Digital Commons Network^™

Localized Surface Plasmon Resonance Enhancement From Complex Nanoscale Geometries, Jacob T. Waitkus

Theses

The unprecedented COVID-19 pandemic highlights the need for portable, sensitive, and accurate biosensors. Here, a novel biosensor that takes advantage of localized surface plasmonic resonance (LSPR) through unique nanoscale geometries was fabricated for sensitive detection of biomarkers. The formation of an adaptable system capable of combining with other sensing methods, such as CRISPR-Cas13a assays, allowed for the detection of specific targets to be realized. In this system, streptavidin-coated gold nanoparticles (GNPs) hybridize with single-stranded RNA (ssRNA) before binding to the surface of gold nanomushrooms (GNMs). Through LSPR enhancement, this binding event produces a red shift in the resonance wavelength peak …

“Temporal Not Permanent:” The Impact Of Covid-19 Cities Lockdown On Improving Air Quality — A Critical Review Of Africa, Emmanuel Mensah Aboagye, Felix Mensah, Kwaku Obeng Effah, Sandylove Afrane, Jeffrey Dankwa Ampah, Selina Annah Brenyah

Journal of Environmental Sustainability

The WHO declared the novel Covid-19 virus a global pandemic shortly after it broke out. The deadly virus spread has affected human health and seriously halted many economic and industrial activities globally. However, there has been a blight as there has generally been an improvement in air quality since the deadly Covid-19 pandemic caused various governments to lockdown their cities to regulate the spread of the virus. Since the widespread of Covid-19, there have been several researches to measure the extent of impact Covid-19 city lockdown has had global air quality, but no critical review has been done in Africa …

Grains Of Gold - Excavating The Perils Of Sand Mining, Oleena Chaudhuri

Journal of Environmental Sustainability

The human population and the global economy have been growing rapidly, increasing resource consumption. This paper evaluates the extent to which sand mining has an impact on the environment. Through a case study and regulatory analysis of India, this paper has attempted to underscore the consequences of excessive sand excavation while highlighting the case of the Indian “sand mafia”. This secondary research evaluation finds that mining of sand resources is a common practice that leads to the destruction of the environment which adversely affects commercial and non-commercial living resources. While studies do point out that economic activities requiring sand as …

A Customer Agnostic Machine Learning Based Peak Electric Load Days Forecasting Methodology For Consumers With And Without Renewable Electricity Generation, Omar Aponte

Theses

The adoption of electricity generation from renewable sources, as well as the push for a speedy electrification of sectors such as transportation and buildings, makes peak electric load management an essential aspect to ensure the electric grid’s reliability and safety. Utilities have established peak load charges that can amount to up to 70% of electricity costs to transfer the financial burden of managing these loads to the consumers. These pricing schemes have created a need for efficient peak electric load management strategies that consumers can implement in order to reduce the financial impact of this type of load. Research has …

Kf-Loc: A Kalman Filter And Machine Learning Integrated Localization System Using Consumer-Grade Millimeter-Wave Hardware, Abhishek Vashist, Maojia Li, Amlan Ganguly, Sai Manoj P. D., Clark Hochgraf, Raymond Ptucha, Andres Kwasinski, Michael E. Kuhl

Articles

With the ever-increasing demands of e-commerce, the need for smarter warehousing is increasing exponentially. Such warehouses requires industry automation beyond Industry 4.0. In this work, we use consumer-grade millimeter-wave (mmWave) equipment to enable fast, and low-cost implementation of our localization system. However, the consumer-grade mmWave routers suffer from coarse-grained channel state information due to cost-effective antenna array design limiting the accuracy of localization systems. To address these challenges, we present a Machine Learning (ML) and Kalman Filter (KF) integrated localization system (KF-Loc). The ML model learns the complex wireless features for predicting the static position of the robot. When in …

Critical Regionalism- Integrating Traditional Chinese Architecture Into Contemporary High-Rise Residential Building, Yang Li

Theses

In the field of modern architecture, a clear path indicates architecture’s historical development, from the very beginning of ancient Egypt to the emergence of modernism in the first half of the twentieth century, and finally to the dominant architectural style of today. Compared with the Western world, the Chinese developed their unique architectural style thousands of years ago. The style can be summed up as the difference between wood and stone, which deeply influenced surrounding countries. Unfortunately, the evolution of the style was suddenly interrupted at the end of the 19th century, which was the emerging modernism in the West. …

Nlp And Ml Methods For Pre-Processing, Clustering And Classification Of Technical Logbook Datasets, Farhad Akhbardeh

Theses

Technical logbooks are a challenging and under-explored text type in automated event identification. These texts are typically short and written in non-standard yet technical language, posing challenges to off-the-shelf NLP pipelines. These datasets typically represent a domain (a technical field such as automotive) and an application (e.g., maintenance). The granularity of issue types described in these datasets additionally leads to class imbalance, making it challenging for models to accurately predict which issue each logbook entry describes. In this research, we focus on the problem of technical issue pre-processing, clustering, and classification by considering logbook datasets from the automotive, aviation, and …

Dynamic Algorithms And Asymptotic Theory For Lp-Norm Data Analysis, Mayur Dhanaraj

Theses

The focus of this dissertation is the development of outlier-resistant stochastic algorithms for Principal Component Analysis (PCA) and the derivation of novel asymptotic theory for Lp-norm Principal Component Analysis (Lp-PCA). Modern machine learning and signal processing applications employ sensors that collect large volumes of data measurements that are stored in the form of data matrices, that are often massive and need to be efficiently processed in order to enable machine learning algorithms to perform effective underlying pattern discovery. One such commonly used matrix analysis technique is PCA. Over the past century, PCA has been extensively used in areas such as …

Leveraging Formulae And Text For Improved Math Retrieval, Behrooz Mansouri

Theses

Large collections containing millions of math formulas are available online. Retrieving math expressions from these collections is challenging. Users can use formula, formula+text, or math questions to express their math information needs. The structural complexity of formulas requires specialized processing. Despite the existence of math search systems and online community question-answering websites for math, little is known about mathematical information needs. This research first explores the characteristics of math searches using a general search engine. The findings show how math searches are different from general searches. Then, test collections for math-aware search are introduced. The ARQMath test collections have two …

Smart And Intelligent Automation For Industry 4.0 Using Millimeter-Wave And Deep Reinforcement Learning, Abhishek Vashist

Theses

Innovations in communication systems, compute hardware, and deep learning algorithms have led to the advancement of smart industry automation. Smart automation includes industrial sectors such as intelligent warehouse management, smart infrastructure for first responders, and smart monitoring systems. Automation aims to maximize efficiency, safety, and reliability. Autonomous forklifts can significantly increase productivity, reduce safety-related accidents, and improve operation speed to enhance the efficiency of a warehouse. Forklifts or robotic agents are required to perform different tasks such as position estimation, mapping, and dispatching. Each of the tasks involves different requirements and design constraints. Smart infrastructure for first responder applications requires …

Design And Fabrication Of Astrophysical Instrumentation For Distinguishing Infrared Sources, Serena M. Tramm

Theses

Observations in the near- and far-infrared allow us to probe through cosmic clouds of gas and dust to uncover processes that are obscured at other wavelengths such as young stars embedded in dust clouds, thermal emission from the interstellar medium, and the birth of galaxies. However, cosmic infrared light is obstructed by Earth's atmosphere. As a result, infrared astrophysical instrumentation is often designed for use in space. As specific examples of technologies addressing the kind of science that can be performed at these wavelengths, I will discuss my contributions to two applications of infrared instrumentation: a Fourier transform spectrometer (FTS) …

A Physiological Computing System To Improve Human-Robot Collaboration By Using Human Comfort Index, Celal Savur

Theses

Fluent human-robot collaboration requires a robot teammate to understand, learn, and adapt to the human's psycho-physiological state. Such collaborations require a physiological computing system that monitors human biological signals during human-robot collaboration (HRC) to quantitatively estimate a human's level of comfort, which we have termed in this research as comfortability index (CI) and uncomfortability index (UnCI). We proposed a human comfort index estimation system (CIES) that uses biological signals and subjective metrics. Subjective metrics (surprise, anxiety, boredom, calmness, and comfortability) and physiological signals were collected during a human-robot collaboration experiment that varied the robot's behavior. The emotion circumplex model is …

Credit Card Fraud Detection Using Machine Learning, Meera Alemad

Theses

The purpose of this project is to detect the fraudulent transactions made by credit cards by the use of machine learning techniques, to stop fraudsters from the unauthorized usage of customers’ accounts. The increase of credit card fraud is growing rapidly worldwide, which is the reason actions should be taken to stop fraudsters. Putting a limit for those actions would have a positive impact on the customers as their money would be recovered and retrieved back into their accounts and they won’t be charged for items or services that were not purchased by them which is the main goal of …

Optimizing Vaccine Procurement For Affordability And Profits, Bruno Alves-Maciel

Theses

This thesis presents methods to increase savings in the global vaccine market without compromising its sustainability. Considering a hypothetically coordinated vaccine market (HCVM), where one or more coordinating entities are responsible for negotiating quantities and prices of vaccines on behalf of countries with different purchasing powers, this work explores four different and complementary issues related to market configurations impacting global affordability and profits: (1) the level of cooperation among coordinating entities; (2) optimal country assignment to coordinating entities; (3) benefits in procuring vaccines through tenders of formularies rather than purchasing doses individually; and (4) the value of a dollar saved …

Cyber Attacks On Power System Substations, Turkei Abdulla Aldarrai

Theses

Modern cyber-physical smart power grids are increasingly relying on their associated cyber networks to fulfill a variety of monitoring and control activities, which unavoidably results in a rise in cyber vulnerabilities. In light of the likely cyber risks, it is crucial to create efficient ways for evaluating the overall sufficiency of power systems thoroughly. Cybersecurity must be implemented inside the smart grid to ensure the safe and dependable operation of this new type of electrical network. The use of standardized procedures and solutions is generally agreed upon amongst experts as the best starting point. The past several years have seen …

Towards A Robust And Long-Lasting Antimicrobial Surface, Justin A. Silva

Theses

Bacterial adhesion to surfaces such as medical devices causes approximately 1.7 million infections and 75,000 deaths each year in the United States. Throughout the past several years, many studies have been conducted on micro- and nanostructure-enabled antimicrobial surfaces. However, this idea is controversial, as some studies have shown that superhydrophobic micro and nanostructures cannot prevent bacterial growth over time and that they are not a practical solution to reduce healthcare-related infections. The objective of this study is to develop a mechanically durable and long-lasting antimicrobial surface to prevent bacterial growth. High-aspect-ratio micro mushroom structures are fabricated by high resolution stereolithography …

R-Cad: Rare Cyber Alert Signature Relationship Extraction Through Temporal Based Learning, Gordon Werner

Theses

The large number of streaming intrusion alerts make it challenging for security analysts to quickly identify attack patterns. This is especially difficult since critical alerts often occur too rarely for traditional pattern mining algorithms to be effective. Recognizing the attack speed as an inherent indicator of differing cyber attacks, this work aggregates alerts into attack episodes that have distinct attack speeds, and finds attack actions regularly co-occurring within the same episode. This enables a novel use of the constrained SPADE temporal pattern mining algorithm to extract consistent co-occurrences of alert signatures that are indicative of attack actions that follow each …

Sustainable Building Without Certification: An Exploration Of Implications And Trends, Yewande Abraham, Lisa Greenwood, Jennifer Schneider

Articles

Background: Sustainable buildings play a pivotal role in meeting the United Nations Sustainable Development Goals (UN SDGs). However, the criteria and process for certification associated with sustainable building rating systems have been seen by many as either cumbersome or too expensive. As a result, some buildings are constructed following sustainable building guidelines without necessarily pursuing external certification. This paper takes a critical look at sustainable building without certification in the US and addresses 3 questions: (1) What is the rationale behind not pursuing certification? (2) When certification is not part of the objective, how are particular sustainability criteria selected? (3) …

Introduction: 2021 Frameless Xr Symposium, Susan Lakin

Frameless

The Frameless XR Symposium is an interdisciplinary conference focused on research, innovation, and artistic creation in the fields of augmented reality, virtual reality, and mixed reality. As we continue to navigate the global pandemic and follow the Rochester Institute of Technology (RIT) COVID-19 Dashboard guidelines, the symposium organizing committee choose a hybrid format for safety and flexibility, as well as in the spirit of extended reality.

Memory Protection With Dynamic Authentication Trees, Matthew T. Millar, Marcin Łukowiak, Stanisław Radziszowski

Presentations and other scholarship

As embedded devices increase in use and handle more critical information and functionalities, the importance of security grows even greater. Defense against bus attacks such as spoofing, splicing, and replay attacks is of particular concern. Traditional memory authentication techniques, such as hashes and message authentication codes, require significant amounts of on-chip memory and introduce a large performance impact when protecting off-chip memory during run-time. Balanced authentication trees such as the well-known Merkle tree or TEC-Tree can be used to reduce this cost. This work proposes a new method of dynamic authentication trees, which updates a tree structure based on a …

Investigation Of Thickness And Robustness Of Ultrathin Parylene Membranes For Tissue Barrier And Co-Culture Models, Shayan Gholizadeh

Theses

Ultrathin porous membranes are currently used for various biomedical applications, ranging from co-culture systems to tissue barrier models. In the blood-brain barrier model, the intimate proximity between endothelial and glial cells plays a significant role in cell-cell communication and transmigration across the barrier, which cannot be physiologically modeled with conventional co-culture systems typically separating cell types by more than 10 μm. A thickness gradient membrane can enable studies where the goal is to understand the physical characteristics of co-culture arrangement in the barrier model. Here, a robust method to generate ultrathin porous Parylene C (UPP) membranes is developed not just …

High Error Rate Qubit Routing, Sean Bonaventure

Theses

The current state of quantum computers is characterized by its limited resources and high noise levels. These are known as Noisy Intermediate Scale Quantum Computing (NISQ). Reduction of noise is addressed at the technology level, while noise mitigation strategies have been proposed through the compilation process of quantum circuits. The compilation process entails a number of steps that are computationally intensive and scale poorly as the problems grow in size and resource usage. This paper addresses the problems associated with noise by proposing a noise aware qubit routing algorithm. This algorithm attempts to improve accuracy of circuits by using SWAP …

Implementation And Evaluation Of Deep Neural Networks In Commercially Available Processing In Memory Hardware, Prangon Das

Theses

Deep Neural Networks (DNN), specifically Convolutional Neural Networks (CNNs) are often associated with a large number of data-parallel computations. Therefore, data-centric computing paradigms, such as Processing in Memory (PIM), are being widely explored for CNN acceleration applications. A recent PIM architecture, developed and commercialized by the UPMEM company, has demonstrated impressive performance boost over traditional CPU-based systems for a wide range of data parallel applications. However, the application domain of CNN acceleration is yet to be explored on this PIM platform. In this work, successful implementations of CNNs on the UPMEM PIM system are presented. Furthermore, multiple operation mapping schemes …

Post-Season Depression, But Not Anxiety, Differs From In-Season Status In Ncaa Diii Lacrosse Student-Athletes, Jimmy Spillane

Theses

Objective: While mental health among collegiate athletes is receiving increased attention, research on when student-athletes suffer the most from anxious or depressive thoughts is limited. Therefore, the purpose of the study is to examine how anxiety and depression levels change in student-athletes between in-season, and after completion of the playoffs (after-season), timepoints.

Design: The present study was a pre-post study design with a repeated measures assessment six weeks apart that collected data using the Beck Anxiety Inventory (BAI) and Harvard Department of Psychiatry/National Depression Screening Day Scale (HANDS). The survey set was delivered using the Qualtrics Platform (Qualtrics International Inc, …

Quantifying The Effects Of Customer Predictability On Battery Energy Storage System Cost And Emissions, Mudit Bhadresh Purohit

Theses

Electricity providers are using variable pricing to encourage customers to shift energy consumption from “peak” times. This presents a great opportunity for the customers to reduce their bills using this variable pricing strategy coupled with a battery energy storage system (BESS). The schedule of charging and discharging the BESS depends on the forecasted customer demand data. Forecasts are bound to have a certain degree of error associated with them, and hence it becomes important to understand the effect of these forecasting errors on the cost and the emissions associated with scheduling of the BESS. In this work, a method has …

Supporting The Maintenance Of Identifier Names: A Holistic Approach To High-Quality Automated Identifier Naming, Anthony S. Peruma

Theses

A considerable part of the source code is identifier names-- unique lexical tokens that provide information about entities, and entity interactions, within the code. Identifier names provide human-readable descriptions of classes, functions, variables, etc. Poor or ambiguous identifier names (i.e., names that do not correctly describe the code behavior they are associated with) will lead developers to spend more time working towards understanding the code's behavior. Bad naming can also have detrimental effects on tools that rely on natural language clues; degrading the quality of their output and making them unreliable. Additionally, misinterpretations of the code, caused by poor names, …

Biophysical Mechanisms Of Cell-Substrate Interactions Over Porous Membranes, Zahra Allahyari

Theses

There is an increasing interest in the development of microphysiological systems due to their applications in drug discoveries and development of therapeutic strategies. Porous membranes are major components of these in vitro biological systems. The interest in using porous membranes, especially ultrathin ones, stems from their capabilities in providing physiologically relevant tissue interfaces and cell-cell communication between the co-cultured cells. Despite the wide applications of these membranes in biomimetic platforms, their role in modulating different cellular behaviors and the enabling mechanism for these effects are not fully understood. It has been previously shown that porous membranes generally weaken cell-substrate interaction. …

Low-Rank Clustering Via Lp1-Pca, Matt Krol

Theses

In recent years, subspace clustering has found many practical use cases which include, for example, image segmentation, motion segmentation, and facial clustering. The image and video data that is common to these types of applications often has high dimensionality. Rather than viewing high dimensionality as a drawback, we propose a novel algorithm for subspace clustering that takes advantage of the high dimensional nature of such data. We call this algorithm LP1-PCA Spectral Clustering. Specifically, we introduce a concept that we call cluster-ID sparsity, and we propose an algorithm called LP1-PCA to attain this in low data dimensions. Our novel LP1-PCA …

Evaluation Of The Accessibility Of Touchscreens For Individuals Who Are Blind Or Have Low Vision: Where To Go From Here, Elizabeth Codick

Theses

Touchscreen devices are well integrated into daily life and can be found in both personal and public spaces, but the inclusion of accessible features and interfaces continues to lag behind technology’s exponential advancement. This thesis aims to explore the experiences of individuals who are blind or have low vision (BLV) while interacting with non-tactile touchscreens, such as smartphones, tablets, smartwatches, coffee machines, smart home devices, kiosks, ATM machines, and more. The goal of this research is to create a set of recommended guidelines that can be used in designing and developing either personal devices or shared public technologies with accessible …

Buyer Prediction Through Machine Learning, Rashed Ibrahim Karmostaje

Theses

Targeted marketing has grown in popularity in recent years, as well as recognizing when a consumer will desire a commodity may be extremely important to a business. Predicting this demand, however, is a complex procedure. Businesses, promoters/marketers, and sellers are using machine learning approaches to execute buyer prediction. This study focuses on when a customer would buy fast-moving retail merchandise by evaluating a customer’s purchase history at partner vendors. The projections should be used to customize special discounts for customers who are about to make a purchase. In addition, buying behavior is a set of consumption habits that can be …