Electrical and Computer Engineering Commons^™

Particle Accelerator Spin-Transparent Storage Rings For Beyond State-Of-The-Art Science, R. Suleiman, Y. Derbenev, M. Grau, V. Morozov

Physics Faculty Publications

We will describe spin-transparent storage rings that exhibit spin-coherence times of several hours and store a large number of particles and their use in novel applications. For example, these rings can be used to directly measure the electric dipole moment of the electron, relevant to CP violation and matter-antimatter asymmetry in the universe, and to search for dark energy and ultra-light dark matter*. These rings can also serve as a compelling platform for quantum computing. In this presentation, we will describe how spin-transparent rings can be used in conjunction with ion traps to enhance scalability and increase quantum coherence times …

An Fpga-Based Eit System For Deep Space Medical Imaging, Kendall R. Farnham

Dartmouth College Ph.D Dissertations

Dangers associated with high radiation and microgravity exposure in space are critical challenges inhibiting us from exploring deep space and pursuing long-duration missions, as current medical systems are unable to monitor, diagnose, or treat tissue injury within physical spacecraft constraints and communication limits. Ultrasound (US) is the current imaging system used on the International Space Station, but this technology relies on telemedical support (or onboard artificial intelligence/autonomous capabilities) for both operation and diagnosis, posing challenges for crews isolated in deep space. Electrical impedance tomography (EIT) is a non-invasive, non-ionizing technology that produces images of the electrical properties of tissues and …

Semiconductor Physics Based Signal Integrity Analysis For 3d Ic, Ze Sun

Doctoral Dissertations

"Three-dimensional integrated circuits (3DICs) are becoming increasingly popular in high-speed electrical systems. 3DICs are made by stacking multiple dies vertically and connecting them with through-silicon vias (TSVs). In this dissertation, three studies were carried out to model the performance of 3DICs in extreme working conditions and the signal integrity (SI) performance of 3DICs.

First, an in-house Monte Carlo particle simulator was developed to characterize extreme working conditions (ESD/EMP) for ICs at the microscopic level instead of the macroscopic level. The ability of the simulator was demonstrated by modeling a voltage regulator diode under forward and reverse bias. The diode simulation …

Modeling And Analysis Of Dc-Dc Converters For Power Distribution Networks Design, Junho Joo

Doctoral Dissertations

"Accurate modeling of power distribution networks (PDN) including voltage regulator module (VRM) is critical for high-performance digital systems including low- to high-power applications such as laptops and mobile platforms. As a consolidated end-to-end power source, PDN can be divided into several parts: the VRM to regulate the external voltage source, printed circuit board (PCB) PDN, package PDN, and on-chip PDN. A transient current drawn from the on-die circuitry will produce an instantaneous voltage drop at the bump. The time domain behavior of such a drop and the subsequent recovery is called voltage droop which is strongly associated with the VRM …

Adversarial Transferability And Generalization In Robust Deep Learning, Tao Wu

Doctoral Dissertations

Despite its remarkable achievements across a multitude of benchmark tasks, deep learning (DL) models exhibit significant fragility to adversarial examples, i.e., subtle modifications applied to inputs during testing yet effective in misleading DL models. These meticulously crafted perturbations possess the remarkable property of transferability: an adversarial example that effectively fools one model often retains its effectiveness against another model, even if the two models were trained independently. This research delves into the characteristics influencing the transferability of adversarial examples from three distinct and complementary perspectives: data, model, and optimization. Firstly, from the data perspective, we propose a new method of …

Deep Reinforcement Learning Based Strategies For Inverter Dominated Microgrids, Oroghene Oboreh-Snapps

Doctoral Dissertations

The integration of inverter-based distributed generators (IBDGs) in modern power systems has ushered in a new era of renewable energy utilization, prompting the rise of Microgrid (MG) architectures. Nevertheless, as IBDG penetration increases, a host of challenges surface. Foremost among these challenges are issues related to frequency stability stemming from the inherent lack of inertia in IBDGs, and inaccurate sharing of reactive and active power, particularly evident in parallel IBDG networks with mismatched feeder impedance. This research presents novel solutions to these challenges by adopting deep reinforcement learning (DRL), specifically, the twin delayed deep deterministic policy gradient (TD3) algorithm, to …

Moisture-Controlled Triboelectrification During Coffee Grinding, Joshua Méndez Harper, Yong-Hyun Kim, Robin E. Bumbaugh, Connor S. Mcdonald, Christopher H. Hendon, Elana J. Cope, Leif E. Lindberg, Justin Pham, Multiple Additional Authors

Electrical and Computer Engineering Faculty Publications and Presentations

Triboelectrification is the physical process where materials acquire surface charge from frictional interactions at their interfaces.The magnitude of charge depends on the interfacial material composition and can be harnessed in emergent technologies for energy generation.

The mechanism of electrostatic accumulation is complex and is further obscured in granular materials where collisions are sufficiently energetic to cause fracturing. In this “fractoelectric” regime, crack initiation and propagation are thought to charge particles through transfer of electrons and/or ions at the hot crack interface.

Whether a material’s charging is dominated by tribo- or fractoelectrification, fracture-generated granular flows often comprise particles whose surface charge …

Psu Esi Review (Doe-Psu-0000922-6), Tylor Slay, Jaime Kolln, Robert B. Bass

Electrical and Computer Engineering Faculty Publications and Presentations

A guide to developing an Energy Service Interface (ESI) was created as part of the Grid Modernization Laboratory Consortium 2.5.2 ESI project. The approach applies device-agnostic and service-oriented ESI principles and leverages documents such as the Interoperability Maturity Model and Common Grid Service Definitions to provide a methodology to review, develop, and update standards and profiles to engage distributed energy resources to provide grid services. This document evaluates the ESI developed by Portland State University’s Power Engineering Group under the Electric Grid of Things project funded by the U.S. Department of Energy. The evaluation explores the compliance of this specific …

Trust Model Utilization For Energy Grid Communication, N. Sonali Fernando, John M. Acken, Robert B. Bass

Electrical and Computer Engineering Faculty Publications and Presentations

The internet information that is used by the Energy Grid of Things requires both preventative security measures as well as surveillance measures. The preventative security measures include certificates, encryption, and all of the basic security protocols as defined by published standards. The surveillance measures include monitoring information flow activities and evaluating these messages for indications of potential security attacks. We describe in this paper the utilization of a Distributed Trust Model that was developed specifically for monitoring communication within an Energy Grid of Things. The goal for the Distributed Trust Models is to provide a level of aggregate trust that …

A Magnetic Constant Torque Mechanism, Gozde Sivka, Dawei Che, Bert Dechant, Colton Bruce, Jonathan Z. Bird

Electrical and Computer Engineering Faculty Publications and Presentations

This paper presents the design of a new type of magnetic constant torque mechanism. The constant torque is shown to be created over a prescribed ±65° stroke length and is shown to be highly uniform. The value of the constant torque is adjustable through the relative axial motion of the rotors. The constant torque is created using a simple magnet arrangement which makes use of the axial transverse-flux path of one rotor's magnetic field relative to the azimuthal field flow of the fixed secondary rotor. It is shown that the use of segmented magnets introduces torque ripple.

Mud Acoustics, Charles W. Holland, Stan E. Dosso, Jason D. Chaytor

Electrical and Computer Engineering Faculty Publications and Presentations

Imaging the ocean requires an understanding of how different seafloor sediments interact with sound waves.

A Transverse-Flux Constant Force Mechanism, Gozde Sivka, Bertrand Dechant, Colton Bruce, Jonathan Z. Bird

Electrical and Computer Engineering Faculty Publications and Presentations

A new type of constant force magnet mechanism is presented that relies on the interaction of magnets alone to create a constant force over a prescribed stroke length. It is demonstrated that by using a transverse field path a very uniform radial field along the travel length is created. This can then be used to create a constant force over a long stroke length. The constat force value is shown to be adjustable through the rotation of the stator relative to the translator. The operating principle of the presented transverse-flux constant force mechanism is validated by using 3-D finite element …

Ring Resonators Intergrating With Dichoric Materials And In Spin-Valley Controlled Photonic Topological System, Yuma Kawaguchi

Dissertations and Theses

Photonic technology plays an important role in the development of diverse applications, ranging from optical telecommunications to sensing and imaging. The utilization of photonic circuits emerges as a promising platform for the establishment of advanced communication systems characterized by high speed and large capacity. This advancement is crucial to facilitate fast and efficient data transfer while concurrently managing multiple devices. It is imperative that essential components such as lasers, modulators, and isolators exhibit compactness without incurring significant losses.

Non-reciprocal devices represent a valuable addition to photonic circuits, enabling the creation of unidirectional waveguides that function as optical isolators, preventing signals …

Natural Convection Heat Transfer Characteristics Of Sierpinski Carpet Fractal Fins In Horizontal And Vertical Orientations, Ayomide Ayoola

College of Graduate Studies: Theses & Dissertations

This study investigates the thermal performance of fins with perforations inspired by the Sierpinski carpet fractal pattern under natural convection conditions in both vertical and horizontal orientations. The objective is to analyze the effects of fractal iteration levels on three primary performance metrics: efficiency, effectiveness, and effectiveness per unit mass. Experimental evaluations were conducted for fractal fins from iterations 0 through 4, utilizing aluminum material, with each fin heated to controlled conditions. The results reveal that efficiency generally decreases with higher fractal iterations, reflecting increased thermal resistance associated with greater porosity and complex geometry. However, effectiveness per unit mass improves …

Neurosymbolic Value-Inspired Ai (Why, What, And How), Amit Sheth, Kaushik Roy

Publications

The rapid progression of Artificial Intelligence (AI) systems, facilitated by the advent of Large Language Models (LLMs), has resulted in their widespread application to provide human assistance across diverse industries. This trend has sparked significant discourse centered around the ever-increasing need for LLM-based AI systems to function among humans as part of human society, sharing human values, especially as these systems are deployed in high-stakes settings (e.g., healthcare, autonomous driving, etc.). Towards this end, neurosymbolic AI systems are attractive due to their potential to enable easy-tounderstand and interpretable interfaces for facilitating valuebased decision-making, by leveraging explicit representations of shared values. …

Tailored Micromagnet Sorting Gate For Simultaneous Multiple Cell Screening In Portable Magnetophoretic Cell-On-Chip Platforms, Jonghwan Yoon, Yumin Kang, Hyeonseol Kim, Abbas Ali, Keonmok Kim, Sri Ramulu Torati, Mi-Young Im, Changyeop Jeon, Byeonghwa Lim, Cheolgi Kim

Center for Bioelectronics Publications

Conventional magnetophoresis techniques for manipulating biocarriers and cells predominantly rely on large-scale electromagnetic systems, which is a major obstacle to the development of portable and miniaturized cell-on-chip platforms. Herein, a novel magnetic engineering approach by tailoring a nanoscale notch on a disk micromagnet using two-step optical and thermal lithography is developed. Versatile manipulations are demonstrated, such as separation and trapping, of carriers and cells by mediating changes in the magnetic domain structure and discontinuous movement of magnetic energy wells around the circumferential edge of the micromagnet caused by a locally fabricated nano-notch in a low magnetic field system. The motion …

Femtosecond Laser–Inscribed Fiber Bragg Grating Sensors: Enabling Distributed High- Temperature Measurements And Strain Monitoring In Steelmaking And Foundry Applications, Ogbole Collins Inalegwu, Yeshwanth Reddy Mekala, Rony Kumer Saha, Farhan Mumtaz, Dinesh Reddy Alla, Deva Prasaad Neelakandan, Jeffrey D. Smith, Ronald J. O'Malley, Rex Gerald, Jie Huang

PSMRC Faculty Research

This study demonstrates the use of fiber Bragg grating (FBG) sensors for distributed temperature and strain monitoring in steelmaking and foundry applications. Integrated into inexpensive optical fibers, FBGs offer accurate and real-time remote sensing, detecting shifts in wavelength due to temperature (up to 1,800 °C), strain, or structural wear and tear. Furthermore, the intrinsic features of FBG sensors: compact size, immunity to electromagnetic interference and corrosion, robustness to vibration, ease of integration into existing composite structures, and non-intrusive measurement capacity in harsh environments make them ideal for steelmaking. FBGs optimize production, ensure quality, and enhance safety within the steel industry.

Enhanced Bottom Anode Monitoring In Dc Electric Arc Furnaces Using Fiber Optic Sensors, Yeshwanth Reddy Mekala, Rony Kumer Saha, Ogbole Collins Inalegwu, Muhammad Roman, Farhan Mumtaz, Rex Gerald, Jeffrey D. Smith, Jie Huang, Ronald J. O'Malley

PSMRC Faculty Research

A pin style bottom anode employs conductive steel rods that serve as the pathway for the high electrical power through rammed refractory at the bottom of a DC Electric Arc Furnace (EAF). Anode wear during operation is important to monitor, as anode replacement is expensive and impacts EAF productivity. Liquid steel penetration into the un-sintered refractory layer can result from rapid electrical power ramp-up, dips in furnace temperature, or operating the anode for too long between EAF campaigns. In extreme cases, the liquid steel may penetrate the bottom of the furnace when anode wear progresses too close to the bottom …

Fiber-Optic Raman Probe For On-Line Eaf Slag Analysis, Bohong Zhang, Hanok Tekle, Jeffrey D. Smith, Todd Sander, Ronald J. O'Malley, Jie Huang

PSMRC Faculty Research

In Electric Arc Furnace (EAF) steelmaking, the push for improved efficiency requires accurate analysis of the chemical composition of its slag system to control slag foaming, provide refractory protection, and maintain high furnace iron yield. Therefore, the ability to obtain real-time slag chemistry data would provide a useful tool to improve the control and efficiency of the process. The work reported here aims to assess the structure and chemistry of EAF slags at high temperatures using a portable fiber-optic Raman probe. The ability to relate Raman spectra peaks to chemistry and structure is demonstrated in experimental result with EAF slags …

Methylene Blue-Mediated Photodynamic Therapy In Combination With Doxorubicin: A Novel Approach In The Treatment Of Ht-29 Colon Cancer Cells, Nima Rastegar-Pouyani, Jaber Zafari, Alireza Nasirpour, Hossein Vazini, Nabbaa Najjar, Seyedeh Zohreh Azarshin, Fatemeh Javani Jouni

Electrical & Computer Engineering Faculty Publications

Introduction: With an alarmingly growing number of patients diagnosed with colorectal cancer, adopting innovative anti-cancer approaches has recently garnered great attention. One interesting concept is the co-administration of cytotoxic agents and safer modalities such as photodynamic therapy (PDT), which can subsequently improve therapeutic efficacy and potentially reduce the risks of severe adverse effects and drug resistance. In the course of PDT, a locally injected photosensitizer (PS) is irradiated with a light source, which subsequently generates reactive oxygen species (ROS) and induces programmed cell death in tumor cells.

Methods: In this study, to evaluate the potential anti-cancer effects of chemotherapy combined …

Accelerating Cavity Fault Prediction Using Deep Learning At Jefferson Laboratory, Md M. Rahman, A. Carpenter, K. Iftekharuddin, C. Tennant

Electrical & Computer Engineering Faculty Publications

Accelerating cavities are an integral part of the continuous electron beam accelerator facility (CEBAF) at Jefferson Laboratory. When any of the over 400 cavities in CEBAF experiences a fault, it disrupts beam delivery to experimental user halls. In this study, we propose the use of a deep learning model to predict slowly developing cavity faults. By utilizing pre-fault signals, we train a long short-term memory-convolutional neural network binary classifier to distinguish between radio-frequency (RF) signals during normal operation and RF signals indicative of impending faults. We optimize the model by adjusting the fault confidence threshold and implementing a multiple consecutive …

Guarding The Grid: Exploring Iot And Iiot Security Vulnerabilities In Smart Power Systems, Nicole G. Parker

Honors Undergraduate Theses

The Internet of Things (IoT) encompasses the collective network of electrical devices and the technology that enables them to send and receive data. The use of IoT technologies in industrial settings, such as transportation, manufacturing, and energy is referred to as the Industrial Internet of Things (IIoT). With the expansion of IoT in homes and IIoT in the energy sector has come an increase in the number of devices connected with each other. Engineers have utilized this network to develop sophisticated smart systems that combine sensing, processing, actuation, and control to produce smart environments. Along with the benefits of IoT …

Enhancing Research Productivity: Seamless Integration Of Personal Devices And Hpc Resources With The Cybershuttle Notebook Gateway, Yasith Jayawardana, Dimuthu Wannipurage, Eroma Abeysinghe, Suresh Marru

Computer Science Faculty Publications

Scientists often utilize personal laptops and workstations for initial research stages and turn to high-performance computing (HPC) supercomputers for compute-intensive tasks. However, seamless transitions between these environments are vital for enhancing productivity and accelerating research progress. Our paper presents the Cybershuttle Notebook Gateway, an open-source framework crafted to streamline this transition, optimize resource utilization, and reduce time-to-science for researchers. Leveraging JupyterLab, the framework extends kernel mechanics for seamless provisioning and connection to remote HPC cluster kernels. We delve into its architecture, which separates user authentication, kernel provisioning, and remote file system access. Additionally, we highlight practical capabilities like analyzing network …

An Analysis Of Precision: Occlusion And Perspective Geometry’S Role In 6d Pose Estimation, Jeffrey Choate, Derek Worth, Scott Nykl, Clark N. Taylor, Brett J. Borghetti, Christine M. Schubert Kabban

Faculty Publications

Achieving precise 6 degrees of freedom (6D) pose estimation of rigid objects from color images is a critical challenge with wide-ranging applications in robotics and close-contact aircraft operations. This study investigates key techniques in the application of YOLOv5 object detection convolutional neural network (CNN) for 6D pose localization of aircraft using only color imagery. Traditional object detection labeling methods suffer from inaccuracies due to perspective geometry and being limited to visible key points. This research demonstrates that with precise labeling, a CNN can predict object features with near-pixel accuracy, effectively learning the distinct appearance of the object due to perspective …

Causal Event Graph-Guided Language-Based Spatiotemporal Question Answering, Kaushik Roy, Alessandro Oltramari, Yuxin Zi, Chathurangi Shyalika, Vignesh Narayanan, Amit Sheth

Publications

Large Language Models have excelled at encoding and leveraging language patterns in large text-based corpora for various tasks, including spatiotemporal event-based question answering (QA). However, due to encoding a text-based projection of the world, they have also been shown to lack a fullbodied understanding of such events, e.g., a sense of intuitive physics, and cause-and-effect relationships among events. In this work, we propose using causal event graphs (CEGs) to enhance language understanding of spatiotemporal events in language models, using a novel approach that also provides proofs for the model’s capture of the CEGs. A CEG consists of events denoted by …

Accurate And Time Efficient Signal Integrity And Power Integrity Modeling Of High-Speed Digital Systems, Chaofeng Li

Doctoral Dissertations

"Signal integrity (SI) and power integrity (PI) play an important role in the modern high-speed digital system design, which are closely related to the printed circuit board (PCB) dielectric material property, the PCB interconnect performance, and the power delivery network (PDN) on PCB. Generally, the full-wave simulation is used to accurately analyze and evaluate the designed PCB. But full-wave simulation is not a good option for the complex PCB structure with high aspect ratio, for example, PCB vias, and PDN, which will require significant computing time and storage resources. Equivalent circuit models have been developed to efficiently predict the electrical …

Lightweight Neural Network Pipeline Model For Drone Collision Prediction System Using Limited Computing Resources, Rifqi Nabila Zufar

Chulalongkorn University Theses and Dissertations (Chula ETD)

This thesis focuses on developing a drone collision prediction algorithm by adapting the Neural Network Pipeline (NNP) for use in limited computing resources devices. While NNP is effective with sufficient computation, its performance in constrained environments remains untested. To address this issue, an adaptation of NNP using post-training quantization is developed. This method effectively compresses the model by reducing the precision of the 32-bit floating point. This adaptation aims at reducing model complexity and offers significant benefits in terms of reduced model size, increased processing speed, and reduced power consumption. It results in the expense of prediction accuracy. The MobileNetV3Small …

Study On Skin Cancer Classification Architectures Based-On Cnn With Channel And Spatial Attention Mechanism, Zahid Maqbool

Chulalongkorn University Theses and Dissertations (Chula ETD)

The early and accurate detection of skin cancer is crucial for effective treatment and improving patient survival rates. This research explores innovative CNN-based architectures enhanced with Channel Attention and Spatial Attention (CASA) mechanisms for classifying skin cancer types from dermoscopic images. Building on the strengths of transfer learning, the study applies pre-trained models VGG16, InceptionResNetV2, and EfficientNet variants (EfficientNet B0 to B7) and customizes them for skin cancer classification using CASA modules. The CASA integration allows the models to selectively focus on critical features within images, enhancing both spatial and channel-based feature extraction. Experiments utilize the HAM10000 dataset. Which contains …

Enhanced Cross-Modality Mri Segmentation Using Dilated Convolutions And Multi-Scale Gradient Map, Ghulam Murtaza

Chulalongkorn University Theses and Dissertations (Chula ETD)

Magnetic resonance imaging (MRI) segmentation is critical for accurate medical diagnosis, but cross-modal segmentation presents significant challenges due to domain shifts between imaging modalities and limited labeled data. This study proposes an enhanced U-Net framework designed to improve segmentation accuracy and generalization across different MRI modalities. The method incorporates dilated convolutions in the encoder to expand the receptive field, allowing for better contextual information capture without increasing the number of parameters. Additionally, squeeze and excite (SE) blocks are introduced to recalibrate channel-wise feature importance, addressing variations in tissue contrast across modalities. A multi-scale gradient map fusion strategy, based on holistic …

Classification Of Bearing Failure In Adverse Industrial Conditions, Bawar Masih

Chulalongkorn University Theses and Dissertations (Chula ETD)

In industrial environments, the detection of machinery reliability is frequently compromised by surrounding noise. This interference can lead to unexpected machine failures, resulting in costly downtime and extensive repair expenses. This study investigates the application of CNN and LSTM networks for fault diagnosis under noisy conditions to achieve high classification accuracy across varying SNRs. The CNN model, designed to capture localized features, proved particularly effective in extracting critical information from complex vibration data despite substantial background noise. In contrast, the LSTM model, while optimized for sequential data analysis, showed limited performance under high-noise conditions. Using the CWRU Bearing dataset, the …