Engineering Commons^™

Dynamic Prediction Of Runway Configuration And Airport Acceptance Rate, Yuan Wang

USF Tampa Graduate Theses and Dissertations

Automated prediction of runway configuration and airport capacity is critical for the future generation of air traffic management. In the future aviation industry, multi-sources weather forecast information will be available for air traffic decision-making units; how to use these data efficiently is key for overall efficiency of air traffic management. Currently, air traffic management personnel lack tools to assist them to translate weather forecast data into real-time airport capacity. Runway configurations and AARs of airports in a multi-airport system are determined by different air traffic controller personnel. The lack of synchronization may lead to the loss of efficiency of the …

Automated Segmentation Of Temporal Bone Structures, Daniel Allen

Electronic Thesis and Dissertation Repository

Mastoidectomy is a challenging surgical procedure that is difficult to perform and practice. As supplementation to current training techniques, surgical simulators have been developed with the ability to visualize and operate on temporal bone anatomy. Medical image segmentation is done to create three-dimensional models of anatomical structures for simulation. Manual segmentation is an accurate but time-consuming process that requires an expert to label each structure on images. An automatic method for segmentation would allow for more practical model creation. The objective of this work was to create an automated segmentation algorithm for structures of the temporal bone relevant to mastoidectomy. …

Acute Angle Repositioning In Mobile C-Arm Using Image Processing And Deep Learning, Armin Yazdanshenas

Mechanical Engineering Theses

During surgery, medical practitioners rely on the mobile C-Arm medical x-ray system (C-Arm) and its fluoroscopic functions to not only perform the surgery but also validate the outcome. Currently, technicians reposition the C-Arm arbitrarily through estimation and guesswork. In cases when the positioning and repositioning of the C-Arm are critical for surgical assessment, uncertainties in the angular position of the C-Arm components hinder surgical performance. This thesis proposes an integrated approach to automatically reposition C-Arms during critically acute movements in orthopedic surgery. Robot vision and control with deep learning are used to determine the necessary angles of rotation for desired …

Semantic Image Segmentation Via A Dense Parallel Network, Jiyang Wang

Theses - ALL

Image segmentation has been an important area of study in computer vision. Image segmentation is a challenging task, since it involves pixel-wise annotation, i.e. labeling each pixel according to the class to which it belongs. In image classification task, the goal is to predict to which class an entire image belongs. Thus, there is more focus on the abstract features extracted by Convolutional Neural Networks (CNNs), with less emphasis on the spatial information. In image segmentation task, on the other hand, the abstract information and spatial information are needed at the same time. One class of work in image segmentation …

Security Framework For The Internet Of Things Leveraging Network Telescopes And Machine Learning, Farooq Israr Ahmed Shaikh

USF Tampa Graduate Theses and Dissertations

The recent advancements in computing and sensor technologies, coupled with improvements in embedded system design methodologies, have resulted in the novel paradigm called the Internet of Things (IoT). IoT is essentially a network of small embedded devices enabled with sensing capabilities that can interact with multiple entities to relay information about their environments. This sensing information can also be stored in the cloud for further analysis, thereby reducing storage requirements on the devices themselves. The above factors, coupled with the ever increasing needs of modern society to stay connected at all times, has resulted in IoT technology penetrating all facets …

Strawberry Detection Under Various Harvestation Stages, Yavisht Fitter

Master's Theses

This paper analyzes three techniques attempting to detect strawberries at various stages in its growth cycle. Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP) and Convolutional Neural Networks (CNN) were implemented on a limited custom-built dataset. The methodologies were compared in terms of accuracy and computational efficiency. Computational efficiency is defined in terms of image resolution as testing on a smaller dimensional image is much quicker than larger dimensions. The CNN based implementation obtained the best results with an 88% accuracy at the highest level of efficiency as well (600x800). LBP generated moderate results with a 74% detection accuracy …

Learning Models For Corrupted Multi-Dimensional Data: Fundamental Limits And Algorithms, Ishan Jindal

Wayne State University Dissertations

Developing machine learning models for unstructured multi-dimensional datasets such as datasets with unreliable labels and noisy multi-dimensional signals with or without missing information have becoming a central necessity. We are not always fortunate enough to get noise-free datasets for developing classification and representation models. Though there is a number of techniques available to deal with noisy datasets, these methods do not exploit the multi-dimensional structures of the signals, which could be used to improve the overall classification and representation performance of the model.

In this thesis, we develop a Kronecker-structure (K-S) subspace model that exploits the multi-dimensional structure of the …

Reinforcement Learning And Game Theory For Smart Grid Security, Shuva Paul

Electronic Theses and Dissertations

This dissertation focuses on one of the most critical and complicated challenges facing electric power transmission and distribution systems which is their vulnerability against failure and attacks. Large scale power outages in Australia (2016), Ukraine (2015), India (2013), Nigeria (2018), and the United States (2011, 2003) have demonstrated the vulnerability of power grids to cyber and physical attacks and failures. These incidents clearly indicate the necessity of extensive research efforts to protect the power system from external intrusion and to reduce the damages from post-attack effects. We analyze the vulnerability of smart power grids to cyber and physical attacks and …

Artificial Intelligence In The Assessment Of Transmission And Distribution Systems Under Natural Disasters Using Machine Learning And Deep Learning Techniques In A Knowledge Discovery Framework, Rossana Villegas

Open Access Theses & Dissertations

Warming trends and increasing temperatures have been observed and reported by federal agencies, such as the National Oceanic and Atmospheric Administration (NOAA). Extreme-weather events, especially hurricanes, tornadoes and winter storms, are among the highly devastating natural disasters responsible for massive and prolonged power outages in Electrical Transmission and Distribution Systems (ETDS). Moreover, the failure rate probability of any system component under extreme-weather tends to increase in the impacted geographic area. This Dissertation proposes an Artificial Intelligence (AI) Decision Support System that can predict damage in the ETDS and allow operators to mitigate disastrous extreme weather events. The document reports the …

Dedicated Hardware For Machine/Deep Learning: Domain Specific Architectures, Angel Izael Solis

Open Access Theses & Dissertations

Artificial intelligence has come a very long way from being a mere spectacle on the silver screen in the 1920s [Hml18]. As artificial intelligence continues to evolve, and we begin to develop more sophisticated Artificial Neural Networks, the need for specialized and more efficient machines (less computational strain while maintaining the same performance results) becomes increasingly evident. Though these “new” techniques, such as Multilayer Perceptron’s, Convolutional Neural Networks and Recurrent Neural Networks, may seem as if they are on the cutting edge of technology, many of these ideas are over 60 years old! However, many of these earlier models, at …

The Real-Time Extractiion Of Neural Spikes For Brain-Machine Interface Application Using Deep Learning Algorithm, Sahaj Anilbhai Patel

All ETDs from UAB

The detection of neural spikes in real-time and accurately has become the center of interest for the researchers in the field of brain-machine/computer interface (BMI/BCI). The primary challenge in the Brain-Machine interface is to translate raw neuronal response signals into the control of electrical actuators. Only accurate and rapid classification of neural response can help efficiently and conveniently to disable peoples, particularly those suffering from spinal cord injury, stocks, etc., Recording from neurons and analyzing them with many different methods are not new. However, the primary challenge here is the real-time recording and classifying the spikes with higher accuracy. In …

Deep Neural Network Learning-Based Classifier Design For Big-Data Analytics, Krishnan Raghavan

Doctoral Dissertations

"In this digital age, big-data sets are commonly found in the field of healthcare, manufacturing and others where sustainable analysis is necessary to create useful information. Big-data sets are often characterized by high-dimensionality and massive sample size. High dimensionality refers to the presence of unwanted dimensions in the data where challenges such as noise, spurious correlation and incidental endogeneity are observed. Massive sample size, on the other hand, introduces the problem of heterogeneity because complex and unstructured data types must analyzed. To mitigate the impact of these challenges while considering the application of classification, a two step analysis approach is …

The Application Of Index Based, Region Segmentation, And Deep Learning Approaches To Sensor Fusion For Vegetation Detection, David L. Stone

Theses and Dissertations

This thesis investigates the application of index based, region segmentation, and deep learning methods to the sensor fusion of omnidirectional (O-D) Infrared (IR) sensors, Kinnect sensors, and O-D vision sensors to increase the level of intelligent perception for unmanned robotic platforms. The goals of this work is first to provide a more robust calibration approach and improve the calibration of low resolution and noisy IR O-D cameras. Then our goal was to explore the best approach to sensor fusion for vegetation detection. We looked at index based, region segmentation, and deep learning methods and compared them with a goal of …

Efficient Detection Of Diseases By Feature Engineering Approach From Chest Radiograph, Avishek Mukherjee

Legacy Theses & Dissertations (2009 - 2024)

Deep Learning is the new state-of-the-art technology in Image Processing. We applied Deep Learning techniques for identification of diseases from Radiographs made publicly available by NIH. We applied some Feature Engineering approach to augment the data from Anterior-Posterior position to Posterior-Anterior position and vice-versa for all the diseases, at the same point we suppressed ‘No Finding’ radiographs which contributed to more than 50% (approximately 60,000) of the dataset to top 1000 images. We also prepared a model by adding a huge amount of noise to the augmented data, which if need be can be deployed at rural locations which lack …

Emotion Forecasting In Dyadic Conversation : Characterizing And Predicting Future Emotion With Audio-Visual Information Using Deep Learning, Sadat Shahriar

Legacy Theses & Dissertations (2009 - 2024)

Emotion forecasting is the task of predicting the future emotion of a speaker, i.e., the emotion label of the future speaking turn–based on the speaker’s past and current audio-visual cues. Emotion forecasting systems require new problem formulations that differ from traditional emotion recognition systems. In this thesis, we first explore two types of forecasting windows(i.e., analysis windows for which the speaker’s emotion is being forecasted): utterance forecasting and time forecasting. Utterance forecasting is based on speaking turns and forecasts what the speaker’s emotion will be after one, two, or three speaking turns. Time forecasting forecasts what the speaker’s emotion will …

A Deep Learning Framework For Medical Image Segmentation, Zheng Zhang

All ETDs from UAB

Deep Learning (DL) has rapidly become a methodology of choice for analyzing medical images and increasingly attracts researcher’s attention in the medical research community. The brain is one of the most important organs in the human body. Within the context of human brain disease, research and care, accurately detecting, evaluating and segmenting human brain abnormalities play an important role in brain disease diagnosis, prognosis, and treatment planning. A significant challenge in developing good brain abnormalities segmentation methods is the high variation of brain abnormalities such as differences in shape, size, location, appearance, and regularity. Deep Learning approach ad-dresses this challenge …

A Deep Learning Approach For Identifying Key Biomarkers In Medical Imaging Applications, David Odaibo

All ETDs from UAB

Although Deep Leaning has achieved great success in many domains in recent years, research into its applicability and effectiveness in medical applications has been limited for various reasons. Some of these barriers are related to the historic impression that neural networks are black boxes, especially when applied to medical diagnosis and offer no in terpretation into prediction. In this dissertation, I demonstrated that deep learning can be an effective utility in medical diagnosis and biomarker detection. In particular, I have developed deep learning models that can identify biomarkers in brain hemorrhage, brain tumor, pneumonia, diabetic retinopathy and Parkinsons disease, with …

Knowledge Graph Reasoning Over Unseen Rdf Data, Bhargavacharan Reddy Kaithi

Browse all Theses and Dissertations

In recent years, the research in deep learning and knowledge engineering has made a wide impact on the data and knowledge representations. The research in knowledge engineering has frequently focused on modeling the high level human cognitive abilities, such as reasoning, making inferences, and validation. Semantic Web Technologies and Deep Learning have an interest in creating intelligent artifacts. Deep learning is a set of machine learning algorithms that attempt to model data representations through many layers of non-linear transformations. Deep learning is in- creasingly employed to analyze various knowledge representations mentioned in Semantic Web and provides better results for Semantic …

Eaglebot: A Chatbot Based Multi-Tier Question Answering System For Retrieving Answers From Heterogeneous Sources Using Bert, Muhammad Rana

Electronic Theses and Dissertations

This paper proposes to tackle Question Answering on a specific domain by developing a multi-tier system using three different types of data storage for storing answers. For testing our system on University domain we have used extracted data from Georgia Southern University website. For the task of faster retrieval we have divided our answer data sources into three distinct types and utilized Dialogflow's Natural Language Understanding engine for route selection. We compared different word and sentence embedding techniques for making a semantic question search engine and BERT sentence embedding gave us the best result and for extracting answer from a …