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Title: Cache optimization for real-time embedded systems.
Creator: Asaduzzaman, Abu Sadath Mohammad, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Cache memory is used, in most single-core and multi-core processors, to improve performance by bridging the speed gap between the main memory and CPU. Even though cache increases performance, it poses some serious challenges for embedded systems running real-time applications. Cache introduces execution time unpredictability due to its adaptive and dynamic nature and cache consumes vast amount of power to be operated. Energy requirement and execution time predictability are crucial for the...
Show moreCache memory is used, in most single-core and multi-core processors, to improve performance by bridging the speed gap between the main memory and CPU. Even though cache increases performance, it poses some serious challenges for embedded systems running real-time applications. Cache introduces execution time unpredictability due to its adaptive and dynamic nature and cache consumes vast amount of power to be operated. Energy requirement and execution time predictability are crucial for the success of real-time embedded systems. Various cache optimization schemes have been proposed to address the performance, power consumption, and predictability issues. However, currently available solutions are not adequate for real-time embedded systems as they do not address the performance, power consumption, and execution time predictability issues at the same time. Moreover, existing solutions are not suitable for dealing with multi-core architecture issues. In this dissertation, we develop a methodology through cache optimization for real-time embedded systems that can be used to analyze and improve execution time predictability and performance/power ratio at the same time. This methodology is effective for both single-core and multi-core systems. First, we develop a cache modeling and optimization technique for single-core systems to improve performance. Then, we develop a cache modeling and optimization technique for multi-core systems to improve performance/power ratio. We develop a cache locking scheme to improve execution time predictability for real-time systems. We introduce Miss Table (MT) based cache locking scheme with victim cache (VC) to improve predictability and performance/power ratio. MT holds information about memory blocks, which may cause more misses if not locked, to improve cache locking performance., VC temporarily stores the victim blocks from level-1 cache to improve cache hits. In addition, MT is used to improve cache replacement performance and VC is used to improve cache hits by supporting stream buffering. We also develop strategies to generate realistic workload by characterizing applications to simulate cache optimization and cache locking schemes. Popular MPEG4, H.264/AVC, FFT, MI, and DFT applications are used to run the simulation programs. Simulation results show that newly introduced Miss Table based cache locking scheme with victim cache significantly improves the predictability and performance/power ratio. In this work, a reduction of 33% in mean delay per task and a reduction of 41% in total power consumption are achieved by using MT and VCs while locking 25% of level-2 cache size in an 4-core system. It is also observed that execution time predictability can be improved by avoiding more than 50% cache misses while locking one-fourth of the cache size.
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Date Issued: 2009
PURL: http://purl.flvc.org/FAU/359919
Subject Headings: Real-time embedded systems and components, Embedded computer systems, Programming, Computer architecture, Integrated circuits, Design and construction, Signal processing, Digital techniques, Object-oriented methods (Computer science)
Format: Document (PDF)

Title: Campus driver assistant on an Android platform.
Creator: Zankina, Iana., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: College campuses can be large, confusing, and intimidating for new students and visitors. Finding the campus may be easy using a GPS unit or Google Maps directions, but this is not the case when you are actually on the campus. There is no service that provides directional assistance for the campus itself. This thesis proposes a driver assistant application running on an Android platform that can direct drivers to different buildings and parking lots in the campus. The application's user...
Show moreCollege campuses can be large, confusing, and intimidating for new students and visitors. Finding the campus may be easy using a GPS unit or Google Maps directions, but this is not the case when you are actually on the campus. There is no service that provides directional assistance for the campus itself. This thesis proposes a driver assistant application running on an Android platform that can direct drivers to different buildings and parking lots in the campus. The application's user interface lets the user select a user type, a campus, and a destination through use of drop down menus and buttons. Once the user submits the needed information, then the next portion of the application runs in the background. The app retrieves the Campus Map XML created by the mapping tool that was constructed for this project. The XML data containing all the map elements is then parsed and stored in a hierarchal data structure. The resulting objects are then used to construct a campus graph, on which an altered version of Dijkstra's Shortest Path algorithm is executed. When the path to the destination has been discovered, the campus map with the computed path overlaid is displayed on the user's device, showing the route to the desired destination.
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Date Issued: 2012
PURL: http://purl.flvc.org/FAU/3359159
Subject Headings: Mobile computing, Software engineering, Application software, Development
Format: Document (PDF)

Title: Bioinformatic analysis of viral genomic sequences and concepts of genome-specific national vaccine design.
Creator: Chatterjee, Sharmistha P., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: This research is concerned with analyzing a set of viral genomes to elucidate the underlying characteristics and determine the information-theoretic aspects of the genomic signatures. The goal of this study thereof, is tailored to address the following: (i) Reviewing various methods available to deduce the features and characteristics of genomic sequences of organisms in general, and particularly focusing on the genomes pertinent to viruses; (ii) applying the concepts of information...
Show moreThis research is concerned with analyzing a set of viral genomes to elucidate the underlying characteristics and determine the information-theoretic aspects of the genomic signatures. The goal of this study thereof, is tailored to address the following: (i) Reviewing various methods available to deduce the features and characteristics of genomic sequences of organisms in general, and particularly focusing on the genomes pertinent to viruses; (ii) applying the concepts of information-theoretics (entropy principles) to analyze genomic sequences; (iii) envisaging various aspects of biothermodynamic energetics so as to determine the framework and architecture that decide the stability and patterns of the subsequences in a genome; (iv) evaluating the genomic details using spectral-domain techniques; (v) studying fuzzy considerations to ascertain the overlapping details in genomic sequences; (vi) determining the common subsequences among various strains of a virus by logistically regressing the data obtained via entropic, energetics and spectral-domain exercises; (vii) differentiating informational profiles of coding and non-coding regions in a DNA sequence to locate aberrant (cryptic) attributes evolved as a result of mutational changes and (viii) finding the signatures of CDS of genomes of viral strains toward rationally conceiving plausible designs of vaccines. Commensurate with the topics indicated above, necessary simulations are proposed and computational exercises are performed (with MatLabTM R2009b and other software as needed). Extensive data gathered from open-literature are used thereof and, simulation results are verified. Lastly, results are discussed, inferences are made and open-questions are identified for future research.
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Date Issued: 2013
PURL: http://purl.flvc.org/FAU/3360772
Subject Headings: Genetic engineering, Bioinformatics, Genomics, DNA microarrays, Proteomics
Format: Document (PDF)

Title: Bioinformatics-inspired binary image correlation: application to bio-/medical-images, microsarrays, finger-prints and signature classifications.
Creator: Pappusetty, Deepti, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: The efforts addressed in this thesis refer to assaying the extent of local features in 2D-images for the purpose of recognition and classification. It is based on comparing a test-image against a template in binary format. It is a bioinformatics-inspired approach pursued and presented as deliverables of this thesis as summarized below: 1. By applying the so-called 'Smith-Waterman (SW) local alignment' and 'Needleman-Wunsch (NW) global alignment' approaches of bioinformatics, a test 2D-image...
Show moreThe efforts addressed in this thesis refer to assaying the extent of local features in 2D-images for the purpose of recognition and classification. It is based on comparing a test-image against a template in binary format. It is a bioinformatics-inspired approach pursued and presented as deliverables of this thesis as summarized below: 1. By applying the so-called 'Smith-Waterman (SW) local alignment' and 'Needleman-Wunsch (NW) global alignment' approaches of bioinformatics, a test 2D-image in binary format is compared against a reference image so as to recognize the differential features that reside locally in the images being compared 2. SW and NW algorithms based binary comparison involves conversion of one-dimensional sequence alignment procedure (indicated traditionally for molecular sequence comparison adopted in bioinformatics) to 2D-image matrix 3. Relevant algorithms specific to computations are implemented as MatLabTM codes 4. Test-images considered are: Real-world bio-/medical-images, synthetic images, microarrays, biometric finger prints (thumb-impressions) and handwritten signatures. Based on the results, conclusions are enumerated and inferences are made with directions for future studies.
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Date Issued: 2011
PURL: http://purl.flvc.org/FAU/3333052
Subject Headings: Bioinformatics, Statistical methods, Diagnostic imaging, Digital techniques, Image processing, Digital techniques, Pattern perception, Data processing, DNA microarrays
Format: Document (PDF)

Title: Biological Computation: the development of a genomic analysis pipeline to identify cellular genes modulated by the transcription / splicing factor srsf1.
Creator: Clark, Evan, Asghar, Waseem, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: SRSF1 is a widely expressed mammalian protein with multiple functions in the regulation of gene expression through processes including transcription, mRNA splicing, and translation. Although much is known of SRSF1 role in alternative splicing of specific genes little is known about its functions as a transcription factor and its global effect on cellular gene expression. We utilized a RNA sequencing (RNA-¬‐Seq) approach to determine the impact of SRSF1 in on cellular gene expression and...
Show moreSRSF1 is a widely expressed mammalian protein with multiple functions in the regulation of gene expression through processes including transcription, mRNA splicing, and translation. Although much is known of SRSF1 role in alternative splicing of specific genes little is known about its functions as a transcription factor and its global effect on cellular gene expression. We utilized a RNA sequencing (RNA-¬‐Seq) approach to determine the impact of SRSF1 in on cellular gene expression and analyzed both the short term (12 hours) and long term (48 hours) effects of SRSF1 expression in a human cell line. Furthermore, we analyzed and compared the effect of the expression of a naturally occurring deletion mutant of SRSF1 (RRM12) to the full-¬‐length protein. Our analysis reveals that shortly after SRSF1 is over-¬‐expressed the transcription of several histone coding genes is down-¬‐regulated, allowing for a more relaxed chromatin state and efficient transcription by RNA Polymerase II. This effect is reversed at 48 hours. At the same time key genes for the immune pathways are activated, more notably Tumor Necrosis Factor-¬‐Alpha (TNF-¬‐α), suggesting a role for SRSF1 in T cell functions.
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Date Issued: 2017
PURL: http://purl.flvc.org/fau/fd/FA00004858, http://purl.flvc.org/fau/fd/FA00004858
Subject Headings: Gene expression., Computational biology., Markov processes., Bioinformatics., Genetic engineering., Molecular biology.
Format: Document (PDF)

Title: Bilingual Sentiment Analysis of Spanglish Tweets.
Creator: Serrano, Melissa, Shankar, Ravi, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Sentiment Analysis has been researched in a variety of contexts but in this thesis, the focus is on sentiment analysis in Twitter, which poses its own unique challenges such as the use of slang, abbreviations, emoticons, hashtags, and user mentions. The 140-character restriction on the length of tweets can also lead to text that is difficult even for a human to determine its sentiment. Specifically, this study will analyze sentiment analysis of bilingual (U.S. English and Spanish language)...
Show moreSentiment Analysis has been researched in a variety of contexts but in this thesis, the focus is on sentiment analysis in Twitter, which poses its own unique challenges such as the use of slang, abbreviations, emoticons, hashtags, and user mentions. The 140-character restriction on the length of tweets can also lead to text that is difficult even for a human to determine its sentiment. Specifically, this study will analyze sentiment analysis of bilingual (U.S. English and Spanish language) Tweets. The hypothesis here is that Bilingual sentiment analysis is more accurate than sentiment analysis in a single language (English or Spanish) when analyzing bilingual tweets. In general, currently sentiment analysis in bilingual tweets is done against an English dictionary. For each of the test cases in this thesis’ experiment we will use the Python NLTK sentiment package.
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Date Issued: 2017
PURL: http://purl.flvc.org/fau/fd/FA00004877, http://purl.flvc.org/fau/fd/FA00004877
Subject Headings: Twitter., Online social networks., Connotation (Linguistics), Mass media--Audiences., Spanish language--Usage.
Format: Document (PDF)

Title: Changing Consumer Behavior through Ambient Displays in Smart Cafeterias and Detecting Anomalous Reporting Behavior in Wireless Sensors.
Creator: Hughes, Shiree, Hallstrom, Jason O., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Food availability and food waste are signi cant global problems which can be mitigated through the use of sensor networks. Current methods of monitoring food waste require manual data collection and are implemented infrequently, providing imprecise information. The use of sensors to automate food waste measurement allows constant monitoring, provides a better dataset for analysis, and enables real- time feedback, which can be used to affect behavioral change in consumers. The data from such...
Show moreFood availability and food waste are signi cant global problems which can be mitigated through the use of sensor networks. Current methods of monitoring food waste require manual data collection and are implemented infrequently, providing imprecise information. The use of sensors to automate food waste measurement allows constant monitoring, provides a better dataset for analysis, and enables real- time feedback, which can be used to affect behavioral change in consumers. The data from such networks can be used to drive ambient displays designed to educate a target audience, and ultimately reduce the amount of waste generated. We present WASTE REDUCE, a system for automating the measurement of food waste and affecting behavioral change. The challenges and results of deploying such a system are presented. To assess the bene ts of using WASTE REDUCE, two case studies are conducted. The rst study evaluates three different displays, and the second reevaluates one of these displays in a separate location. These studies con rm that the combination of automated monitoring and ambient feedback can reduce food waste for targeted groups.
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Date Issued: 2017
PURL: http://purl.flvc.org/fau/fd/FA00004896, http://purl.flvc.org/fau/fd/FA00004896
Subject Headings: Consumer behavior., Intelligent sensors., Wireless sensor networks., Wireless communication systems., Environmental economics., Food consumption--Measurement., Food industry and trade--Safety measures., Food supply--Globalization.
Format: Document (PDF)

Title: Channel assignment in multi-radio networks.
Creator: Mihnea, Amalya, Cardei, Mihaela, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Channel assignment in multi-radio networks is a topic of great importance because the use of multiple channels and multiple radios reduces interference and increases the network throughput. The goal of our research is to design algorithms that maximize the use of available resources while providing robustness to primary users that could reclaim one or more channels. Our algorithms could be used in ad hoc networks, mesh networks, and sensor networks where nodes are equipped with multiple...
Show moreChannel assignment in multi-radio networks is a topic of great importance because the use of multiple channels and multiple radios reduces interference and increases the network throughput. The goal of our research is to design algorithms that maximize the use of available resources while providing robustness to primary users that could reclaim one or more channels. Our algorithms could be used in ad hoc networks, mesh networks, and sensor networks where nodes are equipped with multiple radios. We design algorithms for channel assignment which provide robustness to primary users without assuming an accurate primary user behavior model. We also compute bounds for capacity in grid networks and discuss how the capacity of a network changes when multiple channels are available. Since preserving energy is very important in wireless networks, we focus on algorithms that do not require powerful resources and which use a reduced number of messages.
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Date Issued: 2015
PURL: http://purl.flvc.org/fau/fd/FA00004393, http://purl.flvc.org/fau/fd/FA00004393
Subject Headings: Adaptive signal processing, Long Term Evolution (Telecommunications), MIMO systems, Mobile communication systems, Wireless communication systems, Wireless sensor networks
Format: Document (PDF)

Title: Certificate authentication security and the preservation of privacy in wireless access in vehicular environment (wave).
Creator: Kemp, Clifford Allen, Mahgoub, Imad, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Future vehicles will exchange safety-critical information messages wirelessly with other vehicles on the road. We must provide secure mechanisms to validate the authenticity and integrity of these messages. The IEEE Standard 1609.2 defines the format of secure messages and identifies security algorithms and mechanism for use in Wireless Access in Vehicular Environment (WAVE). We propose an organizational structure for Central Management Entities (CMEs) to address these goals and functional...
Show moreFuture vehicles will exchange safety-critical information messages wirelessly with other vehicles on the road. We must provide secure mechanisms to validate the authenticity and integrity of these messages. The IEEE Standard 1609.2 defines the format of secure messages and identifies security algorithms and mechanism for use in Wireless Access in Vehicular Environment (WAVE). We propose an organizational structure for Central Management Entities (CMEs) to address these goals and functional requirements, and to balance the security of communications with protection of user privacy. A concern in vehicular communications is the privacy of vehicle owners. Privacy must be preserved and the user related information has to be protected from unauthorized access, while the authorities can trace the sender when there is a dispute. This thesis also presents a field operational test using IEEE 802.11a hardware. Vehicular test infrastructures can then be established in a cost effective manner to help support VANET research.
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Date Issued: 2013
PURL: http://purl.flvc.org/fau/fd/FA00004253
Format: Document (PDF)

Title: Collabortive filtering using machine learning and statistical techniques.
Creator: Su, Xiaoyuan., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Collaborative filtering (CF), a very successful recommender system, is one of the applications of data mining for incomplete data. The main objective of CF is to make accurate recommendations from highly sparse user rating data. My contributions to this research topic include proposing the frameworks of imputation-boosted collaborative filtering (IBCF) and imputed neighborhood based collaborative filtering (INCF). We also proposed a model-based CF technique, TAN-ELR CF, and two hybrid CF...
Show moreCollaborative filtering (CF), a very successful recommender system, is one of the applications of data mining for incomplete data. The main objective of CF is to make accurate recommendations from highly sparse user rating data. My contributions to this research topic include proposing the frameworks of imputation-boosted collaborative filtering (IBCF) and imputed neighborhood based collaborative filtering (INCF). We also proposed a model-based CF technique, TAN-ELR CF, and two hybrid CF algorithms, sequential mixture CF and joint mixture CF. Empirical results show that our proposed CF algorithms have very good predictive performances. In the investigation of applying imputation techniques in mining incomplete data, we proposed imputation-helped classifiers, and VCI predictors (voting on classifications from imputed learning sets), both of which resulted in significant improvement in classification performance for incomplete data over conventional machine learned classifiers, including kNN, neural network, one rule, decision table, SVM, logistic regression, decision tree (C4.5), random forest, and decision list (PART), and the well known Bagging predictors. The main imputation techniques involved in these algorithms include EM (expectation maximization) and BMI (Bayesian multiple imputation).
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Date Issued: 2008
PURL: http://purl.flvc.org/FAU/186301
Subject Headings: Filters (Mathematics), Machine learning, Data mining, Technological innovations, Database management, Combinatorial group theory
Format: Document (PDF)

Title: Big Data Analytics and Engineering for Medicare Fraud Detection.
Creator: Herland, Matthew Andrew, Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: The United States (U.S.) healthcare system produces an enormous volume of data with a vast number of financial transactions generated by physicians administering healthcare services. This makes healthcare fraud difficult to detect, especially when there are considerably less fraudulent transactions than non-fraudulent. Fraud is an extremely important issue for healthcare, as fraudulent activities within the U.S. healthcare system contribute to significant financial losses. In the U.S., the...
Show moreThe United States (U.S.) healthcare system produces an enormous volume of data with a vast number of financial transactions generated by physicians administering healthcare services. This makes healthcare fraud difficult to detect, especially when there are considerably less fraudulent transactions than non-fraudulent. Fraud is an extremely important issue for healthcare, as fraudulent activities within the U.S. healthcare system contribute to significant financial losses. In the U.S., the elderly population continues to rise, increasing the need for programs, such as Medicare, to help with associated medical expenses. Unfortunately, due to healthcare fraud, these programs are being adversely affected, draining resources and reducing the quality and accessibility of necessary healthcare services. In response, advanced data analytics have recently been explored to detect possible fraudulent activities. The Centers for Medicare and Medicaid Services (CMS) released several ‘Big Data’ Medicare claims datasets for different parts of their Medicare program to help facilitate this effort. In this dissertation, we employ three CMS Medicare Big Data datasets to evaluate the fraud detection performance available using advanced data analytics techniques, specifically machine learning. We use two distinct approaches, designated as anomaly detection and traditional fraud detection, where each have very distinct data processing and feature engineering. Anomaly detection experiments classify by provider specialty, determining whether outlier physicians within the same specialty signal fraudulent behavior. Traditional fraud detection refers to the experiments directly classifying physicians as fraudulent or non-fraudulent, leveraging machine learning algorithms to discriminate between classes. We present our novel data engineering approaches for both anomaly detection and traditional fraud detection including data processing, fraud mapping, and the creation of a combined dataset consisting of all three Medicare parts. We incorporate the List of Excluded Individuals and Entities database to identify real world fraudulent physicians for model evaluation. Regarding features, the final datasets for anomaly detection contain only claim counts for every procedure a physician submits while traditional fraud detection incorporates aggregated counts and payment information, specialty, and gender. Additionally, we compare cross-validation to the real world application of building a model on a training dataset and evaluating on a separate test dataset for severe class imbalance and rarity.
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Date Issued: 2019
PURL: http://purl.flvc.org/fau/fd/FA00013215
Subject Headings: Big data, Medicare fraud, Data analytics, Machine learning
Format: Document (PDF)

Title: CEREBROSPINAL FLUID SHUNT SYSTEM WITH AUTO-FLOW REGULATION.
Creator: Mutlu, Caner, Asghar, Waseem, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: A cerebrospinal fluid (CSF) shunt system is used for treatment of hydrocephalus and abnormal intracranial pressure (ICP) conditions. Mostly a shunt system is placed under skin for creating a low resistance pathway between intracranial space and appropriate discharge sites within body by doing so excess CSF volume can exit the intracranial space. Displaced intracranial CSF volume normally results in lowered ICP. Thereby, a CSF shunt can manage ICP. In a healthy person, normal ICP is primarily...
Show moreA cerebrospinal fluid (CSF) shunt system is used for treatment of hydrocephalus and abnormal intracranial pressure (ICP) conditions. Mostly a shunt system is placed under skin for creating a low resistance pathway between intracranial space and appropriate discharge sites within body by doing so excess CSF volume can exit the intracranial space. Displaced intracranial CSF volume normally results in lowered ICP. Thereby, a CSF shunt can manage ICP. In a healthy person, normal ICP is primarily maintained by CSF production and reabsorption rate as a natural tendency of body. If intracranial CSF volume starts increasing due to under reabsorption, this mostly results in raised ICP. Abnormal ICP can be treated by discharging excess CSF volume via use of a shunt system. Once a shunt system is placed subcutaneously, a patient is expected to live a normal life. However, shunt failure as well as flow regulatory problems are major issues with current passive shunt systems which leaves patients with serious consequences of under-/over CSF drainage condition. In this research, a shunt system is developed which is resistant to most shunt-related causes of under-/over CSF drainage. This has been made possible via use of an on-board medical monitoring (diagnostic) and active flow control mechanism. The developed shunt system, in this research, has full external ventricular drainage (EVD) capability. Further miniaturization will make it possible for an implantable shunt.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013489
Subject Headings: Cerebrospinal Fluid Shunts
Format: Document (PDF)

Title: Classification techniques for noisy and imbalanced data.
Creator: Napolitano, Amri E., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Machine learning techniques allow useful insight to be distilled from the increasingly massive repositories of data being stored. As these data mining techniques can only learn patterns actually present in the data, it is important that the desired knowledge be faithfully and discernibly contained therein. Two common data quality issues that often affect important real life classification applications are class noise and class imbalance. Class noise, where dependent attribute values are...
Show moreMachine learning techniques allow useful insight to be distilled from the increasingly massive repositories of data being stored. As these data mining techniques can only learn patterns actually present in the data, it is important that the desired knowledge be faithfully and discernibly contained therein. Two common data quality issues that often affect important real life classification applications are class noise and class imbalance. Class noise, where dependent attribute values are recorded erroneously, misleads a classifier and reduces predictive performance. Class imbalance occurs when one class represents only a small portion of the examples in a dataset, and, in such cases, classifiers often display poor accuracy on the minority class. The reduction in classification performance becomes even worse when the two issues occur simultaneously. To address the magnified difficulty caused by this interaction, this dissertation performs thorough empirical investigations of several techniques for dealing with class noise and imbalanced data. Comprehensive experiments are performed to assess the effects of the classification techniques on classifier performance, as well as how the level of class imbalance, level of class noise, and distribution of class noise among the classes affects results. An empirical analysis of classifier based noise detection efficiency appears first. Subsequently, an intelligent data sampling technique, based on noise detection, is proposed and tested. Several hybrid classifier ensemble techniques for addressing class noise and imbalance are introduced. Finally, a detailed empirical investigation of classification filtering is performed to determine best practices.
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Date Issued: 2009
PURL: http://purl.flvc.org/FAU/369201
Subject Headings: Combinatorial group theory, Data mining, Technological innovations, Decision trees, Machine learning, Filters (Mathematics)
Format: Document (PDF)

Title: Cloud-based Skin Lesion Diagnosis System using Convolutional Neural Networks.
Creator: Akar, Esad, Furht, Borko, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Skin cancer is a major medical problem. If not detected early enough, skin cancer like melanoma can turn fatal. As a result, early detection of skin cancer, like other types of cancer, is key for survival. In recent times, deep learning methods have been explored to create improved skin lesion diagnosis tools. In some cases, the accuracy of these methods has reached dermatologist level of accuracy. For this thesis, a full-fledged cloud-based diagnosis system powered by convolutional neural...
Show moreSkin cancer is a major medical problem. If not detected early enough, skin cancer like melanoma can turn fatal. As a result, early detection of skin cancer, like other types of cancer, is key for survival. In recent times, deep learning methods have been explored to create improved skin lesion diagnosis tools. In some cases, the accuracy of these methods has reached dermatologist level of accuracy. For this thesis, a full-fledged cloud-based diagnosis system powered by convolutional neural networks (CNNs) with near dermatologist level accuracy has been designed and implemented in part to increase early detection of skin cancer. A large range of client devices can connect to the system to upload digital lesion images and request diagnosis results from the diagnosis pipeline. The diagnosis is handled by a two-stage CNN pipeline hosted on a server where a preliminary CNN performs quality check on user requests, and a diagnosis CNN that outputs lesion predictions.
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Date Issued: 2018
PURL: http://purl.flvc.org/fau/fd/FA00013150
Subject Headings: Skin Diseases--diagnosis, Skin--Cancer--Diagnosis, Diagnosis--Methodology, Neural networks, Cloud computing
Format: Document (PDF)

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: Content identification using video tomography.
Creator: Leon, Gustavo A., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Video identification or copy detection is a challenging problem and is becoming increasingly important with the popularity of online video services. The problem addressed in this thesis is the identification of a given video clip in a given set of videos. For a given query video, the system returns all the instance of the video in the data set. This identification system uses video signatures based on video tomography. A robust and low complexity video signature is designed and implemented....
Show moreVideo identification or copy detection is a challenging problem and is becoming increasingly important with the popularity of online video services. The problem addressed in this thesis is the identification of a given video clip in a given set of videos. For a given query video, the system returns all the instance of the video in the data set. This identification system uses video signatures based on video tomography. A robust and low complexity video signature is designed and implemented. The nature of the signature makes it independent to the most commonly video transformations. The signatures are generated for video shots and not individual frames, resulting in a compact signature of 64 bytes per video shot. The signatures are matched using simple Euclidean distance metric. The results show that videos can be identified with 100% recall and over 93% precision. The experiments included several transformations on videos.
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Date Issued: 2008
PURL: http://purl.flvc.org/FAU/2783207
Subject Headings: Biometric identification, High performance computing, Image processing, Digital techniques, Multimedia systems, Security measures
Format: Document (PDF)

Title: Deep Learning for Android Application Ransomware Detection.
Creator: Wongsupa, Panupong, Zhu, Xingquan, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Smartphones and mobile tablets are rapidly growing, and very important nowadays. The most popular mobile operating system since 2012 has been Android. Android is an open source platform that allows developers to take full advantage of both the operating system and the applications itself. However, due to the open source community of an Android platform, some Android developers took advantage of this and created countless malicious applications such as Trojan, Malware, and Ransomware. All...
Show moreSmartphones and mobile tablets are rapidly growing, and very important nowadays. The most popular mobile operating system since 2012 has been Android. Android is an open source platform that allows developers to take full advantage of both the operating system and the applications itself. However, due to the open source community of an Android platform, some Android developers took advantage of this and created countless malicious applications such as Trojan, Malware, and Ransomware. All which are currently hidden in a large number of benign apps in official Android markets, such as Google PlayStore, and Amazon. Ransomware is a malware that once infected the victim’s device. It will encrypt files, unlock device system, and display a popup message which asks the victim to pay ransom in order to unlock their device or system which may include medical devices that connect through the internet. In this research, we propose to combine permission and API calls, then use Deep Learning techniques to detect ransomware apps from the Android market. Permissions setting and API calls are extracted from each app file by using a python library called AndroGuard. We are using Permissions and API call features to characterize each application, which can identify which application has potential to be ransomware or is benign. We implement our Android Ransomware Detection framework based on Keras, which uses MLP with back-propagation and a supervised algorithm. We used our method with experiments based on real-world applications with over 2000 benign applications and 1000 ransomware applications. The dataset came from ARGUS’s lab [1] which validated algorithm performance and selected the best architecture for the multi-layer perceptron (MLP) by trained our dataset with 6 various of MLP structures. Our experiments and validations show that the MLPs have over 3 hidden layers with medium sized of neurons achieved good results on both accuracy and AUC score of 98%. The worst score is approximately 45% to 60% and are from MLPs that have 2 hidden layers with large number of neurons.
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Date Issued: 2018
PURL: http://purl.flvc.org/fau/fd/FA00013151
Subject Headings: Deep learning, Android (Electronic resource)--Security measures, Malware (Computer software)--Prevention
Format: Document (PDF)

Title: Emulation of Safety Control Systems for Theme Park Rides.
Creator: Hirapara, Cole P., Alhalabi, Bassem, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Emulation of safety control systems is a form of computerized design validation completed during the design or fabrication stage of an engineering project. Emulation, a method of validating a system between the typical cases of computer simulation and experimental testing, provides a means to test physical systems while removing the limitation of the requirement for physical equipment. The use of emulation could mitigate the unique risks of theme park attractions in engineering design and...
Show moreEmulation of safety control systems is a form of computerized design validation completed during the design or fabrication stage of an engineering project. Emulation, a method of validating a system between the typical cases of computer simulation and experimental testing, provides a means to test physical systems while removing the limitation of the requirement for physical equipment. The use of emulation could mitigate the unique risks of theme park attractions in engineering design and business operation. This thesis considers the unique risks associated with the engineering of safety-related control systems for theme park attractions and rides, and how emulation and the computerization of testing can change the industry.
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Date Issued: 2019
PURL: http://purl.flvc.org/fau/fd/FA00013379
Subject Headings: Amusement rides--Safety measures, Amusement parks, Emulators (Computer programs), Safety
Format: Document (PDF)

Title: Exploiting audiovisual attention for visual coding.
Creator: Torres, Freddy., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Perceptual video coding has been a promising area during the last years. Increases in compression ratios have been reported by applying foveated video coding techniques where the region of interest (ROI) is selected by using a computational attention model. However, most of the approaches for perceptual video coding only use visual features ignoring the auditory component. In recent physiological studies, it has been demonstrated that auditory stimuli affects our visual perception. In this...
Show morePerceptual video coding has been a promising area during the last years. Increases in compression ratios have been reported by applying foveated video coding techniques where the region of interest (ROI) is selected by using a computational attention model. However, most of the approaches for perceptual video coding only use visual features ignoring the auditory component. In recent physiological studies, it has been demonstrated that auditory stimuli affects our visual perception. In this work, we validate some of those physiological tests using complex video sequence. We designed and developed a web-based tool for video quality measurement. After conducting different experiments, we observed that in the general reaction time to detect video artifacts was higher when video was presented with the audio information. We observed that emotional information in audio guide human attention to particular ROI. We also observed that sound frequency change spatial frequency perception in still images.
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Date Issued: 2013
PURL: http://purl.flvc.org/fcla/dt/3361251
Subject Headings: Digital video, Image processing, Digital techniques, Visual perception, Coding theory, Human-computer interaction, Intersensory effects
Format: Document (PDF)

Title: Experimental implementation of the new prototype in Linux.
Creator: Han, Gee Won., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: The Transmission Control Protocol (TCP) is one of the core protocols of the Internet protocol suite. In the wired network, TCP performs remarkably well due to its scalability and distributed end-to-end congestion control algorithms. However, many studies have shown that the unmodified standard TCP performs poorly in networks with large bandwidth-delay products and/or lossy wireless links. In this thesis, we analyze the problems TCP exhibits in the wireless communication and develop TCP...
Show moreThe Transmission Control Protocol (TCP) is one of the core protocols of the Internet protocol suite. In the wired network, TCP performs remarkably well due to its scalability and distributed end-to-end congestion control algorithms. However, many studies have shown that the unmodified standard TCP performs poorly in networks with large bandwidth-delay products and/or lossy wireless links. In this thesis, we analyze the problems TCP exhibits in the wireless communication and develop TCP congestion control algorithm for mobile applications. We show that the optimal TCP congestion control and link scheduling scheme amounts to window-control oriented implicit primaldual solvers for underlying network utility maximization. Based on this idea, we used a scalable congestion control algorithm called QUeueIng-Control (QUIC) TCP where it utilizes queueing-delay based MaxWeight-type scheduler for wireless links developed in [34]. Simulation and test results are provided to evaluate the proposed schemes in practical networks.
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Date Issued: 2013
PURL: http://purl.flvc.org/fcla/dt/3362375
Subject Headings: Ad hoc networks (Computer networks), Wireless sensor networks, Embedded computer systems, Programming, Operating systems (Computers), Network performance (Telecommunication), TCP/IP (Computer network protocol)
Format: Document (PDF)

FAU Theses and Dissertations (735)	+ -
FAU College Collections (9)	+ -

Wireless communication systems (57)	+ -
Neural networks (Computer science) (42)	+ -
Computer network protocols (35)	+ -
Machine learning (34)	+ -
Computer software--Quality control (27)	+ -

Technological innovations (27)	+ -
Computer algorithms (24)	+ -
Computer networks (24)	+ -
Software engineering (22)	+ -
Pattern recognition systems (21)	+ -
Computer architecture (20)	+ -
Fault-tolerant computing (20)	+ -
Mobile communication systems (20)	+ -
Algorithms (19)	+ -
Computer network architectures (18)	+ -
Expert systems (Computer science) (18)	+ -
Artificial intelligence (17)	+ -
Data mining (17)	+ -
Mobile computing (17)	+ -
Computer security (15)	+ -
Digital techniques (15)	+ -
Image processing--Digital techniques (15)	+ -
Data transmission systems (14)	+ -
Image processing (14)	+ -
Mathematical models (14)	+ -
Sensor networks (14)	+ -
Security measures (13)	+ -
Signal processing (13)	+ -
Software measurement (13)	+ -
Big data (12)	+ -

Android (Electronic resource). (2)	+ -
Android. (1)	+ -
Linux. (1)	+ -
OPNET. (1)	+ -

Electronic Thesis or Dissertation (735)	+ -
Program Review (9)	+ -

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