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Title: Neural network approach to Bayesian background modeling for video object segmentation.
Creator: Culibrk, Dubravko., Florida Atlantic University, Furht, Borko, Marques, Oge, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Object segmentation in a video sequence is an essential task in video processing and forms the foundation of content analysis, scene understanding, object-based video encoding (e.g. MPEG-4), various surveillance and 2D-to-pseudo-3D conversion applications. Popularization and availability of video sequences with increased spatial resolution requires development of new, more efficient algorithms for object detection and segmentation. This dissertation discusses a novel neural-network-based...
Show moreObject segmentation in a video sequence is an essential task in video processing and forms the foundation of content analysis, scene understanding, object-based video encoding (e.g. MPEG-4), various surveillance and 2D-to-pseudo-3D conversion applications. Popularization and availability of video sequences with increased spatial resolution requires development of new, more efficient algorithms for object detection and segmentation. This dissertation discusses a novel neural-network-based approach to background modeling for motion-based object segmentation in video sequences. In particular, we show how Probabilistic Neural Network (PNN) architecture can be extended to form an unsupervised Bayesian classifier for the domain of video object segmentation. The constructed Background Modeling Neural Network (BNN) is capable of efficiently handling segmentation in natural-scene sequences with complex background motion and changes in illumination. The weights of the proposed neural network serve as an exclusive model of the background and are temporally updated to reflect the observed background statistics. The proposed approach is designed to enable an efficient, highly-parallelized hardware implementation. Such a system would be able to achieve real-time segmentation of high-resolution image sequences.
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Date Issued: 2006
PURL: http://purl.flvc.org/fcla/dt/12214
Subject Headings: Neural networks (Computer science), Application software--Development, Data structures (Computer science), Bayesian field theory
Format: Document (PDF)

Title: Dynamic simulation and control of an autonomous surface vehicle.
Creator: VanZwieten, Tannen S., Florida Atlantic University, Leonessa, Alexander, College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
Abstract/Description: Autonomous Surface Vehicle (ASV) research and development is inspired by the navigating and communicatiog challenges of Autonomous Underwater Vehicles (AUVs). The development objective is to provide real time positioning of and communication with AUVs through the air-sea interface. Despite extensive research on AUVs, the ASV has had limited research. The NAVY's desire to make AUV's defense capabilities realizable adds to the project's appeal. Guidance and control play an integral part in the...
Show moreAutonomous Surface Vehicle (ASV) research and development is inspired by the navigating and communicatiog challenges of Autonomous Underwater Vehicles (AUVs). The development objective is to provide real time positioning of and communication with AUVs through the air-sea interface. Despite extensive research on AUVs, the ASV has had limited research. The NAVY's desire to make AUV's defense capabilities realizable adds to the project's appeal. Guidance and control play an integral part in the ASV's success, motivating this thesis work. The overall vehicle dynamics were modeled and numerically simulated for 3 DOF lateral motion. These are development tools for the testing and tuning of PID and adaptive control algorithms. The results show the adaptive controller to be advantageous in terms of tuning, robustness and tracking performances. It uses a single layer neural network that bypasses the need for information about the system's dynamic structure and characteristics and provides portability.
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Date Issued: 2003
PURL: http://purl.flvc.org/fcla/dt/13081
Subject Headings: Hydrodynamics, Adaptive control systems--Computer simulation, PID controllers--Computer simulation, Neural networks (Computer science)
Format: Document (PDF)

Title: Performance analysis of K-means algorithm and Kohonen networks.
Creator: Syed, Afzal A., Florida Atlantic University, Pandya, Abhijit S., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: K-means algorithm and Kohonen network possess self-organizing characteristics and are widely used in different fields currently. The factors that influence the behavior of K-means are the choice of initial cluster centers, number of cluster centers and the geometric properties of the input data. Kohonen networks have the ability of self-organization without any prior input about the number of clusters to be formed. This thesis looks into the performances of these algorithms and provides a...
Show moreK-means algorithm and Kohonen network possess self-organizing characteristics and are widely used in different fields currently. The factors that influence the behavior of K-means are the choice of initial cluster centers, number of cluster centers and the geometric properties of the input data. Kohonen networks have the ability of self-organization without any prior input about the number of clusters to be formed. This thesis looks into the performances of these algorithms and provides a unique way of combining them for better clustering. A series of benchmark problem sets are developed and run to obtain the performance analysis of the K-means algorithm and Kohonen networks. We have attempted to obtain the better of these two self-organizing algorithms by providing the same problem sets and extract the best results based on the users needs. A toolbox, which is user-friendly and written in C++ and VC++ is developed for applications on both images and feature data sets. The tool contains K-means algorithm and Kohonen networks code for clustering and pattern classification.
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Date Issued: 2004
PURL: http://purl.flvc.org/fcla/dt/13112
Subject Headings: Self-organizing maps, Neural networks (Computer science), Cluster analysis--Computer programs, Computer algorithms
Format: Document (PDF)

Title: An improved neural net-based approach for predicting software quality.
Creator: Guasti, Peter John., Florida Atlantic University, Khoshgoftaar, Taghi M., Pandya, Abhijit S.
Abstract/Description: Accurately predicting the quality of software is a major problem in any software development project. Software engineers develop models that provide early estimates of quality metrics which allow them to take action against emerging quality problems. Most often the predictive models are based upon multiple regression analysis which become unstable when certain data assumptions are not met. Since neural networks require no data assumptions, they are more appropriate for predicting software...
Show moreAccurately predicting the quality of software is a major problem in any software development project. Software engineers develop models that provide early estimates of quality metrics which allow them to take action against emerging quality problems. Most often the predictive models are based upon multiple regression analysis which become unstable when certain data assumptions are not met. Since neural networks require no data assumptions, they are more appropriate for predicting software quality. This study proposes an improved neural network architecture that significantly outperforms multiple regression and other neural network attempts at modeling software quality. This is demonstrated by applying this approach to several large commercial software systems. After developing neural network models, we develop regression models on the same data. We find that the neural network models surpass the regression models in terms of predictive quality on the data sets considered.
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Date Issued: 1995
PURL: http://purl.flvc.org/fcla/dt/15134
Subject Headings: Neural networks (Computer science), Computer software--Development, Computer software--Quality control, Software engineering
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: Determination of receptive fields in neural networks using Alopex.
Creator: Shah, Gaurang G., Florida Atlantic University, Pandya, Abhijit S., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: This research aims at proposing a model for visual pattern recognition inspired by the neural circuitry in the brain. Our attempt is to propose few modifications in the Alopex algorithm and try to use it for the calculations of the receptive fields of neurons in the trained network. We have developed a small-scale, four-layered neural network model for simple character recognition as well as complex image patterns, which can recognize the patterns transformed by affine conversion. Here Alopex...
Show moreThis research aims at proposing a model for visual pattern recognition inspired by the neural circuitry in the brain. Our attempt is to propose few modifications in the Alopex algorithm and try to use it for the calculations of the receptive fields of neurons in the trained network. We have developed a small-scale, four-layered neural network model for simple character recognition as well as complex image patterns, which can recognize the patterns transformed by affine conversion. Here Alopex algorithm is presented as an iterative and stochastic processing method, which was proposed for optimization of a given cost function over hundreds or thousands of iterations. In this case the receptive fields of the neurons in the output layers are obtained using the Alopex algorithm.
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Date Issued: 2005
PURL: http://purl.flvc.org/fcla/dt/13298
Subject Headings: Pattern recognition systems, Neural networks (Computer science), Computer algorithms, Neuroanatomy, Image processing
Format: Document (PDF)

Title: DETECTION AND CATEGORIZATION OF LUNG CANCER USING CONVOLUTIONAL NEURAL NETWORK.
Creator: Mostafanazhad, Shahabeddin Aslmarand, Muhammad, Wazir, Florida Atlantic University, Department of Physics, Charles E. Schmidt College of Science
Abstract/Description: Medical professionals use CT images to get information about the size, shape, and location of any lung nodules. This information will help radiologist and oncologist to identify the type of cancer and create a treatment plan. However, most of the time, the diagnosis regarding the types of lung cancer is error-prone and time-consuming. One way to address these problems is by using convolutional neural networks. In this Thesis, we developed a convolutional neural network that can detect...
Show moreMedical professionals use CT images to get information about the size, shape, and location of any lung nodules. This information will help radiologist and oncologist to identify the type of cancer and create a treatment plan. However, most of the time, the diagnosis regarding the types of lung cancer is error-prone and time-consuming. One way to address these problems is by using convolutional neural networks. In this Thesis, we developed a convolutional neural network that can detect abnormalities in lung CT scans and further categorize the abnormalities to benign, malignant adenocarcinoma and malignant squamous cell carcinoma. Our network is based on DenseNet, which utilizes dense connections between layers (dense blocks), so that all layers are connected. Because of all layers being connected, different layers can reuse features from previous layers which speeds up the process and make this network computationally efficient. To retrain this network we used CT images for 314 patients (over 1500 CT images) consistent of 42 Lung Adenocarcinoma and 78 Squamous Cell Carcinoma, 118 Non cancer and 76 benign were acquired from the National Lung Screening Trial (NLST). These images were divided to two categories of Training and Validation with 70% being training dataset and 30% as validation dataset. We trained our network on Training dataset and then checked the accuracy of our model using the validation dataset. Our model was able to categorize lung cancer with an accuracy of 88%. Afterwards we calculated the the confusion matrix, Precision (Sensitivity), Recall (Positivity) and F1 score of our model for each category. Our model is able to classify Normal CT images with Normal Accuracy of 89% Precision of 94% and F1 score of 93%. For benign nodules Accuracy was 92% precision of 97% and F1 score 86%, while for Adenocarcinoma and squamous cell cancer the Accuracy was 98% and 93%, Precision 85% and 84% and F1 score 92% and 86.9%. The relatively high accuracy of our model shows that convolutional neural networks can be a valuable tool for the classification of lung cancer, especially in a small city or underdeveloped rural hospital settings and can play a role in achieving healthcare equality.
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Date Issued: 2022
PURL: http://purl.flvc.org/fau/fd/FA00013965
Subject Headings: Lungs--Cancer, Neural networks (Computer science), Tomography, X-Ray Computed
Format: Document (PDF)

Title: Intelligent systems using GMDH algorithms.
Creator: Gupta, Mukul., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Design of intelligent systems that can learn from the environment and adapt to the change in the environment has been pursued by many researchers in this age of information technology. The Group Method of Data Handling (GMDH) algorithm to be implemented is a multilayered neural network. Neural network consists of neurons which use information acquired in training to deduce relationships in order to predict future responses. Most software tool during the simulation of the neural network based...
Show moreDesign of intelligent systems that can learn from the environment and adapt to the change in the environment has been pursued by many researchers in this age of information technology. The Group Method of Data Handling (GMDH) algorithm to be implemented is a multilayered neural network. Neural network consists of neurons which use information acquired in training to deduce relationships in order to predict future responses. Most software tool during the simulation of the neural network based algorithms in a sequential, single processor machine like Pascal, C or C++ takes several hours or even days. But in this thesis, the GMDH algorithm was modified and implemented into a software tool written in Verilog HDL and tested with specific application (XOR) to make the simulation faster. The purpose of the development of this tool is also to keep it general enough so that it can have a wide range of uses, but robust enough that it can give accurate results for all of those uses. Most of the applications of neural networks are basically software simulations of the algorithms only but in this thesis the hardware design is also developed of the algorithm so that it can be easily implemented on hardware using Field Programmable Gate Array (FPGA) type devices. The design is small enough to require a minimum amount of memory, circuit space, and propagation delay.
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Date Issued: 2010
PURL: http://purl.flvc.org/FAU/2976442
Subject Headings: GMDH algorithms, Genetic algorithms, Pattern recognition systems, Expert systems (Computer science), Neural networks (Computer science), Fuzzy logic, Intelligent control systems
Format: Document (PDF)

Title: Development of an intelligent fuzzy obstacle avoidance system using SONAR modeling and simulation.
Creator: Bouxsein, Philip A., Florida Atlantic University, An, Edgar
Abstract/Description: Response time to a threat or incident for coastline security is an area needing improvement. Currently, the U.S. Coast Guard is tasked with monitoring and responding to threats in coastal and port environments using boats or planes, and SCUBA divers. This can significantly hinder the response time to an incident. A solution to this problem is to use autonomous underwater vehicles (AUVs) to continuously monitor a port. The AUV must be able to navigate the environment without colliding into...
Show moreResponse time to a threat or incident for coastline security is an area needing improvement. Currently, the U.S. Coast Guard is tasked with monitoring and responding to threats in coastal and port environments using boats or planes, and SCUBA divers. This can significantly hinder the response time to an incident. A solution to this problem is to use autonomous underwater vehicles (AUVs) to continuously monitor a port. The AUV must be able to navigate the environment without colliding into objects for it to operate effectively. Therefore, an obstacle avoidance system (OAS) is essential to the activity of the AUV. This thesis describes a systematic approach to characterize the OAS performance in terms of environments, obstacles, SONAR configuration and signal processing methods via modeling and simulation. A fuzzy logic based OAS is created using the simulation. Subsequent testing of the OAS demonstrates its effectiveness in unknown environments.
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Date Issued: 2006
PURL: http://purl.flvc.org/fcla/dt/13390
Subject Headings: Fuzzy logic, Submersibles--Automatic control, Neural networks (Computer science), Underwater acoustics--Computer simulation, Sonar--Computer simulation
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: ACCURATE DETECTION OF SELECTIVE SWEEPS WITH TRANSFER LEARNING.
Creator: Sigler, Priya, DeGiorgio, Michael, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Positive natural selection leaves detectable, distinctive patterns in the genome in the form of a selective sweep. Identifying areas of the genome that have undergone selective sweeps is an area of high interest as it enables understanding of species and population evolution. Previous work has accomplished this by evaluating patterns within summary statistics computed across the genome and through application of machine learning techniques to raw population genomic data. When using raw...
Show morePositive natural selection leaves detectable, distinctive patterns in the genome in the form of a selective sweep. Identifying areas of the genome that have undergone selective sweeps is an area of high interest as it enables understanding of species and population evolution. Previous work has accomplished this by evaluating patterns within summary statistics computed across the genome and through application of machine learning techniques to raw population genomic data. When using raw population genomic data, convolutional neural networks have most recently been employed as they can handle large input arrays and maintain correlations among elements. Yet, such models often require massive amounts of training data and can be computationally expensive to train for a given problem. Instead, transfer learning has recently been used in the image analysis literature to improve machine learning models by learning the important features of images from large unrelated datasets beforehand, and then refining these models through subsequent application on smaller and more relevant datasets. We combine transfer learning with convolutional neural networks to improve classification of selective sweeps from raw population genomic data. We show that the combination of transfer learning with convolutional neural networks allows for accurate classification of selective sweeps.
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Date Issued: 2021
PURL: http://purl.flvc.org/fau/fd/FA00013785
Subject Headings: Transfer learning (Machine learning), Neural networks (Computer science), Natural selection, Genomes
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: A novel NN paradigm for the prediction of hematocrit value during blood transfusion.
Creator: Thakkar, Jay., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: During the Leukocytapheresis (LCAP) process used to treat patients suffering from acute Ulcerative Colitis, medical practitioners have to continuously monitor the Hematocrit (Ht) level in the blood to ensure it is within the acceptable range. The work done, as a part of this thesis, attempts to create an early warning system that can be used to predict if and when the Ht values will deviate from the acceptable range. To do this we have developed an algorithm based on the Group Method of Data...
Show moreDuring the Leukocytapheresis (LCAP) process used to treat patients suffering from acute Ulcerative Colitis, medical practitioners have to continuously monitor the Hematocrit (Ht) level in the blood to ensure it is within the acceptable range. The work done, as a part of this thesis, attempts to create an early warning system that can be used to predict if and when the Ht values will deviate from the acceptable range. To do this we have developed an algorithm based on the Group Method of Data Handling (GMDH) and compared it to other Neural Network algorithms, in particular the Multi Layer Perceptron (MLP). The standard GMDH algorithm captures the fluctuation very well but there is a time lag that produces larger errors when compared to MLP. To address this drawback we modified the GMDH algorithm to reduce the prediction error and produce more accurate results.
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Date Issued: 2011
PURL: http://purl.flvc.org/FAU/3174078
Subject Headings: Neural networks (Computer science), Scientific applications, GMDH algorithms, Pattern recognition systems, Genetic algorithms, Fuzzy logic
Format: Document (PDF)

Title: A planar cable-driven robotic device for physical therapy assistance.
Creator: Morris, Melissa M., College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
Abstract/Description: The design and construction of a tri-cable, planar robotic device for use in neurophysical rehabilitation is presented. The criteria for this system are based primarily on marketability factors, rather than ideal models or mathematical outcomes. The device is designed to be low cost and sufficiently safe for a somewhat disabled individual to use unsupervised at home, as well as in a therapist's office. The key features are the use of a barrier that inhibits the user from coming into contact...
Show moreThe design and construction of a tri-cable, planar robotic device for use in neurophysical rehabilitation is presented. The criteria for this system are based primarily on marketability factors, rather than ideal models or mathematical outcomes. The device is designed to be low cost and sufficiently safe for a somewhat disabled individual to use unsupervised at home, as well as in a therapist's office. The key features are the use of a barrier that inhibits the user from coming into contact with the cables as well as a "break-away" joystick that the user utilizes to perform the rehabilitation tasks. In addition, this device is portable, aesthetically acceptable and easy to operate. Other uses of this system include sports therapy, virtual reality and teleoperation of remote devices.
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Date Issued: 2007
PURL: http://purl.flvc.org/FAU/FADTsup3358410p
Subject Headings: Medical physics, Robotics, Biomechanics, Physical therapy, Technological innovations, Neural networks (Computer science)
Format: Set of related objects

Title: Real Time Traffic Monitoring System from a UAV Platform.
Creator: Biswas, Debojit, Su, Hongbo, Florida Atlantic University, College of Engineering and Computer Science, Department of Civil, Environmental and Geomatics Engineering
Abstract/Description: Today transportation systems are facing big transitions all over the world. We created fly overs, roads under the ground, bridges over the river and ocean to get efficient access and to increase the road connectivity. Our transportation system is more intelligent than ever. Our traffic signaling system became adaptive. Our vehicles equipped with new gadgets and we developed new tools for more efficient analysis of traffic. Our research relies on existing traffic infrastructure to generate...
Show moreToday transportation systems are facing big transitions all over the world. We created fly overs, roads under the ground, bridges over the river and ocean to get efficient access and to increase the road connectivity. Our transportation system is more intelligent than ever. Our traffic signaling system became adaptive. Our vehicles equipped with new gadgets and we developed new tools for more efficient analysis of traffic. Our research relies on existing traffic infrastructure to generate better understanding of traffic. More specifically, this research focused on traffic and UAV cameras to extract information about the traffic. Our first goal was to create an automatic system to count the cars using traffic cameras. To achieve this goal, we implemented Background Subtraction Method (BSM) and OverFeat Framework. BSM compares consecutive frames to detect the moving objects. Because BSM only works for ideal lab conditions, therefor we implemented a Convolutional Neural Network (CNN) based classification algorithm called OverFeat Framework. We created different segments on the road in various lanes to tabulate the number of passing cars. We achieved 96.55% accuracy for car counting irrespective of different visibility conditions of the day and night. Our second goal was to find out traffic density. We implemented two CNN based algorithms: Single Shot Detection (SSD) and MobileNet-SSD for vehicle detection. These algorithms are object detection algorithms. We used traffic cameras to detect vehicles on the roads. We utilized road markers and light pole distances to determine distances on the road. Using the distance and count information we calculated density. SSD is a more resource intense algorithm and it achieved 92.97% accuracy. MobileNet-SSD is a lighter algorithm and it achieved 79.30% accuracy. Finally, from a moving platform we estimated the velocity of multiple vehicles. There are a lot of roads where traffic cameras are not available, also traffic monitoring is necessary for special events. We implemented Faster R-CNN as a detection algorithm and Discriminative Correlation Filter (with Channel and Spatial Reliability Tracking) for tracking. We calculated the speed information from the tracking information in our study. Our framework achieved 96.80% speed accuracy compared to manual observation of speeds.
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Date Issued: 2019
PURL: http://purl.flvc.org/fau/fd/FA00013188
Subject Headings: Traffic monitoring, Intelligent transportation systems, Neural networks (Computer science), Vehicle detectors, Unmanned aerial vehicles
Format: Document (PDF)

Title: PREDICTING TROPICAL CYCLONE INTENSITY FROM GEOSYNCHRONOUS SATELLITE IMAGES USING DEEP NEURAL NETWORKS.
Creator: Udumulla, Niranga Mahesh, Motta, Francis, Florida Atlantic University, Department of Mathematical Sciences, Charles E. Schmidt College of Science
Abstract/Description: Tropical cyclones are among the most devastating natural disasters for human beings and the natural and manmade assets near to Atlantic basin. Estimating the current and future intensity of these powerful storms is crucial to protect life and property. Many methods and models exist for predicting the evolution of Atlantic basin cyclones, including numerical weather prediction models that simulate the dynamics of the atmosphere which require accurate measurements of the current state of the...
Show moreTropical cyclones are among the most devastating natural disasters for human beings and the natural and manmade assets near to Atlantic basin. Estimating the current and future intensity of these powerful storms is crucial to protect life and property. Many methods and models exist for predicting the evolution of Atlantic basin cyclones, including numerical weather prediction models that simulate the dynamics of the atmosphere which require accurate measurements of the current state of the atmosphere (NHC, 2019). Often these models fail to capture dangerous aspects of storm evolution, such as rapid intensification (RI), in which a storm undergoes a steep increase in intensity over a short time. To improve prediction of these events, scientists have turned to statistical models to predict current and future intensity using readily collected satellite image data (Pradhan, 2018). However, even the current-intensity prediction models have shown limited success in generalizing to unseen data, a result we confirm in this study. Therefore, building models for the estimating the current and future intensity of hurricanes is valuable and challenging. In this study we focus on to estimating cyclone intensity using Geostationary Operational Environmental Satellite images. These images represent five spectral bands covering the visible and infrared spectrum. We have built and compared various types of deep neural models, including convolutional networks based on long short term memory models and convolutional regression models that have been trained to predict the intensity, as measured by maximum sustained wind speed.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013626
Subject Headings: Tropical cyclones, Cyclones--Tropics--Forecasting, Geosynchronous satellites, Neural networks (Computer science)
Format: Document (PDF)

Title: MULTI-MODEL DEEP LEARNING FOR GROUPER SOUND CLASSIFICATION AND SEIZURE PREDICTION.
Creator: Ibrahim, Ali K., Zhuang, Hanqi, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Deep learning models have been successfully applied to a variety of machine learning tasks, including image identification, image segmentation, object detection, speaker recognition, natural language processing, bioinformatics and drug discovery, among other things. This dissertation introduces Multi-Model Deep Learning (MMDL), a new ensemble deep learning approach for signal classification and event forecasting. The ultimate goal of the MMDL method is to improve classification and...
Show moreDeep learning models have been successfully applied to a variety of machine learning tasks, including image identification, image segmentation, object detection, speaker recognition, natural language processing, bioinformatics and drug discovery, among other things. This dissertation introduces Multi-Model Deep Learning (MMDL), a new ensemble deep learning approach for signal classification and event forecasting. The ultimate goal of the MMDL method is to improve classification and forecasting performances of individual classifiers by fusing results of participating deep learning models. The performance of such an ensemble model, however, depends heavily on the following two design features. Firstly, the diversity of the participating (or base) deep learning models is crucial. If all base deep learning models produce similar classification results, then combining these results will not provide much improvement. Thus, diversity is considered to be a key design feature of any successful MMDL system. Secondly, the selection of a fusion function, namely, a suitable function to integrate the results of all the base models, is important. In short, building an effective MMDL system is a complex and challenging process which requires deep knowledge of the problem context and a well-defined prediction process. The proposed MMDL method utilizes a bank of Convolutional Neural Networks (CNNs) and Stacked AutoEncoders (SAEs). To reduce the design complexity, a randomized generation process is applied to assign values to hyperparameters of base models. To speed up the training process, new feature extraction procedures which captures time-spatial characteristics of input signals are also explored. The effectiveness of the MMDL method is validated in this dissertation study with three real-world case studies. In the first case study, the MMDL model is applied to classify call types of groupers, an important fishery resource in the Caribbean that produces sounds associated with reproductive behaviors during yearly spawning aggregations. In the second case study, the MMDL model is applied to detect upcalls of North Atlantic Right Whales (NARWs), a type of endangered whales. NARWs use upcalls to communicate among themselves. In the third case study, the MMDL model is modified to predict seizure episodes. In all these cases, the proposed MMDL model outperforms existing state-of-the-art methods.
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Date Issued: 2019
PURL: http://purl.flvc.org/fau/fd/FA00013382
Subject Headings: Deep Learning, Machine Learning, Neural networks (Computer science), Groupers, Whales, Vocalization, Animal, Seizures
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: Determination of probability density from statistical moments by neural network approach.
Creator: Zheng, Zhiyin., Florida Atlantic University, Cai, Guo-Qiang, College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
Abstract/Description: It is known that response probability densities, although important in failure analysis, are seldom achievable for stochastically excited systems except for linear systems under additive excitations of Gaussian processes. Most often, statistical moments are obtainable analytically or experimentally. It is proposed in this thesis to determine the probability density from the known statistical moments using artificial neural networks. A multi-layered feed-forward type of neural networks with...
Show moreIt is known that response probability densities, although important in failure analysis, are seldom achievable for stochastically excited systems except for linear systems under additive excitations of Gaussian processes. Most often, statistical moments are obtainable analytically or experimentally. It is proposed in this thesis to determine the probability density from the known statistical moments using artificial neural networks. A multi-layered feed-forward type of neural networks with error back-propagation training algorithm is proposed for the purpose and the parametric method is adopted for identifying the probability density function. Three examples are given to illustrate the applicability of the approach. All three examples show that the neural network approach gives quite accurate results in comparison with either the exact or simulation ones.
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Date Issued: 1996
PURL: http://purl.flvc.org/fcla/dt/15330
Subject Headings: Distribution (Probability theory), Moments method (Statistics), Estimation theory, Structural failures--Investigation, Neural networks (Computer science)
Format: Document (PDF)

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