Open Access. Powered by Scholars. Published by Universities.®
- Institution
-
- Old Dominion University (24)
- University of Nevada, Las Vegas (20)
- University of Denver (14)
- University of Arkansas, Fayetteville (11)
- Western University (11)
-
- Chulalongkorn University (9)
- University of South Florida (9)
- Missouri University of Science and Technology (6)
- New Jersey Institute of Technology (6)
- University of Tennessee, Knoxville (6)
- Air Force Institute of Technology (5)
- City University of New York (CUNY) (4)
- Michigan Technological University (4)
- Purdue University (4)
- University of Louisville (4)
- University of New Orleans (4)
- University of Texas at El Paso (4)
- California Polytechnic State University, San Luis Obispo (3)
- California State University, San Bernardino (3)
- Louisiana State University (3)
- University of North Florida (3)
- West Virginia University (3)
- Embry-Riddle Aeronautical University (2)
- Northern Illinois University (2)
- The University of Southern Mississippi (2)
- Union College (2)
- University of Windsor (2)
- American University in Cairo (1)
- Boise State University (1)
- Clemson University (1)
- Keyword
-
- Machine learning (28)
- Deep learning (19)
- Machine Learning (18)
- Robotics (15)
- Daniel Felix Ritchie School of Engineering and Computer Science (14)
-
- Artificial intelligence (13)
- Computer vision (12)
- Deep Learning (10)
- Electrical and Computer Engineering (9)
- Clustering (5)
- Neural network (5)
- Reinforcement Learning (5)
- Computer Science (4)
- Computer Vision (4)
- #antcenter (3)
- Applied sciences (3)
- Artificial Intelligence (3)
- Artificial neural networks (3)
- Control (3)
- Deep neural networks (3)
- Emotion recognition (3)
- Facial expression recognition (3)
- Internet of Things (3)
- Natural language processing (3)
- Neural Networks (3)
- Neural networks (3)
- Algorithms (2)
- Analysis (2)
- Autonomous surface vehicle (2)
- Autonomous systems (2)
- Publication Year
- Publication
-
- Electronic Theses and Dissertations (21)
- UNLV Theses, Dissertations, Professional Papers, and Capstones (20)
- Electrical & Computer Engineering Theses & Dissertations (15)
- Electronic Thesis and Dissertation Repository (11)
- Chulalongkorn University Theses and Dissertations (Chula ETD) (9)
-
- Theses and Dissertations (9)
- USF Tampa Graduate Theses and Dissertations (9)
- Doctoral Dissertations (7)
- Computer Science and Computer Engineering Undergraduate Honors Theses (6)
- Dissertations (6)
- Graduate Theses and Dissertations (5)
- Masters Theses (5)
- Dissertations, Master's Theses and Master's Reports (4)
- Open Access Theses & Dissertations (4)
- University of New Orleans Theses and Dissertations (4)
- Computational Modeling & Simulation Engineering Theses & Dissertations (3)
- Dissertations, Theses, and Capstone Projects (3)
- Electronic Theses, Projects, and Dissertations (3)
- Graduate Theses, Dissertations, and Problem Reports (3)
- Master's Theses (3)
- Open Access Dissertations (3)
- UNF Graduate Theses and Dissertations (3)
- Computer Engineering (2)
- Computer Science Theses & Dissertations (2)
- Dissertations and Theses (2)
- Doctoral Dissertations and Master's Theses (2)
- Graduate Research Theses & Dissertations (2)
- Honors Theses (2)
- LSU Master's Theses (2)
- Mechanical & Aerospace Engineering Theses & Dissertations (2)
Articles 1 - 30 of 191
Full-Text Articles in Computer Engineering
Planetary Exploration Via Fully Automatic Topological Structure Extraction Using Adaptive Resonance, Jonathan Kissi
Planetary Exploration Via Fully Automatic Topological Structure Extraction Using Adaptive Resonance, Jonathan Kissi
Electronic Thesis and Dissertation Repository
Renewed interest in Solar System exploration, along with ongoing improvements in computing, robotics and instrumentation technologies, have reinforced the case for remote science acquisition systems development in space exploration. Testing systems and procedures that allow for autonomously collected science has been the focus of analogue field deployments and mission planning for some time, with such systems becoming more relevant as missions increase in complexity and ambition. The introduction of lidar and laser scanning-type instruments into the geological and planetary sciences has proven popular, and, just as with the established image and photogrammetric methods, has found widespread use in several research …
Computational Modeling And Analysis Of Facial Expressions And Gaze For Discovery Of Candidate Behavioral Biomarkers For Children And Young Adults With Autism Spectrum Disorder, Megan Anita Witherow
Computational Modeling And Analysis Of Facial Expressions And Gaze For Discovery Of Candidate Behavioral Biomarkers For Children And Young Adults With Autism Spectrum Disorder, Megan Anita Witherow
Electrical & Computer Engineering Theses & Dissertations
Facial expression production and perception in autism spectrum disorder (ASD) suggest the potential presence of behavioral biomarkers that may stratify individuals on the spectrum into prognostic or treatment subgroups. High-speed internet and the ease of technology have enabled remote, scalable, affordable, and timely access to medical care, such as measurements of ASDrelated behaviors in familiar environments to complement clinical observation. Machine and deep learning (DL)-based analysis of video tracking (VT) of expression production and eye tracking (ET) of expression perception may aid stratification biomarker discovery for children and young adults with ASD. However, there are open challenges in 1) facial …
Time Series Models For Predicting Application Gpu Utilization And Power Draw Based On Trace Data, Dorothy Xiaoshuang Parry
Time Series Models For Predicting Application Gpu Utilization And Power Draw Based On Trace Data, Dorothy Xiaoshuang Parry
Electrical & Computer Engineering Theses & Dissertations
This work explores collecting performance metrics and leveraging various statistical and machine learning time series predictive models on a memory-intensive application, Inception v3. Trace data collected using nvidia-smi measured GPU utilization and power draw for two runs of Inception3. Experimental results from the statistical and machine learning-based time series predictive algorithms showed that the predictions from statistical-based models were unable to capture the complex changes in the trace data. The Probabilistic TNN model provided the best results for the power draw trace, according to the test evaluation metrics. For the GPU utilization trace, the RNN models produced the most accurate …
Attribution Robustness Of Neural Networks, Sunanda Gamage
Attribution Robustness Of Neural Networks, Sunanda Gamage
Electronic Thesis and Dissertation Repository
While deep neural networks have demonstrated excellent learning capabilities, explainability of model predictions remains a challenge due to their black box nature. Attributions or feature significance methods are tools for explaining model predictions, facilitating model debugging, human-machine collaborative decision making, and establishing trust and compliance in critical applications. Recent work has shown that attributions of neural networks can be distorted by imperceptible adversarial input perturbations, which makes attributions unreliable as an explainability method. This thesis addresses the research problem of attribution robustness of neural networks and introduces novel techniques that enable robust training at scale.
Firstly, a novel generic framework …
A Memory Efficient Deep Recurrent Q-Learning Approach For Autonomous Wildfire Surveillance, Jeremy A. Cantor
A Memory Efficient Deep Recurrent Q-Learning Approach For Autonomous Wildfire Surveillance, Jeremy A. Cantor
UNF Graduate Theses and Dissertations
Previous literature demonstrates that autonomous UAVs (unmanned aerial vehicles) have the po- tential to be utilized for wildfire surveillance. This advanced technology empowers firefighters by providing them with critical information, thereby facilitating more informed decision-making processes. This thesis applies deep Q-learning techniques to the problem of control policy design under the objective that the UAVs collectively identify the maximum number of locations that are under fire, assuming the UAVs can share their observations. The prohibitively large state space underlying the control policy motivates a neural network approximation, but prior work used only convolutional layers to extract spatial fire information from …
Transformer-Enabled Deep Reinforcement Learning For Coverage Path Planning, Daniel B. Tiu
Transformer-Enabled Deep Reinforcement Learning For Coverage Path Planning, Daniel B. Tiu
UNF Graduate Theses and Dissertations
Coverage path planning (CPP) is the problem of covering all points in an environment and is a well-researched topic in robotics due to its sheer practical relevance. This paper investigates such an offline CPP problem where the primary objective is to minimize the path length to achieve complete coverage. Furthermore, the literature suggests that taking turns leads to a higher energy use than going straight. To this end, we design a novel objective function that aims to minimize the number of turns as well. We have proposed a deep reinforcement learning (DRL)-based framework that uses a Transformer model. Unlike state-of-the-art …
The Integration Of Neuromorphic Computing In Autonomous Robotic Systems, Md Abu Bakr Siddique
The Integration Of Neuromorphic Computing In Autonomous Robotic Systems, Md Abu Bakr Siddique
Dissertations, Master's Theses and Master's Reports
Deep Neural Networks (DNNs) have come a long way in many cognitive tasks by training on large, labeled datasets. However, this method has problems in places with limited data and energy, like when planetary robots are used or when edge computing is used [1]. In contrast to this data-heavy approach, animals demonstrate an innate ability to learn by communicating with their environment and forming associative memories among events and entities, a process known as associative learning [2-4]. For instance, rats in a T-maze learn to associate different stimuli with outcomes through exploration without needing labeled data [5]. This learning paradigm …
Cross-Layer Design Of Highly Scalable And Energy-Efficient Ai Accelerator Systems Using Photonic Integrated Circuits, Sairam Sri Vatsavai
Cross-Layer Design Of Highly Scalable And Energy-Efficient Ai Accelerator Systems Using Photonic Integrated Circuits, Sairam Sri Vatsavai
Theses and Dissertations--Electrical and Computer Engineering
Artificial Intelligence (AI) has experienced remarkable success in recent years, solving complex computational problems across various domains, including computer vision, natural language processing, and pattern recognition. Much of this success can be attributed to the advancements in deep learning algorithms and models, particularly Artificial Neural Networks (ANNs). In recent times, deep ANNs have achieved unprecedented levels of accuracy, surpassing human capabilities in some cases. However, these deep ANN models come at a significant computational cost, with billions to trillions of parameters. Recent trends indicate that the number of parameters per ANN model will continue to grow exponentially in the foreseeable …
Brain-Inspired Spatio-Temporal Learning With Application To Robotics, Thiago André Ferreira Medeiros
Brain-Inspired Spatio-Temporal Learning With Application To Robotics, Thiago André Ferreira Medeiros
USF Tampa Graduate Theses and Dissertations
The human brain still has many mysteries and one of them is how it encodes information. The following study intends to unravel at least one such mechanism. For this it will be demonstrated how a set of specialized neurons may use spatial and temporal information to encode information. These neurons, called Place Cells, become active when the animal enters a place in the environment, allowing it to build a cognitive map of the environment. In a recent paper by Scleidorovich et al. in 2022, it was demonstrated that it was possible to differentiate between two sequences of activations of a …
Implementation Of Adas And Autonomy On Unlv Campus, Zillur Rahman
Implementation Of Adas And Autonomy On Unlv Campus, Zillur Rahman
UNLV Theses, Dissertations, Professional Papers, and Capstones
The integration of Advanced Driving Assistance Systems (ADAS) and autonomous driving functionalities into contemporary vehicles has notably surged, driven by the remarkable progress in artificial intelligence (AI). These AI systems, capable of learning from real-world data, now exhibit the capability to perceive their surroundings via a suite of sensors, create optimal routes from source to destination, and execute vehicle control akin to a human driver.
Within the context of this thesis, we undertake a comprehensive exploration of three distinct yet interrelated ADAS and Autonomy projects. Our central objective is the implementation of autonomous driving(AD) technology at UNLV campus, culminating in …
Accelerating Machine Learning Inference For Satellite Component Feature Extraction Using Fpgas., Andrew Ekblad
Accelerating Machine Learning Inference For Satellite Component Feature Extraction Using Fpgas., Andrew Ekblad
Theses and Dissertations
Running computer vision algorithms requires complex devices with lots of computing power, these types of devices are not well suited for space deployment. The harsh radiation environment and limited power budgets have hindered the ability of running advanced computer vision algorithms in space. This problem makes running an on-orbit servicing detection algorithm very difficult. This work proposes using a low powered FPGA to accelerate the computer vision algorithms that enable satellite component feature extraction. This work uses AMD/Xilinx’s Zynq SoC and DPU IP to run model inference. Experiments in this work centered around improving model post processing by creating implementations …
Autonomous Shipwreck Detection & Mapping, William Ard
Autonomous Shipwreck Detection & Mapping, William Ard
LSU Master's Theses
This thesis presents the development and testing of Bruce, a low-cost hybrid Remote Operated Vehicle (ROV) / Autonomous Underwater Vehicle (AUV) system for the optical survey of marine archaeological sites, as well as a novel sonar image augmentation strategy for semantic segmentation of shipwrecks. This approach takes side-scan sonar and bathymetry data collected using an EdgeTech 2205 AUV sensor integrated with an Harris Iver3, and generates augmented image data to be used for the semantic segmentation of shipwrecks. It is shown that, due to the feature enhancement capabilities of the proposed shipwreck detection strategy, correctly identified areas have a 15% …
Terrain And Adversary-Aware Autonomous Robot Navigation, Aniekan Ufot Inyang
Terrain And Adversary-Aware Autonomous Robot Navigation, Aniekan Ufot Inyang
Electronic Theses and Dissertations
In autonomous robot navigation, the robot is able to understand the environment around it for intelligent navigation. From its world model of this environment, it generates a global plan for navigation from a position to a goal based on different factors. This research aims to implement autonomous robot navigation by learning terrain affordances: traversability (moving quickly) and concealment (staying hidden from an adversary) using the Preference-based Inverse Reward Learning (PbIRL) methodology. The PbIRL methodology reduces the barrier of generating initial demonstration data to learn the terrain affordances by using a human expert’s preferences to learn individual weights over the terrain …
Facial Expression Recognition Using Convolutional Neural Networks (Cnns) And Generative Adversarial Networks (Gans) For Data Augmentation And Image Generation, Shekhar Singh
UNLV Theses, Dissertations, Professional Papers, and Capstones
Facial expressions play a crucial role in human communication, serving as a powerful means to convey emotions. However, classifying facial expressions using artificial intelligence (AI) can be challenging, especially with small datasets and images. Facial Expression Recognition (FER) is an active area of research, with Convolutional Neural Networks (CNNs) being widely employed for classification. In this research, we propose a CNN-based approach for FER that utilizes both original and augmented datasets to enhance classification accuracy. Experimental results on the FER2013 dataset show test accuracies of 63.39% and 64.59% for the original and augmented datasets, respectively, in a seven-class classification task. …
Controllable Language Generation Using Deep Learning, Rohola Zandie
Controllable Language Generation Using Deep Learning, Rohola Zandie
Electronic Theses and Dissertations
The advent of deep neural networks has sparked a revolution in Artificial Intelligence (AI), notably with the creation of Transformer models like GPT-X and ChatGPT. These models have surpassed previous methods in various Natural Language Processing (NLP) tasks. As the NLP field evolves, there is a need to further understand and question the capabilities of these models. Text generation, a crucial part of NLP, remains an area where our comprehension is limited while being critical in research.
This dissertation focuses on the challenging problem of controlling the general behaviors of language models such as sentiment, topical focus, and logical reasoning. …
Generalizable Deep-Learning-Based Wireless Indoor Localization, Ali Owfi
Generalizable Deep-Learning-Based Wireless Indoor Localization, Ali Owfi
All Theses
The growing interest in indoor localization has been driven by its wide range of applications in areas such as smart homes, industrial automation, and healthcare. With the increasing reliance on wireless devices for location-based services, accurate estimation of device positions within indoor environments has become crucial. Deep learning approaches have shown promise in leveraging wireless parameters like Channel State Information (CSI) and Received Signal Strength Indicator (RSSI) to achieve precise localization. However, despite their success in achieving high accuracy, these deep learning models suffer from limited generalizability, making them unsuitable for deployment in new or dynamic environments without retraining. To …
Towards Intelligent Runtime Framework For Distributed Heterogeneous Systems, Polykarpos Thomadakis
Towards Intelligent Runtime Framework For Distributed Heterogeneous Systems, Polykarpos Thomadakis
Computer Science Theses & Dissertations
Scientific applications strive for increased memory and computing performance, requiring massive amounts of data and time to produce results. Applications utilize large-scale, parallel computing platforms with advanced architectures to accommodate their needs. However, developing performance-portable applications for modern, heterogeneous platforms requires lots of effort and expertise in both the application and systems domains. This is more relevant for unstructured applications whose workflow is not statically predictable due to their heavily data-dependent nature. One possible solution for this problem is the introduction of an intelligent Domain-Specific Language (iDSL) that transparently helps to maintain correctness, hides the idiosyncrasies of lowlevel hardware, and …
Towards A Robust Defense: A Multifaceted Approach To The Detection And Mitigation Of Neural Backdoor Attacks Through Feature Space Exploration And Analysis, Liuwan Zhu
Electrical & Computer Engineering Theses & Dissertations
From voice assistants to self-driving vehicles, machine learning(ML), especially deep learning, revolutionizes the way we work and live, through the wide adoption in a broad range of applications. Unfortunately, this widespread use makes deep learning-based systems a desirable target for cyberattacks, such as generating adversarial examples to fool a deep learning system to make wrong decisions. In particular, many recent studies have revealed that attackers can corrupt the training of a deep learning model, e.g., through data poisoning, or distribute a deep learning model they created with “backdoors” planted, e.g., distributed as part of a software library, so that the …
Characterization And Estimation Of Musculoskeletal Pain Using Machine Learning, Boluwatife Faremi
Characterization And Estimation Of Musculoskeletal Pain Using Machine Learning, Boluwatife Faremi
Master's Theses
Traditional scales utilized for recording pain are known to be highly subjective and biased due to inaccuracies in recollecting actual pain intensities. As a result, machine learning (ML) models that are trained using these scores as ground truth are reported to have low performance for objective pain classification because of the huge disparity between what was felt in moments of pain and the scores recorded afterward.
In the present study, two devices were designed for gathering real-time, continuous in-session subjective pain scores and the recording of the autonomic nervous system (ANS) altered endodermal (EDA) activity. 24 participants were recruited to …
Secure And Efficient Federated Learning, Xingyu Li
Secure And Efficient Federated Learning, Xingyu Li
Theses and Dissertations
In the past 10 years, the growth of machine learning technology has been significant, largely due to the availability of large datasets for training. However, gathering a sufficient amount of data on a central server can be challenging. Additionally, with the rise of mobile networking and the large amounts of data generated by IoT devices, privacy and security issues have become a concern, resulting in government regulations such as GDPR, HIPAA, CCPA, and ADPPA. Under these circumstances, traditional centralized machine learning methods face a problem in that sensitive data must be kept locally for privacy reasons, making it difficult to …
Modeling, Simulation And Control Of Microrobots For The Microfactory., Zhong Yang
Modeling, Simulation And Control Of Microrobots For The Microfactory., Zhong Yang
Electronic Theses and Dissertations
Future assembly technologies will involve higher levels of automation in order to satisfy increased microscale or nanoscale precision requirements. Traditionally, assembly using a top-down robotic approach has been well-studied and applied to the microelectronics and MEMS industries, but less so in nanotechnology. With the boom of nanotechnology since the 1990s, newly designed products with new materials, coatings, and nanoparticles are gradually entering everyone’s lives, while the industry has grown into a billion-dollar volume worldwide. Traditionally, nanotechnology products are assembled using bottom-up methods, such as self-assembly, rather than top-down robotic assembly. This is due to considerations of volume handling of large …
Information-Theoretic Model Diagnostics (Infomod), Armin Esmaeilzadeh
Information-Theoretic Model Diagnostics (Infomod), Armin Esmaeilzadeh
UNLV Theses, Dissertations, Professional Papers, and Capstones
Model validation is a critical step in the development, deployment, and governance of machine learning models. During the validation process, the predictive power of a model is measured on unseen datasets with a variety of metrics such as Accuracy and F1-Scores for classification tasks. Although the most used metrics are easy to implement and understand, they are aggregate measures over all the segments of heterogeneous datasets, and therefore, they do not identify the performance variation of a model among different data segments. The lack of insight into how the model performs over segments of unseen datasets has raised significant challenges …
Enhanced Iot-Based Electrocardiogram Monitoring System With Deep Learning, Jian Ni
Enhanced Iot-Based Electrocardiogram Monitoring System With Deep Learning, Jian Ni
UNLV Theses, Dissertations, Professional Papers, and Capstones
Due to the rapid development of computing and sensing technologies, Internet of Things (IoT)-based cardiac monitoring plays a crucial role in providing patients with cost-efficient solutions for long-term, continuous, and pervasive electrocardiogram (ECG) monitoring outside a hospital setting. In a typical IoT-based ECG monitoring system, ECG signals are picked up by sensors located on the edge, and then uploaded to the remote cloud servers. ECG interpretation is performed for the collected ECGs in the cloud servers and the analysis results can be made instantly available to the patients as well as their healthcare providers.In this dissertation, we first examine the …
Wearable Sensor Gait Analysis For Fall Detection Using Deep Learning Methods, Haben Girmay Yhdego
Wearable Sensor Gait Analysis For Fall Detection Using Deep Learning Methods, Haben Girmay Yhdego
Electrical & Computer Engineering Theses & Dissertations
World Health Organization (WHO) data show that around 684,000 people die from falls yearly, making it the second-highest mortality rate after traffic accidents [1]. Early detection of falls, followed by pneumatic protection, is one of the most effective means of ensuring the safety of the elderly. In light of the recent widespread adoption of wearable sensors, it has become increasingly critical that fall detection models are developed that can effectively process large and sequential sensor signal data. Several researchers have recently developed fall detection algorithms based on wearable sensor data. However, real-time fall detection remains challenging because of the wide …
Ai Applications On Planetary Rovers, Alexis David Pascual
Ai Applications On Planetary Rovers, Alexis David Pascual
Electronic Thesis and Dissertation Repository
The rise in the number of robotic missions to space is paving the way for the use of artificial intelligence and machine learning in the autonomy and augmentation of rover operations. For one, more rovers mean more images, and more images mean more data bandwidth required for downlinking as well as more mental bandwidth for analyzing the images. On the other hand, light-weight, low-powered microrover platforms are being developed to accommodate the drive for planetary exploration. As a result of the mass and power constraints, these microrover platforms will not carry typical navigational instruments like a stereocamera or a laser …
Design, Determination, And Evaluation Of Gender-Based Bias Mitigation Techniques For Music Recommender Systems, Sunny Shrestha
Design, Determination, And Evaluation Of Gender-Based Bias Mitigation Techniques For Music Recommender Systems, Sunny Shrestha
Electronic Theses and Dissertations
The majority of smartphone users engage with a recommender system on a daily basis. Many rely on these recommendations to make their next purchase, download the next game, listen to the new music or find the next healthcare provider. Although there are plenty of evidence backed research that demonstrates presence of gender bias in Machine Learning (ML) models like recommender systems, the issue is viewed as a frivolous cause that doesn’t merit much action. However, gender bias poses to effect more than half of the population as by default ML systems are designed to cater to a cisgender man. This …
Reference Frames In Human Sensory, Motor, And Cognitive Processing, Dongcheng He
Reference Frames In Human Sensory, Motor, And Cognitive Processing, Dongcheng He
Electronic Theses and Dissertations
Reference-frames, or coordinate systems, are used to express properties and relationships of objects in the environment. While the use of reference-frames is well understood in physical sciences, how the brain uses reference-frames remains a fundamental question. The goal of this dissertation is to reach a better understanding of reference-frames in human perceptual, motor, and cognitive processing. In the first project, we study reference-frames in perception and develop a model to explain the transition from egocentric (based on the observer) to exocentric (based outside the observer) reference-frames to account for the perception of relative motion. In a second project, we focus …
A Path Planning Framework For Multi-Agent Robotic Systems Based On Multivariate Skew-Normal Distributions, Peter Estephan
A Path Planning Framework For Multi-Agent Robotic Systems Based On Multivariate Skew-Normal Distributions, Peter Estephan
Theses, Dissertations and Capstones
This thesis presents a path planning framework for a very-large-scale robotic (VLSR) system in an known obstacle environment, where the time-varying distributions of agents are applied to represent the multi-agent robotic system (MARS). A novel family of the multivariate skew-normal (MVSN) distributions is proposed based on the Bernoulli random field (BRF) referred to as the Bernoulli-random-field based skew-normal (BRF-SN) distribution. The proposed distributions are applied to model the agents’ distributions in an obstacle-deployed environment, where the obstacle effect is represented by a skew function and separated from the no-obstacle agents’ distributions. First, the obstacle layout is represented by a Hilbert …
Effects Of Morphology On Genetic Assimilation Of Learned Behavior, Natalie L. Tolley
Effects Of Morphology On Genetic Assimilation Of Learned Behavior, Natalie L. Tolley
Graduate College Dissertations and Theses
The Baldwin effect is an evolutionary theory regarding the assimilation of ontogenetic changes into a population's genome via selection pressure to entrench beneficial phenotypes discovered through learning. In evolutionary computation, the incorporation of learning into non-embodied agents allows them to navigate otherwise rough fitness landscapes by allowing for local exploration at particular points in that landscape. Prior work investigating the specific mechanisms by which learned behavior is genetically assimilated is almost entirely limited to non-situated, non-embodied simulations such as bitstring manipulation. However, recent research has demonstrated that genetic assimilation can be observed in embodied agents. Learning more about the ways …
Assessing The Performance Of A Particle Swarm Optimization Mobility Algorithm In A Hybrid Wi-Fi/Lora Flying Ad Hoc Network, William David Paredes
Assessing The Performance Of A Particle Swarm Optimization Mobility Algorithm In A Hybrid Wi-Fi/Lora Flying Ad Hoc Network, William David Paredes
UNF Graduate Theses and Dissertations
Research on Flying Ad-Hoc Networks (FANETs) has increased due to the availability of Unmanned Aerial Vehicles (UAVs) and the electronic components that control and connect them. Many applications, such as 3D mapping, construction inspection, or emergency response operations could benefit from an application and adaptation of swarm intelligence-based deployments of multiple UAVs. Such groups of cooperating UAVs, through the use of local rules, could be seen as network nodes establishing an ad-hoc network for communication purposes.
One FANET application is to provide communication coverage over an area where communication infrastructure is unavailable. A crucial part of a FANET implementation is …