Engineering Commons^™

Multi-Atlas Segmentation Of The Facial Nerve, Bradley M. Gare

Electronic Thesis and Dissertation Repository

Medical image segmentation is an important step to identify the shape and position of patient anatomy prior to surgical simulation, surgical rehearsal, and surgical planning. It is crucial that the facial nerve (FN) is segmented accurately as damage to this nerve can severely impact facial expression, speech, and taste. Manual segmentation provides accurate results but is time-consuming and labor-intensive; semi-automatic methods of segmentation are more feasible in a clinical setting and can provide accurate results with minimal user involvement. The objective of this work was to create a novel, open-source, multi-atlas based segmentation algorithm of the entire FN requiring minimal …

Towards Efficient Intrusion Detection Using Hybrid Data Mining Techniques, Fadi Salo

Electronic Thesis and Dissertation Repository

The enormous development in the connectivity among different type of networks poses significant concerns in terms of privacy and security. As such, the exponential expansion in the deployment of cloud technology has produced a massive amount of data from a variety of applications, resources and platforms. In turn, the rapid rate and volume of data creation in high-dimension has begun to pose significant challenges for data management and security. Handling redundant and irrelevant features in high-dimensional space has caused a long-term challenge for network anomaly detection. Eliminating such features with spectral information not only speeds up the classification process, but …

Analysis, Design And Demonstration Of Control Systems Against Insider Attacks In Cyber-Physical Systems, Xirong Ning

Electronic Thesis and Dissertation Repository

This dissertation aims to address the security issues of insider cyber-physical attacks and provide a defense-in-depth attack-resilient control system approach for cyber-physical systems.

Firstly, security analysis for cyber-physical systems is investigated to identify potential risks and potential security enhancements. Vulnerabilities of the system and existing security solutions, including attack prevention, attack detection and attack mitigation strategies are analyzed.

Subsequently, a methodology to analyze and mathematically characterize insider attacks is developed. An attack pattern is introduced to represent key features in an insider cyber-physical attack, which includes attack goals, resources, constraints, modes, as well as probable attack paths. Patterns for such …

Forecasting Building Energy Consumption With Deep Learning: A Sequence To Sequence Approach, Ljubisa Sehovac, Cornelius Nesen, Katarina Grolinger

Electrical and Computer Engineering Publications

Energy Consumption has been continuously increasing due to the rapid expansion of high-density cities, and growth in the industrial and commercial sectors. To reduce the negative impact on the environment and improve sustainability, it is crucial to efficiently manage energy consumption. Internet of Things (IoT) devices, including widely used smart meters, have created possibilities for energy monitoring as well as for sensor based energy forecasting. Machine learning algorithms commonly used for energy forecasting such as feedforward neural networks are not well-suited for interpreting the time dimensionality of a signal. Consequently, this paper uses Recurrent Neural Networks (RNN) to capture time …

Intraoperative Localization Of Subthalamic Nucleus During Deep Brain Stimulation Surgery Using Machine Learning Algorithms, Mahsa Khosravi

Electronic Thesis and Dissertation Repository

This thesis presents a novel technique for localizing the Subthalamic Nucleus (STN) during Deep Brain Stimulation (DBS) surgery. DBS is an accepted treatment for individuals living with Parkinson's Disease (PD). This surgery involves implantation of a permanent electrode inside the STN to deliver electrical current. The STN is a small grey matter structure within the brain, which makes accurate placement a challenging task for the surgical team. Prior to placement of the permanent electrode, intraoperative microelectrode recordings (MERs) of neural activity are used to localize the STN. The placement of the permanent electrode and the success of the stimulation therapy …

Gabor Filter Initialization And Parameterization Strategies In Convolutional Neural Networks, Long Pham

Electronic Thesis and Dissertation Repository

Convolutional neural networks (CNN) have been widely known in literature to be extremely effective for classifying images. Some of the filters learned during training of the first layer of a CNN resemble the Gabor filter. Gabor filters are extremely good at extracting features within an image. We have taken this as an incentive by replacing the first layer of a CNN with the Gabor filter to increase speed and accuracy for classifying images. We created two simple 5-layer AlexNet-like CNNs comparing grid-search to random-search for initializing the Gabor filter bank. We trained on MNIST, CIFAR-10, and CIFAR-100 as well as …

Design, Implementation And Evaluation Of A Redundancy Management System For Fault-Tolerant Wireless Devices In Harsh Environments, Madison Mccarthy

Electronic Thesis and Dissertation Repository

Wireless sensor networks (WSNs), when deployed in harsh environments, can fail prematurely due to elevated rates of component failures. To counteract this problem, fault-tolerant techniques, such as redundancy, may be used. A redundant design requires a management system. Built-in tests (BITs) are one of the most commonly used approaches for managing redundancy, but it suffers from issues such as imperfect fault coverage and common-cause failures (CCFs). In this work, a BIT based redundancy management system has been designed that makes use of a supervisory unit and a modular architecture to address issues with imperfect fault coverage and CCFs. The design …

Development And Assessment Of Signal Processing Algorithms For Assistive Hearing Devices, Farid Moshgelani

Electronic Thesis and Dissertation Repository

Speech identification in the presence of background noise is difficult for children with auditory processing disorder and adults with sensorineural hearing loss. The listening difficulty arises from deficits in their temporal, spectral, binaural, and/ or cognitive processing. Given the lack of improvement with conventional assistive hearing devices, alternate speech processing methodologies, which exaggerate the temporal and spectral cues, need to be developed to improve speech intelligibility for individuals who have poor temporal and/ or spectral processing.

This thesis first, reports results from a series of experiments on subjective and objective assessments of two different schemes of envelope enhancement algorithms (dynamic …

Investigation Of Radiation-Hardened Design Of Electronic Systems With Applications To Post-Accident Monitoring For Nuclear Power Plants, Qiang Huang

Electronic Thesis and Dissertation Repository

This research aims at improving the robustness of electronic systems used-in high level radiation environments by combining with radiation-hardened (rad-hardened) design and fault-tolerant techniques based on commercial off-the-shelf (COTS) components. A specific of the research is to use such systems for wireless post-accident monitoring in nuclear power plants (NPPs). More specifically, the following methods and systems are developed and investigated to accomplish expected research objectives: analysis of radiation responses, design of a radiation-tolerant system, implementation of a wireless post-accident monitoring system for NPPs, performance evaluation without repeat physical tests, and experimental validation in a radiation environment.

A method is developed …

Autonomous And Real Time Rock Image Classification Using Convolutional Neural Networks, Alexis David Pascual

Electronic Thesis and Dissertation Repository

Autonomous image recognition has numerous potential applications in the field of planetary science and geology. For instance, having the ability to classify images of rocks would allow geologists to have immediate feedback without having to bring back samples to the laboratory. Also, planetary rovers could classify rocks in remote places and even in other planets without needing human intervention. In 2017, Shu et. al. used a Support Vector Machine (SVM) classification algorithm to classify 9 different types of rock images using a with the image features extracted autonomously. Through this method, they achieved a test accuracy of 96.71%. Within the …

Deep Learning: Edge-Cloud Data Analytics For Iot, Katarina Grolinger, Ananda M. Ghosh

Electrical and Computer Engineering Publications

Sensors, wearables, mobile and other Internet of Thing (IoT) devices are becoming increasingly integrated in all aspects of our lives. They are capable of collecting massive quantities of data that are typically transmitted to the cloud for processing. However, this results in increased network traffic and latencies. Edge computing has a potential to remedy these challenges by moving computation physically closer to the network edge where data are generated. However, edge computing does not have sufficient resources for complex data analytics tasks. Consequently, this paper investigates merging cloud and edge computing for IoT data analytics and presents a deep learning-based …

A Virtual-Reality Training Simulator For Cochlear Implant Surgery, Blake Jones, Seyed Alireza Rohani, Nelson Ong, Tarek Tayeh, Hanif M. Ladak, Ahmad Chalabi, Sumit K. Agrawal

Electrical and Computer Engineering Publications

No abstract provided.