Current Search: College of Engineering and Computer Science (x) » Pandya, Abhijit S. (x)
View All Items
(1 - 20 of 37)
Pages
- Title
- A Biometrics Based Secure Communication Scheme for Bluetooth Environment.
- Creator
- Soni, Puneet, Pandya, Abhijit S., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
A novel personnel authentication and verification system for devices communicating through Bluetooth protocol has been proposed in this thesis. Unlike existing verification systems which provide password or a PIN as a key, the system uses biometrics features as a key. In the implementation of the scheme, ridges and bifurcation based parameters are derived to generate a 128 bit Bluetooth pairing PIN. In this thesis a unique translational and rotational invariant feature set has been developed....
Show moreA novel personnel authentication and verification system for devices communicating through Bluetooth protocol has been proposed in this thesis. Unlike existing verification systems which provide password or a PIN as a key, the system uses biometrics features as a key. In the implementation of the scheme, ridges and bifurcation based parameters are derived to generate a 128 bit Bluetooth pairing PIN. In this thesis a unique translational and rotational invariant feature set has been developed. These extracted feature data, unlike traditional systems which include the extracted data into payload, is used for device connection by generating the 128 bit PIN. The system performance is analyzed using the pairing PIN for inter-sample and intra-sample recognition. To validate the stability of the system the performance is analyzed with external samples which are not a part of the internal database.
Show less - Date Issued
- 2007
- PURL
- http://purl.flvc.org/fau/fd/FA00012556
- Subject Headings
- Bluetooth technology--Security measures, Network performance (Telecommunication), Computer network protocols
- 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.
Show less - 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
- A Phased Approach to Evaluate the Performance of Handoff by Mobile Handsets.
- Creator
- Siddiqui, Arjumand Fatima, Pandya, Abhijit S., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
The ever increasing demand for capacity in wireless cellular networks is resolved by decreasing the size of the cells. The smaller cells created inside large cells are called microcells and they cover smaller and dense areas. As a result of this, the mobile device ends up changing the base stations or performing handover at a much higher rate. To maintain the reliability and quality of the call, it is obligatory to ascertain that the handover is performed at a proper location. If the handover...
Show moreThe ever increasing demand for capacity in wireless cellular networks is resolved by decreasing the size of the cells. The smaller cells created inside large cells are called microcells and they cover smaller and dense areas. As a result of this, the mobile device ends up changing the base stations or performing handover at a much higher rate. To maintain the reliability and quality of the call, it is obligatory to ascertain that the handover is performed at a proper location. If the handover is delayed or the mobile handset drags its base station, it is more probable that either the call will drop or the quality of the call will be compromised. The last thing a mobile handset user would want is to experience any of these two consequences. In this thesis we study the methods to analyze the performance ofhandover by the mobile handsets in the drive setup under test. This thesis presents a model for analyzing the performance ofhandoffbased on the measurements ofthe received signal strength indicator and the color code recorded by the mobile handset in the drive test field path. The model from the simulations is seen to yield results that agree with other research about the performance of mobile handsets.
Show less - Date Issued
- 2007
- PURL
- http://purl.flvc.org/fau/fd/FA00012551
- Subject Headings
- Mobile communication systems--Design, Wireless communication systems--Technological innovations, Cellular telephones--Design
- Format
- Document (PDF)
- Title
- Artificial Intelligence Based Electrical Impedance Tomography for Local Tissue.
- Creator
- Rao, Manasa, Pandya, Abhijit S., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
This research aims at proposing the use of Electrical Impedance Tomography (EIT), a non-invasive technique that makes it possible to measure two or three dimensional impedance of living local tissue in a human body which is applied for medical diagnosis of diseases. In order to achieve this, electrodes are attached to the part of human body and an image of the conductivity or permittivity of living tissue is deduced from surface electrodes. In this thesis we have worked towards alleviating...
Show moreThis research aims at proposing the use of Electrical Impedance Tomography (EIT), a non-invasive technique that makes it possible to measure two or three dimensional impedance of living local tissue in a human body which is applied for medical diagnosis of diseases. In order to achieve this, electrodes are attached to the part of human body and an image of the conductivity or permittivity of living tissue is deduced from surface electrodes. In this thesis we have worked towards alleviating drawbacks of EIT such as estimating parameters by incorporating it in an electrode structure and determining a solution to spatial distribution of bio-impedance to a close proximity. We address the issue of initial parameter estimation and spatial resolution accuracy of an electrode structure by using an arrangement called "divided electrode" for measurement of bio-impedance in a cross section of a local tissue. Its capability is examined by computer simulations, where a distributed equivalent circuit is utilized as a model for the cross section tissue. Further, a novel hybrid model is derived which is a combination of artificial intelligence based gradient free optimization technique and numerical integration in order to estimate parameters. This arne! iorates the achievement of spatial resolution of equivalent circuit model to the closest accuracy.
Show less - Date Issued
- 2008
- PURL
- http://purl.flvc.org/fau/fd/FA00012544
- Subject Headings
- Electrical impedance tomography, Diagnostic imaging--Data processing, Computational intelligence
- Format
- Document (PDF)
- Title
- An artificial neural network architecture for interpolation, function approximation, time series modeling and control applications.
- Creator
- Luebbers, Paul Glenn., Florida Atlantic University, Pandya, Abhijit S., Sudhakar, Raghavan, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
A new artificial neural network architecture called Power Net (PWRNET) and Orthogonal Power Net (OPWRNET) has been developed. Based on the Taylor series expansion of the hyperbolic tangent function, this novel architecture can approximate multi-input multi-layer artificial networks, while requiring only a single layer of hidden nodes. This allows a compact network representation with only one layer of hidden layer weights. The resulting trained network can be expressed as a polynomial...
Show moreA new artificial neural network architecture called Power Net (PWRNET) and Orthogonal Power Net (OPWRNET) has been developed. Based on the Taylor series expansion of the hyperbolic tangent function, this novel architecture can approximate multi-input multi-layer artificial networks, while requiring only a single layer of hidden nodes. This allows a compact network representation with only one layer of hidden layer weights. The resulting trained network can be expressed as a polynomial function of the input nodes. Applications which cannot be implemented with conventional artificial neural networks, due to their intractable nature, can be developed with these network architectures. The degree of nonlinearity of the network can be directly controlled by adjusting the number of hidden layer nodes, thus avoiding problems of over-fitting which restrict generalization. The learning algorithm used for adapting the network is the familiar error back propagation training algorithm. Other learning algorithms may be applied and since only one hidden layer is to be trained, the training performance of the network is expected to be comparable to or better than conventional multi-layer feed forward networks. The new architecture is explored by applying OPWRNET to classification, function approximation and interpolation problems. These applications show that the OPWRNET has comparable performance to multi-layer perceptrons. The OPWRNET was also applied to the prediction of noisy time series and the identification of nonlinear systems. The resulting trained networks, for system identification tasks, can be expressed directly as discrete nonlinear recursive polynomials. This characteristic was exploited in the development of two new neural network based nonlinear control algorithms, the Linearized Self-Tuning Controller (LSTC) and a variation of a Neural Adaptive Controller (NAC). These control algorithms are compared to a linear self-tuning controller and an artificial neural network based Inverse Model Controller. The advantages of these new controllers are discussed.
Show less - Date Issued
- 1994
- PURL
- http://purl.flvc.org/fcla/dt/12357
- Subject Headings
- Neural networks (Computer science)
- Format
- Document (PDF)
- Title
- A case study: Performance enhancement of nonlinear combinational optimization problem by neural networks.
- Creator
- Soni, Saurabh., Florida Atlantic University, Pandya, Abhijit S., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
Artificial Neural Networks have been widely used for obtaining solutions for combinational optimization problems. Traveling Salesman problem is a well known nonlinear combinational optimization problem. In Traveling Salesman problem, a fixed number of cities is given. An optimal tour of all these cities is required such that each city is visited only once and the total tour distance to be covered has to be minimized. Hopfield Networks have been applied for generating an optimal solution....
Show moreArtificial Neural Networks have been widely used for obtaining solutions for combinational optimization problems. Traveling Salesman problem is a well known nonlinear combinational optimization problem. In Traveling Salesman problem, a fixed number of cities is given. An optimal tour of all these cities is required such that each city is visited only once and the total tour distance to be covered has to be minimized. Hopfield Networks have been applied for generating an optimal solution. However there are certain factors which result in instability and local optimization of Hopfield Networks. In such cases the solutions obtained may not be optimal and feasible. In this thesis, the application of the K-Means algorithm is combined with the Hopfield Networks to generate more stable and optimum solutions to traveling salesperson problem.
Show less - Date Issued
- 2004
- PURL
- http://purl.flvc.org/fcla/dt/13108
- Subject Headings
- Neural networks (Computer science), Traveling-salesman problem
- Format
- Document (PDF)
- Title
- Concord: A proactive lightweight middleware to enable seamless connectivity in a pervasive environment.
- Creator
- Mutha, Mahesh., Florida Atlantic University, Hsu, Sam, Pandya, Abhijit S., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
One of the major components of any pervasive system is its proactive behavior. Various models have been developed to provide system wide changes which would enable proactive behavior. A major drawback of these approaches is that they do not address the need to make use of existing applications whose design cannot be changed. To overcome this drawback, a middleware architecture called "Concord" is proposed. Concord is based on a simple model which consists of Lookup Server and Database. The...
Show moreOne of the major components of any pervasive system is its proactive behavior. Various models have been developed to provide system wide changes which would enable proactive behavior. A major drawback of these approaches is that they do not address the need to make use of existing applications whose design cannot be changed. To overcome this drawback, a middleware architecture called "Concord" is proposed. Concord is based on a simple model which consists of Lookup Server and Database. The rewards for this simple model are many. First, Concord uses the existing computing infrastructure. Second, Concord standardizes the interfaces for all services and platforms. Third new services can be added dynamically without any need for reconfiguration. Finally, Concord consists of Database that can maintain and publish the active set of available resources. Thus Concord provides a solid system for integration of various entities to provide seamless connectivity and enable proactive behavior.
Show less - Date Issued
- 2005
- PURL
- http://purl.flvc.org/fcla/dt/13234
- Subject Headings
- CONCORD (Computer architecture), Middleware, Computer architecture, Database management
- Format
- Document (PDF)
- Title
- A connectionist approach to adaptive reasoning: An expert system to predict skid numbers.
- Creator
- Reddy, Mohan S., Florida Atlantic University, Pandya, Abhijit S., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
This project illustrates the neural network approach to constructing a fuzzy logic decision system. This technique employs an artificial neural network (ANN) to recognize the relationships that exit between the various inputs and outputs. An ANN is constructed based on the variables present in the application. The network is trained and tested. Various training methods are explored, some of which include auxiliary input and output columns. After successful testing, the ANN is exposed to new...
Show moreThis project illustrates the neural network approach to constructing a fuzzy logic decision system. This technique employs an artificial neural network (ANN) to recognize the relationships that exit between the various inputs and outputs. An ANN is constructed based on the variables present in the application. The network is trained and tested. Various training methods are explored, some of which include auxiliary input and output columns. After successful testing, the ANN is exposed to new data and the results are grouped into fuzzy membership sets based membership evaluation rules. This data grouping forms the basis of a new ANN. The network is now trained and tested with the fuzzy membership data. New data is presented to the trained network and the results form the fuzzy implications. This approach is used to compute skid resistance values from G-analyst accelerometer readings on open grid bridge decks.
Show less - Date Issued
- 1996
- PURL
- http://purl.flvc.org/fcla/dt/15239
- Subject Headings
- Artificial intelligence, Fuzzy logic, Neural networks (Computer science), Pavements--Skid resistance
- Format
- Document (PDF)
- Title
- Cross-domain authentication for multi-protocol phones.
- Creator
- Thakker, 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 last decade has seen a surge in demand for cellular and WLAN networks. With the introduction of Voice Over IP, cellular companies are looking at WLAN-Cellular integrated networks that shall provide users with economical voice and data services. These networks shall be complimentary to the existing cellular networks. A lot of money is spent in registering and authenticating new users, since they are separately authenticated and registered on the WLAN and Cellular domains. This leads to...
Show moreThe last decade has seen a surge in demand for cellular and WLAN networks. With the introduction of Voice Over IP, cellular companies are looking at WLAN-Cellular integrated networks that shall provide users with economical voice and data services. These networks shall be complimentary to the existing cellular networks. A lot of money is spent in registering and authenticating new users, since they are separately authenticated and registered on the WLAN and Cellular domains. This leads to extra costs for the company. Thus for the integrated networks to have an impact on the market some issues such as simpler authentication and registration must be resolved. Therefore we propose a new inter-working model that shall addresses the authentication and registration problem for an integrated network for voice and data. The Single authentication system of the new inter-working model, shall authenticate the user in an integrated network using the SIM credentials, this authentication shall be valid for both voice and data. Also registration costs will be saved by preventing separate registration of users in the WLAN and Cellular domain.
Show less - Date Issued
- 2005
- PURL
- http://purl.flvc.org/fcla/dt/13260
- Subject Headings
- Integrated services digital networks, Digital telephone systems, Wireless communication systems--Technological innovations, Mobile communication systems--Technological innovations
- 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.
Show less - 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
- 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.
Show less - 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
- Firewall formulation driven by risk analysis.
- Creator
- Srinivasan, Sriram, Jr., Florida Atlantic University, Pandya, Abhijit S., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
At the turn of the new millennium, the focus of Information Technology Management turned to Information and Systems Security, as opposed to competitive advantage investment. In catering to the security needs of various firms and institutions, it is seen that different entities require varying Information Security configurations. This thesis attempts to utilize Risk Analysis, a commonly used procedure in business realms, to formulate customized Firewalls based on the specific needs of a...
Show moreAt the turn of the new millennium, the focus of Information Technology Management turned to Information and Systems Security, as opposed to competitive advantage investment. In catering to the security needs of various firms and institutions, it is seen that different entities require varying Information Security configurations. This thesis attempts to utilize Risk Analysis, a commonly used procedure in business realms, to formulate customized Firewalls based on the specific needs of a network, subsequently building an effective system following the "Defense in Depth" strategy. This is done by first choosing an efficient Risk Analysis model which suits the process of creating Firewall policies, and then applying it to a particular case study. A network within Florida Atlantic University is used as an experimental test case, and by analyzing the traffic to which it is subject while behind a single Firewall layer, a specific Security Policy is arrived at and implemented.
Show less - Date Issued
- 2006
- PURL
- http://purl.flvc.org/fcla/dt/13348
- Subject Headings
- Computer networks--Security measures, Electronic data processing departments--Security measures, Firewalls (Computer security), Risk assessment
- Format
- Document (PDF)
- Title
- Forecasting foreign exchange rates using neural networks.
- Creator
- Talati, Amit H., Florida Atlantic University, Pandya, Abhijit S., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
Time series is a phenomena which appears in the financial world in various forms. One of the objectives of time series is to forecast the future based on the past. The goal of this thesis is to use foreign exchange time series, and predict its future values and trends using neural networks. The thesis covers background work in this area and discusses the results obtained by other researchers. A neural network is then developed to predict the future values of the USD/GBP and USD/DEM exchange...
Show moreTime series is a phenomena which appears in the financial world in various forms. One of the objectives of time series is to forecast the future based on the past. The goal of this thesis is to use foreign exchange time series, and predict its future values and trends using neural networks. The thesis covers background work in this area and discusses the results obtained by other researchers. A neural network is then developed to predict the future values of the USD/GBP and USD/DEM exchange rates. Both single-step and iterated multi-step predictions are considered. The performance of neural networks strongly depends on the inputs supplied. The effect of the changes in the number of inputs is also considered, and a method suggested for deciding on the optimum number. The forecasting of foreign exchange rates is a challenge because of the dynamic nature of the FOREX market and its dependencies on world events. The tool used for building the neural network and validating the approach is "Brainmaker".
Show less - Date Issued
- 2000
- PURL
- http://purl.flvc.org/fcla/dt/12699
- Subject Headings
- Foreign exchange rates--Mathmematical models, Foreign exchange--Forecasting--Mathematical models, Neural networks (Computer science)
- Format
- Document (PDF)
- Title
- Fuzzy vault fingerprint cryptography: Experimental and simulation studies.
- Creator
- Kotlarchyk, Alex J., Florida Atlantic University, Pandya, Abhijit S., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
The fuzzy vault scheme introduced by Juels and Sudan [Jue02] was implemented in a fingerprint cryptography system using COTS software. This system proved to be unsuccessful. Failure analysis led to a series of simulations to investigate the parameters and system thresholds necessary for such a system to perform adequately and as guidance for constructing similar systems in the future. First, a discussion of the role of biometrics in data security and cryptography is presented, followed by a...
Show moreThe fuzzy vault scheme introduced by Juels and Sudan [Jue02] was implemented in a fingerprint cryptography system using COTS software. This system proved to be unsuccessful. Failure analysis led to a series of simulations to investigate the parameters and system thresholds necessary for such a system to perform adequately and as guidance for constructing similar systems in the future. First, a discussion of the role of biometrics in data security and cryptography is presented, followed by a review of the key developments leading to the development of the fuzzy vault scheme. The relevant mathematics and algorithms are briefly explained. This is followed by a detailed description of the implementation and simulation of the fuzzy vault scheme. Finally, conclusions drawn from analysis of the results of this research are presented.
Show less - Date Issued
- 2006
- PURL
- http://purl.flvc.org/fcla/dt/13360
- Subject Headings
- Computer networks--Security measures, Computer security, Data encryption (Computer science)
- Format
- Document (PDF)
- Title
- The human face recognition problem: A solution based on third-order synthetic neural networks and isodensity analysis.
- Creator
- Uwechue, Okechukwu A., Florida Atlantic University, Pandya, Abhijit S., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
Third-order synthetic neural networks are applied to the recognition of isodensity facial images extracted from digitized grayscale facial images. A key property of neural networks is their ability to recognize invariances and extract essential parameters from complex high-dimensional data. In pattern recognition an input image must be recognized regardless of its position, size, and angular orientation. In order to achieve this, the neural network needs to learn the relationships between the...
Show moreThird-order synthetic neural networks are applied to the recognition of isodensity facial images extracted from digitized grayscale facial images. A key property of neural networks is their ability to recognize invariances and extract essential parameters from complex high-dimensional data. In pattern recognition an input image must be recognized regardless of its position, size, and angular orientation. In order to achieve this, the neural network needs to learn the relationships between the input pixels. Pattern recognition requires the nonlinear subdivision of the pattern space into subsets representing the objects to be identified. Single-layer neural networks can only perform linear discrimination. However, multilayer first-order networks and high-order neural networks can both achieve this. The most significant advantage of a higher-order net over a traditional multilayer perceptron is that invariances to 2-dimensional geometric transformations can be incorporated into the network and need not be learned through prolonged training with an extensive family of exemplars. It is shown that a third-order network can be used to achieve translation-, scale-, and rotation-invariant recognition with a significant reduction in training time over other neural net paradigms such as the multilayer perceptron. A model based on an enhanced version of the Widrow-Hoff training algorithm and a new momentum paradigm are introduced and applied to the complex problem of human face recognition under varying facial expressions. Arguments for the use of isodensity information in the recognition algorithm are put forth and it is shown how the technique of coarse-coding is applied to reduce the memory required for computer simulations. The combination of isodensity information and neural networks for image recognition is described and its merits over other image recognition methods are explained. It is shown that isodensity information coupled with the use of an "adaptive threshold strategy" (ATS) yields a system that is relatively impervious to image contrast noise. The new momentum paradigm produces much faster convergence rates than ordinary momentum and renders the network behaviour independent of its training parameters over a broad range of parameter values.
Show less - Date Issued
- 1996
- PURL
- http://purl.flvc.org/fcla/dt/12464
- Subject Headings
- Image processing, Face perception, Neural networks (Computer science)
- 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.
Show less - Date Issued
- 2002
- PURL
- http://purl.flvc.org/fcla/dt/12996
- Subject Headings
- GMDH algorithms, Neural networks (Computer science), Time-series analysis, Pattern recognition systems
- Format
- Document (PDF)
- Title
- An intelligent neural network forecaster to predict the Standard & Poor 500's index.
- Creator
- Shah, Sulay Bipin., 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 we present an intelligent forecaster based on neural network technology to capture the future path of the market indicator. This thesis is about the development of a new methodology in financial forecasting. An effort is made to develop a neural network forecaster using the financial indicators as the input variables. A complex recurrent neural network is used to capture the behavior of the nonlinear characteristics of the S&P 500. The main outcome of this research is, a...
Show moreIn this thesis we present an intelligent forecaster based on neural network technology to capture the future path of the market indicator. This thesis is about the development of a new methodology in financial forecasting. An effort is made to develop a neural network forecaster using the financial indicators as the input variables. A complex recurrent neural network is used to capture the behavior of the nonlinear characteristics of the S&P 500. The main outcome of this research is, a systematic way of constructing a forecaster for nonlinear and non-stationary data series of S&P 500 that leads to very good out-of-sample prediction. The results of the training and testing of the network are presented along with conclusion. The tool used for the validation of this research is "Brainmaker". This thesis also contains a brief survey of available tools for financial forecasting.
Show less - Date Issued
- 1999
- PURL
- http://purl.flvc.org/fcla/dt/15741
- Subject Headings
- Neural networks (Computer science), Stock price forecasting, Time-series analysis
- Format
- Document (PDF)
- Title
- An intelligent system for predicting bridge condition rating.
- Creator
- Thiruppathi, Arulseelan., Florida Atlantic University, Pandya, Abhijit S., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
A neural network based model for prediction of bridge condition rating is proposed. The back-propagation algorithm is used to train the network to recognize the pattern of deterioration of bridges and use this knowledge in predicting the future condition rating of a bridge. The various factors which influence the deterioration rate are considered as input to the system. The model then predicts the condition rating of the three major sub-components of a bridge viz. the deck, sub-structure and...
Show moreA neural network based model for prediction of bridge condition rating is proposed. The back-propagation algorithm is used to train the network to recognize the pattern of deterioration of bridges and use this knowledge in predicting the future condition rating of a bridge. The various factors which influence the deterioration rate are considered as input to the system. The model then predicts the condition rating of the three major sub-components of a bridge viz. the deck, sub-structure and the super-structure. Fuzzy logic is used to evaluate the overall condition rating of the bridge using the condition rating of the components. To demonstrate the superiority of the neural network model over the traditional models, the history of the deterioration rates for the components were also considered in the prediction of their future condition. The proposed system is versatile and can be easily extended to include other parameters and updated from time to time without much effort.
Show less - Date Issued
- 1994
- PURL
- http://purl.flvc.org/fcla/dt/15096
- Subject Headings
- Bridges--Maintenance and repair, Neural networks (Computer science)
- Format
- Document (PDF)
- Title
- iVEST A: Interactive Data Visualization and Analysis for Drive Test Data Evaluation.
- Creator
- Lee, Yongsuk, Zhu, Xingquan, Pandya, Abhijit S., Hsu, Sam, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
In this thesis, a practical solution for drive test data evaluation and a real application are studied. We propose a system framework to project high dimensional Drive Test Data (DTD) to well-organized web pages, such that users can visually review phone performance with respect to different factors. The proposed application, iVESTA (interactive Visualization and Evaluation System for driven Test dAta), employs a web-based architecture which enables users to upload DTD and immediately...
Show moreIn this thesis, a practical solution for drive test data evaluation and a real application are studied. We propose a system framework to project high dimensional Drive Test Data (DTD) to well-organized web pages, such that users can visually review phone performance with respect to different factors. The proposed application, iVESTA (interactive Visualization and Evaluation System for driven Test dAta), employs a web-based architecture which enables users to upload DTD and immediately visualize the test results and observe phone and network performances with respect to different factors such as dropped call rate, signal quality, vehicle speed, handover and network delays. iVESTA provides practical solutions for mobile phone manufacturers and network service providers to perform comprehensive study on their products from the real-world DTD.
Show less - Date Issued
- 2007
- PURL
- http://purl.flvc.org/fau/fd/FA00012532
- Subject Headings
- Information visualization--Data processing, Object-oriented programming (Computer science), Information technology--Management, Application software--Development
- Format
- Document (PDF)
- Title
- Knowledge Discovery Through Drive Test Data Visualization.
- Creator
- Saxena, Shalini, Pandya, Abhijit S., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
With the increasing number of cellular phone service subscribers, the telecommunications service providers have placed immense emphasis on improving audio quality and ensure fewer dropped calls. Handoff behavior of all handsets is an important factor in quality of service of a mobile phone service. This thesis focuses on the analysis of large volumes of diagnostic data collected from mobile phones in the real world and the identification of aberrant behavior of a mobile handset under test by...
Show moreWith the increasing number of cellular phone service subscribers, the telecommunications service providers have placed immense emphasis on improving audio quality and ensure fewer dropped calls. Handoff behavior of all handsets is an important factor in quality of service of a mobile phone service. This thesis focuses on the analysis of large volumes of diagnostic data collected from mobile phones in the real world and the identification of aberrant behavior of a mobile handset under test by means of drive test data visualization. Our target was to identify poor mobility decisions that are made by the handsets in calls. Premature, delayed or exceedingly sensitive decisions are considered poor mobility decisions. The goal was to compare a set of behaviors from a baseline unit (one accepted to generally operate well). We were able to identify a particular call that was exhibiting a different path (talking to a different cell than expected or taking longer to move to a new cell). We designed a chi-square statistical test to evaluate the performance of specific mobile handset models. We also developed a mobility tool that evaluated the handset's performance by means of mapping the handoffs on the Google Maps. The mapping of the handoffs by means of the Google Maps were very powerful in identifying the above mentioned mobility patterns.
Show less - Date Issued
- 2007
- PURL
- http://purl.flvc.org/fau/fd/FA00012548
- Subject Headings
- Mobile communication systems--Quality control, Wireless communication systems--Technological innovations, Cellular telephones--Design
- Format
- Document (PDF)
