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Title: 2D/3D face recognition.
Creator: Guan, Xin., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: This dissertation introduces our work on face recognition using a novel approach based on creating 3D face model from 2D face images. Together with the pose angle estimation and illumination compensation, this method can be used successfully to recognize 2D faces with 3D recognition algorithms. The results reported here were obtained partially with our own face image database, which had 2D and 3D face images of 50 subjects, with 9 different pose angles. It is shown that by applying even the...
Show moreThis dissertation introduces our work on face recognition using a novel approach based on creating 3D face model from 2D face images. Together with the pose angle estimation and illumination compensation, this method can be used successfully to recognize 2D faces with 3D recognition algorithms. The results reported here were obtained partially with our own face image database, which had 2D and 3D face images of 50 subjects, with 9 different pose angles. It is shown that by applying even the simple PCA algorithm, this new approach can yield successful recognition rates using 2D probing images and 3D gallery images. The insight gained from the 2D/3D face recognition study was also extended to the case of involving 2D probing and 2D gallery images, which offers a more flexible approach since it is much easier and practical to acquire 2D photos for recognition. To test the effectiveness of the proposed approach, the public AT&T face database, which had 2D only face photos of 40 subjects, with 10 different images each, was utilized in the experimental study. The results from this investigation show that with our approach, the 3D recognition algorithm can be successfully applied to 2D only images. The performance of the proposed approach was further compared with some of the existing face recognition techniques. Studies on imperfect conditions such as domain and pose/illumination variations were also carried out. Additionally, the performance of the algorithms on noisy photos was evaluated. Pros and cons of the proposed face recognition technique along with suggestions for future studies are also given in the dissertation.
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Date Issued: 2012
PURL: http://purl.flvc.org/FAU/3342104
Subject Headings: Pattern recognition systems, Optical pattern recognition, Biometric identification, Face perception, Artificial intellingence
Format: Document (PDF)

Title: A Novel Method for Human Face Enhancement for Video Images.
Creator: Salas, Ernesto Anel, Pandya, Abhijit 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 on images extracted from surveillance videos that have a low resolution and are taken under low illumination. In recent years, great advances have been made in face recognition and many studies mention results of 80% and 90% of recognition efficiency, however, most of these studies reported results using face images under controlled conditions. Current surveillance systems are equipped with low resolution cameras and are located in places with changing...
Show moreThe focus of this research is on images extracted from surveillance videos that have a low resolution and are taken under low illumination. In recent years, great advances have been made in face recognition and many studies mention results of 80% and 90% of recognition efficiency, however, most of these studies reported results using face images under controlled conditions. Current surveillance systems are equipped with low resolution cameras and are located in places with changing illumination, as opposed to a controlled environment. To be used in face recognition, images extracted from videos need to be normalized, enlarged and preprocessed. There is a multitude of processing algorithms for image enhancement, and each algorithm faces its advantages and disadvantages. This thesis presents a novel method for image enlargement of human faces applied to low quality video recordings. Results and comparison to traditional methods are also presented.
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Date Issued: 2007
PURL: http://purl.flvc.org/fau/fd/FA00012547
Subject Headings: Human face recognition (Computer science), Biometric identification, Image processing--Digital techniques, Pattern recognition systems
Format: Document (PDF)

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: 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: Automated biometrics of audio-visual multiple modals.
Creator: Huang, Lin, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Biometrics is the science and technology of measuring and analyzing biological data for authentication purposes. Its progress has brought in a large number of civilian and government applications. The candidate modalities used in biometrics include retinas, fingerprints, signatures, audio, faces, etc. There are two types of biometric system: single modal systems and multiple modal systems. Single modal systems perform person recognition based on a single biometric modality and are affected by...
Show moreBiometrics is the science and technology of measuring and analyzing biological data for authentication purposes. Its progress has brought in a large number of civilian and government applications. The candidate modalities used in biometrics include retinas, fingerprints, signatures, audio, faces, etc. There are two types of biometric system: single modal systems and multiple modal systems. Single modal systems perform person recognition based on a single biometric modality and are affected by problems like noisy sensor data, intra-class variations, distinctiveness and non-universality. Applying multiple modal systems that consolidate evidence from multiple biometric modalities can alleviate those problems of single modal ones. Integration of evidence obtained from multiple cues, also known as fusion, is a critical part in multiple modal systems, and it may be consolidated at several levels like feature fusion level, matching score fusion level and decision fusion level. Among biometric modalities, both audio and face modalities are easy to use and generally acceptable by users. Furthermore, the increasing availability and the low cost of audio and visual instruments make it feasible to apply such Audio-Visual (AV) systems for security applications. Therefore, this dissertation proposes an algorithm of face recognition. In addition, it has developed some novel algorithms of fusion in different levels for multiple modal biometrics, which have been tested by a virtual database and proved to be more reliable and robust than systems that rely on a single modality.
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Date Issued: 2010
PURL: http://purl.flvc.org/FAU/1927864
Subject Headings: Pattern recognition systems, Optical pattern recognition, Biometric identification, Identification, Automation, Automatic speech recognition
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: Design of analog building blocks useful for artificial neural networks.
Creator: Renavikar, Ajit Anand., Florida Atlantic University, Shankar, Ravi, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Software simulations of a scaleable VLSI implementable architecture and algorithm for character recognition by a research group at Florida Atlantic University (FAU) have shown encouraging results. We address here hardware implementation issues pertinent to the classification phase of character recognition. Using the digit classification techniques developed at FAU as a foundation, we have designed and simulated general purpose building blocks useful for a possible implementation of a Digital ...
Show moreSoftware simulations of a scaleable VLSI implementable architecture and algorithm for character recognition by a research group at Florida Atlantic University (FAU) have shown encouraging results. We address here hardware implementation issues pertinent to the classification phase of character recognition. Using the digit classification techniques developed at FAU as a foundation, we have designed and simulated general purpose building blocks useful for a possible implementation of a Digital & Analog CMOS VLSI chip that is suitable for a variety of artificial neural network (ANN) architectures. HSPICE was used to perform circuit-level simulations of the building blocks. We present here the details of implementation of the recognition chip including the architecture, circuit design and the simulation results.
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Date Issued: 1996
PURL: http://purl.flvc.org/fcla/dt/15328
Subject Headings: Neural networks (Computer science), Artificial intelligence, Optical character recognition devices, 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: Development of handprinting character recognition system using two stage shape and stroke classification.
Creator: Tse, Hing Wing., Florida Atlantic University, Sudhakar, Raghavan, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: This thesis deals with the recognition of digitized handprinting characters. Digitized character images are thresholded, binarized and converted into 32 x 32 matrices. The binarized character matrices are preprocessed to remove noise and thin down to one pixel per linewidth. For dominant features, namely, (1) number of loops, (2) number of end-pixels, (3) number of 3-branch-pixels, and (4) number of 4-branch-pixels, are used as criteria to pre-classify characters into 14 groups. Characters...
Show moreThis thesis deals with the recognition of digitized handprinting characters. Digitized character images are thresholded, binarized and converted into 32 x 32 matrices. The binarized character matrices are preprocessed to remove noise and thin down to one pixel per linewidth. For dominant features, namely, (1) number of loops, (2) number of end-pixels, (3) number of 3-branch-pixels, and (4) number of 4-branch-pixels, are used as criteria to pre-classify characters into 14 groups. Characters belonging to larger groups are encoded into chain code and compiled into a data base. Recognition of characters belonging to larger groups is achieved by data base look-up and or decision tree tests if ambiguities occur in the data base entries. Recognition of characters belonging to the smaller groups is doned by decision tree tests.
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Date Issued: 1988
PURL: http://purl.flvc.org/fcla/dt/14486
Subject Headings: Optical character recognition devices, Pattern recognition systems
Format: Document (PDF)

Title: DIGITAL IMAGE PROCESSING APPLIED TO CHARACTER RECOGNITION.
Creator: BEGUN, RALPH MURRAY., Florida Atlantic University, Erdol, Nurgun
Abstract/Description: Surveys are made of both character recognition and image processing. The need to apply image processing techniques to character recognition is pointed out. The fields are then combined and tested in sample programs. Simulations are made of recognition systems with and without image preprocessing. Processing techniques applied utilize Walsh-Hadamard transforms and l ocal window operators. Results indicate that image prepro c ess i ng improves recognition rates when noise degrades input images....
Show moreSurveys are made of both character recognition and image processing. The need to apply image processing techniques to character recognition is pointed out. The fields are then combined and tested in sample programs. Simulations are made of recognition systems with and without image preprocessing. Processing techniques applied utilize Walsh-Hadamard transforms and l ocal window operators. Results indicate that image prepro c ess i ng improves recognition rates when noise degrades input images. A system architecture is proposed for a hardware based video speed image processor operating on local image windows. The possible implementation of this processor is outlined.
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Date Issued: 1982
PURL: http://purl.flvc.org/fcla/dt/14120
Subject Headings: Image processing--Digital techniques, Optical character recognition devices, Pattern recognition systems
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: Exploring the electromagnetics of millimeter-wave through terahertz spectrum: de novo studies vis-à-vis materials science, biomedical applications and wireless communication.
Creator: Sharma, Bharti, Neelakanta, Perambur S., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: The present research is a targeted endeavor to study the underlying characteristics and novel applications of millimeter (mm) wave through terahertz (THz) spectrum of electromagnetic (EM) energy. Focused thereof are the following specific tasks broadly considered pertinent to the said EM spectral range: (i) To elucidate the material characteristics vis-à-vis the interaction with EM energy at the test frequencies; (ii) to identify biomedical applications based on the material characteristics...
Show moreThe present research is a targeted endeavor to study the underlying characteristics and novel applications of millimeter (mm) wave through terahertz (THz) spectrum of electromagnetic (EM) energy. Focused thereof are the following specific tasks broadly considered pertinent to the said EM spectral range: (i) To elucidate the material characteristics vis-à-vis the interaction with EM energy at the test frequencies; (ii) to identify biomedical applications based on the material characteristics studied and applied to biomedia; and (iii) to model the wireless communication channels supporting EM waves at the test frequency bands of interest. Commensurate with the scope as above, the objectives of the research are as follows:
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Date Issued: 2014
PURL: http://purl.flvc.org/fau/fd/FA00004330, http://purl.flvc.org/fau/fd/FA00004330
Subject Headings: Electromagnetic waves -- Scattering, Pattern recognition systems, Scattering (Physics), Terahertz technology, Wireless communication systems
Format: Document (PDF)

Title: Financial prediction using time series.
Creator: Srinivasan, Arunkumar., Florida Atlantic University, Pandya, Abhijit S., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: This thesis discusses the implementation of a feed forward NN using time series model to predict the sudden rise or sudden crash of a company's stock prices. The theory behind this prediction system is Pattern recognition. Pattern recognition techniques for time-series prediction are based on structural matching of the current state of the time-series with previously occurring states in historical data for making predictions. This study reports the result of attempts to predict the Motorola...
Show moreThis thesis discusses the implementation of a feed forward NN using time series model to predict the sudden rise or sudden crash of a company's stock prices. The theory behind this prediction system is Pattern recognition. Pattern recognition techniques for time-series prediction are based on structural matching of the current state of the time-series with previously occurring states in historical data for making predictions. This study reports the result of attempts to predict the Motorola stock price index using artificial neural networks (ANN). Daily data from January 1999 to December 2001 were taken from the NYSE. These data are classified based on criteria of an n% fall or rise of price corresponding to the previous day close price. A novel method using Hurst exponent is used in selecting the data set. These data are fed into a Back Propagated Neural Network. The number of hidden layers and number of neurons are systematically selected to implement a better predicting machine. The implemented model is tested using both interpolated and extrapolated data. Fundamental limitations and inherent difficulties when using neural networks for processing of high noise, small sample size signals are also discussed. Results of the prediction are presented and an elaborate discussion is made comparing the results.
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Date Issued: 2003
PURL: http://purl.flvc.org/fcla/dt/13045
Subject Headings: Pattern recognition systems, Neural networks (Computer science), Stock exchanges
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: How the Spatial Organization of Objects Affects Perceptual Processing of a Scene.
Creator: Rashford, Stacey, Barenholtz, Elan, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Psychology
Abstract/Description: How does spatial organization of objects affect the perceptual processing of a scene? Surprisingly, little research has explored this topic. A few studies have reported that, when simple, homogenous stimuli (e.g., dots), are presented in a regular formation, they are judged to be more numerous than when presented in a random configuration (Ginsburg, 1976; 1978). However, these results may not apply to real-world objects. In the current study, fewer objects were believed to be on organized...
Show moreHow does spatial organization of objects affect the perceptual processing of a scene? Surprisingly, little research has explored this topic. A few studies have reported that, when simple, homogenous stimuli (e.g., dots), are presented in a regular formation, they are judged to be more numerous than when presented in a random configuration (Ginsburg, 1976; 1978). However, these results may not apply to real-world objects. In the current study, fewer objects were believed to be on organized desks than their disorganized equivalents. Objects that are organized may be more likely to become integrated, due to classic Gestalt principles. Consequently, visual search may be more difficult. Such object integration may diminish saliency, making objects less apparent and more difficult to find. This could explain why, in the present study, objects on disorganized desks were found faster.
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Date Issued: 2015
PURL: http://purl.flvc.org/fau/fd/FA00004537, http://purl.flvc.org/fau/fd/FA00004537
Subject Headings: Image analysis, Optical pattern recognition, Pattern recognition systems, Phenomenological psychology, Visual perception
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: 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: 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: 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: Model-based classification of speech audio.
Creator: Thoman, Chris., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: This work explores the process of model-based classification of speech audio signals using low-level feature vectors. The process of extracting low-level features from audio signals is described along with a discussion of established techniques for training and testing mixture model-based classifiers and using these models in conjunction with feature selection algorithms to select optimal feature subsets. The results of a number of classification experiments using a publicly available speech...
Show moreThis work explores the process of model-based classification of speech audio signals using low-level feature vectors. The process of extracting low-level features from audio signals is described along with a discussion of established techniques for training and testing mixture model-based classifiers and using these models in conjunction with feature selection algorithms to select optimal feature subsets. The results of a number of classification experiments using a publicly available speech database, the Berlin Database of Emotional Speech, are presented. This includes experiments in optimizing feature extraction parameters and comparing different feature selection results from over 700 candidate feature vectors for the tasks of classifying speaker gender, identity, and emotion. In the experiments, final classification accuracies of 99.5%, 98.0% and 79% were achieved for the gender, identity and emotion tasks respectively.
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Date Issued: 2009
PURL: http://purl.flvc.org/FAU/210518
Subject Headings: Signal processing, Digital techniques, Speech processing systems, Sound, Recording and reproducing, Digital techniques, Pattern recognition systems
Format: Document (PDF)

Neural networks (Computer science) (9)	+ -
Biometric identification (5)	+ -
GMDH algorithms (5)	+ -
Image processing (4)	+ -
Optical character recognition devices (4)	+ -

Optical pattern recognition (4)	+ -
Artificial intelligence (3)	+ -
Computer algorithms (3)	+ -
Digital techniques (3)	+ -
Visual perception (3)	+ -
Computer vision (2)	+ -
Fuzzy logic (2)	+ -
Genetic algorithms (2)	+ -
Image processing--Digital techniques (2)	+ -
Imaging systems (2)	+ -
Security measures (2)	+ -
Application software (1)	+ -
Artificial intellingence (1)	+ -
Automatic speech recognition (1)	+ -
Automation (1)	+ -
Chaotic behavior in systems (1)	+ -
Character sets (Data processing) (1)	+ -
Computational intelligence (1)	+ -
Computer network architectures (1)	+ -
Computer security (1)	+ -
Computer systems (1)	+ -
Context effects (Psychology) (1)	+ -
Data encryption (Computer science) (1)	+ -
Data mining (1)	+ -

College of Engineering and Computer Science (22)	+ -
Department of Computer and Electrical Engineering and Computer Science (21)	+ -
Florida Atlantic University (17)	+ -
Charles E. Schmidt College of Science (6)	+ -
Pandya, Abhijit S. (6)	+ -

Department of Psychology (4)	+ -
Barenholtz, Elan (2)	+ -
Shankar, Ravi (2)	+ -
BEGUN, RALPH MURRAY. (1)	+ -
Bruhns, Stefan (1)	+ -
Calderon, Rodrigo (1)	+ -
Castellanos Jimenez, Ricardo Augusto. (1)	+ -
Center for Complex Systems and Brain Sciences (1)	+ -
Davidson, Meredith. (1)	+ -
Department of Mathematical Sciences (1)	+ -
Department of Ocean and Mechanical Engineering (1)	+ -
Dong, Yuhong. (1)	+ -
Erdol, Nurgun (1)	+ -
Gilbar, Thomas C. (1)	+ -
Greene, Samuel. (1)	+ -
Guan, Xin. (1)	+ -
Gupta, Mukul. (1)	+ -
Hadlock, Frank O. (1)	+ -
Hsu, Sam (1)	+ -
Huang, Lin (1)	+ -
KING, ALLAN KAI-CHUNG (1)	+ -
Khoshgoftaar, Taghi M. (1)	+ -
Madabushi, Vasudhevan. (1)	+ -
Mallen, Brenton. (1)	+ -
Manuel, Sara. (1)	+ -

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