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Title: Context-aware hybrid data dissemination in vehicular networks.
Creator: Rathod, Monika M., Mahgoub, Imad, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: This work presents the development of the Context-Aware Hybrid Data Dissemination protocol for vehicular networks. The importance of developing vehicular networking data dissemination protocols is exemplified by the recent announcement by the U.S. Department of Transportation (DOT) National Highway Traffic Safety Administration (NHTSA) to enable vehicle-to-vehicle (V2V) communication technology. With emphasis on safety, other useful applications of V2V communication include but are not...
Show moreThis work presents the development of the Context-Aware Hybrid Data Dissemination protocol for vehicular networks. The importance of developing vehicular networking data dissemination protocols is exemplified by the recent announcement by the U.S. Department of Transportation (DOT) National Highway Traffic Safety Administration (NHTSA) to enable vehicle-to-vehicle (V2V) communication technology. With emphasis on safety, other useful applications of V2V communication include but are not limited to traffic and routing, weather, construction and road hazard alerts, as well as advertisement and entertainment. The core of V2V communication relies on the efficient dispersion of relevant data through wireless broadcast protocols for these varied applications. The challenges of vehicular networks demand an adaptive broadcast protocol capable of handling diverse applications. This research work illustrates the design of a wireless broadcast protocol that is context-aware and adaptive to vehicular environments taking into consideration vehicle density, road topology, and type of data to be disseminated. The context-aware hybrid data dissemination scheme combines store-and-forward and multi-hop broadcasts, capitalizing on the strengths of both these categories and mitigates the weaknesses to deliver data with maximum efficiency to a widest possible reach. This protocol is designed to work in both urban and highway mobility models. The behavior and performance of the hybrid data dissemination scheme is studied by varying the broadcast zone radius, aggregation ratio, data message size and frequency of the broadcast messages. Optimal parameters are determined and the protocol is then formulated to become adaptive to node density by keeping the field size constant and increasing the number of nodes. Adding message priority levels to propagate safety messages faster and farther than non-safety related messages is the next context we add to our adaptive protocol. We dynamically set the broadcast region to use multi-hop which has lower latency to propagate safety-related messages. Extensive simulation results have been obtained using realistic vehicular network scenarios. Results show that Context-Aware Hybrid Data Dissemination Protocol benefits from the low latency characteristics of multi-hop broadcast and low bandwidth consumption of store-and-forward. The protocol is adaptive to both urban and highway mobility models.
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Date Issued: 2014
PURL: http://purl.flvc.org/fau/fd/FA00004152, http://purl.flvc.org/fau/fd/FA00004152
Subject Headings: Context aware computing, Convergence (Telecommunication), Intelligent transportation systems, Internetworking (Telecommunication), Routing (Computer network management), Routing protocols (Computer network protocols), Vehicular ad hoc networks (Computer networks)
Format: Document (PDF)

Title: Compliance Issues In Cloud Computing Systems.
Creator: Yimam, Dereje, Fernandez, Eduardo B., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Appealing features of cloud services such as elasticity, scalability, universal access, low entry cost, and flexible billing motivate consumers to migrate their core businesses into the cloud. However, there are challenges about security, privacy, and compliance. Building compliant systems is difficult because of the complex nature of regulations and cloud systems. In addition, the lack of complete, precise, vendor neutral, and platform independent software architectures makes compliance even...
Show moreAppealing features of cloud services such as elasticity, scalability, universal access, low entry cost, and flexible billing motivate consumers to migrate their core businesses into the cloud. However, there are challenges about security, privacy, and compliance. Building compliant systems is difficult because of the complex nature of regulations and cloud systems. In addition, the lack of complete, precise, vendor neutral, and platform independent software architectures makes compliance even harder. We have attempted to make regulations clearer and more precise with patterns and reference architectures (RAs). We have analyzed regulation policies, identified overlaps, and abstracted them as patterns to build compliant RAs. RAs should be complete, precise, abstract, vendor neutral, platform independent, and with no implementation details; however, their levels of detail and abstraction are still debatable and there is no commonly accepted definition about what an RA should contain. Existing approaches to build RAs lack structured templates and systematic procedures. In addition, most approaches do not take full advantage of patterns and best practices that promote architectural quality. We have developed a five-step approach by analyzing features from available approaches but refined and combined them in a new way. We consider an RA as a big compound pattern that can improve the quality of the concrete architectures derived from it and from which we can derive more specialized RAs for cloud systems. We have built an RA for HIPAA, a compliance RA (CRA), and a specialized compliance and security RA (CSRA) for cloud systems. These RAs take advantage of patterns and best practices that promote software quality. We evaluated the architecture by creating profiles. The proposed approach can be used to build RAs from scratch or to build new RAs by abstracting real RAs for a given context. We have also described an RA itself as a compound pattern by using a modified POSA template. Finally, we have built a concrete deployment and availability architecture derived from CSRA that can be used as a foundation to build compliance systems in the cloud.
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Date Issued: 2015
PURL: http://purl.flvc.org/fau/fd/FA00004559, http://purl.flvc.org/fau/fd/FA00004559
Subject Headings: Biometric identification, Client/server computing -- Security measures, Cloud computing -- Security measures, Computational intelligence, Computer software -- Quality control, Electronic information resources -- Access control
Format: Document (PDF)

Title: Intelligent systems using GMDH algorithms.
Creator: Gupta, Mukul., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Design of intelligent systems that can learn from the environment and adapt to the change in the environment has been pursued by many researchers in this age of information technology. The Group Method of Data Handling (GMDH) algorithm to be implemented is a multilayered neural network. Neural network consists of neurons which use information acquired in training to deduce relationships in order to predict future responses. Most software tool during the simulation of the neural network based...
Show moreDesign of intelligent systems that can learn from the environment and adapt to the change in the environment has been pursued by many researchers in this age of information technology. The Group Method of Data Handling (GMDH) algorithm to be implemented is a multilayered neural network. Neural network consists of neurons which use information acquired in training to deduce relationships in order to predict future responses. Most software tool during the simulation of the neural network based algorithms in a sequential, single processor machine like Pascal, C or C++ takes several hours or even days. But in this thesis, the GMDH algorithm was modified and implemented into a software tool written in Verilog HDL and tested with specific application (XOR) to make the simulation faster. The purpose of the development of this tool is also to keep it general enough so that it can have a wide range of uses, but robust enough that it can give accurate results for all of those uses. Most of the applications of neural networks are basically software simulations of the algorithms only but in this thesis the hardware design is also developed of the algorithm so that it can be easily implemented on hardware using Field Programmable Gate Array (FPGA) type devices. The design is small enough to require a minimum amount of memory, circuit space, and propagation delay.
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Date Issued: 2010
PURL: http://purl.flvc.org/FAU/2976442
Subject Headings: GMDH algorithms, Genetic algorithms, Pattern recognition systems, Expert systems (Computer science), Neural networks (Computer science), Fuzzy logic, Intelligent control systems
Format: Document (PDF)

Title: Low Cost Robotic Car as a Way to Teach Mathematics.
Creator: Aguerrevere, Santiago Andres, Shankar, Ravi, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: This report describes the development of a low cost open source semiautonomous robotic car and a way to communicate with it. It is a continuation of prior research done by other students at FAU and published in recent ASEE conferences. The objective of this project was the development of a new robotic platform with improved precision over the original, while still keeping the cost down. It was developed with the aim to allow a hands-on approach to the teaching of mathematics topics that are...
Show moreThis report describes the development of a low cost open source semiautonomous robotic car and a way to communicate with it. It is a continuation of prior research done by other students at FAU and published in recent ASEE conferences. The objective of this project was the development of a new robotic platform with improved precision over the original, while still keeping the cost down. It was developed with the aim to allow a hands-on approach to the teaching of mathematics topics that are taught in the K-12 syllabus. Improved robustness and reliability of the robotic platform for visually solving math problems was achieved using a combination of PID loops to keep track of distance and rotation. The precision was increased by changing the position of the encoders to the shafts of each motor. A mobile application was developed to allow the student to draw the geometric shapes on the screen before the car draws them. The mobile application consists of two parts, the canvas that the user uses to draw the figure and the configure section that lets the user change the parameters of the controller. Results show that the robot can draw standard geometric and complex geometric shapes. It has high precision and sufficient accuracy, the accuracy can be improved with some mechanical adjustments. During testing a Pythagorean triangle was drawn to show visually the key mathematics concept. The eventual goal of this project will be a K-12 class room study to obtain the feedback of the teachers and students on the feasibility of using a robotic car to teach math. Subsequent to that necessary changes will be made to manufacture a unit that is easy to assemble by the teacher.
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Date Issued: 2016
PURL: http://purl.flvc.org/fau/fd/FA00004712, http://purl.flvc.org/fau/fd/FA00004712
Subject Headings: Adaptive control systems, Applied mathematics, Artificial intelligence, Computers, Special purpose, Mathematics -- Study and teaching, User interfaces (Computer systems)
Format: Document (PDF)

Title: Application of artificial neural networks to deduce robust forecast performance in technoeconomic contexts.
Creator: Dabbas, Mohammad A., Neelakanta, Perambur 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 concerned with performing forecasting in technoeconomic contexts using a set of certain novel artificial neural networks (ANNs). Relevant efforts in general, entail the task of quantitatively estimating the details about the likelihood of future events (or unknown outcomes/effects) based on past and current information on the observed events (or known causes). Commensurate with the scope and objectives of the research, the specific topics addressed are as follows...
Show moreThe focus of this research is concerned with performing forecasting in technoeconomic contexts using a set of certain novel artificial neural networks (ANNs). Relevant efforts in general, entail the task of quantitatively estimating the details about the likelihood of future events (or unknown outcomes/effects) based on past and current information on the observed events (or known causes). Commensurate with the scope and objectives of the research, the specific topics addressed are as follows: A review on various methods adopted in technoeconomic forecasting and identified are econometric projections that can be used for forecasting via artificial neural network (ANN)-based simulations Developing and testing a compatible version of ANN designed to support a dynamic sigmoidal (squashing) function that morphs to the stochastical trends of the ANN input. As such, the network architecture gets pruned for reduced complexity across the span of iterative training schedule leading to the realization of a constructive artificial neural-network (CANN). Formulating a training schedule on an ANN with sparsely-sampled data via sparsity removal with cardinality enhancement procedure (through Nyquist sampling) and invoking statistical bootstrapping technique of resampling applied on the cardinality-improved subset so as to obtain an enhanced number of pseudoreplicates required as an adequate ensemble for robust training of the test ANN: The training and prediction exercises on the test ANN corresponds to optimally elucidating output predictions in the context of the technoeconomics framework of the power generation considered Prescribing a cone-of-error to alleviate over- or under-predictions toward prudently interpreting the results obtained; and, squeezing the cone-of-error to get a final cone-of-forecast rendering the forecast estimation/inference to be more precise Designing an ANN-based fuzzy inference engine (FIE) to ascertain the ex ante forecast details based on sparse sets of ex post data gathered in technoeconomic contexts - Involved thereof a novel method of .fusing fuzzy considerations and data sparsity.Lastly, summarizing the results with essential conclusions and identifying possible research items for future efforts identified as open-questions.
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Date Issued: 2014
PURL: http://purl.flvc.org/fau/fd/FA00004097, http://purl.flvc.org/fau/fd/FA00004097
Subject Headings: Artificial intelligence, Fuzzy systems, Long waves (Economics), Multisensor data fusion, Neural networks (Computer science) -- Mathematical models
Format: Document (PDF)

Title: An evaluation of machine learning algorithms for tweet sentiment analysis.
Creator: Prusa, Joseph D., Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Sentiment analysis of tweets is an application of mining Twitter, and is growing in popularity as a means of determining public opinion. Machine learning algorithms are used to perform sentiment analysis; however, data quality issues such as high dimensionality, class imbalance or noise may negatively impact classifier performance. Machine learning techniques exist for targeting these problems, but have not been applied to this domain, or have not been studied in detail. In this thesis we...
Show moreSentiment analysis of tweets is an application of mining Twitter, and is growing in popularity as a means of determining public opinion. Machine learning algorithms are used to perform sentiment analysis; however, data quality issues such as high dimensionality, class imbalance or noise may negatively impact classifier performance. Machine learning techniques exist for targeting these problems, but have not been applied to this domain, or have not been studied in detail. In this thesis we discuss research that has been conducted on tweet sentiment classification, its accompanying data concerns, and methods of addressing these concerns. We test the impact of feature selection, data sampling and ensemble techniques in an effort to improve classifier performance. We also evaluate the combination of feature selection and ensemble techniques and examine the effects of high dimensionality when combining multiple types of features. Additionally, we provide strategies and insights for potential avenues of future work.
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Date Issued: 2015
PURL: http://purl.flvc.org/fau/fd/FA00004460, http://purl.flvc.org/fau/fd/FA00004460
Subject Headings: Social media., Natural language processing (Computer science), Machine learning., Algorithms., Fuzzy expert systems., Artificial intelligence.
Format: Document (PDF)

Title: A Network Telescope Approach for Inferring and Characterizing IoT Exploitations.
Creator: Neshenko, Nataliia, Bou-Harb, Elias, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: While the seamless interconnection of IoT devices with the physical realm is envisioned to bring a plethora of critical improvements on many aspects and in diverse domains, it will undoubtedly pave the way for attackers that will target and exploit such devices, threatening the integrity of their data and the reliability of critical infrastructure. The aim of this thesis is to generate cyber threat intelligence related to Internet-scale inference and evaluation of malicious activities...
Show moreWhile the seamless interconnection of IoT devices with the physical realm is envisioned to bring a plethora of critical improvements on many aspects and in diverse domains, it will undoubtedly pave the way for attackers that will target and exploit such devices, threatening the integrity of their data and the reliability of critical infrastructure. The aim of this thesis is to generate cyber threat intelligence related to Internet-scale inference and evaluation of malicious activities generated by compromised IoT devices to facilitate prompt detection, mitigation and prevention of IoT exploitation. In this context, we initially provide a unique taxonomy, which sheds the light on IoT vulnerabilities from five di↵erent perspectives. Subsequently, we address the task of inference and characterization of IoT maliciousness by leveraging active and passive measurements. To support large-scale empirical data analytics in the context of IoT, we made available corresponding raw data through an authenticated platform.
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Date Issued: 2018
PURL: http://purl.flvc.org/fau/fd/FA00013089
Subject Headings: Internet of things., Internet of things--Security measures., Cyber intelligence (Computer security)
Format: Document (PDF)

Title: Development of a Flapping Actuator Based on Oscillating Electromagnetic Fields.
Creator: Spragg, Donald Oakley, Curet, Oscar M., Florida Atlantic University, College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
Abstract/Description: In this work a bio-inspired flapping actuator based on varied magnetic fields is developed, controlled and characterized. The actuator is sought to contribute to the toolbox of options for bio-mimetics research. The design is that of a neodymium bar magnet on one end of an armature which is moved by two air core electromagnetic coils in the same manner as agonist and antagonist muscle pairs function in biological systems. The other end of the armature is fitted to a rigid fin extending beyond...
Show moreIn this work a bio-inspired flapping actuator based on varied magnetic fields is developed, controlled and characterized. The actuator is sought to contribute to the toolbox of options for bio-mimetics research. The design is that of a neodymium bar magnet on one end of an armature which is moved by two air core electromagnetic coils in the same manner as agonist and antagonist muscle pairs function in biological systems. The other end of the armature is fitted to a rigid fin extending beyond the streamline enclosure body to produce propulsion. A series of tests in still water were performed to measure the kinematics and propulsive force for different control schemes including the effect of adding antagonistic resistance to the control schemes. Control methods based on armature position and based on setpoint error were tested and antagonist force was found to increase consistency of control of the systems in certain cases.
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Date Issued: 2016
PURL: http://purl.flvc.org/fau/fd/FA00004699, http://purl.flvc.org/fau/fd/FA00004699
Subject Headings: Actuators -- Materials, Artificial intelligence -- Biological applications, Biomimetics, Biophysics, Natural computation, Robotics, Robots -- Kinematics
Format: Document (PDF)

Title: Dosimetric comparison of inverse planning by simulated annealing (IPSA) and dose points optimized treatment plans in high dose rate (HDR) brachytherapy of skin lesions using Freiburg flap applicator.
Creator: Ghebremichael, Bereket Tewolde, Ouhib, Zoubir, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Physics
Abstract/Description: A detailed dosimetric comparison between Inverse Planning by Simulated Annealing (IPSA) and Dose Points (DP) optimized treatment plans has been performed for High Dose Rate (HDR) brachytherapy of skin lesions using Freiburg Flap applicator in order to find out whether or not IPSA offers better clinical dosimetric outcomes for lesions categorized into four different curvatures. Without compromising target coverage, IPSA reduced the volume of Planning Target Volume (lesion) that received at...
Show moreA detailed dosimetric comparison between Inverse Planning by Simulated Annealing (IPSA) and Dose Points (DP) optimized treatment plans has been performed for High Dose Rate (HDR) brachytherapy of skin lesions using Freiburg Flap applicator in order to find out whether or not IPSA offers better clinical dosimetric outcomes for lesions categorized into four different curvatures. Without compromising target coverage, IPSA reduced the volume of Planning Target Volume (lesion) that received at least 125% of the prescription dose on average by 41%. It also reduced the volume of the healthy skin surrounding the lesion that receives at least 100% of the prescription dose on average by 42%. IPSA did not show any advantage over DP in sparing normal structures underlying the lesions treated. Although DP optimization algorithm has been regularly used at Lynn Cancer Institute for HDR brachytherapy of skin lesions, recent upgrades in IPSA software have made IPSA more amenable to rapid treatment planning and therefore IPSA can be used either in place of DP or as its alternative.
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Date Issued: 2014
PURL: http://purl.flvc.org/fau/fd/FA00004285
Subject Headings: Artificial intelligence -- Medical applications, Cancer -- Treatment, Computational intellingence, Imaging systems in medicine, Medical physics
Format: Document (PDF)

Title: Activity analysis and detection of falling and repetitive motion.
Creator: Carryl, Clyde, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: This thesis examines the use of motion detection and analysis systems to detect falls and repetitive motion patterns of at-risk individuals. Three classes of motion are examined: Activities of daily living (ADL), falls, and repetitive motion. This research exposes a simple relationship between ADL and non-ADL movement, and shows how to use Principal Component Analysis and a kNN classifier to tell the 2 classes of motion apart with 100% sensitivity and specificity. It also identifies a more...
Show moreThis thesis examines the use of motion detection and analysis systems to detect falls and repetitive motion patterns of at-risk individuals. Three classes of motion are examined: Activities of daily living (ADL), falls, and repetitive motion. This research exposes a simple relationship between ADL and non-ADL movement, and shows how to use Principal Component Analysis and a kNN classifier to tell the 2 classes of motion apart with 100% sensitivity and specificity. It also identifies a more complex relationship between falls and repetitive motion, which both produce bodily accelerations exceeding 3G but differ with regard to their periodicity. This simplifies the classification problem of falls versus repetitive motion when taking into account that their data representations are similar except that repetitive motion displays a high degree of periodicity as compared to falls.
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Date Issued: 2013
PURL: http://purl.flvc.org/FAU/3360774
Subject Headings: Perpetual-motion processes, Human locomotion, Neural networks (Computer science), Artificial intelligence
Format: Document (PDF)

Title: Which Way is It? Spatial Navigation and the Genetics of Head Direction Cells.
Creator: Lora, Joan C., Stackman, Robert W., Florida Atlantic University, Charles E. Schmidt College of Science, Department of Psychology
Abstract/Description: From locating a secure home, foraging for food, running away from predators, spatial navigation is an integral part of everyday life. Multiple brain regions work together to form a three-dimensional representation of our environment; specifically, place cells, grid cells, border cells & head direction cells are thought to interact and influence one another to form this cognitive map. Head direction (HD) cells fire as the animal moves through space, according to directional orientation of the...
Show moreFrom locating a secure home, foraging for food, running away from predators, spatial navigation is an integral part of everyday life. Multiple brain regions work together to form a three-dimensional representation of our environment; specifically, place cells, grid cells, border cells & head direction cells are thought to interact and influence one another to form this cognitive map. Head direction (HD) cells fire as the animal moves through space, according to directional orientation of the animal’s head with respect to the laboratory reference frame, and are therefore considered to represent the directional sense. Interestingly, inactivation of head direction cell-containing brain regions has mixed consequences on spatial behavior. Current methods of identifying HD cells are limited to in vivo electrophysiological recordings in a dry-land environment. We first developed a dry-land version of the MWM in order to carry out behavioral-recording paired studies. Additionally, to learn about HD cells function we quantified expression of neuronal activation marker (c-Fos), and L-amino acid transporter 4 (Lat4) in neurons found within the HD cell dense anterodorsal thalamic nucleus (ADN) in mice after exploratory behavior in an open field, or forward unidirectional movement on a treadmill. We hypothesize that the degree to which ADN neurons are activated during exploratory behavior is influenced by the range of heading directions sampled. Additionally, we hypothesize that c-Fos and Lat4 are colocalized within ADN neurons following varying amounts of head direction exposure. Results indicate that following free locomotion of mice in an open field arena, which permitted access to 360° of heading, a greater number of ADN neurons express c-Fos protein compared to those exposed to a limited range of head directions during locomotion in a treadmill. These findings suggest that the degree of ADN neuronal activation was dependent upon the range of head directions sampled. We observed a high degree of colocalization of c-Fos and Lat4 within ADN suggesting that Lat4 may be a useful tool to manipulate neuronal activity of HD cells. Identifying genetic markers specific to ADN helps provide an essential understanding of the spatial navigation system, and supports development of therapies for cognitive disorders affecting navigation.
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Date Issued: 2017
PURL: http://purl.flvc.org/fau/fd/FA00004931, http://purl.flvc.org/fau/fd/FA00004931
Subject Headings: Psychobiology., Spatial behavior in animals., Mice as laboratory animals., Navigation--Psychological aspects., Computational intelligence.
Format: Document (PDF)

Title: Statistical and Entropy Considerations for Ultrasound Tissue Characterization.
Creator: Navumenka, Khrystsina, Aalo, Valentine A., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Modern cancerous tumor diagnostics is nearly impossible without invasive methods, such as biopsy, that may require involved surgical procedures. In recent years some work has been done to develop alternative non-invasive methods of medical diagnostics. For this purpose, the data obtained from an ultrasound image of the body crosssection, has been analyzed using statistical models, including Rayleigh, Rice, Nakagami, and K statistical distributions. The homodyned-K (H-K) distribution has been...
Show moreModern cancerous tumor diagnostics is nearly impossible without invasive methods, such as biopsy, that may require involved surgical procedures. In recent years some work has been done to develop alternative non-invasive methods of medical diagnostics. For this purpose, the data obtained from an ultrasound image of the body crosssection, has been analyzed using statistical models, including Rayleigh, Rice, Nakagami, and K statistical distributions. The homodyned-K (H-K) distribution has been found to be a good statistical tool to analyze the envelope and/or the intensity of backscattered signal in ultrasound tissue characterization. However, its use has usually been limited due to the fact that its probability density function (PDF) is not available in closed-form. In this work we present a novel closed-form representation for the H-K distribution. In addition, we propose using the first order approximation of the H-K distribution, the I-K distribution that has a closed-form, for the ultrasound tissue characterization applications. More specifically, we show that some tissue conditions that cause the backscattered signal to have low effective density values, can be successfully modeled by the I-K PDF. We introduce the concept of using H-K PDF-based and I-K PDF-based entropies as additional tools for characterization of ultrasonic breast tissue images. The entropy may be used as a goodness of fit measure that allows to select a better-fitting statistical model for a specific data set. In addition, the values of the entropies as well as the values of the statistical distribution parameters, allow for more accurate classification of tumors.
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Date Issued: 2017
PURL: http://purl.flvc.org/fau/fd/FA00004922, http://purl.flvc.org/fau/fd/FA00004922
Subject Headings: Ultrasonics in medicine., Artificial intelligence., Computer vision in medicine., Diagnostic ultrasonic imaging., Bioinformatics.
Format: Document (PDF)

Title: Real Time Traffic Monitoring System from a UAV Platform.
Creator: Biswas, Debojit, Su, Hongbo, Florida Atlantic University, College of Engineering and Computer Science, Department of Civil, Environmental and Geomatics Engineering
Abstract/Description: Today transportation systems are facing big transitions all over the world. We created fly overs, roads under the ground, bridges over the river and ocean to get efficient access and to increase the road connectivity. Our transportation system is more intelligent than ever. Our traffic signaling system became adaptive. Our vehicles equipped with new gadgets and we developed new tools for more efficient analysis of traffic. Our research relies on existing traffic infrastructure to generate...
Show moreToday transportation systems are facing big transitions all over the world. We created fly overs, roads under the ground, bridges over the river and ocean to get efficient access and to increase the road connectivity. Our transportation system is more intelligent than ever. Our traffic signaling system became adaptive. Our vehicles equipped with new gadgets and we developed new tools for more efficient analysis of traffic. Our research relies on existing traffic infrastructure to generate better understanding of traffic. More specifically, this research focused on traffic and UAV cameras to extract information about the traffic. Our first goal was to create an automatic system to count the cars using traffic cameras. To achieve this goal, we implemented Background Subtraction Method (BSM) and OverFeat Framework. BSM compares consecutive frames to detect the moving objects. Because BSM only works for ideal lab conditions, therefor we implemented a Convolutional Neural Network (CNN) based classification algorithm called OverFeat Framework. We created different segments on the road in various lanes to tabulate the number of passing cars. We achieved 96.55% accuracy for car counting irrespective of different visibility conditions of the day and night. Our second goal was to find out traffic density. We implemented two CNN based algorithms: Single Shot Detection (SSD) and MobileNet-SSD for vehicle detection. These algorithms are object detection algorithms. We used traffic cameras to detect vehicles on the roads. We utilized road markers and light pole distances to determine distances on the road. Using the distance and count information we calculated density. SSD is a more resource intense algorithm and it achieved 92.97% accuracy. MobileNet-SSD is a lighter algorithm and it achieved 79.30% accuracy. Finally, from a moving platform we estimated the velocity of multiple vehicles. There are a lot of roads where traffic cameras are not available, also traffic monitoring is necessary for special events. We implemented Faster R-CNN as a detection algorithm and Discriminative Correlation Filter (with Channel and Spatial Reliability Tracking) for tracking. We calculated the speed information from the tracking information in our study. Our framework achieved 96.80% speed accuracy compared to manual observation of speeds.
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Date Issued: 2019
PURL: http://purl.flvc.org/fau/fd/FA00013188
Subject Headings: Traffic monitoring, Intelligent transportation systems, Neural networks (Computer science), Vehicle detectors, Unmanned aerial vehicles
Format: Document (PDF)

Title: Real-time traffic incidents prediction in vehicular networks using big data analytics.
Creator: Al-Najada, Hamzah, Mahgoub, Imad, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: The United States has been going through a road accident crisis for many years. The National Safety Council estimates 40,000 people were killed and 4.57 million injured on U.S. roads in 2017. Direct and indirect loss from tra c congestion only is more than $140 billion every year. Vehicular Ad-hoc Networks (VANETs) are envisioned as the future of Intelligent Transportation Systems (ITSs). They have a great potential to enable all kinds of applications that will enhance road safety and...
Show moreThe United States has been going through a road accident crisis for many years. The National Safety Council estimates 40,000 people were killed and 4.57 million injured on U.S. roads in 2017. Direct and indirect loss from tra c congestion only is more than $140 billion every year. Vehicular Ad-hoc Networks (VANETs) are envisioned as the future of Intelligent Transportation Systems (ITSs). They have a great potential to enable all kinds of applications that will enhance road safety and transportation efficiency. In this dissertation, we have aggregated seven years of real-life tra c and incidents data, obtained from the Florida Department of Transportation District 4. We have studied and investigated the causes of road incidents by applying machine learning approaches to this aggregated big dataset. A scalable, reliable, and automatic system for predicting road incidents is an integral part of any e ective ITS. For this purpose, we propose a cloud-based system for VANET that aims at preventing or at least decreasing tra c congestions as well as crashes in real-time. We have created, tested, and validated a VANET traffic dataset by applying the connected vehicle behavioral changes to our aggregated dataset. To achieve the scalability, speed, and fault-tolerance in our developed system, we built our system in a lambda architecture fashion using Apache Spark and Spark Streaming with Kafka. We used our system in creating optimal and safe trajectories for autonomous vehicles based on the user preferences. We extended the use of our developed system in predicting the clearance time on the highway in real-time, as an important component of the traffic incident management system. We implemented the time series analysis and forecasting in our real-time system as a component for predicting traffic flow. Our system can be applied to use dedicated short communication (DSRC), cellular, or hybrid communication schema to receive streaming data and send back the safety messages. The performance of the proposed system has been extensively tested on the FAUs High Performance Computing Cluster (HPCC), as well as on a single node virtual machine. Results and findings confirm the applicability of the proposed system in predicting traffic incidents with low processing latency.
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Date Issued: 2018
PURL: http://purl.flvc.org/fau/fd/FA00013114
Subject Headings: Vehicular ad hoc networks (Computer networks), Big data, Intelligent transportation systems, Prediction, traffic incidents
Format: Document (PDF)

Title: Sparse Coding and Compressed Sensing: Locally Competitive Algorithms and Random Projections.
Creator: Hahn, William E., Barenholtz, Elan, Florida Atlantic University, Charles E. Schmidt College of Science, Center for Complex Systems and Brain Sciences
Abstract/Description: For an 8-bit grayscale image patch of size n x n, the number of distinguishable signals is 256(n2). Natural images (e.g.,photographs of a natural scene) comprise a very small subset of these possible signals. Traditional image and video processing relies on band-limited or low-pass signal models. In contrast, we will explore the observation that most signals of interest are sparse, i.e. in a particular basis most of the expansion coefficients will be zero. Recent developments in sparse...
Show moreFor an 8-bit grayscale image patch of size n x n, the number of distinguishable signals is 256(n2). Natural images (e.g.,photographs of a natural scene) comprise a very small subset of these possible signals. Traditional image and video processing relies on band-limited or low-pass signal models. In contrast, we will explore the observation that most signals of interest are sparse, i.e. in a particular basis most of the expansion coefficients will be zero. Recent developments in sparse modeling and L1 optimization have allowed for extraordinary applications such as the single pixel camera, as well as computer vision systems that can exceed human performance. Here we present a novel neural network architecture combining a sparse filter model and locally competitive algorithms (LCAs), and demonstrate the networks ability to classify human actions from video. Sparse filtering is an unsupervised feature learning algorithm designed to optimize the sparsity of the feature distribution directly without having the need to model the data distribution. LCAs are defined by a system of di↵erential equations where the initial conditions define an optimization problem and the dynamics converge to a sparse decomposition of the input vector. We applied this architecture to train a classifier on categories of motion in human action videos. Inputs to the network were small 3D patches taken from frame di↵erences in the videos. Dictionaries were derived for each action class and then activation levels for each dictionary were assessed during reconstruction of a novel test patch. We discuss how this sparse modeling approach provides a natural framework for multi-sensory and multimodal data processing including RGB video, RGBD video, hyper-spectral video, and stereo audio/video streams.
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Date Issued: 2016
PURL: http://purl.flvc.org/fau/fd/FA00004713, http://purl.flvc.org/fau/fd/FA00004713
Subject Headings: Artificial intelligence, Expert systems (Computer science), Image processing -- Digital techniques -- Mathematics, Sparse matrices
Format: Document (PDF)

Title: Brain Computer Interface And Neuroprosthetics.
Creator: Calderon, Rodrigo, Morgera, Salvatore D., Florida Atlantic University
Abstract/Description: For many years people have consider the possibility that brain activity might provide a new channel for communication between a person's brain and the external world. Brain Computer Interface allows humans to control electronic devices using only their thoughts. The goal of this project is to provide the users with a basic control of a prosthetic arm using the signal acquired by an Electroencephalogram (EEG). The main objective of the research is to demonstrate and provide a system that...
Show moreFor many years people have consider the possibility that brain activity might provide a new channel for communication between a person's brain and the external world. Brain Computer Interface allows humans to control electronic devices using only their thoughts. The goal of this project is to provide the users with a basic control of a prosthetic arm using the signal acquired by an Electroencephalogram (EEG). The main objective of the research is to demonstrate and provide a system that allows individuals to obtain control of the device with very little training and very few electrodes. The research includes the development of an elaborate signal-processing algorithm that uses an Artificial Neural Network to determine the intentions of the user and their translation into commands to operate the prosthetic arm.
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Date Issued: 2007
PURL: http://purl.flvc.org/fau/fd/FA00012509
Subject Headings: Neural networks (Computer science), Pattern recognition systems, Prosthesis--Technological innovations, Artificial intelligence
Format: Document (PDF)

Title: Design of analog building blocks useful for artificial neural networks.
Creator: Renavikar, Ajit Anand., Florida Atlantic University, Shankar, Ravi, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Software simulations of a scaleable VLSI implementable architecture and algorithm for character recognition by a research group at Florida Atlantic University (FAU) have shown encouraging results. We address here hardware implementation issues pertinent to the classification phase of character recognition. Using the digit classification techniques developed at FAU as a foundation, we have designed and simulated general purpose building blocks useful for a possible implementation of a Digital ...
Show moreSoftware simulations of a scaleable VLSI implementable architecture and algorithm for character recognition by a research group at Florida Atlantic University (FAU) have shown encouraging results. We address here hardware implementation issues pertinent to the classification phase of character recognition. Using the digit classification techniques developed at FAU as a foundation, we have designed and simulated general purpose building blocks useful for a possible implementation of a Digital & Analog CMOS VLSI chip that is suitable for a variety of artificial neural network (ANN) architectures. HSPICE was used to perform circuit-level simulations of the building blocks. We present here the details of implementation of the recognition chip including the architecture, circuit design and the simulation results.
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Date Issued: 1996
PURL: http://purl.flvc.org/fcla/dt/15328
Subject Headings: Neural networks (Computer science), Artificial intelligence, Optical character recognition devices, Pattern recognition systems
Format: Document (PDF)

Title: 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.
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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: Hardware in the loop simulation of generic nodes using Lontalk.
Creator: Gupta, Sangeeta., Florida Atlantic University, Evett, Matthew P.
Abstract/Description: Designing a dependable network for a highly sustainable system gives a challenging network design problem. The network must be highly adaptive to the changes in the network environment. It should also sustain any damages occurring in the network and recover itself quickly and efficiently. This thesis ultimately maps a real network to simulated network by developing a concept of generic nodes and experimentally investigates different parameters that affects the reliability of the system. The...
Show moreDesigning a dependable network for a highly sustainable system gives a challenging network design problem. The network must be highly adaptive to the changes in the network environment. It should also sustain any damages occurring in the network and recover itself quickly and efficiently. This thesis ultimately maps a real network to simulated network by developing a concept of generic nodes and experimentally investigates different parameters that affects the reliability of the system. The work includes designing a simulation for generation of network traffic in a simulated network and studying the behavior of the network with different parameters. The experiment helped us in determining the optimum values of these parameters. For the selected set of experiments and further implies that simulation can determine the nodes different parameter in a control network and will result in a Dependable system.
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Date Issued: 1998
PURL: http://purl.flvc.org/fcla/dt/15596
Subject Headings: Intelligent control systems, Adaptive control systems, Local area networks (Computer networks)
Format: Document (PDF)

Title: Lifeline structures under earthquake excitations.
Creator: Reddy, Kondakrindhi Praveen., Florida Atlantic University, Yong, Yan, College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
Abstract/Description: An analytical method is proposed for the response analysis of lifeline structures subjected to earthquake excitations. The main feature of the approach is to consider the vibrational motion as a result of the wave motion in a waveguide-like lifeline structure. Based on the theory of wave propagation, scattering matrices are derived to characterize the wave propagation in individual segments and wave reflections and transmissions at supports and boundaries. Response solution is derived in a...
Show moreAn analytical method is proposed for the response analysis of lifeline structures subjected to earthquake excitations. The main feature of the approach is to consider the vibrational motion as a result of the wave motion in a waveguide-like lifeline structure. Based on the theory of wave propagation, scattering matrices are derived to characterize the wave propagation in individual segments and wave reflections and transmissions at supports and boundaries. Response solution is derived in a closed form, suitable for stochastic analysis when the input is an earthquake excitation. A space-time earthquake ground motion model that accounts for both coherent decay and seismic wave propagation is used to specify motions at supports. The proposed technique can be used to obtain lifeline structural response accurately and determine the correlation between any two locations in an effective manner. The computational aspects of its implementation are also discussed. Numerical examples are presented to illustrate the application and efficiency of the proposed analytical scheme.
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Date Issued: 1993
PURL: http://purl.flvc.org/fcla/dt/14898
Subject Headings: Artificial intelligence, Fuzzy logic, Neural networks (Computer science), Pavements--Skid resistance
Format: Document (PDF)

Artificial intelligence (20)	+ -
Neural networks (Computer science) (16)	+ -
Artificial intelligence. (9)	+ -
Computational intelligence (6)	+ -
Adaptive control systems (4)	+ -

Intelligent control systems (4)	+ -
Intelligent transportation systems (4)	+ -
Mobile computing. (4)	+ -
Pattern recognition systems (4)	+ -
Routing protocols (Computer network protocols) (4)	+ -
Back propagation (Artificial intelligence) (3)	+ -
Fuzzy logic (3)	+ -
Intelligent transportation systems. (3)	+ -
Machine learning (3)	+ -
System design (3)	+ -
Wireless communication systems. (3)	+ -
Bioinformatics (2)	+ -
Computer Science. (2)	+ -
Computer algorithms. (2)	+ -
Computer communication systems. (2)	+ -
Computer science. (2)	+ -
Computer vision (2)	+ -
Computer vision. (2)	+ -
Cyber intelligence (Computer security) (2)	+ -
Deep learning (Machine learning) (2)	+ -
Expert systems (Computer science) (2)	+ -
Genetic algorithms (2)	+ -
Image processing (2)	+ -
Intelligent control systems. (2)	+ -
Intelligent transportation systems--Mathematical models. (2)	+ -

Florida Atlantic University (51)	+ -
College of Engineering and Computer Science (40)	+ -
Department of Computer and Electrical Engineering and Computer Science (32)	+ -
Charles E. Schmidt College of Science (9)	+ -
Mahgoub, Imad (6)	+ -

Department of Ocean and Mechanical Engineering (5)	+ -
Khoshgoftaar, Taghi M. (4)	+ -
Pandya, Abhijit S. (4)	+ -
Barenholtz, Elan (3)	+ -
Department of Civil, Environmental and Geomatics Engineering (3)	+ -
Department of Psychology (3)	+ -
Dorothy F. Schmidt College of Arts and Letters (3)	+ -
Bou-Harb, Elias (2)	+ -
Center for Complex Systems and Brain Sciences (2)	+ -
Department of Mathematical Sciences (2)	+ -
Marques, Oge (2)	+ -
Neshenko, Nataliia (2)	+ -
School of Communication and Multimedia Studies (2)	+ -
Shankar, Ravi (2)	+ -
Stevanovic, Aleksandar (2)	+ -
Aalo, Valentine A. (1)	+ -
Abtahi, Homayoon (1)	+ -
Aguerrevere, Santiago Andres (1)	+ -
Al-Najada, Hamzah (1)	+ -
Altoaimy, Lina (1)	+ -
Alwakeel, Ahmed M. (1)	+ -
Amento, Brittanney Jaclyn (1)	+ -
Arellano, Wilmer (1)	+ -
Biswas, Debojit (1)	+ -
Bucak, Ayse Papatya (1)	+ -

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