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Full-Text Articles in Engineering
Considerations For Radio Frequency Fingerprinting Across Multiple Frequency Channels, Jose A. Gutierrez Del Arroyo, Brett J. Borghetti, Michael A. Temple
Considerations For Radio Frequency Fingerprinting Across Multiple Frequency Channels, Jose A. Gutierrez Del Arroyo, Brett J. Borghetti, Michael A. Temple
Faculty Publications
Radio Frequency Fingerprinting (RFF) is often proposed as an authentication mechanism for wireless device security, but application of existing techniques in multi-channel scenarios is limited because prior models were created and evaluated using bursts from a single frequency channel without considering the effects of multi-channel operation. Our research evaluated the multi-channel performance of four single-channel models with increasing complexity, to include a simple discriminant analysis model and three neural networks. Performance characterization using the multi-class Matthews Correlation Coefficient (MCC) revealed that using frequency channels other than those used to train the models can lead to a deterioration in performance from …
Surface Defect Detection In Aircraft Skin & Visual Navigation Based On Forced Feature Selection Through Segmentation, Tyler B. Hussey
Surface Defect Detection In Aircraft Skin & Visual Navigation Based On Forced Feature Selection Through Segmentation, Tyler B. Hussey
Theses and Dissertations
Visual inspection of aircraft skin for surface defects is an area of maintenance that is particularly intensive for time and manpower. One novel way to combat this problem is through the use of computer vision and the advent of Artificial Neural Networks (ANN), or more specifically, semantic segmentation via Convolutional Neural Networks (CNN). The research in the paper explores the use of semantic segmentation of aerial imagery as a way to force feature selection onto key areas of an image that might be more likely to correspond under seasonal variations. Utilizing feature selection and matching on the masked aerial image …
Deep Learning-Based, Passive Fault Tolerant Control Facilitated By A Taxonomy Of Cyber-Attack Effects, Dean C. Wardell
Deep Learning-Based, Passive Fault Tolerant Control Facilitated By A Taxonomy Of Cyber-Attack Effects, Dean C. Wardell
Theses and Dissertations
In the interest of improving the resilience of cyber-physical control systems to better operate in the presence of various cyber-attacks and/or faults, this dissertation presents a novel controller design based on deep-learning networks. This research lays out a controller design that does not rely on fault or cyber-attack detection. Being passive, the controller’s routine operating process is to take in data from the various components of the physical system, holistically assess the state of the physical system using deep-learning networks and decide the subsequent round of commands from the controller. This use of deep-learning methods in passive fault tolerant control …
Object Detection With Deep Learning To Accelerate Pose Estimation For Automated Aerial Refueling, Andrew T. Lee
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 …
Improving Aeromagnetic Calibration Using Artificial Neural Networks, Mitchell C. Hezel
Improving Aeromagnetic Calibration Using Artificial Neural Networks, Mitchell C. Hezel
Theses and Dissertations
The Global Positioning System (GPS) has proven itself to be the single most accurate positioning system available, and no navigation suite is found without a GPS receiver. Even basic GPS receivers found in most smartphones can easily provide high quality positioning information at any time. Even with its superb performance, GPS is prone to jamming and spoofing, and many platforms requiring accurate positioning information are in dire need of other navigation solutions to compensate in the event of an outage, be the cause hostile or natural. Indeed, there has been a large push to achieve an alternative navigation capability which …
Improved Ground-Based Monocular Visual Odometry Estimation Using Inertially-Aided Convolutional Neural Networks, Josiah D. Watson
Improved Ground-Based Monocular Visual Odometry Estimation Using Inertially-Aided Convolutional Neural Networks, Josiah D. Watson
Theses and Dissertations
While Convolutional Neural Networks (CNNs) can estimate frame-to-frame (F2F) motion even with monocular images, additional inputs can improve Visual Odometry (VO) predictions. In this thesis, a FlowNetS-based [1] CNN architecture estimates VO using sequential images from the KITTI Odometry dataset [2]. For each of three output types (full six degrees of freedom (6-DoF), Cartesian translation, and transitional scale), a baseline network with only image pair input is compared with a nearly identical architecture that is also given an additional rotation estimate such as from an Inertial Navigation System (INS). The inertially-aided networks show an order of magnitude improvement over the …