Engineering Commons^™

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 …

A Survey On Few-Shot Class-Incremental Learning, Songsong Tian, Lusi Li, Weijun Li, Hang Ran, Xin Ning, Prayag Tiwari

Computer Science Faculty Publications

Large deep learning models are impressive, but they struggle when real-time data is not available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for deep neural networks to learn new tasks from just a few labeled samples without forgetting the previously learned ones. This setup can easily leads to catastrophic forgetting and overfitting problems, severely affecting model performance. Studying FSCIL helps overcome deep learning model limitations on data volume and acquisition time, while improving practicality and adaptability of machine learning models. This paper provides a comprehensive survey on FSCIL. Unlike previous surveys, we aim to synthesize few-shot learning and incremental …

Approximating Discrimination Within Models When Faced With Several Non-Binary Sensitive Attributes, Yijun Bian, Yujie Luo, Ping Xu

Electrical and Computer Engineering Faculty Publications

Discrimination mitigation with machine learning (ML) models could be complicated because multiple factors may interweave with each other including hierarchically and historically. Yet few existing fairness measures are able to capture the discrimination level within ML models in the face of multiple sensitive attributes. To bridge this gap, we propose a fairness measure based on distances between sets from a manifold perspective, named as ‘harmonic fairness measure via manifolds (HFM)’ with two optional versions, which can deal with a fine-grained discrimination evaluation for several sensitive attributes of multiple values. To accelerate the computation of distances of sets, we further propose …

Studying Ht-Mcss In Ieee 802.11n Networks Via Simulations, Jun Peng

Electrical and Computer Engineering Faculty Publications

We study the High Throughput Modulation and Coding Schemes (HT-MCS) in IEEE 802.11n In networks in this paper. The HT-MCSs are designed to deal with various channel conditions in the networks. We used ns-3 simulations to study HT-MCS 0 to HT-MCS 31 under various radio propagation models. The simulation results are presented in this paper. In our simulations the HT-MCSs were tested in networks with Friis, Nakagami, and log-distance propagation models for studying their performance under various signal attenuation and fading effects. The HT-MCSs provided consistent performance under most channel conditions in our simulations. However, they showed significantly degraded performance …

Low Finesse Extrinsic Fabry-Perot Interferometer (Efpi) Demodulation For High Temperature Gap Measurements, Abhishek Prakash Hungund

Masters Theses

"In continuous casting steel industries, mold flux is added to provide thermal and chemical insulation for molten steel. The mold flux absorbs detrimental inclusions from the steel and promotes uniform heat distribution to prevent sticking. To promote flux infiltration, mold oscillation is used, but this creates oscillation marks that reduce local shell growth and increase temperatures. Wide (2-3 mm) and deep (0.5-0.9 mm) oscillation marks with areas of 1.1-2.5 mm² are observed, affecting the steel quality, which results in a loss of 6% per billet to the industry. To address this challenge, we propose an extrinsic Fabry-Perot interferometer (EFPI) sensor …

Microwave Materials Characterization Of Geopolymers, Jared Sinkey

Masters Theses

"The purpose of this work was to develop a process for studying geopolymer materials using a microwave materials characterization approach. Such an approach is known to provide information about chemical and physical properties of materials. As such, in this work, the focus is on the role of water in the geopolymer curing process. To do this, specimens were prepared and cast and microwave measurements conducted throughout the curing process using a short-circuited rectangular waveguide (SC-RWG) measurement technique. In this technique, the complex reflection properties/coefficient of the sample are measured. The effect of sample length and dielectric properties on materials characterization …

In Situ High Temperature Fiber-Optic Raman Sensor For Industrial Applications, Bohong Zhang

Doctoral Dissertations

"Continuous casting in steel production uses specially developed oxyfluoride glasses (mold fluxes) to lubricate a mold and control the solidification of the steel in the mold. The composition of the flux impacts properties, including basicity, viscosity, and crystallization rate, all of which affect the stability of the casting process and the quality of the solidified steel. However, mold fluxes interact with steel during the casting process, resulting in flux chemistry changes that must be considered in the flux design. Currently, the chemical composition of mold flux must be determined by extracting flux samples from the mold during casting and then …

Modeling And Analysis Methods For Esd And Emi Problems, Xin Yan

Doctoral Dissertations

"Electrostatic discharge (ESD) failures and Electromagnetic interference (EMI) problems are becoming more critical in electronic devices and large systems. In this work, four studies are presented to model and analyze ESD and EMI problems.

First, a simplified physical-based model for deep-snapback transient voltage suppressors (TVS) is developed. While based on physics, the number of parameters and components is minimized. Results show that the proposed model captures the most important behaviors of the TVS response using a limited number of parameters, allowing the model to be tuned relatively easily using data obtained only from package-level transient and quasi-static measurements. Second, a …

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 …

Power And Waiting Time Efficiency At Automatic Toll Gates With The Contactless Card Payment Implementation, Ujang Wiharja, Sri Hartanto

ASEAN Journal on Science and Technology for Development

One method of Electronic Toll Collection (ETC) in Automatic Toll Gate (ATG) currently uses contactless transactions using Radio Frequency IDentification (RFID) technology. Tracking and monitoring objects (the car) with RFID is carried out in real-time and is required to keep up with the speed of an object (the car). The On-Board Unit (OBU) transponder installed on the car's windshield and the Road Side Unit (RSU) installed on the ATG are the main components of the Dedicated Short-Range Communication (DSRC) system, which allows the car and ATG to communicate with each other and carry out transactions, including online toll payments, without …

Photoluminescence Switching In Quantum Dots Connected With Fluorinated And Hydrogenated Photochromic Molecules, Ephraiem S. Sarabamoun, Jonathan M. Bietsch, Pramod Aryal, Amelia G. Reid, Maurice Curran, Grayson Johnson, Esther H. R. Tsai, Charles W. Machan, Guijun Wang, Joshua J. Choi

Chemistry & Biochemistry Faculty Publications

We investigate switching of photoluminescence (PL) from PbS quantum dots (QDs) crosslinked with two different types of photochromic diarylethene molecules, 4,4'-(1-cyclopentene-1,2-diyl)bis[5-methyl-2-thiophenecarboxylic acid] (1H) and 4,4'-(1-perfluorocyclopentene-1,2-diyl)bis[5-methyl-2-thiophenecarboxylic acid] (2F). Our results show that the QDs crosslinked with the hydrogenated molecule (1H) exhibit a greater amount of switching in photoluminescence intensity compared to QDs crosslinked with the fluorinated molecule (2F). With a combination of differential pulse voltammetry and density functional theory, we attribute the different amount of PL switching to the different energy levels between 1H and 2F molecules which result in different potential barrier …

Aggregation And Oligomerization Characterization Of Ss-Lactoglobulin Protein Using A Solid-State Nanopore Sensor, Mitu C. Acharjee, Brad Ledden, Brian Thomas, Xianglan He, Troy Messina, Jason Giurleo, David Talaga, Jiali Li

Physics Faculty Publications and Presentations

Protein aggregation is linked to many chronic and devastating neurodegenerative human diseases and is strongly associated with aging. This work demonstrates that protein aggregation and oligomerization can be evaluated by a solid-state nanopore method at the single molecule level. A silicon nitride nanopore sensor was used to characterize both the amyloidogenic and native-state oligomerization of a model protein ß-lactoglobulin variant A (βLGa). The findings from the nanopore measurements are validated against atomic force microscopy (AFM) and dynamic light scattering (DLS) data, comparing βLGa aggregation from the same samples at various stages. By calibrating with linear and circular dsDNA, this study …

Non-Invasive Monitoring Device For Early Detection Of Breast Cancer Related Lymphedema, Amy Prendergast

Honors Theses and Capstones

Breast Cancer Related Lymphedema (BCRL) is a common co-morbidity in cancer survivors following neoadjuvant therapies such as chemotherapy, radiation, and/or surgery. It is brought about by the disruption in the lymphatic system (think lymph node biopsy) that leads to a buildup of lymphatic fluid in the arm. Current diagnostic strategies for this condition are merely retroactive, and fairly limited in the parameters that are examined to ensure patient well-being long term. We hypothesize that with an approach that mimics bioimpedance spectroscopy analysis, we will be able to provide a clinical support tool that would better determine early stages of lymphedema …

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 …

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. …

Tutorial: Knowledge-Infused Artificial Intelligence For Mental Healthcare, Kaushik Roy

Publications

Artificial Intelligence (AI) systems for mental healthcare (MHCare) have been ever-growing after realizing the importance of early interventions for patients with chronic mental health (MH) conditions. Social media (SocMedia) emerged as the go-to platform for supporting patients seeking MHCare. The creation of peer-support groups without social stigma has resulted in patients transitioning from clinical settings to SocMedia supported interactions for quick help. Researchers started exploring SocMedia content in search of cues that showcase correlation or causation between different MH conditions to design better interventional strategies. User-level Classification-based AI systems were designed to leverage diverse SocMedia data from various MH conditions, …

Ontolog Summit 2024 Talk Report: Healthcare Assistance Challenges-Driven Neurosymbolic Ai, Kaushik Roy

Publications

Although Artificial Intelligence technology has proven effective in providing healthcare assistance by analyzing health data, it still falls short in supporting decision-making. This deficiency largely stems from the predominance of opaque neural networks, particularly in mental health care AI applications, which raise concerns about their unpredictable and unverifiable nature. This skepticism hinders the transition from information support to decision support. This presentation will explore neurosymbolic approaches that combine neural networks with symbolic control and verification mechanisms. These approaches aim to unlock AI’s full potential by enhancing information analysis and decision-making support for healthcare assistance1.

Electromagnetic Near-Field Scanning With A Spatially Sparse Sampling Strategy Utilizing Kriging-Dmd, Yanming Zhang, Steven Gao, Lijun Jiang

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

This paper proposes a hybrid method for time-resolved electromagnetic near-field scanning, merging model-based (Gaussian processes regression model, a.k.a. Kriging method) and data-driven (dynamic mode decomposition) techniques. Specifically, Latin hypercube sampling enables spatially sparse measurements, followed by dynamic mode decomposition to analyze resulting sparse spatial-temporal data, extracting frequency information and sparse dynamic modes. The Kriging method is then employed for full-state reconstruction. The proposed approach is evaluated using crossed dipole antennas. Results indicate that, even with a spatial subsampling factor of 130, achieving a fully reconstructed field distribution suitable for engineering applications with frequency information extraction is feasible. This hybrid framework …

A Hybrid Algorithm To Dual Sparse Sampling Measurement In Time-Resolved Electromagnetic Near-Field Scanning, Yanming Zhang, Steven Gao, Lijun Jiang

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Time-resolved electromagnetic near-field scanning is vital for antenna measurement and addressing complex electromagnetic interference and compatibility issues. However, the swift acquisition of high-resolution spatiotemporal data remains challenging due to physical constraints, such as moving the probe position and allowing sufficient time for sampling. This paper introduces a novel hybrid approach that combines Kriging for sparse spatial measurement, compressed sensing (CS) for sparse temporal sampling, and dynamic mode decomposition (DMD) for a comprehensive analysis of dual-sparse sampling electromagnetic near-field data. CS optimizes sparse sampling in the time domain, capitalizing on the inherent sparsity within electromagnetic radiated signals, resulting in reliable representation …

A Data-Driven Approach To Time-Domain Electromagnetic Modeling Based On Dynamic Mode Decomposition, Yanming Zhang, Steven Gao, Lijun Jiang

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

This paper presents a data-driven methodology that utilizes Dynamic Mode Decomposition (DMD) for the time-domain (TD) electromagnetic (EM) modeling of microwave devices. As an unsupervised machine learning technique, DMD leverages a limited set of unlabeled spatio-temporal electromagnetic (EM) data to determine DMD eigenvalues and eigenmodes. Then, the obtained DMD model reconstructs the dynamics as a series of exponential terms based on linear assumptions. The effectiveness of this approach is demonstrated through the TD EM modeling of photonic crystal waveguides. Comparative analysis with the finite-difference time-domain (FDTD) method shows that the DMD model not only achieves precise modeling but also facilitates …

An Unsupervised Learning Framework For Determining The Excitation Coefficients Using Near-Field Antenna Measurements, Yanming Zhang, Peifeng Ma, Steven Gao, Lijun Jiang

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

This article presents a novel unsupervised learning framework based on multiscale dynamic mode decomposition for determining the excitation coefficients of antennas using time-domain near-field measurements. The proposed framework integrates temporal multiscale analysis to extract a joint distribution of frequency, damping factors, and spatial modes, enabling precise extraction of excitation frequencies, rising/falling edges, and phases without labeled data. We validate the effectiveness of the proposed approach through two examples involving on-off keying modulation and a phase-shift dipole antenna. It is found that the proposed method performs well in handling nonstationary excitation signals and proves particularly advantageous for calibrating tunable antenna systems. …

Spatiotemporal Variance Image Reconstruction For Thermographic Inspections, Logan M. Wilcox, Emily M. Johnson, Emma T. Bohannon, Catherine E. Johnson, Kristen M. Donnell

Mining Engineering Faculty Research & Creative Works

Active microwave thermography (AMT) is a nondestructive testing and evaluation (NDT&E) technique that utilizes a radiating antenna to induce a thermal increase on or within a specimen under test (SUT). The radiated power density is spatially nonuniform and therefore results in a spatially nonuniform thermal excitation, which may result in missed or false indications of defects. To this end, this work proposes a novel image reconstruction technique for nonuniform excitation/heating and is referred to as Spatiotemporal Variance Reconstruction (STVR). STVR utilizes the spatial and temporal variance of the surface thermal profile. STVR is advantageous in that it does not require …

Modified Thermographic Signal-To-Noise Ratio For Active Microwave Thermography, Logan M. Wilcox, Kristen M. Donnell

Electrical and Computer Engineering Faculty Research & Creative Works

Active microwave thermography (AMT) is an active thermographic nondestructive testing and evaluation (NDT&E) technique that utilizes an active electromagnetic-based excitation. This excitation is achieved through a radiating antenna and is spatially nonuniform in nature. As such, the electromagnetically-induced heat is also spatially nonuniform, as it is directly related to the radiated power density incident on the specimen under test (SUT). After excitation, infrared measurements on the surface of the SUT are completed using an infrared camera. Common post processing techniques including thermal contrast (TC) and signal-to-noise ratio (SNR) are often applied to these measured results. As these post processing techniques …

A Comprehensive Review Of Piezoelectric Ultrasonic Motors: Classifications, Characterization, Fabrication, Applications, And Future Challenges, Sidra Naz, Tian-Bing Xu

Mechanical & Aerospace Engineering Faculty Publications

Piezoelectric ultrasonic motors (USMs) are actuators that use ultrasonic frequency piezoelectric vibration-generated waves to transform electrical energy into rotary or translating motion. USMs receive more attention because they offer distinct qualities over traditional magnet-coil-based motors, such as miniaturization, great accuracy, speed, non-magnetic nature, silent operation, straightforward construction, broad temperature operations, and adaptability. This review study focuses on the principle of USMs and their classifications, characterization, fabrication methods, applications, and future challenges. Firstly, the classifications of USMs, especially, standing-wave, traveling-wave, hybrid-mode, and multi-degree-of-freedom USMs, are summarized, and their respective functioning principles are explained. Secondly, finite element modeling analysis for design and …

Lidar Technology For Future Wireless Networks: Use Cases And Challenges, Omar Rinchi, Ahmad Alsharoa

Electrical and Computer Engineering Faculty Research & Creative Works

Next-generation wireless networks are beset with challenges such as line-of-sight (LoS) shadowing and multipath scattering, significantly impacting service quality and reliability. Despite technological advances aimed at mitigating these issues, achieving consistent quality of service (QoS) standards continues to be an uphill task, particularly within the ever-changing urban landscapes and the intricate process of melding new technologies with pre-existing infrastructures. This paper delves into the integration of light detection and ranging (LiDAR) sensors as a novel solution to these persistent problems. We explore two potential use cases of LiDAR integration in wireless networks, offering detailed insights into the underlying motivation, technical …

A Hybrid Model-Based Data-Driven Framework For The Electromagnetic Near-Field Scanning, Yanming Zhang, Lijun Jiang

Electrical and Computer Engineering Faculty Research & Creative Works

This article presents a novel hybrid approach for electromagnetic near-field scanning, combining model-based, i.e., Gaussian processes regression, and data-driven, i.e., dynamic mode decomposition, techniques. We first leverage the Latin hypercube sampling technique to achieve spatially sparse measurements. Subsequently, dynamic mode decomposition is applied to analyze the resulting spatiotemporal data with sparse spatial sampling, enabling the extraction of both frequency information and sparse dynamic modes. Finally, the Gaussian processes regression, also known as the Kriging method, is adopted for the full-state reconstruction. The proposed hybrid approach is benchmarked by an example of the crossed dipole antennas. The obtained results demonstrate that …

Profitability Analysis Of Time-Restricted Double-Spending Attack On Pow-Based Large Scale Blockchains With The Aid Of Multiple Attacks, Yiming Jiang, Jiangfan Zhang

Electrical and Computer Engineering Faculty Research & Creative Works

We consider the time-restricted double-spending attack (TR-DSA) on the Proof-of-Work-based blockchain, where an adversary conducts a DSA within a finite timeframe and simultaneously launches multiple types of attacks on the blockchain. To be specific, the adversary can conduct attacks to isolate some honest miners and cause block propagation delays among miners to enhance the success probability of the TR-DSA. We first develop the closed-form expression for the success probability of a TR-DSA with the aid of multiple types of attacks, which is leveraged to develop the closed-form expression for the expected profit of a TR-DSA. The numerical analysis reveals that …

Deep Learning For Uav Detection And Classification Via Radio Frequency Signal Analysis, Prajoy Podder, Maciej Zawodniok, Sanjay Madria

Electrical and Computer Engineering Faculty Research & Creative Works

Unmanned Aerial Vehicles (UAVs) are advertised as great tool that benefits society and humanity. However, UAVs also pose significant security threats ranging from privacy invasions, to interfering with commercial aircraft landing and takeoff, to accidently crashing into vehicles or people, to military or terrorist attacks. Consequently, there is a pressing need to detect and identify UAVs to mitigate such potential risks. While image-based methods are crucial for UAV detection, radio frequency (RF) emissions offer additional valuable insights. Analyzing RF signals, such as those used in UAV-ground station communications, can provide information about UAV types based on distinct frequency usage or …

Additively Manufactured Aperture-Based Fss, Alexander Hook, Doyle T. Motes, Cody Morrow, Kristen M. Donnell

Electrical and Computer Engineering Faculty Research & Creative Works

Frequency selective surfaces (FSSs) are arrays of patch- or aperture-based elements with specific high frequency reflective and/or transmissive properties. The specific FSS response of a given design is dictated by the element dimensions and spacing relative to adjacent elements (collectively referred to as the unit cell), along with the substrate (and superstrate if applicable) properties. As it relates to sensing, the FSS response may be affected by environmental (e.g., temperature) or structural (e.g., strain) changes. To this end, FSS-based sensors have been considered in recent years for a myriad of sensing applications including structural health monitoring (SHM). Concurrent to this, …

Angle-Dependent Photoinduced Changes Of Near-Infrared Transmission In Amorphous Selenium Films, Kaitlin Hellier, Emily Enlow, Seyedehnajmeh Montazeri, Mina Esmaeelpour, Shiva Abbaszadeh

Electrical and Computer Engineering Faculty Research & Creative Works

Amorphous selenium (a-Se) is a promising semiconductor for a variety of photoconductive applications, predominantly in X-ray detection. In addition, material properties introduce several other potential applications in nonlinear optics. Photodarkening (PD) presents an interesting area of study; when exposed to band-region light, metastable structural changes induce a shift in the band edge and increase tail absorption. In this work, we investigate these effects utilizing a near-infrared (NIR) probe to avoid generating any darkening during beam transmission. We observe an unexpected result; here, we present the effects of band region exposure in NIR transmission (1570 nm). We observe a shift from …