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- Title
- An Intelligent Method For Violence Detection in Live Video Feeds.
- Creator
- Eneim, Maryam, Marques, Oge, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
In the past few years, violence detection has become an increasingly rele- vant topic in computer vision with many proposed solutions by researchers. This thesis proposes a solution called Criminal Aggression Recognition Engine (CARE), an OpenCV based Java implementation of a violence detection system that can be trained with video datasets to classify action in a live feed as non-violent or violent. The algorithm extends existing work on fast ght detection by implementing violence detection...
Show moreIn the past few years, violence detection has become an increasingly rele- vant topic in computer vision with many proposed solutions by researchers. This thesis proposes a solution called Criminal Aggression Recognition Engine (CARE), an OpenCV based Java implementation of a violence detection system that can be trained with video datasets to classify action in a live feed as non-violent or violent. The algorithm extends existing work on fast ght detection by implementing violence detection of live video, in addition to prerecorded video. The results for violence detection in prerecorded videos are comparable to other popular detection systems and the results for live video are also very encouraging, making the work proposed in this thesis a solid foundation for improved live violence detection systems.
Show less - Date Issued
- 2016
- PURL
- http://purl.flvc.org/fau/fd/FA00004775, http://purl.flvc.org/fau/fd/FA00004775
- Subject Headings
- Multimedia systems., Image analysis., Computer vision., Visual communication--Social aspects., Social problems--21st century., Pattern recognition systems., Optical pattern recognition.
- Format
- Document (PDF)
- Title
- An Ant Inspired Dynamic Traffic Assignment for VANETs: Early Notification of Traffic Congestion and Traffic Incidents.
- Creator
- Arellano, Wilmer, Mahgoub, Imad, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
Vehicular Ad hoc NETworks (VANETs) are a subclass of Mobile Ad hoc NETworks and represent a relatively new and very active field of research. VANETs will enable in the near future applications that will dramatically improve roadway safety and traffic efficiency. There is a need to increase traffic efficiency as the gap between the traveled and the physical lane miles keeps increasing. The Dynamic Traffic Assignment problem tries to dynamically distribute vehicles efficiently on the road...
Show moreVehicular Ad hoc NETworks (VANETs) are a subclass of Mobile Ad hoc NETworks and represent a relatively new and very active field of research. VANETs will enable in the near future applications that will dramatically improve roadway safety and traffic efficiency. There is a need to increase traffic efficiency as the gap between the traveled and the physical lane miles keeps increasing. The Dynamic Traffic Assignment problem tries to dynamically distribute vehicles efficiently on the road network and in accordance with their origins and destinations. We present a novel dynamic decentralized and infrastructure-less algorithm to alleviate traffic congestions on road networks and to fill the void left by current algorithms which are either static, centralized, or require infrastructure. The algorithm follows an online approach that seeks stochastic user equilibrium and assigns traffic as it evolves in real time, without prior knowledge of the traffic demand or the schedule of the cars that will enter the road network in the future. The Reverse Online Algorithm for the Dynamic Traffic Assignment inspired by Ant Colony Optimization for VANETs follows a metaheuristic approach that uses reports from other vehicles to update the vehicle’s perceived view of the road network and change route if necessary. To alleviate the broadcast storm spontaneous clusters are created around traffic incidents and a threshold system based on the level of congestion is used to limit the number of incidents to be reported. Simulation results for the algorithm show a great improvement on travel time over routing based on shortest distance. As the VANET transceivers have a limited range, that would limit messages to reach at most 1,000 meters, we present a modified version of this algorithm that uses a rebroadcasting scheme. This rebroadcasting scheme has been successfully tested on roadways with segments of up to 4,000 meters. This is accomplished for the case of traffic flowing in a single direction on the roads. It is anticipated that future simulations will show further improvement when traffic in the other direction is introduced and vehicles travelling in that direction are allowed to use a store carry and forward mechanism.
Show less - Date Issued
- 2016
- PURL
- http://purl.flvc.org/fau/fd/FA00004566, http://purl.flvc.org/fau/fd/FA00004566
- Subject Headings
- Vehicular ad hoc networks (Computer networks)--Technological innovations., Routing protocols (Computer network protocols), Artificial intelligence., Intelligent transportation systems., Intelligent control systems., Mobile computing., Computer algorithms., Combinatorial optimization.
- Format
- Document (PDF)
- Title
- An Augmentative System with Facial and Emotion Recognition for Improving the Skills of Children with Autism Spectrum Disorders.
- Creator
- Alharbi, Mohammed N., Huang, Shihong, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
Autism spectrum disorders (ASDs) are one of the complex, pervasive, and multifactorial neurodevelopmental conditions which affect one in 68 children. Scientific research has proven the efficiency of using technologies to improve communication and social skills of autistic children. The use of technological devices, such as mobile applications and multimedia, increase the interest of autistic children to learn while playing games. This thesis presents the re-engineering, extension, and...
Show moreAutism spectrum disorders (ASDs) are one of the complex, pervasive, and multifactorial neurodevelopmental conditions which affect one in 68 children. Scientific research has proven the efficiency of using technologies to improve communication and social skills of autistic children. The use of technological devices, such as mobile applications and multimedia, increase the interest of autistic children to learn while playing games. This thesis presents the re-engineering, extension, and evolution of an existing prototype Windows-based mobile application called Ying to become an Android mobile application which is augmented with facial and emotion recognition. This mobile app complements different approaches of traditional therapy, such as Applied Behavior Analysis (ABA). Ying integrates different computer-assisted technologies, including speech recognition, audio and visual interaction, and mobile applications to enhance autistic children’s social behavior and verbal communication skills. An evaluation of the efficacy of using Ying has been conducted and its results are presented in the thesis.
Show less - Date Issued
- 2018
- PURL
- http://purl.flvc.org/fau/fd/FA00005981
- Subject Headings
- Autism spectrum disorders, Human-computer interaction, Mobile apps
- Format
- Document (PDF)
- Title
- Analysis of quality of service (QoS) in WiMAX networks.
- Creator
- Talwalkar, Rohit., College of Engineering and Computer Science, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
In last few years there has been significant growth in the area of wireless communication. Quality of Service (QoS) has become an important consideration for supporting variety of applications that utilize the network resources. These applications include voice over IP, multimedia services, like, video streaming, video conferencing etc. IEEE 802.16/WiMAX is a new network which is designed with quality of service in mind. This thesis focuses on analysis of quality of service as implemented by...
Show moreIn last few years there has been significant growth in the area of wireless communication. Quality of Service (QoS) has become an important consideration for supporting variety of applications that utilize the network resources. These applications include voice over IP, multimedia services, like, video streaming, video conferencing etc. IEEE 802.16/WiMAX is a new network which is designed with quality of service in mind. This thesis focuses on analysis of quality of service as implemented by the WiMAX networks. First, it presents the details of the quality of service architecture in WiMAX network. In the analysis, a WiMAX module developed based on popular network simulator ns-2, is used. Various real life scenarios like voice call, video streaming are setup in the simulation environment. Parameters that indicate quality of service, such as, throughput, packet loss, average jitter and average delay, are analyzed for different types of service flows as defined in WiMAX. Results indicate that better quality of service is achieved by using service flows designed for specific applications.
Show less - Date Issued
- 2008
- PURL
- http://purl.flvc.org/fcla/flaent/EN00154040/68_2/98p0143h.pdf, http://purl.flvc.org/FAU/58012
- Subject Headings
- Wireless communication systems, Broadband communication systems, Wireless LANs, Design and construction, Computer networks, Management, Quality control
- Format
- Document (PDF)
- Title
- A Clinical Decision Support System for the Identification of Potential Hospital Readmission Patients.
- Creator
- Baechle, Christopher, Agarwal, Ankur, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
Recent federal legislation has incentivized hospitals to focus on quality of patient care. A primary metric of care quality is patient readmissions. Many methods exist to statistically identify patients most likely to require hospital readmission. Correct identification of high-risk patients allows hospitals to intelligently utilize limited resources in mitigating hospital readmissions. However, these methods have seen little practical adoption in the clinical setting. This research attempts...
Show moreRecent federal legislation has incentivized hospitals to focus on quality of patient care. A primary metric of care quality is patient readmissions. Many methods exist to statistically identify patients most likely to require hospital readmission. Correct identification of high-risk patients allows hospitals to intelligently utilize limited resources in mitigating hospital readmissions. However, these methods have seen little practical adoption in the clinical setting. This research attempts to identify the many open research questions that have impeded widespread adoption of predictive hospital readmission systems. Current systems often rely on structured data extracted from health records systems. This data can be expensive and time consuming to extract. Unstructured clinical notes are agnostic to the underlying records system and would decouple the predictive analytics system from the underlying records system. However, additional concerns in clinical natural language processing must be addressed before such a system can be implemented. Current systems often perform poorly using standard statistical measures. Misclassification cost of patient readmissions has yet to be addressed and there currently exists a gap between current readmission system evaluation metrics and those most appropriate in the clinical setting. Additionally, data availability for localized model creation has yet to be addressed by the research community. Large research hospitals may have sufficient data to build models, but many others do not. Simply combining data from many hospitals often results in a model which performs worse than using data from a single hospital. Current systems often produce a binary readmission classification. However, patients are often readmitted for differing reasons than index admission. There exists little research into predicting primary cause of readmission. Furthermore, co-occurring evidence discovery of clinical terms with primary diagnosis has seen only simplistic methods applied. This research addresses these concerns to increase adoption of predictive hospital readmission systems.
Show less - Date Issued
- 2017
- PURL
- http://purl.flvc.org/fau/fd/FA00004880, http://purl.flvc.org/fau/fd/FA00004880
- Subject Headings
- Health services administration--Management., Medical care--Quality control--Statistical methods., Medical care--Quality control--Data processing., Medical care--Decision making., Evidence-based medicine., Outcome assessment (Medical care)
- Format
- Document (PDF)
- Title
- CONNECTED MULTI-DOMAIN AUTONOMY AND ARTIFICIAL INTELLIGENCE: AUTONOMOUS LOCALIZATION, NETWORKING, AND DATA CONFORMITY EVALUATION.
- Creator
- Tountas, Konstantinos, Pados, Dimitris, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
- Abstract/Description
-
The objective of this dissertation work is the development of a solid theoretical and algorithmic framework for three of the most important aspects of autonomous/artificialintelligence (AI) systems, namely data quality assurance, localization, and communications. In the era of AI and machine learning (ML), data reign supreme. During learning tasks, we need to ensure that the training data set is correct and complete. During operation, faulty data need to be discovered and dealt with to...
Show moreThe objective of this dissertation work is the development of a solid theoretical and algorithmic framework for three of the most important aspects of autonomous/artificialintelligence (AI) systems, namely data quality assurance, localization, and communications. In the era of AI and machine learning (ML), data reign supreme. During learning tasks, we need to ensure that the training data set is correct and complete. During operation, faulty data need to be discovered and dealt with to protect from -potentially catastrophic- system failures. With our research in data quality assurance, we develop new mathematical theory and algorithms for outlier-resistant decomposition of high-dimensional matrices (tensors) based on L1-norm principal-component analysis (PCA). L1-norm PCA has been proven to be resistant to irregular data-points and will drive critical real-world AI learning and autonomous systems operations in the future. At the same time, one of the most important tasks of autonomous systems is self-localization. In GPS-deprived environments, localization becomes a fundamental technical problem. State-of-the-art solutions frequently utilize power-hungry or expensive architectures, making them difficult to deploy. In this dissertation work, we develop and implement a robust, variable-precision localization technique for autonomous systems based on the direction-of-arrival (DoA) estimation theory, which is cost and power-efficient. Finally, communication between autonomous systems is paramount for mission success in many applications. In the era of 5G and beyond, smart spectrum utilization is key.. In this work, we develop physical (PHY) and medium-access-control (MAC) layer techniques that autonomously optimize spectrum usage and minimizes intra and internetwork interference.
Show less - Date Issued
- 2020
- PURL
- http://purl.flvc.org/fau/fd/FA00013617
- Subject Headings
- Artificial intelligence, Machine learning, Tensor algebra
- Format
- Document (PDF)
- 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.
Show less - 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.
Show less - 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.
Show less - 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.
Show less - 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.
Show less - 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.
Show less - 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.
Show less - 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.
Show less - 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.
Show less - 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
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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).
Show less - 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
- 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
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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.
Show less - 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
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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.
Show less - 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
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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.
Show less - 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
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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.
Show less - 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)
