Signal Processing Commons^™

Data Forgery Detection In Automatic Generation Control: Exploration Of Automated Parameter Generation And Low-Rate Attacks, Yatish R. Dubasi

Computer Science and Computer Engineering Undergraduate Honors Theses

Automatic Generation Control (AGC) is a key control system utilized in electric power systems. AGC uses frequency and tie-line power flow measurements to determine the Area Control Error (ACE). ACE is then used by the AGC to adjust power generation and maintain an acceptable power system frequency. Attackers might inject false frequency and/or tie-line power flow measurements to mislead AGC into falsely adjusting power generation, which can harm power system operations. Various data forgery detection models are studied in this thesis. First, to make the use of predictive detection models easier for users, we propose a method for automated generation …

Laser Illuminated Imaging: Beam And Scene Deconvolution Algorithm, Benjamin W. Davis

Theses and Dissertations

Laser illuminated imaging systems deal with several physical challenges that must be overcome to achieve high-resolution images of the target. Noise sources like background noise, photon counting noise, and laser speckle noise will all greatly affect the imaging systems ability to produce a high-resolution image. An even bigger challenge to laser illuminated imaging systems is atmospheric turbulence and the effect that it will have on the imaging system. The illuminating beam will experience tilt, causing the beam to wander off the center of the target during propagation. The light returning to the detector will similarly be affected by turbulence, and …

Comparison Of Conic Ray Tracing For Occlusion Determination On 3d Point Cloud Data, Henry Cho

Theses and Dissertations

The US Air Force has been increasing the use of automation in its weapon systems to include the remotely piloted aircraft (RPA) platforms. The RPA career field has had issues with poor pilot retention due to job stressors. For example, RPA operators spend a lot of time and attention surveilling a suspect on the ground for many hours, so adding automation to this activity could help improve pilot retention. The research problem in this thesis attempted to automate the process of observing a ground target. This thesis presents a method termed conic ray tracing for determining visibility and occlusion of …

Indoor Navigation Using Convolutional Neural Networks And Floor Plans, Ricky D. Anderson

Theses and Dissertations

The goal of this thesis is to evaluate a new indoor navigation technique by incorporating floor plans along with monocular camera images into a CNN as a potential means for identifying camera position. Building floor plans are widely available and provide potential information for localizing within the building. This work sets out to determine if a CNN can learn the architectural features of a floor plan and use that information to determine a location. In this work, a simulated indoor data set is created and used to train two CNNs. A classification CNN, which breaks up the floor plan into …

Weakly Supervised Learning For Multi-Image Synthesis, Muhammad Usman Rafique

Theses and Dissertations--Electrical and Computer Engineering

Machine learning-based approaches have been achieving state-of-the-art results on many computer vision tasks. While deep learning and convolutional networks have been incredibly popular, these approaches come at the expense of huge amounts of labeled data required for training. Manually annotating large amounts of data, often millions of images in a single dataset, is costly and time consuming. To deal with the problem of data annotation, the research community has been exploring approaches that require less amount of labelled data.

The central problem that we consider in this research is image synthesis without any manual labeling. Image synthesis is a classic …

Light Field Compression And Manipulation Via Residual Convolutional Neural Network, Eisa Hedayati

Dissertations, Master's Theses and Master's Reports

Light field (LF) imaging has gained significant attention due to its recent success in microscopy, 3-dimensional (3D) displaying and rendering, augmented and virtual reality usage. Postprocessing of LF enables us to extract more information from a scene compared to traditional cameras. However, the use of LF is still a research novelty because of the current limitations in capturing high-resolution LF in all of its four dimensions. While researchers are actively improving methods of capturing high-resolution LF's, using simulation, it is possible to explore a high-quality captured LF's properties. The immediate concerns following the LF capture are its storage and processing …

Cybersecurity Strategy Against Cyber Attacks Towards Smart Grids With Pvs, Fangyu Li, Maria Valero, Liang Zhao, Yousef Mahmoud

KSU Proceedings on Cybersecurity Education, Research and Practice

Cyber attacks threaten the security of distribution power grids, such as smart grids. The emerging renewable energy sources such as photovoltaics (PVs) with power electronics controllers introduce new potential vulnerabilities. Based on the electric waveform data measured by waveform sensors in the smart grids, we propose a novel cyber attack detection and identification approach. Firstly, we analyze the cyber attack impacts (including cyber attacks on the solar inverter causing unusual harmonics) on electric waveforms in distribution power grids. Then, we propose a novel deep learning based mechanism including attack detection and attack diagnosis. By leveraging the electric waveform sensor data …

Cleanpage: Fast And Clean Document And Whiteboard Capture, Jane Courtney

Articles

The move from paper to online is not only necessary for remote working, it is also significantly more sustainable. This trend has seen a rising need for the high-quality digitization of content from pages and whiteboards to sharable online material. However, capturing this information is not always easy nor are the results always satisfactory. Available scanning apps vary in their usability and do not always produce clean results, retaining surface imperfections from the page or whiteboard in their output images. CleanPage, a novel smartphone-based document and whiteboard scanning system, is presented. CleanPage requires one button-tap to capture, identify, crop, and …

Improving Closely Spaced Dim Object Detection Through Improved Multiframe Blind Deconvolution, Ronald M. Aung

Theses and Dissertations

This dissertation focuses on improving the ability to detect dim stellar objects that are in close proximity to a bright one, through statistical image processing using short exposure images. The goal is to improve the space domain awareness capabilities with the existing infrastructure. Two new algorithms are developed. The first one is through the Neighborhood System Blind Deconvolution where the data functions are separated into the bright object, the neighborhood system, and the background functions. The second one is through the Dimension Reduction Blind Deconvolution, where the object function is represented by the product of two matrices. Both are designed …

Hail Detection Using Dual Polarization Weather Radar, Alfonso Ladino Rincon

International Programs

This poster highlights how active remote sensors such as weather radar are completely useful for hail detection given its feature and the information they produce. Hail detection is already well studied by the atmospheric scientific community and dual polarimetric variables values for hail signature are presented according to those advances. Then, a supervised classification technique is showed to illustrated how machine learning can be integrated to radar information for automatic hail detection. However, this fuzzy logic algorithm has the capability to distinguish between meteorological and non-meteorological echoes. This automatic information might help forecasters from National Weather Services – NWS to …

Three-Dimensional (3d) Reconstruction Of Ultrasound Foetal Images Using Visualisation Toolkit (Vtk), Henry Dusim Grace Anabela

Student Works (2020-2029)

Three-dimensional (3D) reconstruction of two-dimensional (2D) ultrasound images has been used for easier analysing of anatomy related to abnormalities. 3D image reconstruction is required as it helps doctors interpret results and assess any defects quickly. Not only does it improve the accuracy of diagnosis, but it also saves a lot of time. In this thesis, 295 slices of 2D ultrasound images of a foetal are used to do 3D reconstruction using surface rendering, volume rendering, and multiplanar rendering technique. A similar software known as 3D Slicer is also used to reconstruct the ultrasound image for comparison purpose. VTK can easily …

Cup-Net: Compressed Ultrafast Photography Using Convolutional Neural Networks, Matthew Parker

ENGS 88 Honors Thesis (AB Students)

Compressed ultrafast photography (CUP) is a cutting-edge imaging technique that uses a variation of the traditional streak camera to obtain video at 100 billion frames per second with a single exposure. In order to achieve this level of temporal detail, CUP leverages compressed sensing (CS). Compressed sensing theory states that a compressed representation of an image can be directly acquired using a non-adaptive measurement matrix so long as the encoding matrix follows certain properties such as restrictive isometry and incoherence. This compressed representation of the original scene can later be reconstructed back into the original form. CUP applies CS by …

An Fpga-Based Hardware Accelerator For The Digital Image Correlation Engine, Keaten Stokke

Graduate Theses and Dissertations

The work presented in this thesis was aimed at the development of a hardware accelerator for the Digital Image Correlation engine (DICe) and compare two methods of data access, USB and Ethernet. The original DICe software package was created by Sandia National Laboratories and is written in C++. The software runs on any typical workstation PC and performs image correlation on available frame data produced by a camera. When DICe is introduced to a high volume of frames, the correlation time is on the order of days. The time to process and analyze data with DICe becomes a concern when …

Partial Discharge Classification For Xlpe Cable Joints Using K Nearest Neighbors Algorithm, Mohd Salleh Muhammad Shairazi

Student Works (2020-2029)

Due to excellent mechanical and electrical properties, cross-linked polyethylene (XLPE) cables are commonly used in the power industry. However, cable joints are the weakest part of XLPE cables and are susceptible to insulation failures. Cable joint insulation breakup can cause large losses for power companies. It is therefore necessary to evaluate the consistency of the insulation for early detection of insulation failure. It is known that there is a link between the partial discharge (PD) and the quality of the insulation. PD analysis is an important tool for assessing the quality of insulation in cable joints. In this study, XLPE …

Classification Of Labour Pain Using Electroencephalogram Signal Based On Wavelet Method, Chong Yeh Sai

Student Works (2020-2029)

Electroencephalogram (EEG) is the recording of electrical activity of the cerebral cortex through electrodes placed on the scalp. EEG is used to acquire neurophysiological signals for application in clinical diagnosis and brain computer interface (BCI). However, in practical settings the EEG signals are often contaminated by signal artifacts known as the biological and environmental artifacts. These artifacts degrade EEG signals, thereby obstructing clinical diagnosis or BCI applications by distorting the observed power spectrum. Procedures for automated removal of EEG artifacts are frequently sought after in pre-processing and filtering of the EEG signals. In recent years, a combination of independent component …

Development Of Fully Balanced Ssfp And Computer Vision Applications For Mri-Assisted Radiosurgery (Mars), Jeremiah Sanders

Dissertations and Theses (Open Access)

Prostate cancer is the second most common cancer in men and the second-leading cause of cancer death in men. Brachytherapy is a highly effective treatment option for prostate cancer, and is the most cost-effective initial treatment among all other therapeutic options for low to intermediate risk patients of prostate cancer. In low-dose-rate (LDR) brachytherapy, verifying the location of the radioactive seeds within the prostate and in relation to critical normal structures after seed implantation is essential to ensuring positive treatment outcomes.

One current gap in knowledge is how to simultaneously image the prostate, surrounding anatomy, and radioactive seeds within the …

Deep Cellular Recurrent Neural Architecture For Efficient Multidimensional Time-Series Data Processing, Lasitha S. Vidyaratne

Electrical & Computer Engineering Theses & Dissertations

Efficient processing of time series data is a fundamental yet challenging problem in pattern recognition. Though recent developments in machine learning and deep learning have enabled remarkable improvements in processing large scale datasets in many application domains, most are designed and regulated to handle inputs that are static in time. Many real-world data, such as in biomedical, surveillance and security, financial, manufacturing and engineering applications, are rarely static in time, and demand models able to recognize patterns in both space and time. Current machine learning (ML) and deep learning (DL) models adapted for time series processing tend to grow in …

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 …

Extracting Range Data From Images Using Focus Error, Erik M. Madden

Theses and Dissertations

Air-to-air refueling (AAR) has become a staple when performing long missions with aircraft. With modern technology, however, people have begun to research how to perform this task autonomously. Automated air-to-air refueling (A3R) is this exact concept. Combining many different systems, the idea is to allow computers on the aircraft to link up via the refueling boom, refuel, and detach before resuming pilot control. This document lays out one of the systems that is needed to perform A3R, namely, the system that extracts range data. While stereo cameras perform such tasks, there is interest in finding other ways of accomplishing the …

Maximizing Accuracy Through Stereo Vision Camera Positioning For Automated Aerial Refueling, Kirill A. Sarantsev

Theses and Dissertations

Aerial refueling is a key component of the U.S. Air Force strategic arsenal. When two aircraft interact in an aerial refueling operation, the accuracy of relative navigation estimates are critical for the safety, accuracy and success of the mission. Automated Aerial Refueling (AAR) looks to improve the refueling process by creating a more effective system and allowing for Unmanned Aerial Vehicle(s) (UAV) support. This paper considers a cooperative aerial refueling scenario where stereo cameras are used on the tanker to direct a \boom" (a large, long structure through which the fuel will ow) into a port on the receiver aircraft. …

Use Of Lidar In Automated Aerial Refueling To Improve Stereo Vision Systems, Michael R. Crowl

Theses and Dissertations

The United States Air Force (USAF) executes five Core Missions, four of which depend on increased aircraft range. To better achieve global strike and reconnaissance, unmanned aerial vehicles (UAVs) require aerial refueling for extended missions. However, current aerial refueling capabilities are limited to manned aircraft due to technical difficulties to refuel UAVs mid-flight. The latency between a UAV operator and the UAV is too large to adequately respond for such an operation. To overcome this limitation, the USAF wants to create a capability to guide the refueling boom into the refueling receptacle. This research explores the use of light detection …

Coverage Guided Differential Adversarial Testing Of Deep Learning Systems, Jianmin Guo, Houbing Song, Yue Zhao, Yu Jiang

Publications

Deep learning is increasingly applied to safety-critical application domains such as autonomous cars and medical devices. It is of significant importance to ensure their reliability and robustness. In this paper, we propose DLFuzz, the coverage guided differential adversarial testing framework to guide deep learing systems exposing incorrect behaviors. DLFuzz keeps minutely mutating the input to maximize the neuron coverage and the prediction difference between the original input and the mutated input, without manual labeling effort or cross-referencing oracles from other systems with the same functionality. We also design multiple novel strategies for neuron selection to improve the neuron coverage. The …

Image Restoration Using Automatic Damaged Regions Detection And Machine Learning-Based Inpainting Technique, Chloe Martin-King

Computational and Data Sciences (PhD) Dissertations

In this dissertation we propose two novel image restoration schemes. The first pertains to automatic detection of damaged regions in old photographs and digital images of cracked paintings. In cases when inpainting mask generation cannot be completely automatic, our detection algorithm facilitates precise mask creation, particularly useful for images containing damage that is tedious to annotate or difficult to geometrically define. The main contribution of this dissertation is the development and utilization of a new inpainting technique, region hiding, to repair a single image by training a convolutional neural network on various transformations of that image. Region hiding is also …

Paper-Based Flexible Electrode Using Chemically-Modified Graphene And Functionalized Multiwalled Carbon Nanotube Composites For Electrophysiological Signal Sensing, Md Faruk Hossain, Jae Sang Heo, John Nelson, Insoo Kim

Bioelectrics Publications

Flexible paper-based physiological sensor electrodes were developed using chemically-modified graphene (CG) and carboxylic-functionalized multiwalled carbon nanotube composites (f@MWCNTs). A solvothermal process with additional treatment was conducted to synthesize CG and f@MWCNTs to make CG-f@MWCNT composites. The composite was sonicated in an appropriate solvent to make a uniform suspension, and then it was drop cast on a nylon membrane in a vacuum filter. A number of batches (0%~35% f@MWCNTs) were prepared to investigate the performance of the physical characteristics. The 25% f@MWCNT-loaded composite showed the best adhesion on the paper substrate. The surface topography and chemical bonding of the proposed CG-f@MWCNT …

Demonstration Of Visible And Near Infrared Raman Spectrometers And Improved Matched Filter Model For Analysis Of Combined Raman Signals, Alexander Matthew Atkinson

Electrical & Computer Engineering Theses & Dissertations

Raman spectroscopy is a powerful analysis technique that has found applications in fields such as analytical chemistry, planetary sciences, and medical diagnostics. Recent studies have shown that analysis of Raman spectral profiles can be greatly assisted by use of computational models with achievements including high accuracy pure sample classification with imbalanced data sets and detection of ideal sample deviations for pharmaceutical quality control. The adoption of automated methods is a necessary step in streamlining the analysis process as Raman hardware becomes more advanced. Due to limits in the architectures of current machine learning based Raman classification models, transfer from pure …

An Underground Radio Wave Propagation Prediction Model For Digital Agriculture, Abdul Salam

Faculty Publications

Underground sensing and propagation of Signals in the Soil (SitS) medium is an electromagnetic issue. The path loss prediction with higher accuracy is an open research subject in digital agriculture monitoring applications for sensing and communications. The statistical data are predominantly derived from site-specific empirical measurements, which is considered an impediment to universal application. Nevertheless, in the existing literature, statistical approaches have been applied to the SitS channel modeling, where impulse response analysis and the Friis open space transmission formula are employed as the channel modeling tool in different soil types under varying soil moisture conditions at diverse communication distances …

End-To-End Deep Learning Systems For Scene Understanding, Path Planning And Navigation In Fire Fighter Teams, Manish Bhattarai

Shared Knowledge Conference

Firefighting is a dynamic activity with many operations occurring simultaneously. Maintaining situational awareness, defined as knowledge of current conditions and activities at the scene, are critical to accurate decision making. Firefighters often carry various sensors in their personal equipment, namely thermal cameras, gas sensors, and microphones. Improved data processing techniques can mine this data more effectively and be used to improve situational awareness at all times thereby improving real-time decision making and minimizing errors in judgment induced by environmental conditions and anxiety levels. This objective of this research employs state of the art Machine Learning (ML) techniques to create an …

Deep Neural Network Architectures For Modulation Classification Using Principal Component Analysis, Sharan Ramjee, Shengtai Ju, Diyu Yang, Aly El Gamal

The Summer Undergraduate Research Fellowship (SURF) Symposium

In this work, we investigate the application of Principal Component Analysis to the task of wireless signal modulation recognition using deep neural network architectures. Sampling signals at the Nyquist rate, which is often very high, requires a large amount of energy and space to collect and store the samples. Moreover, the time taken to train neural networks for the task of modulation classification is large due to the large number of samples. These problems can be drastically reduced using Principal Component Analysis, which is a technique that allows us to reduce the dimensionality or number of features of the samples …

Mitigating The Effects Of Boom Occlusion On Automated Aerial Refueling Through Shadow Volumes, Zachary C. Paulson

Theses and Dissertations

In flight refueling of Unmanned Aerial Vehicles (UAVs) is critical to the United States Air Force (USAF). However, the large communication latency between a ground-based operator and his/her remote UAV makes docking with a refueling tanker unsafe. This latency may be mitigated by leveraging a tanker-centric stereo vision system. The vision system observes and computes an approaching receiver's relative position and orientation offering a low-latency, high frequency docking solution. Unfortunately, the boom -- an articulated refueling arm responsible for physically pumping fuel into the receiver -- occludes large portions of the receiver especially as the receiver approaches and docks with …

Rss-Based Device-Free Passive Detection And Localization Using Home Automation Network Radio Frequencies, Tiffany M. Phan

Theses and Dissertations

This research provided a proof of concept for a device-free passive (DfP) system capable of detecting and localizing a target through exploitation of a home automation network’s radio frequency (RF) signals. The system was developed using Insteon devices with a 915 MHz center frequency. Without developer privileges, limitations of the Insteon technology like no intrinsic received signal strength (RSS) field and silent periods between messages were overcome by using software-defined radios to simulate Insteon devices capable of collecting and reporting RSS, and by creating a message generation script and implementing a calibrated filter threshold to reduce silent periods. Evaluation of …