Digital Commons Network^™

The Main Errors Of The Ultrasonic Sensor In Measuring Water Flow In Open Channels, Anvar Urolovich Djalilov

Chemical Technology, Control and Management

This article analyzes the use of ultrasonic sensors in measuring water flow and the main errors that may occur in this process. In the conducted scientific research, a time-pulse ultrasonic sensor was tested. The absolute, relative and repeatability errors of the sensor during water flow measurement were studied. The absolute error represents the largest difference between the value recorded by the sensor and the real value, affecting the overall accuracy of the measurement system. This error can vary depending on environmental factors, the design and operating principles of the sensor. During the experiment, the performance of this sensor was …

Pid Control For Lower Limb Exoskeletons: A Framework Evaluation, Javlonbek Rakhmatillaev, Vytautas Bučinskas

Chemical Technology, Control and Management

This research provides a detailed guideline for implementing and evaluating Proportional Integral Derivative (PID) control frameworks in lower limb rehabilitation exoskeleton robotics. It examines the role of control systems within rehabilitation robotics, outlines the principles of PID control, describes exoskeleton architecture, explores applications of PID control, reviews optimization strategies, presents experimental validations, and considers future developments in the field. The proposed control framework incorporates aspects of mechanical design, actuator and sensor selection, and PID-based control algorithms, thereby promoting safe, accurate, and individualized rehabilitation support. Recommendations and effective guidance for future work are also presented.

Review Of Modern Methods For Identification, Forecasting, And Intelligent Control Of Wastewater Biological Treatment Processes, Mirkhalil Agzamovich Ismailov, Boburbek Zokirjon O'G'Li Mannobjonov

Chemical Technology, Control and Management

This article presents a comprehensive review of contemporary approaches to the automation and intelligent control of wastewater biological treatment processes. Particular emphasis is placed on the digitalisation of wastewater treatment plants, ranging from the implementation of automated process control systems (APCS/SCADA-based solutions) to the application of predictive algorithms and the development of digital twins of bioreactors.

Special attention is devoted to mathematical models that underpin the control of bioprocesses. The evolution of the most widely used activated sludge models—ASM1, ASM2d, and ASM3—is examined, as these models describe key processes such as microbial community growth, nitrification, denitrification, and phosphorus removal. It …

Hybrid Cnn–Gru-Based Demand–Supply Forecasting To Enhance Sustainability In Renewable-Integrated Smart Grids, Süleyman Emre Eyimaya, Necmi Altin

Faculty Publications

The rapid integration of renewable energy sources in smart grids has introduced significant uncertainty in both power generation and consumption patterns, posing challenges to environmental, economic, and operational sustainability. Accurate short-term forecasting of energy demand and supply is essential for achieving optimal scheduling, grid stability, and resilient operation in renewable-integrated power systems. This study proposes a hybrid deep learning framework combining Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU) for intelligent joint demand–supply forecasting in smart grids. The model was developed and implemented in MATLAB using real-world datasets comprising electricity consumption, photovoltaic (PV) generation, temperature, and irradiance variables. Comparative …

Demystifying Hardware Formal Verification For Undergraduate Education: A Risc-V Processor Case Study With Coursework Implementation, Riley A. Peters

Master's Theses

Hardware verification engineers apply formal methods to prove that a digital device always behaves according to its specification. This differs from traditional functional verification, in which engineers establish correctness by repeatedly sending test inputs to the device and comparing the outputs against a reference model. With the growing complexity of integrated circuits, the demand for digital verification engineers with formal methods experience has continued to increase. However, California Polytechnic State University: San Luis Obispo's current curriculum lacks dedicated material to prepare students for these roles.

This thesis seeks to address the lack of formal methods material through two efforts. First, …

Portable Ice Cube Maker, Ayden Ziegler, Emiliano Hansen, Wyatt Engdahl, Dane Hansen

Mechanical Engineering

This Final Design Report outlines the senior design project undertaken by a team of mechanical engineering students at California Polytechnic State University, San Luis Obispo, for the development of a portable backpacking ice cube maker. The project aims to design, build, and test a lightweight, compact device that produces ice cubes for backpackers in remote outdoor environments. The goal is to create a functional prototype that is durable, user-friendly, and suitable for backcountry use. This document details background research, project objectives, and project plan, and current design status.

Microwave Dielectric Properties Of Geopolymer Precursor Powders, Linh T. Duong, Abu Naser Rashid Reza, Kristen M. Donnell, Christopher R. Shearer

Electrical and Computer Engineering Faculty Research & Creative Works

Geopolymers are sustainable structural materials with properties similar to ordinary Portland cement concrete. To better understand the fundamental reaction mechanisms of geopolymers, a microwave materials characterization approach is used. In this research work, the dielectric properties (permittivity and loss factor) of common precursor powders used to make geopolymers (GPPs)—fly ash, lime, metakaolin, silica fume, slag, and zeolite—are measured over the S- and X-band frequency ranges (i.e., 2.0–4.0 GHz and 8.2–12.4 GHz, respectively). The physical characteristics, elemental composition, mineralogical properties, and phase characterization of the GPPs are then correlated to dielectric properties. Permittivity of GPP, classified as pozzolanic or latent hydraulic, …

Sustainabake, Kevin Kunta Swanson Ii

Electrical Engineering

This senior project outlines the development of an oven that utilizes renewable energy to operate safely and effectively. It aims to test building an oven using mostly basic hardware students acquired during their time at Cal Poly and provide a starting point for those interested in creating a solar-powered oven. As our society continues to innovate and increasingly relies on electricity, it is important to place more emphasis on how we can make our lives sustainable to take care of our environment without introducing excessive pollutants. Food preparation is a daily activity that typically involves significant energy consumption, making this …

Effects Of Asphalt Or Concrete On Dynamic Wireless Power Transfer, Yuou Peng, Daniela Wolter Ferreira Touma

Shelby Hall Graduate Research Forum Posters

Dynamic Wireless Power Transfer (DWPT) allows measurements should be done for 100 kHz and 77 kHz. electrical energy to be transferred wirelessly to a moving object, and it also possesses the ability to charge vehicles continuously while they are traveling down a specially equipped road.

A method of embedding the coils in the surface course shows that all coils achieve more than 54 kW of power at an efficiency of 95% in the power transmission evaluation. Advantage: Ability to charge vehicles continuously while they are traveling down a specially equipped road. Objective: Investigation of physical and structural feasibility of embedding …

Efficient Active Training For Deep Lidar Odometry, Beibei Zhou, Zhiyuan Zhang, Zhenbo Song, Jianhui Guo, Hui Kong

Research Collection School Of Computing and Information Systems

Robust and efficient deep LiDAR odometry models are crucial for accurate localization and 3D reconstruction, but typically require extensive and diverse training data to adapt to diverse environments, leading to inefficiencies. To tackle this, we introduce an active training framework designed to selectively extract training data from diverse environments, thereby reducing the training load and enhancing model generalization. Our framework is based on two key strategies: Initial Training Set Selection (ITSS) and Active Incremental Selection (AIS). ITSS begins by breaking down motion sequences from general weather into nodes and edges for detailed trajectory analysis, prioritizing diverse sequences to form a …

Current Research Progress On Electrode Materials For All-Vanadium Redox Flow Batteries, Wen-Qi Wang, Jie Jin, Li-Min Wang, Xin-Yue Liu, Tao Cheng, Yong Hou, Han Xue, Zhi-Yu Wang, Bo Liu, Jia-Bao Liu, Xu-Bin Lu

Journal of Electrochemistry

The redox active species in all-vanadium redox flow batteries (VRFBs) reside in the electrolyte, while the heterogeneous reactions occur on the electrode surface; the electrode is therefore the decisive platform for dynamic adsorption, electron transfer, and ion conversion, especially for the VO²⁺/VO₂⁺ and V²⁺/V³⁺ couples. One of the major challenges for VRFBs is the slow charge transfer in VO²⁺/VO₂⁺ and V²⁺/V³⁺ reactions, mainly caused by poor catalytic performance of electrodes and weak adhesion of catalysts to electrodes. This review focuses on the key challenges and recent …

Design And Control Of A Multi-Modal Electromagnetic Floor Array For Foot-Based Human Locomotion And Stabilization In Microgravity, Aryan Anand

Electrical Engineering Theses

Long-duration living and working in microgravity creates everyday mobility problems such as drifting, loss of stable footing, higher effort to move, and difficulty doing routine tasks safely. Many solutions have been proposed in literature, including handrails, restraint systems, and concepts for artificial gravity using rotation. Artificial gravity could improve comfort, but it is complex to build and operate for large spacecraft, especially when future missions may include not only trained astronauts but also common people. With companies like SpaceX pushing toward large-scale travel and long-term settlement goals, there is a need for simpler mobility support technologies that can work inside …

Remote Sensing Of Dynamic Ground Motion Via A Moiré-Based Apparatus, Adrian Ali Moazzam, Nontawat Srisapan, Gregory Waite, Durdu Guney, Roohollah Askari

Michigan Tech Publications

Highlights: What are the main findings? A Moiré-based optical apparatus enables long-range, non-contact ground displacement measurement in hazardous environments. Controlled indoor and outdoor experiments demonstrate reliable detection of dynamic and seismic-like ground motions with sub-millimeter resolution. What are the implications of the main findings? System performance is evaluated under atmospheric turbulence and wind, revealing key limits and mitigation strategies for field deployment. The proposed approach provides a low-cost, scalable complement to traditional seismic and geodetic monitoring techniques. Ground-based remote sensing of seismic and geophysical displacements remains a major challenge due to environmental hazards, signal attenuation, and practical deployment limitations of …

Uas Path Planning With Dynamic Rerouting Using A Space-Time Graph, Kimoy Williams

Electronic Theses and Dissertations 2020 - Present

UAS systems have emerged as multifaceted technologies with applications across a wide range of sectors. Their ability to access areas that are difficult or unsafe for manned systems has made them invaluable tools in various domains. As a result, UAS have transformed numerous industries, including infrastructure inspection, delivery and logistics, military and defense, as well as precision agriculture and environmental monitoring.

The advancement in UAS technology is fundamentally reliant upon ongoing research efforts in the specialized area of UAS path planning. Optimal flight planning is essential for a UAV to effectively execute its mission’s task safely, effectively, and in congruence …

A Capability Maturity Model For Artificial Intelligence Integration In Supply Chain Management, Lordt Becklines

Dissertations

Artificial Intelligence (AI) is transforming Supply Chain Management (SCM), yet many organizations struggle to assess their readiness for AI adoption and to understand how AI capabilities develop across maturity stages. This dissertation addresses this gap by developing a Capability Maturity Model (CMM) for AI integration in SCM, grounded in Organizational Information Processing Theory (OIPT), the Resource-Based View, and related capability frameworks. The model provides a structured approach for evaluating an organization's information-processing requirements, resource configurations, and alignment needed for effective AI-enabled supply chain operations.

Using a design science research approach, the AI-SCM CMM and its associated assessment instrument were derived …

Strategic Research On Rebco High-Temperature Superconducting Tapes, Tianping Ying, Dongliang Wang, Pengtao Yang, Lin Zhao, Zhongtang Xu, Ziyi Liu, Chao Yao, Yanwei Ma, Xingjiang Zhou, Jinguang Cheng, Zhong Fang

Bulletin of Chinese Academy of Sciences (Chinese Version)

High-temperature superconducting materials, represented by REBCO (REBa2Cu3O7-δ, where RE denotes rare-earth elements), are of significant strategic importance in fields such as energy, healthcare, and large-scale scientific facilities due to their excellent performance in the liquid nitrogen temperature range. However, they still face severe challenges in long-tape uniformity, production cost, and engineering reliability. Future development must shift toward a “material-processing-application” collaborative innovation model, aiming at enhancing flux pinning, optimizing the interfaces and mechanical properties of the multilayer structure, and integrating scalable and intelligent fabrication technologies to promote the low-cost and stable production of high-performance tapes. This study analyzes the core application …

A Holistic Modelling Framework For Functionally Safe Software Architectures In Embedded Control Systems, Thomas Barth

Doctoral

Embedded control systems are integral to most modern electrified products and an essential backbone of ongoing digitalisation [1]. In this context, these systems increasingly perform safety-critical functions where failures can lead to severe personal injury, environmental damage, or significant economic loss [2]. Consequently, they fall more often within the scope of regulation such as IEC 61508 and its derivatives [3]. At the same time, driven by hardware evolution and market demands, embedded control systems continue to grow in both integration density and functional complexity [4]. These demands necessitate structured development methods that balance compliance with cost-efficiency and development agility. A …

Exploring Noise Induced Extreme Events In Neuronal Oscillators Networks And Machine Learning Forecasts, Hariharan S Mr

Theses and Dissertations

This doctoral dissertation comprehensively investigates the underexplored phenomenon of noise-induced extreme events (EE). The word “extreme” is accompanied by an occurring “event” when the deviation is extreme or higher than that of regular occurrences. These extreme occurrences are rare, abrupt, sudden, and irregular, often causing a profound impact on the system and its surroundings. Tsunami, earthquakes, solar flares, and tornadoes are such events that do not occur often but still significantly cause damage to mankind. This thesis particularly focuses on EE in neuronal systems where sudden synchronization can trigger seizures, tremors, and strokes which serve as classic examples of such …

Scalable And Fault-Tolerant Network Architectures For Real-Time Video Transmission In Industrial Networked Control Systems, Moustafa Awad

Theses and Dissertations

This thesis addresses the challenge of transporting supervisory video alongside time-critical control traffic in industrial Networked Control Systems (NCS) without violating stringent real-time constraints. A simple yet scalable network architecture is developed and evaluated for a plant-level deployment comprising three interconnected workcells with sensors, controllers, actuators, and cameras. The design explicitly accommodates bandwidth-intensive video streams while preserving the responsiveness of watchdog/control traffic. Analytical delay modeling decomposes end-to-end latency into transmission, propagation, processing, and queuing components, and Riverbed-based simulations are used to validate the model under realistic mixed-traffic conditions. A traffic-engineering strategy - phase-shifting supervisory camera transmissions - effectively desynchronizes frame …

Dynamic Multi-Layered Hardware Obfuscation With Behavioral Drift Locking For Sat-Resistant Designs, Ahmed Yehia Salah Mohamed Emish

Theses and Dissertations

The globalization of the semiconductor supply chain has introduced critical vulnerabilities, including intellectual property (IP) piracy, reverse engineering, and hardware tampering. While traditional logic locking offers a baseline of defense, the emergence of powerful Boolean satisfiability (SAT) solvers has rendered many static obfuscation techniques ineffective. This work proposes a Dynamic Multi- Layered Hardware Obfuscation Framework that utilizes Behavioral Drift Locking (BDL) to provide a robust defense-in-depth against advanced adversarial models. The methodology integrates four synergistic layers: • Dynamic Key Management using a time-dependent rotation mechanism that updates keys every clock cycle to prevent static analysis. • Control Obfuscation through opcode …

Contrasting Coastal Dune Environments In Chile, Aurora Christianson

Student Research Symposium (SRS)

Emerging coastalization and urbanization threats to the prehistoric Concón Dunes and Humedal de Mantagua coastal area of Chile is being investigated by researchers via uncrewed aircraft systems (UAS). Four UAS were utilized: Anzu Raptor T, DJI Mavic 3E, DJI Mavic 3M, and DJI Air 3 to collect various images of the coastal dunes. Multispectral and RGB cameras gather images by photogrammetry to create orthomosaics and monitor vegetation indexes in Pix4Dmapper. Thermal cameras provided images in rainbow, white hot infrared, and black hot infrared schemes to monitor wildlife and vegetation. The normalized difference vegetation index (NDVI) was calculated to visualize overall …

Electromagnetic Simulations For Vulnerable Road User Safety In 5g/6g Wireless Systems, Colin Mcnerny

Theses and Dissertations

Protecting vulnerable road users is imperative. As 5G wireless technology matures and 6G standards are established, new operational technology emerges to reduce traffic accidents. There is widespread evidence to support the need for pedestrian awareness in modern traffic ecosystems as a significant number of vulnerable road users are injured or killed every year due to observable traffic failures. Computational electromagnetic (CEM) solvers are used to model, simulate, and analyze the performance of antennas deployed to meet this challenge. Validation studies using the Altair FEKO CEM solver demonstrate scenarios where vulnerable road users are at risk. The simulation data in this …

Silent Sabotage: Internal State Triggered Backdoor Attacks On Llm-Powered Robotic Systems, Doniyorkhon Obidov, Shivayogi Akki, Tan Chen, Kaichen Yang

Michigan Tech Publications

The integration of Large Language Models (LLMs) into robotic control systems is enabling a new generation of autonomous agents capable of complex reasoning and planning. While this paradigm shift accelerates progress, it also introduces novel security risks that remain largely unexplored. Current research into LLM backdoors has focused on attacks triggered by external stimuli, such as specific words, visual objects, or environmental states. These attacks, while potent, overlook a more insidious class of vulnerability where the trigger is internal to the agent’s own operational logic. This paper presents the first comprehensive study of history-based backdoor attacks on LLM-powered robotic systems. …

Neurocore: A Gnn Approach To Configurable Ip Core Identification In Fpga Netlists, Dallin Dahl, Keenan Faulkner, James Usevitch, Jeffrey Goeders

Student Works

Netlist reverse engineering enables many applications, including detecting IP theft, verifying CAD tool correctness, and detecting hardware trojans. However, reconstructing high-level information and circuit structure from a flat, nameless netlist is challenging. In this work we focus on the problem of locating known IP cores in an FPGA netlist, which is especially challenging due to the prevalence of highly configurable IP cores. We present Neurocore: a graph neural network-based approach to classifying nodes in a netlist as instances of known IP cores, and present and evaluate different models for different use cases. We have created a large open-source dataset of …

Biologically-Inspired Multiscale Neuromorphic Architecture, Christian O'Reilly, Ramtin Zand

Publications

This white paper proposes a biologically-inspired multiscale neuromorphic architecture that bridges key gaps between artificial neural networks (ANNs), spiking neural networks (SNNs), and biological neural networks (BNNs). While SNNs offer promising energy efficiency, their broader adoption remains limited by suboptimal performance and the need for novel learning paradigms. To address these challenges, the proposed framework integrates structural and functional principles observed in the brain, including hierarchical organization, sparse and modular connectivity, predictive coding, and diverse neuronal dynamics.

The architecture operates across micro-, meso-, and macro-scales, incorporating neuron-level diversity (e.g., excitatory/inhibitory and principal/support cells), canonical microcircuits (CMCs), and large-scale hierarchical organization. …

The Impact Of A Structured Training Program On The Depth Perception Of Ab Initio Drone Pilots, John Murray, Steven Richardson, Keith Joiner, Graham Wild

Research outputs 2022 to 2026

Highlights: What are the main findings? First empirical assessment of depth-perception improvement from an official RPAS training program. Demonstrates measurable perceptual-skill gains despite no explicit syllabus objective for depth perception. What are the implications of the main findings? Conducted in authentic operational settings under regulated conditions, ensuring strong external validity. Establishes a validated baseline for future comparative and longitudinal studies on perceptual training effectiveness. Flying remotely requires accurate perception of the environment to ensure safe operation. While remotely piloted aircraft (RPA) bring unique opportunities, they also present new challenges for the pilot, including exercising accurate depth perception. The impact of …

The Role Of Spatial Abilities In Stem Learning And The Influence Of Individual Differences, Styliani Malkogeorgou

Masters

Students’ decisions to pursue education and careers in Science, Technology, Engineering, and Mathematics (STEM) are shaped by an interplay of cognitive, social, and motivational factors. Spatial ability is among the most reliable predictors of STEM success, yet less is known about how it relates to students’ STEM attitudes and aspirations, and whether visuospatial working memory (VSWM) explains this relationship. This study tested the hypotheses that (a) stronger spatial abilities and VSWM would be associated with more positive STEM attitudes and stronger STEM aspirations, and (b) VSWM would mediate the relationship between spatial abilities and STEM attitudes/aspirations, while examining the influence …

A Runtime-Adaptive Transformer Neural Network Accelerator On Fpgas, Ehsan Kabir, Jason D. Bakos, David Andrews, Miaoqing Huang

Electrical Engineering and Computer Science Faculty Publications and Presentations

Transformer neural networks (TNN) excel in natural language processing (NLP), machine translation, and computer vision (CV) without relying on recurrent or convolutional layers. However, they have high computational and memory demands, particularly on resource constrained devices like FPGAs. Moreover, transformer models vary in processing time across applications, requiring custom models with specific parameters. Designing custom accelerators for each model is complex and time-intensive. Some custom accelerators exist with no runtime adaptability, and they often rely on sparse matrices to reduce latency. However, hardware designs become more challenging due to the need for application-specific sparsity patterns. This paper introduces ADAPTOR, a …

Optical Fiber Sensors Using Vernier Effect In Cascaded Fiber Interferometers, Zhouchen Wang

Dissertations

The optical Vernier effect has emerged as a powerful tool to enhance the sensitivity of optical fiber interferometer-based sensors, opening new opportunities for developing highly sensitive fiber sensing systems. Optical fiber interferometric sensors based on the Vernier effect are widely used for various applications due to their ultra-compact size, high sensitivity, immunity to electromagnetic interference, electrical isolation, resistance to harsh environments, flexibility, multiplexing capability, and remote operation. The aim of this doctoral thesis was to gain a deeper fundamental understanding of the Vernier effect in optical fiber structures and to develop and investigate a series of novel Vernier effect optical …

Machine Learning In Peak Demand Forecasting: Foundations, Trends, And Insights, Shuang Dai, Fanlin Meng, Hongsheng Dai, Qian Wang, Xizhong Chen, Wenlei Bai, Peizhi Shi, Richard Allmendinger, Yuchen Zhang, Jian Liu

Electrical and Computer Engineering Faculty Research & Creative Works

Peak demand forecasting involves predicting the maximum electricity demand within a specific period, which plays a key role in maintaining the efficiency and stability of power systems. The rapid evolution of power systems, driven by advanced metering infrastructure, local energy applications such as electric vehicles, and the increasing adoption of intermittent renewable energy, has introduced greater randomness and reduced predictability in peak demand. Given the pressing need to address more diverse implementation requirements across different contexts, accurate and reliable peak demand forecasting has become increasingly important. To the best of our knowledge, this study is the first to provide a …