Digital Commons Network^™

Understanding Timing Error Characteristics From Overclocked Systolic Multiply–Accumulate Arrays In Fpgas, Andrew Chamberlin, Andrew Gerber, Mason Palmer, Tim Goodale, Noel Daniel Gundi, Koushik Chakraborty, Sanghamitra Roy

Electrical and Computer Engineering Faculty Publications

Artificial Intelligence (AI) hardware accelerators have seen tremendous developments in recent years due to the rapid growth of AI in multiple fields. Many such accelerators comprise a Systolic Multiply–Accumulate Array (SMA) as its computational brain. In this paper, we investigate the faulty output characterization of an SMA in a real silicon FPGA board. Experiments were run on a single Zybo Z7-20 board to control for process variation at nominal voltage and in small batches to control for temperature. The FPGA is rated up to 800 MHz in the data sheet due to the max frequency of the PLL, but the …

Clpfudatabase: A Suite Of R Packages For Energy Conversion Chain Analysis, Matthew Kuperus Heun, Zeke Marshall, Emmanuel Aramendia

University Faculty Publications and Creative Works

Energy flowing through societal energy conversion chains (ECCs) enables economic activity and facilitates human flourishing. To understand economic growth and human well-being, the field of societal energy analysis (Common, 1976) evaluates ECCs from the primary stage (resources extracted from the environment, such as coal, oil, natural gas, wind, and solar), to the final stage (energy purchased by consumers, such as refined petroleum and electricity), to the useful stage (energy desired by the end user, such as heat, motion, and light), and sometimes to energy services (such as thermal comfort, transport, and illumination). Societal exergy analysis (SEA) (Ertesvåg, 2001), an extension …

Energy Services Interface (Doe-Psu-0000922-1), Robert B. Bass

Electrical and Computer Engineering Faculty Publications and Presentations

This document defines a set of rules known as the Energy Services Interface (ESI), which “establish a bi-directional, service-oriented, logical interface to support secure, trustworthy information exchange between an aggregator and distributed energy resources (DERs). These exchanges facilitate energy interactions between the DERs and the aggregator, thereby allowing the aggregator to provide grid services through dispatch of the DERs.” The ESI serves as an umbrella, ensuring the information exchange between an aggregator and DER owners conforms to expectations: protect privacy, provide security, develop trustworthiness, and ensure interoperability. DERMS developers use the ESI to ensure that information exchange meets these expectations.

Implementation Profile: Modeling Environment (Doe-Psu-0000922-3), Sean Keene, Midrar Adham, Robert B. Bass

Electrical and Computer Engineering Faculty Publications and Presentations

This implementation profile provides the scope, background, and requirements necessary to implement a Modeling Environment (ME) to test a Distributed Energy Resource (DER) Management System (DERMS). A DERMS is used by an aggregator to dispatch large numbers of DERs in order to provide grid services to a Grid Operator. The ME addresses scalability issues inherent to Hardware-in-the-Loop DERMS simulation; a large number of assets are needed in order to observe effects on the grid from deployment and dispatch of DERs.

Implementation Profile: Egot Derms Server/Client System (Doe-Psu-0000922-2), Tylor Slay, Robert B. Bass

Electrical and Computer Engineering Faculty Publications and Presentations

This implementation profile (IP) provides the scope and requirements necessary to implement a distributed energy resource management system (DERMS), which networks large numbers of DER within an energy grid of things (EGoT). This document originated as part of a U.S. DOE-funded project to develop a DERMS based on a set of rules known as the Energy Services Interface (ESI). The ESI serves as an umbrella, ensuring the information exchange between an aggregator and DER owners conforms to expectations: protect privacy, provide security, develop trustworthiness, and ensure interoperability. DERMS developers use the ESI to ensure that information exchange meets these expectations. …

Product Specification: Distributed Control Module (Doe-Psu-0000922-5), Nicole Henderson, Kai Zeng, Robert B. Bass

Electrical and Computer Engineering Faculty Publications and Presentations

This product specification describes the architecture, implementation, and hardware descriptions of a Distributed Control Module (DCM) prototype. A DCM is an enabling technology for distributed energy resources (DER). DERs are grid-enabled generation, storage, and load devices that are owned by utility customers. DCMs enable information exchange between a distributed energy resource management system (DERMS) and DERs for the purpose of networking large numbers of DERs. The DCM prototype described within this document enables DER participation in a service-oriented aggregation system. A DERMS server provides IEEE 2030.5 smart energy resource services to DCM clients using a request/response information exchange process. DCMs …

Psu Derms Operating Manual And Egot System Reference (Doe-Psu-0000922-7), Tylor Slay, Robert B. Bass

Electrical and Computer Engineering Faculty Publications and Presentations

This document guides the user of the Portland State University Distributed Energy Resource Management System in configuration and normal operation. For direct access to the underlying code and its usage see the accompanying PSU EGoT System Reference. The system reference outlines all classes and methods used through the Energy Grid of Things system including applications, models, interfaces and the entity component system.

Voxlabs: Real-Time Vocal Trainer, Madhav Danturthi, Aidan Galia

Electrical and Computer Engineering Senior Theses

With the rapid evolution of society, the barriers to entry into the world of music are more constricting than ever. The impacts of race, age, gender, and language on the quality of musical education and the accessibility to education in general is growing more and more. That is why it is more important than ever to develop a new accessible method of vocal training focused around improving craft through gamification and rewards, all while inspiring a next generation of ready to learn musicians.

VoxLabs is a project created to help music lovers learn how to sing through an entertaining yet …

Electronic Prototype Of A Smart Window System, Kelly Pennington

Electrical and Computer Engineering Senior Theses

As demands and cost for energy increase, the importance of energy saving devices will increase. Yet, as of now, the potential of such devices has yet to be fully realized. The Santa Clara University Smart Window considers the importance of energy saving devices and examines the potential of saving energy through a fully automated window. Ultimately, the Smart Window establishes a more efficient use of outdoor and indoor "energy" to create a more comfortable and user-friendly environment. This paper discusses the electronic design and implementation portion of the Smart Window research project. In particular, it focuses on the sensing, communications …

A Low-Noise, Low-Power Cmos Readout Ic For Amperometric Electrochemical Sensing System, Manu Chilukuri

Electrical Engineering Dissertations - Archive

Electrochemical sensing systems have become essential tools in numerous fields due to their exceptional sensitivity, selectivity, and versatility. They play a critical role in modern research, industry, environmental monitoring, and healthcare applications. In environmental monitoring, these systems are utilized to detect pollutants, heavy metals, and toxic gases, enabling real-time monitoring and ensuring environmental protection. In the biomedical sector, electrochemical sensing systems are vital for diagnosing diseases, monitoring biomarkers, and transforming healthcare practices.

Affordable wearable electrochemical sensing devices are crucial for making healthcare more accessible and improving public health outcomes. These devices allow for continuous monitoring of physiological parameters, supporting early …

Unsupervised Machine Learning In Wireless Sensor Measurements And Hyperspectral Imaging, Abrar U. Alam

Electrical Engineering Dissertations - Archive

The increasing demand for real-time analysis of sensor data in dynamic environments necessitates innovative approaches to data clustering. This work introduces a novel Online Kernel Clustering (OKC) framework that efficiently determines time-varying clustering configurations without requiring training data. The proposed method employs sparse kernel factorization, guided by a time-dependent metric to quantify the closeness of kernel similarity matrices to a block diagonal structure. By processing data sequentially, the OKC framework is tailored for non-stationary settings. The optimization process integrates block coordinate descent, difference-of-convex functions minimization, and projected sub-gradient descent to iteratively update kernel covariance matrices and cluster memberships online. Numerical …

Study Of A Multi-Agent Containment Problem Under Communication Constraints And Defense From Malicious Agents, Braulio Mora

Mechanical and Aerospace Engineering Theses - Archive

This thesis investigates a multi-agent containment problem where a fast evader, moving at a constant speed and heading, attempts to escape a circular containment region. A team of slow pursuers possess a nonzero capture radius and seek to capture the evader before escape. The research addresses two primary scenarios: one involving the team of pursuers, with no communication network, being constrained to the edge of the containment region, and the second involving a team of pursuers, with a communication network, not being constrained to the edge of the containment region. Both scenarios study the influence of malicious agents in the …

Dueling Robot, Abigail Coy

All Undergraduate Projects

This project was undertaken as a personal challenge to combine a Mechanical Engineering Technology degree and an Electrical Engineering Technology degree into a single final project showing the cumulative knowledge gained. It was determined that a robot to participate in Western Allied Robotics would be designed and manufactured for the 3 lb. weight class.

A two-part power system was designed to provide more power to the weapon arm. This design combined a compressive spring’s kinetic energy with the torque from the electric motor. Research determined all of the purchased components required and analysis were performed to determine the geometry and …

Reinforcement Learning For Optimal Kicking Actions In Humanoid Robotics: Advancing Robotic Autonomy And Versatility, Suresh Dodda, Sathish Kumar Chintala, Sukender Reddy Mallreddy, Sharath Chandra Macha, Yashwanth Vasa, Sapan Bharadwaj Bonala, Navin Kamuni, Sujatha Alla

Engineering Management & Systems Engineering Faculty Publications

Acquiring the necessary skills to perform a work effectively and efficiently requires a significant investment of time and computing power. Previous applications of Reinforcement Learning (RL) for action optimization in humanoid robotics have shown how promising this technology is for moving robotics towards true autonomy and versatility. Therefore, this study offers the first use of RL to create an entirely optimal kicking action for the Alderbaran Nao robot. Kicking motions that were steady, precise, quick, and able to kick farther than any existing RoboCup squad were generated by optimizing for a multi-objective reward function. We demonstrate that the ideal kicking …

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

Center for Bioelectronics Publications

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

Applications Of Plasmonic Biosensors In Chiral And Achiral Sensing, Aritra Biswas

Graduate Thesis and Dissertation 2023-2024

Monitoring biological systems is crucial in healthcare, driving the need for reliable and noninvasive solutions. The proliferation of unverified drugs in the market necessitates reliable methods for their detection and identification, especially amidst advancements in pharmaceuticals. Plasmonic biosensors emerge as a great platform for ultra-sensitive detection, identification, and manipulation of biomolecular systems. This dissertation report addresses the critical need for precise detection and monitoring of biomolecules and drugs, presenting innovative solutions through the design of a plasmonic biosensor to tackle challenges in sensitivity, selectivity, and label-free detection and identification. We introduce a robust and tunable, cavity-integrated plasmonic nanopatterned sensor that …

Exploratory Prompting Of Large Language Models To Act As Co-Pilots For Augmenting Business Process Work In Document Classification, Jose Ramon Ilagan, Joseph Benjamin R. Ilagan, Claire Louisse Basallo, Zachary Matthew Alabastro

Quantitative Methods and Information Technology Faculty Publications

Businesses deal with different types of documents containing unstructured documents. The data in these documents must be converted into digital forms other automated systems could only process. One generic use case is document classification, which usually involves manual transformation due to human understanding needed in the process. These documents go beyond those generated through regular business transactions and operations and also include web-based content such as online news, blogs, e-mails, and various digital libraries. Recent developments in robotic process automation (RPA) and artificial intelligence (AI) aim to automate the otherwise expensive, time-consuming, and repetitive manual steps. Through more powerful natural …

Otfs Modulation For High Mobility Wireless Channels, Sandip K C

All Graduate Theses, Dissertations, and Other Capstone Projects

In today's world, billions of devices are being connected to the internet primarily through wireless means, making this connectivity increasingly important. Managing these connections becomes complex, especially for moving devices such as drones and high-speed trains, which require seamless and reliable connectivity even at high speeds. Maintaining a stable connection in fast-moving environments is challenging but essential for the operation of these technologies. Traditional communication systems like OFDM face significant challenges in high mobility environments, struggling to cope with the rapid changes and dynamic conditions that high-speed movements introduce. To address this, OTFS is proposed as a solution. This novel …

A Wearable Sensing System For High-Speed Interface Of Wide Range Electrochemical Sensor Array, Shanthala Lakshminarayana

Electrical Engineering Dissertations - Archive

Electrochemical sensing systems have emerged as indispensable tools in various fields due to their remarkable sensitivity, selectivity, and versatility. They play a pivotal role in modern research, industry, environmental monitoring, and healthcare devices. In environmental monitoring, these systems are deployed for detecting pollutants, heavy metals, and toxic gases, facilitating real-time monitoring, and ensuring environmental safety. Whereas, in the biomedical field, electrochemical sensing systems play a crucial role in diagnosing diseases, monitoring biomarkers etc, thereby revolutionizing healthcare practices.

Cost-effective wearable electrochemical sensing devices are significant for democratizing healthcare and improving public health outcomes. These devices enable continuous monitoring of physiological parameters, …

Exploring Machine Learning Techniques For Embedded Hardware, Neel R. Vora

Computer Science and Engineering Theses - Archive

This thesis delves into the intricate symbiosis between machine learning (ML) methodologies and embedded hardware systems, with a primary focus on augmenting efficiency and real-time processing capabilities across diverse application domains. It confronts the formidable challenge of deploying sophisticated ML algorithms on resource-constrained embedded hardware, aiming not only to optimize performance but also to minimize energy consumption. Innovative strategies are explored to tailor ML models for streamlined execution on embedded platforms, with validation conducted across various real-world application domains. Notable contributions include the development of a deep-learning framework leveraging a variational autoencoder (VAE) for compressing physiological signals from wearables while …

Li-Ion Battery Resistance During External Short Circuits And Cyclic Ageing, Kaynat Zia

Electrical Engineering Dissertations - Archive

External short circuit testing (ESCT) is one of the many steps towards ensuring lithium-ion (Li-ion) batteries are safe for different applications. The nature of testing is sensitive due to the concern of safety and data integrity. Great care and planning must be done before conducting these tests. Bouncing in recorded data is observed in literatures with high sampling rates due to switching and moving mechanical parts. This impacts the estimation accuracy of battery internal parameter values especially the effective ohmic resistance (Ro). This dissertation proposes a zero bouncing circuit design to eliminate this problem. Multi-rate Pulse Discharge Testing (MPDT) is …

All-Optical Signal Processing With Fiber-Based Parametric Wavelength Converters, Cheng Guo

Electrical Engineering Dissertations - Archive

The optical signal degradation by optical amplifier noise set the fundamental limit of link reach in the fiber-optics networks. The industrial solution is to use the optical-electrical-optical (OEO) regenerator to clean up the noise at the expense of high-speed electronics and extra cost of laser and photodetectors. All-optical signal processing, enabled by nonlinear optics and optical fiber, intrinsically provides 2-order of magnitude higher processing bandwidth and seamless interface to fiber communication channels. However, there is no robust phase-preserving regenerator that has been experimentally demonstrated without sophisticated polarization tuning and without instable interferometric structure. In this project, we explore the applications …

Neural Net Estimation Of Discriminants Posterior Probability Vector, Harshvardhan Harshvardhan

Electrical Engineering Theses - Archive

Interpreting multi-layer perceptron (MLP) classifier outputs as posterior probabilities is a well-established practice in machine learning and is supported in the literature. However, several authors point out that MLP outputs are very poor estimates of the posterior probabilities. This is demonstrated for classifiers with and without nonlinear output activation. Achieving this reliability depends on key factors such as model complexity, sufficient training data availability, and optimization techniques' effectiveness. In practice, these requirements are not met, resulting in suboptimal probability estimates. Our approach introduces an innovative method based on the softmax output. The method aim to refine MLP discriminants into more …

Advanced Digital Wideband Receiver Design: High Dynamic Range And Enhanced Multi-Signal Detection With Fpga-Based Custom Fft And Nyquist Folding, Kiran Jayarama

Browse all Theses and Dissertations

In modern wideband receiver standards, efficient frequency spectrum utilization is essential to meet demands for high data rates, reduced latency, and enhanced connectivity. The Fast Fourier Transform (FFT) stands as a pivotal technology, particularly in radar signal processing, where it supports tasks such as target detection, range estimation, and velocity estimation by analyzing the frequency content of the received radar signals. This dissertation introduces the design of an advanced digital wideband receiver featuring a high dynamic range for multiple signals, with a focus on improved performance, compact size, and reduced power consumption, implemented on an FPGA using custom hardware. Key …

Intelligent Millimeter-Wave System For Human Activity Monitoring For Telemedicine, Abdullah K. Alhazmi, Mubarak A. Alanazi, Awwad H. Alshehry, Saleh M. Alshahry, Jennifer Jaszek, Cameron Djukic, Anna Brown, Kurt Jackson, Vamsy P. Chodavarapu

Electrical and Computer Engineering Faculty Publications

Telemedicine has the potential to improve access and delivery of healthcare to diverse and aging populations. Recent advances in technology allow for remote monitoring of physiological measures such as heart rate, oxygen saturation, blood glucose, and blood pressure. However, the ability to accurately detect falls and monitor physical activity remotely without invading privacy or remembering to wear a costly device remains an ongoing concern. Our proposed system utilizes a millimeter-wave (mmwave) radar sensor (IWR6843ISK-ODS) connected to an NVIDIA Jetson Nano board for continuous monitoring of human activity. We developed a PointNet neural network for real-time human activity monitoring that can …

Ecgconvt : A Hybrid Cnn And Vision Transformer Model For 12-Lead Ecg Images Classification, Mudassar Khalid

Chulalongkorn University Theses and Dissertations (Chula ETD)

Cardiovascular diseases (CVDs) are the most common cause of human deaths globally, and they can be controlled by early diagnosis and accurate classification of the electrocardiograms. The Electrocardiogram, commonly known as an ECG, is a very affordable technique to know about the health of the heart from 12 different angles by using 12-Leads to capture the heart's electrical activity. A hybrid deep learning model is proposed in this study to classify the different heart diseases. The deep learning model is composed of a CNN(Xception) to capture all the local feature-maps of the images and a vision transformer (ViT) to study …

Parameter Identifiability Analysis And Calibration For Power System Dynamic Models, Lei Wang

Electronic Theses and Dissertations

Accurate dynamic models are crucial for stable and reliable power system operation, making their validation essential. The accuracy of parameter estimation is influenced by several factors, including available observables, model structure, and estimation methods. However, in dynamic models, estimating parameters can be difficult when some of them are unidentifiable, meaning their values cannot be uniquely determined from the available data. This challenge may stem from limited impact of parameters on the measured outputs, parameter interactions, or poor data quality. Therefore, an identifiability analysis should be conducted prior to parameter estimation or model calibration. This preliminary step helps to determine which …

Evaluating The Performance Of Egemaps Features In Depression Detection Using E-Daic Subsets, Joshua Turnipseed

Graduate Research Theses & Dissertations

This paper investigates the performance of the eGeMAPS (extended Geneva Minimalistic Acoustic Parameter Set) feature set in detecting depression from audio samples using subsets of the E-DAIC (Extended Distress Analysis Interview Corpus) database. With depression affecting a significant portion of the U.S. adult population, efficient detection methods are critical for timely diagnosis and treatment. Various classifiers in the WEKA machine learning toolbox are used to evaluate the performance of eGeMAPS features in distinguishing between depressed and nondepressed (D&ND) individuals. Our methodology involves creating balanced subsets of E-DAIC, extracting eGeMAPS features using openSMILE, and testing different machine learning models. This study …

Energy Efficiency In Additive Manufacturing: Condensed Review, Ismail Fidan, Vivekanand Naikwadi, Suhas Alkunte, Roshan Mishra, Khalid Tantawi

Engineering Technology Faculty Publications

Today, it is significant that the use of additive manufacturing (AM) has growing in almost every aspect of the daily life. A high number of sectors are adapting and implementing this revolutionary production technology in their domain to increase production volumes, reduce the cost of production, fabricate light weight and complex parts in a short period of time, and respond to the manufacturing needs of customers. It is clear that the AM technologies consume energy to complete the production tasks of each part. Therefore, it is imperative to know the impact of energy efficiency in order to economically and properly …

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, …