Long-Distance Propagation Of 162 Mhz Shipping Information Links Associated With Sporadic E,
2022
Air Force Institute of Technology
Long-Distance Propagation Of 162 Mhz Shipping Information Links Associated With Sporadic E, Alex T. Chartier, Thomas R. Hanley, Daniel J. Emmons
Faculty Publications
This is a study of anomalous long-distance (>1000 km) radio propagation that was identified in United States Coast Guard monitors of automatic identification system (AIS) shipping transmissions at 162 MHz. Our results indicate this long-distance propagation is caused by dense sporadic E layers in the daytime ionosphere, which were observed by nearby ionosondes at the same time. This finding is surprising because it indicates these sporadic E layers may be far more dense than previously thought.
Iris Detection Authenticator,
2022
California Polytechnic State University, San Luis Obispo
Iris Detection Authenticator, Nathan D. Tang, Bryan K. Chau
Electrical Engineering
The development of iris biometric identification recognition is presented. Iris recognition differs from other methods because data acquisition is non-physical and is more accessible. It has been proven that the iris does not change as an individual ages and is well protected from external damages due to the eyelid and cornea, acting as a shield to the iris. In addition, the iris is almost impossible to forge due to its complex patterns and the current limitations in technology. Using Canny Edge Detection, Hough Transform, rubber-sheet normalization, Histogram of Gradient feature extraction, and the MultiMedia University iris database as our subjects, …
Development Of A Smartphone Application As An Asset To Pavement Management Engineers, Smartp3m,
2022
University of Texas at Tyler
Development Of A Smartphone Application As An Asset To Pavement Management Engineers, Smartp3m, Damien Stephens
Electrical Engineering Theses
An application specific multi-platform smartphone application can utilize on-board accelerometer, gyroscope, and GPS sensors, along with software derived signals from the same sensors, to sample vibrational and geolocation datasets to capture pavement distresses such as potholes when mounted in a standardized configuration in a vehicle. Several observations were made with regard to the signals obtained from the accelerometer, gyroscope, and GPS sensors, and it was determined that the raw sensor outputs are capable of sampling statistically significant datasets which can be used to distinguish pavement distress from normal driving conditions. Furthermore, an approximate sensor noise margin is established, and a …
Device Free Indoor Localization Of Human Target Using Wifi Fingerprinting,
2022
Louisiana State University and Agricultural and Mechanical College
Device Free Indoor Localization Of Human Target Using Wifi Fingerprinting, Prasanga Neupane
LSU Master's Theses
Indoor localization of human objects has many important applications nowadays. Proposed here is a new device free approach where all the transceiver devices are fixed in an indoor environment so that the human target doesn't need to carry any transceiver device with them. This work proposes radio-frequency fingerprinting for the localization of human targets which makes this even more convenient as radio-frequency wireless signals can be easily acquired using an existing wireless network in an indoor environment. This work explores different avenues for optimal and effective placement of transmitter devices for better localization. In this work, an experimental environment is …
A Design Flow For Additively Manufactured 3d Metasurface Antennas,
2022
Embry-Riddle Aeronautical University
A Design Flow For Additively Manufactured 3d Metasurface Antennas, Justin Parkhurst
Doctoral Dissertations and Master's Theses
Metasurface (MTS) antennas are complex arrays consisting of hundreds or thousands of individual elements that each exert their own influence on the performance of the antenna. Due to this, the process of designing and developing a MTS antenna can be intensive in terms of both the time to understand the how these antennas operate and time running calculations that achieve optimal performance. Through the use of automation for geometry creation in ANSYS HFSS, the work involved in making a MTS antenna can be greatly simplified. The overall objective of this thesis is to reduce the burden of constructing a MTS …
Hardware Security For Wireless Communications Systems Using Antenna-Based Radio Frequency Fingerprint Engineering,
2022
Embry-Riddle Aeronautical University
Hardware Security For Wireless Communications Systems Using Antenna-Based Radio Frequency Fingerprint Engineering, Noemi Miguelez Gomez
Doctoral Dissertations and Master's Theses
The design and application of novel physical-layer security techniques have been increasing in the last decades as means to enhance the security that more traditional techniques provide to wireless communications systems. Well-known hardware security techniques, such as radio frequency fingerprinting, use unintended manufacturing process variations and unique hardware structures in the semiconductors for applications such as identification and classification of the source of different transmitted signals, and detection of hardware modifications. The uniqueness of the features that two different modules present, even maintaining the same design, can be used for modules characterization at a lower cost and complexity than other …
Predicting Insulin Pump Therapy Settings,
2022
Southern Methodist University & Tandem Diabetes Care, Inc
Predicting Insulin Pump Therapy Settings, Riccardo L. Ferraro, David Grijalva, Alex Trahan
SMU Data Science Review
Millions of people live with diabetes worldwide [7]. To mitigate some of the many symptoms associated with diabetes, an estimated 350,000 people in the United States rely on insulin pumps [17]. For many of these people, how effectively their insulin pump performs is the difference between sleeping through the night and a life threatening emergency treatment at a hospital. Three programmed insulin pump therapy settings governing effective insulin pump function are: Basal Rate (BR), Insulin Sensitivity Factor (ISF), and Carbohydrate Ratio (ICR). For many people using insulin pumps, these therapy settings are often not correct, given their physiological needs. While …
Evaluating Large Delay Estimation Techniques For Assisted Living Environments,
2022
Technological University Dublin
Evaluating Large Delay Estimation Techniques For Assisted Living Environments, Swarnadeep Bagchi, Ruairí De Fréin
Articles
Abstract Phase wraparound due to large inter-sensor spacings in multi-channel demixing limits the range of relative delays that many time–frequency relative delay estimators can estimate. The performance of a large relative delay estimation method, called the elevatogram, is evaluated in the presence of significant phase wraparound. This paper compares the elevatogram with the popular relative delay estimator used in DUET and the brute-force approach in D-AdRess and analyses its computational efficiency. The elevatogram can accurately estimate relative delays of speech signals of up to 800 samples, whereas DUET and D-AdRess were limited to delays of 7 and 35 samples, given …
Height Information Aided 3d Real-Time Large-Scale Underground User Positioning,
2022
Embry-Riddle Aeronautical University
Height Information Aided 3d Real-Time Large-Scale Underground User Positioning, Houbing Song, Chengkai Tang, Cunle Zhang, Lingling Zhang, Yi Zhang
Publications
Due to the cost of inertial navigation and visual navigation equipment and lake of satellite navigation signals, they cannot be used in large‐scale underground mining environment. To solve this problem, this study proposes large‐scale underground 3D real‐time positioning method with seam height assistance. This method uses the ultrawide band positioning base station as the core and is combined with seam height information to build a factor graph confidence transfer model to realise3D positioning. The simulation results show that the proposed real‐time method is superior to the existing algorithms in positioning accuracy and can meet the needs of large‐scale underground users.
Distribution Of Dds-Cerberus Authenticated Facial Recognition Streams,
2022
Air Force Institute of Technology
Distribution Of Dds-Cerberus Authenticated Facial Recognition Streams, Andrew T. Park, Nathaniel Peck, Richard Dill, Douglas D. Hodson, Michael R. Grimaila, Wayne C. Henry
Faculty Publications
Successful missions in the field often rely upon communication technologies for tactics and coordination. One middleware used in securing these communication channels is Data Distribution Service (DDS) which employs a publish-subscribe model. However, researchers have found several security vulnerabilities in DDS implementations. DDS-Cerberus (DDS-C) is a security layer implemented into DDS to mitigate impersonation attacks using Kerberos authentication and ticketing. Even with the addition of DDS-C, the real-time message sending of DDS also needs to be upheld. This paper extends our previous work to analyze DDS-C’s impact on performance in a use case implementation. The use case covers an artificial …
An Empirical Comparison Of The Security And Performance Characteristics Of Topology Formation Algorithms For Bitcoin Networks,
2022
University of Bournemouth
An Empirical Comparison Of The Security And Performance Characteristics Of Topology Formation Algorithms For Bitcoin Networks, Muntadher Sallal, Ruairí De Fréin, Ali Malik, Benjamin Aziz
Articles
There is an increasing demand for digital crypto-currencies to be more secure and robust to meet the following business requirements: (1) low transaction fees and (2) the privacy of users. Nowadays, Bitcoin is gaining traction and wide adoption. Many well-known businesses have begun accepting bitcoins as a means of making financial payments. However, the susceptibility of Bitcoin networks to information propagation delay, increases the vulnerability to attack of the Bitcoin network, and decreases its throughput performance. This paper introduces and critically analyses new network clustering methods, named Locality Based Clustering (LBC), Ping Time Based Approach (PTBC), Super Node Based Clustering …
Acoustic Source Localization Using Straight Line Approximations,
2022
Technological University Dublin
Acoustic Source Localization Using Straight Line Approximations, Swarnadeep Bagchi, Ruairí De Fréin
Conference papers
The short paper extends an acoustic signal delay estimation method to general anechoic scenario using image processing techniques. The technique proposed in this paper localizes acoustic speech sources by creating a matrix of phase versus frequency histograms, where the same phases are stacked in appropriate bins. With larger delays and multiple sources coexisting in the same matrix, it becomes cluttered with activated bins. This results in high intensity spots on the spectrogram, making source discrimination difficult. In this paper, we have employed morphological filtering, chain-coding and straight line approximations to ignore noise and enhance the target signal features. Lastly, Hough …
Development Of The Subwave Rov And Neural-Inertial Positioning System,
2022
Mississippi State University
Development Of The Subwave Rov And Neural-Inertial Positioning System, Jason Farmer
Theses and Dissertations
This report documents the development of the Subwave, a remotely-operated underwater vehicle (ROV), and an artificial neural network based inertial positioning system. The Subwave uses the open-source ArduSub software framework, commercial-off-the-shelf hardware components, and several custom systems. It is designed as a platform for researching autonomous underwater vehicles (AUVs). The first step for an AUV is navigating waypoints, which requires the AUV to know its global position. Since global navigation satellite systems (GNSSs) are denied underwater, the available underwater positioning systems were surveyed and determined that all the available systems were too large and expensive for the Subwave. It was …
Exploratory Analysis Of Machine Learning For Images: Methods And Applications,
2022
University of New Mexico - Main Campus
Exploratory Analysis Of Machine Learning For Images: Methods And Applications, Meenu Ajith
Electrical and Computer Engineering ETDs
This research focuses on implementing four different applications of machine learning on images. The various categories of digital images considered for these applications are grayscale, RGB, and infra-red images. The first framework uses an unsupervised learning strategy for detecting fire and smoke from an infra-red image dataset. This problem was solved using a classical machine learning algorithm since the dataset was small and unlabeled. Next, a semi-supervised deep learning model was used for facial expression recognition. Here we detect emotions from a moderately large dataset containing labeled and unlabeled grayscale images. The third application focused on single image superresolution, which …
Hybrid Smart Transformer For Enhanced Power System Protection Against Dc With Advanced Grid Support,
2022
Clemson University
Hybrid Smart Transformer For Enhanced Power System Protection Against Dc With Advanced Grid Support, Moazzam Nazir
All Dissertations
The traditional grid is rapidly transforming into smart substations and grid assets incorporating advanced control equipment with enhanced functionalities and rapid self-healing features. The most important and strategic equipment in the substation is the transformer and is expected to perform a variety of functions beyond mere voltage conversion and isolation. While the concept of smart solid-state transformers (SSTs) is being widely recognized, their respective lifetime and reliability raise concerns, thus hampering the complete replacement of traditional transformers with SSTs. Under this scenario, introducing smart features in conventional transformers utilizing simple, cost-effective, and easy to install modules is a highly desired …
Deep Learning Based Localization Of Zigbee Interference Sources Using Channel State Information,
2022
Clemson University
Deep Learning Based Localization Of Zigbee Interference Sources Using Channel State Information, Dylan Kensler
All Theses
As the field of Internet of Things (IoT) continues to grow, a variety of wireless signals fill the ambient wireless environment. These signals are used for communication, however, recently wireless sensing has been studied, in which these signals can be used to gather information about the surrounding space. With the development of 802.11n, a newer standard of WiFi, more complex information is available about the environment a signal propagates through. This information called Channel State Information (CSI) can be used in wireless sensing. With the help of Deep Learning, this work attempts to generate a fingerprinting technique for localizing a …
Emotion Detection Using An Ensemble Model Trained With Physiological Signals And Inferred Arousal-Valence States,
2022
Old Dominion University
Emotion Detection Using An Ensemble Model Trained With Physiological Signals And Inferred Arousal-Valence States, Matthew Nathanael Gray
Electrical & Computer Engineering Theses & Dissertations
Affective computing is an exciting and transformative field that is gaining in popularity among psychologists, statisticians, and computer scientists. The ability of a machine to infer human emotion and mood, i.e. affective states, has the potential to greatly improve human-machine interaction in our increasingly digital world. In this work, an ensemble model methodology for detecting human emotions across multiple subjects is outlined. The Continuously Annotated Signals of Emotion (CASE) dataset, which is a dataset of physiological signals labeled with discrete emotions from video stimuli as well as subject-reported continuous emotions, arousal and valence, from the circumplex model, is used for …
Combinatorial Cnn Meta-Structures In Deep Learning Applications,
2022
University of New Mexico - Main Campus
Combinatorial Cnn Meta-Structures In Deep Learning Applications, Aswathy Rajendra Kurup
Electrical and Computer Engineering ETDs
The study aims in applying combinatorial structures motivated from the basic CNN in various applications. Convolutional neural networks (CNNs) have grown to be very popular in the field of deep learning. The ability of such networks to learn both Spatial and temporal characteristics in the data have helped in deploying them in various fields. Through this research we explore different kinds of CNN-derived architectures and how these structures can be trained and setup in combinatorial environment to solve problems in deep learning applications. First, we introduce the basic idea of a deep learning meta-structures which is used in the application …
Computational Models To Detect Radiation In Urban Environments: An Application Of Signal Processing Techniques And Neural Networks To Radiation Data Analysis, Jose Nicolas Gachancipa
Beyond: Undergraduate Research Journal
Radioactive sources, such as uranium-235, are nuclides that emit ionizing radiation, and which can be used to build nuclear weapons. In public areas, the presence of a radioactive nuclide can present a risk to the population, and therefore, it is imperative that threats are identified by radiological search and response teams in a timely and effective manner. In urban environments, such as densely populated cities, radioactive sources may be more difficult to detect, since background radiation produced by surrounding objects and structures (e.g., buildings, cars) can hinder the effective detection of unnatural radioactive material. This article presents a computational model …
Credit Card Fraud Detection Using Machine Learning Techniques,
2022
BIS Helwan University
Credit Card Fraud Detection Using Machine Learning Techniques, Nermin Samy Elhusseny, Shimaa Mohamed Ouf, Amira M. Idrees Ami
Future Computing and Informatics Journal
This is a systematic literature review to reflect the previous studies that dealt with credit card fraud detection and highlight the different machine learning techniques to deal with this problem. Credit cards are now widely utilized daily. The globe has just begun to shift toward financial inclusion, with marginalized people being introduced to the financial sector. As a result of the high volume of e-commerce, there has been a significant increase in credit card fraud. One of the most important parts of today's banking sector is fraud detection. Fraud is one of the most serious concerns in terms of monetary …
