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2022

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Nanobubble Capillary Force Between Parallel Plates, Eric Bird, Zhi Liang Jan 2022

Nanobubble Capillary Force Between Parallel Plates, Eric Bird, Zhi Liang

Mechanical and Aerospace Engineering Faculty Research & Creative Works

The Aggregation of Fine or Ultrafine Particles in Liquid Due to the Nanobubble (NB) Bridging Capillary Force is a Key Process for Many Industrial Applications. the Magnitude and Direction of the NB Capillary Force (NBCF) Are Directly Affected by the Geometry of the NB Gas Bridge between Two Adjoining Particles. Although the NBCF Can Be Accurately Measured using Experimental Techniques, It is Very Challenging to Monitor the Images of a NB Capillary Bridge in Nanoscale Spatial Resolution in NBCF Measuring Experiments. as a Result, the Capillary Force Model that Predicts the Dependence of the NBCF on the Geometry of the …

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New Hybrid Model For Evaluating The Frequency-Dependent Leakage Inductance Of A Variable Inductance Transformer (Vit), Angshuman Sharma, Jonathan W. Kimball Jan 2022

New Hybrid Model For Evaluating The Frequency-Dependent Leakage Inductance Of A Variable Inductance Transformer (Vit), Angshuman Sharma, Jonathan W. Kimball

Electrical and Computer Engineering Faculty Research & Creative Works

Skin and proximity effects can cause a significant drop in the effective leakage inductance of a transformer when the operating frequency is increased. Although the magnetic image method-based double-2-D model can calculate the low-frequency leakage inductance with sufficient accuracy, it is inherently a frequency-independent model. While Dowell's 1-D model uses frequency-dependent relations to account for both skin and proximity effects, its accuracy is severely affected by the assumed winding geometry. In this paper, a hybrid model is proposed that uses superposition to combine a modified Dowell's model with the double-2-D model. The proposed model is investigated on a variable inductance …

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A Spline-Based Partial Element Equivalent Circuit Method For Electrostatics, Riccardo Torchio, Maximilian Nolte, Sebastian Schops, Albert E. Ruehli Jan 2022

A Spline-Based Partial Element Equivalent Circuit Method For Electrostatics, Riccardo Torchio, Maximilian Nolte, Sebastian Schops, Albert E. Ruehli

Electrical and Computer Engineering Faculty Research & Creative Works

This contribution investigates the connection between Isogeometric Analysis (IgA) and the Partial Element Equivalent Circuit (PEEC) method for electrostatic problems. We demonstrate that using the spline-based geometry concepts from IgA allows for extracting circuit elements without an explicit meshing step. Moreover, the proposed IgA-PEEC method converges for complex geometries up to three times faster than the conventional PEEC approach and, in turn, it requires a significantly lower number of degrees of freedom to solve a problem with comparable accuracy. The resulting method is closely related to the isogeometric boundary element method. However, it uses lowest-order basis functions to allow for …

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Chimeranet: U-Net For Hair Detection In Dermoscopic Skin Lesion Images, Norsang Lama, Reda Kasmi, Jason R. Hagerty, R. Joe Stanley, Reagan Harris Young, Jessica Miinch, Januka Nepal, Anand Nambisan, William V. Stoecker Jan 2022

Chimeranet: U-Net For Hair Detection In Dermoscopic Skin Lesion Images, Norsang Lama, Reda Kasmi, Jason R. Hagerty, R. Joe Stanley, Reagan Harris Young, Jessica Miinch, Januka Nepal, Anand Nambisan, William V. Stoecker

Electrical and Computer Engineering Faculty Research & Creative Works

Hair and ruler mark structures in dermoscopic images are an obstacle preventing accurate image segmentation and detection of critical network features. Recognition and removal of hairs from images can be challenging, especially for hairs that are thin, overlapping, faded, or of similar color as skin or overlaid on a textured lesion. This paper proposes a novel deep learning (DL) technique to detect hair and ruler marks in skin lesion images. Our proposed ChimeraNet is an encoder-decoder architecture that employs pretrained EfficientNet in the encoder and squeeze-and-excitation residual (SERes) structures in the decoder. We applied this approach at multiple image sizes …

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Clock Duty Cycle Tuning For Desense Mitigation In Modulation -Involved Cases, Shengxuan Xia, Jun Fan, Chulsoon Hwang Jan 2022

Clock Duty Cycle Tuning For Desense Mitigation In Modulation -Involved Cases, Shengxuan Xia, Jun Fan, Chulsoon Hwang

Electrical and Computer Engineering Faculty Research & Creative Works

Nowadays consumers' electronic devices are highly integrated, and modules and integrated circuits (ICs) are usually placed close to each other due to the compact size. The modules and ICs may interfere with the radio frequency (RF) antennas and cause desense issues. In recent years, desense caused by direct coupling from the noise sources to the victim RF antennas has been well studied. However, more complicated mechanisms such as modulations between transmitting signals and low-frequency clock or data signals can also result in desense problems, especially in frequency divide duplex (FDD) applications. Typical solutions to desense problems will focus on suppressing …

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A Dnn-Ensemble Method For Error Reduction And Training Data Selection In Dnn Based Modeling, Ling Zhang, Da Li, Jiayi He, Bhyrav Mutnury, Bo Pu, Xiao Ding Cai, Chulsoon Hwang, Jun Fan, James L. Drewniak, Er Ping Li Jan 2022

A Dnn-Ensemble Method For Error Reduction And Training Data Selection In Dnn Based Modeling, Ling Zhang, Da Li, Jiayi He, Bhyrav Mutnury, Bo Pu, Xiao Ding Cai, Chulsoon Hwang, Jun Fan, James L. Drewniak, Er Ping Li

Electrical and Computer Engineering Faculty Research & Creative Works

Deep neural networks (DNNs) have been widely adopted in modeling electromagnetic compatibility (EMC) problems, but the training data acquisition is usually time-consuming through various simulators. This paper presents a powerful approach using an ensemble of DNN s to effectively reduce the training data size in DNN-based modeling problems. A batch of training data with the largest uncertainties is selected using active learning through the variance among the ensemble of DNNs. Subsequently, a greedy sampling algorithm is applied to select a data subset using diversity. Thus, the proposed method can achieve both uncertainty and diversity in data selection. By averaging the …

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Peec-Based On-Chip Pdn Impedance Modeling Using Layered Green's Function, Chaofeng Li, Biyao Zhao, Bo Pu, Xu Wang, Donghyun Kim, Jun Fan Jan 2022

Peec-Based On-Chip Pdn Impedance Modeling Using Layered Green's Function, Chaofeng Li, Biyao Zhao, Bo Pu, Xu Wang, Donghyun Kim, Jun Fan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents an impedance model of on-chip power distribution network (PDN), which is an efficient criterion for estimating simultaneous switching noises (SSNs) on 3-D integrated circuit (IC). The impedance of on-chip PDN, including the effect of silicon substrate, is accurately modeled based on partial element equivalent circuit (PEEC) and layered Green's function (LGF). The equivalent circuit model of PDN is extracted based on the physical dimensions and electrical material characteristic of PDN at first. And then the LGF is used to consider the effect of silicon substrate for improving the accuracy of on-chip PDN impedance model. The effectiveness of …

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A Fast Cascading Method For Predicting The Coupling From External Plane Waves To Pcbs, Shengxuan Xia, James Hunter, Aaron Harmon, Mohamed Z.M. Hamdalla, Ahmed M. Hassan, Chulsoon Hwang, Victor Khilkevich, Daryl G. Beetner Jan 2022

A Fast Cascading Method For Predicting The Coupling From External Plane Waves To Pcbs, Shengxuan Xia, James Hunter, Aaron Harmon, Mohamed Z.M. Hamdalla, Ahmed M. Hassan, Chulsoon Hwang, Victor Khilkevich, Daryl G. Beetner

Electrical and Computer Engineering Faculty Research & Creative Works

The radio frequency (RF) coupling to electronic devices impacts their EMC performance. The functionalities of a working electronic device may be disrupted when the electromagnetic (EM) coupling reaches a certain level. Studies of the EM coupling to printed circuit boards (PCBs) are therefore essential for RF susceptibility and EMC purposes. For decades, researchers focused on the analytical modeling of EM coupling to transmission lines. However, when it comes to more realistic PCBs the analysis usually still relies heavily on full-wave simulations because of the complexity of the structures and the lack of analytical solutions. Using a traditional full-wave modeling approach, …

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A Practical Simulation Flow For Singing Capacitor Based Acoustic Noise Analysis, Xin Yan, Songping Wu, Mingfeng Xue, Chi Kin Benjamin Leung, Daryl G. Beetner, Jianmin Zhang Jan 2022

A Practical Simulation Flow For Singing Capacitor Based Acoustic Noise Analysis, Xin Yan, Songping Wu, Mingfeng Xue, Chi Kin Benjamin Leung, Daryl G. Beetner, Jianmin Zhang

Electrical and Computer Engineering Faculty Research & Creative Works

Multilayer ceramic capacitors (MLCCs) are widely used in modern electronics. Due to the piezoelectric effect of the ceramic material, however, MLCCs subjected to electrical noise may vibrate and generate acoustic noise, as 'singing'. Acoustic noise can be annoying for users, especially within mobile devices, so it becomes important to perform acoustic noise analysis before a product is released. In this paper, a practical simulation flow for singing capacitor based acoustic noise is presented. The simulation flow and analysis method are developed on Ansys Sherlock and Mechanical. In Ansys Sherlock, local library and Approved Vendor List (AVL) files were used to …

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Optimizing The Placement Of Non-Functional Pads On Signal Vias Using Multiple Reflection Analysis, Muqi Ouyang, Kevin Cai, Chaofeng Li, Anna Gao, Felen Fu, Hannah Bian, Bidyut Sen, Donghyun Kim Jan 2022

Optimizing The Placement Of Non-Functional Pads On Signal Vias Using Multiple Reflection Analysis, Muqi Ouyang, Kevin Cai, Chaofeng Li, Anna Gao, Felen Fu, Hannah Bian, Bidyut Sen, Donghyun Kim

Electrical and Computer Engineering Faculty Research & Creative Works

In this study, the effects of using non-functional pads to optimize the performance of high-speed signal vias are investigated based on multiple reflection analysis. The non-functional pads on signal vias introduce more capacitive coupling and are possible to improve the response of the via structure if the original via has relatively larger impedance compared to the system reference impedance. The effectiveness of the non-functional pad optimization is validated through a numerical example, and the eye diagram of the via structure without and with non-functional pads are compared. The eye opening becomes 5.4 times larger after the via optimization using non-functional …

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A Methodical Approach For Pcb Pdn Decoupling Minimizing Overdesign With Genetic Algorithm Optimization, F. De Paulis, Y. Ding, M. Cocchini, Chulsoon Hwang, S. Connor, M. Doyle, S. Scearce, W. D. Becker, Albert E. Ruehli, James L. Drewniak Jan 2022

A Methodical Approach For Pcb Pdn Decoupling Minimizing Overdesign With Genetic Algorithm Optimization, F. De Paulis, Y. Ding, M. Cocchini, Chulsoon Hwang, S. Connor, M. Doyle, S. Scearce, W. D. Becker, Albert E. Ruehli, James L. Drewniak

Electrical and Computer Engineering Faculty Research & Creative Works

An optimization routine is applied for the decoupling capacitor placement on Power Distribution Networks to identify the limit beyond which the placement of additional decaps is no longer effective, thus leading to wasting layout area and components, and to a cost increase. A specific test example from a real design is used together with the required target impedance and frequency band of interest for the PDN design. The effectiveness of the decap placement while selecting different layers of the stack-up, and while moving the upper limit of the PDN design band is analyzed. Such analysis leads to helpful insights based …

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Physics-Based Modeling For Determining Transient Current Flow In Multi-Layer Pcb Pi Designs, Yifan Ding, Matthew S. Doyle, Samuel Connor, Dale Becker, James L. Drewniak Jan 2022

Physics-Based Modeling For Determining Transient Current Flow In Multi-Layer Pcb Pi Designs, Yifan Ding, Matthew S. Doyle, Samuel Connor, Dale Becker, James L. Drewniak

Electrical and Computer Engineering Faculty Research & Creative Works

A physics-based modeling methodology for determining the transient current flow path in multi-layer PI designs is given in this paper using a commercial board with a complicated structure as an example. Board structure analysis is done first to provide a physical basis of post-layout analytical and equivalent circuit modeling. A match of the PDN impedance between commercial tool simulation, post-layout analytical calculation, and the physics-based equivalent circuit modeling was achieved to support the model for the transient simulation. By analyzing the current response in all the vias, a clear representation of transient current flow across all via segments can be …

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Modeling Of Rf Interference Caused By Solid-State Drive Noise, Junho Joo, Woncheol Song, Taelim Song, Sunkyu Kong, Jungcheol Lim, Inmyung Song, Jongjoo Lee, Chulsoon Hwang Jan 2022

Modeling Of Rf Interference Caused By Solid-State Drive Noise, Junho Joo, Woncheol Song, Taelim Song, Sunkyu Kong, Jungcheol Lim, Inmyung Song, Jongjoo Lee, Chulsoon Hwang

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, modeling of RFI problem caused by a solid-state drive (SSD) in a laptop is proposed. Two noise sources (one outside and one inside a cavity) in the SSD are reconstructed as dipole moments with magnitude-only near-field scanning data. The dipole moment inside a cavity is then replaced by a Huygens' box covering four side surfaces of the cavity using a numerical simulation. The noise voltage at an RF antenna port is calculated by combining the two reconstructed noise sources with measured transfer functions. The model is successfully validated through a comparison of the calculation with measurement results.

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Concurrent Learning-Based Neuro-Adaptive Robust Tracking Control Of Wheeled Mobile Robot: An Event-Triggered Design, Krishanu Nath, Manas Kumar Bera, Sarangapani Jagannathan Jan 2022

Concurrent Learning-Based Neuro-Adaptive Robust Tracking Control Of Wheeled Mobile Robot: An Event-Triggered Design, Krishanu Nath, Manas Kumar Bera, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, an event-based neuro-adaptive robust tracking controller for a perturbed and networked differential drive mobile robot (DMR) is designed with concurrent learning. A radial basis function neural network, which approximates an unknown perturbation, is used to design an adaptive sliding mode controller (SMC). The RBFNN weights and SMC parameters are estimated online using an adaptive tuning law to ensure performance with reduced chattering. To improve the convergence of RBFNN weight estimation error, a concurrent learning-based adaptive law is derived, which uses measured online and recorded data. Further, a suitable triggering condition is designed to achieve a reduced number …

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An Energy-Efficient Internet Of Things Relaying System For Delay-Constrained Applications, Abdullah M. Almasoud, Ahmad Alsharoa, Daji Qiao, Ahmed E. Kamal Jan 2022

An Energy-Efficient Internet Of Things Relaying System For Delay-Constrained Applications, Abdullah M. Almasoud, Ahmad Alsharoa, Daji Qiao, Ahmed E. Kamal

Electrical and Computer Engineering Faculty Research & Creative Works

The emerging Internet-of-things (IoT) systems contain a large number of small wireless devices with limited energy, communication, and computational capabilities. In such systems, a helping station located between the IoT devices and backhaul servers can be deployed to broadcast the IoT devices to the backhaul networks. This paper investigates a hybrid energy-efficient framework using multiple energy harvested relays with data buffering capabilities. The relays are powered by a hybrid energy supply consisting of a traditional electric grid and renewable energy grid. We propose an energy efficient novel approach aiming to support the wireless uplink transmission from IoT devices to backhaul …

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Pulsed-Active Microwave Thermography, Logan M. Wilcox, Mathias Bonmarin, Kristen M. Donnell Jan 2022

Pulsed-Active Microwave Thermography, Logan M. Wilcox, Mathias Bonmarin, Kristen M. Donnell

Electrical and Computer Engineering Faculty Research & Creative Works

Active microwave thermography (AMT) is a thermographic nondestructive testing and evaluation technique that utilizes an electromagnetic-based excitation with a subsequent infrared measurement of the surface thermal profile of the material or structure of interest. AMT has been successfully applied to several aerospace and civil infrastructure applications. This work seeks to expand the performance of AMT by incorporating a signal processing technique common to traditional (flash-lamp) thermography, referred to as pulsed thermography (PT). PT operates on the premise of a pulsed excitation, as opposed to a constant or step excitation (ST) over a given time-period that is typical to traditional active …

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Generation Expansion Planning Considering Discrete Storage Model And Renewable Energy Uncertainty: A Bi-Interval Optimization Approach, Siyuan Wang, Guangchao Geng, Qy Jiang, Rui Bo Jan 2022

Generation Expansion Planning Considering Discrete Storage Model And Renewable Energy Uncertainty: A Bi-Interval Optimization Approach, Siyuan Wang, Guangchao Geng, Qy Jiang, Rui Bo

Electrical and Computer Engineering Faculty Research & Creative Works

Both discrete storage model (DSM) and continuous storage model (CSM) have been used in the power system planning literature. In this work, we conduct a sizing-error analysis for the use of CSM in generation expansion planning (GEP), which shows more reasonable storage sizing decisions are offered by the DSM in comparison to the CSM. However, when the DSM is considered in the context of interval optimization, the discrete status variables in mutually exclusive constraints and the strong temporal coupling in state-of-charge (SOC) constraints create significant challenges. To tackle this, a tailored interval optimization approach is proposed to consider both DSM …

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Review Of Fiber Optic Displacement Sensors, Chen Zhu, Yiyang Zhuang, Bo Liu, Jie Huang Jan 2022

Review Of Fiber Optic Displacement Sensors, Chen Zhu, Yiyang Zhuang, Bo Liu, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

Displacement Measurements Are of Significant Importance in a Variety of Critical Scientific and Engineering Fields, Such as Gravitational Wave Detection, Geophysical Research, and Manufacturing Industries. Due to the Inherent Advantages Such as Compactness, High Sensitivity, and Immunity to Electromagnetic Interference, in Recent Years, Fiber Optic Sensors Have Been Widely Used in an Expansive Range of Sensing Applications, Ranging from Infrastructural Health Monitoring to Chemical and Biological Sensing. of Particular Interest Here, Fiber Optic Displacement Sensors Have Gained Wide Interest and Have Evolved from Basic Intensity Modulation-Based Configurations to More Advanced Structures, Such as Fiber Bragg Grating (FBG)-Based and Interferometric Configurations. …

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Computing Leaky Modes Of Optical Fibers Using A Feast Algorithm For Polynomial Eigenproblems, Jay Gopalakrishnan, B. Q. Parker, Pieter Vandenberge Jan 2022

Computing Leaky Modes Of Optical Fibers Using A Feast Algorithm For Polynomial Eigenproblems, Jay Gopalakrishnan, B. Q. Parker, Pieter Vandenberge

Mathematics and Statistics Faculty Publications and Presentations

An efficient contour integral technique to approximate a cluster of nonlinear eigenvalues of a polynomial eigenproblem, circumventing certain large inversions from a linearization, is presented. It is applied to the nonlinear eigenproblem that arises from a frequency-dependent perfectly matched layer. This approach is shown to result in an accurate method for computing leaky modes of optical fibers. Extensive computations on an antiresonant fiber with a complex transverse microstructure are reported. This structure is found to present substantial computational difficulties: Even when employing over one million degrees of freedom, the fiber model appears to remain in a preasymptotic …

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Prediction Of Formation Conditions Of Gas Hydrates Using Machine Learning And Genetic Programming, Anupama Kumari, Mukund Madhaw, Vishnu S. Pendyala Jan 2022

Prediction Of Formation Conditions Of Gas Hydrates Using Machine Learning And Genetic Programming, Anupama Kumari, Mukund Madhaw, Vishnu S. Pendyala

Faculty Research, Scholarly, and Creative Activity

The formation of gas hydrates in the pipelines of oil, gas, chemical, and other industries has been a significant problem for many years because the formation of gas hydrates may block the pipelines. Hence, the knowledge of the phase equilibrium conditions of gas hydrate became necessary for the economic and safe working of oil, gas, chemical industries. Various thermodynamic approaches with various mathematical techniques are available for the prediction of formation conditions of gas hydrates. In this chapter, the authors have discussed the least square support vector machine and artificial neural network models for the prediction of stability conditions of …

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An Overview Of Carbon Footprint Mitigation Strategies. Machine Learning For Societal Improvement, Modernization, And Progress, Vishnu S. Pendyala, Saritha Podali Jan 2022

An Overview Of Carbon Footprint Mitigation Strategies. Machine Learning For Societal Improvement, Modernization, And Progress, Vishnu S. Pendyala, Saritha Podali

Faculty Research, Scholarly, and Creative Activity

Among the most pressing issues in the world today is the impact of globalization and energy consumption on the environment. Despite the growing regulatory framework to prevent ecological degradation, sustainability continues to be a problem. Machine learning can help with the transition toward a net-zero carbon society. Substantial work has been done in this direction. Changing electrical systems, transportation, buildings, industry, and land use are all necessary to reduce greenhouse gas emissions. Considering the carbon footprint aspect of sustainability, this chapter provides a detailed overview of how machine learning can be applied to forge a path to ecological sustainability in …

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3d-Printed Gelatin-Alginate Hydrogel Dressings For Burn Wound Healing: A Comprehensive Study, Fateme Fayyazbakhsh, Michael J. Khayat, Ming-Chuan Leu Jan 2022

3d-Printed Gelatin-Alginate Hydrogel Dressings For Burn Wound Healing: A Comprehensive Study, Fateme Fayyazbakhsh, Michael J. Khayat, Ming-Chuan Leu

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Burn Wound Treatment is Still a Clinical Challenge Due to the Severity of Tissue Damage and Dehydration. among Various Wound Dressings, Hydrogel Materials Have Gained Significant Attention for Burn Wound Treatment in Clinical Practice Due to their Soothing and Moisturizing Activity. in This Study, 3D-Printed Dressings Were Fabricated using Clinically Relevant Hydrogels for Deep Partial-Thickness Burn (PTB) Wounds. Different Ratios of Gelatin and Alginate Mixture Were 3D-Printed and Examined in Terms of Rheological Behavior, Shear Thinning Behavior, Mechanical Properties, Degradation Rate, and Hydration Activity to Tune the Hydrogel Composition for Best Functionality. the Cell-Laden Dressings Were Bioprinted to Evaluate the …

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Satellite Drag Coefficient Modeling For Thermosphere Science And Mission Operations, Piyush M. Mehta, Smriti N. Paul, Nicholas H. Crisp, Philip L. Sheridan, Christian Siemes, Günther March, Sean Bruinsma Jan 2022

Satellite Drag Coefficient Modeling For Thermosphere Science And Mission Operations, Piyush M. Mehta, Smriti N. Paul, Nicholas H. Crisp, Philip L. Sheridan, Christian Siemes, Günther March, Sean Bruinsma

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Satellite drag modeling remains the largest source of uncertainty affecting space operations in low Earth orbit. The uncertainty stems from inaccurate models for mass density and drag coefficient. Drag coefficient modeling also impacts scientific knowledge on the physics and dynamics of the upper atmosphere through the estimation of high-fidelity mass density from measurements of acceleration on-board satellites. Efforts over the last decade have pushed drag coefficient modeling in the right direction, however, have resulted in multiple methods and tools. We provide a comprehensive review of the drag coefficient modeling methods and tools. Current scale differences between thermospheric data sets mostly …

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Effects Of Wheel Rotation On Long-Period Wake Dynamics Of The Drivaer Fastback Model, Matthew Aultman, Rodrigo Auza-Gutierrez, Kevin Disotell, Lian Duan Jan 2022

Effects Of Wheel Rotation On Long-Period Wake Dynamics Of The Drivaer Fastback Model, Matthew Aultman, Rodrigo Auza-Gutierrez, Kevin Disotell, Lian Duan

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Lattice Boltzmann method (LBM) simulations were performed to capture the long-period dynamics within the wake of a realistic DrivAer fastback model with stationary and rotating wheels. The simulations showed that the wake developed as a low-pressure torus regardless of whether the wheels were rotating. This torus shrank in size on the base in the case of rotating wheels, leading to a reduction in the low-pressure footprint on the base, and consequently a 7% decrease in the total vehicle drag in comparison to the stationary wheels case. Furthermore, the lateral vortex shedding experienced a long-period switching associated with the bi-stability in …

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Discrimination Experiences During Covid-19 Among A National, Multi-Lingual, Community-Based Sample Of Asian Americans And Pacific Islanders: Compass Findings, Van M. Ta Park, Marcelle M. Dougan, Oanh L. Meyer, Bora Nam, Marian Tzuang, Linda G. Park, Quyen Vuong, Joon Bang, Janice Y. Tsoh Jan 2022

Discrimination Experiences During Covid-19 Among A National, Multi-Lingual, Community-Based Sample Of Asian Americans And Pacific Islanders: Compass Findings, Van M. Ta Park, Marcelle M. Dougan, Oanh L. Meyer, Bora Nam, Marian Tzuang, Linda G. Park, Quyen Vuong, Joon Bang, Janice Y. Tsoh

Faculty Research, Scholarly, and Creative Activity

Reports of escalated discrimination among Asian Americans and Pacific Islanders (AAPIs) due to COVID-19 are alarming, making this a public health priority. However, there are limited empirical studies on the scope and impact of COVID-19-related discrimination among AAPIs. Using the COVID-19 Effects on the Mental and Physical Health of AAPI Survey Study (COMPASS) data (N = 4971; survey period: October 2020–February 2021), which is a U.S.-wide multi-lingual survey, we examined the prevalence of, and factors associated with discrimination experiences attributable to being an AAPI during the COVID-19 pandemic. Overall, 60.7% reported experiencing discrimination; the group prevalence ranged from 80.0% (Hmong) …

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Word Embeddings For Fake Malware Generation, Quang Duy Tran, Fabio Di Troia Jan 2022

Word Embeddings For Fake Malware Generation, Quang Duy Tran, Fabio Di Troia

Faculty Research, Scholarly, and Creative Activity

Signature and anomaly-based techniques are the fundamental methods to detect malware. However, in recent years this type of threat has advanced to become more complex and sophisticated, making these techniques less effective. For this reason, researchers have resorted to state-of-the-art machine learning techniques to combat the threat of information security. Nevertheless, despite the integration of the machine learning models, there is still a shortage of data in training that prevents these models from performing at their peak. In the past, generative models have been found to be highly effective at generating image-like data that are similar to the actual data …

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Robustness Of Image-Based Malware Analysis, Katrina Tran, Fabio Di Troia, Mark Stamp Jan 2022

Robustness Of Image-Based Malware Analysis, Katrina Tran, Fabio Di Troia, Mark Stamp

Faculty Research, Scholarly, and Creative Activity

In previous work, “gist descriptor” features extracted from images have been used in malware classification problems and have shown promising results. In this research, we determine whether gist descriptors are robust with respect to malware obfuscation techniques, as compared to Convolutional Neural Networks (CNN) trained directly on malware images. Using the Python Image Library (PIL), we create images from malware executables and from malware that we obfuscate. We conduct experiments to compare classifying these images with a CNN as opposed to extracting the gist descriptor features from these images to use in classification. For the gist descriptors, we consider a …

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Fake Malware Generation Using Hmm And Gan, Harshit Trehan, Fabio Di Troia Jan 2022

Fake Malware Generation Using Hmm And Gan, Harshit Trehan, Fabio Di Troia

Faculty Research, Scholarly, and Creative Activity

In the past decade, the number of malware attacks have grown considerably and, more importantly, evolved. Many researchers have successfully integrated state-of-the-art machine learning techniques to combat this ever present and rising threat to information security. However, the lack of enough data to appropriately train these machine learning models is one big challenge that is still present. Generative modelling has proven to be very efficient at generating image-like synthesized data that can match the actual data distribution. In this paper, we aim to generate malware samples as opcode sequences and attempt to differentiate them from the real ones with the …

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Crowdfunding Non-Fungible Tokens On The Blockchain, Sean Basu, Kimaya Basu, Thomas H. Austin Jan 2022

Crowdfunding Non-Fungible Tokens On The Blockchain, Sean Basu, Kimaya Basu, Thomas H. Austin

Faculty Research, Scholarly, and Creative Activity

Non-fungible tokens (NFTs) have been used as a way of rewarding content creators. Artists publish their works on the blockchain as NFTs, which they can then sell. The buyer of an NFT then holds ownership of a unique digital asset, which can be resold in much the same way that real-world art collectors might trade paintings. However, while a deal of effort has been spent on selling works of art on the blockchain, very little attention has been paid to using the blockchain as a means of fundraising to help finance the artist’s work in the first place. Additionally, while …

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A Blockchain-Based Tamper-Resistant Logging Framework, Thomas H. Austin, Fabio Di Troia Jan 2022

A Blockchain-Based Tamper-Resistant Logging Framework, Thomas H. Austin, Fabio Di Troia

Faculty Research, Scholarly, and Creative Activity

Since its introduction in Bitcoin, the blockchain has proven to be a versatile data structure. In its role as an immutable ledger, it has grown beyond its initial use in financial transactions to be used in recording a wide variety of other useful information. In this paper, we explore the application of the blockchain outside of its traditional decentralized, financial domain. We show how, even with only a single “mining” node, a proof-of-work blockchain can be the cornerstone of a tamper resistant logging framework. By attaching a proof-of-work to blocks of logging messages, we make it increasingly difficult for an …

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