Physical Sciences and Mathematics Commons^™

Exploration Of Digital Synthesis, Angelo Indre

Williams Honors College, Honors Research Projects

“An Exploration of Digital Synthesis” is a comprehensive investigation into the world of digital audio and music production. The paper explores the fundamental concepts of sound synthesis, including MIDI, virtual instruments (VSTs), and the JUCE framework. The central focus of the paper is the implementation of a custom synthesizer, which serves as a case study for the practical application of digital synthesis. The paper addresses the key question of how to create a functioning synthesizer from scratch, providing detailed insights into the programming and design process. Overall, the paper represents a significant contribution to the fields of digital audio and …

Liquid Tab, Nathan Hulet

Williams Honors College, Honors Research Projects

Guitar transcription is a complex task requiring significant time, skill, and musical knowledge to achieve accurate results. Since most music is recorded and processed digitally, it would seem like many tools to digitally analyze and transcribe the audio would be available. However, the problem of automatic transcription presents many more difficulties than are initially evident. There are multiple ways to play a guitar, many diverse styles of playing, and every guitar sounds different. These problems become even more difficult considering the varying qualities of recordings and levels of background noise.

Machine learning has proven itself to be a flexible tool …

Textural And Mineral Analysis Of Pegmatites From The Pala And Mesa Grande Districts In San Diego, California, Gabrielle Potter

Williams Honors College, Honors Research Projects

San Diego pegmatites of the Pala and Mesa Grande mining districts are world renowned for their gem-quality minerals and their Li-bearing mineral phases. Pegmatite genesis, especially in San Diego mines, has been a debated topic since the 1900’s (Morgan and London, 2012). Pegmatite genesis is caused by partial melting processes and fractional crystallization that form granitic melts with high concentrations of rare earth elements (REE), such as lithium. Assessing the exact mineralogy and determining the overall textures can provide insight into the crystallization history of pegmatites. In the San Diego pegmatites, lithium-bearing phases, such as lepidolite and pink elbaite (a …

The Future Between Quantum Computing And Cybersecurity, Daniel Dorazio

Williams Honors College, Honors Research Projects

Quantum computing, a novel branch of technology based on quantum theory, processes information in ways beyond the capabilities of classical computers. Traditional computers use binary digits [bits], but quantum computers use quantum binary digits [qubits] that can exist in multiple states simultaneously. Since developing the first two-qubit quantum computer in 1998, the quantum computing field has experienced rapid growth.

Cryptographic algorithms such as RSA and ECC, essential for internet security, rely on the difficulty of complex math problems that classical computers can’t solve. However, the advancement of quantum technology threatens these encryption systems. Algorithms, such as Shor’s, leverage the power …

Urban Air Mobility: Vision, Challenges And Opportunities, Debjyoti Sengupta, Sajal K. Das

Computer Science Faculty Research & Creative Works

Urban Air Mobility (UAM) involving piloted or autonomous aerial vehicles, is envisioned as emerging disruptive technology for next-generation transportation addressing mobility challenges in congested cities. This paradigm may include aircrafts ranging from small unmanned aerial vehicles (UAVs) or drones, to aircrafts with passenger carrying capacity, such as personal air vehicles (PAVs). This paper highlights the UAM vision and brings out the underlying fundamental research challenges and opportunities from computing, networking, and service perspectives for sustainable design and implementation of this promising technology providing an innovative infrastructure for urban mobility. Important research questions include, but are not limited to, real-Time autonomous …

Smartlens: Robust Detection Of Rogue Device Via Frequency Domain Features In Lora-Enabled Iiot, Subir Halder, Amrita Ghosal, Thomas Newe, Sajal K. Das

Computer Science Faculty Research & Creative Works

A challenging problem in Long Range (LoRa) communications enabled Industrial Internet of Things (IIoT) is the detection of rogue devices, which attempt to impersonate real devices by spoofing their authentic identifications in order to steal information and gain access to the system. Although machine learning (ML) offers a promising approach to detecting rogue devices, existing ML models rely on domain knowledge yet exhibit low detection accuracy and vulnerability against adversarial attacks. This paper proposes SmartLens, a novel real-time frequency domain feature based rogue device detection system, using a lightweight statistical ML algorithm and Mahalanobis distance to achieve high accuracy and …

Inter-Rater Agreement For The Annotation Of Neurologic Signs And Symptoms In Electronic Health Records, Chelsea Oommen, Quentin Howlett-Prieto, Michael D. Carrithers, Daniel B. Hier

Chemistry Faculty Research & Creative Works

The extraction of patient signs and symptoms recorded as free text in electronic health records is critical for precision medicine. Once extracted, signs and symptoms can be made computable by mapping to signs and symptoms in an ontology. Extracting signs and symptoms from free text is tedious and time-consuming. Prior studies have suggested that inter-rater agreement for clinical concept extraction is low. We have examined inter-rater agreement for annotating neurologic concepts in clinical notes from electronic health records. After training on the annotation process, the annotation tool, and the supporting neuro-ontology, three raters annotated 15 clinical notes in three rounds. …

Geo-Distributed Multi-Tier Workload Migration Over Multi-Timescale Electricity Markets, Sourav Kanti Addya, Anurag Satpathy, Bishakh Chandra Ghosh, Sandip Chakraborty, Soumya K. Ghosh, Sajal K. Das

Computer Science Faculty Research & Creative Works

Virtual machine (VM) migration enables cloud service providers (CSPs) to balance workload, perform zero-downtime maintenance, and reduce applications' power consumption and response time. Migrating a VM consumes energy at the source, destination, and backbone networks, i.e., intermediate routers and switches, especially in a Geo-distributed setting. In this context, we propose a VM migration model called Low Energy Application Workload Migration (LEAWM) aimed at reducing the per-bit migration cost in migrating VMs over Geo-distributed clouds. With a Geo-distributed cloud connected through multiple Internet Service Providers (ISPs), we develop an approach to find out the migration path across ISPs leading to the …

Dispatching Point Selection For A Drone-Based Delivery System Operating In A Mixed Euclidean–Manhattan Grid, Francesco Betti Sorbelli, Federico Corò, Sajal K. Das, Cristina M. Pinotti, Anil Shende

Computer Science Faculty Research & Creative Works

In this paper, we present a drone-based delivery system that assumes to deal with a mixed-area, i.e., two areas, one rural and one urban, placed side-by-side. In the mixed-areas, called EM-grids, the distances are measured with two different metrics, and the shortest path between two destinations concatenates the Euclidean and Manhattan metrics. Due to payload constraints, the drone serves a single customer at a time returning back to the dispatching point (DP) after each delivery to load a new parcel for the next customer. In this paper, we present the 1 -Median Euclidean–Manhattan grid Problem (MEMP) for EM-grids, whose goal …

Welcome From General Chairs, Sajal K. Das, Wen Zhan Song

Computer Science Faculty Research & Creative Works

No abstract provided.

Detection Of False Data Injection In Smart Water Metering Infrastructure, Ayanfeoluwa Oluyomi, Shameek Bhattacharjee, Sajal K. Das

Computer Science Faculty Research & Creative Works

Smart water metering (SWM) infrastructure collects real-Time water usage data that is useful for automated billing, leak detection, and forecasting of peak periods. Cyber/physical attacks can lead to data falsification on water usage data. This paper proposes a learning approach that converts smart water meter data into a Pythagorean mean-based invariant that is highly stable under normal conditions but deviates under attacks. We show how adversaries can launch deductive or camouflage attacks in the SWM infrastructure to gain benefits and impact the water distribution utility. Then, we apply a two-Tier approach of stateless and stateful detection, reducing false alarms without …

Cyber-Agricultural Systems For Crop Breeding And Sustainable Production, Soumik Sarkar, Baskar Ganapathysubramanian, Arti Singh, Fateme Fotouhi, Soumyashree Kar, Koushik Nagasubramanian, Girish Chowdhary, Sajal K. Das, George Kantor, Adarsh Krishnamurthy, Nirav Merchant, Asheesh K. Singh

Computer Science Faculty Research & Creative Works

The Cyber-Agricultural System (CAS) Represents an overarching Framework of Agriculture that Leverages Recent Advances in Ubiquitous Sensing, Artificial Intelligence, Smart Actuators, and Scalable Cyberinfrastructure (CI) in Both Breeding and Production Agriculture. We Discuss the Recent Progress and Perspective of the Three Fundamental Components of CAS – Sensing, Modeling, and Actuation – and the Emerging Concept of Agricultural Digital Twins (DTs). We Also Discuss How Scalable CI is Becoming a Key Enabler of Smart Agriculture. in This Review We Shed Light on the Significance of CAS in Revolutionizing Crop Breeding and Production by Enhancing Efficiency, Productivity, Sustainability, and Resilience to Changing …

Robust Federated Learning Against Backdoor Attackers, Priyesh Ranjan, Ashish Gupta, Federico Corò, Sajal K. Das

Computer Science Faculty Research & Creative Works

Federated Learning is a Privacy-Preserving Alter-Native for Distributed Learning with No Involvement of Data Transfer. as the Server Does Not Have Any Control on Clients' Actions, Some Adversaries May Participate in Learning to Introduce Corruption into the Underlying Model. Backdoor Attacker is One Such Adversary Who Injects a Trigger Pattern into the Data to Manipulate the Model Outcomes on a Specific Sub-Task. This Work Aims to Identify Backdoor Attackers and to Mitigate their Effects by Isolating their Weight Updates. Leveraging the Correlation between Clients' Gradients, We Propose Two Graph Theoretic Algorithms to Separate Out Attackers from the Benign Clients. under …

Disagreement Matters: Exploring Internal Diversification For Redundant Attention In Generic Facial Action Analysis, Xiaotian Li, Zheng Zhang, Xiang Zhang, Taoyue Wang, Zhihua Li, Huiyuan Yang, Umur Ciftci, Qiang Ji, Jeffrey Cohn, Lijun Yin

Computer Science Faculty Research & Creative Works

This paper demonstrates the effectiveness of a diversification mechanism for building a more robust multi-attention system in generic facial action analysis. While previous multi-attention (e.g., visual attention and self-attention) research on facial expression recognition (FER) and Action Unit (AU) detection have been thoroughly studied to focus on "external attention diversification", where attention branches localize different facial areas, we delve into the realm of "internal attention diversification" and explore the impact of diverse attention patterns within the same Region of Interest (RoI). Our experiments reveal that variability in attention patterns significantly impacts model performance, indicating that unconstrained multi-attention plagued by redundancy …

Strategic Information Design In Selfish Routing With Quantum Response Travelers, Sainath Sanga, Venkata Sriram Siddhardh Nadendla, Mukund Telukunta, Sajal K. Das

Computer Science Faculty Research & Creative Works

Selfish routing begets inefficiency in multi-agent transportation systems, leading to significant economic losses in our society. Although several powerful techniques (e. g., marginal cost pricing) have been proposed to mitigate price-of-anarchy (a measure of inefficiency), social welfare maximization still remains a huge challenge in selfish routing, especially when travelers deviate from maximizing their own expected utilities. This paper proposes a novel informational intervention to improve the efficiency of selfish routing, especially in the presence of quantal response travelers. Specifically, modeling the interaction between the system and travelers as a Stackelberg game, and develop a novel approximate algorithm, called LoRI (which …

Optimizing Federated Learning In Leo Satellite Constellations Via Intra-Plane Model Propagation And Sink Satellite Scheduling, Mohamed Elmahallawy, Tie (Tony) T. Luo

Computer Science Faculty Research & Creative Works

The advances in satellite technology developments have recently seen a large number of small satellites being launched into space on Low Earth orbit (LEO) to collect massive data such as Earth observational imagery. The traditional way which downloads such data to a ground station (GS) to train a machine learning (ML) model is not desirable due to the bandwidth limitation and intermittent connectivity between LEO satellites and the GS. Satellite edge computing (SEC), on the other hand, allows each satellite to train an ML model onboard and uploads only the model to the GS which appears to be a promising …

Analyzing Ground Motion Records With Cvi Fuzzy Art, Dustin Tanksley, Xinzhe Yuan, Genda Chen, Donald C. Wunsch

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

This paper explores using Cluster Validity Indices Fuzzy Adaptative Resonance Theory (CVI Fuzzy ART) to cluster ground motion records (GMRs). Clustering the features extracted from a supervised network trained for predicting the structure damage results in less overfitting from the trained network. Using Cluster Validity Indices (CVIs) to evaluate the clustering gives feedback to how well the data is being classified, allowing further separation of the data. By using CVI Fuzzy ART in combination with features extracted from a trained Convolutional Neural Network (CNN), we were able to form additional clusters in the data. Within the primary clusters, accuracy was …

Natural Product Co-Metabolism And The Microbiota–Gut–Brain Axis In Age-Related Diseases, Mark Obrenovich, Sandeep Kumar Singh, Yi Li, George Perry, Bushra Siddiqui, Waqas Haq, V. Prakash Reddy

Chemistry Faculty Research & Creative Works

Complementary alternative medicine approaches are growing treatments of diseases to standard medicine practice. Many of these concepts are being adopted into standard practice and orthomolecular medicine. Age-related diseases, in particular neurodegenerative disorders, are particularly difficult to treat and a cure is likely a distant expectation for many of them. Shifting attention from pharmaceuticals to phytoceuticals and "bugs as drugs" represents a paradigm shift and novel approaches to intervention and management of age-related diseases and downstream effects of aging. Although they have their own unique pathologies, a growing body of evidence suggests Alzheimer's disease (AD) and vascular dementia (VaD) share common …

Natural Product Co-Metabolism And The Microbiota–Gut–Brain Axis In Age-Related Diseases, Mark Obrenovich, Sandeep Kumar Singh, Yi Li, George Perry, Bushra Siddiqui, Waqas Haq, V. Prakash Reddy

Chemistry Faculty Research & Creative Works

Complementary alternative medicine approaches are growing treatments of diseases to standard medicine practice. Many of these concepts are being adopted into standard practice and orthomolecular medicine. Age-related diseases, in particular neurodegenerative disorders, are particularly difficult to treat and a cure is likely a distant expectation for many of them. Shifting attention from pharmaceuticals to phytoceuticals and "bugs as drugs" represents a paradigm shift and novel approaches to intervention and management of age-related diseases and downstream effects of aging. Although they have their own unique pathologies, a growing body of evidence suggests Alzheimer's disease (AD) and vascular dementia (VaD) share common …

Comprehensive Evaluation Of A Novel Re-Crosslinkable Preformed Particle Gel For The Water Management Of Reservoir With Concentrated Divalent Ions, Tao Song, Mohamed Ahdaya, Zhanmiao Zhai, Thomas P. Schuman, Baojun Bai

Chemistry Faculty Research & Creative Works

As one of the most widely used technology to ameliorate the reservoir's heterogeneity, polymer gels have been applied for more than 60 years. However, how to plug fractured reservoirs with significant abnormal features, high temperature and high salinity, especially the divalent cations, is still a challenging target. This work systematically evaluated a novel salt-resistant re-crosslinkable preformed particle gel (SR-RPPG) designed for fractured reservoirs with excellent salt resistance (up to 5 % CaCl₂). We evaluated the swelling kinetics, thermal stability and plugging efficiency of this SR-RPPG. We assessed the swelling kinetic and re-crosslinking behavior of the SR-RPPG through the …

Nanostructured Ternary Nickel-Based Mixed Anionic (Telluro)-Selenide As A Superior Catalyst For Oxygen Evolution Reaction, Ibrahim Munkaila Abdullahi, Siby Thomas, Alessio Gagliardi, Mohsen Asle Zaeem, Manashi Nath

Chemistry Faculty Research & Creative Works

Developing Protocols for Designing High-Efficiency, Durable, Cost-Effective Electrocatalysts for Oxygen Evolution Reaction (OER) Necessitates Deeper Understanding of Structure–property Correlation as a Function of Composition. Herein, It Has Been Demonstrated that Incorporating Tellurium into Binary Nickel Chalcogenide (NiSe) and Creating a Mixed Anionic Phase Perturbs its Electronic Structure and Significantly Enhances the OER Activity. a Series of Nanostructured Nickel Chalcogenides Comprising a Layer-By-Layer Morphology Along with Mixed Anionic Ternary Phase Are Grown in Situ on Nickel Foam with Varying Morphological Textures using Simple Hydrothermal Synthesis Route. Comprehensive X-Ray Diffraction, X-Ray Photoelectron Spectroscopy, and in Situ Raman Spectroscopy Analysis Confirms the Formation …

Epitaxial Electrodeposition Of Ordered Inorganic Materials, Jay A. Switzer, Avishek Banik

Chemistry Faculty Research & Creative Works

Conspectus The quality of technological materials generally improves as the crystallographic order is increased. This is particularly true in semiconductor materials, as evidenced by the huge impact that bulk single crystals of silicon have had on electronics. Another approach to producing highly ordered materials is the epitaxial growth of crystals on a single-crystal surface that determines their orientation. Epitaxy can be used to produce films and nanostructures of materials with a level of perfection that approaches that of single crystals. It may be used to produce materials that cannot be grown as large single crystals due to either economic or …

Temperature Dependence Of The Electronic Absorption Spectrum Of No2, Steve Ndengué, Ernesto Quintas Sánchez, Richard Dawes, Christopher C. Blackstone, David L. Osborn

Chemistry Faculty Research & Creative Works

The nitrogen dioxide (NO₂) radical is composed of the two most abundant elements in the atmosphere, where it can be formed in a variety of ways including combustion, detonation of energetic materials, and lightning. Relevant also to smog and ozone cycles, together these processes span a wide range of temperatures. Remarkably, high-resolution NO₂ electronic absorption spectra have only been reported in a narrow range below about 300 K. Previously, we reported [ J. Phys. Chem. A 2021, 125, 5519−5533 ] the construction of quasi-diabatic potential energy surfaces (PESs) for the lowest four electronic states (X̃, Ã, B̃, …

Nuclear Magnetic Resonance Study Of Co2 Capture By Fluoroalkylamines: Ammonium Ion Pka Depression Via Fluorine Modification And Thermochemistry Of Carbamylation, Brian Jameson, Kari Knobbe, Rainer Glaser

Chemistry Faculty Research & Creative Works

We are developing energy-efficient and reversible carbon capture and release (CCR) systems that mimic the Lys201 carbamylation reaction in the active site of ribulose-1,5-bisphosphate carboxylase-oxygenase (RuBisCO). The multiequilibria scenario ammonium ion Xa ⇌ amine Xb ⇌ carbamic acid Xc ⇌ carbamate Xd requires the presence of both free amine and CO2 for carbamylation and is affected by the pKa(Xa). Two fluorination strategies aimed at ammonium ion pKa depression and low pH carbamylation were analyzed with (2,2,2-trifluoroethyl) butylamine 2b and 2,2-difluoropropylamine 3b and compared to butylamine 1b. The determination of K1 and ΔG1 of the carbamylation reactions requires the solution of …

Topology Driven And Soft Phonon Mode Enabled Na-Ion Diffusion In Quaternary Chalcogenides, Na3zngax4 (X = S, And Se), Sajan Kumar, Mayanak K. Gupta, Ranjan Mittal, Santhoshkumar Sundaramoorthy, Amitava Choudhury, Naresh C. Osti, Alexander I. Kolesnikov, Matthew B. Stone, Yongqiang Cheng, Samrath L. Chaplot

Chemistry Faculty Research & Creative Works

The compounds Na3ZnGaX4 (X = S, Se) are potential solid electrolyte materials in sodium-based batteries, which have certain advantages over oxide materials and have shown significant ionic conductivity at ambient temperature. In this paper, we bring out atomic-level features of the diffusion process in these new materials using the microscopic techniques of inelastic neutron scattering (INS), Quasi elastic neutron scattering (QENS), and ab initio molecular dynamics (AIMD) simulations. The insights obtained from these techniques are unique and not available from other macroscopic experiments. Neutron scattering experiments have been performed at temperatures from 100 to 700 K. The simulations have been …

An Explainable Deep Learning Model For Prediction Of Severity Of Alzheimer's Disease, Godwin Ekuma, Daniel B. Hier, Tayo Obafemi-Ajayi

Chemistry Faculty Research & Creative Works

Deep Convolutional Neural Networks (CNNs) have become the go-To method for medical imaging classification on various imaging modalities for binary and multiclass problems. Deep CNNs extract spatial features from image data hierarchically, with deeper layers learning more relevant features for the classification application. Despite the high predictive accuracy, usability lags in practical applications due to the black-box model perception. Model explainability and interpretability are essential for successfully integrating artificial intelligence into healthcare practice. This work addresses the challenge of an explainable deep learning model for the prediction of the severity of Alzheimer's disease (AD). AD diagnosis and prognosis heavily rely …

Extracellular Poly(Hydroxybutyrate) Bioplastic Production Using Surface Display Techniques, Kevin Beaver, Ashwini Dantanarayana, Willisa Liou, Markus Babst, Shelley D. Minteer

Chemistry Faculty Research & Creative Works

Poly(hydroxybutyrate) is a biocompatible, biodegradable polyester synthesized naturally in a variety of microbial species. A greener alternative to petroleum-based plastics and sought after for biomedical applications, poly(hydroxybutyrate) has failed to break through as a leading material in the plastic industry due to its high cost of production. Specifically, the extraction of this material from within bacterial cells requires lysis of cells, which takes time, uses harsh chemicals, and starts the process again with growing new living cells. Recently, surface display of enzymes on bacterial membranes has become an emerging technique for extracellular biocatalysis. In this work, a fusion protein lpp-ompA-phaC …

Advances In Bioelectrode Design For Developing Electrochemical Biosensors, Nabajyoti Kalita, Sudarshan Gogoi, Shelley D. Minteer, Pranab Goswami

Chemistry Faculty Research & Creative Works

The critical performance factors such as selectivity, sensitivity, operational and storage stability, and response time of electrochemical biosensors are governed mainly by the function of their key component, the bioelectrode. Suitable design and fabrication strategies of the bioelectrode interface are essential for realizing the requisite performance of the biosensors for their practical utility. A multifaceted attempt to achieve this goal is visible from the vast literature exploring effective strategies for preparing, immobilizing, and stabilizing biorecognition elements on the electrode surface and efficient transduction of biochemical signals into electrical ones (i.e., current, voltage, and impedance) through the bioelectrode interface with the …

Continual Reinforcement Learning Formulation For Zero-Sum Game-Based Constrained Optimal Tracking, Behzad Farzanegan, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This study provides a novel reinforcement learning-based optimal tracking control of partially uncertain nonlinear discrete-time (DT) systems with state constraints using zero-sum game (ZSG) formulation. To address optimal tracking, a novel augmented system consisting of tracking error and its integral value, along with an uncertain desired trajectory, is constructed. A barrier function (BF) with a tradeoff factor is incorporated into the cost function to keep the state trajectories to remain within a compact set and to balance safety with optimality. Next, by using the modified value functional, the ZSG formulation is introduced wherein an actor–critic neural network (NN) framework is …

Securing The Transportation Of Tomorrow: Enabling Self-Healing Intelligent Transportation, Elanor Jackson, Sahra Sedigh Sarvestani

Electrical and Computer Engineering Faculty Research & Creative Works

The safety of autonomous vehicles relies on dependable and secure infrastructure for intelligent transportation. The doctoral research described in this paper aims to enable self-healing and survivability of the intelligent transportation systems required for autonomous vehicles (AV-ITS). The proposed approach is comprised of four major elements: qualitative and quantitative modeling of the AV-ITS, stochastic analysis to capture and quantify interdependencies, mitigation of disruptions, and validation of efficacy of the self-healing process. This paper describes the overall methodology and presents preliminary results, including an agent-based model for detection of and recovery from disruptions to the AV-ITS.