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Title: Shift-invariant recognition of rotationally deformed ship silhouettes at multiple resolution scales.
Creator: Schmalz, Mark S., Caimi, F. M., Harbor Branch Oceanographic Institute
Date Issued: 1986
PURL: http://purl.flvc.org/FCLA/DT/3180388
Subject Headings: Pattern recognition, Fractals
Format: Document (PDF)

$Geometric properties of non-differentiable contours: concurrent spatial harmonic and fractal analyses$

Title: Geometric properties of non-differentiable contours: concurrent spatial harmonic and fractal analyses.
Creator: Caimi, F. M., Schmalz, Mark S., Harbor Branch Oceanographic Institute
Date Issued: 1985
PURL: http://purl.flvc.org/FCLA/DT/3180376
Subject Headings: Fractals, Pattern recognition, Spatial analysis, Fractal geometry
Format: Document (PDF)

Title: AN ECOLOGICAL APPROACH TO SETTLEMENT PATTERNS IN THE RED DESERT, SOUTH-CENTRAL WYOMING, UTILIZING DATA OBTAINED FROM THE WAMSUTTER BLOCK AREA SURVEY.
Creator: SILVIA, DIANE ELIZABETH., Florida Atlantic University, Kennedy, William J., Dorothy F. Schmidt College of Arts and Letters, Department of Anthropology
Abstract/Description: The purpose of this research project is to determine if any correlation exists between ecological factors and the settlement patterns exhibited within Townships 18 and 19 North, Range 93 West, of the Wamsutter Block Area Survey, Carbon County, Wyoming. The Wamsutter project area, located in a region known as the Red Desert, lies mostly in the Great Divide Basin and extends slightly into the Washakie Basin of south-central Wyoming. The environmental effect on cultural adaptations may be...
Show moreThe purpose of this research project is to determine if any correlation exists between ecological factors and the settlement patterns exhibited within Townships 18 and 19 North, Range 93 West, of the Wamsutter Block Area Survey, Carbon County, Wyoming. The Wamsutter project area, located in a region known as the Red Desert, lies mostly in the Great Divide Basin and extends slightly into the Washakie Basin of south-central Wyoming. The environmental effect on cultural adaptations may be reflected in the archaeological remains. Prior to the Wamsutter survey project, this area was the subject of several surveys by various institutions. The previous surveys were limited compared with the massive and intensive undertaking of this project. It is hoped that through this investigation the utility of the large data base generated by contract archaeology will be demonstrated.
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Date Issued: 1982
PURL: http://purl.flvc.org/fcla/dt/14103
Subject Headings: Land settlement patterns, Prehistoric--Wyoming
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: A SYNTACTIC APPROACH TO HAND PRINTED CHARACTER RECOGNITION.
Creator: KING, ALLAN KAI-CHUNG, Florida Atlantic University, Hadlock, Frank O., Charles E. Schmidt College of Science, Department of Mathematical Sciences
Abstract/Description: A study was made on the feasibility of the syntactic approach to the problem of hand printed character recognition. The characters are represented as postfix expressions in Picture Description Language. By comparing them with the prototype expressions, each character is classified as the prototype that is closest to it. Programs written in the Pascal language, which generate the postfix expressions for the characters, and recognize the characters, are presented.
Date Issued: 1983
PURL: http://purl.flvc.org/fcla/dt/14168
Subject Headings: Pattern recognition systems, Character sets (Data processing)
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: 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: 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: Peripheral Object Recognition in Naturalistic Scenes.
Creator: Schlangen, Derrick, Barenholtz, Elan, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Psychology
Abstract/Description: Most of the human visual field falls in the periphery, and peripheral processing is important for normal visual functioning. Yet, little is known about peripheral object recognition in naturalistic scenes and factors that modulate this ability. We propose that a critical function of scene and object memory is in order to facilitate visual object recognition in the periphery. In the first experiment, participants identified objects in scenes across different levels of familiarity and...
Show moreMost of the human visual field falls in the periphery, and peripheral processing is important for normal visual functioning. Yet, little is known about peripheral object recognition in naturalistic scenes and factors that modulate this ability. We propose that a critical function of scene and object memory is in order to facilitate visual object recognition in the periphery. In the first experiment, participants identified objects in scenes across different levels of familiarity and contextual information within the scene. We found that familiarity with a scene resulted in a significant increase in the distance that objects were recognized. Furthermore, we found that a semantically consistent scene improved the distance that object recognition is possible, supporting the notion that contextual facilitation is possible in the periphery. In the second experiment, the preview duration of a scene was varied in order to examine how a scene representation is built and how memory of that scene and the objects within it contributes to object recognition in the periphery. We found that the closer participants fixated to the object in the preview, the farther on average they recognized that target object in the periphery. However, only a preview duration of the scenes for 5000 ms produced significantly farther peripheral object recognition compared to not previewing the scene. Overall, these experiments introduce a novel research paradigm for object recognition in naturalistic scenes, and demonstrates multiple factors that have systematic effects on peripheral object recognition.
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Date Issued: 2016
PURL: http://purl.flvc.org/fau/fd/FA00004669, http://purl.flvc.org/fau/fd/FA00004669
Subject Headings: Context effects (Psychology), Human information processing, Optical pattern recognition, Pattern recognition systems, Recognition (Psychology), Visual perception
Format: Document (PDF)

Title: Spatiotemporal patterns of neural fields in a spherical cortex with general connectivity.
Creator: Tayefeh, Vahid, Fuchs, Armin, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Physics
Abstract/Description: The human brain consists of billions of neurons and these neurons pool together in groups at different scales. On one hand, these neural entities tend to behave as single units and on the other hand show collective macroscopic patterns of activity. The neural units communicate with each other and process information over time. This communication is through small electrical impulses which at the macroscopic scale are measurable as brain waves. The electric field that is produced collectively...
Show moreThe human brain consists of billions of neurons and these neurons pool together in groups at different scales. On one hand, these neural entities tend to behave as single units and on the other hand show collective macroscopic patterns of activity. The neural units communicate with each other and process information over time. This communication is through small electrical impulses which at the macroscopic scale are measurable as brain waves. The electric field that is produced collectively by macroscopic groups of neurons within the brain can be measured on the surface of the skull via a brain imaging modality called Electroencephalography (EEG). The brain as a neural system has variant connection topology, in which an area might not only be connected to its adjacent neighbors homogeneously but also distant areas can directly transfer brain activity [16]. Timing of these brain activity communications between different neural units bring up overall emerging spatiotemporal patterns. The dynamics of these patterns and formation of neural activities in cortical surface is influenced by the presence of long-range connections between heterogeneous neural units. Brain activity at large-scale is thought to be involved in the information processing and the implementation of cognitive functions of the brain. This research aims to determine how the spatiotemporal pattern formation phenomena in the brain depend on its connection topology. This connection topology consists of homogeneous connections in local cortical areas alongside the couplings between distant functional units as heterogeneous connections. Homogeneous connectivity or synaptic weight distribution representing the large-scale anatomy of cortex is assumed to depend on the Euclidean distance between interacting neural units. Altering characteristics of inhomogeneous pathways as control parameters guide the brain pattern formation through phase transitions at critical points. In this research, linear stability analysis is applied to a macroscopic neural field in a one-dimensional circular and a twodimensional spherical model of the brain in order to find destabilization mechanism and subsequently emerging patterns.
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Date Issued: 2018
PURL: http://purl.flvc.org/fau/fd/FA00013119
Subject Headings: Cerebral cortex, Neural circuitry, Electroencephalography, Neural fields, Spatiotemporal patterns
Format: Document (PDF)

Title: PRGMDH algorithm for neural network development and its applications.
Creator: Tangadpelli, Chetan., Florida Atlantic University, Pandya, Abhijit S., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: The existing Group Method of Data Handling (GMDH) algorithm has characteristics that are ideal for neural network design. This thesis introduces a new algorithm that applies some of the best characteristics of GMDH to neural network design and develops a Pruning based Regenerated Network by discarding the neurons in a layer which don't contribute for the creation of neurons in next layer. Unlike other conventional algorithms, which generate a network which is a black box, the new algorithm...
Show moreThe existing Group Method of Data Handling (GMDH) algorithm has characteristics that are ideal for neural network design. This thesis introduces a new algorithm that applies some of the best characteristics of GMDH to neural network design and develops a Pruning based Regenerated Network by discarding the neurons in a layer which don't contribute for the creation of neurons in next layer. Unlike other conventional algorithms, which generate a network which is a black box, the new algorithm provides visualization of the network displaying all the neurons in the network. The algorithm is general enough that it will accept any number of inputs and any sized training set. To show the flexibility of the Pruning based Regenerated Network, this algorithm is used to analyze different combinations of drugs and determine which pathways in these networks interact and determine the combination of drugs that take advantage of these interactions to maximize a desired effect on genes.
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Date Issued: 2006
PURL: http://purl.flvc.org/fcla/dt/13397
Subject Headings: Neural networks (Computer science), GMDH algorithms, Pattern recognition 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: A new GMDH type algorithm for the development of neural networks for pattern recognition.
Creator: Gilbar, Thomas C., Florida Atlantic University, Pandya, Abhijit S., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Researchers from a wide range of fields have discovered the benefits of applying neural networks to pattern recognition problems. Although applications for neural networks have increased, development of tools to design these networks has been slower. There are few comprehensive network development methods. Those that do exist are slow, inefficient, and application specific, require predetermination of the final network structure, and/or result in large, complicated networks. Finding optimal...
Show moreResearchers from a wide range of fields have discovered the benefits of applying neural networks to pattern recognition problems. Although applications for neural networks have increased, development of tools to design these networks has been slower. There are few comprehensive network development methods. Those that do exist are slow, inefficient, and application specific, require predetermination of the final network structure, and/or result in large, complicated networks. Finding optimal neural networks that balance low network complexity with accuracy is a complicated process that traditional network development procedures are incapable of achieving. Although not originally designed for neural networks, the Group Method of Data Handling (GMDH) has characteristics that are ideal for neural network design. GMDH minimizes the number of required neurons by choosing and keeping only the best neurons and filtering out unneeded inputs. In addition, GMDH develops the neurons and organizes the network simultaneously, saving time and processing power. However, some of the qualities of the network must still be predetermined. This dissertation introduces a new algorithm that applies some of the best characteristics of GMDH to neural network design. The new algorithm is faster, more flexible, and more accurate than traditional network development methods. It is also more dynamic than current GMDH based methods, capable of creating a network that is optimal for an application and training data. Additionally, the new algorithm virtually guarantees that the number of neurons progressively decreases in each succeeding layer. To show its flexibility, speed, and ability to design optimal networks, the algorithm was used to successfully design networks for a wide variety of real applications. The networks developed using the new algorithm were compared to other development methods and network architectures. The new algorithm's networks were more accurate and yet less complicated than the other networks. Additionally, the algorithm designs neurons that are flexible enough to meet the needs of the specific applications, yet similar enough to be implemented using a standardized hardware cell. When combined with the simplified network layout that naturally occurs with the algorithm, this results in networks that can be implemented using Field Programmable Gate Array (FPGA) type devices.
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Date Issued: 2002
PURL: http://purl.flvc.org/fcla/dt/11994
Subject Headings: GMDH algorithms, Neural networks (Computer science), 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: An Intelligent Method For Violence Detection in Live Video Feeds.
Creator: Eneim, Maryam, Marques, Oge, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: In the past few years, violence detection has become an increasingly rele- vant topic in computer vision with many proposed solutions by researchers. This thesis proposes a solution called Criminal Aggression Recognition Engine (CARE), an OpenCV based Java implementation of a violence detection system that can be trained with video datasets to classify action in a live feed as non-violent or violent. The algorithm extends existing work on fast ght detection by implementing violence detection...
Show moreIn the past few years, violence detection has become an increasingly rele- vant topic in computer vision with many proposed solutions by researchers. This thesis proposes a solution called Criminal Aggression Recognition Engine (CARE), an OpenCV based Java implementation of a violence detection system that can be trained with video datasets to classify action in a live feed as non-violent or violent. The algorithm extends existing work on fast ght detection by implementing violence detection of live video, in addition to prerecorded video. The results for violence detection in prerecorded videos are comparable to other popular detection systems and the results for live video are also very encouraging, making the work proposed in this thesis a solid foundation for improved live violence detection systems.
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Date Issued: 2016
PURL: http://purl.flvc.org/fau/fd/FA00004775, http://purl.flvc.org/fau/fd/FA00004775
Subject Headings: Multimedia systems., Image analysis., Computer vision., Visual communication--Social aspects., Social problems--21st century., Pattern recognition systems., Optical pattern recognition.
Format: Document (PDF)

Title: A hybrid color‐based foreground object detection method for automated marine surveillance.
Creator: Furht, Borko, Kalva, Hari, Marques, Oge, Culibrk, Dubravko, Socek, Daniel
Date Issued: 2005
PURL: http://purl.flvc.org/fcla/dt/358420
Subject Headings: Computer vision., Automatic tracking., Digital video., Image processing., Optical pattern recognition.
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: 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)

FAU Theses and Dissertations (64)	+ -
FAU College Collections (2)	+ -
HBOI Faculty Contributions (2)	+ -

Pattern recognition systems (31)	+ -
Optical pattern recognition (13)	+ -
Neural networks (Computer science) (11)	+ -
Biometric identification (8)	+ -
Pattern recognition systems. (6)	+ -

GMDH algorithms (5)	+ -
Image processing (5)	+ -
Visual perception (5)	+ -
Artificial intelligence. (4)	+ -
Computer vision (4)	+ -
Computer vision. (4)	+ -
Digital techniques (4)	+ -
Optical character recognition devices (4)	+ -
Optical pattern recognition. (4)	+ -
Artificial intelligence (3)	+ -
Computer algorithms (3)	+ -
Image processing--Digital techniques (3)	+ -
Imaging systems (3)	+ -
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