Digital Commons Network^™

Extending Geometric Acoustic Ray Tracing To Multi-Room Environments: A Case Study On Gunshot Sound Transmission Between Adjacent Rooms, Tyler Ton

Theses and Dissertations

Accurate localization of gunshots in multi-room building environments remains a challenging problem in acoustic forensics and public safety applications. Existing approaches model sound propagation within a single room, neglecting the transmission of acoustic energy through walls and other building materials. This thesis presents a study on modeling multi-room gunshot acoustic transmission, combining geometric ray tracing with structural acoustic transmission-loss modeling to generate impulse responses for two horizontally adjacent rooms separated by a shared wall, providing a foundation for future inter-room gunshot localization work. The proposed system uses GSound-SIR, a geometric acoustics engine, to simulate sound propagation in both of the …

Lrq-Solver: A Transformer-Based Neural Operator For Fast And Accurate Solving Of Large-Scale 3d Pdes, Peijian Zeng, Guan Wang, Haohao Gu, Xiaoguang Hu, Tiezhu Gao, Zhuowei Wang, Aimin Yang, Xiaoyu Song

Electrical and Computer Engineering Faculty Publications and Presentations

Solving large-scale PDEs on complex three-dimensional geometries remains a central challenge in scientific and engineering computing, often due to expensive pre-processing stages and high computational overhead. We present Low-Rank Query-based PDE Solver (LRQ-Solver), a physics-integrated deep learning framework for efficient CAE simulations of complex three-dimensional geometries in CAD-driven design analysis. Built upon the Parameter-Conditioned Lagrangian Modeling (PCLM) that embeds physical consistency into the learning process and the Low-Rank Query Attention (LR-QA) module that reduces attention complexity from O(N2) to O(NC2+C3) via covariance decomposition, LRQ-Solver supports multi-configuration analysis within iterative design workflows. On two benchmark datasets, it achieves a 28.6% error …

Estimation Of Unmeasurable External Disturbances Of A Dynamic Object Based On A Robust Adaptive Kalman Filter, Bunyod Mamurjon Ugli Buronov

Technical science and innovation

This article considers the problem of directly estimating unmeasurable external disturbances acting on a dynamic object under the condition that the state vector of the object is available for measurement. In the considered case, the problem is reduced to the estimation of an unknown input of a dynamic system. It is shown that when the system state is measurable, both a direct inversion scheme based on the object’s dynamic equation and filtering methods that eliminate the need for coarse numerical differentiation can be applied. For slowly varying disturbances, a random walk model with a small process noise covariance is proposed, …

Automatic +1 Diffraction Order Detection In Off-Axis Digital Holographic Interferometry Using An Energy-Based Spectral Model, Nigora Alimdjanovna Akbarova, Dilnoza Ibrokhim Kizi Abdulkhayeva

Technical science and innovation

This paper presents an automatic +1 diffraction order detection algorithm for off-axis digital holographic interferometry (DHI Manual spectral filtering is widely used to find out the desired order of diffraction in the Fourier domain in conventional digital holographic reconstruction. Nevertheless, this approach is very subjective and must be subject to expert guidance, thus, the reconstruction reproducibility and stability can often deteriorate. To address these limitations, this designed scheme constructs a spectral model utilizing energy-maximization as well as adaptive DC suppression and dynamic filter radius estimates. The algorithm itself can automatically observe the distribution of energy in the hologram spectrum and …

Evaluation Of The Metrological Characteristics Of An Ultrasonic Flowmeter For Water Resources, Makhsud Idrisovich Makhmudov, Uktam Farkhodovich Mamirov, Siroj Sobirovich Nurov

Technical science and innovation

This paper presents the results of evaluating the metrological characteristics of a developed ultrasonic flowmeter intended for measuring water flow in open channels. The study analyzes the main sources of measurement errors and classifies them into methodological, instrumental, additional, systematic, and random components. Experimental investigations were carried out to estimate the random and systematic errors under steady-state flow conditions. Using the least-squares method, the systematic error was separated into additive and multiplicative components, providing a basis for calibration and correction of the measurement results. The reliability of the developed measuring instrument was evaluated using the failure rate method based on …

Evaluation Of Level Measurement Uncertainty For Three Types Of Laser Level Meters Using The Gum Methodology: Calculated Uncertainty Budgets, Uncertainty Profile, And Hardware Component Requirements, Lazizbek Furkatjanovich Saidoripov, Barat Makhmudovich Akhmedov

Technical science and innovation

A methodology is presented for evaluating the uncertainty of liquid petroleum product level measurements using laser level meters based on different measurands’ informative parameters: propagation time, phase difference, and image coordinates. Based on analytical expressions for error components and a traceability chain to the national length standard, calculated uncertainty budgets are developed in accordance with JCGM 100 for time-of-flight, phase-based, and laser–television level meters. Under the assumed input conditions, the expanded uncertainties (k = 2) are 1.66, 0.73, and 1.02 mm, respectively. For the laser–television level meter budget, the Gaussian approximation is verified using the Monte Carlo method (JCGM 101): …

Analysis And Study Of The Content Of Chloride Salts In Oil, Natalia Evgenievna Sheina, Muborak Akramovna Mirshomilova

Technical science and innovation

The article addresses the critical issue of crude oil quality control, specifically focusing on the determination of chloride salts (sodium, calcium, and magnesium), which exert a destructive impact on processing equipment. The paper provides a detailed analysis of the adverse effects of salts, which lead to severe equipment corrosion, scaling, and a reduced yield of valuable oil fractions. The chemical principles and specific applications of two standardized methods for analyzing aqueous extracts according to GOST 21534-2021 and ASTM D3230 are described: indicator (argentometric and mercurimetric) and potentiometric titration. The experimental section presents the results of a comparative analysis of crude …

Integrating Gis With Interim Payment Valuation In Road Construction Projects: A Conceptual Framework, Abdulrahman I. Iro, Juma M. Matindana, Julian Ijumulana

Tanzania Journal of Engineering and Technology (TJET)

Abstract

Interim Payment Valuation is a critical process in road construction contract administration, yet conventional valuation practices remain heavily dependent on manual measurements, fragmented documentation, spreadsheets, and professional judgement. These limitations can affect measurement accuracy, transparency, traceability, and the timeliness of payment certification. Although Geographic Information Systems have increasingly been applied to construction planning, quantity measurement, progress monitoring, infrastructure management, and decision support, their integration with contractual and financial processes for interim payment valuation remains insufficiently explored. This study therefore develops a conceptual framework for integrating GIS with Interim Payment Valuation in road construction projects. A PRISMA-guided structured literature review …

Voltage-Related Power Quality Issues And Impacts On Distribution Networks With Sensitive Loads - A Review, Godwin Elinazi Mnkeni, Jackson Justo, Aviti Thadei Mushi, Bakari M. M. Mwinyiwiwa

Tanzania Journal of Engineering and Technology (TJET)

Voltage disturbances are the most important power quality (PQ) complications that customers and power utilities face in this smart era. The growing adoption of sophisticated electronic equipment and integration of renewable energy sources (RES) into power grids has increased the susceptibility of power distribution networks (PDNs) to voltage sags, swells, interruptions, flicker, and voltage imbalance. These disturbances, mainly caused by upstream faults, switching operations, and RES integration, compromise voltage PQ and system reliability. Consequently, they accelerate equipment degradation, increase electronic waste (e-waste), raise reactive power demand and maintenance costs, increase power losses, and impose substantial economic losses on customers and …

Implementation Of The Regfm_B1 Virtual Synchronous Machine Model With Supplementary Damping And Power System Stabilizer Extensions In Pstess., Kwabena Amadu

Honors Theses

The increasing penetration of inverter-based resources (IBRs) has motivated the development and verification of accurate grid-forming (GFM) control models for use in transient stability and dynamic simulation studies. This thesis presents an implementation of the REGFM_B1 virtual synchronous machine (VSM) model within the Power System Toolbox (PSTess) simulation environment. The implementation extends the baseline REGFM_B1 algorithm with a supplementary relative frequency damping path and a power system stabilizer (PSS). The implemented controller is verified on a simple three-machine test system and validated against an independent ideal VSM baseline implementation through simulations. This work establishes a validated PSTess implementation intended to …

Disturbance-Learning Inertia Estimation Using Artificial Neural Networks For Power System Stability, Sospeter Igaanja Gabriel, Francis Mwasilu, Peter Makolo Dr.

Tanzania Journal of Engineering and Technology (TJET)

ABSTRACT

Power systems are progressively shifting towards low inertia as a result of incorporating significant amounts of intermittent and converter-based renewable energy sources, such as wind and solar power, into the current power grid network. This integration poses considerable problems to inertia and frequency control within the network due to a reduction in the proportion of synchronous generators. Furthermore, rapid frequency deviations occur due to the disparity between supply and demand during contingencies, complicating the maintenance of frequency stability within the power system. The disturbance-learning inertia estimation method for power system stability is presented. The simulation analysis is performed using …

Fast Sparse Image Reconstruction Models In Through-The-Wall Radars: A Review, Aude Kileo, Hashimu U. Iddi, Abdi Abdalla

Tanzania Journal of Science

Through-the-Wall Radar Imaging (TWRI) is a modern technology that uses electromagnetic waves to detect objects behind walls, with key applications in surveillance, rescue operations, and reconnaissance. Achieving high resolution in both down-range and cross-range requires ultra-wideband signals and long apertures, resulting in large data volumes, increased acquisition time, and high memory demands. TWRI employs Compressive Sensing (CS) to reduce computational time, which has proved its significance in many recent TWRI applications. However, in CS, image reconstruction approaches shift the computational burden from the sensing stage to the recovery stage, which prolongs the reconstruction times, making it unsuitable in time-sensitive applications. …

Context Matters: Evaluating Llm-Generated Knowledge Graph Schemas, Ritvik Garimella, Riju Marwah, Atishay Jain, Khusham Bansal, Amit Sheth

Publications

Knowledge graph (KG) schema engineering is labor-intensive and resists automation at scale. We investigate whether LLMs can generate domain-specific KG schemas of sufficient quality for downstream symbolic reasoning. We propose a tiered contextual framework that varies domain context richness across four levels: zero context, domain scope, task requirements, and data distribution. Generated schemas are evaluated intrinsically on BioRED (600 PubMed abstracts, multi-type entities and relations), where automated tiered schemas match an established KG construction baseline at 79.9% EC, with edge conformance rising from 47.5% at L1 to a stable 78–80% from L2 onward. Extrinsic evaluation on a 50-record MedHop controlled …

Efficient Acidic H2O2 Electrosynthesis Over Co Atoms Anchored On Nitrogen-Doped Hierarchical Porous Carbon, Xiao Huang, Zi-Hao Zhan, Hao-Xu Niu, Guan-Yu Luo, Jin-Tao Huang, Bo-Xuan Jin, De-Li Wang

Journal of Electrochemistry

The two-electron oxygen reduction reaction (2e⁻ ORR) presents a promising route for the on-site production of hydrogen peroxide (H₂O₂), offering a green alternative to energy-consuming anthraquinone process. However, the high selectivity toward the competing 4e⁻ ORR over the desired 2e⁻ pathway leads to low Faradaic efficiency for H₂O₂, posing a critical challenge in catalyst design. In this work, a nitrogen-doped hollow hierarchical porous carbon with anchored Co atoms (CoN/HPC) was constructed for high-performance H₂O₂ production. The as-prepared Co-N/HPC catalyst showed excellent 2e⁻ ORR performance, achieving …

Dense Platinum Nanoparticles Confined In Mn-N-C Nanocages For Robust Heavy-Duty Pemfcs, Lei Zhao, Zhen-Min Cao, Jia-Yu Zuo, Ming-Liang Yang, Hong-Yan Qiao, Jun-Song Chen, Rui Wu

Journal of Electrochemistry

High-loading Pt cathodes are essential for heavy-duty proton exchange membrane fuel cells but suffer from a critical tradeoff between ionomer sulfonate poisoning and nanoparticle instability. Herein, we report a spatial confinement strategy to encapsulate dense Pt nanoparticles (~51.8 wt%) within Mn/N-co-doped mesoporous carbon nanocages (denoted as Pt-MnNC). This architecture excludes bulky ionomers to create an ionomer-shielded environment against sulfonate poisoning, while Mn-N_x-mediated strong metal-support interactions anchor the Pt nanoparticles to prevent agglomeration and further boost durability. In the 5 × 5 cm² membrane electrode assembly tests, the Pt-MnNC catalyst delivers an exceptional power density of 1.26 W·cm …

Scheduling The Charging Of Battery-Electric Vehicles Under Heterogeneous Scheduling Constraints, Justin Whitaker, Greg Droge, Mario Harper

Electrical and Computer Engineering Student Research

Adopting battery electric vehicles (EVs) for vehicle fleets requires scheduling charging alongside day-to-day operations. This problem is complicated by complex utility cost structures, limited battery capacity, and competition for shared charging resources. Existing methods that simultaneously schedule routes and charging neither address the full cost structure nor employ high-fidelity charging models, and no prior work analyzes fleets containing vehicles with heterogeneous routing constraints. This work addresses these gaps by combining a state-of-the-art flexible-schedule formulation with time-of-use (TOU) demand costs and a non-linear, variable-rate charging model as drawn from additional state-of-the-art works. The proposed method is validated against two state-of-the-art methods, …

Multi-Solution Ternary Grover’S Algorithm For Logic-Based Quantum Machine Learning With Pseudo-Kronecker Reed–Muller Form Minimization, Sophia Lee, Ali Al-Bayaty, Marek Perkowski

Electrical and Computer Engineering Faculty Publications and Presentations

Optimization problems in machine-learning (ML) applications can be computationally challenging for classical methods, particularly when they involve large, unstructured search spaces. Quantum computing offers a promising approach to combinatorial optimization by utilizing quantum superposition and amplitude amplification. This paper presents a new methodology using logic-based quantum machine learning (QML) as a complete framework for employing a multi-solution ternary Grover’s algorithm to minimize incomplete binary functions represented by Pseudo-Kronecker Reed–Muller (PKRO) expansions, consistent with Occam’s razor principle. Unlike previous quantum approaches based on Kronecker Reed–Muller (KRO) or fixed-polarity Reed–Muller (FPRM) representations, our work introduces the first Grover-based optimization framework for PKRO …

Hybrid Transformer-Bilstm Model For Early Fault Detection And Multiclass Classification Of Wind Turbine Faults Using Scada Data, Hassan Y. Mkindu

Tanzania Journal of Engineering and Technology (TJET)

Early and accurate fault detection in wind turbines is essential for improving operational reliability, reducing maintenance costs, and minimizing unplanned downtime. This study proposes a Hybrid Transformer-BiLSTM deep learning model for early fault detection and multiclass fault classification using Supervisory Control and Data Acquisition (SCADA) data. The proposed architecture combines the Transformer's self-attention mechanism to capture global temporal dependencies with the Bidirectional Long Short-Term Memory (BiLSTM) network's ability to model sequential fault evolution, enabling effective learning of multivariate time-series data. The model was developed and evaluated using the recently introduced CARE SCADA dataset, classifying five operating states: No Fault, Transformer …

Challenges, Trends, And The Role Of Lstm In Ai-Based Predictive Maintenance Of Electrolyzers For Solar Hydrogen Systems: A Review, Yani Koerniawan Kuatno, Muhamad Zahim Sujod

Turkish Journal of Electrical Engineering and Computer Sciences

The transition toward low-carbon energy systems has increased interest in hydrogen as a clean energy carrier, with solar-driven water electrolysis emerging as a promising technology due to its high efficiency and compatibility with renewable energy sources. However, dynamic operating conditions and intermittent renewable input accelerate electrolyzer degradation, reducing reliability and system lifespan. Predictive maintenance (PdM), supported by artificial intelligence (AI), offers a data-driven approach to anticipate failures and improve operational durability. This review systematically investigates AI-based PdM approaches for electrolyzers, with an emphasis on long short-term memory (LSTM) networks and Internet of things (IoT) integration. Following PRISMA 2020 guidelines, 35 …

Cuip-X25: A Real-World Network Intrusion Dataset For Next-Generation Ai-Driven Security, Arshad Iqbal, Sohail Asghar

Turkish Journal of Electrical Engineering and Computer Sciences

The efficacy of artificial intelligence (AI) in intrusion detection systems (IDS) is critically dependent on high-fidelity training data. However, as detailed in the manuscript's literature review, existing benchmark datasets are predominantly synthetic, outdated, or imbalanced and fail to capture the complexity of the contemporary threat landscape. To bridge this gap, this study introduces CUIP-X25, a novel real-world cyber-attack dataset captured over a four-month period using a dionaea honeypot deployed on a public network. Unlike synthetic alternatives, this dataset provides an authentic representation of modern adversarial tactics, techniques, and procedures, encompassing 3.16 million real events across ten distinct attack categories, including …

Empowering Edge Intelligence Through Reparameterized Lightweight Transformers And Distributed Inference, Hosein Esmaeili, Mohammad Ali Afshar Kazemi, Reza Radfar, Nazanin Pilevari

Turkish Journal of Electrical Engineering and Computer Sciences

Deploying advanced transformer-based models on resource-constrained edge devices remains a significant challenge due to their high memory footprint and substantial compute requirements. In this paper, we propose a reparameterized transformer framework that integrates High-Rank Factorization (HRF) during training, layer merging at inference, and dynamic, load-balanced distributed inference across multiple devices. To further reduce resource usage, our framework supports mixed-precision quantization down to 4-bit, enabling flexible accuracy–latency–energy trade-offs. Experimental evaluations on the ESC-50 environmental sound dataset demonstrate that our method matches or exceeds the performance of larger baseline models while using 20–30% fewer parameters, achieving up to 48% latency reduction in …

Measurement-Aware Zero-Phase Iterative Learning Control For Robust Regulation Of Nonideal Boost Converters In Electric Vehicle Fast Charging, Aytaç Altan, Mohammed S. Alzaidi, Cağfer Yanarateş

Turkish Journal of Electrical Engineering and Computer Sciences

Integrating battery energy storage with DC-DC boost converters for electric vehicle fast charging exposes the regulator to ageing-induced parameter drift, periodic load pulses, and, critically, the nonidealities in the output-voltage sensing chain. This paper proposes a measurement-aware, zero-phase iterative learning control scheme for robust output-voltage regulation of a nonideal boost converter whose parameters are matched to those of a commercial Texas Instruments TPS6102x battery regulator. The controller combines an inner proportional-integral stabilizing loop with an outer zero-phase learning law that updates a feedforward correction based on the sensor-captured output trajectory; a forward-backward robustness filter suppresses the amplification of measurement noise …

Doing Less With More: A First-Principles Exploration Of The Suitability Of Agentic Computing Over Alternative Architectural Choices, Ritvik Garimella, Biplav Srivastava, Amit Sheth

Publications

There is growing interest in automating business activities with Agentic Artificial Intelligence (AI) due to latter's seeming ease of use. Never has it been easier, or costlier, to do less with more. However, little is known about when agents are preferable to established alternatives such as local computation, Representational State Transfer (REST), the Simple Object Access Protocol (SOAP), and the Model Context Protocol (MCP), particularly when development speed, performance, and operational cost are considered. We investigate this question using a controlled mathematical task that compares seven methods on a benchmark of 1,000 arithmetic expressions where semantics of operator precedence has …

Ai-Based Displacement Forecasting For Real-Time Landslide Risk Assessment In The Danube Region, Amina Čehaja, Asja Muharemović, Jasmin Kevrić, Dejan Jokić, Mirza Ponjavić

Communications of the IIMA

This paper presents a dual-layered AI framework for real-time landslide risk assessment developed under the GeoNetSee project within the Interreg Danube Region Programme. The first layer employs a fuzzy logic model, inspired by the Slovenian MASPREM system, which integrates Landslide Susceptibility Maps (LSS) with high-resolution precipitation forecasts from the Open-Meteo API to generate a Predicted Landslide Hazard (PLSH) score on a 0–5 scale, updated every 6–12 hours. The second layer focuses on real-time ground displacement detection by fusing low-cost dual-frequency GNSS receivers with MEMS accelerometers and applying machine learning regression algorithms, including Support Vector Regression (SVR), Long Short-Term Memory (LSTM), …

The Evolution Of Reliability Methods For Nondestructive Evaluation (Nde): From Probability Of Detection (Pod) Conception To Contemporary Practices, Christine E. Knott, Jennifer Brown, John Aldrin, Christine M. Schubert Kabban

Faculty Publications

Nondestructive evaluation (NDE) methods are powerful tools for detecting and characterizing flaws in structural components, but their reliability must be evaluated before they can be used in critical applications. For more than 50 years, probabilistic and statistical methods have been used effectively to estimate reliability by describing an NDE system’s Probability of Detection (POD) for flaws of realistic sizes. The POD methods used by the USAF and NASA, like Hit/Miss, Signal-Response (â vs. a), and Point Estimate method (PEM, a.k.a. 29/29) have evolved, alongside newer approaches like Limited Sample POD (LS-POD) method, and Model Assisted Probability of Detection …

Harmonicthreads: A Formative Evaluation Of A Fabric-Based Digital Musical Instrument Toward Inclusive Music-Making, Ellie Nguyen, Franceli L. Cibrian

Engineering Faculty Articles and Research

Background:

Inclusive music-making requires instruments that support varied bodies, abilities, musical backgrounds, and forms of participation. Digital musical instruments provide diverse approaches to sound creation, and fabric-based interfaces offer an alternative interaction modality that may support participation for some users and contexts. Their tactile and deformable properties enable forms of interaction that differ from conventional rigid or screen-based controllers and may offer inclusive possibilities in particular settings.

Objective:

This paper presents HarmonicThreads as a formative interaction-design case of a fabric-based digital musical instrument. The prototype explores how tactile cues, fabric deformation, projected visual feedback, and assisted accompaniment can support low-barrier …

Mechatronics: Fundamentals, Design, Integration, And Validation, Guoming Zhu

Mechatronics

This textbook is a product of Co-DREAM OER (Collaborative Development of Robotics, Mechatronics, and Advanced Manufacturing Open Educational Resources), an initiative funded by the U.S. Department of Education to develop open educational resource textbooks on robotics, mechatronics, and advanced manufacturing processes. The texts are written for students enrolled in 2-year associate’s, 4-year bachelor’s, and graduate-level courses. This specific text has been created by a team of scholars, students, support staff, and other professionals from across the country. It is intended for mechatronics courses for 4-year bachelor’s and graduate-level programs.

Robust-Adaptive Estimation Of Unmeasurable States And External Disturbances Of A Dynamic Object Based On An Augmented Kalman Filter, Husan Igamberdiev, Uktam Farkhodovich Mamirov, Lu Liu, Bohong Wang, Bunyod Buronov

Chemical Technology, Control and Management

The paper addresses the problem of joint estimation of the directly unmeasurable states of a dynamic object and of external disturbances when information is available only on the control inputs and on the measured output signals. To solve this problem, a robust-adaptive algorithm based on an augmented Kalman filter is proposed, in which the external disturbance is included in the extended state vector. Two operating modes of the algorithm are considered. For slowly varying disturbances, a random-walk model with a constant process-noise covariance is used, whereas for rapidly varying inputs a signal adaptation  driven by the normalized innovation is proposed. …

Development And Uncertainty Assessment Of Secondary Method For Ph Measurement In Physicochemical Measurements, Sarvar Rakhmatullaev, Dostonbek Xujakulov, Mohinur Nigmatova

Chemical Technology, Control and Management

In physicochemical measurements across various industries, laboratory analyses commonly require the determination of pH as an indicator of hydrogen ion activity in aqueous solutions. Ensuring the accuracy and reliability of these measurements, obtaining precise readings from pH measuring instruments, and maintaining overall measurement quality represent critical challenges in guaranteeing the safety of both products and processes. This requires constant monitoring, accuracy assessment, calibration, metrological verification, and control of measuring instruments. In this case, it should be carried out not with solutions prepared in any laboratory, but with certified reference materials (СRM) with appropriate regulatory framework, ensuring metrological observation. …

One Size Does Not Fit All: Revisiting World Models And Neurosymbolic Ai, Amit P. Sheth, Madhur Thareja, Anushka Pawar, Niyati Rawal

Publications

World models are being built twice, from opposite ends, without a shared theory of how the two halves should meet. One lineage grounds the world model in perception: a self-supervised, latent-predictive encoder – exemplified by Joint Embedding Predictive Architectures (JEPA) – that learns the structure of sensory experi-ence. A second, older lineage grounds the world model in cognition: an explicit, inspectable structure of entities, rules, and constraints, ranging from knowledge graphs to formal logic to physical law. Neither lineage alone has produced a world model that is simultane-ously adaptive and auditable. We argue this is not solved by picking a …