Engineering Commons^™

Accelerating Machine Learning Inference For Satellite Component Feature Extraction Using Fpgas., Andrew Ekblad

Theses and Dissertations

Running computer vision algorithms requires complex devices with lots of computing power, these types of devices are not well suited for space deployment. The harsh radiation environment and limited power budgets have hindered the ability of running advanced computer vision algorithms in space. This problem makes running an on-orbit servicing detection algorithm very difficult. This work proposes using a low powered FPGA to accelerate the computer vision algorithms that enable satellite component feature extraction. This work uses AMD/Xilinx’s Zynq SoC and DPU IP to run model inference. Experiments in this work centered around improving model post processing by creating implementations …

Adversary Aware Continual Learning, Muhammad Umer

Theses and Dissertations

Continual learning approaches are useful as they help the model to learn new information (classes) sequentially, while also retaining the previously acquired information (classes). However, these approaches are adversary agnostic, i.e., they do not consider the possibility of malicious attacks. In this dissertation, we have demonstrated that continual learning approaches are extremely vulnerable to the adversarial backdoor attacks, where an intelligent adversary can introduce small amount of misinformation to the model in the form of imperceptible backdoor pattern during training to cause deliberate forgetting of a specific class at test time. We then propose a novel defensive framework to counter …

Methodology For Designing Assistive Robots For Activities Of Daily Living Assistance, Javier Dario Sanjuan De Caro

Theses and Dissertations

The growing prevalence of Upper or Lower Extremities Dysfunctions (ULED), often linked to central nervous disorders such as stroke, Spinal Cord Injury (SCI), and Multiple Sclerosis (MS), underscores the urgent need for innovative support solutions. Over 5.35 million Americans currently live with ULED, a situation that places a significant socioeconomic burden on families and society. Despite invaluable support from caregivers and family members, the need for more scalable, practical solutions persists.

Wheelchair-mounted assistive robots emerge as a promising alternative in this context. These devices, offering continuous and reliable assistance, significantly alleviate caregiver fatigue and enhance the independence and quality of …

Secure And Efficient Federated Learning, Xingyu Li

Theses and Dissertations

In the past 10 years, the growth of machine learning technology has been significant, largely due to the availability of large datasets for training. However, gathering a sufficient amount of data on a central server can be challenging. Additionally, with the rise of mobile networking and the large amounts of data generated by IoT devices, privacy and security issues have become a concern, resulting in government regulations such as GDPR, HIPAA, CCPA, and ADPPA. Under these circumstances, traditional centralized machine learning methods face a problem in that sensitive data must be kept locally for privacy reasons, making it difficult to …

A Machine Learning Framework For Automatic Speech Recognition In Air Traffic Control Using Word Level Binary Classification And Transcription, Fowad Shahid Sohail

Theses and Dissertations

Advances in Artificial Intelligence and Machine learning have enabled a variety of new technologies. One such technology is Automatic Speech Recognition (ASR), where a machine is given audio and transcribes the words that were spoken. ASR can be applied in a variety of domains to improve general usability and safety. One such domain is Air Traffic Control (ATC). ASR in ATC promises to improve safety in a mission critical environment. ASR models have historically required a large amount of clean training data. ATC environments are noisy and acquiring labeled data is a difficult, expertise dependent task. This thesis attempts to …

Development Of A Novel Telemanipulation Framework For Human-Robot Collaboration Using Ptc Thingworx And Vuforia Studio, Preet Parag Modi

Theses and Dissertations

Significant advancements in contemporary telehealth care applications are enforcing the demand for effective and intuitive telerehabilitation tools. The current techniques for observing live process parameters of robots frequently require complex and inefficient methods, which fundamentally limits the human administrator's ability to settle on the most educated decisions possible. Telemanipulation can minimize the distance and costs in varieties of robot applications, including industry for object manipulation and robot-aided rehabilitation. This research aims to develop a novel telemanipulation framework to deliver robot-assisted rehabilitation using PTC ThingWorx’s Industrial Internet of Things (IIoT), and Vuforia Studio’s Augmented Reality (AR) platforms. This communication architecture is …

Identifying Characteristics For Success Of Robotic Process Automations, Charles M. Unkrich

Theses and Dissertations

In the pursuit of digital transformation, the Air Force creates digital airmen. Digital airmen are robotic process automations designed to eliminate the repetitive high-volume low-cognitive tasks that absorb so much of our Airmen's time. The automation product results in more time to focus on tasks that machines cannot sufficiently perform data analytics and improving the Air Force's informed decision-making. This research investigates the assessment of potential automation cases to ensure that we choose viable tasks for automation and applies multivariate analysis to determine which factors indicate successful projects. The data is insufficient to provide significant insights.

Analysis Of Generalized Artificial Intelligence Potential Through Reinforcement And Deep Reinforcement Learning Approaches, Jonathan Turner

Theses and Dissertations

Artificial Intelligence is the next competitive domain; the first nation to develop human level artificial intelligence will have an impact similar to the development of the atomic bomb. To maintain the security of the United States and her people, the Department of Defense has funded research into the development of artificial intelligence and its applications. This research uses reinforcement learning and deep reinforcement learning methods as proxies for current and future artificial intelligence agents and to assess potential issues in development. Agent performance were compared across two games and one excursion: Cargo Loading, Tower of Hanoi, and Knapsack Problem, respectively. …

Approximate Dynamic Programming For An Unmanned Aerial Vehicle Routing Problem With Obstacles And Stochastic Target Arrivals, Kassie M. Gurnell

Theses and Dissertations

The United States Air Force is investing in artificial intelligence (AI) to speed analysis in efforts to modernize the use of autonomous unmanned combat aerial vehicles (AUCAVs) in strike coordination and reconnaissance (SCAR) missions. This research examines an AUCAVs ability to execute target strikes and provide reconnaissance in a SCAR mission. An orienteering problem is formulated as anMarkov decision process (MDP) model wherein a single AUCAV must optimize its target route to aid in eliminating time-sensitive targets and collect imagery of requested named areas of interest while evading surface-to-air missile (SAM) battery threats imposed as obstacles. The AUCAV adjusts its …

Team Air Combat Using Model-Based Reinforcement Learning, David A. Mottice

Theses and Dissertations

We formulate the first generalized air combat maneuvering problem (ACMP), called the MvN ACMP, wherein M friendly AUCAVs engage against N enemy AUCAVs, developing a Markov decision process (MDP) model to control the team of M Blue AUCAVs. The MDP model leverages a 5-degree-of-freedom aircraft state transition model and formulates a directed energy weapon capability. Instead, a model-based reinforcement learning approach is adopted wherein an approximate policy iteration algorithmic strategy is implemented to attain high-quality approximate policies relative to a high performing benchmark policy. The ADP algorithm utilizes a multi-layer neural network for the value function approximation regression mechanism. One-versus-one …

Application Of Machine Learning Models With Numerical Simulations Of An Experimental Microwave Induced Plasma Gasification Reactor, Owen D. Sedej

Theses and Dissertations

This thesis aims to contribute to the future development of this technology by providing an in-depth literature review of how this technology physically operates and can be numerically modeled. Additionally, this thesis reviews literature of machine learning models that have been applied to gasification to make accurate predictions regarding the system. Finally, this thesis provides a framework of how to numerically model an experimental plasma gasification reactor in order to inform a variety of machine learning models.

Learning Robot Motion From Creative Human Demonstration, Charles C. Dietzel

Theses and Dissertations

This thesis presents a learning from demonstration framework that enables a robot to learn and perform creative motions from human demonstrations in real-time. In order to satisfy all of the functional requirements for the framework, the developed technique is comprised of two modular components, which integrate together to provide the desired functionality. The first component, called Dancing from Demonstration (DfD), is a kinesthetic learning from demonstration technique. This technique is capable of playing back newly learned motions in real-time, as well as combining multiple learned motions together in a configurable way, either to reduce trajectory error or to generate entirely …

Smart City Management Using Machine Learning Techniques, Mostafa Zaman

Theses and Dissertations

In response to the growing urban population, "smart cities" are designed to improve people's quality of life by implementing cutting-edge technologies. The concept of a "smart city" refers to an effort to enhance a city's residents' economic and environmental well-being via implementing a centralized management system. With the use of sensors and actuators, smart cities can collect massive amounts of data, which can improve people's quality of life and design cities' services. Although smart cities contain vast amounts of data, only a percentage is used due to the noise and variety of the data sources. Information and communication technology (ICT) …

Universal Design In Bci: Deep Learning Approaches For Adaptive Speech Brain-Computer Interfaces, Srdjan Lesaja

Theses and Dissertations

In the last two decades, there have been many breakthrough advancements in non-invasive and invasive brain-computer interface (BCI) systems. However, the majority of BCI model designs still follow a paradigm whereby neural signals are preprocessed and task-related features extracted using static, and generally customized, data-independent designs. Such BCI designs commonly optimize narrow task performance over generalizability, adaptability, and robustness, which is not well suited to meeting individual user needs. If one day BCIs are to be capable of decoding our higher-order cognitive commands and conceptual maps, their designs will need to be adaptive architectures that will evolve and grow in …

Energy Planning Model Design For Forecasting The Final Energy Consumption Using Artificial Neural Networks, Haidy Eissa

Theses and Dissertations

“Energy Trilemma” has recently received an increasing concern among policy makers. The trilemma conceptual framework is based on three main dimensions: environmental sustainability, energy equity, and energy security. Energy security reflects a nation’s capability to meet current and future energy demand. Rational energy planning is thus a fundamental aspect to articulate energy policies. The energy system is huge and complex, accordingly in order to guarantee the availability of energy supply, it is necessary to implement strategies on the consumption side. Energy modeling is a tool that helps policy makers and researchers understand the fluctuations in the energy system. Over the …

Medical Image Segmentation Using Machine Learning, Masoud Khani

Theses and Dissertations

Image segmentation is the most crucial step in image processing and analysis. It can divide an image into meaningfully descriptive components or pathological structures. The result of the image division helps analyze images and classify objects. Therefore, getting the most accurate segmented image is essential, especially in medical images. Segmentation methods can be divided into three categories: manual, semiautomatic, and automatic. Manual is the most general and straightforward approach. Manual segmentation is not only time-consuming but also is imprecise. However, automatic image segmentation techniques, such as thresholding and edge detection, are not accurate in the presence of artifacts like noise …

Development Of A 7 Dof Exoskeleton Robot For Rehabilitation Of Lower Extremity, Sk Hasan

Theses and Dissertations

The World Health Organization reports that worldwide about 1 billion people have some form ofdisability. Of these, 110-190 million people have significant difficulties in functioning (mainly upper and lower extremity disability) independently. The major causes of human lower extremity disability include stroke, trauma, spinal cord injuries, and muscular dystrophy. Every 40 seconds, someone in the United States has a stroke. A statistic shows that approximately 65% of post-stroke patients suffer lower extremity impairment. Rehabilitation programs are the main method to promote functional recovery in disabled individuals. The conventional therapeutic approach requires a long commitment from a therapist or a clinician. …

Stock Markets Performance During A Pandemic: How Contagious Is Covid-19?, Yara Abushahba

Theses and Dissertations

Background and Motivation: The coronavirus (“COVID-19”) pandemic, the subsequent policies and lockdowns have unarguably led to an unprecedented fluid circumstance worldwide. The panic and fluctuations in the stock markets were unparalleled. It is inarguable that real-time availability of news and social media platforms like Twitter played a vital role in driving the investors’ sentiment during such global shock.

Purpose:The purpose of this thesis is to study how the investor sentiment in relation to COVID-19 pandemic influenced stock markets globally and how stock markets globally are integrated and contagious. We analyze COVID-19 sentiment through the Twitter posts and investigate its …

Development Of Novel Compound Controllers To Reduce Chattering Of Sliding Mode Control, Mehran Rahmani

Theses and Dissertations

The robotics and dynamic systems constantly encountered with disturbances such as micro electro mechanical systems (MEMS) gyroscope under disturbances result in mechanical coupling terms between two axes, friction forces in exoskeleton robot joints, and unmodelled dynamics of robot manipulator. Sliding mode control (SMC) is a robust controller. The main drawback of the sliding mode controller is that it produces high-frequency control signals, which leads to chattering. The research objective is to reduce chattering, improve robustness, and increase trajectory tracking of SMC. In this research, we developed controllers for three different dynamic systems: (i) MEMS, (ii) an Exoskeleton type robot, and …

Low-Cost Terrestrial Demonstration Of Autonomous Satellite Proximity Operations, Zackary R. Hewitt

Theses and Dissertations

The lack of satellite servicing capabilities significantly impacts the development and operation of current orbital assets. With autonomous solutions under consideration for servicing, the purpose of this research is to build and validate a low-cost hardware platform to expedite the development of autonomous satellite proximity operations. This research aims to bridge the gap between simulation and existing higher fidelity hardware testing with an affordable alternative. An omnidirectional variant of the commercially available TurtleBot3 mobile robot is presented as a 3-DOF testbed that demonstrates a satellite servicing inspection scenario. Reference trajectories for the scenario are generated via optimal control using the …

Applied Machine Learning In Extrusion-Based Bioprinting, Shuyu Tian

Theses and Dissertations

Optimization of extrusion-based bioprinting (EBB) parameters have been systematically conducted through experimentation. However, the process is time and resource-intensive and not easily translatable across different laboratories. A machine learning (ML) approach to EBB parameter optimization can accelerate this process for laboratories across the field through training using data collected from published literature. In this work, regression-based and classification-based ML models were investigated for their abilities to predict printing outcomes of cell viability and filament diameter for cell-containing alginate and gelatin composite hydrogels. Regression-based models were investigated for their ability to predict suitable extrusion pressure given desired cell viability when keeping …

Intelligent Therapeutic Robot: Design, Development, And Control, Asif Al Zubayer Swapnil

Theses and Dissertations

This research contributes to developing an Intelligent Therapeutic Robot (iTbot) designed to provide therapy to patients with upper limb impairment due to stroke, injury, and other trauma. This robot aims to implement robotic rehabilitation based on principles of motor rehabilitation and Neuroplasticity. The iTbot, as developed in this research, can provide end-effector type rehabilitation exercises in various configurations, including motion in the vertical and horizontal plane. It can provide passive, active, and active-assisted rehabilitation therapies to patients with limited upper limb mobility.

The iTbot has been designed with simplicity in mind with a minimum viability approach. With a minimum amount …

Detection Of Stealthy False Data Injection Attacks Against State Estimation In Electric Power Grids Using Deep Learning Techniques, Qingyu Ge

Theses and Dissertations

Since communication technologies are being integrated into smart grid, its vulnerability to false data injection is increasing. State estimation is a critical component which is used for monitoring the operation of power grid. However, a tailored attack could circumvent bad data detection of the state estimation, thus disturb the stability of the grid. Such attacks are called stealthy false data injection attacks (FDIAs). This thesis proposed a prediction-based detector using deep learning techniques to detect injected measurements. The proposed detector adopts both Convolutional Neural Networks and Recurrent Neural Networks, making full use of the spatial-temporal correlations in the measurement data. …

Monte Carlo Tree Search Applied To A Modified Pursuit/Evasion Scotland Yard Game With Rendezvous Spaceflight Operation Applications, Joshua A. Daughtery

Theses and Dissertations

This thesis takes the Scotland Yard board game and modifies its rules to mimic important aspects of space in order to facilitate the creation of artificial intelligence for space asset pursuit/evasion scenarios. Space has become a physical warfighting domain. To combat threats, an understanding of the tactics, techniques, and procedures must be captured and studied. Games and simulations are effective tools to capture data lacking historical context. Artificial intelligence and machine learning models can use simulations to develop proper defensive and offensive tactics, techniques, and procedures capable of protecting systems against potential threats. Monte Carlo Tree Search is a bandit-based …

Object Detection With Deep Learning To Accelerate Pose Estimation For Automated Aerial Refueling, Andrew T. Lee

Theses and Dissertations

Remotely piloted aircraft (RPAs) cannot currently refuel during flight because the latency between the pilot and the aircraft is too great to safely perform aerial refueling maneuvers. However, an AAR system removes this limitation by allowing the tanker to directly control the RP A. The tanker quickly finding the relative position and orientation (pose) of the approaching aircraft is the first step to create an AAR system. Previous work at AFIT demonstrates that stereo camera systems provide robust pose estimation capability. This thesis first extends that work by examining the effects of the cameras' resolution on the quality of pose …

Extracting Range Data From Images Using Focus Error, Erik M. Madden

Theses and Dissertations

Air-to-air refueling (AAR) has become a staple when performing long missions with aircraft. With modern technology, however, people have begun to research how to perform this task autonomously. Automated air-to-air refueling (A3R) is this exact concept. Combining many different systems, the idea is to allow computers on the aircraft to link up via the refueling boom, refuel, and detach before resuming pilot control. This document lays out one of the systems that is needed to perform A3R, namely, the system that extracts range data. While stereo cameras perform such tasks, there is interest in finding other ways of accomplishing the …

Algorithm Selection Framework: A Holistic Approach To The Algorithm Selection Problem, Marc W. Chalé

Theses and Dissertations

A holistic approach to the algorithm selection problem is presented. The “algorithm selection framework" uses a combination of user input and meta-data to streamline the algorithm selection for any data analysis task. The framework removes the conjecture of the common trial and error strategy and generates a preference ranked list of recommended analysis techniques. The framework is performed on nine analysis problems. Each of the recommended analysis techniques are implemented on the corresponding data sets. Algorithm performance is assessed using the primary metric of recall and the secondary metric of run time. In six of the problems, the recall of …

Pedestrian Navigation Using Artificial Neural Networks And Classical Filtering Techniques, David J. Ellis

Theses and Dissertations

The objective of this thesis is to explore the improvements achieved through using classical filtering methods with Artificial Neural Network (ANN) for pedestrian navigation techniques. ANN have been improving dramatically in their ability to approximate various functions. These neural network solutions have been able to surpass many classical navigation techniques. However, research using ANN to solve problems appears to be solely focused on the ability of neural networks alone. The combination of ANN with classical filtering methods has the potential to bring beneficial aspects of both techniques to increase accuracy in many different applications. Pedestrian navigation is used as a …

Machine Intelligence For Advanced Medical Data Analysis: Manifold Learning Approach, Fereshteh S Bashiri

Theses and Dissertations

In the current work, linear and non-linear manifold learning techniques, specifically Principle Component Analysis (PCA) and Laplacian Eigenmaps, are studied in detail. Their applications in medical image and shape analysis are investigated.

In the first contribution, a manifold learning-based multi-modal image registration technique is developed, which results in a unified intensity system through intensity transformation between the reference and sensed images. The transformation eliminates intensity variations in multi-modal medical scans and hence facilitates employing well-studied mono-modal registration techniques. The method can be used for registering multi-modal images with full and partial data.

Next, a manifold learning-based scale invariant global shape …

Design And Implementation Of A Domain Specific Language For Deep Learning, Xiao Bing Huang

Theses and Dissertations

\textit {Deep Learning} (DL) has found great success in well-diversified areas such as machine vision, speech recognition, big data analysis, and multimedia understanding recently. However, the existing state-of-the-art DL frameworks, e.g. Caffe2, Theano, TensorFlow, MxNet, Torch7, and CNTK, are programming libraries with fixed user interfaces, internal representations, and execution environments. Modifying the code of DL layers or data structure is very challenging without in-depth understanding of the underlying implementation. The optimization of the code and execution in these tools is often limited and relies on the specific DL computation graph manipulation and scheduling that lack systematic and universal strategies. Furthermore, …