Engineering Commons^™

Modeling And Testing Chain Trencher Excavator And Cone Penetrometer Robotic Prospecting Systems For Lunar In-Situ Resource Utilization, Marcello C. Guadagno

Dissertations, Master's Theses and Master's Reports

The Artemis Missions aim to establish a sustained human presence on the Moon, requiring In-Situ Resource Utilization (ISRU) of water-ice deposits in permanently shadowed regions. Two critical capabilities must be developed to enable ISRU at scale: prospecting instruments that can characterize the spatial distribution and geotechnical properties of icy regolith in situ, and excavation systems that can extract hardened icy regolith efficiently. This dissertation addresses both needs through the development and testing of a Percussive Heated Cone Penetrometer (PHCP) for geotechnical prospecting and a chain trencher excavator for icy regolith mining.

The PHCP was developed across eight design iterations and …

Antenna-Based Sensors For Dielectric Characterization Of Materials, Hilary Scott Nkimbeng Cho

Dissertations, Master's Theses and Master's Reports

This research introduces antenna-based sensors for dielectric characterization aimed at overcoming the limitations of conventional microwave sensors. Although traditional microwave sensors are widely used for their noncontact operation, high sensitivity, and ability to penetrate various materials, they often face challenges such as large size and high-power consumption. To address these issues, antenna-based sensors are explored for their compactness, ease of fabrication, and flexible design adaptability across diverse sensing applications. An in-depth analysis of multiple antenna sensor configurations is conducted, followed by the design and development of high-performance sensing structures. A saw-tooth slot antenna sensor is developed for highly sensitive liquid …

High-Performance Circuit Manufacturing And Testing Exercises, Benjamin S. Keppers

Dissertations, Master's Theses and Master's Reports

An advanced demonstrator Printed Circuit Board (PCB) has been designed and implemented providing a framework for advancing students’ knowledge in hands-on PCB design and manufacturing process through industry recognized test coupons, stack ups, and transmission lines. Students are guided through several key aspects of design and simulation relating to manufacturing and qualifications. Manufacturing allows students to refine process development and analyze performance data with respect to qualification tests specified by Global Electronics Association standards. Results are then used to build a stackup model, and complete a design activity for calculating expected test results for a series of controlled impedance electrical …

Robust State Estimation And Scene Understanding For Autonomous Ground Vehicles In Challenging Environments, Nader J. Abu-Alrub

Dissertations, Master's Theses and Master's Reports

Autonomous ground vehicles require reliable state estimation and scene understanding to operate safely in challenging environments. Poor illumination, adverse weather, degraded visibility, snow-covered roads, and sensor noise can reduce the reliability of conventional localization and perception pipelines. Addressing these challenges requires sensing and algorithmic strategies that exploit the complementary strengths of different modalities. Radar is particularly attractive because of its resilience to lighting and weather variations, while inertial, camera, and lidar measurements provide additional motion, appearance, and geometric information. This dissertation investigates compact radar-based and sensor-fusion approaches for improving autonomous ground-vehicle state estimation and scene understanding under degraded sensing conditions. …

Using Automotive Lidar To Reduce The Energy Consumption Of An Ego Autonomous Vehicle, Logan P. Schexnaydre

Dissertations, Master's Theses and Master's Reports

There is significant potential to reduce the energy consumption of the transportation sector through autonomous vehicles. Prior work on autonomous vehicle energy efficiency focuses on the whole system or the control subsystem. Yet, the sensing and processing components, which have direct and indirect effects on net energy use, are less explored. This dissertation fills this gap by modeling and evaluating these effects for lidar sensors, which provide high-resolution spatial data at the cost of high power and processing demands. I apply lidar to the energy-saving tasks of automated vehicle following and road surface profiling. For automated vehicle following, I model …

Analytical And Data-Driven Modeling And Control Of Nonlinear Point Absorber Wave Energy Converters With Application To Cone–Cone Buoy Geometries, Houssein Yassin

Dissertations, Master's Theses and Master's Reports

This dissertation develops analytical modeling, numerical simulation, experimental validation, and data driven control methods for nonlinear point absorber wave energy converters, with particular emphasis on cone--cone buoy geometries. Geometry dependent nonlinear force models are first derived using pressure field and displaced volume formulations. These models show how buoy geometry produces nonlinear Froude--Krylov and restoring forces, including the dominant cubic behavior of cone--cone geometries.

The derived models are incorporated into a feedback linearization framework that compensates selected nonlinear dynamics while retaining the incident wave terms. An analytical optimal control formulation is also developed to maximize harvested energy in nonlinear, nonautonomous systems …

The Effects Of Physical Boundaries On The Complexity Of Resonance Processes In Finite-Size Dielectric Metasurfaces, Varsha Vijay Kumar

Dissertations, Master's Theses and Master's Reports

This work investigates dramatic effects produced by the presence of physical boundaries of finite-size metasurfaces, composed of identical cylindrical dielectric resonators without broken symmetry, on the complexity of their responses, observed at normal plane wave incidence, when constituent particles can support only dipolar Mie resonances. Owing to their periodicity, metasurfaces are conventionally analysed using their unit cells with periodic boundary conditions, so that their “finiteness” remains largely under-studied, even though all practical systems are finite.  We demonstrate the opportunities to excite in these finite structures extra high-quality resonance modes, quite distinct from the resonant modes in infinite structures and explain …

Hybrid Learning And Optimization Methods For Solving Capacitated Vehicle Routing Problem, Monit Sharma, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

We propose a hybrid quantum–classical framework for the Capacitated Vehicle Routing Problem (CVRP) that integrates the Augmented Lagrangian Method (ALM) with deep reinforcement learning (RL). Directly solving CVRP via Variational Quantum Eigensolver (VQE) requires a slack-based QUBO formulation, where converting inequalities to equalities greatly increases the qubit count. To circumvent this, we employ an ALM-based reformulation that enforces constraints through Lagrange terms instead of slack variables, drastically reducing quantum resource demands. An RL agent, trained with Soft Actor–Critic, adaptively tunes the Lagrange penalties to improve convergence and feasibility. Experiments show that RL-Q-ALM outperforms static-penalty and plain VQE baselines in both …

From Circuits To Hardware: Benchmarking Standard And Qubit-Efficient Quantum Optimization On Real Hardware, Monit Sharma, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Despite rapid progress in quantum optimization, the field lacks broad real-hardware benchmarks comparing multiple algorithmic families across diverse classically hard combinatorial problems under one protocol. We present a hardware-aware benchmark of gate-based quantum optimization across four NP-hard problems: the multi-dimensional knapsack problem (MDKP), maximum independent set (MIS), quadratic assignment problem (QAP), and market-share problem, spanning variational methods (VQE, CVaR-VQE), standard, multi-angle, and warm-start QAOA, and qubit-efficient encodings (Pauli correlation encoding (PCE), QRAO), executed on IBM Heron r1/r2 processors under resilience-level-2 mitigation. To our knowledge this includes the first real-hardware QRAO results and the first multi-problem PCE hardware benchmark. Across 247 …

Scale-Net: A Hierarchical U-Net Framework For Cross-Scale Generalization In Multi-Task Vehicle Routing, Suyu Liu, Zhiguang Cao, Nan Yin, Yew-Soon Ong

Research Collection School Of Computing and Information Systems

Neural solvers for Vehicle Routing Problems (VRPs) have shown great advantages in solving various kinds of problem types. However, they also face critical challenges in generalizing from small-scale training to large-scale problems and in identifying the most salient topological information for decision-making. To mitigate these gaps, we introduce ScaleNet, a novel hierarchical framework that integrates a U-Net architecture into a unified, multi-task VRP solver. Scale-Net explicitly captures multi-scale structural patterns by processing a nested hierarchy of input graph instances. This enriched, coarse-to-fine representation is extracted by the encoder and fed directly into the decoder, empowering decoder module with superior topological …

Instance Generation For Meta-Black-Box Optimization Through Latent Space Reverse Engineering, Chen Wang, Yue-Jiao Gong, Zhiguang Cao, Zeyuan Ma

Research Collection School Of Computing and Information Systems

To relieve intensive human-expertise required to design optimization algorithms, recent Meta-Black-Box Optimization (MetaBBO) researches leverage generalization strength of meta-learning to train neural network-based algorithm design policies over a predefined training problem set, which automates the adaptability of the low-level optimizers on unseen problem instances. Currently, a common training problem set choice in existing MetaBBOs is well-known benchmark suites CoCo-BBOB. Although such choice facilitates the MetaBBO's development, problem instances in CoCo-BBOB are more or less limited in diversity, raising the risk of overfitting of MetaBBOs, which might further results in poor generalization. In this paper, we propose an instance generation approach, …

Bridging Synthetic And Real Routing Problems Via Llm-Guided Instance Generation And Progressive Adaptation, Jianghan Zhu, Yaoxin Wu, Zhuoyi Lin, Zhengyuan Zhang, Haiyan Yin, Zhiguang Cao, Senthilnath Jayavelu, Xiaoli Li

Research Collection School Of Computing and Information Systems

Recent advances in Neural Combinatorial Optimization (NCO) methods have significantly improved the capability of neural solvers to handle synthetic routing instances. Nonetheless, existing neural solvers typically struggle to generalize effectively from synthetic, uniformly-distributed training data to real-world VRP scenarios, including widely recognized benchmark instances from TSPLib and CVRPLib. To bridge this generalization gap, we present Evolutionary Realistic Instance Synthesis (EvoReal), which leverages an evolutionary module guided by large language models (LLMs) to generate synthetic instances characterized by diverse and realistic structural patterns. Specifically, the evolutionary module produces synthetic instances whose structural attributes statistically mimics those observed in authentic real-world instances. …

Securing The Flow: Strategic Storage And Corridor Design For Maritime Energy Security Under Chokepoint Disruptions, Monit Sharma, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Maritime disruption planning requires joint strategic storage and transport capacity decisions under uncertainty. We develop a two-stage stochastic multi-commodity network-design model for national energy security that integrates corridor commitments, port--commodity inventories, storage expansion, and scenario-dependent routing for crude oil, LNG, LPG, and fertilizer. The formulation incorporates decision-dependent probabilities, corridor concentration limits, and mean--CVaR risk, admitting an exact MILP reformulation. In a data-anchored India-inspired case study, stochastic modeling outperforms the expected-value policy by 1.35%, with decision-dependent probabilities worth 0.67%. Service levels are heterogeneous: crude and fertilizer maintain full coverage, while LNG and LPG drop to 34.6% and 33.3% under worst-case scenarios. …

Artificial Neural Network Based Protection For Dc Bus Using Solid State Dc Breaker, Eswaraiah Giddalur, Askani Jaya Laxmi

ASEAN Journal on Science and Technology for Development

Low voltage Direct Current (LVDC) microgrids have been extensively used as a hopeful technology with high access to distributed energy resources (DERs) and DC loads, due to the absence of multiple conversions. A high amplitude Fault current together with the vulnerability of electronic devices, poses a great challenge to the line protection in LVDC microgrids. Electromechanical switches are slow and take long tripping time to clear the short circuit fault. Power electronic circuit breakers are used to achieve fast fault interruption recent progress in areas like machine learning and natural language processing have affected nearly every assiduity and area of …

Extraction And Characterization Of Fresh And Dehydrated Cactus (Opuntia Ficus Indica) Polysaccharide For Hydrogel Preparation, Aye Thwe Thwesoe, May Myat Khine

ASEAN Journal on Science and Technology for Development

In this research, extraction of polysaccharide compounds from Cactus (Opuntia Ficus Indica) leaves in both fresh and dehydrated condition by solvent precipitation method using three types of water, acid (HCL) and NaOH. Before extraction, the physicochemical properties were examined to determine optimum yield (%) of extracted polysaccharide by optimization of Box-Behnken Design (BBD) of response surface methodology (RSM). The polysaccharide-based acrylamide hydrogel was prepared by free radical polymerization. The functional and structural characterization was done by FTIR, XRD and SEM for examination of extracted polysaccharide as raw polymer backbone in hydrogel preparation and prepared hydrogel as adsorbent for metal removal. …

Hybrid Model For Phishing Website Detection Using Transfer Learning, Atul Dubal, Mansi Subhedar, Santosh Dhamala, Manasi Patil

ASEAN Journal on Science and Technology for Development

The rapid digitization of human activities has intensified reliance on internet-based platforms, creating fertile ground for cybercriminal exploits such as phishing. Despite advancements in detection mechanisms, phishing attacks continue to evolve, leveraging sophisticated visual mimicry to deceive users. This paper proposes a robust vision-based phishing detection system using ensemble deep learning to analyse webpage screenshots. The framework integrates transfer learning with pre-trained VGG16 and DenseNet121 models, extracting complementary low-level texture features (edges, gradients) and high-level hierarchical patterns (logos, layouts). These features are fused through a custom classifier with dropout regularization to mitigate overfitting. A balanced dataset of 3,000 webpage screenshots …

Multiclass Skin Lesions Classification And Early Skin Cancer Detection Using An Attention-Enhanced Xception Architecture, Shamira Arshad Shaikh, Yogita Mistry

ASEAN Journal on Science and Technology for Development

Skin disorders are considered one of the major healthcare problems globally and need proper, prompt, effective and early diagnosis to enhance clinical practice. Traditional machine learning and deep learning approaches often exhibit limited generalization capability and reduced effectiveness in extracting discriminative features from complex multiclass dermatological datasets. For this purpose, an interpretable attention-enhanced Xception framework for automatic multiclass skin disease diagnosis based on dermoscopic images has been designed through the use of the International Skin Imaging Collaboration (ISIC) dataset. The dataset includes 2,357 dermoscopic images that belong to nine classes of skin lesions. The proposed framework integrates channel attention and …