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 …
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 …
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 …
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 …
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 …
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 …
A Comparison Of Correlation-Agnostic Techniques For Magnetic Navigation,
2022
Air Force Institute of Technology
A Comparison Of Correlation-Agnostic Techniques For Magnetic Navigation, Clark N. Taylor, Josh Hiatt
Faculty Publications
Navigation using a Global Navigation Satellite System (GNSS) is common for autonomous vehicles (ground or air). Unfortunately, GNSS-based navigation solutions are often susceptible to jamming, interference, and a limited number of satellites. A proposed technique to aid in navigation when a GNSS-based system fails is magnetic navigation - navigation using the Earth's magnetic anomaly field. This solution comes with its own set of problems including the need for quality magnetic maps in every area in which magnetic navigation will be used. Many of the currently available magnetic maps are generated from a combination of dated magnetic surveys, resulting in maps …
Automatic Contact Tracing Using Bluetooth Low Energy Signals And Imu Sensor Readings,
2022
Dublin City University, Ireland
Automatic Contact Tracing Using Bluetooth Low Energy Signals And Imu Sensor Readings, Suriyadeepan Ramamoorthy, Joyce Mahon, Michael O'Mahony, Jean Francois Itangayenda, Tendai Mukande, Tlamelo Makati
Other resources
In this report, we present our solution to the challenge provided by the SFI Centre for Machine Learning (ML-Labs) in which the distance between two phones needs to be estimated. It is a modified version of the NIST Too Close For Too Long (TC4TL) Challenge, as the time aspect is excluded. We propose a feature-based approach based on Bluetooth RSSI and IMU sensory data, that outperforms the previous state of the art by a significant margin, reducing the error down to 0.071. We perform an ablation study of our model that reveals interesting insights about the relationship between the distance …
Soft-Mask De-Mixing For Anechoic Mixtures,
2022
Technological University Dublin
Soft-Mask De-Mixing For Anechoic Mixtures, Swarnadeep Bagchi, Ruairí De Fréin
Articles
This paper extends a computationally efficient, soft-mask based source separation (SS) technique called Redress, to anechoic mixing scenarios. SS methods are an integral part of hearing aid research. We call the resulting method D-Redress. In its original form, Redress was intended for instantaneous mixing scenarios. Numerical evaluations demonstrate that soft-mask based techniques reduce the level of artifacts in the separated speech. Monte Carlo trials on 1000 real speech mixtures demonstrate that the D-Redress successfully extends Redress in terms of Overall-Perceptual (OPS), Target-Perceptual (TPS) scores and Human-Ear Intelligibility (HEI).
Photon Attenuation Layer Silicon-Based X-Ray Detector: Quantum Efficiency Investigation And Algorithmic Processing Of Individual Detection Events,
2022
Dartmouth College
Photon Attenuation Layer Silicon-Based X-Ray Detector: Quantum Efficiency Investigation And Algorithmic Processing Of Individual Detection Events, Kevin Larkin
ENGS 88 Honors Thesis (AB Students)
State-of-the-art X-ray detectors either use silicon direct detection—which is inefficient, as silicon is mostly transparent to high-energy X-rays—or rely on expensive scintillators to first down-convert X-ray photons to lower energies. Dr. Eldred Lee of Dartmouth College recently completed doctoral research that suggested the promise of a novel alternative: silicon-based X-ray detectors that utilize photon attenuation layers rather than scintillators. While Dr. Lee’s work is certainly rigorous and meritorious, background noise controls were rendered less effective by circumstances. The work presented in this thesis succeeded in implementing much better background noise controls for a series of new tests to compare the …
Effects Of Motion Measurement Errors On Radar Target Detection,
2022
Air Force Institute of Technology
Effects Of Motion Measurement Errors On Radar Target Detection, Darnell D. Parker
Theses and Dissertations
This thesis investigates the relationships present between signal-to-clutter ratios, motion measurement errors, image quality metrics, and the task of target detection, in order to discover what factor merit greater focus in order to attain the highest probability of target detection success. This investigation is accomplished by running a high number of Monte Carlo trials through a coherent target detector and analyzing the results. The aforementioned relationships are demonstrated via sample synthetic aperture radar imagery, histograms, receiver operating characteristics curves, and error bar plots.
Polarization-Based Image Segmentation And Height Estimation For Interferometric Sar,
2022
Air Force Institute of Technology
Polarization-Based Image Segmentation And Height Estimation For Interferometric Sar, Augusta J. Vande Hey
Theses and Dissertations
To find scatterers in a synthetic aperture radar (SAR) image, a modification is proposed to improve peak region segmentation (PRS) with region merging. The modification considers the polarization of each pixel before it is added to a segment to ensure the segment only contains pixels of the same polarization. Prior to region merging, the polarization of the segments is compared, so that only segments with the same polarization are merged into a single region. The segmented regions are used to find the height of each scatterer through interferometric SAR (IFSAR) processing. Multiple methods of IFSAR are examined to find the …
Methods For Focal Plane Array Resolution Estimation Using Random Laser Speckle In Non-Paraxial Geometries,
2022
Air Force Institute of Technology
Methods For Focal Plane Array Resolution Estimation Using Random Laser Speckle In Non-Paraxial Geometries, Phillip J. Plummer
Theses and Dissertations
The infrared (IR) imaging community has a need for direct IR detector evaluation due to the continued demand for small pixel pitch detectors, the emergence of strained-layer-super-lattice devices, and the associated lateral carrier diffusion issues. Conventional laser speckle-based modulation transfer function (MTF) estimation is dependent on Fresnel propagation and a wide-sense-stationary input random process, limiting the use of this approach for lambda (wavelength)-scale IR devices. This dissertation develops two alternative methodologies for speckle-based resolution evaluation of IR focal plane arrays (FPAs). Both techniques are formulated using Rayleigh-Sommerfield electric field propagation, making them valid in the non-paraxial geometries dictated for resolution …
