

Intelligent Resource Allocation For Sdn/Nfv-Enabled Networks Through Reinforcement Learning, Jing Su

Computer Science and Engineering Theses and Dissertations

Software-Defined Networking (SDN) and Network Functions Virtualization (NFV) are two emerging paradigms that enable the feasible and scalable deployment of Virtual Network Functions (VNFs) in commercial-off-the-shelf (COTS) devices, which deliver a range of network services with reduced cost. The deployment of these services requires efficient resource allocation that fulfills the requirements in terms of Quality of Service (QoS) and Service-Level Agreement (SLA) while considering the constraints of the underlying infrastructure, such as maximum latency tolerance and affinity policies.

An optimized resource allocation result can benefit the network in various aspects, such as energy-saving, performance boost, and latency reduction. To achieve …

Design And Analysis Of Passive Correlator Radio-Frequency Identification Tagging, Albert Kilgore

McKelvey School of Engineering Graduate Student Theses & Dissertations

Radio Frequency Identification (RFID) is a technology used in many industries to locate and track assets. Passive RFID tags are popular because they are inexpensive and flexible, but they have limited accuracy. My thesis aims to improve the accuracy of passive RFID tags by investigating two solutions: (a) Using orthogonal RFID configuration and exploiting phase information available to an RFID reader, in addition to the received signal strength indicator (RSSI) metric; and (b) Designing a novel voltage multiplier-based correlator circuit that can be integrated directly onto the tag. To further reduce power consumption and silicon area, we propose an architecture …

Engineering Composite Gridlines For Improved Solar Module Reliability, Andre Chavez

Electrical and Computer Engineering ETDs

Renewable energy sources, such as solar power, are rapidly expanding worldwide to meet increased energy demands. However, these sources are exposed to challenging environmental stressors, such as extreme wind, heavy snow loads, and hailstorms that can cause irreversible damage to the crystalline semiconductor solar cells. Today, single crystalline silicon solar cells dominate the market, and our work is focused on improving their reliability against environmental stressors. One of the main degradation mechanisms caused by environmental stressors is cell cracks. These microcracks can go virtually undetected and lead to reduced power output from solar modules over time. To solve this engineering …