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Title: Estimation of Internet transit times using a fast-computing artificial neural network (FC-ANN).
Creator: Fasulo, Joseph V., Florida Atlantic University, Neelakanta, Perambur S.
Abstract/Description: The objective of this research is to determine the macroscopic behavior of packet transit-times across the global Internet cloud using an artificial neural network (ANN). Specifically, the problem addressed here refers to using a "fast-convergent" ANN for the purpose indicated. The underlying principle of fast-convergence is that, the data presented in training and prediction modes of the ANN is in the entropy (information-theoretic) domain, and the associated annealing process is "tuned" to...
Show moreThe objective of this research is to determine the macroscopic behavior of packet transit-times across the global Internet cloud using an artificial neural network (ANN). Specifically, the problem addressed here refers to using a "fast-convergent" ANN for the purpose indicated. The underlying principle of fast-convergence is that, the data presented in training and prediction modes of the ANN is in the entropy (information-theoretic) domain, and the associated annealing process is "tuned" to adopt only the useful information content and discard the posentropy part of the data presented. To demonstrate the efficacy of the research pursued, a feedforward ANN structure is developed and the necessary transformations required to convert the input data from the parametric-domain to the entropy-domain (and a corresponding inverse transformation) are followed so as to retrieve the output in parametric-domain. The fast-convergent or fast-computing ANN (FC-ANN) developed is deployed to predict the packet-transit performance across the Internet. (Abstract shortened by UMI.)
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Date Issued: 2001
PURL: http://purl.flvc.org/fcla/dt/12835
Subject Headings: Neural networks (Computer science), Information theory, Packet switching (Data transmission), Internet
Format: Document (PDF)

Title: THE EFFECT OF LANE CHANGE VOLATILITY ON REAL TIME ACCIDENT PREDICTION.
Creator: Tesheira, Hamilton, Mahgoub, Imad, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: According to a March 2019 publication by the National Highway Transportation Safety Administration(NHTSA), 62% of all police-reported accidents in the United States between 2011 and 2015 could have been prevented or mitigated with the use of five groups of collision avoidance technologies in passenger vehicles: (1) forward collision prevention, (2) lane keeping, (3) blind zone detection, (4) forward pedestrian impact, and (5) backing collision avoidance. These technologies work mostly by...
Show moreAccording to a March 2019 publication by the National Highway Transportation Safety Administration(NHTSA), 62% of all police-reported accidents in the United States between 2011 and 2015 could have been prevented or mitigated with the use of five groups of collision avoidance technologies in passenger vehicles: (1) forward collision prevention, (2) lane keeping, (3) blind zone detection, (4) forward pedestrian impact, and (5) backing collision avoidance. These technologies work mostly by reducing or removing the risks involved in a lane change maneuver; yet, the Broward transportation management system does not directly address these risk. Therefore, we are proposing a Machine Learning based approach to real-time accident prediction for Broward I-95 using the C5.1 Decision Tree and the Multi-Layer Perceptron Neural Network to address them. To do this, we design a new measure of volatility, Lane Change Volatility(LCV), which measures the potential for a lane change in a segment of the highway. Our research found that LCV is an important predictor of accidents in an exit zone and when considered in tandem with current system variable, such as lighting conditions, the machine learning classifiers are able to predict accidents in the exit zone with an accuracy rate of over 98%.
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Date Issued: 2019
PURL: http://purl.flvc.org/fau/fd/FA00013420
Subject Headings: Traffic accidents, Traffic accidents--Forecasting, Automobile driving--Lane changing, Perceptrons, Neural networks (Computer science)
Format: Document (PDF)

Title: A neural network-based receiver for interference cancellation in multi-user environment for DS/CDMA systems.
Creator: Shukla, Kunal Hemang., Florida Atlantic University, Pandya, Abhijit S., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: The objective of this work is to apply and investigate the performance of a neural network-based receiver for interference cancellation in multiuser direct sequence code division multiple access (DSCDMA) wireless networks. This research investigates a Receiver model which uses Neural Network receiver in combination with a conventional receiver system to provide an efficient mechanism for the Interference Suppression in DS/CDMA systems. The Conventional receiver is used for the time during...
Show moreThe objective of this work is to apply and investigate the performance of a neural network-based receiver for interference cancellation in multiuser direct sequence code division multiple access (DSCDMA) wireless networks. This research investigates a Receiver model which uses Neural Network receiver in combination with a conventional receiver system to provide an efficient mechanism for the Interference Suppression in DS/CDMA systems. The Conventional receiver is used for the time during which the neural network receiver is being trained. Once the NN receiver is trained the conventional receiver system is deactivated. It is demonstrated that this receiver when used along with an efficient Neural network model can outperform MMSE receiver or DFFLE receiver with significant advantages, such as improved bit-error ratio (BER) performance, adaptive operation, single-user detection in DS/CDMA environment and a near far resistant system.
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Date Issued: 2003
PURL: http://purl.flvc.org/fcla/dt/12975
Subject Headings: Neural networks (Computer science), Wireless communication systems, Code division multiple access
Format: Document (PDF)

Title: A new methodology to predict certain characteristics of stock market using time-series phenomena.
Creator: Shah, Trupti U., Florida Atlantic University, Pandya, Abhijit S., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: The goal of time series forecasting is to identify the underlying pattern and use these patterns to predict the future path of the series. To capture the future path of a dynamic stock market variable is one of the toughest challenges. This thesis is about the development of a new methodology in financial forecasting. An effort is made to develop a neural network forecaster using time-series phenomena. The main outcome of this new approach for financial forecasting is a systematic way of...
Show moreThe goal of time series forecasting is to identify the underlying pattern and use these patterns to predict the future path of the series. To capture the future path of a dynamic stock market variable is one of the toughest challenges. This thesis is about the development of a new methodology in financial forecasting. An effort is made to develop a neural network forecaster using time-series phenomena. The main outcome of this new approach for financial forecasting is a systematic way of constructing a Neural Network Forecaster for nonlinear and non-stationary time-series data that leads to very good out-of-sample prediction. The tool used for the validation of this research is "Brainmaker". This thesis also contains a small survey of available tools used for financial forecasting.
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Date Issued: 1999
PURL: http://purl.flvc.org/fcla/dt/15677
Subject Headings: Time-series analysis, Neural networks (Computer science), Stock price forecasting
Format: Document (PDF)

Title: A study of Internet-based control of processes.
Creator: Popescu, Cristian., Florida Atlantic University, Zhuang, Hanqi, Wang, Yuan, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: In certain applications, one needs to control physical plants that operate in hazardous conditions. In such situations, it is necessary to acquire access to the controller from a different (remote) location through data communication networks, in order to interconnect the remote location and the controller. The use of such network linking between the plant and the controller may introduce network delays, which would affect adversely the performance of the process control. The main theoretical...
Show moreIn certain applications, one needs to control physical plants that operate in hazardous conditions. In such situations, it is necessary to acquire access to the controller from a different (remote) location through data communication networks, in order to interconnect the remote location and the controller. The use of such network linking between the plant and the controller may introduce network delays, which would affect adversely the performance of the process control. The main theoretical contribution of this thesis is to answer the following question: How large can a network delay be tolerated such that the delayed closed-loop system is locally asymptotically stable? An explicit time-independent bound for the delay is derived. In addition, various practical realizations for the remote control tasks are presented, utilizing a set of predefined classes for serial communication, data-acquisition modules and stream-based sockets. Due to the presence of a network, implementing an efficient control scheme is a not trivial problem. Hence, two practical frameworks for Internet-based control are illustrated in this thesis. Related implementation issues are addressed in detail. Examples and case studies are provided to demonstrate the effectiveness of the proposal approach.
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Date Issued: 2003
PURL: http://purl.flvc.org/fcla/dt/13073
Subject Headings: Time delay systems, Process control, Computer networks--Remote access, World Wide Web
Format: Document (PDF)

Title: Use of GIS to improve the user interface design of an airline reservation web site.
Creator: Wilson, Jennifer L., Florida Atlantic University, Ivy, Russell L.
Abstract/Description: As use of the Internet becomes pervasive, user interface design of web sites becomes increasingly important. Consumers must be able to easily and quickly perform the functions they desire. Travel industry applications have a large market potential on the Internet. Because of the geographical relationships of locations and functions in the travel industry, the use of cartography and GIS can be very beneficial to user interfaces of these applications. This paper examines functions, inputs, and...
Show moreAs use of the Internet becomes pervasive, user interface design of web sites becomes increasingly important. Consumers must be able to easily and quickly perform the functions they desire. Travel industry applications have a large market potential on the Internet. Because of the geographical relationships of locations and functions in the travel industry, the use of cartography and GIS can be very beneficial to user interfaces of these applications. This paper examines functions, inputs, and user interface of current airline reservation web sites, and looks at some current examples of GIS use on the Internet. It then discusses ways to improve the user interface design of airline reservation web sites using GIS to create more powerful and easy-to-use applications that also incorporate other aspects of the travel industry.
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Date Issued: 1999
PURL: http://purl.flvc.org/fcla/dt/15669
Subject Headings: Geographic information systems, Airlines--Reservation systems--Computer network resources, World Wide Web
Format: Document (PDF)

Title: FINANCIAL TIME-SERIES ANALYSIS WITH DEEP NEURAL NETWORKS.
Creator: Rimal, Binod, Hahn, William Edward, Florida Atlantic University, Department of Mathematical Sciences, Charles E. Schmidt College of Science
Abstract/Description: Financial time-series data are noisy, volatile, and nonlinear. The classic statistical linear models may not capture those underlying structures of the data. The rapid advancement in artificial intelligence and machine learning techniques, availability of large-scale data, and increased computational capabilities of a machine opens the door to developing sophisticated deep learning models to capture the nonlinearity and hidden information in the data. Creating a robust model by unlocking the...
Show moreFinancial time-series data are noisy, volatile, and nonlinear. The classic statistical linear models may not capture those underlying structures of the data. The rapid advancement in artificial intelligence and machine learning techniques, availability of large-scale data, and increased computational capabilities of a machine opens the door to developing sophisticated deep learning models to capture the nonlinearity and hidden information in the data. Creating a robust model by unlocking the power of a deep neural network and using real-time data is essential in this tech era. This study constructs a new computational framework to uncover the information in the financial time-series data and better inform the related parties. It carries out the comparative analysis of the performance of the deep learning models on stock price prediction with a well-balanced set of factors from fundamental data, macroeconomic data, and technical indicators responsible for stock price movement. We further build a novel computational framework through a merger of recurrent neural networks and random compression for the time-series analysis. The performance of the model is tested on a benchmark anomaly time-series dataset. This new computational framework in a compressed paradigm leads to improved computational efficiency and data privacy. Finally, this study develops a custom trading simulator and an agent-based hybrid model by combining gradient and gradient-free optimization methods. In particular, we explore the use of simulated annealing with stochastic gradient descent. The model trains a population of agents to predict appropriate trading behaviors such as buy, hold, or sell by optimizing the portfolio returns. Experimental results on S&P 500 index show that the proposed model outperforms the baseline models.
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Date Issued: 2022
PURL: http://purl.flvc.org/fau/fd/FA00014009
Subject Headings: Neural networks (Computer science), Deep learning (Machine learning), Time-series analysis, Stocks, Simulated annealing (Mathematics)
Format: Document (PDF)

Title: PRESERVING KNOWLEDGE IN SIMULATED BEHAVIORAL ACTION LOOPS.
Creator: St.Clair, Rachel, Barenholtz, Elan, Hahn, William, Florida Atlantic University, Center for Complex Systems and Brain Sciences, Charles E. Schmidt College of Science
Abstract/Description: One basic goal of artificial learning systems is the ability to continually learn throughout that system’s lifetime. Transitioning between tasks and re-deploying prior knowledge is thus a desired feature of artificial learning. However, in the deep-learning approaches, the problem of catastrophic forgetting of prior knowledge persists. As a field, we want to solve the catastrophic forgetting problem without requiring exponential computations or time, while demonstrating real-world relevance....
Show moreOne basic goal of artificial learning systems is the ability to continually learn throughout that system’s lifetime. Transitioning between tasks and re-deploying prior knowledge is thus a desired feature of artificial learning. However, in the deep-learning approaches, the problem of catastrophic forgetting of prior knowledge persists. As a field, we want to solve the catastrophic forgetting problem without requiring exponential computations or time, while demonstrating real-world relevance. This work proposes a novel model which uses an evolutionary algorithm similar to a meta-learning objective, that is fitted with a resource constraint metrics. Four reinforcement learning environments are considered with the shared concept of depth although the collection of environments is multi-modal. This system shows preservation of some knowledge in sequential task learning and protection of catastrophic forgetting in deep neural networks.
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Date Issued: 2022
PURL: http://purl.flvc.org/fau/fd/FA00013896
Subject Headings: Artificial intelligence, Deep learning (Machine learning), Reinforcement learning, Neural networks (Computer science)
Format: Document (PDF)

Title: DATA-DRIVEN IDENTIFICATION AND CONTROL OF TURBULENT STRUCTURES USING DEEP NEURAL NETWORKS.
Creator: Jagodinski, Eric, Verma, Siddhartha, Florida Atlantic University, Department of Ocean and Mechanical Engineering, College of Engineering and Computer Science
Abstract/Description: Wall-bounded turbulent flows are pervasive in numerous physics and engineering applications. Such flows tend to have a strong impact on the design of ships, airplanes and rockets, industrial chemical mixing, wind and hydrokinetic energy, utility infrastructure and innumerable other fields. Understanding and controlling wall bounded turbulence has been a long-pursued endeavor yielding plentiful scientific and engineering discoveries, but there is much that remains unexplained from a...
Show moreWall-bounded turbulent flows are pervasive in numerous physics and engineering applications. Such flows tend to have a strong impact on the design of ships, airplanes and rockets, industrial chemical mixing, wind and hydrokinetic energy, utility infrastructure and innumerable other fields. Understanding and controlling wall bounded turbulence has been a long-pursued endeavor yielding plentiful scientific and engineering discoveries, but there is much that remains unexplained from a fundamental viewpoint. One unexplained phenomenon is the formation and impact of coherent structures like the ejections of slow near-wall fluid into faster moving ow which have been shown to correlate with increases in friction drag. This thesis focuses on recognizing and regulating organized structures within wall-bounded turbulent flows using a variety of machine learning techniques to overcome the nonlinear nature of this phenomenon. Deep Learning has provided new avenues of analyzing large amounts of data by applying techniques modeled after biological neurons. These techniques allow for the discovery of nonlinear relationships in massive, complex systems like the data found frequently in fluid dynamics simulation. Using a neural network architecture called Convolutional Neural Networks that specializes in uncovering spatial relationships, a network was trained to estimate the relative intensity of ejection structures within turbulent flow simulation without any a priori knowledge of the underlying flow dynamics. To explore the underlying physics that the trained network might reveal, an interpretation technique called Gradient-based Class Activation Mapping was modified to identify salient regions in the flow field which most influenced the trained network to make an accurate estimation of these organized structures. Using various statistical techniques, these salient regions were found to have a high correlation to ejection structures, and to high positive kinetic energy production, low negative production, and low energy dissipation regions within the flow. Additionally, these techniques present a general framework for identifying nonlinear causal structures in general three-dimensional data in any scientific domain where the underlying physics may be unknown.
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Date Issued: 2022
PURL: http://purl.flvc.org/fau/fd/FA00014119
Subject Headings: Turbulent flow, Turbulence, Neural networks (Computer science), Deep learning (Machine learning)
Format: Document (PDF)

Title: FROM DNA TO GRAVITATIONAL WAVES: APPLICATIONS OF STATISTICS AND MACHINE LEARNING.
Creator: Alemrajabi, Mahsa Firouzabad, Tichy, Wolfgang, Assis, Raquel, Florida Atlantic University, Department of Physics, Charles E. Schmidt College of Science
Abstract/Description: In the current world of fast-paced data production, statistics and machine learning tools are essential for interpreting and utilizing the full potential of this data. This dissertation comprises three studies employing statistical analysis and Convolutional Neural Network models. First, the research investigates the genetic evolution of the SARS-CoV-2 RNA molecule, emphasizing the role of epistasis in the RNA virus’s ability to adapt and survive. Through statistical tests, this study...
Show moreIn the current world of fast-paced data production, statistics and machine learning tools are essential for interpreting and utilizing the full potential of this data. This dissertation comprises three studies employing statistical analysis and Convolutional Neural Network models. First, the research investigates the genetic evolution of the SARS-CoV-2 RNA molecule, emphasizing the role of epistasis in the RNA virus’s ability to adapt and survive. Through statistical tests, this study validates the significant impacts of genetic interactions and mutations on the virus’s structural changes over time, offering insights into its evolutionary dynamics. Secondly, the dissertation explores medical diagnosis by implementing Convolutional Neural Networks to differentiate between lung CT-scans of COVID-19 and non-COVID patients. This portion of the research demonstrates the capability of deep learning to enhance diagnostic processes, thereby reducing time and increasing accuracy in clinical settings. Lastly, we delve into gravitational wave detection, an area of astrophysics requiring precise data analysis to identify signals from cosmic events such as black hole mergers. Our goal is to utilize Convolutional Neural Network models in hopes of improving the sensitivity and accuracy of detecting these difficult to catch signals, pushing the boundaries of what we can observe in the universe. The findings of this dissertation underscore the utility of combining statistical methods and machine learning models to solve problems that are not only varied but also highly impactful in their respective fields.
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Date Issued: 2024
PURL: http://purl.flvc.org/fau/fd/FA00014454
Subject Headings: Neural networks (Computer science), Gravitational waves, Deep learning (Machine learning), Diagnosis, Epistasis, Genetic
Format: Document (PDF)

Title: INFRASTRUCTURE AND METHODS FOR WIFI-BASED PASSIVE DEVICE LOCALIZATION, FINGERPRINTING, AND RE-IDENTIFICATION FOR MOBILITY INTELLIGENCE.
Creator: Mazokha, Stepan, Hallstrom, Jason O., Sklivanitis, George, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Mobility monitoring in urban environments can provide valuable insights into pedestrian and vehicle movement – where people want to go, how they get there, and the challenges they face along the way. Today, local governments can automate the acquisition of such data using video surveillance to understand the potential impact of investment and policy decisions. However, public disapproval of computer vision due to privacy concerns opens opportunities for research into alternative tools built...
Show moreMobility monitoring in urban environments can provide valuable insights into pedestrian and vehicle movement – where people want to go, how they get there, and the challenges they face along the way. Today, local governments can automate the acquisition of such data using video surveillance to understand the potential impact of investment and policy decisions. However, public disapproval of computer vision due to privacy concerns opens opportunities for research into alternative tools built with privacy constraints at the core of the design. WiFi sensing emerges as a promising solution. Modern mobile devices ubiquitously support the 802.11 standard and regularly emit WiFi probe requests for network discovery. We can passively monitor this traffic to estimate the levels of congestion in public spaces. In this dissertation, we address three fundamental research problems pertaining to developing streetscape-scale mobility intelligence: scalable infrastructure for WiFi signal capture, passive device localization, and device re-identification.
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Date Issued: 2024
PURL: http://purl.flvc.org/fau/fd/FA00014551
Subject Headings: Deep learning (Machine learning), IEEE 802.11 (Standard), Wireless sensor networks, Computer engineering
Format: Document (PDF)

Title: Performance analyses of Slotted ALOHA protocol in a Weibull fading environment.
Creator: Rene, Jean N., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: In past and recent literature, random access protocols have been investigated with growing interest. In particular, the Slotted ALOHA protocol has been extensively used in satellite communications, and has also attracted considerable attention in many areas of wireless communication systems, especially in the cellular mobile environment. In this thesis, we investigate the performance of Slotted ALOHA, an effective random access protocol, in a Weibull fading environment. We study the...
Show moreIn past and recent literature, random access protocols have been investigated with growing interest. In particular, the Slotted ALOHA protocol has been extensively used in satellite communications, and has also attracted considerable attention in many areas of wireless communication systems, especially in the cellular mobile environment. In this thesis, we investigate the performance of Slotted ALOHA, an effective random access protocol, in a Weibull fading environment. We study the performance metrics based on the signal-to-interference-and-noise ratio (SINR) model, in a cellular network system, assuming two captures models. The capture effect, also called co-channel interference tolerance, is the ability to correctly receive a strong signal from one transmitter despite significant interference from other transmitters. We derive closed-formed expressions and numerical evaluations for both the capture probability and the system throughput. he analytical results will be validated with computer simulations. Finally, to mitigate the effects of Weibull fading channel we also consider the effect of dual selection diversity that will increase the capture probability and the system throughput.
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Date Issued: 2013
PURL: http://purl.flvc.org/fcla/dt/3362567
Subject Headings: Multiple access protocols (Computer network protocols), Wireless communication systems, Packet switching (Data transmission), Computer simulation, Radio frequency identification systems, Computer simulation
Format: Document (PDF)

Title: Social Interaction on Facebook.
Creator: Hanrahan, Jeffrey, Maniaci, Michael, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Psychology
Abstract/Description: How we share our good news with people can have a significant effect on our lives. Sharing good news on social media sites involves a process called capitalization. Capitalization has been shown to increase well-being when others provide appropriate responses in face-to-face interactions. To see if this effect on well-being extends to our online presence, this study utilized the social media site Facebook to observe if capitalization predicted well-being and relationship satisfaction. This...
Show moreHow we share our good news with people can have a significant effect on our lives. Sharing good news on social media sites involves a process called capitalization. Capitalization has been shown to increase well-being when others provide appropriate responses in face-to-face interactions. To see if this effect on well-being extends to our online presence, this study utilized the social media site Facebook to observe if capitalization predicted well-being and relationship satisfaction. This study used data collected from 137 participants recruited from an undergraduate participant pool and from Amazon Mechanical Turk. Consistent with hypotheses, participants who reported receiving active and constructive responses after sharing a positive event on Facebook also reported greater personal well-being and relationship satisfaction. Although future experimental research is needed to establish causality, the current results suggest that the ways in which friends respond to social media posts are associated with personal and relationship well-being.
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Date Issued: 2016
PURL: http://purl.flvc.org/fau/fd/FA00004602, http://purl.flvc.org/fau/fd/FA00004602
Subject Headings: Social media., Online social networks., Social networks--Psychological aspects., Social networks--Health aspects., Mobile communication systems--Social aspects., Human-computer interaction--Psychological aspects., Information society., Interpersonal communication--Psychological aspects.
Format: Document (PDF)

Title: Determining the Effectiveness of Human Interaction in Human-in-the-Loop Systems by Using Mental States.
Creator: Lloyd, Eric, Huang, Shihong, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: A self-adaptive software is developed to predict the stock market. It’s Stock Prediction Engine functions autonomously when its skill-set suffices to achieve its goal, and it includes human-in-the-loop when it recognizes conditions benefiting from more complex, expert human intervention. Key to the system is a module that decides of human participation. It works by monitoring three mental states unobtrusively and in real time with Electroencephalography (EEG). The mental states are drawn from...
Show moreA self-adaptive software is developed to predict the stock market. It’s Stock Prediction Engine functions autonomously when its skill-set suffices to achieve its goal, and it includes human-in-the-loop when it recognizes conditions benefiting from more complex, expert human intervention. Key to the system is a module that decides of human participation. It works by monitoring three mental states unobtrusively and in real time with Electroencephalography (EEG). The mental states are drawn from the Opportunity-Willingness-Capability (OWC) model. This research demonstrates that the three mental states are predictive of whether the Human Computer Interaction System functions better autonomously (human with low scores on opportunity and/or willingness, capability) or with the human-in-the-loop, with willingness carrying the largest predictive power. This transdisciplinary software engineering research exemplifies the next step of self-adaptive systems in which human and computer benefit from optimized autonomous and cooperative interactions, and in which neural inputs allow for unobtrusive pre-interactions.
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Date Issued: 2016
PURL: http://purl.flvc.org/fau/fd/FA00004764, http://purl.flvc.org/fau/fd/FA00004764
Subject Headings: Cognitive neuroscience., Neural networks (Computer science), Pattern recognition systems., Artificial intelligence., Self-organizing systems., Human-computer interaction., Human information processing.
Format: Document (PDF)

Title: A VLSI implementable learning algorithm.
Creator: Ruiz, Laura V., Florida Atlantic University, Pandya, Abhijit S., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: A top-down design methodology using hardware description languages (HDL's) and powerful design, analysis, synthesis and layout software tools for electronic circuit design is described and applied to the design of a single layer artificial neural network that incorporates on-chip learning. Using the perception learning algorithm, these simple neurons learn a classification problem in 10.55 microseconds in one application. The objective is to describe a methodology by following the design of a...
Show moreA top-down design methodology using hardware description languages (HDL's) and powerful design, analysis, synthesis and layout software tools for electronic circuit design is described and applied to the design of a single layer artificial neural network that incorporates on-chip learning. Using the perception learning algorithm, these simple neurons learn a classification problem in 10.55 microseconds in one application. The objective is to describe a methodology by following the design of a simple network. This methodology is later applied in the design of a novel architecture, a stochastic neural network. All issues related to algorithmic design for VLSI implementability are discussed and results of layout and timing analysis given over software simulations. A top-down design methodology is presented, including a brief introduction to HDL's and an overview of the software tools used throughout the design process. These tools make it possible now for a designer to complete a design in a relative short period of time. In-depth knowledge of computer architecture, VLSI fabrication, electronic circuits and integrated circuit design is not fundamental to accomplish a task that a few years ago would have required a large team of specialized experts in many fields. This may appeal to researchers from a wide background of knowledge, including computer scientists, mathematicians, and psychologists experimenting with learning algorithms. It is only in a hardware implementation of artificial neural network learning algorithms that the true parallel nature of these architectures could be fully tested. Most of the applications of neural networks are basically software simulations of the algorithms run on a single CPU executing sequential simulations of a parallel, richly interconnected architecture. This dissertation describes a methodology whereby a researcher experimenting with a known or new learning algorithm will be able to test it as it was intentionally designed for, on a parallel hardware architecture.
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Date Issued: 1996
PURL: http://purl.flvc.org/fcla/dt/12453
Subject Headings: Integrated circuits--Very large scale integration--Design and construction, Neural networks (Computer science)--Design and construction, Computer algorithms, Machine learning
Format: Document (PDF)

Title: Energy Efficient Cluster-Based Target Tracking Strategy.
Creator: AL-Ghanem, Waleed Khalid, Mahgoub, Imad, Florida Atlantic University
Abstract/Description: This research proposes a cluster-based target tracking strategy for one moving object using wireless sensor networks. The sensor field is organized in 3 hierarchal levels. 1-bit message is sent when a node detects the target. Otherwise the node stays silent. Since in wireless sensor network nodes have limited computational resources, limited storage resources, and limited battery, the code for predicting the target position should be simple, and fast to execute. The algorithm proposed in this...
Show moreThis research proposes a cluster-based target tracking strategy for one moving object using wireless sensor networks. The sensor field is organized in 3 hierarchal levels. 1-bit message is sent when a node detects the target. Otherwise the node stays silent. Since in wireless sensor network nodes have limited computational resources, limited storage resources, and limited battery, the code for predicting the target position should be simple, and fast to execute. The algorithm proposed in this research is simple, fast, and utilizes all available detection data for estimating the location of the target while conserving energy. lbis has the potential of increasing the network life time. A simulation program is developed to study the impact of the field size and density on the overall performance of the strategy. Simulation results show that the strategy saves energy while estimating the location of the target with an acceptable error margin.
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Date Issued: 2008
PURL: http://purl.flvc.org/fau/fd/FA00012501
Subject Headings: Wireless communication systems--Technological innovations, Sensor networks--Security measures, High performance computing, Adaptive signal processing, Target acquisition, Expert systems (Computer science)
Format: Document (PDF)

Title: Finding and evaluating medical and health information on the internet: a beginner's reference.
Creator: Lomax, Eleanor
Date Issued: 1999-01
PURL: http://purl.flvc.org/fcla/dt/11520
Subject Headings: Electronic resources, Nursing--Computer network resources, Web sites, Internet--Resource Guides, Nursing--Resource Guides
Format: Document (PDF)

Title: Sensitivity analysis of blind separation of speech mixtures.
Creator: Bulek, Savaskan., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Blind source separation (BSS) refers to a class of methods by which multiple sensor signals are combined with the aim of estimating the original source signals. Independent component analysis (ICA) is one such method that effectively resolves static linear combinations of independent non-Gaussian distributions. We propose a method that can track variations in the mixing system by seeking a compromise between adaptive and block methods by using mini-batches. The resulting permutation...
Show moreBlind source separation (BSS) refers to a class of methods by which multiple sensor signals are combined with the aim of estimating the original source signals. Independent component analysis (ICA) is one such method that effectively resolves static linear combinations of independent non-Gaussian distributions. We propose a method that can track variations in the mixing system by seeking a compromise between adaptive and block methods by using mini-batches. The resulting permutation indeterminacy is resolved based on the correlation continuity principle. Methods employing higher order cumulants in the separation criterion are susceptible to outliers in the finite sample case. We propose a robust method based on low-order non-integer moments by exploiting the Laplacian model of speech signals. We study separation methods for even (over)-determined linear convolutive mixtures in the frequency domain based on joint diagonalization of matrices employing time-varying second order statistics. We investigate the sources affecting the sensitivity of the solution under the finite sample case such as the set size, overlap amount and cross-spectrum estimation methods.
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Date Issued: 2010
PURL: http://purl.flvc.org/FAU/2953201
Subject Headings: Blind source separation, Mathematical models, Signal processing, Digital techniques, Neural networks (Computer science), Automatic speech recognition, Speech processing systems
Format: Document (PDF)

Title: Artificial neural network prediction of ground-level ozone concentration in Palm Beach County.
Creator: Crumiere, Mylene., Florida Atlantic University, Scarlatos, Panagiotis (Pete) D.
Abstract/Description: The purpose of this study was to develop a user-friendly mathematical model for prediction of daily, ground level ozone concentration in Palm Beach County, Florida. The focus of this project was to investigate the correlation between hourly ozone concentrations and pre-existing pollutant levels and meteorological data. An artificial neural network model was applied, involving a backpropagation algorithm and the tangent sigmoid as the transfer function. Surface meteorological data and upper...
Show moreThe purpose of this study was to develop a user-friendly mathematical model for prediction of daily, ground level ozone concentration in Palm Beach County, Florida. The focus of this project was to investigate the correlation between hourly ozone concentrations and pre-existing pollutant levels and meteorological data. An artificial neural network model was applied, involving a backpropagation algorithm and the tangent sigmoid as the transfer function. Surface meteorological data and upper air data such as pressure, temperature, dew point temperature, wind speed and wind direction were included in the model, along with the ozone concentration in the hour previous to the forecast. Based on the model results, the 8-hour average ozone concentration is to be forecasted. This will assist state and local air pollution officials in providing the general public with early notice of an impending air quality problem.
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Date Issued: 1999
PURL: http://purl.flvc.org/fcla/dt/15723
Subject Headings: Neural networks (Computer science), Air--Pollution--Mathematical models, Air--Pollution--Florida--Palm Beach County, Ozone--Forecasting
Format: Document (PDF)

Title: Prediction of crude oil product quality parameters using neural networks.
Creator: Bawazeer, Khalid Ahmed., Florida Atlantic University, Zilouchian, Ali
Abstract/Description: Inferential analysis using neural networks technology is being proposed for the Ras Tanura Refinery crude fractionation section. Plant data for a three month operation period is analyzed in order to construct a neural network model with backpropagation training algorithm. The proposed neural network model can predict various properties associated with crude oil products. The simulation results for modeling Naphtha 95% cut point and Naphtha Reid vapor pressure properties are analyzed. A fuzzy...
Show moreInferential analysis using neural networks technology is being proposed for the Ras Tanura Refinery crude fractionation section. Plant data for a three month operation period is analyzed in order to construct a neural network model with backpropagation training algorithm. The proposed neural network model can predict various properties associated with crude oil products. The simulation results for modeling Naphtha 95% cut point and Naphtha Reid vapor pressure properties are analyzed. A fuzzy neural network model is also proposed that takes into account the fuzziness in both process variables and the corresponding product quality parameter. The training algorithm is derived based on the backpropagation technique. The results of the proposed study can ultimately enhance the on-line prediction of crude oil product quality parameters for crude fractionation processes in the Ras Tanura Oil Refinery.
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Date Issued: 1996
PURL: http://purl.flvc.org/fcla/dt/15302
Subject Headings: Petroleum products--Analysis, Petroleum products--Testing, Petroleum industry and trade--Quality control, Neural networks (Computer science)
Format: Document (PDF)

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