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Title: An Empirical Study of Performance Metrics for Classifier Evaluation in Machine Learning.
Creator: Bruhns, Stefan, Khoshgoftaar, Taghi M., Florida Atlantic University
Abstract/Description: A variety of classifiers for solving classification problems is available from the domain of machine learning. Commonly used classifiers include support vector machines, decision trees and neural networks. These classifiers can be configured by modifying internal parameters. The large number of available classifiers and the different configuration possibilities result in a large number of combinatiorrs of classifier and configuration settings, leaving the practitioner with the problem of...
Show moreA variety of classifiers for solving classification problems is available from the domain of machine learning. Commonly used classifiers include support vector machines, decision trees and neural networks. These classifiers can be configured by modifying internal parameters. The large number of available classifiers and the different configuration possibilities result in a large number of combinatiorrs of classifier and configuration settings, leaving the practitioner with the problem of evaluating the performance of different classifiers. This problem can be solved by using performance metrics. However, the large number of available metrics causes difficulty in deciding which metrics to use and when comparing classifiers on the basis of multiple metrics. This paper uses the statistical method of factor analysis in order to investigate the relationships between several performance metrics and introduces the concept of relative performance which has the potential to case the process of comparing several classifiers. The relative performance metric is also used to evaluate different support vector machine classifiers and to determine if the default settings in the Weka data mining tool are reasonable.
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Date Issued: 2008
PURL: http://purl.flvc.org/fau/fd/FA00012508
Subject Headings: Machine learning, Computer algorithms, Pattern recognition systems, Data structures (Computer science), Kernel functions, Pattern perception--Data processing
Format: Document (PDF)

Title: Assessing Children’s Performance on the Facial Emotion Recognition Task with Familiar and Unfamiliar Faces: An Autism Study.
Creator: Shanok, Nathaniel, Jones, Nancy Aaron, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Psychology
Abstract/Description: Studies exploring facial emotion recognition (FER) abilities in autism spectrum disorder (ASD) samples have yielded inconsistent results despite the widely-accepted finding that an impairment in emotion recognition is a core component of ASD. The current study aimed to determine if an FER task featuring both unfamiliar and familiar faces would highlight additional group differences between ASD children and typically developing (TD) children. We tested the two groups of 4- to 8-year-olds on...
Show moreStudies exploring facial emotion recognition (FER) abilities in autism spectrum disorder (ASD) samples have yielded inconsistent results despite the widely-accepted finding that an impairment in emotion recognition is a core component of ASD. The current study aimed to determine if an FER task featuring both unfamiliar and familiar faces would highlight additional group differences between ASD children and typically developing (TD) children. We tested the two groups of 4- to 8-year-olds on this revised task, and also compared their resting-state brain activity using electroencephalogram (EEG) measurements. As hypothesized, the TD group had significantly higher overall emotion recognition percent scores. In addition, there was a significant interaction effect of group by familiarity, with the ASD group recognizing emotional expressions significantly better in familiar faces than in unfamiliar ones. This finding may be related to the preference of children with autism for people and situations which they are accustomed to. TD children did not demonstrate this pattern, as their recognition scores were approximately the same for familiar faces and unfamiliar ones. No significant group differences existed for EEG alpha power or EEG alpha asymmetry in frontal, central, temporal, parietal, or occipital brain regions. Also, neither of these EEG measurements were strongly correlated with the group FER performances. Further evidence is needed to assess the association between neurophysiological measurements and behavioral symptoms of ASD. The behavioral results of this study provide preliminary evidence that an FER task featuring both familiar and unfamiliar expressions produces a more optimal assessment of emotion recognition ability.
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Date Issued: 2017
PURL: http://purl.flvc.org/fau/fd/FA00004908, http://purl.flvc.org/fau/fd/FA00004908
Subject Headings: Emotions in children., Social skills in children., Nonverbal communication., Pattern recognition systems., Face perception.
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: 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: Identification of others using biological motion.
Creator: Manuel, Sara., Charles E. Schmidt College of Science, Department of Psychology
Abstract/Description: The literature regarding biological motion suggests that people may accurately identify and recognize the gender of others using movement cues in the absence of typical identifiers. This study compared identification and gender judgments of traditional point-light stimuli to skeleton stimuli. Controlling for previous experience and execution of actions, the frequency and familiarity of movements was also considered. Watching action clips, participants learned to identify 4 male and 4 female...
Show moreThe literature regarding biological motion suggests that people may accurately identify and recognize the gender of others using movement cues in the absence of typical identifiers. This study compared identification and gender judgments of traditional point-light stimuli to skeleton stimuli. Controlling for previous experience and execution of actions, the frequency and familiarity of movements was also considered. Watching action clips, participants learned to identify 4 male and 4 female actors. Participants then identified the corresponding point-light or skeleton displays. Although results indicate higher than chance performance, no difference was observed between stimuli conditions. Analyses did show better gender recognition for common as well as previously viewed actions. This suggests that visual experience influences extraction and application of biological motion. Thus insufficient practice in relying on movement cues for identification could explain the significant yet poor performance in biological motion point-light research.
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Date Issued: 2012
PURL: http://purl.flvc.org/FAU/3355623
Subject Headings: Pattern recognition systems, Visual perception, Human body, Social aspects, Biometric identification, Psychophysiology
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 systematic evaluation of object detection and recognition approaches with context capabilities.
Creator: Giusti Urbina, Rafael J., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Contemporary computer vision solutions to the problem of object detection aim at incorporating contextual information into the process. This thesis proposes a systematic evaluation of the usefulness of incorporating knowledge about the geometric context of a scene into a baseline object detection algorithm based on local features. This research extends publicly available MATLABRª implementations of leading algorithms in the field and integrates them in a coherent and extensible way....
Show moreContemporary computer vision solutions to the problem of object detection aim at incorporating contextual information into the process. This thesis proposes a systematic evaluation of the usefulness of incorporating knowledge about the geometric context of a scene into a baseline object detection algorithm based on local features. This research extends publicly available MATLABRª implementations of leading algorithms in the field and integrates them in a coherent and extensible way. Experiments are presented to compare the performance and accuracy between baseline and context-based detectors, using images from the recently published SUN09 dataset. Experimental results demonstrate that adding contextual information about the geometry of the scene improves the detector performance over the baseline case in 50% of the tested cases.
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Date Issued: 2011
PURL: http://purl.flvc.org/FAU/3183127
Subject Headings: Imaging systems, Mathematical models, Cognitive science, Optical pattern recognition, Computer vision, Logistic regression analysis
Format: Document (PDF)

Title: Signature system for video identification.
Creator: Medellin, Sebastian Possos., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Video signature techniques based on tomography images address the problem of video identification. This method relies on temporal segmentation and sampling strategies to build and determine the unique elements that will form the signature. In this thesis an extension for these methods is presented; first a new feature extraction method, derived from the previously proposed sampling pattern, is implemented and tested, resulting in a highly distinctive set of signature elements, second a robust...
Show moreVideo signature techniques based on tomography images address the problem of video identification. This method relies on temporal segmentation and sampling strategies to build and determine the unique elements that will form the signature. In this thesis an extension for these methods is presented; first a new feature extraction method, derived from the previously proposed sampling pattern, is implemented and tested, resulting in a highly distinctive set of signature elements, second a robust temporal video segmentation system is used to replace the original method applied to determine shot changes more accurately. Under a very exhaustive set of tests the system was able to achieve 99.58% of recall, 100% of precision and 99.35% of prediction precision.
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Date Issued: 2010
PURL: http://purl.flvc.org/FAU/2683534
Subject Headings: Biometric identification, Image processing, Digital techniques, Pattern recognition systems, Data encryption (Computer science)
Format: Document (PDF)

Title: Proud elastic target discrimination using low-frequency sonar signatures.
Creator: Mallen, Brenton., College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
Abstract/Description: This thesis presents a comparative analysis of various low-frequency sonar signature representations and their ability to discriminate between proud targets of varying physical parameters. The signature representations used include: synthetic aperture sonar (SAS) beamformed images, acoustic color plot images, and bispectral images. A relative Mean-Square Error (rMSE) performance metric and an effective Signal-to-Noise Ratio (SNReff) performance metric have been developed and implemented to...
Show moreThis thesis presents a comparative analysis of various low-frequency sonar signature representations and their ability to discriminate between proud targets of varying physical parameters. The signature representations used include: synthetic aperture sonar (SAS) beamformed images, acoustic color plot images, and bispectral images. A relative Mean-Square Error (rMSE) performance metric and an effective Signal-to-Noise Ratio (SNReff) performance metric have been developed and implemented to quantify the target differentiation. The analysis is performed on a subset of the synthetic sonar stave data provided by the Naval Surface Warfare Center - Panama City Division (NSWC-PCD). The subset is limited to aluminum and stainless steel, thin-shell, spherical targets in contact with the seafloor (proud). It is determined that the SAS signature representation provides the best, least ambiguous, target differentiation with a minimum mismatch difference of 14.5802 dB. The acoustic color plot and bispectrum representations resulted in a minimum difference of 9.1139 dB and 1.8829 dB, respectively
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Date Issued: 2012
PURL: http://purl.flvc.org/FAU/3342210
Subject Headings: Pattern recognition systems, Frequency response (Dynamics), Signal theory (Telecommunication), Random noise theory
Format: Document (PDF)

Title: Learning to match faces and voices.
Creator: Davidson, Meredith., Charles E. Schmidt College of Science, Department of Psychology
Abstract/Description: This study examines whether forming a single identity is crucial to learning to bind faces and voices, or if people are equally able to do so without tying this information to an identity. To test this, individuals learned paired faces and voices that were in one of three different conditions: True voice, Gender Matched, or Gender Mismatched conditions. Performance was measured in a training phase as well as a test phase, and results show that participants were able to learn more quickly and...
Show moreThis study examines whether forming a single identity is crucial to learning to bind faces and voices, or if people are equally able to do so without tying this information to an identity. To test this, individuals learned paired faces and voices that were in one of three different conditions: True voice, Gender Matched, or Gender Mismatched conditions. Performance was measured in a training phase as well as a test phase, and results show that participants were able to learn more quickly and have higher overall performance for learning in the True Voice and Gender Matched conditions. During the test phase, performance was almost at chance in the Gender Mismatched condition which may mean that learning in the training phase was simply memorization of the pairings for this condition. Results support the hypothesis that learning to bind faces and voices is a process that involves forming a supramodal identity from multisensory learning.
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Date Issued: 2010
PURL: http://purl.flvc.org/FAU/2683140
Subject Headings: Sensorimotor integration, Senses and sensation, Intersensory effects, Perceptual learning, Pattern recognition systems
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: Application level intrusion detection using a sequence learning algorithm.
Creator: Dong, Yuhong., Florida Atlantic University, Hsu, Sam, Rajput, Saeed, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: An un-supervised learning algorithm on application level intrusion detection, named Graph Sequence Learning Algorithm (GSLA), is proposed in this dissertation. Experiments prove its effectiveness. Similar to most intrusion detection algorithms, in GSLA, the normal profile needs to be learned first. The normal profile is built using a session learning method, which is combined with the one-way Analysis of Variance method (ANOVA) to determine the value of an anomaly threshold. In the proposed...
Show moreAn un-supervised learning algorithm on application level intrusion detection, named Graph Sequence Learning Algorithm (GSLA), is proposed in this dissertation. Experiments prove its effectiveness. Similar to most intrusion detection algorithms, in GSLA, the normal profile needs to be learned first. The normal profile is built using a session learning method, which is combined with the one-way Analysis of Variance method (ANOVA) to determine the value of an anomaly threshold. In the proposed approach, a hash table is used to store a sparse data matrix in triple data format that is collected from a web transition log instead of an n-by-n dimension matrix. Furthermore, in GSLA, the sequence learning matrix can be dynamically changed according to a different volume of data sets. Therefore, this approach is more efficient, easy to manipulate, and saves memory space. To validate the effectiveness of the algorithm, extensive simulations have been conducted by applying the GSLA algorithm to the homework submission system at our computer science and engineering department. The performance of GSLA is evaluated and compared with traditional Markov Model (MM) and K-means algorithms. Specifically, three major experiments have been done: (1) A small data set is collected as a sample data, and is applied to GSLA, MM, and K-means algorithms to illustrate the operation of the proposed algorithm and demonstrate the detection of abnormal behaviors. (2) The Random Walk-Through sampling method is used to generate a larger sample data set, and the resultant anomaly score is classified into several clusters in order to visualize and demonstrate the normal and abnormal behaviors with K-means and GSLA algorithms. (3) Multiple professors' data sets are collected and used to build the normal profiles, and the ANOVA method is used to test the significant difference among professors' normal profiles. The GSLA algorithm can be made as a module and plugged into the IDS as an anomaly detection system.
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Date Issued: 2006
PURL: http://purl.flvc.org/fcla/dt/12220
Subject Headings: Data mining, Parallel processing (Electronic computers), Computer algorithms, Computer security, Pattern recognition systems
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: An intelligent GMDH forecaster for forecasting certain variables in financial markets.
Creator: Mehta, Sandeep., Florida Atlantic University, Pandya, Abhijit S., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: In this thesis, application of GMDH Algorithm to real life problems is studied. A particular type of GMDH Algorithm namely TMNN is chosen for this purpose. An effort is made to forecast S&P Index Closing Value with the help of the forecaster. The performance of the TMNN Algorithm is simulated by implementing a tool in C++ for developing forecast models. The validation of this simulation tool is carried out with Sine Wave Values and performance analysis is done in a noisy environment. The...
Show moreIn this thesis, application of GMDH Algorithm to real life problems is studied. A particular type of GMDH Algorithm namely TMNN is chosen for this purpose. An effort is made to forecast S&P Index Closing Value with the help of the forecaster. The performance of the TMNN Algorithm is simulated by implementing a tool in C++ for developing forecast models. The validation of this simulation tool is carried out with Sine Wave Values and performance analysis is done in a noisy environment. The noisy environment tests the TMNN forecaster for its robustness. The primary goal of this research is to develop a simulation software based on TMNN Algorithm for forecasting stock market index values. The main inputs are previous day's closing values and the output is predicted closing index.
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Date Issued: 2002
PURL: http://purl.flvc.org/fcla/dt/12996
Subject Headings: GMDH algorithms, Neural networks (Computer science), Time-series analysis, Pattern recognition systems
Format: Document (PDF)

Title: An Algorithm for the Automated Interpretation of Cardiac Auscultation.
Creator: Lieber, Claude, Erdol, Nurgun, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Cardiac auscultation, an important part of the physical examination, is difficult for many primary care providers. As a result, diagnoses are missed or auscultatory signs misinterpreted. A reliable, automated means of interpreting cardiac auscultation should be of benefit to both the primary care provider and to patients. This paper explores a novel approach to this problem and develops an algorithm that can be expanded to include all the necessary electronics and programming to develop such...
Show moreCardiac auscultation, an important part of the physical examination, is difficult for many primary care providers. As a result, diagnoses are missed or auscultatory signs misinterpreted. A reliable, automated means of interpreting cardiac auscultation should be of benefit to both the primary care provider and to patients. This paper explores a novel approach to this problem and develops an algorithm that can be expanded to include all the necessary electronics and programming to develop such a device. The algorithm is explained and its shortcomings exposed. The potential for further development is also expounded.
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Date Issued: 2016
PURL: http://purl.flvc.org/fau/fd/FA00004609, http://purl.flvc.org/fau/fd/FA00004609
Subject Headings: Phonocardiography., Signal processing., Pattern recognition systems., Imaging systems in medicine., Decision support systems., Medicine--Data processing.
Format: Document (PDF)

Title: Event detection in surveillance video.
Creator: Castellanos Jimenez, Ricardo Augusto., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Digital video is being used widely in a variety of applications such as entertainment, surveillance and security. Large amount of video in surveillance and security requires systems capable to processing video to automatically detect and recognize events to alleviate the load on humans and enable preventive actions when events are detected. The main objective of this work is the analysis of computer vision techniques and algorithms used to perform automatic detection of events in video...
Show moreDigital video is being used widely in a variety of applications such as entertainment, surveillance and security. Large amount of video in surveillance and security requires systems capable to processing video to automatically detect and recognize events to alleviate the load on humans and enable preventive actions when events are detected. The main objective of this work is the analysis of computer vision techniques and algorithms used to perform automatic detection of events in video sequences. This thesis presents a surveillance system based on optical flow and background subtraction concepts to detect events based on a motion analysis, using an event probability zone definition. Advantages, limitations, capabilities and possible solution alternatives are also discussed. The result is a system capable of detecting events of objects moving in opposing direction to a predefined condition or running in the scene, with precision greater than 50% and recall greater than 80%.
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Date Issued: 2010
PURL: http://purl.flvc.org/FAU/1870694
Subject Headings: Computer systems, Security measures, Image processing, Digital techniques, Imaging systems, Mathematical models, Pattern recognition systems, Computer vision, Digital video
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: Object recognition on Android mobil platform using speeded up robust features.
Creator: Tyagi, Vivek K., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: In recent years there has been great interest in implementing object recognition frame work on mobile phones. This has stemmed from the fact the advances in object recognition algorithm and mobile phone capabilities have built a congenial ecosystem. Application developers on mobile platforms are trying to utilize the object recognition technology to build better human computer interfaces. This approach is in the nascent phase and proper application framework is required. In this thesis, we...
Show moreIn recent years there has been great interest in implementing object recognition frame work on mobile phones. This has stemmed from the fact the advances in object recognition algorithm and mobile phone capabilities have built a congenial ecosystem. Application developers on mobile platforms are trying to utilize the object recognition technology to build better human computer interfaces. This approach is in the nascent phase and proper application framework is required. In this thesis, we propose a framework to overcome design challenges and provide an evaluation methodology to assess the system performance. We use the emerging Android mobile platform to implement and test the framework. We performed a case study using the proposal and reported the test result. This assessment will help developers make wise decisions about their application design. Furthermore, the Android API developers could use this information to provide better interfaces to the third party developers. The design and evaluation methodology could be extended to other mobile platforms for a wider consumer base.
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Date Issued: 2010
PURL: http://purl.flvc.org/FAU/2683531
Subject Headings: Mobile computing, Application software, Development, Object-oriented programming (Computer software), Pattern recognition systems, Development
Format: Document (PDF)

Title: Optical 2D Positional Estimation for a Biomimetic Station-Keeping Autonomous Underwater Vehicle.
Creator: Nunes, Christopher, Dhanak, Manhar R., Florida Atlantic University, College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
Abstract/Description: Underwater vehicles often use acoustics or dead reckoning for global positioning, which is impractical for low cost, high proximity applications. An optical based positional feedback system for a wave tank operated biomimetic station-keeping vehicle was made using an extended Kalman filter and a model of a nearby light source. After physical light model verification, the filter estimated surge, sway, and heading with 6 irradiance sensors and a low cost inertial measurement unit (~$15)....
Show moreUnderwater vehicles often use acoustics or dead reckoning for global positioning, which is impractical for low cost, high proximity applications. An optical based positional feedback system for a wave tank operated biomimetic station-keeping vehicle was made using an extended Kalman filter and a model of a nearby light source. After physical light model verification, the filter estimated surge, sway, and heading with 6 irradiance sensors and a low cost inertial measurement unit (~$15). Physical testing with video feedback suggests an average error of ~2cm in surge and sway, and ~3deg in yaw, over a 1200 cm2 operational area. This is 2-3 times better, and more consistent, than adaptations of prior art tested alongside the extended Kalman filter feedback system. The physical performance of the biomimetic platform was also tested. It has a repeatable forward velocity response with a max of 0.3 m/s and fair stability in surface testing conditions.
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Date Issued: 2015
PURL: http://purl.flvc.org/fau/fd/FA00004528, http://purl.flvc.org/fau/fd/FA00004528
Subject Headings: Biometric identification, Feedback control systems, Oceanographic submersibles -- Design and construction, Optical pattern recognition, Remote submersibles -- Design and construction
Format: Document (PDF)

Title: Patterns for secure interactions in social networks in Web 2.0.
Creator: Marin, Carolina, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: A social network is a structure of individuals and organizations, which are connected by one or more types of interdependency, such as friendship, affinity, common interests or knowledge. Social networks use Web 2.0 technology, which is mostly based on a service-oriented architecture. We are studying patterns for social networks in this environment. A pattern is an encapsulated solution to a software problem in a given context, secure threats are possible in this context. We present a...
Show moreA social network is a structure of individuals and organizations, which are connected by one or more types of interdependency, such as friendship, affinity, common interests or knowledge. Social networks use Web 2.0 technology, which is mostly based on a service-oriented architecture. We are studying patterns for social networks in this environment. A pattern is an encapsulated solution to a software problem in a given context, secure threats are possible in this context. We present a collection of patterns associated with the most important aspects of social networks, with emphasis on controlling the actions of the users of these networks.
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Date Issued: 2012
PURL: http://purl.flvc.org/FAU/3342203
Subject Headings: Web 2.0, Computer network architectures, Online social networks, Security measures, Social media, Pattern recognition systems
Format: Document (PDF)

Pattern recognition systems (31)	+ -
Neural networks (Computer science) (10)	+ -
Biometric identification (7)	+ -
Pattern recognition systems. (6)	+ -
GMDH algorithms (5)	+ -

Optical pattern recognition (5)	+ -
Artificial intelligence. (4)	+ -
Image processing (4)	+ -
Optical character recognition devices (4)	+ -
Artificial intelligence (3)	+ -
Computer algorithms (3)	+ -
Computer vision (3)	+ -
Computer vision. (3)	+ -
Digital techniques (3)	+ -
Imaging systems (3)	+ -
Optical character recognition devices--Computer simulation (3)	+ -
Optical pattern recognition. (3)	+ -
Pattern recognition systems--Computer simulation (3)	+ -
Visual perception (3)	+ -
Expert systems (Computer science) (2)	+ -
Fuzzy logic (2)	+ -
Genetic algorithms (2)	+ -
Human face recognition (Computer science) (2)	+ -
Image processing--Digital techniques (2)	+ -
Information visualization. (2)	+ -
Latent structure analysis. (2)	+ -
Mathematical models (2)	+ -
Security measures (2)	+ -
Algorithms (1)	+ -
Application software (1)	+ -

College of Engineering and Computer Science (31)	+ -
Florida Atlantic University (30)	+ -
Department of Computer and Electrical Engineering and Computer Science (28)	+ -
Charles E. Schmidt College of Science (8)	+ -
Department of Psychology (6)	+ -

Pandya, Abhijit S. (6)	+ -
Shankar, Ravi (5)	+ -
Barenholtz, Elan (3)	+ -
Department of Ocean and Mechanical Engineering (3)	+ -
Erdol, Nurgun (2)	+ -
Marques, Oge (2)	+ -
Abtahi, Homayoon (1)	+ -
Agba, Lawrence C. (1)	+ -
BEGUN, RALPH MURRAY. (1)	+ -
Banuru, Prashanth K. (1)	+ -
Bidari, Ravindra Chandrashekar. (1)	+ -
Blandon, Jatni. (1)	+ -
Bruhns, Stefan (1)	+ -
Calderon, Rodrigo (1)	+ -
Castellanos Jimenez, Ricardo Augusto. (1)	+ -
Center for Complex Systems and Brain Sciences (1)	+ -
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Gilbar, Thomas C. (1)	+ -
Giusti Urbina, Rafael J. (1)	+ -
Greene, Samuel. (1)	+ -

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