Computer Engineering Commons^™

Design Of Plastic Contaminant Eliminator In Seed Cotton, Joshua H. Tandio

Theses and Dissertations

Plastic contamination in cotton is a problem in cotton industry and researchers have worked on this problem with different approaches. This thesis documents the design of mechanical and electronic real-time systems for detecting and removing plastic contaminants. The mechanical system was designed to expose plastic embedded inside the seed cotton to the sensor and to separate plastic contaminated cotton from the process stream. The detection system consisted of an embedded computer interfaced with a USB camera and Neural Network (NN) software running in it. Two NN models were tested, a transfer learning model and a built-from-scratch original model. The original …

3d Shape Estimation Of Negative Obstacles Using Lidar Point Cloud Data, Viswadeep Lebakula

Theses and Dissertations

Obstacle detection and avoidance plays a crucial role in the autonomous navigation of unmanned ground vehicles (UGV). Information about the obstacles decreases as the distance between the UGV and obstacles increases. However, this information decreases much more rapidly for negative obstacles than for positive obstacles. UGV navigation becomes more challenging in off-road environments due to the higher probability of finding negative obstacles (e.g., potholes, ditches, trenches, etc.) compared with on-road environments. One approach to solve this problem is to avoid the candidate path with a negative obstacle, but in off-road environments avoiding negative obstacles in all situations is not possible. …

Autonomous Control And Signal Acquisition System, Rion Cadell Krampe

Honors College Theses

Our goal is to continue a previous teams project to build a NDE autonomous control and signal acquisition system that is more precise, more customizable with both code and mechanical parts, and cheaper than a similar system bought by the school. This goal has two stages to it. First, to repair the system from considerable damage it received during transportation. Secondly, to continue designing and developing the system to make considerable progress towards the goal of a fully functional NDE system. Along with making progress we must consider the team after us and create an easy stepping off point for …

Synchronous Drones For Autonomous Package Delivery, Ryan Kruegel

Honors Theses

As technology progresses, drones are becoming a more realistic substitute for humans in the workplace. Using drones to move a payload allows for higher mobility and faster delivery times. This project explores the idea of using drones as delivery devices. The project itself is a continuation of the Synchronous Drones project started by Adam Dubs, Saleh Almaden, and Jonathan Emhoff. The main objective is to have two drones operate in sync to deliver a load to a designated location. The benefit of this is that if multiple drones can be synchronized, even larger loads can be moved. For the scope …

Iot Greenhouse Monitoring System, Raj Basnet

Honors Theses

Our project is a greenhouse monitoring system. The customer states that they need a complete monitoring system for their greenhouse. There are a lot of items within the greenhouse that need to be watered at the right time and kept at a certain temperature. The customer is not always around to check the status of these items due to their busy lifestyle. They would like a system to monitor all these items so they can check it on their smartphone no matter how far away they are from the greenhouse. The customer wants this to be a low-cost and energyefficient …

Computational Solutions To Exosomal Microrna Biomarker Detection In Pancreatic Cancer, Thuy T. An

School of Computing: Dissertations, Theses, and Student Research

Pancreatic cancer is the fourth leading cause of cancer death in the United States and the 5-year survival rate is only 5% to 10%. There are only a few non-specific symptoms associated with the early-stage cancer, therefore most patients are diagnosed in a late stage. Due to the lack of effective treatments and the fact that the early stage has a 39% 5-year survival rate, the biggest hope to control this disease is early detection. Therefore, discovery of effective and reliable non-invasive biomarkers for early detection of pancreatic cancer has been a major topic. Very recently, exosomal microRNAs have become …

3d Bionic Arm, Anny Baez Silfa

Publications and Research

Previous work had been done on what was an adaptation of a robotic arm for Roboqueen, which is a persistent research project of the Department of Computer Engineering Technology. This robotic arm consisted of removing the cardboard hands from the Roboqueen and being replaced by 3D printed fingers and wrists to add functionality that did not exist before. Servo motors connected to Arduinos hidden in the forearm will be used to move the fingers and pick up and hold objects in the hand. Currently, the Finger Myoware is an update of the robotic arm in order to understand how prosthetic …

Messiness: Automating Iot Data Streaming Spatial Analysis, Christopher White, Atilio Barreda Ii

Publications and Research

The spaces we live in go through many transformations over the course of a year, a month, or a day; My room has seen tremendous clutter and pristine order within the span of a few hours. My goal is to discover patterns within my space and formulate an understanding of the changes that occur. This insight will provide actionable direction for maintaining a cleaner environment, as well as provide some information about the optimal times for productivity and energy preservation.

Using a Raspberry Pi, I will set up automated image capture in a room in my home. These images will …

Child Detection System For Preventing Hot-Car Death, Touheda Khanom, Fahmeda Khanom

Publications and Research

According to the Safety Organization Kids and Cars, an average of 38 children die each year from hot cars due to lack of attention from parents. Existing alert system uses sensors that detect child presence and care less about detecting temperature inside the car. In order to save children from heatstroke, our research focuses on providing information about the temperature inside the car using very effective sensors with high precision. In our research, we have tested different sensors and figured out three sensors that can provide precise information about the presence of a child without an adult. Our child detection …

Semantically Meaningful Sentence Embeddings, Rojina Deuja

School of Computing: Dissertations, Theses, and Student Research

Text embedding is an approach used in Natural Language Processing (NLP) to represent words, phrases, sentences, and documents. It is the process of obtaining numeric representations of text to feed into machine learning models as vectors (arrays of numbers). One of the biggest challenges in text embedding is representing longer text segments like sentences. These representations should capture the meaning of the segment and the semantic relationship between its constituents. Such representations are known as semantically meaningful embeddings. In this thesis, we seek to improve upon the quality of sentence embeddings that capture semantic information.

The current state-of-the-art models are …

Digitization Of Academic Libraries Through Cloud Environment, Sivankalai S, Virumandi A, Sivasekaran K, Jeyanthi R, M Sharmila

Library Philosophy and Practice (e-journal)

Libraries may soon be able to establish and manage their own data centres. This paradigm would allow libraries to control the apps and data stores that include sensitive and private information about their users' personal and financial information. The provisioning and management of infrastructure for a Web-based digital library present several complicated difficulties for library administrators. In this article, we address the challenges that digital libraries confront, and the efforts being made to solve those challenges. Infrastructure virtualization and cloud Environment are incredibly enticing options, but they are being challenged by the expansion of the indexed document collection, the addition …

Improving Noise Immunity And Efficiency Using High-Precision Iterative Codes, Sherzod Shukhratovich Atadjanov, Aziza Ahmadjanovna Tursunova

Bulletin of TUIT: Management and Communication Technologies

The article discusses the issues of ensuring noise immunity in digital broadcasting systems, shows the importance of the transition to the optimal code and the need to use it in the field of noiseless coding in various areas of telecommunication transmission and reception of digital signals. The previous algorithms and error-correcting coding methods based on the Gray code, which are used in multi-level digital broadcast modulation schemes to minimize the intensity of bit errors, are highlighted. A model of error-correcting coding by the Gray method and methods for estimating the probability of error for the Gray code are presented. Based …

Contrastive Learning For Unsupervised Auditory Texture Models, Christina Trexler

Computer Science and Computer Engineering Undergraduate Honors Theses

Sounds with a high level of stationarity, also known as sound textures, have perceptually relevant features which can be captured by stimulus-computable models. This makes texture-like sounds, such as those made by rain, wind, and fire, an appealing test case for understanding the underlying mechanisms of auditory recognition. Previous auditory texture models typically measured statistics from auditory filter bank representations, and the statistics they used were somewhat ad-hoc, hand-engineered through a process of trial and error. Here, we investigate whether a better auditory texture representation can be obtained via contrastive learning, taking advantage of the stationarity of auditory textures to …

Automated Report Based System To Encourage A Greener Commute To Campus, Ronald Velasquez

Computer Science and Computer Engineering Undergraduate Honors Theses

This project consists of the design and implementation of a tool to encourage greener commutes to the University of Arkansas. Trends in commuting of the last few years show a decline in not so environment-friendly commute modes. Nevertheless, ensuring that this trend continues is vital to assure a significant impact. The created tool is an automated report system. The report displays information about different commute options. A Google form allows users to submit report requests, and a web app allows the sustainability office to process them in batches. This system was built in the Apps Script platform. It implements several …

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 …

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. …

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 …

Deep Learning Based Speech Enhancement And Its Application To Speech Recognition, Ju Lin

All Dissertations

Speech enhancement is the task that aims to improve the quality and the intelligibility of a speech signal that is degraded by ambient noise and room reverberation. Speech enhancement algorithms are used extensively in many audio- and communication systems, including mobile handsets, speech recognition, speaker verification systems and hearing aids. Recently, deep learning has achieved great success in many applications, such as computer vision, nature language processing and speech recognition. Speech enhancement methods have been introduced that use deep-learning techniques, as these techniques are capable of learning complex hierarchical functions using large-scale training data. This dissertation investigates the deep learning …

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 …

Towards Characterizing Adversarial Opportunity And Behavior In Critical Infrastructure Network, Md Sharif Ullah

Electrical & Computer Engineering Theses & Dissertations

The growth of information and communication technology is constantly revolutionizing various domains, e.g., energy utility systems, healthcare, the internet of things, etc. This inception of widespread cyber technology enables reliability and first operability of the system, yet simultaneously imposed a risk of significant impact due to disruption of safe and secure operation. The attack surface is expanding, creating a cyber exposure gap which indicates a higher threat landscape and increased risk of compromise. Motivated by this increased threat exposure, this dissertation investigates the attack surface as a static and dynamic indicator of adversary propagation, seeking an efficient modeling paradigm to …

Joint Linear And Nonlinear Computation With Data Encryption For Efficient Privacy-Preserving Deep Learning, Qiao Zhang

Electrical & Computer Engineering Theses & Dissertations

Deep Learning (DL) has shown unrivalled performance in many applications such as image classification, speech recognition, anomalous detection, and business analytics. While end users and enterprises own enormous data, DL talents and computing power are mostly gathered in technology giants having cloud servers. Thus, data owners, i.e., the clients, are motivated to outsource their data, along with computationally-intensive tasks, to the server in order to leverage the server’s abundant computation resources and DL talents for developing cost-effective DL solutions. However, trust is required between the server and the client to finish the computation tasks (e.g., conducting inference for the newly-input …

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 …

Risk-Based Machine Learning Approaches For Probabilistic Transient Stability, Umair Shahzad

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

Power systems are getting more complex than ever and are consequently operating close to their limit of stability. Moreover, with the increasing demand of renewable wind generation, and the requirement to maintain a secure power system, the importance of transient stability cannot be overestimated. Considering its significance in power system security, it is important to propose a different approach for enhancing the transient stability, considering uncertainties. Current deterministic industry practices of transient stability assessment ignore the probabilistic nature of variables (fault type, fault location, fault clearing time, etc.). These approaches typically provide a conservative criterion and can result in expensive …

Comparative Analysis Of Kmer Counting And Estimation Tools, Ankitha Vejandla

School of Computing: Dissertations, Theses, and Student Research

The rapid development of next-generation sequencing (NGS) technologies for determining the sequence of DNA has revolutionized genome research in recent years. De novo assemblers are the most commonly used tools to perform genome assembly. Most of the assemblers use de Bruijn graphs that break the sequenced reads into smaller sequences (sub-strings), called kmers, where k denotes the length of the sub-strings. The kmer counting and analysis of kmer frequency distribution are important in genome assembly. The main goal of this research is to provide a detailed analysis of the performance of different kmer counting and estimation tools that are currently …

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 …

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 …

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 …

Similarity Identification Of Large-Scale Biomedical Documents Using Cosine Similarity And Parallel Computing, Merlinda Wibowo, Christoph Quix, Nur Syahela Hussien, Herman Yuliansyah, Faisal Dharma Adhinata

Knowledge Engineering and Data Science

Document similarity computation is an important research topic in information retrieval, and it is a crucial issue for automatic document categorization. The similarity value is between 0 and 1, then the closest value to 1 is represented both documents is considered more relevant, vice versa. However, the large scale of textual information has created the problem of finding the relevance level between documents. Therefore, the relevance between mesh heading text in the PubMed documents is higher than the relevance of the abstract text in the PubMed documents. Furthermore, parallel computing is implemented to speed up the large-scale documents similarity identification …

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 …