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Articles 1 - 30 of 610
Full-Text Articles in Physical Sciences and Mathematics
Feasibility Study Of Off-The-Shelf Components On A Split-Cycle Motor And Esc Testbed, Hayden C. Lotspeich
Feasibility Study Of Off-The-Shelf Components On A Split-Cycle Motor And Esc Testbed, Hayden C. Lotspeich
Computer Science and Engineering Theses
This project aims to create a testbed for split-cycle flapping wing systems that allows for testing of different motors and ESC protocols to find a suitable set for a flapping wing system. In order for a flapping-wing drone to be able to maneuver, it has to be able to flap its wings at different speeds when flapping forward and flapping backwards. The arching back-and-forth motion is what the output wing would be connected to, so this system is used to calculate the maximum split-cycle time ratio that can be achieved when set up with different motors and ESC protocols.
Enhancing Biomedical Imaging With Ai: Compression, Prediction, And Multi-Modal Integration For Clinical Advancement, Mohammad Sadegh Nasr
Enhancing Biomedical Imaging With Ai: Compression, Prediction, And Multi-Modal Integration For Clinical Advancement, Mohammad Sadegh Nasr
Computer Science and Engineering Dissertations
This dissertation delves into the enhancement of biomedical image analysis through the deployment of artificial intelligence methodologies, focusing on the transition from theoretical innovation to practical clinical utility. Spanning four cornerstone projects, the work encapsulates the development of predictive models for spatial transcriptomics, efficient image compression for cancer pathology slides, and critical evaluations of histopathology slide search engines. The first project employs Random Forest Regression and spatial point processes to forecast cell distribution patterns, thereby offering a novel perspective on gene expression in embryogenesis at a single-molecule resolution. The second venture introduces a Variational Autoencoder (VAE) that sets a new …
An Intelligent Multi-Modal Framework Towards Assessing Human Cognition, Ashish Jaiswal
An Intelligent Multi-Modal Framework Towards Assessing Human Cognition, Ashish Jaiswal
Computer Science and Engineering Dissertations
Cognition is the mental process of acquiring knowledge and understanding through thought, experience, and senses. Fatigue is a loss in cognitive or physical performance due to physiological factors such as insufficient sleep, long work hours, stress, and physical exertion. It adversely affects the human body and can slow reaction times, reduce attention, and limit short-term memory. Hence, there is a need to monitor a person's state to avoid extreme fatigue conditions that can result in physiological complications. However, tools to understand and assess fatigue are minimal. This thesis primarily focuses on building an experimental setup that induces cognitive fatigue (CF) …
Homln-Sd: Substructure Discovery In Homogeneous Multilayer Networks, Arshdeep Singh
Homln-Sd: Substructure Discovery In Homogeneous Multilayer Networks, Arshdeep Singh
Computer Science and Engineering Theses
Substructure discovery is a process in data analysis and data mining that involves identifying and extracting meaningful patterns, structures, or components within a larger dataset. These substructures can be of various types, such as frequent patterns, motifs, or any other relevant features within the data. The growth of the internet and the proliferation of mobile devices have led to the generation of enormous amounts of data. Companies like Facebook and Twitter can generate large datasets from user interactions on their websites, such as connections between users and user generated content. Moreover, advances in processing power and storage capacity have made …
Enhancing Indoors Robotic Traversability Estimation With Sensor Fusion, Christos Sevastopoulos
Enhancing Indoors Robotic Traversability Estimation With Sensor Fusion, Christos Sevastopoulos
Computer Science and Engineering Dissertations
Generally speaking, traversability estimation illustrates the ability to navigate or move through a particular environment (indoors or outdoors). Indoor environments are governed by uncertainty and stochasticity arising from their complex structures encapsulating both static elements like furniture and walls, as well as entities such as moving humans. In our research, we underline the importance of blending semantic and spatial information for ensuring secure navigation for a mobile robot. We show that RGB sensors suffer from constrained situational awareness of the surroundings, thus highlighting the need to incorporate spatial and geometric data, which can collaborate synergistically to enhance overall perception and …
Deep Generative Sculpting Models For Single Image 3d Reconstruction, Jason Jennings
Deep Generative Sculpting Models For Single Image 3d Reconstruction, Jason Jennings
Computer Science and Engineering Dissertations
In the field of computer vision, learning representations of images is an important task. This dissertation introduces deep generative sculpting models (DGSM), deep learning models that learn 3D representations of objects from 2D images. DGSMs use convolutional networks combined with a differentiable renderer to attempt to "sculpt" a base 3D mesh, such as a sphere, to faithfully represent an object in the scene, and render it to reconstruct the input image. The core methodology revolves around the encoding of the input image into latent variables. These variables are decoded into interpretable scene parameters, describing the object's translation, rotation, scale, texture, …
Constructing Large Open-Source Corpora And Leveraging Language Models For Simulink Toolchain Testing And Analysis, Sohil Lal Shrestha
Constructing Large Open-Source Corpora And Leveraging Language Models For Simulink Toolchain Testing And Analysis, Sohil Lal Shrestha
Computer Science and Engineering Dissertations
In several safety-critical industries such as automotive, aerospace, healthcare, and industrial automation, MATLAB/Simulink has emerged as the de-facto standard tool for system modeling and analysis, model compilation into executable code, and code deployment onto embedded hardware. Within the context of cyber-physical system (CPS) development, it is imperative to both rigorously test the development tools, such as MathWorks’ Simulink, and understand modeling practices and model evolution. The existing body of work faces limitations primarily stemming from two factors: (1) contemporary testing methodologies often prove inefficient in identifying critical toolchain bugs due to a paucity of explicit toolchain specifications and (2) there …
Design Of Single Precision Floating Point Unit (32-Bit Numbers) According To Ieee 754 Standard Using Verilog, And Creation Of An Education Model For Advanced Digital Logic And Design Courses, Kartikey Sharan
Computer Science and Engineering Theses
In today’s day and age of arithmetic, Floating Point Arithmetic is by far the most industry sanctioned way of approximating real number arithmetic for making numerical calculations on all computers used by industries on an everyday basis. In the year 1985, IEEE 754 standard was established that defined a single universal standard for all different arithmetic formats [1]. Before this, for a long period each computer had a different arithmetic format and size for bases, significand, and exponents. This format allowed industries all around the world to compute floating point arithmetic in a universal way and facilitated open communication between …
Hemln-Sd: Substructure Discovery In Heterogeneous Multilayer Networks, Kiran Bolaj
Hemln-Sd: Substructure Discovery In Heterogeneous Multilayer Networks, Kiran Bolaj
Computer Science and Engineering Theses
Graph mining analyzes the real-world graphs for finding core substructures in chemical compounds (e.g., Benzene), identify the structure that occurs frequently in a given graph or forest. These identified structures are important as they reveal an inherent feature or property in the given graph or forest. Substructures represent interesting and repeating patterns found within an application, offering insights into hidden regularities. Therefore, the process of finding these interesting and frequent patterns in an unsupervised manner is known as substructure discovery. SUBDUE was the first main-memory algorithm developed for substructure discovery. Since then, for scalability, the algorithm has been extended to …
Enhancing The Classification Of Autism Spectrum Disorder From Rs-Fmri Functional Connectivity Data Using Temporal Information, Mihir Yashwant Ingole
Enhancing The Classification Of Autism Spectrum Disorder From Rs-Fmri Functional Connectivity Data Using Temporal Information, Mihir Yashwant Ingole
Computer Science and Engineering Theses
Autism Spectrum Disorder (ASD) affects the patient’s cognitive development which leads to difficulties in social functioning, daily tasks, and independent living. This necessitates intervention at an early age to take preventive measures and provide vital care. Manual diagnosis methods like Autism Diagnostic Observation Schedule (ADOS) assessment adopts symptom-based criteria which typically manifest at a later age. To automate this process, correlations computed from BOLD (Blood Oxygen-level dependent) signals obtained through resting state functional magnetic resonance imaging (rs-fMRI) data of patients across sparse brain regions has been used recently as a measure of functional connectivity. The goal of this study is …
Towards Nuclei Segmentation With Limited Annotations, Mohammad Minhazul Haq
Towards Nuclei Segmentation With Limited Annotations, Mohammad Minhazul Haq
Computer Science and Engineering Dissertations
Nuclei segmentation is a fundamental but challenging task in histopathology image analysis. For semantic segmentation of nuclei, Convolutional Neural Network (CNN), and Vision Transformer (VT) models give very promising results. However, to successfully train fully-supervised CNN and VT models we need significant amount of annotated data which is highly rare in biomedical domain. Also, collecting an unannotated histopathology dataset first, and then manually doing pixel-level labeling is expensive, time-consuming and tedious process. Therefore, we require to discover a way for training nuclei segmentation models with unlabeled datasets. In this thesis, I present my work towards solving this critical problem by …
Fuzz Testing Of Zigbee Protocol Implementations, Mengfei Ren
Fuzz Testing Of Zigbee Protocol Implementations, Mengfei Ren
Computer Science and Engineering Dissertations
ABSTRACT: In recent years, we have witnessed the increasing of the Internet of Things (IoT) devices deployed by many areas, such as home automation, healthcare, manufacture, and smart vehicle. Among the numerous IoT wireless standards available, Zigbee stands out as one of the most globally popular choices, with major companies like Amazon, Samsung, IKEA, Huawei, and Xiaomi incorporating it into their products. Notably, Zigbee has even been utilized in NASA's Mars mission, where it serves as the communication radio between the flying drone and the Perseverance rover. However, with the rapid growth of Zigbee's global market presence, the incentive for …
Compact Representatives Of Databases And Responsible Data Management, Suraj Shetiya
Compact Representatives Of Databases And Responsible Data Management, Suraj Shetiya
Computer Science and Engineering Dissertations
With the advent of advanced computational models, we are being constantly judged by AI systems, complex algorithmic systems based on data that has been collected about us. These analysis are critical as they span many wide spread areas of our lives. For instance, these systems have been shown to find effective ways to fight back and make informed decisions during the COVID-19 pandemic. The wide spread use of these models naturally give rise to a few questions regarding explaining decisions that these systems have made, fairness questions in various parts of these systems. In this dissertation, we present three important …
Deep Learning For Molecular Property Prediction, Hehuan Ma
Deep Learning For Molecular Property Prediction, Hehuan Ma
Computer Science and Engineering Dissertations
Drug discovery has always been a crucial task for society, and molecular property prediction is one of the fundamental problem. It is responsible for identifying the target properties or severe side-effects, so that certain molecules can be selected as the candidates of drugs. Traditional methods usually conduct a series of biochemical experiments to test the molecular properties, which may take up to decades. Nowadays, this process can be facilitated due to the rapid growth of deep learning methods. I present my work toward solving this critical problem by utilizing deep learning techniques. My research study can be summarized in three …
Generative And Implicit Methods For 3d Point Cloud Processing, Mohammad Samiul Arshad
Generative And Implicit Methods For 3d Point Cloud Processing, Mohammad Samiul Arshad
Computer Science and Engineering Dissertations
3D point clouds are a popular form of data representation with many applications in computer vision, computer graphics, and robotics. As the output of range sensing devices, point clouds have gained popularity with the current interest in self-driving vehicles. More formally, point clouds are an unordered set of irregular points collected from the surface of an object. Each point consists of a Cartesian coordinate, along with additional information such as an RGB color value and surface normal estimate. However, deep learning methods fall short in the processing of 3D point clouds due to the irregular and permutation-invariant nature of the …
On-Line Environment Adaptation For User Performance Optimization, Subharag Sarkar
On-Line Environment Adaptation For User Performance Optimization, Subharag Sarkar
Computer Science and Engineering Dissertations
In today’s fast-paced and globally connected world, businesses are creating products with more significance to user personalization and customization. This has amplified the importance of capturing and learning user preferences as more information from users can lead to the designing and development of products that will improve user engagement and performance. Numerous algorithms based on collaborative filtering and recommender systems have been used to learn user preferences, but almost all of them require big datasets to train on. This creates a dependency on collecting more and more user information which might lead to ethical considerations and privacy concerns. To solve …
Procedural Level Generation For A Top-Down Roguelike Game, Kieran Ahn, Tyler Edmiston
Procedural Level Generation For A Top-Down Roguelike Game, Kieran Ahn, Tyler Edmiston
Honors Thesis
In this file, I present a sequence of algorithms that handle procedural level generation for the game Fragment, a game designed for CMSI 4071 and CMSI 4071 in collaboration with students from the LMU Animation department. I use algorithms inspired by graph theory and implementing best practices to the best of my ability. The full level generation sequence is comprised of four algorithms: the terrain generation, boss room placement, player spawn point selection, and enemy population. The terrain generation algorithm takes advantage of tree traversal methods to create a connected graph of walkable tiles. The boss room placement algorithm randomly …
Context-Aware Gaze-Based Interface For Smart Wheelchair, Tien Pham
Context-Aware Gaze-Based Interface For Smart Wheelchair, Tien Pham
Computer Science and Engineering Theses
Human-Computer Interfaces (HCI) is an essential aspect of modern technology that has revolutionized the way we interact with machines. With the revolution of computers and smart devices and the advent of autonomous vehicles and other machines, there has been a significant advancement in this area that brings convenience to users to interact with technology intuitively and efficiently. However, the importance of HCI goes beyond the convenience of everyday technology. It has become crucial in the development of assistive technologies that empower people with disabilities to live more independently. Person with disabilities, who lack control of one or more parts of …
Neural Network Architecture Optimization Using Reinforcement Learning, Raghav Vadhera
Neural Network Architecture Optimization Using Reinforcement Learning, Raghav Vadhera
Computer Science and Engineering Dissertations
Deep learning has emerged as an increasingly valuable tool, employed across a myriad of applications. However, the intricacies of deep learning systems, stemming from their sensitivity to specific network architectures, have rendered them challenging for non-experts to harness, thus highlighting the need for automatic network architecture optimization. Prior research predominantly optimizes a network for a single problem through architecture search, necessitating extensive training of various architectures during optimization.\\ To tackle this issue and unlock the potential for transferability across tasks, this dissertation presents a groundbreaking approach that employs Reinforcement Learning to develop a network optimization policy based on an abstract …
Practical Indirect Control Flow Analysis For Binary Executables, Haotian Zhang
Practical Indirect Control Flow Analysis For Binary Executables, Haotian Zhang
Computer Science and Engineering Dissertations
Resolving indirect control flow is one of the fundamental challenges in binary analysis. Improving the accuracy of the indirect control flow analysis is vital to the binary analysis domain. Many analysis algorithms and security techniques rely on a precise indirect control flow result, such as recursive disassembling, control flow integrity, data-flow analysis, etc. Incorrect or even inaccuracy indirect control flow analysis results can compromise or even break the assumptions of these analyses. This thesis explores this topic from two directions, altering the indirect control flow analysis to make it more suitable for different scenarios and improving the accuracy of indirect …
Toward A Deeper Integration Of Low-Fidelity Sketches Into Mobile Application Development, Soumik Mohian
Toward A Deeper Integration Of Low-Fidelity Sketches Into Mobile Application Development, Soumik Mohian
Computer Science and Engineering Dissertations
Mobile application development often starts with creating low-fidelity sketches of user interfaces. Integrating these sketches into the software development process can reduce repetition, narrow the gap between user perception and final implementation, and improve app resilience. In this study, we introduce the DoodleUINet dataset, which comprises over 10K sketches of UI elements. Our Doodle2App tool converts low-fidelity sketches into a single-page, compilable Android app. At the same time, our PSDoodle provides an interactive, partial sketch-based search engine with a top-10 screen retrieval accuracy comparable to the state-of-the-art SWIRE line of work but with a 50% reduction in the average required …
Enhancing Health Tweet Classification: An Evaluation Of Transformer-Based Models For Comprehensive Analysis, Foram Pankajbhai Patel
Enhancing Health Tweet Classification: An Evaluation Of Transformer-Based Models For Comprehensive Analysis, Foram Pankajbhai Patel
Computer Science and Engineering Theses
The task of health tweet classification entails identifying whether a given tweet is health-related or not. While existing research in this area has made significant progress in classifying tweets into specific sub-domains of health, such as mental health, COVID-19, or specific diseases, there is a need for a more comprehensive approach that considers a broader range of health-related topics. This thesis addresses this need by proposing a diverse and comprehensive dataset that includes various existing health-related datasets, data collected through a keyword-based approach, and manually annotated data. However, the use of health-related keywords in a figurative or non-health context poses …
Toward Digital Phenotyping: Human Activity Representation For Embodied Cognition Assessment, Mohammad Zakizadehghariehali
Toward Digital Phenotyping: Human Activity Representation For Embodied Cognition Assessment, Mohammad Zakizadehghariehali
Computer Science and Engineering Dissertations
Cognition is the mental process of acquiring knowledge and understanding through thought, experience and senses. Based on Embodied Cognition theory, physical activities are an important manifestation of cognitive functions. As a result, they can be employed to both assess and train cognitive skills. In order to assess various cognitive measures, the ATEC system has been proposed. It consists of physical exercises with different variations and difficulty levels, designed to provide assessment of executive and motor functions. This thesis focuses on obtaining human activity representation from recorded videos of ATEC tasks in order to automatically assess embodied cognition performance. Representation learning …
Optimizing Resource Utilization, Efficiency And Scalability In Deep Learning Systems, Xiaofeng Wu
Optimizing Resource Utilization, Efficiency And Scalability In Deep Learning Systems, Xiaofeng Wu
Computer Science and Engineering Dissertations
This thesis addresses the challenges of utilization, efficiency, and scalability faced by deep learning systems, which are essential for high-performance training and serving of deep learning models. Deep learning systems play a critical role in developing accurate and complex models for various applications, including image recognition, natural language understanding, and speech recognition. This research focuses on understanding and developing deep learning systems that encompass data preprocessing, resource management, multi-tenancy, and distributed model training. The thesis proposes several solutions to improve the performance, scalability, and efficiency of deep learning applications. Firstly, we introduce SwitchFlow, a scheduling framework that addresses the limitations …
Graph Representation Learning For Heterogeneous Multimodal Biomedical Data, Nhat Chau Tran
Graph Representation Learning For Heterogeneous Multimodal Biomedical Data, Nhat Chau Tran
Computer Science and Engineering Dissertations
The emergence of high-throughput sequencing technology has generated a wealth of “multi-omics” data, capturing information about different types of biomolecules at multiple levels. Since large-scale genomics, transcriptomics, and proteomics data are becoming publicly available, integrated systems analysis utilizing these data sources has taken the front seat in deriving valuable insights for identifying cancer biomarkers or predicting interactions and functions for novel molecules such as LncRNAs. The graph representation learning paradigm can address these challenging tasks as among the most promising approaches to improve predictions over sparsely annotated molecular entities and to provide representation capacity and interpretability over heterogeneous and hierarchically …
Intuitive Robot Integration Via Virtual Reality Workspaces, Minh Tram
Intuitive Robot Integration Via Virtual Reality Workspaces, Minh Tram
Computer Science and Engineering Theses
As robots become increasingly prominent in diverse industrial settings, the desire for an accessible and reliable system has correspondingly increased. Yet, the task of meaningfully assessing the feasibility of introducing a new robotic component, or adding more robots into an existing infrastructure, remains a challenge. This is due to both the logistics of acquiring a robot and the need for expert knowledge in setting it up. In this paper, we address these concerns by developing a purely virtual simulation of a robotic system. Our proposed framework enables natural human-robot interaction through a visually immersive representation of the workspace. The main …
Approximate Query Processing Using Deep Learning And Database Techniques, Shohedul Hasan
Approximate Query Processing Using Deep Learning And Database Techniques, Shohedul Hasan
Computer Science and Engineering Dissertations
Data is generated at an unprecedented rate surpassing our ability to analyze them. In real applications, it is often impractical to find an exact answer by traversing the entire data. As a result, Approximate Query Processing (AQP) is getting extremely popular, which finds an approximate answer in a quick time by sacrificing a fraction of accuracy. This dissertation focuses on developing different AQP techniques to solve fundamental database problems using deep learning. Moreover, we build a fast and scalable algorithm for Quantile Regression, a well-known regression technique that can help minimize the uncertainty in the recent deep learning-based AQP solutions, …
Data Discovery Analysis On Complex Time Series Data, Peter Lawrence Severynen
Data Discovery Analysis On Complex Time Series Data, Peter Lawrence Severynen
Computer Science and Engineering Theses
Complex time series are a ubiquitous form of data in the modern world. They have wide application across many different fields of scientific inquiry and business endeavor. Time series are used to understand and forecast weather patterns, voting patterns, computer network traffic, population health outcomes, demographic changes, the results of scientific experiments, and the performance of stocks and mutual funds. But time series can be difficult to analyze by conventional methods when the data is multivariate, incomplete, or in different formats. To address these issues, an investigation of several multivariate time series datasets was performed using the methods of automatic …
Understanding Human Actions: Cognitive Assessment And Action Segmentation Using Human Object Interaction, Saif Sayed
Understanding Human Actions: Cognitive Assessment And Action Segmentation Using Human Object Interaction, Saif Sayed
Computer Science and Engineering Dissertations
Automatic understanding of human behavior has several applications in medicine and surveillance. Analysing human actions can enable cognitive assessment of children by measuring their hyperactivity and response inhibition which can give physicians better understanding of their cognitive state. Automatic and non-invasive assessment for cognitive disorders will increase the affordability and reach for these detection methods and can prove life-changing in child’s development. Human activity can also be analysed in common settings such as cooking in kitchen and understanding the information of human object interaction can give priors on the underlying activity they are performing. In the first section, we focus …
Towards High Performance Cancer Staging From Histology Images, Ashwin Raju
Towards High Performance Cancer Staging From Histology Images, Ashwin Raju
Computer Science and Engineering Dissertations
Digital Pathology (DP) has been recently used in replacement to traditional microscopy samples as it easy to navigate and can be analysed, processed and saved. With the invention of Digital pathology, there has been exponential increase of automated process to make the life of Doctors easier. One such automated process is Artificial Intelligence (AI) where the AI is used as an assistant to Humans and to make the analysis and guide the experts. With the advent of AI and in particular Deep Learning, research has been divided and focused to solve multiple problems in Digital Pathology. One such important application …