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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: AN ARTIFICIAL INTELLIGENCE DRIVEN FRAMEWORK FOR MEDICAL IMAGING.
Creator: Sanghvi, Harshal A., Agarwal, Ankur, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: The major objective of this dissertation was to create a framework which is used for medical image diagnosis. In this diagnosis, we brought classification and diagnosing of diseases through an Artificial Intelligence based framework, including COVID, Pneumonia, and Melanoma cancer through medical images. The algorithm ran on multiple datasets. A model was developed which detected the medical images through changing hyper-parameters. The aim of this work was to apply the new transfer learning...
Show moreThe major objective of this dissertation was to create a framework which is used for medical image diagnosis. In this diagnosis, we brought classification and diagnosing of diseases through an Artificial Intelligence based framework, including COVID, Pneumonia, and Melanoma cancer through medical images. The algorithm ran on multiple datasets. A model was developed which detected the medical images through changing hyper-parameters. The aim of this work was to apply the new transfer learning framework DenseNet-201 for the diagnosis of the diseases and compare the results with the other deep learning models. The novelty in the proposed work was modifying the Dense Net 201 Algorithm, changing hyper parameters (source weights, Batch Size, Epochs, Architecture (number of neurons in hidden layer), learning rate and optimizer) to quantify the results. The novelty also included the training of the model by quantifying weights and in order to get more accuracy. During the data selection process, the data were cleaned, removing all the outliers. Data augmentation was used for the novel architecture to overcome overfitting and hence not producing false absurd results the computational performance was also observed. The proposed model results were also compared with the existing deep learning models and the algorithm was also tested on multiple datasets.
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Date Issued: 2023
PURL: http://purl.flvc.org/fau/fd/FA00014274
Subject Headings: Diagnostic imaging, Artificial intelligence, Deep learning (Machine learning)
Format: Document (PDF)

Title: DATA AUGMENTATION IN DEEP LEARNING.
Creator: Shorten, Connor, Khoshgoftaar, Taghi M., Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Recent successes of Deep Learning-powered AI are largely due to the trio of: algorithms, GPU computing, and big data. Data could take the shape of hospital records, satellite images, or the text in this paragraph. Deep Learning algorithms typically need massive collections of data before they can make reliable predictions. This limitation inspired investigation into a class of techniques referred to as Data Augmentation. Data Augmentation was originally developed as a set of label-preserving...
Show moreRecent successes of Deep Learning-powered AI are largely due to the trio of: algorithms, GPU computing, and big data. Data could take the shape of hospital records, satellite images, or the text in this paragraph. Deep Learning algorithms typically need massive collections of data before they can make reliable predictions. This limitation inspired investigation into a class of techniques referred to as Data Augmentation. Data Augmentation was originally developed as a set of label-preserving transformations used in order to simulate large datasets from small ones. For example, imagine developing a classifier that categorizes images as either a “cat” or a “dog”. After initial collection and labeling, there may only be 500 of these images, which are not enough data points to train a Deep Learning model. By transforming these images with Data Augmentations such as rotations and brightness modifications, more labeled images are available for model training and classification! In addition to applications for learning from limited labeled data, Data Augmentation can also be used for generalization testing. For example, we can augment the test set to set the visual style of images to “winter” and see how that impacts the performance of a stop sign detector. The dissertation begins with an overview of Deep Learning methods such as neural network architectures, gradient descent optimization, and generalization testing. Following an initial description of this technology, the dissertation explains overfitting. Overfitting is the crux of Deep Learning methods in which improvements to the training set do not lead to improvements on the testing set. To the rescue are Data Augmentation techniques, of which the Dissertation presents an overview of the augmentations used for both image and text data, as well as the promising potential of generative data augmentation with models such as ChatGPT. The dissertation then describes three major experimental works revolving around CIFAR-10 image classification, language modeling a novel dataset of Keras information, and patient survival classification from COVID-19 Electronic Health Records. The dissertation concludes with a reflection on the evolution of limitations of Deep Learning and directions for future work.
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Date Issued: 2023
PURL: http://purl.flvc.org/fau/fd/FA00014228
Subject Headings: Deep learning (Machine learning), Artificial intelligence, Data augmentation
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: Intelligent Supervisory Switching Control of Unmanned Surface Vehicles.
Creator: Bertaska, Ivan Rodrigues, von Ellenrieder, Karl, Florida Atlantic University, College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
Abstract/Description: novel approach to extend the decision-making capabilities of unmanned surface vehicles (USVs) is presented in this work. A multi-objective framework is described where separate controllers command different behaviors according to a desired trajectory. Three behaviors are examined – transiting, station-keeping and reversing. Given the desired trajectory, the vehicle is able to autonomously recognize which behavior best suits a portion of the trajectory. The USV uses a combination of a...
Show morenovel approach to extend the decision-making capabilities of unmanned surface vehicles (USVs) is presented in this work. A multi-objective framework is described where separate controllers command different behaviors according to a desired trajectory. Three behaviors are examined – transiting, station-keeping and reversing. Given the desired trajectory, the vehicle is able to autonomously recognize which behavior best suits a portion of the trajectory. The USV uses a combination of a supervisory switching control structure and a reinforcement learning algorithm to create a hybrid deliberative and reactive approach to switch between controllers and actions. Reinforcement learning provides a deliberative method to create a controller switching policy, while supervisory switching control acts reactively to instantaneous changes in the environment. Each action is restricted to one controller. Due to the nonlinear effects in these behaviors, two underactuated backstepping controllers and a fully-actuated backstepping controller are proposed for each transiting, reversing and station-keeping behavior, respectively, restricted to three degrees of freedom. Field experiments are presented to validate this system on the water with a physical USV platform under Sea State 1 conditions. Main outcomes of this work are that the proposed system provides better performance than a comparable gain-scheduled nonlinear controller in terms of an Integral of Absolute Error metric. Additionally, the deliberative component allows the system to identify dynamically infeasible trajectories and properly accommodate them.
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Date Issued: 2016
PURL: http://purl.flvc.org/fau/fd/FA00004671, http://purl.flvc.org/fau/fd/FA00004671
Subject Headings: Adaptive control systems, Artificial intelligence, Engineering mathematics, Intelligent control systems, Mechatronics, Nonlinear control theory, Transportation engineering
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: Application of artificial neural networks to deduce robust forecast performance in technoeconomic contexts.
Creator: Dabbas, Mohammad A., Neelakanta, Perambur S., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: The focus of this research is concerned with performing forecasting in technoeconomic contexts using a set of certain novel artificial neural networks (ANNs). Relevant efforts in general, entail the task of quantitatively estimating the details about the likelihood of future events (or unknown outcomes/effects) based on past and current information on the observed events (or known causes). Commensurate with the scope and objectives of the research, the specific topics addressed are as follows...
Show moreThe focus of this research is concerned with performing forecasting in technoeconomic contexts using a set of certain novel artificial neural networks (ANNs). Relevant efforts in general, entail the task of quantitatively estimating the details about the likelihood of future events (or unknown outcomes/effects) based on past and current information on the observed events (or known causes). Commensurate with the scope and objectives of the research, the specific topics addressed are as follows: A review on various methods adopted in technoeconomic forecasting and identified are econometric projections that can be used for forecasting via artificial neural network (ANN)-based simulations Developing and testing a compatible version of ANN designed to support a dynamic sigmoidal (squashing) function that morphs to the stochastical trends of the ANN input. As such, the network architecture gets pruned for reduced complexity across the span of iterative training schedule leading to the realization of a constructive artificial neural-network (CANN). Formulating a training schedule on an ANN with sparsely-sampled data via sparsity removal with cardinality enhancement procedure (through Nyquist sampling) and invoking statistical bootstrapping technique of resampling applied on the cardinality-improved subset so as to obtain an enhanced number of pseudoreplicates required as an adequate ensemble for robust training of the test ANN: The training and prediction exercises on the test ANN corresponds to optimally elucidating output predictions in the context of the technoeconomics framework of the power generation considered Prescribing a cone-of-error to alleviate over- or under-predictions toward prudently interpreting the results obtained; and, squeezing the cone-of-error to get a final cone-of-forecast rendering the forecast estimation/inference to be more precise Designing an ANN-based fuzzy inference engine (FIE) to ascertain the ex ante forecast details based on sparse sets of ex post data gathered in technoeconomic contexts - Involved thereof a novel method of .fusing fuzzy considerations and data sparsity.Lastly, summarizing the results with essential conclusions and identifying possible research items for future efforts identified as open-questions.
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Date Issued: 2014
PURL: http://purl.flvc.org/fau/fd/FA00004097, http://purl.flvc.org/fau/fd/FA00004097
Subject Headings: Artificial intelligence, Fuzzy systems, Long waves (Economics), Multisensor data fusion, Neural networks (Computer science) -- Mathematical models
Format: Document (PDF)

Title: An evaluation of machine learning algorithms for tweet sentiment analysis.
Creator: Prusa, Joseph D., Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Sentiment analysis of tweets is an application of mining Twitter, and is growing in popularity as a means of determining public opinion. Machine learning algorithms are used to perform sentiment analysis; however, data quality issues such as high dimensionality, class imbalance or noise may negatively impact classifier performance. Machine learning techniques exist for targeting these problems, but have not been applied to this domain, or have not been studied in detail. In this thesis we...
Show moreSentiment analysis of tweets is an application of mining Twitter, and is growing in popularity as a means of determining public opinion. Machine learning algorithms are used to perform sentiment analysis; however, data quality issues such as high dimensionality, class imbalance or noise may negatively impact classifier performance. Machine learning techniques exist for targeting these problems, but have not been applied to this domain, or have not been studied in detail. In this thesis we discuss research that has been conducted on tweet sentiment classification, its accompanying data concerns, and methods of addressing these concerns. We test the impact of feature selection, data sampling and ensemble techniques in an effort to improve classifier performance. We also evaluate the combination of feature selection and ensemble techniques and examine the effects of high dimensionality when combining multiple types of features. Additionally, we provide strategies and insights for potential avenues of future work.
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Date Issued: 2015
PURL: http://purl.flvc.org/fau/fd/FA00004460, http://purl.flvc.org/fau/fd/FA00004460
Subject Headings: Social media., Natural language processing (Computer science), Machine learning., Algorithms., Fuzzy expert systems., Artificial intelligence.
Format: Document (PDF)

Title: Analyzing the effect of fin morphology on the propulsive performance of an oscillating caudal fin using a robotic model.
Creator: Fischer, Tyler M., Curet, Oscar M., Florida Atlantic University, College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
Abstract/Description: A bio-inspired robotic underwater vessel was developed to test the effect of fin morphology on the propulsive performance of caudal fin. The robotic vessel, called The Bullet Fish, features a cylindrical body with a hemisphere at the forward section and a conical body at the stern. The vessel uses an oscillating caudal fin for thrust generation. The robotic vessel was tested in a recirculating flume for seven different caudal fins that range different bio-inspired forms and aspect ratios. The...
Show moreA bio-inspired robotic underwater vessel was developed to test the effect of fin morphology on the propulsive performance of caudal fin. The robotic vessel, called The Bullet Fish, features a cylindrical body with a hemisphere at the forward section and a conical body at the stern. The vessel uses an oscillating caudal fin for thrust generation. The robotic vessel was tested in a recirculating flume for seven different caudal fins that range different bio-inspired forms and aspect ratios. The experiments were performed at four different flow velocities and two flapping frequencies: 0.5 and 1.0 Hz. We found that for 1 Hz flapping frequency that in general as the aspect-ratio decreases both thrust production tends and power decrease resulting in a better propulsive efficiency for aspect ratios between 0.9 and 1.0. A less uniform trend was found for 0.5 Hz, where our data suggest multiple efficiency peaks. Additional experiments on the robotic model could help understand the propulsion aquatic locomotion and help the design of bio-inspired underwater vehicles.
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Date Issued: 2017
PURL: http://purl.flvc.org/fau/fd/FA00004944, http://purl.flvc.org/fau/fd/FA00004944
Subject Headings: Robotics., Robots--Kinematics., Artificial intelligence., Biomimetics., Bioinformatics., Stereotypes (Social psychology)
Format: Document (PDF)

Title: Contextual Modulation of Competitive Object Candidates in Early Object Recognition.
Creator: Islam, Mohammed F., Barenholtz, Elan, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Psychology
Abstract/Description: Object recognition is imperfect; often incomplete processing or deprived information yield misperceptions (i.e., misidentification) of objects. While quickly rectified and typically benign, instances of such errors can produce dangerous consequences (e.g., police shootings). Through a series of experiments, this study examined the competitive process of multiple object interpretations (candidates) during the earlier stages of object recognition process using a lexical decision task paradigm....
Show moreObject recognition is imperfect; often incomplete processing or deprived information yield misperceptions (i.e., misidentification) of objects. While quickly rectified and typically benign, instances of such errors can produce dangerous consequences (e.g., police shootings). Through a series of experiments, this study examined the competitive process of multiple object interpretations (candidates) during the earlier stages of object recognition process using a lexical decision task paradigm. Participants encountered low-pass filtered objects that were previously demonstrated to evoke multiple responses: a highly frequented interpretation (“primary candidates”) and a lesser frequented interpretation (“secondary candidates”). When objects were presented without context, no facilitative effects were observed for primary candidates. However, secondary candidates demonstrated evidence for being actively suppressed.
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Date Issued: 2017
PURL: http://purl.flvc.org/fau/fd/FA00004836, http://purl.flvc.org/fau/fd/FA00004836
Subject Headings: Pattern recognition systems., Information visualization., Artificial intelligence., Spatial analysis (Statistics), Latent structure analysis.
Format: Document (PDF)

Title: Development of a Flapping Actuator Based on Oscillating Electromagnetic Fields.
Creator: Spragg, Donald Oakley, Curet, Oscar M., Florida Atlantic University, College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
Abstract/Description: In this work a bio-inspired flapping actuator based on varied magnetic fields is developed, controlled and characterized. The actuator is sought to contribute to the toolbox of options for bio-mimetics research. The design is that of a neodymium bar magnet on one end of an armature which is moved by two air core electromagnetic coils in the same manner as agonist and antagonist muscle pairs function in biological systems. The other end of the armature is fitted to a rigid fin extending beyond...
Show moreIn this work a bio-inspired flapping actuator based on varied magnetic fields is developed, controlled and characterized. The actuator is sought to contribute to the toolbox of options for bio-mimetics research. The design is that of a neodymium bar magnet on one end of an armature which is moved by two air core electromagnetic coils in the same manner as agonist and antagonist muscle pairs function in biological systems. The other end of the armature is fitted to a rigid fin extending beyond the streamline enclosure body to produce propulsion. A series of tests in still water were performed to measure the kinematics and propulsive force for different control schemes including the effect of adding antagonistic resistance to the control schemes. Control methods based on armature position and based on setpoint error were tested and antagonist force was found to increase consistency of control of the systems in certain cases.
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Date Issued: 2016
PURL: http://purl.flvc.org/fau/fd/FA00004699, http://purl.flvc.org/fau/fd/FA00004699
Subject Headings: Actuators -- Materials, Artificial intelligence -- Biological applications, Biomimetics, Biophysics, Natural computation, Robotics, Robots -- Kinematics
Format: Document (PDF)

Title: Dosimetric comparison of inverse planning by simulated annealing (IPSA) and dose points optimized treatment plans in high dose rate (HDR) brachytherapy of skin lesions using Freiburg flap applicator.
Creator: Ghebremichael, Bereket Tewolde, Ouhib, Zoubir, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Physics
Abstract/Description: A detailed dosimetric comparison between Inverse Planning by Simulated Annealing (IPSA) and Dose Points (DP) optimized treatment plans has been performed for High Dose Rate (HDR) brachytherapy of skin lesions using Freiburg Flap applicator in order to find out whether or not IPSA offers better clinical dosimetric outcomes for lesions categorized into four different curvatures. Without compromising target coverage, IPSA reduced the volume of Planning Target Volume (lesion) that received at...
Show moreA detailed dosimetric comparison between Inverse Planning by Simulated Annealing (IPSA) and Dose Points (DP) optimized treatment plans has been performed for High Dose Rate (HDR) brachytherapy of skin lesions using Freiburg Flap applicator in order to find out whether or not IPSA offers better clinical dosimetric outcomes for lesions categorized into four different curvatures. Without compromising target coverage, IPSA reduced the volume of Planning Target Volume (lesion) that received at least 125% of the prescription dose on average by 41%. It also reduced the volume of the healthy skin surrounding the lesion that receives at least 100% of the prescription dose on average by 42%. IPSA did not show any advantage over DP in sparing normal structures underlying the lesions treated. Although DP optimization algorithm has been regularly used at Lynn Cancer Institute for HDR brachytherapy of skin lesions, recent upgrades in IPSA software have made IPSA more amenable to rapid treatment planning and therefore IPSA can be used either in place of DP or as its alternative.
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Date Issued: 2014
PURL: http://purl.flvc.org/fau/fd/FA00004285
Subject Headings: Artificial intelligence -- Medical applications, Cancer -- Treatment, Computational intellingence, Imaging systems in medicine, Medical physics
Format: Document (PDF)

Title: Activity analysis and detection of falling and repetitive motion.
Creator: Carryl, Clyde, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: This thesis examines the use of motion detection and analysis systems to detect falls and repetitive motion patterns of at-risk individuals. Three classes of motion are examined: Activities of daily living (ADL), falls, and repetitive motion. This research exposes a simple relationship between ADL and non-ADL movement, and shows how to use Principal Component Analysis and a kNN classifier to tell the 2 classes of motion apart with 100% sensitivity and specificity. It also identifies a more...
Show moreThis thesis examines the use of motion detection and analysis systems to detect falls and repetitive motion patterns of at-risk individuals. Three classes of motion are examined: Activities of daily living (ADL), falls, and repetitive motion. This research exposes a simple relationship between ADL and non-ADL movement, and shows how to use Principal Component Analysis and a kNN classifier to tell the 2 classes of motion apart with 100% sensitivity and specificity. It also identifies a more complex relationship between falls and repetitive motion, which both produce bodily accelerations exceeding 3G but differ with regard to their periodicity. This simplifies the classification problem of falls versus repetitive motion when taking into account that their data representations are similar except that repetitive motion displays a high degree of periodicity as compared to falls.
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Date Issued: 2013
PURL: http://purl.flvc.org/FAU/3360774
Subject Headings: Perpetual-motion processes, Human locomotion, Neural networks (Computer science), Artificial intelligence
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: Statistical and Entropy Considerations for Ultrasound Tissue Characterization.
Creator: Navumenka, Khrystsina, Aalo, Valentine A., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Modern cancerous tumor diagnostics is nearly impossible without invasive methods, such as biopsy, that may require involved surgical procedures. In recent years some work has been done to develop alternative non-invasive methods of medical diagnostics. For this purpose, the data obtained from an ultrasound image of the body crosssection, has been analyzed using statistical models, including Rayleigh, Rice, Nakagami, and K statistical distributions. The homodyned-K (H-K) distribution has been...
Show moreModern cancerous tumor diagnostics is nearly impossible without invasive methods, such as biopsy, that may require involved surgical procedures. In recent years some work has been done to develop alternative non-invasive methods of medical diagnostics. For this purpose, the data obtained from an ultrasound image of the body crosssection, has been analyzed using statistical models, including Rayleigh, Rice, Nakagami, and K statistical distributions. The homodyned-K (H-K) distribution has been found to be a good statistical tool to analyze the envelope and/or the intensity of backscattered signal in ultrasound tissue characterization. However, its use has usually been limited due to the fact that its probability density function (PDF) is not available in closed-form. In this work we present a novel closed-form representation for the H-K distribution. In addition, we propose using the first order approximation of the H-K distribution, the I-K distribution that has a closed-form, for the ultrasound tissue characterization applications. More specifically, we show that some tissue conditions that cause the backscattered signal to have low effective density values, can be successfully modeled by the I-K PDF. We introduce the concept of using H-K PDF-based and I-K PDF-based entropies as additional tools for characterization of ultrasonic breast tissue images. The entropy may be used as a goodness of fit measure that allows to select a better-fitting statistical model for a specific data set. In addition, the values of the entropies as well as the values of the statistical distribution parameters, allow for more accurate classification of tumors.
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Date Issued: 2017
PURL: http://purl.flvc.org/fau/fd/FA00004922, http://purl.flvc.org/fau/fd/FA00004922
Subject Headings: Ultrasonics in medicine., Artificial intelligence., Computer vision in medicine., Diagnostic ultrasonic imaging., Bioinformatics.
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: Sparse Coding and Compressed Sensing: Locally Competitive Algorithms and Random Projections.
Creator: Hahn, William E., Barenholtz, Elan, Florida Atlantic University, Charles E. Schmidt College of Science, Center for Complex Systems and Brain Sciences
Abstract/Description: For an 8-bit grayscale image patch of size n x n, the number of distinguishable signals is 256(n2). Natural images (e.g.,photographs of a natural scene) comprise a very small subset of these possible signals. Traditional image and video processing relies on band-limited or low-pass signal models. In contrast, we will explore the observation that most signals of interest are sparse, i.e. in a particular basis most of the expansion coefficients will be zero. Recent developments in sparse...
Show moreFor an 8-bit grayscale image patch of size n x n, the number of distinguishable signals is 256(n2). Natural images (e.g.,photographs of a natural scene) comprise a very small subset of these possible signals. Traditional image and video processing relies on band-limited or low-pass signal models. In contrast, we will explore the observation that most signals of interest are sparse, i.e. in a particular basis most of the expansion coefficients will be zero. Recent developments in sparse modeling and L1 optimization have allowed for extraordinary applications such as the single pixel camera, as well as computer vision systems that can exceed human performance. Here we present a novel neural network architecture combining a sparse filter model and locally competitive algorithms (LCAs), and demonstrate the networks ability to classify human actions from video. Sparse filtering is an unsupervised feature learning algorithm designed to optimize the sparsity of the feature distribution directly without having the need to model the data distribution. LCAs are defined by a system of di↵erential equations where the initial conditions define an optimization problem and the dynamics converge to a sparse decomposition of the input vector. We applied this architecture to train a classifier on categories of motion in human action videos. Inputs to the network were small 3D patches taken from frame di↵erences in the videos. Dictionaries were derived for each action class and then activation levels for each dictionary were assessed during reconstruction of a novel test patch. We discuss how this sparse modeling approach provides a natural framework for multi-sensory and multimodal data processing including RGB video, RGBD video, hyper-spectral video, and stereo audio/video streams.
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Date Issued: 2016
PURL: http://purl.flvc.org/fau/fd/FA00004713, http://purl.flvc.org/fau/fd/FA00004713
Subject Headings: Artificial intelligence, Expert systems (Computer science), Image processing -- Digital techniques -- Mathematics, Sparse matrices
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: Brain Computer Interface And Neuroprosthetics.
Creator: Calderon, Rodrigo, Morgera, Salvatore D., Florida Atlantic University
Abstract/Description: For many years people have consider the possibility that brain activity might provide a new channel for communication between a person's brain and the external world. Brain Computer Interface allows humans to control electronic devices using only their thoughts. The goal of this project is to provide the users with a basic control of a prosthetic arm using the signal acquired by an Electroencephalogram (EEG). The main objective of the research is to demonstrate and provide a system that...
Show moreFor many years people have consider the possibility that brain activity might provide a new channel for communication between a person's brain and the external world. Brain Computer Interface allows humans to control electronic devices using only their thoughts. The goal of this project is to provide the users with a basic control of a prosthetic arm using the signal acquired by an Electroencephalogram (EEG). The main objective of the research is to demonstrate and provide a system that allows individuals to obtain control of the device with very little training and very few electrodes. The research includes the development of an elaborate signal-processing algorithm that uses an Artificial Neural Network to determine the intentions of the user and their translation into commands to operate the prosthetic arm.
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Date Issued: 2007
PURL: http://purl.flvc.org/fau/fd/FA00012509
Subject Headings: Neural networks (Computer science), Pattern recognition systems, Prosthesis--Technological innovations, Artificial intelligence
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)

Artificial intelligence (33)	+ -
Neural networks (Computer science) (13)	+ -
Artificial intelligence. (11)	+ -
Machine learning (6)	+ -
Deep learning (Machine learning) (4)	+ -

Robotics (4)	+ -
Adaptive control systems (3)	+ -
Back propagation (Artificial intelligence) (3)	+ -
Computational intelligence (3)	+ -
Pattern recognition systems (3)	+ -
System design (3)	+ -
Artificial intelligence -- Medical applications (2)	+ -
Bioinformatics (2)	+ -
Bioinformatics. (2)	+ -
Computer Science. (2)	+ -
Computer science. (2)	+ -
Computer vision. (2)	+ -
Fuzzy logic (2)	+ -
Healthcare (2)	+ -
Machine learning. (2)	+ -
Medical physics (2)	+ -
Mobile computing. (2)	+ -
Optical pattern recognition. (2)	+ -
Pattern recognition systems. (2)	+ -
Pavements--Skid resistance (2)	+ -
Reinforcement learning (2)	+ -
Theory of computation (2)	+ -
Actuators -- Materials (1)	+ -
Affective Computing (1)	+ -
Algorithms. (1)	+ -

Florida Atlantic University (54)	+ -
College of Engineering and Computer Science (36)	+ -
Department of Computer and Electrical Engineering and Computer Science (29)	+ -
Charles E. Schmidt College of Science (10)	+ -
Department of Ocean and Mechanical Engineering (7)	+ -

Dorothy F. Schmidt College of Arts and Letters (5)	+ -
Khoshgoftaar, Taghi M. (5)	+ -
Marques, Oge (5)	+ -
Barenholtz, Elan (4)	+ -
Department of Psychology (3)	+ -
Pandya, Abhijit S. (3)	+ -
Center for Complex Systems and Brain Sciences (2)	+ -
Curet, Oscar M. (2)	+ -
Department of English (2)	+ -
Department of Mathematical Sciences (2)	+ -
Department of Physics (2)	+ -
School of Communication and Multimedia Studies (2)	+ -
Shankar, Ravi (2)	+ -
Zhong, Xiangnan (2)	+ -
Aalo, Valentine A. (1)	+ -
Abd, Moaed A. (1)	+ -
Abtahi, Homayoon (1)	+ -
Agarwal, Ankur (1)	+ -
Aguerrevere, Santiago Andres (1)	+ -
Amento, Brittanney Jaclyn (1)	+ -
Arellano, Wilmer (1)	+ -
Bagdasarian, Jennifer Ling (1)	+ -
Bertaska, Ivan Rodrigues (1)	+ -
Bucak, Ayse Papatya (1)	+ -
Calderon, Rodrigo (1)	+ -

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