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Articles 2521 - 2550 of 3699
Full-Text Articles in Computer Sciences
Comparing Task Graph Scheduling Algorithms: An Adversarial Approach, Jared Coleman, Bhaskar Krishnamachari
Comparing Task Graph Scheduling Algorithms: An Adversarial Approach, Jared Coleman, Bhaskar Krishnamachari
Computer Science Faculty Works
Scheduling a task graph representing an application over a heterogeneous network of computers is a fundamental problem in distributed computing. It is known to be not only NP-hard but also not polynomial-time approximable within a constant factor. As a result, many heuristic algorithms have been proposed over the past few decades. Yet it remains largely unclear how these algorithms compare to each other in terms of the quality of schedules they produce. We identify gaps in the traditional benchmarking approach to comparing task scheduling algorithms and propose a simulated annealing-based adversarial analysis approach called PISA to help address them. We …
Q-Rung Neutrosophic Sets And Topological Spaces, Michael Gr. Voskoglou, Florentin Smarandache, Mona Mohamed
Q-Rung Neutrosophic Sets And Topological Spaces, Michael Gr. Voskoglou, Florentin Smarandache, Mona Mohamed
Neutrosophic Systems with Applications
The concept of the q-rung orthopair neutrosophic set is introduced in this paper, and fundamental properties of it are studied. Also, the ordinary notion of topological space is extended to the q-rung orthopair neutrosophic environment, as well as the fundamental concepts of convergence, continuity, compactness, and Hausdorff topological space. All these generalizations are illustrated by suitable examples.
Q-Rung Neutrosophic Sets And Topological Spaces, Michael Gr. Voskoglou, Florentin Smarandache, Mona Mohamed
Q-Rung Neutrosophic Sets And Topological Spaces, Michael Gr. Voskoglou, Florentin Smarandache, Mona Mohamed
Neutrosophic Systems with Applications
The concept of the q-rung orthopair neutrosophic set is introduced in this paper, and fundamental properties of it are studied. Also, the ordinary notion of topological space is extended to the q-rung orthopair neutrosophic environment, as well as the fundamental concepts of convergence, continuity, compactness, and Hausdorff topological space. All these generalizations are illustrated by suitable examples.
Wang Tilings In Arbitrary Dimensions, Ian Tassin
Wang Tilings In Arbitrary Dimensions, Ian Tassin
Rose-Hulman Undergraduate Mathematics Journal
This paper makes a new observation about arbitrary dimensional Wang Tilings,
demonstrating that any d -dimensional tile set that can tile periodically along d − 1 axes must be able to tile periodically along all axes.
This work also summarizes work on Wang Tiles up to the present day, including
definitions for various aspects of Wang Tilings such as periodicity and the validity of a tiling. Additionally, we extend the familiar 2D definitions for Wang Tiles and associated properties into arbitrary dimensional spaces. While there has been previous discussion of arbitrary dimensional Wang Tiles in other works, it has been …
“It Is Luring You To Click On The Link With False Advertising” - Mental Models Of Clickbait And Its Impact On User’S Perceptions And Behavior Towards Clickbait Warnings, Ankit Shrestha, Arezou Behfar, Mahdi Nasrullah Al-Ameen
“It Is Luring You To Click On The Link With False Advertising” - Mental Models Of Clickbait And Its Impact On User’S Perceptions And Behavior Towards Clickbait Warnings, Ankit Shrestha, Arezou Behfar, Mahdi Nasrullah Al-Ameen
Computer Science Student Research
Clickbait, a social engineering attack performed through social media, tricks users through sensationalized or misleading posts into clicking on links that direct them to malicious websites. With the recent boom in social media, clickbait has become a substantial security concern, necessitating efforts from platforms and academia to control it. Despite these attempts, clickbait is effective due to the lack of users’ knowledge. Therefore, we explore user mental models (thought processes about how something works) about clickbait to analyze their deficiencies and their influences on users’ behavior towards clickbait warnings. To this end, we conducted an online study with 770 participants …
Sepsis Treatment: Reinforced Sequential Decision-Making For Saving Lives, Dipesh Tamboli, Jiayu Chen, Kiran Pranesh Jotheeswaran, Denny Yu, Vaneet Aggarwal
Sepsis Treatment: Reinforced Sequential Decision-Making For Saving Lives, Dipesh Tamboli, Jiayu Chen, Kiran Pranesh Jotheeswaran, Denny Yu, Vaneet Aggarwal
Graduate Industrial Research Symposium
Sepsis, a life-threatening condition triggered by the body's exaggerated response to infection, demands urgent intervention to prevent severe complications. Existing machine learning methods for managing sepsis struggle in offline scenarios, exhibiting suboptimal performance with survival rates below 50%. Our project introduces the "PosNegDM: Reinforcement Learning with Positive and Negative Demonstrations for Sequential Decision-Making" framework utilizing an innovative transformer-based model and a feedback reinforcer to replicate expert actions while considering individual patient characteristics. A mortality classifier with 96.7% accuracy guides treatment decisions towards positive outcomes. The PosNegDM framework significantly improves patient survival, saving 97.39% of patients and outperforming established machine learning …
Online Class-Incremental Learning For Real-World Food Image Classification, Siddeshwar Raghavan, Jiangpeng He, Fengqing Zhu
Online Class-Incremental Learning For Real-World Food Image Classification, Siddeshwar Raghavan, Jiangpeng He, Fengqing Zhu
Graduate Industrial Research Symposium
Food image classification is essential for monitoring health and tracking dietary in image-based dietary assessment methods. However, conventional systems often rely on static datasets with fixed classes and uniform distribution. In contrast, real-world food consumption patterns, shaped by cultural, economic, and personal influences, involve dynamic and evolving data. Thus, it requires the classification system to cope with continuously evolving data. Online Class Incremental Learning (OCIL) addresses the challenge of learning continuously from a single-pass data stream while adapting to the new knowledge and reducing catastrophic forgetting. Experience Replay (ER) based OCIL methods store a small portion of previous data and …
A Machine Learning Model Of Perturb-Seq Data For Use In Space Flight Gene Expression Profile Analysis, Liam F. Johnson, James Casaletto, Lauren Sanders, Sylvain Costes
A Machine Learning Model Of Perturb-Seq Data For Use In Space Flight Gene Expression Profile Analysis, Liam F. Johnson, James Casaletto, Lauren Sanders, Sylvain Costes
Graduate Industrial Research Symposium
The genetic perturbations caused by spaceflight on biological systems tend to have a system-wide effect which is often difficult to deconvolute it into individual signals with specific points of origin. Single cell multi-omic data can provide a profile of the perturbational effects, but does not necessarily indicate the initial point of interference within the network. The objective of this project is to take advantage of large scale and genome-wide perturbational datasets by using them to train a tuned machine learning model that is capable of predicting the effects of unseen perturbations in new data. Perturb-Seq datasets are large libraries of …
Student Perspectives On Assignment Deadline Policies In Computer Science Courses, J. Kim, Christian Murphy
Student Perspectives On Assignment Deadline Policies In Computer Science Courses, J. Kim, Christian Murphy
Computer Science Faculty Works
While the COVID-19 pandemic encouraged higher education to be more flexible, there is now growing uncertainty regarding the usefulness of flexibility as students return to in-person classes, particularly in fast-paced, rigorous fields such as Computer Science (CS). A big part of providing flexibility in education is through assignment deadlines. On one hand, flexible deadline policies may reduce student stress, help students face unexpected circumstances, and improve student learning; on the other, too much flexibility may lead to procrastination and poor time management. In order to help CS instructors make informed decisions when choosing an assignment deadline policy, we compared student …
Experiences Of Undergraduate Computer Science Students Living With Mental Health Conditions, J. Ji, Christian Murphy, B. Blaser, J. Akullian
Experiences Of Undergraduate Computer Science Students Living With Mental Health Conditions, J. Ji, Christian Murphy, B. Blaser, J. Akullian
Computer Science Faculty Works
Along with the growing number of students with disabilities in higher education comes an opportunity to explore the difficulties they experience, especially in the post-pandemic era, as well as how to better support them, thus making post-secondary education more inclusive. A considerable amount of research has been done in providing accommodation for students with physical disabilities, but other hindrances to accessibility such as mental health conditions are prone to be overlooked, perhaps in part due to the stigmatization and subjective invisibility of this topic, specifically in rigorous, competitive fields such as Computer Science (CS). In order to bridge this gap, …
Learning About Ai: Level Up Your Skills Without Overwhelming Your Mind, Jennifer Freer, Lara Nicosia
Learning About Ai: Level Up Your Skills Without Overwhelming Your Mind, Jennifer Freer, Lara Nicosia
Presentations and other scholarship
OpenAI and deep learning and neural networks, oh my! The swiftly developing landscape of artificial intelligence, combined with its rapid integration into our work and personal lives, can feel confusing and overwhelming. This session will help you think about AI in your own work and plan your own personal AI learning journey, while also providing thoughtful recommendations on how both you and your users can get started. The presenters will discuss common terminology, user-friendly resources for understanding AI, questions to ask when exploring AI tools, and ideas on how to start building your own knowledge on this topic. The information …
Detecting Anomalies In Time Series Using Kernel Density Approaches, Robin Frehner, Kesheng Wu, Alexander Sim, Jinoh Kim
Detecting Anomalies In Time Series Using Kernel Density Approaches, Robin Frehner, Kesheng Wu, Alexander Sim, Jinoh Kim
Faculty Publications
This paper introduces a novel anomaly detection approach tailored for time series data with exclusive reliance on normal events during training. Our key innovation lies in the application of kernel-density estimation (KDE) to scrutinize reconstruction errors, providing an empirically derived probability distribution for normal events post-reconstruction. This non-parametric density estimation technique offers a nuanced understanding of anomaly detection, differentiating it from prevalent threshold-based mechanisms in existing methodologies. In post-training, events are encoded, decoded, and evaluated against the estimated density, providing a comprehensive notion of normality. In addition, we propose a data augmentation strategy involving variational autoencoder-generated events and a smoothing …
Exploring The Design Of Low-End Technology To Increase Patient Connectivity To Electronic Health Records, Rens Kievit, Abdullahi Abubakar Kawu, Mirjam Van Reisen, Dympna O'Sullivan, Lucy Hederman
Exploring The Design Of Low-End Technology To Increase Patient Connectivity To Electronic Health Records, Rens Kievit, Abdullahi Abubakar Kawu, Mirjam Van Reisen, Dympna O'Sullivan, Lucy Hederman
Conference papers
The tracking of the vitals of patients with long term health problems is essential for clinicians to determine proper care. Using Patient Generated Health Data (PGHD) communicated remotely allows patients to be monitored without requiring frequent hospital visits. Issues might arise when the communication of data digitally is difficult or impossible due to a lack of access to internet or a low level of digital literacy as is the case in many African countries. The VODAN-Africa project (van Reisen et al., 2021) started in 2020 and has greatly increased the capabilities of clinics in different countries in both Africa and …
Graph Theory And Graph Neural Network Assisted High-Throughput Crystal Structure Prediction And Screening For Energy Conversion And Storage, Joshua Ojih, Mohammed Al-Fahdi, Yagang Yao, Jianjun Hu, Ming Hu
Graph Theory And Graph Neural Network Assisted High-Throughput Crystal Structure Prediction And Screening For Energy Conversion And Storage, Joshua Ojih, Mohammed Al-Fahdi, Yagang Yao, Jianjun Hu, Ming Hu
Faculty Publications
Prediction of crystal structures with desirable material properties is a grand challenge in materials research, due to the enormous search space of possible combinations of elements and their countless arrangements in 3D space. Despite the recent progress of a few crystal structure prediction algorithms, most of those methods only target a few specific material families or are restricted to simple systems with limited element diversity. Moreover, these algorithms are usually coupled with first principles calculations and thus are computationally expensive and very time consuming. Therefore, establishing a workflow that can generate a large number of hypothetical structures with diverse elements …
A Novel Data Fusion Framework To Enhance Contextual Awareness Of The Autonomous Vehicles For Accurate Decision Making, Henry Alexander Ignatious
A Novel Data Fusion Framework To Enhance Contextual Awareness Of The Autonomous Vehicles For Accurate Decision Making, Henry Alexander Ignatious
Thesis/ Dissertation Defenses
Autonomous driving has the potential to bring significant changes and benefits to various aspects of transportation. Autonomous vehicles (AVs) use a combination of advanced sensors, cameras, radar, lidar, GPS, maps, and AI algorithms to perceive their environment, make decisions, and control their movements. Though there is a significant increase in the AVs utility, there are several challenges associated with the AVs among which ensuring safety and security for a reliable drive is still an existing challenge. The majority of accidents involving the AVs result from faulty decision-making resulting in fatal incidents. Multiple elements contribute to the flawed decision-making in autonomous …
Relative Vectoring Using Dual Object Detection For Autonomous Aerial Refueling, Derek B. Worth, Jeffrey L. Choate, James Lynch, Scott L. Nykl, Clark N. Taylor
Relative Vectoring Using Dual Object Detection For Autonomous Aerial Refueling, Derek B. Worth, Jeffrey L. Choate, James Lynch, Scott L. Nykl, Clark N. Taylor
Faculty Publications
Once realized, autonomous aerial refueling will revolutionize unmanned aviation by removing current range and endurance limitations. Previous attempts at establishing vision-based solutions have come close but rely heavily on near perfect extrinsic camera calibrations that often change midflight. In this paper, we propose dual object detection, a technique that overcomes such requirement by transforming aerial refueling imagery directly into receiver aircraft reference frame probe-to-drogue vectors regardless of camera position and orientation. These vectors are precisely what autonomous agents need to successfully maneuver the tanker and receiver aircraft in synchronous flight during refueling operations. Our method follows a common 4-stage process …
Towards The Design And Evaluation Of Clickbait Education Content: Leveraging User Mental Models And Learning Science Principles, A. Shrestha, A. Flood, B. Hackler, Arezou Behfar, M. N. Al-Ameen
Towards The Design And Evaluation Of Clickbait Education Content: Leveraging User Mental Models And Learning Science Principles, A. Shrestha, A. Flood, B. Hackler, Arezou Behfar, M. N. Al-Ameen
Computer Science Student Research
Clickbait refers to sensationalized or misleading post on social networking sites (e.g., Facebook) that can trick users into clicking on malicious links. Clickbait is often used in cyberattacks, especially to conduct 'social engineering attacks' that direct users to malicious websites, resulting in disclosure of users' personal information or installing malicious software (i.e., malware). Thus, clickbait has become a major security concern with the recent boom in social media use. Therefore, security education has become necessary more than ever for the safe and secure use of social media, where there is dearth in security education literature to explore how we could …
A Social-Aware Gaussian Pre-Trained Model For Effective Cold-Start Recommendation, Siwei Liu, Xi Wang, Craig Macdonald, Iadh Ounis
A Social-Aware Gaussian Pre-Trained Model For Effective Cold-Start Recommendation, Siwei Liu, Xi Wang, Craig Macdonald, Iadh Ounis
Machine Learning Faculty Publications
The use of pre-training is an emerging technique to enhance a neural model's performance, which has been shown to be effective for many neural language models such as BERT. This technique has also been used to enhance the performance of recommender systems. In such recommender systems, pre-training models are used to learn a better initialisation for both users and items. However, recent existing pre-trained recommender systems tend to only incorporate the user interaction data at the pre-training stage, making it difficult to deliver good recommendations, especially when the interaction data is sparse. To alleviate this rcommon data sparsity issue, we …
Considering The Impact Framework To Understand The Ai-Well-Being-Complex From An Interdisciplinary Perspective, Christian Montag, Preslav Nakov, Raian Ali
Considering The Impact Framework To Understand The Ai-Well-Being-Complex From An Interdisciplinary Perspective, Christian Montag, Preslav Nakov, Raian Ali
Natural Language Processing Faculty Publications
Artificial intelligence (AI) is built into many products and has the potential to dramatically impact societies around the world. This short theoretical paper aims to provide a simple framework that might help us understand how the introduction and/or use of products with AI might influence the well-being of humans. It is proposed that considering the dynamic Interplay between variables stemming from Modality, Person, Area, Culture and Transparency categories will help to understand the influence of AI on well-being. The Modality category encompasses areas such as the degree of AI being interactive, informational versus actualizing, or autonomous. The Person variable contains …
Smart Cities And Aging Well: Exploring The Links Between Technological Models And Social Models For Promoting Daily Social Interaction For Geriatric Care, Jocelyne Kiss, Lindenwood University
Smart Cities And Aging Well: Exploring The Links Between Technological Models And Social Models For Promoting Daily Social Interaction For Geriatric Care, Jocelyne Kiss, Lindenwood University
Faculty Scholarship
The aging global population requires a new social model to meet the growing social, economic, and physical needs of seniors. Western social models need to be reconsidered in light of examples that support communal ways of living, which are sustainable through smart city design for more supportive geriatric care systems. To address the complex problems of geriatric care in this growing aging population with specific needs related to increased lifespan and limited financial resources, the use of emerging technologies, such as artificial intelligence (AI) and the Internet of Things (IoT), should be considered. As retirement ages rise and funds for …
Medical Image Super-Resolution For Smart Healthcare Applications: A Comprehensive Survey, Sabina Umirzakova, Shabir Ahmad, Latif U. Khan, Taegkeun Whangbo
Medical Image Super-Resolution For Smart Healthcare Applications: A Comprehensive Survey, Sabina Umirzakova, Shabir Ahmad, Latif U. Khan, Taegkeun Whangbo
Machine Learning Faculty Publications
The digital transformation in healthcare, propelled by the integration of deep learning models and the Internet of Things (IoT), is creating unprecedented opportunities for improving patient care. However, the utilization of low-resolution images, often generated by IoT devices, introduces biases in the deep learning models, thereby affecting the overall clinical decision-making process. While super-resolution techniques have been extensively employed to transform low-resolution images into high-resolution counterparts, the challenge of achieving highly accurate image restoration remains unresolved. This is especially critical in the medical imaging domain, where even minor inaccuracies can lead to significant biases in model training and, consequently, impact …
Why Do We Not Stand Up To Misinformation? Factors Influencing The Likelihood Of Challenging Misinformation On Social Media And The Role Of Demographics, Selin Gurgun, Deniz Cemiloglu, Emily Arden Close, Keith Phalp, Preslav Nakov, Raian Ali
Why Do We Not Stand Up To Misinformation? Factors Influencing The Likelihood Of Challenging Misinformation On Social Media And The Role Of Demographics, Selin Gurgun, Deniz Cemiloglu, Emily Arden Close, Keith Phalp, Preslav Nakov, Raian Ali
Natural Language Processing Faculty Publications
This study investigates the barriers to challenging others who post misinformation on social media platforms. We conducted a survey amongst U.K. Facebook users (143 (57.2 %) women, 104 (41.6 %) men) to assess the extent to which the barriers to correcting others, as identified in literature across disciplines, apply to correcting misinformation on social media. We also group the barriers into factors and explore demographic differences amongst them. It has been suggested that users are generally hesitant to challenge misinformation. We found that most of our participants (58.8 %) were reluctant to challenge misinformation. We also identified moderating roles of …
Why Pavement Cracks Are Mostly Longitudinal, Sometimes Transversal, And Rarely Of Other Directions: A Geometric Explanation, Edgar Daniel Rodriguez Velasquez, Olga Kosheleva, Vladik Kreinovich
Why Pavement Cracks Are Mostly Longitudinal, Sometimes Transversal, And Rarely Of Other Directions: A Geometric Explanation, Edgar Daniel Rodriguez Velasquez, Olga Kosheleva, Vladik Kreinovich
Departmental Technical Reports (CS)
In time, pavements deteriorate, and need maintenance. One of the most typical pavement faults are cracks. Empirically, the most frequent cracks are longitudinal, i.e., following the direction of the road; less frequent are transversal cracks, which are orthogonal to the direction of the road. Sometimes, there are cracks in different directions, but such cracks are much rarer. In this paper, we show that simple geometric analysis and fundamental physical ideas can explain these observed relative frequencies.
Why Linear And Sigmoid Last Layers Work Better In Classification, Lehel Dénes-Fazakas, Lásló Szilágyi, Vladik Kreinovich
Why Linear And Sigmoid Last Layers Work Better In Classification, Lehel Dénes-Fazakas, Lásló Szilágyi, Vladik Kreinovich
Departmental Technical Reports (CS)
Usually, when a deep neural network is used to classify objects, its last layer computes the softmax. Our empirical results show we can improve the classification results if instead, we have linear or sigmoid last layer. In this paper, we provide an explanation for this empirical phenomenon.
Why Two Fish Follow Each Other But Three Fish Form A School: A Symmetry-Based Explanation, Shahnaz Shahbazova, Olga Kosheleva, Vladik Kreinovich
Why Two Fish Follow Each Other But Three Fish Form A School: A Symmetry-Based Explanation, Shahnaz Shahbazova, Olga Kosheleva, Vladik Kreinovich
Departmental Technical Reports (CS)
Recent experiments with fish has shown an unexpected strange behavior: when two fish of the same species are placed in an aquarium, they start following each other, while when three fish are placed there, they form (approximately) an equilateral triangle, and move in the direction (approximately) orthogonal to this triangle. In this paper, we use natural symmetries -- such as rotations, shifts, and permutation of fish -- to show that this observed behavior is actually optimal. This behavior is not just optimal with respect to one specific optimality criterion, it is optimal with respect to any optimality criterion -- as …
Technical Note: Generalizable And Promptable Artificial Intelligence Model To Augment Clinical Delineation In Radiation Oncology, Lian Zhang, Zhengliang Liu, Lu Zhang, Zihao Wu, Xiaowei Yu, Jason Holmes, Hongying Feng, Haixing Dai, Xiang Li, Quanzheng Li, William W. Wong, Sujay A. Vora, Dajiang Zhu, Tianming Liu, Wei Liu
Technical Note: Generalizable And Promptable Artificial Intelligence Model To Augment Clinical Delineation In Radiation Oncology, Lian Zhang, Zhengliang Liu, Lu Zhang, Zihao Wu, Xiaowei Yu, Jason Holmes, Hongying Feng, Haixing Dai, Xiang Li, Quanzheng Li, William W. Wong, Sujay A. Vora, Dajiang Zhu, Tianming Liu, Wei Liu
Computer Science Faculty Research & Creative Works
Background: Efficient and accurate delineation of organs at risk (OARs) is a critical procedure for treatment planning and dose evaluation. Deep learning-based auto-segmentation of OARs has shown promising results and is increasingly being used in radiation therapy. However, existing deep learning-based auto-segmentation approaches face two challenges in clinical practice: generalizability and human-AI interaction. A generalizable and prompt able auto-segmentation model, which segments OARs of multiple disease sites simultaneously and supports on-the-fly human-AI interaction, can significantly enhance the efficiency of radiation therapy treatment planning. Purpose: Meta's segment anything model (SAM) was proposed as a generalizable and prompt able model for next-generation …
Mask2former With Improved Query For Semantic Segmentation In Remote-Sensing Images, Shichen Guo, Qi Wang, Shiming Xiang, Shuwen Wang, Xuezhi Wang
Mask2former With Improved Query For Semantic Segmentation In Remote-Sensing Images, Shichen Guo, Qi Wang, Shiming Xiang, Shuwen Wang, Xuezhi Wang
Computer Science Faculty Publications and Presentations
Semantic segmentation of remote sensing (RS) images is vital in various practical applications, including urban construction planning, natural disaster monitoring, and land resources investigation. However, RS images are captured by airplanes or satellites at high altitudes and long distances, resulting in ground objects of the same category being scattered in various corners of the image. Moreover, objects of different sizes appear simultaneously in RS images. For example, some objects occupy a large area in urban scenes, while others only have small regions. Technically, the above two universal situations pose significant challenges to the segmentation with a high quality for RS …
Navigating Through Chaos, Hoong Chuin Lau
Navigating Through Chaos, Hoong Chuin Lau
Asian Management Insights
How AI and optimisation models can strengthen supply chain resilience.
Screening Through A Broad Pool: Towards Better Diversity For Lexically Constrained Text Generation, Changsen Yuan, Heyan Huang, Yixin Cao, Qianwen Cao
Screening Through A Broad Pool: Towards Better Diversity For Lexically Constrained Text Generation, Changsen Yuan, Heyan Huang, Yixin Cao, Qianwen Cao
Research Collection School Of Computing and Information Systems
Lexically constrained text generation (CTG) is to generate text that contains given constrained keywords. However, the text diversity of existing models is still unsatisfactory. In this paper, we propose a lightweight dynamic refinement strategy that aims at increasing the randomness of inference to improve generation richness and diversity while maintaining a high level of fluidity and integrity. Our basic idea is to enlarge the number and length of candidate sentences in each iteration, and choose the best for subsequent refinement. On the one hand, different from previous works, which carefully insert one token between two words per action, we insert …
Small Unmanned Aircraft System Detection And Tracking With Audio, Computer Vision, And Deep Learning Techniques, Anthony C. Brunson
Small Unmanned Aircraft System Detection And Tracking With Audio, Computer Vision, And Deep Learning Techniques, Anthony C. Brunson
Theses and Dissertations
sUAS present significant risks to local and federal agencies when under the control of negligent, reckless, or criminal operators. In the face of an escalating presence of sUAS in shared airspace with traditional aircraft, and their deployment in protected airspace as potential weapons, safeguarding personnel, facilities, and assets becomes paramount. This research seeks to address this emerging threat by investigating the efficacy of integrating low-cost distributed sensors and Machine learning (ML) models to enhance battlespace awareness and complement existing sensing platforms for real-time sUAS detection, classification, and localization. The thesis introduces the conceptualization and development of a Drone Detection Command …