Computer Engineering Commons^™

Broadband Dielectric Spectroscopic Detection Of Volatile Organic Compounds With Zinc Oxide And Metal-Organic Frameworks As Solid-State Sensor Materials, Papa Kojo Amoah

Electrical & Computer Engineering Theses & Dissertations

The industrial revolution drove technological progress but also increased the release of harmful pollutants, posing significant risks to human health and the environment. Volatile organic compounds (VOCs), which have various anthropogenic and natural sources, are particularly concerning due to their impact on public health, especially in urban areas. Addressing these adverse effects requires comprehensive strategies for mitigation as traditional gas sensing techniques have limitations and there is a need for innovative approaches to VOC detection.

VOCs encompass a diverse group of chemicals with high volatility, emitted from various human activities and natural sources. These compounds play a crucial role in …

Tech Time

DePaul Magazine

DePaul is embracing tech more than ever, incorporating innovative devices and approaches into education in all corners of the university. Here are seven ways DePaul provides hands-on experiences with cutting-edge tools that position students and faculty in the forefront of their industries and disciplines.

Chatgpt As Metamorphosis Designer For The Future Of Artificial Intelligence (Ai): A Conceptual Investigation, Amarjit Kumar Singh (Library Assistant), Dr. Pankaj Mathur (Deputy Librarian)

Library Philosophy and Practice (e-journal)

Abstract

Purpose: The purpose of this research paper is to explore ChatGPT’s potential as an innovative designer tool for the future development of artificial intelligence. Specifically, this conceptual investigation aims to analyze ChatGPT’s capabilities as a tool for designing and developing near about human intelligent systems for futuristic used and developed in the field of Artificial Intelligence (AI). Also with the helps of this paper, researchers are analyzed the strengths and weaknesses of ChatGPT as a tool, and identify possible areas for improvement in its development and implementation. This investigation focused on the various features and functions of ChatGPT that …

Enhanced Neurologic Concept Recognition Using A Named Entity Recognition Model Based On Transformers, Sima Azizi, Daniel B. Hier, Donald C. Wunsch

Chemistry Faculty Research & Creative Works

Although Deep Learning Has Been Applied to the Recognition of Diseases and Drugs in Electronic Health Records and the Biomedical Literature, Relatively Little Study Has Been Devoted to the Utility of Deep Learning for the Recognition of Signs and Symptoms. the Recognition of Signs and Symptoms is Critical to the Success of Deep Phenotyping and Precision Medicine. We Have Developed a Named Entity Recognition Model that Uses Deep Learning to Identify Text Spans Containing Neurological Signs and Symptoms and Then Maps These Text Spans to the Clinical Concepts of a Neuro-Ontology. We Compared a Model based on Convolutional Neural Networks …

Tau Kinetics In Alzheimer's Disease, Daniel B. Hier, Sima Azizi, Matthew S. Thimgan, Donald C. Wunsch

Chemistry Faculty Research & Creative Works

The Cytoskeletal Protein Tau is Implicated in the Pathogenesis of Alzheimer's Disease Which is Characterized by Intra-Neuronal Neurofibrillary Tangles Containing Abnormally Phosphorylated Insoluble Tau. Levels of Soluble Tau Are Elevated in the Brain, the CSF, and the Plasma of Patients with Alzheimer's Disease. to Better Understand the Causes of These Elevated Levels of Tau, We Propose a Three-Compartment Kinetic Model (Brain, CSF, and Plasma). the Model Assumes that the Synthesis of Tau Follows Zero-Order Kinetics (Uncorrelated with Compartmental Tau Levels) and that the Release, Absorption, and Clearance of Tau is Governed by First-Order Kinetics (Linearly Related to Compartmental Tau Levels). …

Du Undergraduate Showcase: Research, Scholarship, And Creative Works: Abstracts, Emma Aggeler, Elena Arroway, Daisy T. Booker, Justin Bravo, Kyle Bucholtz, Megan Burnham, Nicole Choi, Spencer Cockerell, Rosie Contino, Jackson Garske, Kaitlyn Glover, Caroline Hamilton, Haley Hartmann, Madalyne Heiken, Colin Holter, Leah Huzjak, Alyssa Jeng, Cole Jernigan, Chad Kashiwa, Adelaide Kerenick, Emily King, Abigail Langeberg, Maddie Leake, Meredith Lemons, Alec Mackay, Greer Mckinley, Ori Miller, Guy Milliman, Katherine Miromonti, Audrey Mitchell, Lauren Moak, Megan Morrell, Gelella Nebiyu, Zdenek Otruba, Toni V. Panzera, Kassidy Patarino, Sneha Patil, Alexandra Penney, Kevin Persky, Caitlin Pham, Gabriela Recinos, Mary Ringgenberg, Chase Routt, Olivia Schneider, Roman Shrestha, Arlo Simmerman, Alec Smith, Tessa Smith, Nhi-Lac Thai, Kyle Thurmann, Casey Tindall, Amelia Trembath, Maria Trubetskaya, Zachary Vangelisti, Peter Vo, Abby Walker, David Winter, Grayden Wolfe, Leah York

DU Undergraduate Research Journal Archive

Abstracts from the DU Undergraduate Showcase.

Schizophrenia Classification Using Resting State Eeg Functional Connectivity: Source Level Outperforms Sensor Level, Sima Azizi, Daniel B. Hier, Donald C. Wunsch

Chemistry Faculty Research & Creative Works

Disrupted Functional and Structural Connectivity Measures Have Been Used to Distinguish Schizophrenia Patients from Healthy Controls. Classification Methods based on Functional Connectivity Derived from EEG Signals Are Limited by the Volume Conduction Problem. Recorded Time Series at Scalp Electrodes Capture a Mixture of Common Sources Signals, Resulting in Spurious Connections. We Have Transformed Sensor Level Resting State EEG Times Series to Source Level EEG Signals Utilizing a Source Reconstruction Method. Functional Connectivity Networks Were Calculated by Computing Phase Lag Values between Brain Regions at Both the Sensor and Source Level. Brain Complex Network Analysis Was Used to Extract Features and …

Subsumption Reduces Dataset Dimensionality Without Decreasing Performance Of A Machine Learning Classifier, Donald C. Wunsch, Daniel B. Hier

Chemistry Faculty Research & Creative Works

When Features in a High Dimension Dataset Are Organized Hierarchically, There is an Inherent Opportunity to Reduce Dimensionality. Since More Specific Concepts Are Subsumed by More General Concepts, Subsumption Can Be Applied Successively to Reduce Dimensionality. We Tested Whether Sub-Sumption Could Reduce the Dimensionality of a Disease Dataset Without Impairing Classification Accuracy. We Started with a Dataset that Had 168 Neurological Patients, 14 Diagnoses, and 293 Unique Features. We Applied Subsumption Repeatedly to Create Eight Successively Smaller Datasets, Ranging from 293 Dimensions in the Largest Dataset to 11 Dimensions in the Smallest Dataset. We Tested a MLP Classifier on All …

Treated Hfo2 Based Rram Devices With Ru, Tan, Tin As Top Electrode For In-Memory Computing Hardware, Yuvraj Dineshkumar Patel

Theses

The scalability and power efficiency of the conventional CMOS technology is steadily coming to a halt due to increasing problems and challenges in fabrication technology. Many non-volatile memory devices have emerged recently to meet the scaling challenges. Memory devices such as RRAMs or ReRAM (Resistive Random-Access Memory) have proved to be a promising candidate for analog in memory computing applications related to inference and learning in artificial intelligence. A RRAM cell has a MIM (Metal insulator metal) structure that exhibits reversible resistive switching on application of positive or negative voltage. But detailed studies on the power consumption, repeatability and retention …

Development, Manufacture And Application Of A Solid-State Ph Sensor Using Ruthenium Oxide, Wade Lonsdale

Theses: Doctorates and Masters

The measurement of pH is undertaken frequently in numerous settings for many applications. The common glass pH probe is almost ideal for measuring pH, and as such, it is used almost ubiquitously. However, glass is not ideal for all applications due to its relatively large size, fragility, need for recalibration and wet-storage. Therefore, much research has been undertaken on the use of metal oxides as an alternative for the measurement of pH.

Here, a solid-state potentiometric pH sensor is developed using ruthenium metal oxide (RuO2). Initially, pH sensitive RuO2 electrodes were prepared by deposition with radio frequency magnetron sputtering (RFMS) …

In Situ Electron Microscopy Of Plasmon-Mediated Nanocrystal Synthesis, Peter Sutter, Ying Li, Christos Argyropoulos, Eli A. Sutter

Department of Electrical and Computer Engineering: Faculty Publications

Chemical processes driven by nonthermal energy (e.g., visible light) are attractive for future approaches to energy conversion, synthesis, photocatalysis, and so forth. The growth of anisotropic metal nanostructures mediated by excitation of a localized surface plasmon resonance (LSPR) is a prototype example of such a reaction. Important aspects, notably the growth mechanism and a possible role of plasmonic “hot spots” within the metal nanostructures, remain poorly understood. Here, we use in situ electron microscopy to stimulate and image the plasmon-mediated growth of triangular Ag nanoprisms in solution. The quantification of the time-dependent evolution of the lateral size and thickness of …

Light-Activated Photocurrent Degradation And Self-Healing In Perovskite Solar Cells, Wanyi Nie, Jean-Christophe Blancon, Amanda J. Neukirch, Kannatassen Appavoo, Hsinhan Tsai, Manish Chhowalla, Muhammad A. Alam, Matthew Y. Sfeir, Claudine Katan, Jacky Even, Sergei Tretiak, Jared J. Crochet, Gautam Gupta, Aditya D. Mohite

Publications and Research

Solution-processed organometallic perovskite solar cells have emerged as one of the most promising thin-film photovoltaic technology. However, a key challenge is their lack of stability over prolonged solar irradiation. Few studies have investigated the effect of light soaking on hybrid perovskites and have attributed the degradation in the optoelectronic properties to photochemical or field-assisted ion migration. Here we show that the slow photocurrent degradation in thin-film photovoltaic devices is due to the formation of light-activated meta-stable deep-level trap states. However, the devices can self-heal completely by resting them in the dark for <1 min or the degradation can be completely prevented by operating the devices at 0°C. We investigate several physical mechanisms to explain the microscopic origin for the formation of these trap states, among which the creation of small polaronic states involving localized cooperative lattice strain and molecular orientations emerges as a credible microscopic mechanism requiring further detailed studies.

Energy-Efficient Computational Chemistry: Comparison Of X86 And Arm Systems, Kristopher Keipert, Gaurav Mitra, Vaibhav Sunriyal, Sarom S. Leang, Masha Sosonkina, Alistair P. Rendell, Mark S. Gordon

Computational Modeling & Simulation Engineering Faculty Publications

The computational efficiency and energy-to-solution of several applications using the GAMESS quantum chemistry suite of codes is evaluated for 32-bit and 64-bit ARM-based computers, and compared to an x86 machine. The x86 system completes all benchmark computations more quickly than either ARM system and is the best choice to minimize time to solution. The ARM64 and ARM32 computational performances are similar to each other for Hartree-Fock and density functional theory energy calculations. However, for memory-intensive second-order perturbation theory energy and gradient computations the lower ARM32 read/write memory bandwidth results in computation times as much as 86% longer than on the …

Distributed Approach For Peptide Identification, Naga V K Abhinav Vedanbhatla

Masters Theses & Specialist Projects

A crucial step in protein identification is peptide identification. The Peptide Spectrum Match (PSM) information set is enormous. Hence, it is a time-consuming procedure to work on a single machine. PSMs are situated by a cross connection, a factual score, or a probability that the match between the trial and speculative is right and original. This procedure takes quite a while to execute. So, there is demand for enhancement of the performance to handle extensive peptide information sets. Development of appropriate distributed frameworks are expected to lessen the processing time.

The designed framework uses a peptide handling algorithm named C-Ranker, …

Online Learning In A Chemical Perceptron, Peter Banda, Christof Teuscher, Matthew R. Lakin

Computer Science Faculty Publications and Presentations

Autonomous learning implemented purely by means of a synthetic chemical system has not been previously realized. Learning promotes reusability and minimizes the system design to simple input-output specification. In this article we introduce a chemical perceptron, the first full-featured implementation of a perceptron in an artificial (simulated) chemistry. A perceptron is the simplest system capable of learning, inspired by the functioning of a biological neuron. Our artificial chemistry is deterministic and discrete-time, and follows Michaelis-Menten kinetics. We present two models, the weight-loop perceptron and the weight-race perceptron, which represent two possible strategies for a chemical implementation of linear integration and …

Exploring Computational Chemistry On Emerging Architectures, David Dewayne Jenkins

Doctoral Dissertations

Emerging architectures, such as next generation microprocessors, graphics processing units, and Intel MIC cards, are being used with increased popularity in high performance computing. Each of these architectures has advantages over previous generations of architectures including performance, programmability, and power efficiency. With the ever-increasing performance of these architectures, scientific computing applications are able to attack larger, more complicated problems. However, since applications perform differently on each of the architectures, it is difficult to determine the best tool for the job. This dissertation makes the following contributions to computer engineering and computational science. First, this work implements the computational chemistry variational …

Data Mining Of Protein Databases, Christopher Assi

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

Data mining of protein databases poses special challenges because many protein databases are non-relational whereas most data mining and machine learning algorithms assume the input data to be a relational database. Protein databases are non-relational mainly because they often contain set data types. We developed new data mining algorithms that can restructure non-relational protein databases so that they become relational and amenable for various data mining and machine learning tools. We applied the new restructuring algorithms to a pancreatic protein database. After the restructuring, we also applied two classification methods, such as decision tree and SVM classifiers and compared their …

Techniques For Sensor-Integrated Robotic Systems: Raman Spectra Analysis, Image Guidance, And Kinematic Calibration, Luke Anthony Reisner

Wayne State University Dissertations

Robotics and sensor technology have made impressive advancements over the years. There are now robotic systems that help perform surgeries or explore the surface of Mars, and there are sensors that detect trace amounts of explosives or identify diseased human tissue. The most powerful systems integrate robots and sensors, which are natural complements to each other. Sensors can provide information that might otherwise be unavailable due to indirect robotic manipulation (e.g., images of the target environment), and robots can provide suitably precise positioning of an analytical sensor.

To have an effective sensor-integrated robotic system, multiple capabilities are needed in the …

2nd Annual Undergraduate Research Conference Abstract Book, University Of Missouri--Rolla

Undergraduate Research Conference at Missouri S&T

No abstract provided.

An On-Line Acid-Base Titration Applet In The Generic Tutorial System For The Sciences Project, Thomas Lee Gummo

Theses Digitization Project

The purpose of this Master's Project was to develop an Acid-Base Titration Simulator. It was also to be a part of the California State University - San Bernardino's GTSS, Generic Tutorial System for the Sciences, project. The main benefit is that students will be able to conduct titration experiments over the Internet without being in the laboratory and without costly equipment or dangerous chemicals. Instructors at the high school and college level can demonstrate the key principles of titration.

A Dipolar Coupling Based Strategy For Simultaneous Resonance Assignment And Structure Determination Of Protein Backbones, Fang Tian, Homayoun Valafar, James H. Prestegard

Faculty Publications

A new approach for simultaneous protein backbone resonance assignment and structure determination by NMR is introduced. This approach relies on recent advances in high-resolution NMR spectroscopy that allow observation of anisotropic interactions, such as dipolar couplings, from proteins partially aligned in field ordered media. Residual dipolar couplings are used for both geometric information and a filter in the assembly of residues in a sequential manner. Experimental data were collected in less than one week on a small redox protein, rubredoxin, that was 15N enriched but not enriched above 1% natural abundance in 13C. Given the acceleration possible with partial 13C …