Electrical and Computer Engineering Commons^™

Vessel Trajectory Prediction With Recurrent Neural Networks: An Evaluation Of Datasets, Features, And Architectures, Isaac Slaughter, Jagir Laxmichand Charla, Martin Siderius, John Lipor

Electrical and Computer Engineering Faculty Publications and Presentations

Maritime situational awareness tasks such as port management, collision avoidance, and search-and-rescue missions rely on accurate knowledge of vessel locations. The availability of historical vessel trajectory data through the Automatic Identification System (AIS) has enabled the development of prediction methods, with a recent focus on trajectory prediction via recurrent neural networks (RNNs) and other deep learning architectures. While these methods have shown promising performance benefits over kinematic and clustering-based models, comparing among RNN-based models remains difficult due to variations in evaluation datasets, region sizes, vessel types, and numerous other design choices. As a result, it is not clear whether recent …

On The Provenance Of Software Systems: Automating Software Traceability With Knowledge Graph And Large Language Model Synergy, Tyler Procko

Doctoral Dissertations and Master's Theses

The present dissertation delineates a system that enables those engaged in software development to automatically generate and maintain project life cycle provenance. All projects are implemented and made manifest with the development of artifacts, e.g., papers, code files, etc. Tools exist to accelerate artifact creation, but little focus is paid to the processes that produce them. In terms of Ontology, or, from Ancient Greek, the study of being, the two most basic entities in reality are Continuant and Occurrent, or, roughly, “Artifact” and “Process”. This dissertation posits that for any created artifact, its process of creation, i.e., its life …

Developing Fixed-Bias Langmuir Probes For Multi-Point Constellation Deployments, Henry Carter Valentine

Doctoral Dissertations and Master's Theses

Since their initial development in the early 20th century, electrostatic Langmuir probes have proved invaluable in terrestrial and interplanetary ionospheric sounding applications. When deployed aboard rocket and satellite platforms, these probes yield high-cadence, in-situ measurements of key plasma parameters such as electron density, ion density, and electron temperature. Furthermore, the efficacy of Langmuir probes in characterizing the three-dimensional structure and dynamics of ionospheric plasmas can be augmented by the technique of multi-payload deployments. In this work, we discuss the design, development, and analysis of fixed-bias Langmuir probes constructed for two multi-point science campaigns: the Mars-bound, dual-satellite Escape and Plasma Acceleration …

Practical Experiments Conducted On Determining The Air Velocity At The Inlet And Outlet Of The Intensive Separator And Processing Of The Obtained Results, Ilxom Zapirovich Abbazov, Kudrat Sherzadovich Khodjayev, Bobir Nabijon Ugli Sharopov, Rashid Kaldybaev

Technical science and innovation

In the primary processing of cotton raw materials, the means of storing cotton, transporting it from the gins to the departments, and transferring the product between departments are pneumatic conveying devices. Separators are devices for separating cotton from the air and cotton mixture in such pneumatic conveying devices. Separators are used in two types of conditions, namely, in a mobile or stationary state. The device for separating cotton from air consists of three main working parts: they are the parts for feeding cotton into the working chamber, separating it from the air flow, and removing cotton and air. These parts, …

Painting Rich Six-Dimensional Pictures Using Polarized Fluorescence Microscopy, Matthew D. Lew

Electrical & Systems Engineering Publications and Presentations

No abstract provided.

Development And Analysis Of The Kinematic-Dynamic Model Of The Rehabilitation Exoskeleton System, Umidjon Abdumutalipovich Takabaev, Zafar Botirovich Juraev

Technical science and innovation

Designing modern rehabilitation exoskeleton systems requires a comprehensive study of human-machine interactions. In this process, creating kinematic and dynamic mathematical models of the system is extremely important. In this paper, we develop mathematical models that describe rehabilitation exoskeleton systems' kinematic and dynamic properties. Based on the Denavit-Hartenberg method we construct kinematic model of the exoskeleton system. The method gave us to determine the coordinate system for each joint. Furthermore, we also study the motion range of joints of the exoskeleton. Modeling the dynamic properties of the system performed with the second-order Lagrange equation. The exoskeleton construction consisting of knee, thigh, …

Installation Of Self-Controlled Floating Bars At Water Intake Pipelines Of Pumping Stations, Oleg Yakovlevich Glovatsky, Rustam Khujakulov, Fakhriddin Jaylovovich Nasirov, Ilhom Islamov, Jorabek Abdurahman Uralov, Shakhboz Amirov

Technical science and innovation

In modern conditions, to maintain a favorable hydraulic regime on mechanical water lifting systems, it is rational to use protective structures on watercourses that use the energy of the water flow in the form of a floating tank. Based on the obtained theoretical principles, fundamentally new self-controlled floating structures were created and the hydrodynamic characteristics of the movement of an unsteady three-dimensional flow for the parameters of the wing profile were refined. The theory of unsteady three-dimensional movement of a multiphase flow with its artificial stratification and protection from selected phases in depth is used by floating guide systems at …

Adaptive Control System Of Nonlinear Dynamic Object On The Basis Of Neuro-Fuzzy Networks, Isomiddin Xakimovich Siddikov, Dilnoza Maxamadjanovna Umurzakova

Technical science and innovation

The presented paper is devoted to the development of an adaptive control system for a nonlinear dynamic object using neuro-fuzzy networks. The authors note that in real conditions dynamic objects function under conditions of uncertainty, which are characterised by complex and poorly understood relationships between technological variables, the presence of perturbing and random disturbances, as well as nonlinear elements. This makes it difficult to apply traditional linear adaptive control algorithms. The use of self-organising adaptive control is proposed, in which the mathematical model of the controlled system is formed by operational identification during system operation. It is shown that linear …

Development Of Analytical Dependencies For Determining Kinematic Parameters Of The Drive Of The Working Engine Of The Mixing Machine, N. A. Maksudova

Technical science and innovation

The requirement of any technological process of product preparation is intensive mixing. This ensures uniform distribution of components. At the initial stage of designing planetary mechanisms, special attention is paid to determining the kinematic parameters. The dimensions, weight and design of the mechanism depend on the value of these parameters. The main criterion for assessing the scheme of the planetary mechanism is the kinematic parameters of the gear wheels of the mechanism. The article presents a kinematic scheme of the planetary mechanism of the kneading machine. The principle of rotation of the gear wheels of the planetary mechanism of the …

Volatile Organic Compound (Voc) Sensing Trend Among Novel 2d/3d Materials, Mohammad Shakhawat Hossain

USF Tampa Graduate Theses and Dissertations

Volatile Organic Compounds (VOCs) present significant health risks to both humans and animals. Long term exposure to certain VOCs belonging to certain chemical classes can cause both short- and long-term effects including nausea, damage to the central nervous system, and cancer. VOCs can be found in various everyday products like varnishes, paints, cooking items, cleaning products, nail polishes etc. Because of their ubiquitous nature, health concerns and risks regarding VOC exposure have become even more pressing. In this study, the VOC sensing capabilities and trends of novel 2D, and 3D materials (perovskite, and phthalocyanines) have been explored through electrochemical and …

Enhancing Ai Reliability: Probabilistic Inference And Neural Optimization, Behnam Zeinali Rizi

USF Tampa Graduate Theses and Dissertations

Deep neural networks have recently shown better performance than traditional machine learning algorithms in various applications such as computer vision, natural language processing, indoor navigation, and biomedical signal/bio-informatics data processing tasks. However, some main challenges still exist when harnessing these models for different applications. The first challenge is the performance of these methodologies for some specific types of data, such as Biomedical images, IMU measurements, and even bioinformatics data, such as genome expression. Moreover, deploying these models on mobile and IoT devices is computationally challenging. Most devices use cloud computing, where powerful deep-learning models analyze the data on a server. …

A 0.18 Μm Cmos 40 Ghz Miniaturized Coupled-Line Coupler With Enhanced Directivity, Elsayed Ibrahim Elsaidy, Amr H. Hussein Prof.Dr., Mustafa M. Abd Elnaby Prof. Dr., Anwer S. Abd El-Hameed Dr.

Journal of Engineering Research

No abstract provided.

Design And Development Of A Low-Cost Educational Robot: A Scalable And Affordable Software Learning Platform, Diana Refaat Henry Jacob, Mohamed Tarek Elawa, Ashraf Mohamed Hafez

Journal of Engineering Research

This study presents the design and development of a low-cost educational robot to enhance learning experiences in robotics and programming. The system integrates robust software development, ensuring seamless interaction between sensors, actuators, and a custom-designed graphical user interface (GUI). The robot is equipped with various sensors and actuators to support a broad range of experiments and educational applications. The software architecture is detailed, focusing on its modularity and ease of use, enabling students to intuitively program and interact with the robot. Additionally, a series of experiments were conducted to evaluate the performance of the sensors and actuators, providing insights into …

Non-Orthogonal Multiple Access Empowered Physical Layer Security Systems: Review, Issues And Challenges, Aml Fawzy, Mahmoud Selim, Maha Elsabrouty, Sameh Napoleon, Mustafa M. Abd Elnaby

Journal of Engineering Research

As 5G networks advance, the demand for higher data rates, enhanced spectral efficiency, and increased connectivity intensify. Non-orthogonal multiple Access (NOMA) addresses these needs by allowing multiple users to share the same time and frequency resources, thus optimizing network resource utilization and significantly boosting system capacity and throughput. NOMA's influence extends beyond traditional communication scenarios, impacting various vertical industries that require extensive connectivity, such as the Internet of Things (IoT). This transformative approach is crucial for industrial and critical mission applications. Given the importance of safeguarding these communications from potential eavesdroppers, Physical Layer Security (PLS) emerges as a vital tool. …

Trajectory Optimization For Uav-Assisted Energy Harvesting For Internet-Of-Things, Gehad I. Mohammed, Mahmoud M. Selim, Mahmoud A. Attia

Journal of Engineering Research

Unmanned aerial vehicles (UAVs) have gained significant attention for their versatile applications due to their low cost, high mobility, and on-demand deployment capabilities. UAV-assisted wireless communication is a promising technology for future systems; it offers connectivity without the need for terrestrial infrastructure. Similarly, the Internet of Things (IoT) has emerged as a revolutionary technology, connecting smart devices and enabling seamless data exchange. However, IoT networks face significant challenges in environments with weak or unstable infrastructure. This paper investigates UAV-aided energy harvesting (EH) and data collection in an IoT scenario, where a UAV bridges communication between an IOT device that acts …

Role Of Eye-Tracking Technology And Software Algorithms In Enhancing Adhd Detection And Diagnosis: A Systematic Literature Review, Lauren E. Perkins

Honors College Theses

This systematic literature review explores the role of eye-tracking technology and software algorithms in enhancing the detection and diagnosis of ADHD. ADHD, a neurodevelopmental disorder affecting both children and adults, is traditionally diagnosed through behavioral assessments, which may lack objectivity. Recent studies suggest that eye-tracking, specifically focusing on saccades, fixations, and blink rates, offers the potential for more accurate and objective measures of ADHD. The review examines clinical trials, observational studies, and machine learning research to assess the correlation between ADHD and eye movement patterns. Results indicate that individuals with ADHD exhibit distinct eye movement patterns, which can be quantified …

Deep Learning Applications For Predictive Modeling In Cancer Therapy And Enzyme Encoding, Mengmeng Liu

LSU Doctoral Dissertations

Predictive modeling has revolutionized computational biology and molecular bioinformatics, enabling significant advancements in cancer therapy and functional enzyme characterization. Despite considerable progress, significant challenges remain in accurately predicting combinational cancer therapies and systematically representing enzyme functions for computational applications. Traditional methods struggle with capturing the complex interactions between drugs and biological networks, as well as representing hierarchical relationships within enzyme classifications. This dissertation addresses these limitations by developing advanced deep learning models tailored to enhance predictive performance in both domains.

First, a data augmentation strategy is introduced to improve anticancer drug synergy prediction by generating pharmacologically relevant drug pairs based …

Comprehensive Sensing Analysis Of Ofdm Waveform For Future Jrc And Radar Systems, Mehmet Yazgan

USF Tampa Graduate Theses and Dissertations

In today’s interconnected world, sensing is one of the indispensable parts of wireless systems.The need to optimize and diversify the use of the radio frequency spectrum has reached unprecedented importance. Cutting-edge applications, including autonomous technologies, intelligent urban systems, and advanced monitoring, require solutions that can effectively merge communication and sensing functions within limited bandwidth. Orthogonal frequency division multiplexing (OFDM) has emerged as a leading contender in this field due to its superior ability to maximize spectral usage, resilience to multipath interference, and versatile signal configuration options. Reconsidering the use of the OFDM waveform for sensing purposes is undeniably essential due …

The Impact Of Solar Angle And Cloud Shadows On 3d Reconstruction Of Rolling Stock Cargo, Carlina M. Ostrand, Adam D. Reiman, Frank W. Ciarallo, Scott L. Nykl, Clark N. Taylor, Joshua F. Krutz

Faculty Publications

Meeting the relentless demand for more efficient air cargo transportation is of paramount importance for commercial needs and military missions. This study describes an experiment to test an innovative approach that harnesses cutting-edge stereoscopic vision technology to create 3D point clouds of rolling stock cargo across varying solar angles and cloud shadow conditions. Virtual cargo point clouds are generated by calibrating and systematically organizing the depth and location points from an RGB-D camera and then reprojecting them in a virtual environment. Measurement accuracy was rigorously tested across six camera positions in various combinations of weather conditions against physical ground truth …

Arduino-Esp32 Based Smart Irrigation System, Ahmed D. Hassebo, Kevin B. Montes, Erick Cabrera

Publications and Research

In response to the growing demand for efficient water management in agriculture, this research presents the development of an Arduino-ESP32 based automated irrigation system. The project integrates an Arduino microcontroller with an ESP32 module to facilitate real-time data collection and control of irrigation processes. Sensors monitor soil moisture, water levels, and local weather conditions, with data displayed on an LCD I2C screen for user accessibility. The ESP32 module handles alert notifications via text messages, keeping users informed remotely. Key components, including a water pump, valve, relay module, OLED screen, and LED display, are synchronized to ensure precise irrigation control. Additionally, …

A Case Study Of Gray-Box Fuzzing With Byte- And Tree-Level Mutation Strategies In Xml-Based Applications For Exposing Security Vulnerabilities, Şerafetti̇n Şentürk, Vahi̇d Garousi, Nejat Yumuşak

Turkish Journal of Electrical Engineering and Computer Sciences

Fuzzing is an automated process for detecting crashes and vulnerabilities in software system and it is classified as grammar- or mutation-based in terms of input generation. While the grammar-based fuzzing generates inputs from a specification and takes highly-structured inputs, mutation-based fuzzing generates inputs by modifying input files and abstract syntax trees randomly. There are not many case studies comparing the crash detection capabilities in the scope of mutation-based fuzzing. To add to the body of empirical evidence in this area, this case study compares fuzzing with different mutation strategies to evaluate their effectiveness in three aspects: fault detection effectiveness, fault …

Optimizing Parameters For Efficient Computation With Fully Homomorphic Encryption Schemes, Cavi̇dan Yakupoğlu Karaağaç, Kurt Rohloff

Turkish Journal of Electrical Engineering and Computer Sciences

In this study, we aim to provide a parameter selection approach for the BFVrns scheme, one of the prominent fully homomorphic encryption (FHE) schemes. Selecting parameters for lattice-based FHE schemes poses a practical challenge for both experts and nonexperts. To solve this problem, we introduce a hybrid approach that combines theoretical approach with experimental analysis. First, we employ regression analysis to examine the impact of parameters on both performance and security. The varying behavior of FHE parameters in terms of performance, security, and ciphertext expansion factor (CEF) makes parameter selection more challenging. To address this issue, we employ a multi-objective …

Decomposition Lstm With Dual Multi-Head Self-Attention For Wind Turbine Drivetrain State Forecasting, Haikun Jia, Huini Sun, Shuang Bai

Turkish Journal of Electrical Engineering and Computer Sciences

Due to the clean and renewable nature of wind energy, accurate prediction of rotor loads and operating states for wind turbine units has become of paramount importance. Currently, traditional methods relying on expert analysis combined with instrument testing for qualitative reasoning are both time-consuming and labor-intensive, and their accuracy guarantees are limited. In response to wind farm data entailing the interweaving of data from multiple sources and the diverse interrelations across various features and time steps, this study introduces a method for predicting rotor loads and operating states. Initially, we employ an iterative multi-scale seasonal-trend decomposition block to capture latent …

Fuzzy-Virtual Inertia Control To Improve The Frequency Response Of Multi-Area Power Systems, Nourelhouda Djaraf, Yacine Daili, Abderrahim Zemmit, Abdelghani Harrag

Turkish Journal of Electrical Engineering and Computer Sciences

Virtual inertia control (VIC) is essential for power systems dominated by electronic devices to compensate for the lack of inertia and ensure frequency regulation. However, most existing VICs often focus solely on optimizing the virtual inertia parameter to adapt to the high penetration of renewable energy sources (RESs) without considering the damping factor. This oversight can lead to significant fluctuations and power mismatches, especially in interconnected systems where the coordination between MGs is sensitive and essential, and there is a risk of propagation of deviations between MGs, which makes the control more complex. To address these issues, this paper presents …

Multikernel Embedded Fusion Unet (Mkef-Unet): A Robust Deep Learning Approach For Accurate Segmentation Of Chagas Parasites, Preet Kumar, Carlos Brito-Loeza, Lavdie Rada

Turkish Journal of Electrical Engineering and Computer Sciences

This paper introduces a novel approach for segmenting Chagas parasites on stained blood smear samples from mice during the acute phase of infection with Trypanosoma cruzi utilizing a U-Net-based deep learning model named multikernel embedded fusion UNet (MKEF-UNet). Our proposed model incorporates DenseNet-121 for feature extraction, a classifier module for predicting parasite information, and a segmentation decoder with multiscale feature fusion to generate precise segmentation results. Notably, the integration of the embedded vector module, multikernel convolutions with dilations, and advanced data augmentation techniques significantly enhance the model’s robustness and generalization capabilities. In extensive experiments on the Chagas dataset, MKEF-UNet achieves …

Enhancing Spatial-Temporal Video Prediction With Ts-Vq-Vae: A Novel Encoder-Processor-Decoder Approach, Mei Feng, Fan Li

Turkish Journal of Electrical Engineering and Computer Sciences

Video prediction is a significant and actively researched area within the data science community. Its primary objective is to generate future video frames based on historical frames, finding applications in diverse domains such as human motion prediction, climate change analysis, and traffic flow forecasting. Traditional methods combine Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to capture complex correlations in spatial-temporal signals. Recent methods improve video prediction accuracy by introducing external information such as optical flow, semantic maps, and human pose data. However, these methods have limitations, such as not fully exploring the intermediate states of learning representations, overlooking …

Conversational Open-Domain Question Answering For Resource-Constrained Languages, Emrah Budur, Tunga Güngör

Turkish Journal of Electrical Engineering and Computer Sciences

The growing interest in Conversational AI has led to the development of Conversational OpenQA systems as a crucial step for meeting users' information needs in real world scenarios. Conversational OpenQA systems enhance standard OpenQA performance by leveraging conversation history of the users. However, building effective Conversational OpenQA systems requires large-scale Conversational OpenQA datasets, often limited to the English language, hindering progress in low-resource languages. We present a robust Conversational OpenQA system enhanced by conversational context, designed for languages with limited resources and exemplified in our case study for Turkish. To address data limitations in a cost-effective way, we repurpose existing …

Increasing Electric Vehicle Charger Availability With A Mobile, Self-Contained Charging Station, Robert Serrano, Arifa Sultana, Declan Kavanaugh, Hongjie Wang

Electrical and Computer Engineering Student Research

As the transition to sustainable transportation has accelerated with the rise of electric vehicles (EVs), ensuring drivers have access to charging to maximize the electric miles driven is critical to lowering carbon emissions in the transportation sector. Limited charging station capacity and poor reliability, especially during peak travel times, long distance travels, holidays, and events, have hindered the adoption of EVs and threaten the progress toward reducing greenhouse gas emissions. Adaptive, flexible deployment strategies combined with innovative approaches integrating mobility and renewable energy are essential to address these systemic challenges and bridge the current infrastructure gap. To address these challenges, …

Dynamic Wireless Power Transfer, George Mccue, Dionysios Aliprantis, Patrick Patterson, Benjamin O'Brien, Chiara Cervini, Bruno Krause Moras

Purdue Road School

Purdue University, in partnership with INDOT, PC Krause and Associates, White Construction, William Charles Electric, and Cummins, Inc. transitioned the embedment of dynamic wireless power transfer (DWPT) technology from a controlled test setting to real roadway implementation. This presentation delves into the construction process of the quarter-mile DWPT project on northbound US 52/231 in West Lafayette and outlines the plans for testing the technology with a Cummins truck later this year.

Multi-Channel Features Combined With Gray Features For Remote Sensing Image Classification, Khaled Hasan, Hussien Harb, Lamiaa A. Elrefaei, Hala M. Abdel-Kader

Mansoura Engineering Journal

Image classification in remote sensing is crucial for various applications like deforestation monitoring and Land Use/Land Cover (LULC) mapping. While grayscale images offer simplicity, recent advancements have enhanced classification by integrating spatial and relative data. However, relying solely on grayscale images overlooks valuable color information from different channels. To overcome this, researchers have explored two strategies: combining diverse features and utilizing multi-channel features. These methods merge spatial information and exploit shared features across channels. Despite their improved performance compared to grayscale, they face increased dimensionality, necessitating dimensionality reduction techniques. In our study, we propose a novel approach that integrates spatial …