Computer Engineering Commons^™

Design Of Human Inspired Feet For Mithra, A High-Performance Humanoid Robot, Spencer Lyle Brewster

Electronic Theses and Dissertations

This thesis explores the foot design for the new humanoid robot Mithra, with the goal to improve impact mitigation, natural gait throughout the gait cycle and balance. Using human gait parameters, a stiff aluminum foot was designed to emulate human center of pressure rollover trajectories. In addition, a 2D dynamic Finite Element Analysis (FEA) of the gait cycle was created in Abaqus to rapidly prototype foot designs. Data from the FE model provided impact forces, parameters for distal foot power evaluation and maximum lean angles. From this model a flexible nylon foot was optimized for the given design objectives. The …

Design And Control Of Quasi-Direct Drive Actuation For Lightweight And Versatile Wearable Robots, Shuangyue Yu

Dissertations and Theses

Wearable robots have shown great potential for augmenting the physical capabilities of humans in lab settings. However, wearable robots for augmenting the physical capabilities of humans under community-based conditions are the new frontier of robotics. Furthermore, the design and control are still considered to be grand challenges for providing physical augmentation for humans. In terms of design, the state-of-the-art exoskeletons are typically rigid, bulky, and limited to lab settings. In terms of control, most of the rhythmic controllers are not versatile and are focused only on steady-state walking assistance.

The motivation behind my research is to improve both the design …

A Cloud Computing-Based Dashboard For The Visualization Of Motivational Interviewing Metrics, E Jinq Heng

Browse all Theses and Dissertations

Motivational Interviewing (MI) is an evidence-based brief interventional technique that has been demonstrated to be effective in triggering behavior change in patients. To facilitate behavior change, healthcare practitioners adopt a nonconfrontational, empathetic dialogic style, a core component of MI. Despite its advantages, MI has been severely underutilized mainly due to the cognitive overload on the part of the MI dialogue evaluator, who has to assess MI dialogue in real-time and calculate MI characteristic metrics (number of open-ended questions, close-ended questions, reflection, and scale-based sentences) for immediate post-session evaluation both in MI training and clinical settings. To automate dialogue assessment and …

Can We Make Our Robot Play Soccer? Influence Of Collaborating With Preservice Teachers And Fifth Graders On Undergraduate Engineering Students' Learning During A Robotic Design Process (Work In Progress), Krishnanand Kaipa, Jennifer Kidd, Julia Noginova, Francisco Cima, Stacie Ringleb, Orlando Ayala, Pilar Pazos, Kristie Gutierrez, Min Jung Lee

Mechanical & Aerospace Engineering Faculty Publications

This work-in-progress paper describes engineering students’ experiences in an NSF-funded project that partnered undergraduate engineering students with pre-service teachers to plan and deliver robotics lessons to fifth graders at a local school. This project aims to address an apparent gap between what is taught in academia and industry’s expectations of engineers to integrate perspectives from outside their field to solve modern societal problems requiring a multidisciplinary approach. Working in small teams over Zoom, participating engineering, education, and fifth grade students designed, built, and coded bio-inspired COVID companion robots. The goal for the engineering students was to build new interprofessional skills, …

Modeling Document Classification To Automate Mental Health Diagnosis, William M. Tadlock

EWU Masters Thesis Collection

The objective of this study is to determine if diagnosis documents can be used with document classification to automatically diagnose mental health conditions. Document classification allows text documents to be analyzed and organized into their appropriate classes based on the features and words presented in the text. One application of this is within the medical field to automatically classify different patient diagnosis based on medical or patient notes. This research applied mental health diagnosis documents to automatically diagnose a group of patients with a mental health condition based on text-based survey data. This classification was approached through several feature engineering …

Service Placement Optimization For Location-Based Service, Karnkitti Kittikamron

Chulalongkorn University Theses and Dissertations (Chula ETD)

Location-based service (LBS) is necessary and useful for several applications including navigation and games. These real-time applications require high accuracy and low delay. In general, the complexity of indoor localization algorithms used in LBS depends on the size of fingerprint data. This can lead to long delays when operating in large-scale areas. In this paper, we propose a novel optimization framework for edge service placement, aiming at minimizing the overall cost of edge computing deployment and service response time. Our placement strategy is used to solve the formulated edge node placement problems. The simulated annealing approach is then used in …

Machine Learning For Wavelet-Based Valve Stiction Detection, Kris Prasopsanti

Chulalongkorn University Theses and Dissertations (Chula ETD)

Valve stiction presents challenges in industrial process control, leading to oscillations and hindering the regulation of fluid flow. This thesis addresses the detection of valve stiction by exploring the limitations of current methods and proposes a novel approach that combines wavelet reconstruction and convolutional neural networks (CNN) to enhance stiction detection performance. The proposed method utilizes preprocessed process variable versus controller output (PV(OP)) plots as input to the CNN model, capitalizing on the distinctive characteristics of stiction. Training and evaluation employ both simulated and real-world data from the International Stiction Data Base (ISDB), with the F1 score serving as the …

กระบวนการและแบบจำลองสำหรับการคัดกรองเพื่อการจัดการคุณภาพข้อมูลในคราวด์ซอร์สซิงแพลตฟอร์ม, กฤตย์ กังวาลพงศ์พันธุ์

Chulalongkorn University Theses and Dissertations (Chula ETD)

การเก็บรวบรวมข้อมูลด้วยคราวด์ซอร์สซิงเป็นวิธีที่โดยทั่วไปมีความเร็วมากกว่า มีต้นทุนต่ำกว่า และมีความหลากหลายมากกว่าวิธีการเก็บรวบรวมข้อมูลแบบอื่น ๆ อย่างไรก็ตาม คราวด์ซอร์สซิงอาจเผชิญกับปัญหาคุณภาพ เช่น การติดป้ายกำกับผิดหรือการนำมาใช้ในทางที่ไม่เหมาะสม ดังนั้น กระบวนการควบคุมคุณภาพเป็นสิ่งที่จำเป็นสำหรับแพลตฟอร์มคราวด์ซอร์สซิง วิทยานิพนธ์นี้ศึกษาค้นคว้าอุปสรรคและวิธีการแก้ไขที่เป็นไปได้ในการจัดการคุณภาพของผู้ใช้งานแพลตฟอร์มคราวด์ซอร์สซิง ส่วนแรกเน้นวิธีการเพิ่มกระบวนการในคราวด์ซอร์สซิง โดยศึกษา 3 วิธี ได้แก่ 1. งานที่จำเป็นต้องทำก่อน 2. คำถามมาตรฐานแบบทองคำ และ 3. การทำซ้ำของข้อมูล พบว่างานที่จำเป็นต้องทำก่อนเป็นสิ่งจำเป็นเพื่อคัดกรองให้ได้ผู้ปฏิบัติงานที่มีคุณภาพสูง โดยควรเน้นไปที่ลักษณะเฉพาะและรายละเอียดของงาน คำถามที่ตรวจสอบความสอดคล้องระหว่างงานดีกว่าคำถามแบบชัดเจนในการตรวจสอบด้วยคำถามมาตรฐานทองคำ ผู้ตรวจสอบข้อมูลคนเดียวอาจนำไปสู่การปรับปรุงคุณภาพข้อมูลได้มากที่สุด ส่วนที่สองคือ การใช้แบบจำลองการเรียนรู้ของเครื่องที่ใช้ข้อมูลพฤติกรรมในการทำนายคุณภาพของข้อมูล ซึ่งวิธีนี้ยังช่วยคัดกรองข้อมูลคุณภาพต่ำออกไปได้โดยไม่เสียทรัพยากรเพิ่มเติม

แบบจำลองคุณภาพซอฟต์แวร์โอเพนซอร์ซเพื่อการวัดคุณภาพอย่างอัตโนมัติ, อัรกอม มะแดเฮาะ

Chulalongkorn University Theses and Dissertations (Chula ETD)

ปัจจุบันได้มีการนำเสนอแบบจำลองคุณภาพซอฟต์แวร์โอเพนซอร์ซหลายแบบจำลองเพื่อใช้ในการประเมินคุณภาพของซอฟต์แวร์โอเพนซอร์ซ แต่แบบจำลองเหล่านั้นมีข้อจำกัดเนื่องจากเป็นการประเมินตามความคิดส่วนตัวซึ่งต้องอาศัยผู้ใช้ในการประเมิน และแบบจำลองดังกล่าวต้องการข้อมูลจากหลากหลายแหล่ง เพื่อเสริมการประเมินคุณภาพซอฟต์แวร์โอเพนซอร์ซที่เป็นอยู่ให้ทำได้สมบูรณ์มากยิ่งขึ้น วิทยานิพนธ์นี้จึงได้นำเสนอแบบจำลองคุณภาพซอฟต์แวร์โอเพนซอร์ซใหม่ที่ชื่อว่าโอเอสเอส-เอคิวเอ็ม โดยมีเป้าหมายเพื่อการวัดคุณภาพซอฟต์แวร์โอเพนซอร์ซอย่างอัตโนมัติ แบบจำลองโอเอสเอส-เอคิวเอ็มได้นำเสนอตัววัดคุณภาพและเครื่องมืออัตโนมัติที่สามารถดึงข้อมูลเกี่ยวกับซอฟต์แวร์โอเพนซอร์ซจากกิตฮับ ซอร์ซโค้ด โซนาร์คิวบ์ และสแต็กเอกซ์เชนจ์ ทำให้สามารถกำหนดคะแนนคุณภาพของซอฟต์แวร์โอเพนซอร์ซได้ โอเอสเอส-เอคิวเอ็มได้รับการตรวจสอบจากวิศวกรซอฟต์แวร์ที่มีประสบการณ์ในการเลือกใช้ซอฟต์แวร์โอเพนซอร์ซ นอกจากนี้การจัดลำดับซอฟต์แวร์โอเพนซอร์ซโดยเครื่องมือโอเอสเอส-เอคิวเอ็มยังถูกนำไปเปรียบเทียบกับการจัดลำดับด้วยวิธีอื่น และพบว่าการจัดลำดับของโอเอสเอส-เอคิวเอ็มมีสหสัมพันธ์ระดับต่ำมากถึงปานกลางในทิศทางตรงกันข้ามกับวิธีจัดลำดับอื่น ๆ ตามความคิดเห็นและความนิยมของผู้ใช้ และมีสหสัมพันธ์ระดับปานกลางในทิศทางเดียวกันกับวิธีการจัดลำดับอื่นที่เน้นการตรวจสอบความมั่นคงที่ซอฟต์แวร์โดยตรง ทั้งนี้เนื่องจากโอเอสเอส-เอคิวเอ็มครอบคลุมปัจจัยคุณภาพหลายอย่างที่ไม่ได้ถูกพิจารณาโดยวิธีการจัดลำดับอื่น ๆ ดังกล่าว โอเอสเอส-เอคิวเอ็มจึงให้ข้อมูลด้านคุณภาพของซอฟต์แวร์โอเพนซอร์ซในเชิงลึกที่ดีกว่า

Developing Reactive Distributed Aerial Robotics Platforms For Real-Time Contaminant Mapping, Joshua Ashley

Theses and Dissertations--Electrical and Computer Engineering

The focus of this research is to design a sensor data aggregation system and centralized sensor-driven trajectory planning algorithm for fixed-wing aircraft to optimally assist atmospheric simulators in mapping the local environment in real-time. The proposed application of this work is to be used in the event of a hazardous contaminant leak into the atmosphere as a fleet of sensing unmanned aerial vehicles (UAVs) could provide valuable information for evacuation measures. The data aggregation system was designed using a state-of-the-art networking protocol and radio with DigiMesh and a process/data management system in the ROS2 DDS. This system was tested to …

Core Point Pixel-Level Localization By Fingerprint Features In Spatial Domain, Xueyi Ye, Yuzhong Shen, Maosheng Zeng, Yirui Liu, Huahua Chen, Zhijing Zhao

Computational Modeling & Simulation Engineering Faculty Publications

Singular point detection is a primary step in fingerprint recognition, especially for fingerprint alignment and classification. But in present there are still some problems and challenges such as more false-positive singular points or inaccurate reference point localization. This paper proposes an accurate core point localization method based on spatial domain features of fingerprint images from a completely different viewpoint to improve the fingerprint core point displacement problem of singular point detection. The method first defines new fingerprint features, called furcation and confluence, to represent specific ridge/valley distribution in a core point area, and uses them to extract the innermost Curve …

Biomimetic Design, Modeling, And Adaptive Control Of Robotic Gripper For Optimal Grasping, Mushtaq Al-Mohammed

Electronic Theses and Dissertations, 2020-2023

Grasping is an essential skill for almost every assistive robot. Variations in shape and/or weight of different objects involved in Activities of Daily Living (ADL) lead to complications, especially, when the robot is trying to grip novel objects for which it has no prior information –too much force will deform or crush the object while too little force will lead to slipping and possibly dropped objects. Thus, successful grasping requires the gripper to immobilize an object with the minimal force. In Chapter 2, we present the design, analysis, and experimental implementation of an adaptive control to facilitate 1-click grasping of …

Using Long Short-Term Memory Networks To Make And Train Neural Network Based Pseudo Random Number Generator, Aditya Harshvardhan

Electronic Theses and Dissertations

Neural Networks have been used in many decision-making models and been employed in computer vision, and natural language processing. Several works have also used Neural Networks for developing Pseudo-Random Number Generators [2, 4, 5, 7, 8]. However, despite great performance in the National Institute of Standards and Technology (NIST) statistical test suite for randomness, they fail to discuss how the complexity of a neural network affects such statistical results. This work introduces: 1) a series of new Long Short- Term Memory Network (LSTM) based and Fully Connected Neural Network (FCNN – baseline [2] + variations) Pseudo Random Number Generators (PRNG) …

Formation Control With Bounded Controls And Collision Avoidance: Theory And Application To Quadrotor Unmanned Air Vehicles, Zachary S. Lippay

Theses and Dissertations--Mechanical and Aerospace Engineering

This dissertation presents new results on multi-agent formation control and applies the new control algorithms to quadrotor unmanned air vehicles. First, this dissertation presents a formation control algorithm for double-integrator agents, where the formation is time varying and the agents’ controls satisfy a priori bounds (e.g., the controls accommodate actuator saturation). The main analytic results provide sufficient conditions such that all agents converge to the desired time-varying relative positions with one another and the leader, and have a priori bounded controls (if applicable). We also present results from rotorcraft experiments that demonstrate the algorithm with time-varying formations and bounded controls. …

Deepfakes, Shallowfakes, And The Need For A Private Right Of Action, Eric Kocsis

Dickinson Law Review (2017-Present)

For nearly as long as there have been photographs and videos, people have been editing and manipulating them to make them appear to be something they are not. Usually edited or manipulated photographs are relatively easy to detect, but those days are numbered. Technology has no morality; as it advances, so do the ways it can be misused. The lack of morality is no clearer than with deepfake technology.

People create deepfakes by inputting data sets, most often pictures or videos into a computer. A series of neural networks attempt to mimic the original data set until they are nearly …

Book Review: This Is How They Tell Me The World Ends: The Cyberweapons Arms Race (2020) By Nicole Perlroth, Amy C. Gaudion

Dickinson Law Review (2017-Present)

No abstract provided.

Using Satellite Images Datasets For Road Intersection Detection In Route Planning, Fatmaelzahraa Eltaher Ph.D, Susan Mckeever, Ayman Taha, Jane Courtney

Datasets

Understanding road networks plays an important role in navigation applications such as self-driving vehicles and route planning for individual journeys. Intersections of roads are essential components of road networks. Understanding the features of an intersection, from a simple T-junction to larger multi-road junctions is critical to decisions such as crossing roads or selecting safest routes. The identification and profiling of intersections from satellite images is a challenging task. While deep learning approaches offer state-of-the-art in image classification and detection, the availability of training datasets is a bottleneck in this approach. In this paper, a labelled satellite image dataset for the …

On The Dimensionality And Utility Of Convolutional Autoencoder’S Latent Space Trained With Topology-Preserving Spectral Eeg Head-Maps, Arjun Vinayak Chikkankod, Luca Longo

Articles

Electroencephalography (EEG) signals can be analyzed in the temporal, spatial, or frequency domains. Noise and artifacts during the data acquisition phase contaminate these signals adding difficulties in their analysis. Techniques such as Independent Component Analysis (ICA) require human intervention to remove noise and artifacts. Autoencoders have automatized artifact detection and removal by representing inputs in a lower dimensional latent space. However, little research is devoted to understanding the minimum dimension of such latent space that allows meaningful input reconstruction. Person-specific convolutional autoencoders are designed by manipulating the size of their latent space. A sliding window technique with overlapping is employed …

Development And Implementation Of Robot Operating Systems For Undergraduates, Chelsey Spitzner

Dissertations, Master's Theses and Master's Reports

The purpose of this project was to create an undergraduate junior lab to teach students about Robotic Operating System (ROS). The labs were designed to highlight the usefulness of ROS and the process used. Designing algorithms, how to send/receive messages, and the hierarchy of how nodes work with each other are emphasized. Taking packages that are open-source then modifying them is also emphasized. This is done so that students can transfer their knowledge from this course to other robot operating systems.

Off Road Autonomous Vehicle Modeling And Repeatability Using Real World Telemetry Via Simulation, Matthew Paul Spencer

Dissertations, Master's Theses and Master's Reports

One approach to autonomous control of high mobility ground vehicle platforms operating on challenging terrain is with the use of predictive simulation. Using a simulated or virtual world, an autonomous system can optimize use of its control systems by predicting interaction between the vehicle and ground as well as the vehicle actuator state. Such a simulation allows the platform to assess multiple possible scenarios before attempting to execute a path. Physically realistic simulations covering all of these domains are currently computationally expensive, and are unable to provide fast execution times when assessing each individual scenario due to the use of …

Adapting Deep Learning For Underwater Acoustic Communication Channel Modeling, Li Wei

Dissertations, Master's Theses and Master's Reports

The recent emerging applications of novel underwater systems lead to increasing demand for underwater acoustic (UWA) communication and networking techniques. However, due to the challenging UWA channel characteristics, conventional wireless techniques are rarely applicable to UWA communication and networking. The cognitive and software-defined communication and networking are considered promising architecture of a novel UWA system design. As an essential component of a cognitive communication system, the modeling and prediction of the UWA channel impulse response (CIR) with deep generative models are studied in this work.

Firstly, an underwater acoustic communication and networking testbed is developed for conducting various simulations and …

Energy And Area Efficient Machine Learning Architectures Using Spin-Based Neurons, Hossein Pourmeidani

Electronic Theses and Dissertations, 2020-2023

Recently, spintronic devices with low energy barrier nanomagnets such as spin orbit torque-Magnetic Tunnel Junctions (SOT-MTJs) and embedded magnetoresistive random access memory (MRAM) devices are being leveraged as a natural building block to provide probabilistic sigmoidal activation functions for RBMs. In this dissertation research, we use the Probabilistic Inference Network Simulator (PIN-Sim) to realize a circuit-level implementation of deep belief networks (DBNs) using memristive crossbars as weighted connections and embedded MRAM-based neurons as activation functions. Herein, a probabilistic interpolation recoder (PIR) circuit is developed for DBNs with probabilistic spin logic (p-bit)-based neurons to interpolate the probabilistic output of the neurons …

C2 Microservices Api: Ch4rl3sch4l3m4gn3, Thai H. Nguyễn

School of Computer Science & Engineering Undergraduate Publications

In the 21^st century, cyber-based attackers such as advance persistent threats are leveraging bots in the form of botnets to conduct a plethora of cyber-attacks. While there are several social engineering techniques used to get targets to unknowingly download these bots, it is the command-and-control techniques advance persistent threats use to control their bots that is of critical interest to the author. In this research paper, the author aims to develop a command-and-control microservice application programming interface infrastructure to facilitate botnet command-and-control attack simulations. To achieve this the author will develop a simple bot skeletal framework, utilize the latest …

Improving Network Policy Enforcement Using Natural Language Processing And Programmable Networks, Pinyi Shi

Theses and Dissertations--Computer Science

Computer networks are becoming more complex and challenging to operate, manage, and protect. As a result, Network policies that define how network operators should manage the network are becoming more complex and nuanced. Unfortunately, network policies are often an undervalued part of network design, leaving network operators to guess at the intent of policies that are written and fill in the gaps where policies don’t exist. Organizations typically designate Policy Committees to write down the network policies in the policy documents using high-level natural languages. The policy documents describe both the acceptable and unacceptable uses of the network. Network operators …

Design, Development And Benchmarking Of Machine Learning Algorithms In Biomedical Applications, Qi Sun

Theses and Dissertations--Computer Science

Machine learning algorithms are becoming the most effective methods for knowledge discovery from high dimensional datasets. Machine learning seeks to construct predictive models through the analysis of large-scale heterogeneous data. While machine learning has been widely used in many domains including computer vision, natural language processing, product recommendation, its application in biomedical science for clinical diagnosis and treatment is only emerging. However, the wealthy amount of data in the biomedical domain offers not only challenges but also opportunities for machine learning. In this dissertation, we focus on three biomedical applications from vastly different domains to understand the opportunities and challenges …

Protocols And Architecture For Privacy-Preserving Authentication And Secure Message Dissemination In Vehicular Ad Hoc Networks, Shafika Showkat Moni

Theses and Dissertations--Computer Science

The rapid development in the automotive industry and wireless communication technologies have enhanced the popularity of Vehicular ad hoc networks (VANETs). Today, the automobile industry is developing sophisticated sensors that can provide a wide range of assistive features, including accident avoidance, automatic lane tracking, semi-autonomous driving, suggested lane changes, and more. VANETs can provide drivers a safer and more comfortable driving experience, as well as many other useful services by leveraging such technological advancements. Even though this networking technology enables smart and autonomous driving, it also introduces a plethora of attack vectors. However, the main issues to be sorted out …

An Automated Framework To Debug System-Level Concurrency Failures, Tarannum Shaila Zaman

Theses and Dissertations--Computer Science

The ever-increasing parallelism in computer systems has made software more prone to concurrency failures, causing problems during both pre- and post-development. Debugging concurrent programs is difficult because of the non-deterministic behavior and the specific sequences of interleaving in the execution flow. Debugging is a technique where programmers reproduce the bug, identify the root cause, and then take necessary steps to remove the bug from the system. The failure information may come from the bug reports of the testing phase or the production runs. In both cases, there should be steps taken to reproduce and localize the failure. However, reproducing and …

Ensemble Approach To The Semantic Segmentation Of Satellite Images, Brendan Kent

Dissertations

Automatic classification and segmentation of land use land cover(LULC) is extremely important for understanding the relationship between humans and nature. Human pressures on the environment have drastically accelerated in the last decades, risking biodiversity and ecosystem services. Remote sensing via satellite imagery is an excellent tool to study LULC. Research has shown that deep learning encoder-decoder architectures have achieved worthy results in the area of LULC, however the application of an ensemble approach has not been well quantified. Studies have shown it to be useful in the area of medical imaging. Ensembling by pooling together predictions to produce better predictions …

An Analysis On Network Flow-Based Iot Botnet Detection Using Weka, Cian Porteous

Dissertations

Botnets pose a significant and growing risk to modern networks. Detection of botnets remains an important area of open research in order to prevent the proliferation of botnets and to mitigate the damage that can be caused by botnets that have already been established. Botnet detection can be broadly categorised into two main categories: signature-based detection and anomaly-based detection. This paper sets out to measure the accuracy, false-positive rate, and false-negative rate of four algorithms that are available in Weka for anomaly-based detection of a dataset of HTTP and IRC botnet data. The algorithms that were selected to detect botnets …

Evaluating The Performance Of Vision Transformer Architecture For Deepfake Image Classification, Devesan Govindasamy

Dissertations

Deepfake classification has seen some impressive results lately, with the experimentation of various deep learning methodologies, researchers were able to design some state-of-the art techniques. This study attempts to use an existing technology “Transformers” in the field of Natural Language Processing (NLP) which has been a de-facto standard in text processing for the purposes of Computer Vision. Transformers use a mechanism called “self-attention”, which is different from CNN and LSTM. This study uses a novel technique that considers images as 16x16 words (Dosovitskiy et al., 2021) to train a deep neural network with “self-attention” blocks to detect deepfakes. It creates …