Engineering Commons^™

Ce 410 - 102: Constr Schedlng/Estimtng, Avinash Prasad

Civil and Environmental Engineering Syllabi

No abstract provided.

Ce 443 - 002: Foundation Design, Andrew Ciancia

Civil and Environmental Engineering Syllabi

No abstract provided.

Ce 432 - 002, Hm2: Steel Design, Methi Wacharatama

Civil and Environmental Engineering Syllabi

No abstract provided.

Ce 461 - 102: Professional Practice In Cee, Avinash Prasad

Civil and Environmental Engineering Syllabi

No abstract provided.

Ce 485 - 002: St: Osha In Civil Enginering, Frank Golon

Civil and Environmental Engineering Syllabi

No abstract provided.

Ce 461 - 104: Professional Practice In Cee, Avinash Prasad

Civil and Environmental Engineering Syllabi

No abstract provided.

Ce 494 - 106, Hm2: Civil Engr Design I, Joseph Baladi

Civil and Environmental Engineering Syllabi

No abstract provided.

Ce 494 - 108: Civil Engr Design I, Joseph Baladi

Civil and Environmental Engineering Syllabi

No abstract provided.

Ce 495 - 102: Civil Engr Design Ii, Babu Veeragowda

Civil and Environmental Engineering Syllabi

No abstract provided.

Ene 262 - 004, Hm2: Intro To Environmental Engr, Paul Schorr

Civil and Environmental Engineering Syllabi

No abstract provided.

Ene 465 - 101: Sustainable Env Infrastructure, William Pennock

Civil and Environmental Engineering Syllabi

No abstract provided.

Mech 235 - 002,004: Eng Mech: Statics, Sunil Saigal

Civil and Environmental Engineering Syllabi

No abstract provided.

Mech 236 - 002, 004, 006, 008, Hm2: Dynamics, Yuan Ding

Civil and Environmental Engineering Syllabi

No abstract provided.

Mech 234 - 002, 004: Engineering Mechanics: Statics, Geraldine Milano

Civil and Environmental Engineering Syllabi

No abstract provided.

Mech 237 - 002, 004, 014, 016, 106, 108: Strength Of Materials, Eduardo Castro

Civil and Environmental Engineering Syllabi

No abstract provided.

Mech 237 - 010, 012, 110, 112: Strength Of Materials, Geraldine Milano

Civil and Environmental Engineering Syllabi

No abstract provided.

Mech 320 - 102: Statics & Mech Of Materl, Mohamad Saadeghvaziri

Civil and Environmental Engineering Syllabi

No abstract provided.

Ce 341 - 002: Soil Mechanics, Andrew Ciancia

Civil and Environmental Engineering Syllabi

No abstract provided.

Ce 350 - Hm2: Transportation Engr - Honors, Branislav Dimitrijevic

Civil and Environmental Engineering Syllabi

No abstract provided.

Ce 320a - 102, 104, 108: Hydraulics Laboratory, Thomas Olenik

Civil and Environmental Engineering Syllabi

No abstract provided.

Ce 431 - 002: Contruction Materials Lab, Walter Konon

Civil and Environmental Engineering Syllabi

No abstract provided.

Multiobjective Optimal Design Of Hierarchical Control Architectures For Quadcopter Systems: Linear And Hybrid Approaches, Xinhuang Wu

Theses, Dissertations and Capstones

This thesis investigates the design of many-objective optimal controllers for quadcopter unmanned aerial vehicles (UAVs), focusing on both linear and hybrid control structures. In Chapter 2, a multi-input–multi-output (MIMO) optimal control system is developed for a six-degree-of-freedom UAV actuated by four brushless DC motors. The UAV dynamics are first derived and linearized around an operating point, and then organized into a three-loop nested control structure. The outer loop computes the roll and pitch angles required to maneuver the vehicle in the global X and Y directions, the middle loop regulates attitude and altitude, and the inner loop controls the angular …

Nonconvex Optimization Methods Under Inexact Information, Dat Ba Tran

Wayne State University Dissertations

This thesis focuses on the design and convergence analysis of algorithms for solving nonconvex optimization problems under inexact first-order information. We introduce Inexact Reduced Gradient (IRG) methods for general smooth functions and Inexact Gradient Descent (IGD) methods for $\mathcal{C}^{1,1}_L$ functions with relative and absolute errors. Additionally, we develop Inexact Proximal Point and Inexact Proximal Gradient methods for weakly convex functions. Our methods improve the performance of standard inexact proximal point methods, inexact proximal gradient methods, and inexact augmented Lagrangian methods by approximately 2.5 to 10 times in terms of iteration complexity for image processing tasks. Moreover, we propose new derivative-free …

Fusion-Based Utilization And Synthesis Of Efficient Detections, Ethan C. Rogers, Parker H. Liberatore, Thomas G. James

Endeavors: Mississippi State Undergraduate Research Journal

This study aimed to develop hardware and software for an object detection fusion system, using three different sensors. The system was built and studied with the motivating application of autonomous drones searching for and detecting people in a search-and-rescue scenario. The system’s performance was compared to that of individual sensors deployed for the same task. The focus of the research was to prove the competence and benefits of a decision-level fusion method as it was applied to a lightweight object detection architecture, and the driving motivators behind the study were simplicity in implementation and good computational performance. In short, the …

Virtual Fixtures For Teleoperated Robots For The Visually Impaired, Vishwaak Chandran Thamaraiselvan

Computer Science and Engineering Theses - Archive

This paper presents our preliminary study on enabling individuals with visual impairments to safely operate mobile robots and vehicles. To achieve this, we developed a teleoperation with accessibility at its core. The system incorporates features that enhance usability and situational awareness, including assistive control based on artificial potential fields to prevent collisions and ensure smooth navigation. It also provides multimodal feedback through (a) haptic vibrations on the gamepad controller, which convey the proximity of nearby objects detected by the robot’s laser sensor, and (b) color-coded overlays that differentiate paths, obstacles, and people through semantic segmentation performed by a deep neural …

Event-Based Histogram Of Gradients For Lane Detection, Ganesh Gupta

Computer Science and Engineering Theses - Archive

In the rapidly evolving landscape of autonomous driving technology, lane detection systems stand as fundamental guardians of vehicular safety. The National Highway Traffic Safety Administration identifies unintentional lane departures as responsible for approximately one-third of all road accidents—a sobering statistic that underscores the critical importance of robust lane detection methodologies. This thesis embarks on an academic exploration at the intersection of neuromorphic engineering and computer vision, examining how the distinctive properties of event-based cameras might be harnessed to enhance lane detection capabilities under challenging environmental conditions. Unlike conventional frame-based imaging sensors that capture entire scenes at fixed intervals, event-based cameras …

Exploring Instruction Generation For Uavs: Dataset Adaptation, Model Behavior, And Diagnostic Insights, Seyedarman Vaziri Bozorg

Computer Science and Engineering Theses - Archive

This thesis explores the development of an answering agent capable of generating natural language instructions for unmanned aerial vehicles (UAVs), grounded in a limited, real-world dialogue dataset. The objective is to adapt a static dataset into a training pipeline that can support instruction generation and serve as a foundation for future interactive systems involving question-asking agents and internal dialogue. A hybrid architecture is implemented using a semantic teacher model (MPNet) and a T5-base encoder-decoder trained with contrastive and supervised objectives. The adapted training process yields statistically acceptable performance across standard evaluation metrics. However, qualitative analysis reveals a mismatch between metric …

Training Data Privacy In Machine Learning: A Systematization Of Attacks And Defenses, Mohammad Sufyaan Saeed

Computer Science and Engineering Theses - Archive

Training and deploying Machine Learning (ML) models introduce significant data confidentiality risks, as modern models can inadvertently memorize and leak information about their training data. While attacks such as membership inference and model inversion are well studied, the literature remains fragmented, with inconsistent threat models and unclear relationships across attack classes and defenses. This work presents a Systematization of Knowledge (SoK) that unifies the landscape of training-data privacy attacks and defenses, aligning them with the NIST Adversarial Machine Learning (AML) taxonomy to enable standardized threat modeling and comparison. Our analysis shows that, despite significant progress in characterizing attack vectors, defenses …

Methods Of Optimizing Storage And Retrieval Of Structured Data, Neelim Haider

Computer Science and Engineering Theses - Archive

Storing and retrieving large amounts of data reliably is becoming more and more important as time goes on. There are high demands to store highly personal information such as social security numbers, bank account information, and residence information to rapidly changing data such as employee information, inventory information, and stock information. Therefore, the ability of a system to store, remove, and update such information efficiently and correctly is critical. There are different types of data that database systems can potentially hold: structured, unstructured, and semistructured data. Various database models have been developed to provide a framework that allows designers to …

Transformer And Recurrent Architectures For Dynamics Prediction And Policy Learning On Long-Horizon Tasks, Vinal Jitendrabhai Gadhiya

Computer Science and Engineering Theses - Archive

Model-based reinforcement learning promises improved sample efficiency by learning environment dynamics and using them for planning or policy improvement. However, the choice of neural architecture for dynamics prediction significantly impacts the model's ability to capture temporal dependencies and maintain long-term context, capabilities crucial for complex, open-world environments.

This thesis investigates three neural architectures for learning world models: Transformer-based, GRU-based, and a hybrid Transformer+GRU approach. We evaluate these architectures on Crafter, a 2D open-world survival environment that requires long-horizon planning and sequential task completion. In Crafter, agents must perform hierarchical sequences of actions, such as collecting wood, placing a table, and …