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Title: Self-Contained Soft Robotic Jellyfish with Water-Filled Bending Actuators and Positional Feedback Control.
Creator: Frame, Jennifer, Engeberg, Erik, Florida Atlantic University, College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
Abstract/Description: This thesis concerns the design, construction, control, and testing of a novel self-contained soft robotic vehicle; the JenniFish is a free-swimming jellyfish-like soft robot that could be adapted for a variety of uses, including: low frequency, low power sensing applications; swarm robotics; a STEM classroom learning resource; etc. The final vehicle design contains eight PneuNet-type actuators radially situated around a 3D printed electronics canister. These propel the vehicle when inflated...
Show moreThis thesis concerns the design, construction, control, and testing of a novel self-contained soft robotic vehicle; the JenniFish is a free-swimming jellyfish-like soft robot that could be adapted for a variety of uses, including: low frequency, low power sensing applications; swarm robotics; a STEM classroom learning resource; etc. The final vehicle design contains eight PneuNet-type actuators radially situated around a 3D printed electronics canister. These propel the vehicle when inflated with water from its surroundings by impeller pumps; since the actuators are connected in two neighboring groups of four, the JenniFish has bi-directional movement capabilities. Imbedded resistive flex sensors provide actuator position to the vehicle’s PD controller. Other onboard sensors include an IMU and an external temperature sensor. Quantitative constrained load cell tests, both in-line and bending, as well as qualitative free-swimming video tests were conducted to find baseline vehicle performance capabilities. Collected metrics compare well with existing robotic jellyfish.
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Date Issued: 2016
PURL: http://purl.flvc.org/fau/fd/FA00004656, http://purl.flvc.org/fau/fd/FA00004656
Subject Headings: Adaptive control systems, Artificial intelligence, Autonomous robots, Computational intelligence, Robotics
Format: Document (PDF)

Title: AN APPROACH USING AFFECTIVE COMPUTING TO PREDICT INTERACTION QUALITY FROM CONVERSATIONS.
Creator: Matic, Richard N., Maniaci, Michael, Florida Atlantic University, Department of Psychology, Charles E. Schmidt College of Science
Abstract/Description: John Gottman’s mathematical models have been shown to accurately predict a couple’s style of interaction using only the sentiments found in the couple’s conversations. I derived speaker sentiment slopes from 151 recorded dyadic audio conversations from the IEMOCAP dataset through an IBM Watson emotion recognition pipeline and assessed its accuracy as input for a Gottman model by comparing the cumulative speaker sentiment slope for each conversation produced from predicted emotion codes to...
Show moreJohn Gottman’s mathematical models have been shown to accurately predict a couple’s style of interaction using only the sentiments found in the couple’s conversations. I derived speaker sentiment slopes from 151 recorded dyadic audio conversations from the IEMOCAP dataset through an IBM Watson emotion recognition pipeline and assessed its accuracy as input for a Gottman model by comparing the cumulative speaker sentiment slope for each conversation produced from predicted emotion codes to that produced from groundtruth codes provided by IEMOCAP. Watson produced sentiment slopes strongly correlated with those produced by groundtruth emotion codes. An abbreviated pipeline was also assessed consisting just of the Watson textual emotion recognition model using IEMOCAP’s human transcriptions as input. It produced predicted sentiment slopes very strongly correlated with those produced by groundtruth. The research demonstrated that artificial intelligence has potential to be used to predict interaction quality from short samples of conversational data.
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Date Issued: 2022
PURL: http://purl.flvc.org/fau/fd/FA00014023
Subject Headings: Affective Computing, Emotion recognition, Artificial intelligence
Format: Document (PDF)

Title: Generating narratives: a pattern language.
Creator: Greene, Samuel., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: In order to facilitate the development, discussion, and advancement of the relatively new subfield of Artificial Intelligence focused on generating narrative content, the author has developed a pattern language for generating narratives, along with a new categorization framework for narrative generation systems. An emphasis and focus is placed on generating the Fabula of the story (the ordered sequence of events that make up the plot). Approaches to narrative generation are classified into...
Show moreIn order to facilitate the development, discussion, and advancement of the relatively new subfield of Artificial Intelligence focused on generating narrative content, the author has developed a pattern language for generating narratives, along with a new categorization framework for narrative generation systems. An emphasis and focus is placed on generating the Fabula of the story (the ordered sequence of events that make up the plot). Approaches to narrative generation are classified into one of three categories, and a pattern is presented for each approach. Enhancement patterns that can be used in conjunction with one of the core patterns are also identified. In total, nine patterns are identified - three core narratology patterns, four Fabula patterns, and two extension patterns. These patterns will be very useful to software architects designing a new generation of narrative generation systems.
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Date Issued: 2012
PURL: http://purl.flvc.org/FAU/3355559
Subject Headings: Computational intelligence, Pattern recognition systems, Computer vision, Artificial intelligence, Image processing, Digital techiques
Format: Document (PDF)

Title: An Ant Inspired Dynamic Traffic Assignment for VANETs: Early Notification of Traffic Congestion and Traffic Incidents.
Creator: Arellano, Wilmer, Mahgoub, Imad, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Vehicular Ad hoc NETworks (VANETs) are a subclass of Mobile Ad hoc NETworks and represent a relatively new and very active field of research. VANETs will enable in the near future applications that will dramatically improve roadway safety and traffic efficiency. There is a need to increase traffic efficiency as the gap between the traveled and the physical lane miles keeps increasing. The Dynamic Traffic Assignment problem tries to dynamically distribute vehicles efficiently on the road...
Show moreVehicular Ad hoc NETworks (VANETs) are a subclass of Mobile Ad hoc NETworks and represent a relatively new and very active field of research. VANETs will enable in the near future applications that will dramatically improve roadway safety and traffic efficiency. There is a need to increase traffic efficiency as the gap between the traveled and the physical lane miles keeps increasing. The Dynamic Traffic Assignment problem tries to dynamically distribute vehicles efficiently on the road network and in accordance with their origins and destinations. We present a novel dynamic decentralized and infrastructure-less algorithm to alleviate traffic congestions on road networks and to fill the void left by current algorithms which are either static, centralized, or require infrastructure. The algorithm follows an online approach that seeks stochastic user equilibrium and assigns traffic as it evolves in real time, without prior knowledge of the traffic demand or the schedule of the cars that will enter the road network in the future. The Reverse Online Algorithm for the Dynamic Traffic Assignment inspired by Ant Colony Optimization for VANETs follows a metaheuristic approach that uses reports from other vehicles to update the vehicle’s perceived view of the road network and change route if necessary. To alleviate the broadcast storm spontaneous clusters are created around traffic incidents and a threshold system based on the level of congestion is used to limit the number of incidents to be reported. Simulation results for the algorithm show a great improvement on travel time over routing based on shortest distance. As the VANET transceivers have a limited range, that would limit messages to reach at most 1,000 meters, we present a modified version of this algorithm that uses a rebroadcasting scheme. This rebroadcasting scheme has been successfully tested on roadways with segments of up to 4,000 meters. This is accomplished for the case of traffic flowing in a single direction on the roads. It is anticipated that future simulations will show further improvement when traffic in the other direction is introduced and vehicles travelling in that direction are allowed to use a store carry and forward mechanism.
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Date Issued: 2016
PURL: http://purl.flvc.org/fau/fd/FA00004566, http://purl.flvc.org/fau/fd/FA00004566
Subject Headings: Vehicular ad hoc networks (Computer networks)--Technological innovations., Routing protocols (Computer network protocols), Artificial intelligence., Intelligent transportation systems., Intelligent control systems., Mobile computing., Computer algorithms., Combinatorial optimization.
Format: Document (PDF)

Title: The cake is not a lie: narrative structure and aporia in Portal & Portal 2.
Creator: Copeland, Kimberly., Dorothy F. Schmidt College of Arts and Letters, School of Communication and Multimedia Studies
Abstract/Description: As puzzle-driven, character based games, Portal and Portal 2, developed by the Valve Corporation, are not only pioneering in their use of narrative, but they also revolutionize the function of aporia. This thesis explores the role of aporia and use of the narrative in the two video games. It will be argued that the games possess a rigid narrative structure, but while the narrative serves as a peripheral construction, there are other structures that contribute to the experience of gameplay....
Show moreAs puzzle-driven, character based games, Portal and Portal 2, developed by the Valve Corporation, are not only pioneering in their use of narrative, but they also revolutionize the function of aporia. This thesis explores the role of aporia and use of the narrative in the two video games. It will be argued that the games possess a rigid narrative structure, but while the narrative serves as a peripheral construction, there are other structures that contribute to the experience of gameplay. The research aims to determine how the games adapt narrative and use it in combination with other elements to move beyond simple play and storytelling. As video games become more widely studied in academia, it is important that they merit and maintain standing ; Portal and Portal 2 not only provide a rich gameplay experience, but also offer a particular interaction not found in other texts.
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Date Issued: 2012
PURL: http://purl.flvc.org/FAU/3358551
Subject Headings: Computer games, Social aspects, Computer games, Design and construction, Artificial intelligence, Narration (Rhetoric)
Format: Document (PDF)

Title: Analysis of machine learning algorithms on bioinformatics data of varying quality.
Creator: Shanab, Ahmad Abu, Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: One of the main applications of machine learning in bioinformatics is the construction of classification models which can accurately classify new instances using information gained from previous instances. With the help of machine learning algorithms (such as supervised classification and gene selection) new meaningful knowledge can be extracted from bioinformatics datasets that can help in disease diagnosis and prognosis as well as in prescribing the right treatment for a disease. One...
Show moreOne of the main applications of machine learning in bioinformatics is the construction of classification models which can accurately classify new instances using information gained from previous instances. With the help of machine learning algorithms (such as supervised classification and gene selection) new meaningful knowledge can be extracted from bioinformatics datasets that can help in disease diagnosis and prognosis as well as in prescribing the right treatment for a disease. One particular challenge encountered when analyzing bioinformatics datasets is data noise, which refers to incorrect or missing values in datasets. Noise can be introduced as a result of experimental errors (e.g. faulty microarray chips, insufficient resolution, image corruption, and incorrect laboratory procedures), as well as other errors (errors during data processing, transfer, and/or mining). A special type of data noise called class noise, which occurs when an instance/example is mislabeled. Previous research showed that class noise has a detrimental impact on machine learning algorithms (e.g. worsened classification performance and unstable feature selection). In addition to data noise, gene expression datasets can suffer from the problems of high dimensionality (a very large feature space) and class imbalance (unequal distribution of instances between classes). As a result of these inherent problems, constructing accurate classification models becomes more challenging.
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Date Issued: 2015
PURL: http://purl.flvc.org./fau/fd/FA00004425, http://purl.flvc.org/fau/fd/FA00004425
Subject Headings: Artificial intelligence, Bioinformatics, Machine learning, System design, Theory of computation
Format: Document (PDF)

Title: Parallel Distributed Deep Learning on Cluster Computers.
Creator: Kennedy, Robert Kwan Lee, Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Deep Learning is an increasingly important subdomain of arti cial intelligence. Deep Learning architectures, arti cial neural networks characterized by having both a large breadth of neurons and a large depth of layers, bene ts from training on Big Data. The size and complexity of the model combined with the size of the training data makes the training procedure very computationally and temporally expensive. Accelerating the training procedure of Deep Learning using cluster computers faces...
Show moreDeep Learning is an increasingly important subdomain of arti cial intelligence. Deep Learning architectures, arti cial neural networks characterized by having both a large breadth of neurons and a large depth of layers, bene ts from training on Big Data. The size and complexity of the model combined with the size of the training data makes the training procedure very computationally and temporally expensive. Accelerating the training procedure of Deep Learning using cluster computers faces many challenges ranging from distributed optimizers to the large communication overhead speci c to a system with o the shelf networking components. In this thesis, we present a novel synchronous data parallel distributed Deep Learning implementation on HPCC Systems, a cluster computer system. We discuss research that has been conducted on the distribution and parallelization of Deep Learning, as well as the concerns relating to cluster environments. Additionally, we provide case studies that evaluate and validate our implementation.
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Date Issued: 2018
PURL: http://purl.flvc.org/fau/fd/FA00013080
Subject Headings: Deep learning., Neural networks (Computer science)., Artificial intelligence., Machine learning.
Format: Document (PDF)

Title: Machine learning techniques for alleviating inherent difficulties in bioinformatics data.
Creator: Dittman, David, Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: In response to the massive amounts of data that make up a large number of bioinformatics datasets, it has become increasingly necessary for researchers to use computers to aid them in their endeavors. With difficulties such as high dimensionality, class imbalance, noisy data, and difficult to learn class boundaries, being present within the data, bioinformatics datasets are a challenge to work with. One potential source of assistance is the domain of data mining and machine learning, a field...
Show moreIn response to the massive amounts of data that make up a large number of bioinformatics datasets, it has become increasingly necessary for researchers to use computers to aid them in their endeavors. With difficulties such as high dimensionality, class imbalance, noisy data, and difficult to learn class boundaries, being present within the data, bioinformatics datasets are a challenge to work with. One potential source of assistance is the domain of data mining and machine learning, a field which focuses on working with these large amounts of data and develops techniques to discover new trends and patterns that are hidden within the data and to increases the capability of researchers and practitioners to work with this data. Within this domain there are techniques designed to eliminate irrelevant or redundant features, balance the membership of the classes, handle errors found in the data, and build predictive models for future data.
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Date Issued: 2015
PURL: http://purl.flvc.org/fau/fd/FA00004362, http://purl.flvc.org/fau/fd/FA00004362
Subject Headings: Artificial intelligence, Bioinformatics, Machine learning, System design, Theory of computation
Format: Document (PDF)

Title: Artificial Intelligence Based Electrical Impedance Tomography for Local Tissue.
Creator: Rao, Manasa, Pandya, Abhijit S., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: This research aims at proposing the use of Electrical Impedance Tomography (EIT), a non-invasive technique that makes it possible to measure two or three dimensional impedance of living local tissue in a human body which is applied for medical diagnosis of diseases. In order to achieve this, electrodes are attached to the part of human body and an image of the conductivity or permittivity of living tissue is deduced from surface electrodes. In this thesis we have worked towards alleviating...
Show moreThis research aims at proposing the use of Electrical Impedance Tomography (EIT), a non-invasive technique that makes it possible to measure two or three dimensional impedance of living local tissue in a human body which is applied for medical diagnosis of diseases. In order to achieve this, electrodes are attached to the part of human body and an image of the conductivity or permittivity of living tissue is deduced from surface electrodes. In this thesis we have worked towards alleviating drawbacks of EIT such as estimating parameters by incorporating it in an electrode structure and determining a solution to spatial distribution of bio-impedance to a close proximity. We address the issue of initial parameter estimation and spatial resolution accuracy of an electrode structure by using an arrangement called "divided electrode" for measurement of bio-impedance in a cross section of a local tissue. Its capability is examined by computer simulations, where a distributed equivalent circuit is utilized as a model for the cross section tissue. Further, a novel hybrid model is derived which is a combination of artificial intelligence based gradient free optimization technique and numerical integration in order to estimate parameters. This arne! iorates the achievement of spatial resolution of equivalent circuit model to the closest accuracy.
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Date Issued: 2008
PURL: http://purl.flvc.org/fau/fd/FA00012544
Subject Headings: Electrical impedance tomography, Diagnostic imaging--Data processing, Computational intelligence
Format: Document (PDF)

Title: Performance analysis of back propagation algorithm using artificial neural networks.
Creator: Malladi, Sasikanth., Florida Atlantic University, Pandya, Abhijit S., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Backpropagation is a standard algorithm that is widely employed in many neural networks. Due to its wide acceptance and implementation, a standard benchmark for evaluating the performance of the algorithm is a handy tool for software design and development. The object of this thesis is to propose the use of the classic XOR problem for the performance evaluation of the backpropagation algorithm, with some variations on the input data sets. This thesis covers background work in this area and...
Show moreBackpropagation is a standard algorithm that is widely employed in many neural networks. Due to its wide acceptance and implementation, a standard benchmark for evaluating the performance of the algorithm is a handy tool for software design and development. The object of this thesis is to propose the use of the classic XOR problem for the performance evaluation of the backpropagation algorithm, with some variations on the input data sets. This thesis covers background work in this area and discusses the results obtained by other researchers. A series of test cases are then developed and run to perform the performance analysis of the backpropagation algorithm. As the performance of the networks depends strongly on the inputs, the effect of variation of the design parameters for the networks are evaluated and discussed.
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Date Issued: 2002
PURL: http://purl.flvc.org/fcla/dt/12961
Subject Headings: Back propagation (Artificial intelligence), Neural networks (Computer science)
Format: Document (PDF)

Title: ILLUMINATING CYBER THREATS FOR SMART CITIES: A DATA-DRIVEN APPROACH FOR CYBER ATTACK DETECTION WITH VISUAL CAPABILITIES.
Creator: Neshenko, Nataliia, Furht, Borko, Bou-Harb, Elias, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: A modern urban infrastructure no longer operates in isolation but instead leverages the latest technologies to collect, process, and distribute aggregated knowledge to improve the quality of the provided services and promote the efficiency of resource consumption. However, the ambiguity of ever-evolving cyber threats and their debilitating consequences introduce new barriers for decision-makers. Numerous techniques have been proposed to address the cyber misdemeanors against such critical...
Show moreA modern urban infrastructure no longer operates in isolation but instead leverages the latest technologies to collect, process, and distribute aggregated knowledge to improve the quality of the provided services and promote the efficiency of resource consumption. However, the ambiguity of ever-evolving cyber threats and their debilitating consequences introduce new barriers for decision-makers. Numerous techniques have been proposed to address the cyber misdemeanors against such critical realms and increase the accuracy of attack inference; however, they remain limited to detection algorithms omitting attack attribution and impact interpretation. The lack of the latter prompts the transition of these methods to operation difficult to impossible. In this dissertation, we first investigate the threat landscape of smart cities, survey and reveal the progress in data-driven methods for situational awareness and evaluate their effectiveness when addressing various cyber threats. Further, we propose an approach that integrates machine learning, the theory of belief functions, and dynamic visualization to complement available attack inference for ICS deployed in the realm of smart cities. Our framework offers an extensive scope of knowledge as opposed to solely evident indicators of malicious activity. It gives the cyber operators and digital investigators an effective tool to dynamically and visually interact, explore and analyze heterogeneous, complex data, and provide rich context information. Such an approach is envisioned to facilitate the cyber incident interpretation and support a timely evidence-based decision-making process.
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Date Issued: 2021
PURL: http://purl.flvc.org/fau/fd/FA00013813
Subject Headings: Smart cities, Cyber intelligence (Computer security), Visual analytics, Threats
Format: Document (PDF)

Title: OPTIMIZED DEEP LEARNING ARCHITECTURES AND TECHNIQUES FOR EDGE AI.
Creator: Zaniolo, Luiz, Marques, Oge, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: The recent rise of artificial intelligence (AI) using deep learning networks allowed the development of automatic solutions for many tasks that, in the past, were seen as impossible to be performed by a machine. However, deep learning models are getting larger, need significant processing power to train, and powerful machines to use it. As deep learning applications become ubiquitous, another trend is taking place: the growing use of edge devices. This dissertation addresses selected...
Show moreThe recent rise of artificial intelligence (AI) using deep learning networks allowed the development of automatic solutions for many tasks that, in the past, were seen as impossible to be performed by a machine. However, deep learning models are getting larger, need significant processing power to train, and powerful machines to use it. As deep learning applications become ubiquitous, another trend is taking place: the growing use of edge devices. This dissertation addresses selected technical issues associated with edge AI, proposes novel solutions to them, and demonstrates the effectiveness of the proposed approaches. The technical contributions of this dissertation include: (i) architectural optimizations to deep neural networks, particularly the use of patterned stride in convolutional neural networks used for image classification; (ii) use of weight quantization to reduce model size without hurting its accuracy; (iii) systematic evaluation of the impact of image imperfections on skin lesion classifiers' performance in the context of teledermatology; and (iv) a new approach for code prediction using natural language processing techniques, targeted at edge devices.
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Date Issued: 2021
PURL: http://purl.flvc.org/fau/fd/FA00013822
Subject Headings: Artificial intelligence, Deep learning (Machine learning), Neural networks (Computer science)
Format: Document (PDF)

Title: Using a cerebellar model arithmetic computer (CMAC) neural network to control an autonomous underwater vehicle.
Creator: Comoglio, Rick F., Florida Atlantic University, Pandya, Abhijit S.
Abstract/Description: The design of an Autonomous Undersea Vehicle (AUV) control system is a significant challenge in-light of the highly uncertain nature of the ocean environment together with partially known nonlinear vehicle dynamics. This thesis describes a Neural Network architecture called Cerebellar Model Arithmetic Computer (CMAC). CMAC is used to control a model of an Autonomous Underwater Vehicle. The AUV model consists of two input parameters, the rudder and stern plane deflections, controlling six...
Show moreThe design of an Autonomous Undersea Vehicle (AUV) control system is a significant challenge in-light of the highly uncertain nature of the ocean environment together with partially known nonlinear vehicle dynamics. This thesis describes a Neural Network architecture called Cerebellar Model Arithmetic Computer (CMAC). CMAC is used to control a model of an Autonomous Underwater Vehicle. The AUV model consists of two input parameters, the rudder and stern plane deflections, controlling six output parameters; forward velocity, vertical velocity, pitch angle, side velocity, roll angle, and yaw angle. Properties of CMAC and results of computer simulations for identification and control of the AUV model are presented.
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Date Issued: 1991
PURL: http://purl.flvc.org/fcla/dt/14762
Subject Headings: Neural networks (Computer science), Artificial intelligence, Submersibles--Automatic control
Format: Document (PDF)

Title: COMPARISON OF CLASSIFYING HUMAN ACTIONS FROM BIOLOGICAL MOTION WITH ARTIFICIAL NEURAL NETWORKS.
Creator: Wong, Rachel, Barenholtz, Elan, Florida Atlantic University, Department of Psychology, Charles E. Schmidt College of Science
Abstract/Description: The ability to recognize human actions is essential for individuals to navigate through their daily life. Biological motion is the primary mechanism people use to recognize actions quickly and efficiently, but their precision can vary. The development of Artificial Neural Networks (ANNs) has the potential to enhance the efficiency and effectiveness of accomplishing common human tasks, including action recognition. However, the performance of ANNs in action recognition depends on the type of...
Show moreThe ability to recognize human actions is essential for individuals to navigate through their daily life. Biological motion is the primary mechanism people use to recognize actions quickly and efficiently, but their precision can vary. The development of Artificial Neural Networks (ANNs) has the potential to enhance the efficiency and effectiveness of accomplishing common human tasks, including action recognition. However, the performance of ANNs in action recognition depends on the type of model used. This study aimed to improve the accuracy of ANNs in action classification by incorporating biological motion information into the input conditions. The study used the UCF Crime dataset, a dataset containing surveillance videos of normal and criminal activity, and extracted biological motion information with OpenPose, a pose estimation ANN. OpenPose adjusted to create four condition types using the biological motion information (image-only, image with biological motion, only biological motion, and coordinates only) and used either a 3-Dimensional Convolutional Neural Network (3D CNN) or a Gated Recurrent Unit (GRU) to classify the actions. Overall, the study found that including biological motion information in the input conditions led to higher accuracy regardless of the number of action categories in the dataset. Moreover, the GRU model using the 'coordinates only' condition had the best accuracy out of all the action classification models. These findings suggest that incorporating biological motion into input conditions and using numerical format input data can benefit the development of accurate action classification models using ANNs.
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Date Issued: 2023
PURL: http://purl.flvc.org/fau/fd/FA00014164
Subject Headings: Neural networks (Computer science), Human activity recognition, Artificial intelligence
Format: Document (PDF)

Title: AI COMPUTATION OF L1-NORM-ERROR PRINCIPAL COMPONENTS WITH APPLICATIONS TO TRAINING DATASET CURATION AND DETECTION OF CHANGE.
Creator: Varma, Kavita, Pados, Dimitris, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: The aim of this dissertation is to achieve a thorough understanding and develop an algorithmic framework for a crucial aspect of autonomous and artificial intelligence (AI) systems: Data Analysis. In the current era of AI and machine learning (ML), ”data” holds paramount importance. For effective learning tasks, it is essential to ensure that the training dataset is accurate and comprehensive. Additionally, during system operation, it is vital to identify and address faulty data to prevent...
Show moreThe aim of this dissertation is to achieve a thorough understanding and develop an algorithmic framework for a crucial aspect of autonomous and artificial intelligence (AI) systems: Data Analysis. In the current era of AI and machine learning (ML), ”data” holds paramount importance. For effective learning tasks, it is essential to ensure that the training dataset is accurate and comprehensive. Additionally, during system operation, it is vital to identify and address faulty data to prevent potentially catastrophic system failures. Our research in data analysis focuses on creating new mathematical theories and algorithms for outlier-resistant matrix decomposition using L1-norm principal component analysis (PCA). L1-norm PCA has demonstrated robustness against irregular data points and will be pivotal for future AI learning and autonomous system operations. This dissertation presents a comprehensive exploration of L1-norm techniques and their diverse applications. A summary of our contributions in this manuscript follows: Chapter 1 establishes the foundational mathematical notation and linear algebra concepts critical for the subsequent discussions, along with a review of the complexities of the current state-of-the-art in L1-norm matrix decomposition algorithms. In Chapter 2, we address the L1-norm error decomposition problem by introducing a novel method called ”Individual L1-norm-error Principal Component Computation by 3-layer Perceptron” (Perceptron L1 error). Extensive studies demonstrate the efficiency of this greedy L1-norm PC calculator.
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Date Issued: 2024
PURL: http://purl.flvc.org/fau/fd/FA00014460
Subject Headings: Artificial intelligence, Machine learning, Neural networks (Computer science), Data Analysis
Format: Document (PDF)

Title: Quantum Circuits for Cryptanalysis.
Creator: Amento, Brittanney Jaclyn, Steinwandt, Rainer, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Mathematical Sciences
Abstract/Description: Finite elds of the form F2m play an important role in coding theory and cryptography. We show that the choice of how to represent the elements of these elds can have a signi cant impact on the resource requirements for quantum arithmetic. In particular, we show how the Gaussian normal basis representations and \ghost-bit basis" representations can be used to implement inverters with a quantum circuit of depth O(mlog(m)). To the best of our knowledge, this is the rst construction with...
Show moreFinite elds of the form F2m play an important role in coding theory and cryptography. We show that the choice of how to represent the elements of these elds can have a signi cant impact on the resource requirements for quantum arithmetic. In particular, we show how the Gaussian normal basis representations and \ghost-bit basis" representations can be used to implement inverters with a quantum circuit of depth O(mlog(m)). To the best of our knowledge, this is the rst construction with subquadratic depth reported in the literature. Our quantum circuit for the computation of multiplicative inverses is based on the Itoh-Tsujii algorithm which exploits the property that, in a normal basis representation, squaring corresponds to a permutation of the coe cients. We give resource estimates for the resulting quantum circuit for inversion over binary elds F2m based on an elementary gate set that is useful for fault-tolerant implementation. Elliptic curves over nite elds F2m play a prominent role in modern cryptography. Published quantum algorithms dealing with such curves build on a short Weierstrass form in combination with a ne or projective coordinates. In this thesis we show that changing the curve representation allows a substantial reduction in the number of T-gates needed to implement the curve arithmetic. As a tool, we present a quantum circuit for computing multiplicative inverses in F2m in depth O(mlogm) using a polynomial basis representation, which may be of independent interest. Finally, we change our focus from the design of circuits which aim at attacking computational assumptions on asymmetric cryptographic algorithms to the design of a circuit attacking a symmetric cryptographic algorithm. We consider a block cipher, SERPENT, and our design of a quantum circuit implementing this cipher to be used for a key attack using Grover's algorithm as in [18]. This quantum circuit is essential for understanding the complexity of Grover's algorithm.
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Date Issued: 2016
PURL: http://purl.flvc.org/fau/fd/FA00004662, http://purl.flvc.org/fau/fd/FA00004662
Subject Headings: Artificial intelligence, Computer networks, Cryptography, Data encryption (Computer science), Finite fields (Algebra), Quantum theory
Format: Document (PDF)

Title: An Application of Artificial Neural Networks for Hand Grip Classification.
Creator: Gosine, Robbie R., Zhuang, Hanqi, Florida Atlantic University
Abstract/Description: The gripping action as performed by an average person is developed over their life and changes over time. The initial learning is based on trial and error and becomes a natural action which is modified as the physiology of the individual changes. Each grip type is a personal expression and as the grip changes over time to accommodate physiologically changes, it can be considered to be a grip-signature. lt is postulated that an ANN can deliver a classification mechanism that is able to make...
Show moreThe gripping action as performed by an average person is developed over their life and changes over time. The initial learning is based on trial and error and becomes a natural action which is modified as the physiology of the individual changes. Each grip type is a personal expression and as the grip changes over time to accommodate physiologically changes, it can be considered to be a grip-signature. lt is postulated that an ANN can deliver a classification mechanism that is able to make sense of the varying gripping inputs that are linearly inseparable and uniquely attributed to user physiology. Succinctly, in this design, the stifnulus is characterized by a voltage that represents the applied force in a grip. This signature of forces is then used to train an ANN to recognize the grip that produced the signature, the ANN in turn is used to successfully classify three unique states of grip-signatures collected from the gripping action of various individuals as they hold, lift and crush a paper coffee-cup. A comparative study is done for three types of classification: K-Means, Backpropagation Feedforward Neural Networks and Recurrent Neural Networks, with recommendations made in selecting more effective classification methods.
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Date Issued: 2007
PURL: http://purl.flvc.org/fau/fd/FA00012522
Subject Headings: Neural networks (Computer science), Pattern perception, Back propagation (Artificial intelligence), Multivariate analysis (Computer programs)
Format: Document (PDF)

Title: Artificial neural network prediction of alluvial river geometry.
Creator: Hoffman, David Carl., Florida Atlantic University, Scarlatos, Panagiotis (Pete) D.
Abstract/Description: An artificial neural network is used to predict the stable geometry of alluvial rivers. This knowledge is useful for the design of new channels or modification of natural rivers. Given inputs of river discharge, slope and mean particle size, an artificial neural network is trained to predict the corresponding stable channel width and depth. The network is trained using data from several alluvial canals and rivers. Various factors including training set size and composition, number of hidden...
Show moreAn artificial neural network is used to predict the stable geometry of alluvial rivers. This knowledge is useful for the design of new channels or modification of natural rivers. Given inputs of river discharge, slope and mean particle size, an artificial neural network is trained to predict the corresponding stable channel width and depth. The network is trained using data from several alluvial canals and rivers. Various factors including training set size and composition, number of hidden layer nodes, activation function type, and data scaling method are analyzed as variables affecting network performance. These factors are studied to determine impacts on network accuracy and generalizing ability.
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Date Issued: 1995
PURL: http://purl.flvc.org/fcla/dt/15179
Subject Headings: Alluvial streams, Neural networks (Computer science), Back propagation (Artificial intelligence), Sediment transport--Computer programs
Format: Document (PDF)

Title: Reliable Vehicle-to-Vehicle Weighted Localization in Vehicular Networks.
Creator: Altoaimy, Lina, Mahgoub, Imad, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Vehicular Ad Hoc Network (VANET) supports wireless communication among vehicles using vehicle-to-vehicle (V2V) communication and between vehicles and infrastructure using vehicle-to-infrastructure (V2I) communication. This communication can be utilized to allow the distribution of safety and non-safety messages in the network. VANET supports a wide range of applications which rely on the messages exchanged within the network. Such applications will enhance the drivers' consciousness and...
Show moreVehicular Ad Hoc Network (VANET) supports wireless communication among vehicles using vehicle-to-vehicle (V2V) communication and between vehicles and infrastructure using vehicle-to-infrastructure (V2I) communication. This communication can be utilized to allow the distribution of safety and non-safety messages in the network. VANET supports a wide range of applications which rely on the messages exchanged within the network. Such applications will enhance the drivers' consciousness and improve their driving experience. However, the efficiency of these applications depends on the availability of vehicles real-time location information. A number of methods have been proposed to fulfill this requirement. However, designing a V2V-based localization method is challenged by the high mobility and dynamic topology of VANET and the interference noise due to objects and buildings. Currently, vehicle localization is based on GPS technology, which is not always reliable. Therefore, utilizing V2V communication in VANET can enhance the GPS positioning. With V2V-based localization, vehicles can determine their locations by exchanging mobility data among neighboring vehicles. In this research work, we address the above challenges and design a realistic V2V-based localization method that extends the centroid localization (CL) by assigning a weight value to each neighboring vehicle. This weight value is obtained using a weighting function that utilizes the following factors: 1) link quality distance between the neighboring vehicles 2) heading information and 3) map information. We also use fuzzy logic to model neighboring vehicles' weight values. Due to the sensitivity and importance of the exchanged information, it is very critical to ensure its integrity and reliability. Therefore, in this work, we present the design and the integration of a mobility data verification component into the proposed localization method, so that only verified data from trusted neighboring vehicles are considered. We also use subjective logic to design a trust management system to evaluate the trustworthiness of neighboring vehicles based on the formulated subjective opinions. Extensive experimental work is conducted using simulation programs to evaluate the performance of the proposed methods. The results show improvement on the location accuracy for varying vehicle densities and transmission ranges as well as in the presence of malicious/untrusted neighboring vehicles.
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Date Issued: 2016
PURL: http://purl.flvc.org/fau/fd/FA00004564, http://purl.flvc.org/fau/fd/FA00004564
Subject Headings: Vehicular ad hoc networks (Computer networks)--Mathematical models., Computer communication systems., Wireless communication systems., Routing (Computer network management), Intelligent transportation systems., Intelligent control systems.
Format: Document (PDF)

Title: Science fiction girlfriends transgender politics and US science fiction television, 1990–present.
Creator: Cava, Peter, Scodari, Christine, Florida Atlantic University, Dorothy F. Schmidt College of Arts and Letters, School of Communication and Multimedia Studies
Abstract/Description: The 1990s ushered in what historian Susan Stryker describes as “a tremendous burst of new transgender activism” in the United States. Concomitantly, the success of Star Trek: The Next Generation led to a renaissance of US science fiction television. This dissertation asks, what is the relation between transgender (trans) politics and US science fiction (sf) television from 1990 to the present? The theoretical framework is Trans/Elemental feminism, a new paradigm developed in the dissertation....
Show moreThe 1990s ushered in what historian Susan Stryker describes as “a tremendous burst of new transgender activism” in the United States. Concomitantly, the success of Star Trek: The Next Generation led to a renaissance of US science fiction television. This dissertation asks, what is the relation between transgender (trans) politics and US science fiction (sf) television from 1990 to the present? The theoretical framework is Trans/Elemental feminism, a new paradigm developed in the dissertation. The method is multiperspectival cultural studies, which considers how the production, content, and reception of media texts and their metatexts collectively determine the texts’ meaning. The data include trade articles about the television industry; published interviews with producers; 3,175 hours of televisual content; commercial advertisements for television programs; films, novels, and webisodes (Web episodes) in selected media franchises; professional reviews; online discussion boards; fan fiction; and fan videos.
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Date Issued: 2015
PURL: http://purl.flvc.org/fau/fd/FA00004435, http://purl.flvc.org/fau/fd/FA00004435
Subject Headings: Computer science., Computers., Artificial intelligence., Applied mathematics., Engineering mathematics., Statistical physics., Dynamical systems., Vibration., Dynamics., Computer Science.
Format: Document (PDF)

Artificial intelligence (20)	+ -
Neural networks (Computer science) (16)	+ -
Artificial intelligence. (9)	+ -
Computational intelligence (6)	+ -
Adaptive control systems (4)	+ -

Intelligent control systems (4)	+ -
Intelligent transportation systems (4)	+ -
Mobile computing. (4)	+ -
Pattern recognition systems (4)	+ -
Routing protocols (Computer network protocols) (4)	+ -
Back propagation (Artificial intelligence) (3)	+ -
Fuzzy logic (3)	+ -
Intelligent transportation systems. (3)	+ -
Machine learning (3)	+ -
System design (3)	+ -
Wireless communication systems. (3)	+ -
Bioinformatics (2)	+ -
Computer Science. (2)	+ -
Computer algorithms. (2)	+ -
Computer communication systems. (2)	+ -
Computer science. (2)	+ -
Computer vision (2)	+ -
Computer vision. (2)	+ -
Cyber intelligence (Computer security) (2)	+ -
Deep learning (Machine learning) (2)	+ -
Expert systems (Computer science) (2)	+ -
Genetic algorithms (2)	+ -
Image processing (2)	+ -
Intelligent control systems. (2)	+ -
Intelligent transportation systems--Mathematical models. (2)	+ -

Florida Atlantic University (51)	+ -
College of Engineering and Computer Science (40)	+ -
Department of Computer and Electrical Engineering and Computer Science (32)	+ -
Charles E. Schmidt College of Science (9)	+ -
Mahgoub, Imad (6)	+ -

Department of Ocean and Mechanical Engineering (5)	+ -
Khoshgoftaar, Taghi M. (4)	+ -
Pandya, Abhijit S. (4)	+ -
Barenholtz, Elan (3)	+ -
Department of Civil, Environmental and Geomatics Engineering (3)	+ -
Department of Psychology (3)	+ -
Dorothy F. Schmidt College of Arts and Letters (3)	+ -
Bou-Harb, Elias (2)	+ -
Center for Complex Systems and Brain Sciences (2)	+ -
Department of Mathematical Sciences (2)	+ -
Marques, Oge (2)	+ -
Neshenko, Nataliia (2)	+ -
School of Communication and Multimedia Studies (2)	+ -
Shankar, Ravi (2)	+ -
Stevanovic, Aleksandar (2)	+ -
Aalo, Valentine A. (1)	+ -
Abtahi, Homayoon (1)	+ -
Aguerrevere, Santiago Andres (1)	+ -
Al-Najada, Hamzah (1)	+ -
Altoaimy, Lina (1)	+ -
Alwakeel, Ahmed M. (1)	+ -
Amento, Brittanney Jaclyn (1)	+ -
Arellano, Wilmer (1)	+ -
Biswas, Debojit (1)	+ -
Bucak, Ayse Papatya (1)	+ -

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