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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: 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: 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: 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: A VLSI implementation of a hexagonal topology CCD image sensor.
Creator: Madabushi, Vasudhevan., Florida Atlantic University, Shankar, Ravi, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: In this thesis we report a VLSI design implementation of an application specific, full-frame architecture CCD image sensor for a handwritten Optical Character Recognition system. The design is targeted to the MOSIS 2mu, 2-poly/ 2-metal n-buried channel CCD/CMOS technology. The front side illuminated CCD image sensor uses a transparent polysilicon gate structure and is comprised of 84 (H) x 100 (V) pixels arranged in a hexagonal lattice structure. The sensor has unit pixel dimensions of 18...
Show moreIn this thesis we report a VLSI design implementation of an application specific, full-frame architecture CCD image sensor for a handwritten Optical Character Recognition system. The design is targeted to the MOSIS 2mu, 2-poly/ 2-metal n-buried channel CCD/CMOS technology. The front side illuminated CCD image sensor uses a transparent polysilicon gate structure and is comprised of 84 (H) x 100 (V) pixels arranged in a hexagonal lattice structure. The sensor has unit pixel dimensions of 18 lambda (H) x 16 lambda (V). A second layer of metal is used for shielding certain areas from incident light, and the effective pixel photosite area is 8 lambda x 8 lambda. The imaging pixels use a 3-phase structure (with an innovative addressing scheme for the hexagonal lattice) for image sensing and horizontal charge shift. Columns of charge are shifted into the vertical 2-phase CCD shift registers, which shift the charge out serially at high speed. The chip has been laid out on the 'tinychip' (2250 mu m x 2220 (mu m) pad frame and fabrication through MOSIS is planned next.
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Date Issued: 1995
PURL: http://purl.flvc.org/fcla/dt/15123
Subject Headings: Integrated circuits--Very large scale integration, Optical character recognition devices, Pattern recognition systems, Imaging systems
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: 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: 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: A VLSI implementable handwritten digit recognition system using artificial neural networks.
Creator: Agba, Lawrence C., Florida Atlantic University, Shankar, Ravi, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: A VLSI implementable feature extraction scheme, and two VLSI implementable algorithms for feature classification that should lead to a practical handwritten digit recognition system are proposed. The feature extraction algorithm exploits the concept of holon dynamics. Holons can be regarded as a group of cooperative processors with self-organizing property. Two types of artificial neural network-based classifiers have been evolved to classify these features. The United States Post Office...
Show moreA VLSI implementable feature extraction scheme, and two VLSI implementable algorithms for feature classification that should lead to a practical handwritten digit recognition system are proposed. The feature extraction algorithm exploits the concept of holon dynamics. Holons can be regarded as a group of cooperative processors with self-organizing property. Two types of artificial neural network-based classifiers have been evolved to classify these features. The United States Post Office handwritten digit database was used to train and test these networks. The first type of classifier system used limited interconnect multi-layer perceptron (LIMP) modules in a hierarchical configuration. Each classifier in this system was independently trained and designated to recognize a particular digit. A maximum of sixty-one digits were used to train and 464 digits which included the training set were used to test the classifiers. A cumulative performance of 93.75% (correctly recognized digits) was recorded. The second classifier system consists of a cluster of small multi-layer perceptron (CLUMP) networks. Each cell in this system was independently trained to trace the boundary between two or more digits in the recognition plane. A combination of these cells distinguish a digit from the rest. This system was trained with 1796 digits and tested on 1918 different set of digits. On the training set a performance of 95.55% was recorded while 79.35% resulted from the test data. These results, which are expected to further improve, are superior to those obtained by other researchers on the same database. This technique of digit recognition is general enough for application in the development of a universal alphanumeric recognition system. A hybrid VLSI system consisting of both analog and digital circuitry, and utilizing both Bi-CMOS and switched capacitor technologies has been designed. The design is intended for implementation with the current MOSIS 2 $\mu$m, double poly, double metal, and p-well CMOS technology. The integrated circuit is such that both classifier systems can be realized using the same chip.
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Date Issued: 1990
PURL: http://purl.flvc.org/fcla/dt/12260
Subject Headings: Optical character recognition devices--Computer simulation, Pattern recognition systems--Computer simulation
Format: Document (PDF)

Title: Feature extraction implementation for handwritten numeral recognition.
Creator: Banuru, Prashanth K., Florida Atlantic University, Shankar, Ravi
Abstract/Description: Feature extraction for handwritten character recognition has always been a challenging problem for investigators in the field. The problem gets worse due to large variations present for each type of input character. Our algorithm computes directional features for alphanumeric input mapped on to a hexagonal lattice. The algorithm implements size and scale invariance that is a requirement for achieving a reasonably good recognition rate. Functional performance has been verified for an hexagonal...
Show moreFeature extraction for handwritten character recognition has always been a challenging problem for investigators in the field. The problem gets worse due to large variations present for each type of input character. Our algorithm computes directional features for alphanumeric input mapped on to a hexagonal lattice. The algorithm implements size and scale invariance that is a requirement for achieving a reasonably good recognition rate. Functional performance has been verified for an hexagonal lattice mapped input on the data obtained from the US postal service handwritten character database. In this thesis, we implemented the algorithm in a Xilinx FPGA (XC4xxx series).
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Date Issued: 1994
PURL: http://purl.flvc.org/fcla/dt/15103
Subject Headings: Algorithms, Pattern recognition systems--Computer simulation, Optical character recognition devices--Computer simulation
Format: Document (PDF)

Title: Handwritten digit recognition using neural network integrated chips.
Creator: Bidari, Ravindra Chandrashekar., Florida Atlantic University, Shankar, Ravi
Abstract/Description: Development of a handwritten digit recognition system for real time applications is a feasible goal today due to the many advances pertinent to VLSI. In this research we address the issue of mapping our neural net classification algorithm to Intel's commercially available general purpose Neural Network Chip, 80170NX (ETANN). Most of the proposed techniques used for character recognition have been validated by our research group using various software and hardware simulation methods. The...
Show moreDevelopment of a handwritten digit recognition system for real time applications is a feasible goal today due to the many advances pertinent to VLSI. In this research we address the issue of mapping our neural net classification algorithm to Intel's commercially available general purpose Neural Network Chip, 80170NX (ETANN). Most of the proposed techniques used for character recognition have been validated by our research group using various software and hardware simulation methods. The objective of this thesis was to develop a practical hardware system to perform the final step of classification of handwritten digits in an Optical Character Recognition (OCR) system. Such a hardware implementation would increase the classification speed and also would permit testing in a real life application environment. An efficient mapping scheme was evolved to map the modules of a limited interconnect classification algorithm, CLUMP, to a minimum number of ETANN chips. The hardware modules to interface the ETANN chips to MC68000 education board have been developed and tested. The proposed system is estimated to process the features input in 336 $\mu$s, for our specific implementation, with 12 clock phases and 3 ETANN chips.
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Date Issued: 1992
PURL: http://purl.flvc.org/fcla/dt/14838
Subject Headings: Optical character recognition devices--Computer simulation, Pattern recognition systems--Computer simulation
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: 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)

Title: Nonlinear resonance: determining maximal autoresonant response and modulation of spontaneous otoacoustic emissions.
Creator: Witkov, Carey., Charles E. Schmidt College of Science, Center for Complex Systems and Brain Sciences
Abstract/Description: Sustained resonance in a linear oscillator is achievable with a drive whose constant frequency matches the resonant frequency of the oscillator. In oscillators with nonlinear restoring forces, i.e., Dung-type oscillators, resonant frequency changes with amplitude, so a constant frequency drive generates a beat oscillation instead of sustained resonance. Dung-type oscillators can be driven into sustained resonance, called autoresonance (AR), when drive frequency is swept in time to match the...
Show moreSustained resonance in a linear oscillator is achievable with a drive whose constant frequency matches the resonant frequency of the oscillator. In oscillators with nonlinear restoring forces, i.e., Dung-type oscillators, resonant frequency changes with amplitude, so a constant frequency drive generates a beat oscillation instead of sustained resonance. Dung-type oscillators can be driven into sustained resonance, called autoresonance (AR), when drive frequency is swept in time to match the changing resonant frequency of the oscillator. It is found that near-optimal drive linear sweep rates for autoresonance can be estimated from the beat oscillation resulting from constant frequency excitation. Specically, a least squares estimate of the slope of the Teager-Kaiser instantaneous frequency versus time plot for the rising half-cycle of the beat response to a stationary drive provides a near-optimal estimate of the linear drive sweep rate that sustains resonance in the pendulum, Dung and Dung-Van der Pol oscillators. These predictions are confirmed with model-based numerical simulations. A closed-form approximation to the AM-FM nonlinear resonance beat response of a Dung oscillator driven at its low-amplitude oscillator frequency is obtained from a solution to an associated Mathieu equation. AR time responses are found to evolve along a Mathieu equation primary resonance stability boundary. AR breakdown occurs at sweep rates just past optimal and map to a single stable point just off the Mathieu equation primary resonance stability boundary. Optimal AR sweep rates produce oscillating phase dierences with extrema near 90 degrees, allowing extended time in resonance. AR breakdown occurs when phase difference equals 180 degrees. Nonlinear resonance of the van der Pol type may play a role in the extraordinary sensitivity of the human ear., The mechanism for maintaining the cochlear amplifier at its critical point is currently unknown. The possibility of open-loop control of cochlear operating point, maintaining criticality on average through periodically varying damping (super-regeneration) motivates a study of spontaneous otoacoustic emission (SOAE) amplitude modulation on a short (msec) time scale. An example of periodic amplitude modulation within a wide filter bandwidth is found that appears to be a beat oscillation of two SOAEs.
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Date Issued: 2011
PURL: http://purl.flvc.org/FAU/3174314
Subject Headings: Otoacoustic emissions, Chaotic behavior in systems, Nonlinear theories, Pattern recognition systems
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)

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)	+ -
Davidson, Meredith. (1)	+ -
Department of Mathematical Sciences (1)	+ -
Dhanak, Manhar R. (1)	+ -
Dong, Yuhong. (1)	+ -
Eneim, Maryam (1)	+ -
Furht, Borko (1)	+ -
Gilbar, Thomas C. (1)	+ -
Giusti Urbina, Rafael J. (1)	+ -
Greene, Samuel. (1)	+ -

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