Engineering Commons^™

Runtime Power Allocation Based On Multi-Gpu Utilization In Gamess, Masha Sosonkina, Vaibhav Sundriyal, Jorge Luis Galvez Vallejo

Electrical & Computer Engineering Faculty Publications

To improve the power consumption of parallel applications at the runtime, modern processors provide frequency scaling and power limiting capabilities. In this work, a runtime strategy is proposed to maximize performance under a given power budget by distributing the available power according to the relative GPU utilization. Time series forecasting methods were used to develop workload prediction models that provide accurate prediction of GPU utilization during application execution. Experiments were performed on a multi-GPU computing platform DGX-1 equipped with eight NVIDIA V100 GPUs used for quantum chemistry calculations in the GAMESS package. For a limited power budget, the proposed strategy …

Entropy Of Generating Series For Nonlinear Input-Output Systems And Their Interconnections, W. Steven Gray

Electrical & Computer Engineering Faculty Publications

This paper has two main objectives. The first is to introduce a notion of entropy that is well suited for the analysis of nonlinear input-output systems that have a Chen-Fliess series representation. The latter is defined in terms of its generating series over a noncommutative alphabet. The idea is to assign an entropy to a generating series as an element of a graded vector space. The second objective is to describe the entropy of generating series originating from interconnected systems of Chen-Fliess series that arise in the context of control theory. It is shown that one set of interconnections can …

Real-Time Cavity Fault Prediction In Cebaf Using Deep Learning, Md. M. Rahman, K. Iftekharuddin, A. Carptenter, T. Mcguckin, C. Tennant, L. Vidyaratne, Sandra Biedron (Ed.), Evgenya Simakov (Ed.), Stephen Milton (Ed.), Petr M. Anisimov (Ed.), Volker R.W. Schaa (Ed.)

Electrical & Computer Engineering Faculty Publications

Data-driven prediction of future faults is a major research area for many industrial applications. In this work, we present a new procedure of real-time fault prediction for superconducting radio-frequency (SRF) cavities at the Continuous Electron Beam Accelerator Facility (CEBAF) using deep learning. CEBAF has been afflicted by frequent downtime caused by SRF cavity faults. We perform fault prediction using pre-fault RF signals from C100-type cryomodules. Using the pre-fault signal information, the new algorithm predicts the type of cavity fault before the actual onset. The early prediction may enable potential mitigation strategies to prevent the fault. In our work, we apply …

A Primer On Software Defined Radios, Dimitrie C. Popescu, Rolland Vida

Electrical & Computer Engineering Faculty Publications

The commercial success of cellular phone systems during the late 1980s and early 1990 years heralded the wireless revolution that became apparent at the turn of the 21st century and has led the modern society to a highly interconnected world where ubiquitous connectivity and mobility are enabled by powerful wireless terminals. Software defined radio (SDR) technology has played a major role in accelerating the pace at which wireless capabilities have advanced, in particular over the past 15 years, and SDRs are now at the core of modern wireless communication systems. In this paper we give an overview of SDRs that …

A Formal Power Series Approach To Multiplicative Dynamic And Static Output Feedback, Venkatesh Subbarao Guggilam

Electrical & Computer Engineering Faculty Publications

The goal of the paper is two-fold. The first of which is to derive an explicit formula to compute the generating series of a closed-loop system when a plant, given in a Chen-Fliess series description is in multiplicative output feedback connection with another system given in Chen-Fliess series description. In addition, the multiplicative dynamic output feedback connection has a natural interpretation as a transformation group acting on the plant. The second of the two-part goal of this paper is same as the first part albeit when the Chen-Fliess series in the feedback is replaced by a memoryless map, so called …

Qu-Brats: Miccai Brats 2020 Challenge On Quantifying Uncertainty In Brain Tumor Segmentation - Analysis Of Ranking Scores And Benchmarking Results, Raghav Mehta, Angelos Filos, Ujjwal Baid, Chiharu Sako, Richard Mckinley, Michael Rebsamen, Katrin Dätwyler, Raphael Meier, Piotr Radojewski, Gowtham Krishnan Murugesan, Sahil Nalawade, Chandan Ganesh, Ben Wagner, Fang F. Yu, Baowei Fei, Ananth J. Madhuranthakam, Joseph A. Maldjian, Laura Daza, Catalina Gómez, Pablo Arbeláez, Chengliang Dai, Shuo Wang, Hadrien Reynaud, Yuan-Han Mo, Elsa Angelini, Yike Guo, Wenjia Bai, Subhashis Banerjee, Lin-Min Pei, Murat Ak, Sarahi Rosas-González, Ilyess Zemmoura, Clovis Tauber, Minh H. Vu, Tufve Nyholm, Tommy Löfstedt, Laura Mora Ballestar, Veronica Vilaplana, Hugh Mchugh, Gonzalo Maso Talou, Alan Wang, Jay Patel, Ken Chang, Katharina Hoebel, Mishka Gidwani, Nishanth Arun, Sharut Gupta, Mehak Aggarwal, Praveer Singh, Elizabeth R. Gerstner, Jayashree Kalpathy-Cramer, Nicholas Boutry, Alexis Huard, Lasitha Vidyaratne, Md. Monibor Rahman, Khan M. Iftekharuddin, Joseph Chazalon, Elodie Puybareau, Guillaume Tochon, Jun Ma, Mariano Cabezas, Xavier Llado, Arnau Oliver, Liliana Valencia, Sergi Valverde, Mehdi Amian, Mohammadreza Soltaninejad, Andriy Myronenko, Ali Hatamizadeh, Xue Feng, Quan Dou, Nicholas Tustison, Craig Meyer, Nisarg A. Shah, Sanjay Talbar, Marc-André Weber, Abhishek Mahajan, Andras Jakab, Roland Wiest, Hassan M. Fathallah-Shaykh, Arash Nazeri, Mikhail Milchenko1, Daniel Marcus, Aikaterini Kotrotsou, Rivka Colen, John Freymann, Justin Kirby, Christos Davatzikos, Bjoern Menze, Spyridon Bakas, Yarin Gal, Tal Arbel

Electrical & Computer Engineering Faculty Publications

Deep learning (DL) models have provided the state-of-the-art performance in a wide variety of medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder the translation of DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties, could enable clinical review of the most uncertain regions, thereby building trust and paving the way towards clinical translation. Recently, a number of uncertainty estimation methods have been introduced for DL medical image segmentation tasks. …

Design And Performance Analysis Of A Chemically-Etched Flexible Nfc Tag Antenna, Muhammad Enayetur Rahman, Marwan Abdelatti, Manbir Singh Sodhi, Kunal Mankodiya

Electrical & Computer Engineering Faculty Publications

Near Field Communication (NFC) is a perfect example of ubiquitous computing that is secured, short-ranged, low-powered contactless communication. High demand is predicted for NFC, especially with wearables, and the variety of applications may require that this technology be fabricated onto different materials. In this research, we first designed flexible NFC antennas based on mathematical models. A simulation of the NFC coil inductance was performed and verified using the predictive models. Later, two flexible NFC antennas, including 160x80 mm2 (rectangular) and 80x80 mm2 (square) were fabricated through a chemical etching process and verified at 13.56 MHz frequency. Two experiments 1) Read …

State Level Trends In Renewable Energy Procurement Via Solar Installation Versus Green Electricity, Eric Michael Hanson

Masters Theses

“In the past 5 years, consumer options for procuring renewable energy have increased, ranging from rooftop solar installation to utility green pricing to Community Choice Aggregation. These options vary in terms of costs and benefits to the consumer as well as grid integration implications. However, little is known regarding how the presence of a wide range of options for utility-scale renewable procurement affects demand for distributed residential solar installations. In theory, there are three possible relationships, (1) positive correlation, where utility-scale and distributed resources complement each other to increase overall production, (2) negative correlation, where utility-scale and distributed resources are …

Automatic Sparse Esm Scan Using Gaussian Process Regression, Jiangshuai Li

Masters Theses

“Emission source microscopy (ESM) technique can be utilized for localization of electromagnetic interference sources in complex and large systems. In this work a Gaussian process regression (GPR) method is applied in real-time to select sampling points for the sparse ESM imaging using a motorized scanner. The Gaussian process regression is used to estimate the complex amplitude of the scanned field and its uncertainty allowing to select the most relevant areas for scanning. Compared with the randomly selected samples the proposed method allows to reduce the number of samples needed to achieve a certain dynamic range of the image, reducing the …

Optimization And Modeling Of Esd Protection Devices, Li Shen

Masters Theses

“Transient voltage suppressors (TVS) are used to protect ICs (integrated circuits) against overvoltage, ESD (Electrostatic Discharge), inductive load switching, and even lightning strikes. In this research, a transient behavior model framework for ESD protection devices is used for modelling four different types of TVS (non-snapback, snapback, spark gap like device and varistor). The System-Efficient ESD Design (SEED) methodology is utilized to strengthen the trust in the model framework by efficient simulation of ESD interaction of the off-chip ESD protection devices with the IC ESD protection device and associated measurement data.

Improvements in the TVS transient response, accounting for conductivity modulation, …

Machine Learning Land Cover And Land Use Classification Of 4-Band Satellite Imagery, Lorelei Turner, Torrey J. Wagner, Paul Auclair, Brent T. Langhals

Faculty Publications

Land-cover and land-use classification generates categories of terrestrial features, such as water or trees, which can be used to track how land is used. This work applies classical, ensemble and neural network machine learning algorithms to a multispectral remote sensing dataset containing 405,000 28x28 pixel image patches in 4 electromagnetic frequency bands. For each algorithm, model metrics and prediction execution time were evaluated, resulting in two families of models; fast and precise. The prediction time for an 81,000-patch group of predictions wasmodels, and >5s for the precise models, and there was not a significant change in prediction time when a …

Empirical Comparison Of Machine Learning Methods For Wind Power Predictions, Sidny M. Stewart

EWU Masters Thesis Collection

No abstract provided.

Unsupervised Multiview Embedding Of Node Embeddings, Jia Chen, Lizeth Figueroa, Dalia Orozco, Evangelos E. Papalexakis

Electrical and Computer Engineering Faculty Publications

—In this paper, we propose a comprehensive unsupervised framework that leverages existing and novel multiview learning models, towards obtaining a single node embedding from a collection of node embeddings, combining the best of all worlds. Through extensive experiments, we demonstrate that the proposed multiview node embedding is able to perform on par or better than the best of its constituents and provide reliable performance across downstream tasks including node classification and graph reconstruction

Self-Assessment And Robust Anomaly Detection With Bayesian Deep Learning, Giuseppina Carannante, Dimah Dera, Orune Aminul, Nidhal Bouaynaya, Ghulam Rasool

Electrical and Computer Engineering Faculty Publications

Deep Learning (DL) models have achieved or even surpassed human-level accuracy in several areas, including computer vision and pattern recognition. The state-of-art performance of DL models has raised the interest in using them in real-world applications, such as disease diagnosis and clinical decision support systems. However, the challenge remains the lack of trustworthiness and reliability of these DL models. The detection of incorrect decisions or flagging suspicious input samples is essential for the reliability of machine learning models. Uncertainty estimation in the output decision is a key component in establishing the trustworthiness and reliability of these models. In this work, …

Modeling, Simulation, And Hardware-In-The-Loop Implementation Of Distributed Voltage Control In Power Systems With Renewable Energy Sources, Ali Dehghan Banadaki

Graduate Theses, Dissertations, and Problem Reports (ETD)

This dissertation develops and analyzes distributed controllers for power systems with renewable energy sources. A comprehensive state space modeling of voltage source inverters (VSIs) is developed specifically to address the secondary voltage control. This model can be used for simulation and control design. Unlike frequency, voltage is a local phenomenon, meaning that it cannot be controlled from a far distance. Therefore, a voltage zoning matrix that relates the sensitivity of the loads to the sources is proposed. The secondary voltage control is designed by applying the eigenvalue decomposition of the voltage zoning matrix to obtain the reference generators voltages. The …

Inferential Statistics And Information Theoretical Measures: An Approach To Interference Detection In Radio Astronomy, Morgan R. Dameron

Graduate Theses, Dissertations, and Problem Reports (ETD)

In a time when technology is rapidly growing, radio observatories are now able to expand their computational power to achieve higher receiver sensitivity power and a more flexible realtime computing approach to probe the universe for its composition and study new astronomical phenomena. This allows searches to go deeper into the universe, and results in the recording of massive quantities of observed data. At the same time, this increases the amount of radio frequency interference (RFI) found in the obtained observatory data. The high power of RFI easily masks the low power of extraterrestrial signals, making them hard to detect …

A Tool For Biometric Interpretation Of Forensic Str Dna Profiles, Ahmad Jamal Baroudi

Graduate Theses, Dissertations, and Problem Reports (ETD)

Rapid DNA biometric identification applications are becoming more essential and widely used in human identity validation processes. Despite their powerful identification capabilities, processing a sample to generate a forensic DNA profile still takes longer compared with other rapid biometric technologies. Methods used to speed up the analysis could lead to signal artifacts similar to those arising from low copy or degraded DNA samples, making the electropherogram unsuitable for forensic interpretation and analysis. The goal of this research effort is to apply biometrics and mathematical approaches to forensic STR (Short Tandem Repeat) profiles. To accomplish this goal, a multi-function software tool …

Performance Enhancement Of Organic Light-Emitting Diodes By Electronic Doping, David A. Shelhammer

Graduate Theses, Dissertations, and Problem Reports (ETD)

Since the invention of organic light-emitting diodes (OLEDs) nearly 40 years ago, significant effort has been put into realizing their full potential. OLEDs exhibit several properties that make them ideal candidates for applications in displays as well as solid-state lighting: low power consumption, high contrast ratios, mechanical flexibility, and wide viewing angles. However, the low electrical conductivity and poor stability of organic materials remain critical factors that limit device performance. The focus of this work is OLED performance improvement through the introduction of novel inorganic dopants into organic charge transport layers. The results show a significant reduction in operating voltage …

An Efficient Ar Model-Based Method For The Detection Of Forced Oscillations In Power Networks: Implementation And Analysis, Maria Waleska Suarez

Graduate Theses, Dissertations, and Problem Reports (ETD)

An active research topic is the detection of various oscillations that may lead to instability and potential disruption in the operation of a power network. Forced Oscillations (FOs) play a unique role in power system stability among various oscillations. They are perturbances that change the system’s state and are caused for many reasons, including but not limited to persistent load changes and oscillatory load or generation, fault, triplane, and other mechanical anomalies. These factors can hugely affect the power grid by either increasing or decreasing the amplitude, causing corrupt modes leading to blackouts, affecting the equipment involved, delivering poor power …

An Analysis On Adversarial Machine Learning: Methods And Applications, Ali Dabouei

Graduate Theses, Dissertations, and Problem Reports (ETD)

Deep learning has witnessed astonishing advancement in the last decade and revolutionized many fields ranging from computer vision to natural language processing. A prominent field of research that enabled such achievements is adversarial learning, investigating the behavior and functionality of a learning model in presence of an adversary. Adversarial learning consists of two major trends. The first trend analyzes the susceptibility of machine learning models to manipulation in the decision-making process and aims to improve the robustness to such manipulations. The second trend exploits adversarial games between components of the model to enhance the learning process. This dissertation aims to …

Landmark Enforcement And Principal Component Analysis For Improving Gan-Based Morphing, Samuel W. Price

Graduate Theses, Dissertations, and Problem Reports (ETD)

Facial Recognition Systems (FRSs) are a key target for adversaries determined to circumvent security checkpoints. Morph images threaten FRS by presenting as multiple individuals, allowing an adversary to swap identities with another subject. Although morph generation using generative adversarial networks (GANs) results in high-quality morphs without possessing the spatial artifacts caused by landmarkbased methods, there is an apparent loss in identity with standard GAN-based morphing methods. In this thesis, we examine landmark-based and GAN-based morphing methods to fuse the advantages of both methodologies. We propose a novel StyleGAN2 morph generation technique by introducing a landmark enforcement method. Considering this method, …

Modeling, Fabrication, And Characterization Of Rf-Based Passive Wireless Sensors Composed Of Refractory Semiconducting Ceramics For High Temperature Applications, Kavin Sivaneri Varadharajan Idhaiam

Graduate Theses, Dissertations, and Problem Reports (ETD)

Real-time health monitoring of high temperature systems (>500^oC) in harsh environments is necessary to prevent catastrophic events caused by structural failures, varying pressure, and chemical reactions. Conventional solid-state temperature sensors such as resistance temperature detectors (RTDs) and thermocouples are restricted by their operating environments, sensor dimensions and often require external power sources for their operation. The current work presents the research and development of RF-based passive wireless sensing technology targeting high temperatures and harsh environmental conditions. Passive wireless devices are generally classified as near-field and far-field devices based on the interrogation distance. Near-field sensors are placed at …

Air-Assisted Communications Using Line-Of-Sight Links, Shaikha A. Alkandari

Graduate Theses, Dissertations, and Problem Reports (ETD)

Recently, there has been a rapid increase in the use of air-assisted communications involv-
ing the use of airborne platforms such as unmanned aerial vehicles (UAVs). In air-assisted
networks, the UAVs can act like base stations in a traditional cellular network as long as an
appropriate backhaul is available. Alternatively, the UAVs could serve as relays, for instance,
connecting two ground-based users who are within range of the UAV. UAVs have the benefit
of being deployed and reconfigured rapidly and on demand.
Meanwhile, there has been a trend towards the use of higher and higher frequencies,
including those in millimeter-wave …

Network Slicing In 5g: Admission, Scheduling, And Security, Raneem Jassim Alghawi

Graduate Theses, Dissertations, and Problem Reports (ETD)

In the past few decades, there was an increase in the number of devices that have wireless capabilities such as phones, televisions, and home appliances. With the high demand for wireless networking, the fifth generation (5G) of mobile networks was designed to support the different services of new applications. In addition, one of the technical issues that 5G would evolve is the increase in traffic and the need to satisfy the user’s experience. With the evolution of wireless networking and 5G, Network Slicing has been introduced to accommodate the diverse requirements of the applications. Thus, network slicing is the concept …

Modeling Of Inverter-Based Microgrid For Small-Signal And Large-Signal Stability Analysis, Vishal Verma

Graduate Theses, Dissertations, and Problem Reports (ETD)

Integration of inverter-based resources (IBRs) such as solar photovoltaic, wind, and battery storage, is both a boon and a bane for electric power systems. On one hand, IBRs have helped in making electrical energy a clean (carbon-free) source of energy. On other hand, the dynamics of IBRs have changed the way power system studies have been carried out. With the advantages IBRs offer over conventional resources, it is assumed that a small distribution power system (microgrid) will have a 100% IBRs penetration in the future. Such a microgrid can operate in grid-connected mode or in an islanded mode. Both modes …

Multimodal Adversarial Learning, Uche Osahor

Graduate Theses, Dissertations, and Problem Reports (ETD)

Deep Convolutional Neural Networks (DCNN) have proven to be an exceptional tool for object recognition, generative modelling, and multi-modal learning in various computer vision applications. However, recent findings have shown that such state-of-the-art models can be easily deceived by inserting slight imperceptible perturbations to key pixels in the input. A good target detection systems can accurately identify targets by localizing their coordinates on the input image of interest. This is ideally achieved by labeling each pixel in an image as a background or a potential target pixel. However, prior research still confirms that such state of the art targets models …

Stability Bounds On Superluminal Propagation In Active Structures, Robert Duggan, Hady Moussa, Younes Ra'di, Dimitrios L. Sounas, Andrea Alù

Advanced Science Research Center

Active materials have been explored in recent years to demonstrate superluminal group velocities over relatively broad bandwidths, implying a potential path towards bold claims such as information transport beyond the speed of light, as well as antennas and metamaterial cloaks operating over very broad bandwidths. However, causality requires that no portion of an impinging pulse can pass its precursor, implying a fundamental trade-off between bandwidth, velocity and propagation distance. Here, we clarify the general nature of superluminal propagation in active structures and derive a bound on these quantities fundamentally rooted into stability considerations. By applying filter theory, we show that …

High Energy And Power Density Peptidoglycan Musclesthrough Super-Viscous Nanoconfined Water, Haozhen Wang, Zhi-Lun Liu, Jianpei Lao, Sheng Zhang, Rinat Abzalimov, Tong Wang, Xi Chen

Advanced Science Research Center

Water-responsive (WR) materials that reversibly deform in response to humidity changes show great potential for developing muscle-like actuators for miniature and biomimetic robotics. Here, it is presented that Bacillus (B.) subtilis’ peptidoglycan (PG) exhibits WR actuation energy and power densities reaching 72.6 MJ m−3 and 9.1 MW m−3, respectively, orders of magnitude higher than those of frequently used actuators, such as piezoelectric actuators and dielectric elastomers. PG can deform as much as 27.2% within 110 ms, and its actuation pressure reaches ≈354.6 MPa. Surprisingly, PG exhibits an energy conversion efficiency of ≈66.8%, which can be attributed to its super-viscous nanoconfined …

Moiré-Driven Topological Transitions And Extremeanisotropy In Elastic Metasurfaces, Simon Yves, Matheus Inguaggiato Nora Nora Rosa, Mohit Gupta, Massimo Ruzzene, Andrea Alù

Advanced Science Research Center

The twist angle between a pair of stacked 2D materials has been recently shown to control remarkable phenomena, including the emergence of flat-band superconductivity in twisted graphene bilayers, of higher-order topological phases in twisted moiré superlattices, and of topological polaritons in twisted hyperbolic metasurfaces. These discoveries, at the foundations of the emergent field of twistronics, have so far been mostly limited to explorations in atomically thin condensed matter and photonic systems, with limitations on the degree of control over geometry and twist angle, and inherent challenges in the fabrication of carefully engineered stacked multilayers. Here, this work extends twistronics to …

Photonics Of Time-Varying Media, Emanuele Galiffi, Romain Tirole, Shixiong Yin, Huanan Li, Stefano Vezzoli, Paloma A. Huidobro, Mário G. Silveirinha, Riccardo Sapienza, Andrea Alù, J. B. Pendry

Advanced Science Research Center

Time-varying media have recently emerged as a new paradigm for wave manipulation, due to the synergy between the discovery of highly nonlinear materials, such as epsilon-near-zero materials, and the quest for wave applications, such as magnet-free nonreciprocity, multimode light shaping, and ultrafast switching. In this review, we provide a comprehensive discussion of the recent progress achieved with photonic metamaterials whose properties stem from their modulation in time. We review the basic concepts underpinning temporal switching and its relation with spatial scattering and deploy the resulting insight to review photonic time-crystals and their emergent research avenues, such as topological and non-Hermitian …