Other Electrical and Computer Engineering Commons^™

Application Of Quantum Cryptography To Cybersecurity And Critical Infrastructures In Space Communications, Rita Meraz, Linda Vahala

OUR Journal: ODU Undergraduate Research Journal

As society becomes more dependent on technology and the internet, critical infrastructure, which provides the fundamental services that millions of people depend on, becomes more vulnerable to cyber threats. This paper presents the importance of cybersecurity in critical infrastructure addressing the communications sector which is prevailed by space systems. It gives an overview of laser communications via satellite, and it argues the utility that quantum cryptography can provide to secure the data transmitted between communication satellites and ground stations from cyber attacks. Common encryption algorithms are briefly introduced as well as a review on quantum computing. Quantum cryptography is still …

Corrections To ‘‘Glaciernet: A Deep-Learning Approach For Debris-Covered Glacier Mapping’’, Zhiyuan Xie, Umesh K. Haritashya, Vijayan K. Asari, Brennan W. Young, Michael P. Bishop, Jeffrey S. Kargel

Electrical and Computer Engineering Faculty Publications

In the above article [1], Figure 2 was incorrect. Unfortunately, we mixed the color label of "CONV $\to $ BN $\to $ ReLu" and "Unpooling" in the CNN structure section of Figure 2. The color label of "CONV $\to $ BN $\to $ ReLu" should be orange while the color label of "Unpooling" should be green. Also, the word "Decoder" is misspelled. That same figure with the same error is also used for the graphic abstract. The corrected figure is given here. None of the sections in the figure is modified. The only change is in the color label of …

Reliable Navigation For Suas In Complex Indoor Environments, Andrew J. Fabian

Theses and Dissertations

Indoor environments are a particular challenge for Unmanned Aerial Vehicles (UAVs). Effective navigation through these GPS-denied environments require alternative localization systems, as well as methods of sensing and avoiding obstacles while remaining on-task. Additionally, the relatively small clearances and human presence characteristic of indoor spaces necessitates a higher level of precision and adaptability than is common in traditional UAV flight planning and execution. This research blends the optimization of individual technologies, such as state estimation and environmental sensing, with system integration and high-level operational planning.

The combination of AprilTag visual markers, multi-camera Visual Odometry, and IMU data can be used …

Design, Construction, And Characterization Of A Combined Mini-Co₂/Voc Sensor And Gas Chromatograph For Field Research, Rishi Basdeo, Michael Hampton

Digital Repository: Showcase of Undergraduate Research Excellence

No abstract provided.

Ev Charging Behavior Analysis Using Hybrid Intelligence For 5g Smart Grid, Yi Shen, Wei Fang, Feng Ye, Michel Kadoch

Electrical and Computer Engineering Faculty Publications

With the development of the Internet of Things (IoT) and the widespread use of electric vehicles (EV), vehicle-to-grid (V2G) has sparked considerable discussion as an energy-management technology. Due to the inherently high maneuverability of EVs, V2G systems must provide on-demand service for EVs. Therefore, in this work, we propose a hybrid computing architecture based on fog and cloud with applications in 5G-based V2G networks. This architecture allows the bi-directional flow of power and information between schedulable EVs and smart grids (SGs) to improve the quality of service and cost-effectiveness of energy service providers. However, it is very important to select …

Glaciernet: A Deep-Learning Approach For Debris-Covered Glacier Mapping, Zhiyuan Xie, Umesh K. Haritashya, Vijayan K. Asari, Brennan W. Young, Michael P. Bishop, Jeffrey S. Kargel

Electrical and Computer Engineering Faculty Publications

Rising global temperatures over the past decades is directly affecting glacier dynamics. To understand glacier fluctuations and document regional glacier-state trends, glacier-boundary detection is necessary. Debris-covered glacier (DCG) mapping, however, is notoriously difficult using conventional geospatial technology methods. Therefore, in this research for automated DCG mapping, we evaluate the utility of a convolutional neural network (CNN), which is a deep learning feed-forward neural network. The CNN inputs include Landsat satellite images, an Advanced Land Observation Satellite (ALOS) digital elevation model (DEM) and DEM-derived land-surface parameters. Our CNN based deep-learning approach named GlacierNet was designed by appropriately choosing the type, number …

Mitosisnet: End-To-End Mitotic Cell Detection By Multi-Task Learning, Md Zahangir Alom, Theus Aspiras, Tarek M. Taha, Tj Bowen, Vijayan K. Asari

Electrical and Computer Engineering Faculty Publications

Mitotic cell detection is one of the challenging problems in the field of computational pathology. Currently, mitotic cell detection and counting are one of the strongest prognostic markers for breast cancer diagnosis. The clinical visual inspection on histology slides is tedious, error prone, and time consuming for the pathologist. Thus, automatic mitotic cell detection approaches are highly demanded in clinical practice. In this paper, we propose an end-to-end multi-task learning system for mitosis detection from pathological images which is named"MitosisNet". MitosisNet consist of segmentation, detection, and classification models where the segmentation, and detection models are used for mitosis reference region …

Palmprint Gender Classification Using Deep Learning Methods, Minou Khayami

Graduate Theses, Dissertations, and Problem Reports (ETD)

Gender identification is an important technique that can improve the performance of authentication systems by reducing searching space and speeding up the matching process. Several biometric traits have been used to ascertain human gender. Among them, the human palmprint possesses several discriminating features such as principal-lines, wrinkles, ridges, and minutiae features and that offer cues for gender identification. The goal of this work is to develop novel deep-learning techniques to determine gender from palmprint images. PolyU and CASIA palmprint databases with 90,000 and 5502 images respectively were used for training and testing purposes in this research. After ROI extraction and …

Augmented Reality Billiards Assistant, Sarah Medved

Williams Honors College, Honors Research Projects

The game of pool is an extremely popular activity, with approximately 36 million participants yearly. As with any game of pool, the most basic skill the player must possess is the ability to accurately aim and hit the cue ball. It would be helpful to the player if they had a system which would assist with aiming by predicting the trajectories of a shot before the shot is actually made. These trajectory predictions can serve as a guide to the player and allow them to adjust their cue position before each shot. This lets the player explore many different options …

E-Z Door: Hands-Free Front Door Unlocking And Opening Mechanism, Caleb Dyck

Williams Honors College, Honors Research Projects

The E-Z Door Senior Design project is a project with the aim of designing a hands-free system to unlock and open the front door of a home using two-factor security authentication.

The main goal of this project is to help people who may have physical limitations to be able to take advantage of recent technology, making it significantly easier to enter their homes.

Analysis Of Antenna Designs For The Maximum Power Transmission, Lauryn P. Smith

College of Graduate Studies: Theses & Dissertations

Since Nikola Tesla discovered wireless power transmission, it has become a very interesting topic of study in the antennas and wireless propagation community. Various aspects and applications for wireless power transmission are studied today, a few of which are investigated in this work. First, various antenna geometries are analyzed for radiative near-field wireless power transfer in terms of electrical field strength. It is determined that the meander antenna is ideal for maximum power transfer in its radiative near-field region, contrary to its far-field behavior. Next, in the application of radio frequency identification, a directive, UHF RFID tag antenna is designed …

Unique Image Representation As A Tensor, Bharat Rosanlall

Dissertations and Theses

This thesis presents a two dimensional orthonormal transform that represents an image as coefficients in 4 independent channels. The salient feature of these coefficients is that they contain complete position spatial frequency information about the image, in a sense that the original image can be reconstructed from these coefficients with negligible error. These coefficients can be used in various machine learning, AI , and other tasks where data features are used. Popular convolutional layer used in various neural networks reduces information and can not reconstruct original image. In this thesis , we present several examples where these coefficients are used …

On-Demand Electrically Induced Decomposition Of Thin-Film Nitrocellulose Membranes For Wearable Or Implantable Biosensor Systems, Benjamin M. Horstmann

Theses and Dissertations

Implantable or subcutaneous biosensors used for continuous health monitoring have a limited functional lifetime requiring frequent replacement and therefore may be highly discomforting to the patient and become costly. One possible solution to this problem is use of biosensor arrays where each individual reserve sensor can be activated on-demand when the previous one becomes inoperative due to biofouling or enzyme degradation. Each reserve biosensor in the array is housed in an individual Polydimethylsiloxane (PDMS) well and is protected from exposure to bodily fluids such as interstitial fluid ( ISF) by a thin-film nitrocellulose membrane. Controlled activation is achieved by decomposing …

Deep Learning Towards Intelligent Vehicle Fault Diagnosis, Mohammed Al-Zeyadi, Javier Andreu-Perez, Hani Hagras, Chris Royce, Darren Smith, Piotr Rzonsowski, Ali Malik

Conference papers

Recently, the rapid development of automotive industries has given rise to large multidimensional datasets both in the production sites and after-sale services. Fault diagnostic systems are one of the services that the automotive industries provide. As a consequence of the rapid development of cars features, traditional rule-based diagnostic systems became very limited. Therefore, more sophisticated AI approaches need to be investigated towards more efficient solutions. In this paper, we focus on utilising deep learning so as to build a diagnostic system that is able to estimate the required services in an efficient and effective way. We propose a new model, …

Accurate Range-Based Indoor Localization Using Pso-Kalman Filter Fusion, Paul Bupe Jr

College of Graduate Studies: Theses & Dissertations

Accurate indoor localization often depends on infrastructure support for distance estimation in range-based techniques. One can also trade off accuracy to reduce infrastructure investment by using relative positions of other nodes, as in range-free localization. Even for range-based methods where accurate Ultra-WideBand (UWB) signals are used, non line-of-sight (NLOS) conditions pose significant difficulty in accurate indoor localization. Existing solutions rely on additional measurements from sensors and typically correct the noise using a Kalman filter (KF). Solutions can also be customized to specific environments through extensive profiling. In this work, a range-based indoor localization algorithm called PSO - Kalman Filter Fusion …

Estimation Of Atmospheric Conditions Over A Long Horizontal Path Using Multi-Frame Blind Deconvolution (Mfbd) Techniques In Comparison With Delayed Tilt Anisoplanatism (Delta) Software, Hannah Stoll

Dissertations, Master's Theses and Master's Reports

The potential to track and view objects in space from the ground with greater near real time knowledge of the intervening turbulence would be a revolutionary capability. The objective of this thesis is to cross-validate two separate methods used to estimate the Fried parameter. This verification is a step toward a commercial grade product that would make real-time estimates of the turbulence strength along an optical path from a ground-based observatory to a satellite in orbit around the Earth. Michigan Technological University has developed a multi-frame blind deconvolution (MFBD) algorithm used to estimate r0 and it was tested against MZA’s …

Technology-Dependent Quantum Logic Synthesis And Compilation, Kaitlin Smith

Electrical Engineering Theses and Dissertations

The models and rules of quantum computation and quantum information processing (QIP) differ greatly from those that govern classical computation, and these differences have caused the implementation of quantum processing devices with a variety of new technologies. Many platforms have been developed in parallel, but at the time of writing, one method of quantum computing has not shown to be superior to the rest. Because of the variation that exists between quantum platforms, even between those of the same technology, there must be a way to automatically synthesize technology-independent quantum designs into forms that are capable of physical realization on …

Amodal Instance Segmentation And Multi-Object Tracking With Deep Pixel Embedding, Yanfeng Liu

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

This thesis extends upon the representational output of semantic instance segmentation by explicitly including both visible and occluded parts. A fully convolutional network is trained to produce consistent pixel-level embedding across two layers such that, when clustered, the results convey the full spatial extent and depth ordering of each instance. Results demonstrate that the network can accurately estimate complete masks in the presence of occlusion and outperform leading top-down bounding-box approaches.

The model is further extended to produce consistent pixel-level embeddings across two consecutive image frames from a video to simultaneously perform amodal instance segmentation and multi-object tracking. No post-processing …

Optimal Allocation Of Energy Storage And Wind Generation In Power Distribution Systems, Carlos Mendoza

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

The advent of energy storage technologies applications for the electric power system gives new tools for planners to cope with the operation challenges that come from the integration of renewable generation in medium voltage networks. This work proposes and implements an optimization model for Battery Energy Storage System (BESS) and distributed generation allocation in radial distribution networks. The formulation aims to assist distribution system operators in the task of making decisions on energy storage investment, BESSs' operation, and distributed generation penetration's level to minimize electricity costs. The BESSs are required to participate in energy arbitrage and voltage control. In addition, …

Bitcoin Price Prediction Using Neural Networks, Vladislav Killiakov

Electrical Engineering

In this project, I will investigate the performance of several major neural network architectures for the task of Bitcoin price prediction. Bitcoin is a cryptocurrency that is recently becoming increasingly more popular, and more widely adopted as a financial instrument. As a result, more efforts have been made in the past several years to model and predict its price. However, to this moment a large portion of work on Bitcoin price modeling was done using statistical or classical machine learning techniques. At the same time, other artificial intelligence based prediction techniques, and specifically neural networks, have not been explored to …