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Articles 1 - 30 of 46
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 …
Phishing In The Free Waters: A Study Of Phishing Attacks Created Using Free Website Building Services, Sayak Saha Roy, Karanjit Unique, Shirin Nilizadeh
Phishing In The Free Waters: A Study Of Phishing Attacks Created Using Free Website Building Services, Sayak Saha Roy, Karanjit Unique, Shirin Nilizadeh
Computer Science and Engineering Faculty Publications
Free Website Building services (FWBs) provide individuals with a cost-effective and convenient way to create a website without requiring advanced technical knowledge or coding skills. However, malicious actors often abuse these services to host phishing websites. In this work, we propose FreePhish, a scalable framework to continuously identify phishing websites that are created using FWBs. Using FreePhish, we were able to detect and characterize more than 31.4K phishing URLs that were created using 17 unique free website builder services and shared on Twitter and Facebook over a period of six months. We find that FWBs provide attackers with several features …
Expert Knowledge-Aware Image Difference Graph Representation Learning For Difference-Aware Medical Visual Question Answering, Xinyue Hu, Lin Gu, An Qiyuan, Mengliang Zhang, Liu Liangchen, Kobayashi Kazuma, Harada Tatsuya, M. Ronald Summers, Yingying Zhu
Expert Knowledge-Aware Image Difference Graph Representation Learning For Difference-Aware Medical Visual Question Answering, Xinyue Hu, Lin Gu, An Qiyuan, Mengliang Zhang, Liu Liangchen, Kobayashi Kazuma, Harada Tatsuya, M. Ronald Summers, Yingying Zhu
Computer Science and Engineering Faculty Publications
To contribute to automating the medical vision-language model, we propose a novel Chest-Xray Difference Visual Question Answering (VQA) task. Given a pair of main and reference images, this task attempts to answer several questions on both diseases and, more importantly, the differences between them. This is consistent with the radiologist’s diagnosis practice that compares the current image with the reference before concluding the report. We collect a new dataset, namely MIMIC-Diff-VQA, including 700,703 QA pairs from 164,324 pairs of main and reference images. Compared to existing medical VQA datasets, our questions are tailored to the Assessment Diagnosis-Intervention-Evaluation treatment procedure used …
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 …
Offline Handwritten Chinese Character Using Convolutional Neural Network: State-Of-The-Art Methods, Yingna Zhong, Kauthar Mohd Daud, Ain Najiha Binti Mohamad Nor, Richard Adeyemi Ikuesan, Kohbalan Moorthy
Offline Handwritten Chinese Character Using Convolutional Neural Network: State-Of-The-Art Methods, Yingna Zhong, Kauthar Mohd Daud, Ain Najiha Binti Mohamad Nor, Richard Adeyemi Ikuesan, Kohbalan Moorthy
All Works
Given the presence of handwritten documents in human transactions, including email sorting, bank checks, and automating procedures, handwritten characters recognition (HCR) of documents has been invaluable to society. Handwritten Chinese characters (HCC) can be divided into offline and online categories. Online HCC recognition (HCCR) involves the trajectory movement of the pen tip for expressing linguistic content. In contrast, offline HCCR involves analyzing and categorizing the sample binary or grayscale images of characters. As recognition technology develops, academics' interest in Chinese character recognition has continuously increased, as it significantly affects social and economic development. Recent development in this area is promising. …
Remote Operated Human Robot Interactive System Using Hand Gestures For Persons With Disabilities, Enamul Karim, Harish Nambiappan, Sneh Acharya, Fillia Makedon
Remote Operated Human Robot Interactive System Using Hand Gestures For Persons With Disabilities, Enamul Karim, Harish Nambiappan, Sneh Acharya, Fillia Makedon
Computer Science and Engineering Faculty Publications
This paper proposes a novel Human-Robot Interactive System where users can interact with the robotic system using hand gestures, even from a distance, through a smartphone-based IoT-Controller Framework. The system is primarily designed to help people with vocal and hearing impairments. A mobile application records user gestures shown in front of a smartphone and sends the data to a server. The server performs gesture recognition and forms a command which is sent to the robotic system. The robotic system performs the required task based on the given command. The robotic system is set to carry out an object pick and …
Smartfunction: An Immersive Vr System To Assess Attention Using Embodied Cognition, Ashish Jaiswal, Aref Hebri, Pavel Hamza Reza, Zadeh Mohammad Zaki, Fillia Makedon
Smartfunction: An Immersive Vr System To Assess Attention Using Embodied Cognition, Ashish Jaiswal, Aref Hebri, Pavel Hamza Reza, Zadeh Mohammad Zaki, Fillia Makedon
Computer Science and Engineering Faculty Publications
In traditional neuropsychological tests, executive functions (EFs) are typically evaluated using paper and pencil or computer-based sit-down tasks. However, a new assessment framework, the Automated Test of Embodied Cognition (ATEC), has been developed to measure EFs and embodied cognition through physical tasks. This paper proposes integrating the ATEC system with virtual reality (VR) to evaluate and diagnose attention-deficit disorders using embodied cognition (EC) principles. The VR system will utilize Meta Quest 2 VR headsets and controllers with motion sensors to accurately capture users’ physical movements. The collected motion data will be transmitted to a remote server for evaluation through machine …
Detecting Cognitive Fatigue In Subjects With Traumatic Brain Injury From Fmri Scans Using Self-Supervised Learning, Ashish Jaiswal, Ramesh Babu Ashwin, Zadeh Mohammad Zaki, Glenn Wylie, Fillia Makedon
Detecting Cognitive Fatigue In Subjects With Traumatic Brain Injury From Fmri Scans Using Self-Supervised Learning, Ashish Jaiswal, Ramesh Babu Ashwin, Zadeh Mohammad Zaki, Glenn Wylie, Fillia Makedon
Computer Science and Engineering Faculty Publications
Understanding cognitive states from fMRI data have yet to be investigated to its full extent due to its complex nature. In this work, the problem of understanding cognitive fatigue among TBI patients has been formulated as a multi-class classification problem. We built a Spatio-temporal encoder model using convolutions and LSTMs as the building blocks to extract spatial features and to model the 4D nature of fMRI scans. To learn a better representation of the data and the condition, we used a self-supervised learning technique called "Contrastive Learning" to pretrain our encoder with a public dataset BOLD5000 and further fine-tuned our …
An Eeg-Based Cognitive Fatigue Detection System, Karim Enamul, Pavel Hamza Reza, Ashish Jaiswal, Zadeh Mohammad Zaki, Michail Theodanidis, Glenn Wylie, Fillia Makedon
An Eeg-Based Cognitive Fatigue Detection System, Karim Enamul, Pavel Hamza Reza, Ashish Jaiswal, Zadeh Mohammad Zaki, Michail Theodanidis, Glenn Wylie, Fillia Makedon
Computer Science and Engineering Faculty Publications
Mental or Cognitive fatigue (CF) is the exhaustion of the neurological system brought on by prolonged cognitive tasks. It causes performance to decline in day-to-day life. Throughout this paper, we present an experimental setup where we artificially induce cognitive fatigue to participants. During the experimental process, we collected electroencephalogram (EEG) signals from the subjects that participated in the experiment. The goal of the study is to detect the presence or absence of cognitive fatigue. Our proposed solution was able to classify cognitive fatigue of the subjects with an accuracy of 88.17%.
A Teleoperation Framework For Robots Utilizing Control Barrier Functions In Virtual Reality, Aref Hebri, Sneh Acharya, Fillia Makedon
A Teleoperation Framework For Robots Utilizing Control Barrier Functions In Virtual Reality, Aref Hebri, Sneh Acharya, Fillia Makedon
Computer Science and Engineering Faculty Publications
This paper describes a novel shared control teleoperation framework for mobile robots that utilizes Control Barrier Functions (CBFs) as filtering mechanism to prevent a human operator from making dangerous actions. The proposed framework demonstrates the potential to create a CBF controller that enables users with no prior knowledge of robotics to safely tele-navigate mobile robots with limited situational awareness. As formal methods, we utilize a hand-crafted CBF, which acts as a repulsive field to describe unsafe regions withing the robot’s vicinity. The implementation of the application was deemed possible by creating a Virtual Reality (VR) simulation in the Unity Engine …
An Rgb-D Fusion System For Indoor Wheelchair Navigation, Christos Sevastopoulos, Sneh Acharya, Fillia Makedon
An Rgb-D Fusion System For Indoor Wheelchair Navigation, Christos Sevastopoulos, Sneh Acharya, Fillia Makedon
Computer Science and Engineering Faculty Publications
We present a method for extracting high-level semantic information through successful landmark detection using feature fusion between RGB and depth information. We focus on the classification of specific labels (open path, humans, staircases, doorways, obstacles) in the encountered scene, which can be a fundamental source of information enhancing scene understanding, and acting towards the safe navigation of the mobile unit. Experiments are conducted using a manual wheelchair equipped with a stereo RGB-D camera that captures image instances consisting of multiple labels before fine-tuning on a pre-trained Vision Transformer (ViT).
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 …