Computer Engineering Commons^™

Respiratory Compensated Robot For Liver Cancer Treatment: Design, Fabrication, And Benchtop Characterization, Mishek Jair Musa

Graduate Theses and Dissertations

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death in the world. Radiofrequency ablation (RFA) is an effective method for treating tumors less than 5 cm. However, manually placing the RFA needle at the site of the tumor is challenging due to the complicated respiratory induced motion of the liver. This paper presents the design, fabrication, and benchtop characterization of a patient mounted, respiratory compensated robotic needle insertion platform to perform percutaneous needle interventions. The robotic platform consists of a 4-DoF dual-stage cartesian platform used to control the pose of a 1-DoF needle insertion module. The active …

Respiratory Sound Analysis For The Evidence Of Lung Health, Priyanka Sreerama

Dissertations and Theses

Significant changes have been made on audio-based technologies over years in several different fields along with healthcare industry. Analysis of Lung sounds is a potential source of noninvasive, quantitative information along with additional objective on the status of the pulmonary system. To do that medical professionals listen to sounds heard over the chest wall at different positions with a stethoscope which is known as auscultation and is important in diagnosing respiratory diseases. At times, possibility of inaccurate interpretation of respiratory sounds happens because of clinician’s lack of considerable expertise or sometimes trainees such as interns and residents misidentify respiratory sounds. …

English-To-Ipa Transcription, Riley Roberts

Undergraduate Honors Capstone Projects

The purpose of this project was to create a tool that could automate English-to-IPA (International Phonetic Alphabet) transcription. Research was done to determine what would be required to perform such a transcription. After researching and experimenting with existing tools, it was determined that developing the signal processing and Artificial Intelligence model portion of the application would be too intensive to successfully complete within the timeframe of this project.

The choice was made to develop an iOS application, with the Python library Allosaurus used to do the speech processing and as the Artificial Intelligence model. This model was then deployed in …

Synthesis Methodologies For Robust And Reconfigurable Clock Networks, Necati Uysal

Electronic Theses and Dissertations, 2020-2023

In today's aggressively scaled technology nodes, billions of transistors are packaged into a single integrated circuit. Electronic Design Automation (EDA) tools are needed to automatically assemble the transistors into a functioning system. One of the most important design steps in the physical synthesis is the design of the clock network. The clock network delivers a synchronizing clock signal to each sequential element. The clock signal is required to be delivered meeting timing constraints under variations and in multiple operating modes. Synthesizing such clock networks is becoming increasingly difficult with the complex power management methodologies and severe manufacturing variations. Clock network …

Optimizing A Virtual Human Platform For Depression/Suicide Ideation Identification For The American Soldier, Christina M. Monahan

Master's Theses

Suicide surpassed homicide to be the second leading cause of death among people 10-24 years old in the United States \cite{1}. This statistic is alarming especially when combined with the more than eight distinctly different types of clinical depression among society today \cite{2}. To further complicate this health crisis, let’s consider the current worldwide isolating pandemic often referred to as COVID-19 that has spanned 12 months. It is more important than ever to consider how we can get ahead of the crisis by identifying the symptoms as they set in and more importantly ahead of the decision to commit suicide. …

Optimal Feature Learning For Facial Expression Recognition, Kamran Ali

Electronic Theses and Dissertations, 2020-2023

A great deal of research has been done to improve the performance of Facial Expression Recognition (FER) algorithms, but extracting optimal features to represent expressions remains a challenging task. The biggest drawback is that most work on FER ignores the inter-subject variations in facial attributes of individuals present in data. Hence, the representation extracted for the recognition of expressions is polluted by identity-related features that negatively affect the generalization capability of a FER technique on unseen identities. To overcome the effect of subject-identity bias, previous research shows the effectiveness of extracting identity-invariant expression features for FER. However, most of those …

An Analysis Of Camera Configurations And Depth Estimation Algorithms For Triple-Camera Computer Vision Systems, Jared Peter-Contesse

Master's Theses

The ability to accurately map and localize relevant objects surrounding a vehicle is an important task for autonomous vehicle systems. Currently, many of the environmental mapping approaches rely on the expensive LiDAR sensor. Researchers have been attempting to transition to cheaper sensors like the camera, but so far, the mapping accuracy of single-camera and dual-camera systems has not matched the accuracy of LiDAR systems. This thesis examines depth estimation algorithms and camera configurations of a triple-camera system to determine if sensor data from an additional perspective will improve the accuracy of camera-based systems. Using a synthetic dataset, the performance of …

Evaluating Deep-Learning Models For Debris-Covered Glacier Mapping, Zhiyuan Xie, Vijayan K. Asari, Umesh K. Haritashya

Electrical and Computer Engineering Faculty Publications

In recent decades, mountain glaciers have experienced the impact of climate change in the form of accelerated glacier retreat and other glacier-related hazards such as mass wasting and glacier lake outburst floods. Since there are wide-ranging societal consequences of glacier retreat and hazards, monitoring these glaciers as accurately and repeatedly as possible is important. However, the accurate glacier boundary, especially the debriscovered glacier (DCG) boundary, which is one of the primary inputs in many glacier analyses, remains a challenge even after many years of research using conventional remote sensing methods or machine-learning methods. The GlacierNet, a deep-learning-based approach, utilized the …

Augmented Communications : A Solution For Overcoming High Spatial Correlation Of The Massive-Miso Vlc Channel, Monette Khadr

Legacy Theses & Dissertations (2009 - 2024)

A key challenge for future wireless networks is to come upon a riveting compromise between spectral efficiency, complexity, and energy efficiency. The challenge is also intensified due to the pace at which the Internet-of-Things (IoT) technology is arriving, causing an upheaval to pre-existing network infrastructures in terms of elevating spectrum scarcity. To keep pace with the exploding data demand forecasts, a circumvention is required. One realization is by utilizing the high-band spectrum and the rich body of knowledge on multiple-input multiple-output (MIMO) technologies. One of the prominent high frequency technologies is visible light communications (VLC). VLC provide a large unregulated …

Parallel Approach Of Adaptive Image Thresholding Algorithm On Gpu, Adhi Prahara, Andri Pranolo, Nuril Anwar, Yingchi Mao

Knowledge Engineering and Data Science

Image thresholding is used to segment an image into background and foreground using a given threshold. The threshold can be generated using a specific algorithm instead of a pre-defined value obtained from observation or experiment. However, the algorithm involves per pixel operation, histogram calculation, and iterative procedure to search the optimum threshold that is costly for high-resolution images. In this research, parallel implementations on GPU for three adaptive image thresholding methods, namely Otsu, ISODATA, and minimum cross-entropy, were proposed to optimize their computational times to deal with high-resolution images. The approach involves parallel reduction and parallel prefix sum (scan) techniques …

A Comprehensive Analysis Of Reward Function For Adaptive Traffic Signal Control, Abu Rafe Md Jamil, Naushin Nower

Knowledge Engineering and Data Science

Adaptive traffic control systems (ATCS) can play an essential role in reducing traffic congestion in urban areas. The main challenge for ATSC is to determine the proper signal timing. Recently, Deep Reinforcement Learning (DRL) has been used to determine proper signal timing. However, the success of the DRL algorithm depends on the appropriate reward function design. There exist various reward functions for ATSC in the existing research. This research presents a comprehensive analysis of the widely used reward function. The pros and cons of various reward algorithms were discussed, and experimental analysis shows that the multi-objective reward function enhances the …

Recognition Of Handwritten Javanese Script Using Backpropagation With Zoning Feature Extraction, Anik Nur Handayani, Heru Wahyu Herwanto, Katya Lindi Chandrika, Kohei Arai

Knowledge Engineering and Data Science

Backpropagation is part of supervised learning, in which the training process requires a target. The resulting error is transmitted back to the units below in its training process. Backpropagation can solve complicated problems because it consumes less memory than other algorithms. In addition, it also can produce solutions with a low error rate while executing less time. In image pattern recognition, backpropagation can be utilized for cultural preservation in many places worldwide, including Indonesia. It is used to recognize picture patterns in Javanese script writings. This study concluded that feature extraction approaches, zoning, and backpropagation could be utilized to distinguish …

A Comparative Study Of Machine Learning-Based Approach For Network Traffic Classification, Kien Trang, An Hoang Nguyen

Knowledge Engineering and Data Science

Internet usage has increased rapidly and become an essential part of human life, corresponding to the rapid development of network infrastructure in recent years. Thus, protecting users’ confidential information when joining the global network becomes one of the most significant considerations. Even though multiple encryption algorithms and techniques have been applied in different parties, including internet providers, and web hosting, this situation also allows the hacker to attack the network system anonymously. Therefore, the significance of classifying network data streams to improve network system quality and security is attracting increasing study interests. This work introduces a machine learning-based approach to …

Application Of Image Processing And Convolutional Neural Networks For Flood Image Classification And Semantic Segmentation, Jaku Rabinder Rakshit Pally

All Theses

Floods are among the most destructive natural hazards that affect millions of people across the world leading to severe loss of life and damage to property, critical infrastructure, and the environment. Deep learning algorithms are exceptionally valuable tools for collecting and analyzing the catastrophic readiness and countless actionable flood data. Convolutional neural networks (CNNs) are one form of deep learning algorithms widely used in computer vision which can be used to study flood images and assign learnable weights and biases to various objects in the image. Here, we leveraged and discussed how connected vision systems can be used to embed …

Lossy Compression And Its Application On Large Scale Scientific Datasets, Tasmia Reza

All Theses

High Performance Computing (HPC) applications are always expanding in data size and computational complexity. It is becoming necessary to consider fault tolerance and system recovery to reduce computation and resource cost in HPC systems. The computation of modern large scale HPC applications are facing bottleneck due to computation complexities, increased runtime and large data storage requirements. These issues can not be ignored in current supercomputing era. Data compression is one of the effective ways to address data storage issue. Among data compression, the lossy compression is much more feasible and efficient than the traditional lossless compression due to low I/O …

Approaches To Improve The Execution Time Of A Quantum Network Simulation, Joseph B. Tippit

Theses and Dissertations

Evaluating quantum networks is an expensive and time-consuming task that benefits from simulation. A potential improvement is to utilize GPUs, namely by leveraging NVIDIA's programming framework, CUDA. To avoid performance pitfalls of higher level languages and programming models such as the so called "two language problem," the Julia Programming Language provides the basis for the development effort. This research develops a two module prototype quantum network simulation framework using GPUs and Julia. Performance of the software is measured and compared against other languages such as MATLAB.

Design, Development, And Testing Of Embedded Computing On Afit's Control & Autonomy Space Proximity Robot (Caspr), Collin A. Gwaltney

Theses and Dissertations

This thesis reviews RPO algorithm testbeds and discusses the development of the Control and Autonomy Space proximity Robot (CASpR) kinematic testbed housed at the Air Force Institute of Technology (AFIT). CASpR operates on a rail system to propagate the trajectories of two satellites using the Hill-Clohessy-Wiltshire (HCW) Equations of Motion (EOMs). In this study, the implementation of a Jetson TX2i as an onboard flight computer is discussed and accomplished. Each hardware component used in the process of adding embedded computing as well as the software and paths of communication are all discussed in detail. Tests are conducted to assess the …

Self Adaptive Reinforcement Learning For High-Dimensional Stochastic Systems With Application To Robotic Control, Sayyed Jaffar Ali Raza

Electronic Theses and Dissertations, 2020-2023

A long standing goal in the field of artificial intelligence (AI) is to develop agents that can perceive richer problem space and effortlessly plan their activity in minimal duration. Several strides have been made towards this goal over the last few years due to simultaneous advances in compute power, optimized algorithms, and most importantly evident success of AI based machines in nearly every discipline. The progress has been especially rapid in area of reinforcement learning (RL) where computers can now plan-ahead their activities and outperform their human rivals in complex problem domains like chess or Go game. However, despite encouraging …

Integration Of Internet Of Things And Health Recommender Systems, Moonkyung Yang

Electronic Theses, Projects, and Dissertations

The Internet of Things (IoT) has become a part of our lives and has provided many enhancements to day-to-day living. In this project, IoT in healthcare is reviewed. IoT-based healthcare is utilized in remote health monitoring, observing chronic diseases, individual fitness programs, helping the elderly, and many other healthcare fields. There are three main architectures of smart IoT healthcare: Three-Layer Architecture, Service-Oriented Based Architecture (SoA), and The Middleware-Based IoT Architecture. Depending on the required services, different IoT architecture are being used. In addition, IoT healthcare services, IoT healthcare service enablers, IoT healthcare applications, and IoT healthcare services focusing on Smartwatch …

Monitored Compression Therapy: Using Smart Technology To Optimize The Treatment Of Lower Extremity Swelling, James Skelly

UNLV Theses, Dissertations, Professional Papers, and Capstones

Chronic Venous Insufficiency (CVI) and Venous Stasis Ulcers (VSUs) are symptoms which stem from diabetes – a disease effecting over 34 million people in the United States alone as of 2020. This Thesis details the design of a pressure-sensing garment used to enhance the treatment of CVI. The garment uses small force sensors (four levels: the insole of the foot, the lower leg, the lower calf, and the upper calf) to sense the pressure applied by a compression stocking. The sensed data is transmitted wirelessly via Bluetooth to a smartphone application that was developed to display the data and interface …