Digital Commons Network^™

H-Nobs: Achieving Certified Fairness And Robustness In Distributed Learning On Heterogeneous Datasets, Guanqiang Zhou, Ping Xu, Yue Wang, Zhi Tian

Electrical and Computer Engineering Faculty Publications

Fairness and robustness are two important goals in the design of modern distributed learning systems. Despite a few prior works attempting to achieve both fairness and robustness, some key aspects of this direction remain underexplored. In this paper, we try to answer three largely unnoticed and unaddressed questions that are of paramount significance to this topic: (i) What makes jointly satisfying fairness and robustness difficult? (ii) Is it possible to establish theoretical guarantee for the dual property of fairness and robustness? (iii) How much does fairness have to sacrifice at the expense of robustness being incorporated into the system? To …

Real-Time Cyber-Power Testbed Enhancement And Synchrophasor Data Generation For Anomaly Detection Using Physics-Informed Machine Learning, Vasavi Sivaramakrishnan

Graduate Theses, Dissertations, and Problem Reports (ETD)

Advanced sensing and automation are essential to managing the evolving electric power system with tight coupling of information and power system layer. However, this increasing number of cyber-physical devices brings vulnerabilities from cyber threats and extreme weather events can endanger the power system on a physical level as well. Advanced monitoring and control algorithms with human operators in the loop are needed to enable power system resiliency despite these increasing threats. These advanced algorithms require validation using a realistic test system that mimics real-world scenarios.

This work focuses on a) developing a realistic real-time cyber-power testbed with hardware-in-the-loop to generate …

On Uncertainty For Ill-Posed Robot Decision Problems, Jared Joseph Beard

Graduate Theses, Dissertations, and Problem Reports (ETD)

As robots adopt more real world responsibilities, they will be expected to solve more complicated problems. In some cases limited prior knowledge will result in unmodelled environmental conditions; in others, multiple users may have competing perspectives on how to frame a decision problem. Many existing frameworks, namely Markov decision processes (MDP) presuppose users have identified a specific problem with models sufficient to solve or learn a problem. If we wish to extend MDPs to novel problems or those heavily dependent on user feedback, autonomous decision makers must be able to identify limitations in how a given problem is framed and …

Direct Measurement Of Microwave Loss In Nb Films For Superconducting Qubits, B. Abdisatarov, D. Bafia, A. Murthy, G. Eremeev, H. E. Elsayed-Ali, J. Lee, A. Netepenko, C. P. A. Carlos, S. Leith, G. J. Rosaz, A. Romanenko, A. Grassellino

Electrical & Computer Engineering Faculty Publications

Niobium films are a key component in modern two-dimensional superconducting qubits, yet their contribution to the total qubit decay rate is not fully understood. The presence of different layers of materials and interfaces makes it difficult to identify the dominant loss channels in present two-dimensional qubit designs. In this paper, we present the study that directly correlates measurements of RF losses in such films to material parameters by investigating a high-power impulse magnetron sputtered (HiPIMS) film atop a three-dimensional niobium superconducting radio frequency (SRF) resonator. By using a 3D SRF structure, we are able to isolate the niobium film loss …

Underlying Substrate Effect On Electrochemical Activity For Hydrogen Evolution Reaction With Low-Platinum-Loaded Catalysts, Baleeswaraiah Muchharla, Peter V. Sushko, Kishor K. Sadasivuni, Wei Cao, Akash Tomar, Hani Elsayed-Ali, Adetayo Adedeji, Abdennaceur Karoui, Joshua M. Spurgeon, Bijandra Kumar

Electrical & Computer Engineering Faculty Publications

Platinum is known as the best catalyst for the hydrogen evolution reaction (HER) but the scarcity and high cost of Pt limit its widespread applicability. Herein, the role of the underlying substrate on the HER activity of dispersed Pt atoms is uncovered. A direct current magnetron sputtering technique is utilized to deposit transition metal (TM) thin films of W, Ti, and Ta as underlying substrates for extremely low loading of Pt (

Using Feature Selection Enhancement To Evaluate Attack Detection In The Internet Of Things Environment, Khawlah Harahsheh, Rami Al-Naimat, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

The rapid evolution of technology has given rise to a connected world where billions of devices interact seamlessly, forming what is known as the Internet of Things (IoT). While the IoT offers incredible convenience and efficiency, it presents a significant challenge to cybersecurity and is characterized by various power, capacity, and computational process limitations. Machine learning techniques, particularly those encompassing supervised classification techniques, offer a systematic approach to training models using labeled datasets. These techniques enable intrusion detection systems (IDSs) to discern patterns indicative of potential attacks amidst the vast amounts of IoT data. Our investigation delves into various aspects …

Quest For An Optimal Spin-Polarized Electron Source For The Electron-Ion Collider, J. Biswas, E. Wang, O. Rahman, J. Sharitka, K. Kisslinger, Adam Masters, S. Marsillac, T. Lee

Electrical & Computer Engineering Faculty Publications

Superlattice GaAs photocathodes play a crucial role as the primary source of polarized electrons in various accelerator facilities, including the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson National Laboratory and the Electron-Ion Collider (EIC) at Brookhaven National Laboratory. To increase the quantum efficiency (QE) of GaAs/GaAsP superlattice photocathodes, a Distributed Bragg Reflector (DBR) is grown underneath using metal-organic chemical vapor deposition (MOCVD). There are several challenges associated with DBR photocathodes: the resonance peak may not align with the emission threshold of around 780 nm, non-uniform doping density in the top 5 nm may significantly impact QE and spin polarization, …

A Fresh Revisit Of The Issues And Improvements In Impulse Invariance Filter Design For Infinite Impulse Response Filters, Chiman Kwan, Hal Ferguson

Electrical & Computer Engineering Faculty Publications

The objective of this paper is to first present some issues with impulse invariance filter (IIF) design during the design of digital infinite impulse response (IIR) filters. Engineers are often confused about some inconsistent observations. For instance, if the impulse response of a digital filter is designed using the impulse invariance procedure, then the analog and digital filters’ frequency and step responses are very different. Two simple remedies are presented in this paper. One is a post-processing approach that scales the frequency and step responses of the digital filter by the sampling interval T. Another one is a pre-processing approach …

Scene Classification Of Remote Sensing Image Based On Multi-Path Reconfigurable Neural Network, Wenyi Hu, Chunjie Lan, Tian Chen, Shan Liu, Lirong Yin, Lei Wang

Electrical & Computer Engineering Faculty Publications

Land image recognition and classification and land environment detection are important research fields in remote sensing applications. Because of the diversity and complexity of different tasks of land environment recognition and classification, it is difficult for researchers to use a single model to achieve the best performance in scene classification of multiple remote sensing land images. Therefore, to determine which model is the best for the current recognition classification tasks, it is often necessary to select and experiment with many different models. However, finding the optimal model is accompanied by an increase in trial-and-error costs and is a waste of …

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 …

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 …

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 …

Low Power Remote Sensing System For Ceramic Thermocouples, Syed Khaleduzzaman

Graduate Theses, Dissertations, and Problem Reports (ETD)

The ceramic thermocouple sensor is a promising alternative to traditional thermocouple due to their corrosion resistance and cost-effective manufacturing. Currently, no electronic Interface or Integrated Circuit solution is commercially available which can be used to interface these thermocouples at low-cost and low power for accurate temperature measurement. An electronic system is needed to use these ceramic sensors which consumes less amount of power for an extensive period of time with remote monitoring option.

This thesis proposes a low-power electronic remote sensing system that works for ceramic thermocouples. The system consists of three parts. They are battery-operated sensor node electronic circuit, …

Design Of An Adaptive Robust Pi Controller For Dc/Dc Boost Converter Using Reinforcement-Learning Technique And Snake Optimization Algorithm, Seyyedmorteza Ghamari, Mojtaba Hajihosseini, Daryoush Habibi, Asma Aziz

Research outputs 2022 to 2026

The DC/DC Boost converter exhibits a non-minimum phase system with a right half-plane zero structure, posing significant challenges for the design of effective control approaches. This article presents the design of a robust Proportional-Integral (PI) controller for this converter with an online adaptive mechanism based on the Reinforcement-Learning (RL) strategy. Classical PI controllers are simple and easy to build, but they need to be more robust against a wide range of disturbances and more adaptable to operational parameters. To address these issues, the RL adaptive strategy is used to optimize the performance of the PI controller. Some of the main …

Optimal Pilot Length For Key Generation In Correlated Wireless Channels, Danqi Li, Shihao Yan, Jia Zhang, Jiande Sun, Feng Shu

Research outputs 2022 to 2026

This work examines the optimal pilot length for key generation in correlated wireless channels. We first deduce the secret key capacity as a function of the pilot length N and other system parameters. Then, derive two fixed-point equations to identify the optimal N with and without an eavesdropper. Our analysis and numerical results confirm the existence of the optimal N that maximizes the secret key capacity, and both results for optimal N are consistent. Furthermore, the optimal N generally decreases with average signal-to-noise ratio and maximum Doppler shift. Our examination can provide useful guidelines in designing practical key generation systems.

Optimal Operation Of An Islanded Hybrid Energy System Integrating Power And Gas Systems, Mehrdad Ghahramani, Daryoush Habibi, Seyyedmorteza Ghamari, Asma Aziz

Research outputs 2022 to 2026

Remote communities and geographically isolated areas require a secure supply of energy. Isolated hybrid energy systems offer an effective and reliable solution for delivering power to these regions. However, shifting to renewable energy sources introduces uncertainty challenges for low-inertia stand-alone systems. In this paper, we propose a two-stage energy management strategy to address the uncertainties of wind generation and load consumption while minimizing operational expenses. Furthermore, the study explores the integration of multi-carrier energy networks, in this case electricity and gas, to enhance the reliability of hybrid energy systems. Two modeling methods are proposed to tackle the uncertainties. First, a …

Enhancing Energy Efficiency With A Dynamic Trust Measurement Scheme In Power Distribution Network, Yilei Wang, Xin Sun, Guiping Zheng, Ahmar Rashid, Sami Ullah, Hisham Alasmary, Muhammad Waqas

Research outputs 2022 to 2026

The application of Intelligent Internet of Things (IIoT) in constructing distribution station areas strongly supports platform transformation, upgrade, and intelligent integration. The sensing layer of IIoT comprises the edge convergence layer and the end sensing layer, with the former using intelligent fusion terminals for real-time data collection and processing. However, the influx of multiple low-voltage in the smart grid raises higher demands for the performance, energy efficiency, and response speed of the substation fusion terminals. Simultaneously, it brings significant security risks to the entire distribution substation, posing a major challenge to the smart grid. In response to these challenges, a …

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 …

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 …

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 …

Adaptive Critic Optimal Control Of An Uncertain Robot Manipulator With Applications, Ravi Prakash, Laxmidhar Behera, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

Realistic manipulation tasks involve a prolonged sequence of motor skills in varying control environments consisting of uncertain robot dynamic models and end-effector payloads. To address these challenges, this article proposes an adaptive critic (AC)-based basis function neural network (BFNN) optimal controller. Using a single neural network (NN) with a basis function, the proposed optimal controller simultaneously learns task-related optimal cost function, robot internal dynamics, and optimal control law. This is achieved through the development of a novel BFNN tuning law using closed-loop system stability. Therefore, the proposed optimal controller provides real-time, implementable, cost-effective control solutions for practical robotic tasks. The …

Evaluation Of Transfer Learning Models On Traumatic Brain Injury Severity Classification, Ngoc Do, Daniel B. Hier, Tayo Obafemi-Ajayi

Electrical and Computer Engineering Faculty Research & Creative Works

After traumatic brain injury (TBI), clinicians use the Glasgow Coma Scale (GCS) to classify patients by severity and radiologists use the Rotterdam score and the Marshall score to classify CT scans by severity. This work investigates a viable efficient low-cost transfer learning model to use MRI images to predict GCS, Rotterdam, and Marshall severity class after TBI. The enhanced transfer learning model architecture integrates multiple fine-tuning steps in which a few layers of the pre-trained model is unfrozen in each iteration so that the neural network is able to learn layer by layer further aspects of the image for better …

Towards Explainability Of Dimension Reduction Plots Of Unsupervised Learning Model Outcomes, Tony E.Astuhuaman Davila, Daniel B. Hier, Tayo Obafemi-Ajayi

Electrical and Computer Engineering Faculty Research & Creative Works

Dimension reduction methods are used to visualize the output of unsupervised learning models when applied to complex data. These techniques improve interpretability by transforming a high-dimension space to a lower-dimension space (usually 2D or 3D). The results are typically viewed as 2D scatter plots, and class centroids may be added to increase interpretability. Although useful, the relationship of these class centroids to the underlying feature space remains opaque. The innovative aspect of this work is to create a strong link between the dimension-reduced space and the underlying high-dimension feature space by adding selected feature centroids to the 2D scatter plots. …

A Model For Corona Streamer Propagation On Glass During An Air Discharge, Zhekun Peng, Jianchi Zhou, Darryl Kostka, David Pommerenke, Daryl G. Beetner

Electrical and Computer Engineering Faculty Research & Creative Works

Corona discharge to a glass surface is challenging to model due to a poorly understood air and surface ionization process. A modeling methodology based on the transmission line modeling (TLM) approach is proposed to simulate the streamer propagation process. The time-changing corona streamer resistance is estimated using the Rompe and Weizel spark model. The streamer is represented using small segments consisting of the arc resistance, per unit length (PUL) capacitance of the streamer, PUL inductance, a switch representing streamer formation, and a surface discharge gap voltage representing the voltage drop caused by ions within the streamer length. The propagation of …

Design Of Experiment Analysis On Multiple Pim Sources In An Rf Antenna System, Shengxuan Xia, Yuchu He, Haicheng Zhou, Hanfeng Wang, Chulsoon Hwang

Electrical and Computer Engineering Faculty Research & Creative Works

Passive intermodulation (PIM) has been identified as one of the common root causes for receiving sensitivity degradation (desense) on radiofrequency (RF) antennas working in frequency-division duplex mode. The component-level PIM characterization and simulation have been well-established over the years. However, the study has been using an ideal 50 Ω transmission line system while antenna modules are more complicated than a simple transmission line structure. Moreover, the metallic contacts can exist in multiple locations with different connection topologies in real applications. This paper provides a method to simulate the PIM performance caused by the metallic contacts in a practical environment. In …

Mechanisms For Unwanted Magnetic Field Coupling To A Shielded Magnetic Near-Field Probe, Xin Yan, Wei Zhang, Sajjad Sadeghi, Mehdi Gholizadeh, David J. Pommerenke, Daryl G. Beetner

Electrical and Computer Engineering Faculty Research & Creative Works

Unwanted field coupling is a major concern when using magnetic near-field probes for high-frequency scanning. The unwanted coupling of the tangential electric field to a tangential magnetic field probe has been studied extensively. This article shows a second unwanted coupling mechanism: coupling from a vertical magnetic field. The standing wave of a shorted pure TEM microstrip in air is utilized to separate the magnetic and electric field coupling. Simulations show that the eddy currents induced within the probe by the vertical magnetic field couple inductively to the pickup loop if the layer stack up is not symmetric. The unwanted magnetic …

Virtual Inertia Scheduling (Vis) For Microgrids With Static And Dynamic Security Constraints, Buxin She, Fangxing Li, Jinning Wang, Hantao Cui, Xiaofei Wang, Rui Bo

Electrical and Computer Engineering Faculty Research & Creative Works

Microgrids feature a high penetration of inverter-interfaced distributed energy resources (DERs). The low inertia characteristic and fast dynamics of DERs pose challenges to conventional decoupled static economic operation and dynamic control design within microgrids. Hence, this paper proposed virtual inertia scheduling (VIS) for microgrids, aiming to ensure both economy and security. First, a unified framework for device-level control and grid-level operation is introduced, with VIS serving as a key application to address low inertia issues. VIS actively harnesses the controllability and flexibility of DERs to effectively manage microgrid inertia. It updates the conventional economic operation framework by incorporating the virtual …

Coupling-Informed Data-Driven Scheme For Joint Angle And Frequency Estimation In Uniform Linear Array With Mutual Coupling Present, Yanming Zhang, Wenchao Xu, A. Long Jin, Min Li, Peifeng Ma, Lijun Jiang, Steven Gao

Electrical and Computer Engineering Faculty Research & Creative Works

This paper proposes a novel coupling-informed data-driven algorithm tailored for the concurrent estimation of frequency and angle within a uniform linear array (ULA), while addressing the complicating influence of mutual coupling. Leveraging the hybrid dynamic mode decomposition (DMD) methodology, termed as averaged DMD, we incorporate moving average techniques to achieve effective denoising. The averaged DMD further decomposes the received signal into eigenvalues and corresponding eigenvectors. The frequency information is derived from the eigenvalues and the corresponding eigenvectors represent the steering vectors of sources. Subsequently, mutual coupling is informed into the calibration of the steering vector for each source. Specifically, the …

Design Of The Tm010 Mode Cylindrical Cavity Resonator For Pcb Dielectric Characterization, Reza Asadi, Chaofeng Li, Seyedmehdi Mousavi, Seyed Moastafa Mousavi, Reza Vahdani, Xiaoning Ye, Donghyun Kim

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents the study of the TM₀₁₀ mode cylindrical resonator, which can be used for printed circuit board (PCB) material properties extraction, e.g., the dielectric constant (Dk) and the loss tangent (Df) extraction. The theoretical formulas of the resonance frequency and Q-factor of the resonator are presented. In real measurement, the TM₀₁₀ mode cylindrical cavity resonator needs to be excited by the probe. The study emphasizes the impact of probe orientation, location, and field distribution on the accuracy of material property extraction. The relationship between cavity dimensions and resonance frequency is explored, highlighting the influence of cavity …

A Tensor-Based Data-Driven Approach For Multidimensional Harmonic Retrieval And Its Application For Mimo Channel Sounding., Yanming Zhang, Wenchao Xu, A. Long Jin, Min Li, Ping Yuan, Lijun Jiang, Steven Gao

Electrical and Computer Engineering Faculty Research & Creative Works

In wireless channel sounding, accurately estimating multiple parameters within a multipath signal, such as azimuth, elevation, Doppler shift, and delay, necessitates addressing the challenges posed by the multidimensional harmonic retrieval (MHR) problem. To overcome these complexities, we propose a framework based on high-order dynamic mode decomposition (HODMD) that designed for robustly estimating frequencies of interest from high-dimensional sinusoidal signals, particularly in additive white Gaussian noise conditions. The HODMD approach, a hybrid algorithm amalgamating high-order singular value decomposition (HOSVD) and dynamic mode decomposition (DMD), operates by initially decomposing observed tensorial data into a core tensor and R mode matrices through HOSVD. …