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Title: DIGITAL TRANSFORMATION OF HEALTHCARE USING ARTIFICIAL INTELLIGENCE.
Creator: Gogova, Jennifer, Marques, Oge, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Digital transformation is rapidly changing the healthcare industry, and artificial intelligence (AI) is a critical component in this evolution. This thesis investigates three selected challenges that might delay the adoption of AI in healthcare and proposes ways to address them successfully. Challenge #1 states that healthcare professionals may not feel sufficiently knowledgeable about AI. This is addressed by Contribution #1 which is a guide for self-actualization in AI for healthcare...
Show moreDigital transformation is rapidly changing the healthcare industry, and artificial intelligence (AI) is a critical component in this evolution. This thesis investigates three selected challenges that might delay the adoption of AI in healthcare and proposes ways to address them successfully. Challenge #1 states that healthcare professionals may not feel sufficiently knowledgeable about AI. This is addressed by Contribution #1 which is a guide for self-actualization in AI for healthcare professionals. Challenge #2 explores the concept of transdisciplinary teams needing a work protocol to deliver successful results. This is addressed by Contribution #2 which is a step-by-step protocol for medical and AI researchers working on data-intensive projects. Challenge #3 states that the NIH All of Us Research Hub has a steep learning curve, and this is addressed by Contribution #3 which is a pilot project involving transdisciplinary teams using All of Us datasets.
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Date Issued: 2023
PURL: http://purl.flvc.org/fau/fd/FA00014179
Subject Headings: Healthcare, Medical care, Artificial intelligence—Medical applications
Format: Document (PDF)

Title: INVESTIGATING AND IMPROVING FAIRNESS AND BIAS IN MACHINE LEARNING MODELS FOR DERMATOLOGY.
Creator: Corbin, Adam, Marques, Oge, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Advancements in Artificial Intelligence (AI) and Machine Learning (ML) have significantly improved their application in dermatology. However, bias issues in AI systems can result in missed diagnoses and disparities in healthcare, especially for individuals with different skin types. This dissertation aims to investigate and improve the fairness and bias in machine learning models for dermatology by evaluating and enhancing their performance across different Fitzpatrick skin types. The...
Show moreAdvancements in Artificial Intelligence (AI) and Machine Learning (ML) have significantly improved their application in dermatology. However, bias issues in AI systems can result in missed diagnoses and disparities in healthcare, especially for individuals with different skin types. This dissertation aims to investigate and improve the fairness and bias in machine learning models for dermatology by evaluating and enhancing their performance across different Fitzpatrick skin types. The technical contributions of the dissertation include generating metadata for Fitzpatrick Skin Type using Individual Typology Angle; outlining best practices for Explainable AI (XAI) and the use of colormaps; developing and enhancing ML models through skin color transformation and extending the models to include XAI methods for better interpretation and improvement of fairness and bias; and providing a list of steps for successful application of deep learning in medical image analysis. The research findings of this dissertation have the potential to contribute to the development of fair and unbiased AI/ML models in dermatology. This can ultimately lead to better health outcomes and reduced healthcare costs, particularly for individuals with different skin types.
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Date Issued: 2023
PURL: http://purl.flvc.org/fau/fd/FA00014131
Subject Headings: Diagnostic Imaging, Machine learning, Dermatology, Artificial intelligence
Format: Document (PDF)

Title: FEDERATED LEARNING FOR MEDICAL IMAGE CLASSIFICATION.
Creator: Blazanovic, Danica, Zhu, Xingquan, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Machine learning (ML) has traditionally been used to make predictive models by training on local data. However, due to concerns regarding privacy, it is not always possible to collect and combine data from different sources. On the other hand, if there are insufficient data available, it might not be possible to construct accurate models to produce meaningful outcomes. This is where Federated Learning comes to the rescue. Federated Learning (FL) represents a sophisticated distributed machine...
Show moreMachine learning (ML) has traditionally been used to make predictive models by training on local data. However, due to concerns regarding privacy, it is not always possible to collect and combine data from different sources. On the other hand, if there are insufficient data available, it might not be possible to construct accurate models to produce meaningful outcomes. This is where Federated Learning comes to the rescue. Federated Learning (FL) represents a sophisticated distributed machine learning strategy that enables multiple devices hosted at different institutions such as hospitals, to collaboratively train a global model while ensuring that their respective data remains securely stored on-premises. It addresses privacy concerns and data protection regulations, because raw data does not need to be shared or centralized during the training process. This thesis research studies how two different FL architectures, centralized and decentralized FL, affect medical image classification. To study and validate the findings, skin cancer images dataset is used in a federated learning setting with five sites/clients, and a center for centralized FL. Experimental results show that using both centralized and decentralized (peer to peer) version of FL for classification of skin cancer images outperforms using the traditional ML. In addition, two different FL settings, centralized federated learning (CFL) and decentralized federated learning (DFL), are compared using different data distributions across sites/clients. Our study shows that the best accuracy (95.14%) was achieved with the DFL model when tested on the original dataset (without adding bias to the class distributions). This asserts that class distribution imbalance between sites has a significant impact to the federated learning.
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Date Issued: 2023
PURL: http://purl.flvc.org/fau/fd/FA00014205
Subject Headings: Medical imaging, Diagnostic Imaging--classification, Machine learning
Format: Document (PDF)

Title: NETWORK INTRUSION DETECTION AND DEEP LEARNING MECHANISMS.
Creator: Chatterjee, Suvosree, Cardei, Ionut, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Cyber attack is a strong threat to the digital world. So, it’s very essential to keep the network safe. Network Intrusion Detection system is the system to address this problem. Network Intrusion Detection system functions like a firewall, and monitors incoming and outgoing traffic like ingress and egress filtering fire wall. Network Intrusion Detection System does anomaly and hybrid detection for detecting known and unknown attacks. My thesis discusses about the several network cyber attacks...
Show moreCyber attack is a strong threat to the digital world. So, it’s very essential to keep the network safe. Network Intrusion Detection system is the system to address this problem. Network Intrusion Detection system functions like a firewall, and monitors incoming and outgoing traffic like ingress and egress filtering fire wall. Network Intrusion Detection System does anomaly and hybrid detection for detecting known and unknown attacks. My thesis discusses about the several network cyber attacks we face nowadays and I created several Deep learning models to detect accurately, I used NSL-KDD dataset which is a popular dataset, that contains several network attacks. After experimenting with different deep learning models I found some disparities in the training accuracy and validation accuracy, which is a clear indication of overfitting. To reduce the overfitting I introduced regularization and dropout in the models and experimented with different hyperparameters.
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Date Issued: 2023
PURL: http://purl.flvc.org/fau/fd/FA00014128
Subject Headings: Deep learning (Machine learning), Cyberterrorism, Intrusion detection systems (Computer security)
Format: Document (PDF)

Title: AN EFFECTIVE ENSEMBLE LEARNING-BASED REAL-TIME INTRUSION DETECTION SCHEME FOR IN-VEHICLE NETWORK.
Creator: Alalwany, Easa, Mahgoub, Imadeldin, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Connectivity and automation have expanded with the development of autonomous vehicle technology. One of several automotive serial protocols that can be used in a wide range of vehicles is the controller area network (CAN). The growing functionality and connectivity of modern vehicles make them more vulnerable to cyberattacks aimed at vehicular networks. The CAN bus protocol is vulnerable to numerous attacks as it lacks security mechanisms by design. It is crucial to design intrusion detection...
Show moreConnectivity and automation have expanded with the development of autonomous vehicle technology. One of several automotive serial protocols that can be used in a wide range of vehicles is the controller area network (CAN). The growing functionality and connectivity of modern vehicles make them more vulnerable to cyberattacks aimed at vehicular networks. The CAN bus protocol is vulnerable to numerous attacks as it lacks security mechanisms by design. It is crucial to design intrusion detection systems (IDS) with high accuracy to detect attacks on the CAN bus. In this dissertation, to address all these concerns, we design an effective machine learning-based IDS scheme for binary classification that utilizes eight supervised ML algorithms, along with ensemble classifiers, to detect normal and abnormal activities in the CAN bus. Moreover, we design an effective ensemble learning-based IDS scheme for detecting and classifying DoS, fuzzing, replay, and spoofing attacks. These are common CAN bus attacks that can threaten the safety of a vehicle’s driver, passengers, and pedestrians. For this purpose, we utilize supervised machine learning in combination with ensemble methods. Ensemble learning aims to achieve better classification results through the use of different classifiers that are combined into a single classifier. Furthermore, in the pursuit of real-time attack detection and classification, we use the Kappa architecture for efficient data processing, enhancing the IDS’s accuracy and effectiveness. We build this system using the most recent CAN intrusion dataset provided by the IEEE DataPort. We carried out the performance evaluation of the proposed system in terms of accuracy, precision, recall, F1-score, and area under curve receiver operator characteristic (ROC-AUC). For the binary classification, the ensemble classifiers outperformed the individual supervised ML classifiers and improved the effectiveness of the classifier. For detecting and classifying CAN bus attacks, the ensemble learning methods resulted in a robust and accurate multiclassification IDS for common CAN bus attacks. The stacking ensemble method outperformed other recently proposed methods, achieving the highest performance. For the real-time attack detection and classification, the ensemble methods significantly enhance the accuracy the real-time CAN bus attack detection and classification. By combining the strengths of multiple models, the stacking ensemble technique outperformed individual supervised models and other ensembles.
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Date Issued: 2023
PURL: http://purl.flvc.org/fau/fd/FA00014298
Subject Headings: Automated vehicles, Controller Area Network (Computer network), Intrusion detection systems (Computer security)
Format: Document (PDF)

Title: ENHANCING IOT DEVICES SECURITY: ENSEMBLE LEARNING WITH CLASSICAL APPROACHES FOR INTRUSION DETECTION SYSTEM.
Creator: Alotaibi, Yazeed, Ilyas, Mohammad, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: The Internet of Things (IoT) refers to a network of interconnected nodes constantly engaged in communication, data exchange, and the utilization of various network protocols. Previous research has demonstrated that IoT devices are highly susceptible to cyber-attacks, posing a significant threat to data security. This vulnerability is primarily attributed to their susceptibility to exploitation and their resource constraints. To counter these threats, Intrusion Detection Systems (IDS) are...
Show moreThe Internet of Things (IoT) refers to a network of interconnected nodes constantly engaged in communication, data exchange, and the utilization of various network protocols. Previous research has demonstrated that IoT devices are highly susceptible to cyber-attacks, posing a significant threat to data security. This vulnerability is primarily attributed to their susceptibility to exploitation and their resource constraints. To counter these threats, Intrusion Detection Systems (IDS) are employed. This study aims to contribute to the field by enhancing IDS detection efficiency through the integration of Ensemble Learning (EL) methods with traditional Machine Learning (ML) and deep learning (DL) models. To bolster IDS performance, we initially utilize a binary ML classification approach to classify IoT network traffic as either normal or abnormal, employing EL methods such as Stacking and Voting. Once this binary ML model exhibits high detection rates, we extend our approach by incorporating a ML multi-class framework to classify attack types. This further enhances IDS performance by implementing the same Ensemble Learning methods. Additionally, for further enhancement and evaluation of the intrusion detection system, we employ DL methods, leveraging deep learning techniques, ensemble feature selections, and ensemble methods. Our DL approach is designed to classify IoT network traffic. This comprehensive approach encompasses various supervised ML, and DL algorithms with ensemble methods. The proposed models are trained on TON-IoT network traffic datasets. The ensemble approaches are evaluated using a comprehensive metrics and compared for their effectiveness in addressing this classification tasks. The ensemble classifiers achieved higher accuracy rates compared to individual models, a result attributed to the diversity of learning mechanisms and strengths harnessed through ensemble learning. By combining these strategies, we successfully improved prediction accuracy while minimizing classification errors. The outcomes of these methodologies underscore their potential to significantly enhance the effectiveness of the Intrusion Detection System.
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Date Issued: 2023
PURL: http://purl.flvc.org/fau/fd/FA00014304
Subject Headings: Internet of things, Intrusion detection systems (Computer security), Machine learning
Format: Document (PDF)

Title: TACKLING BIAS, PRIVACY, AND SCARCITY CHALLENGES IN HEALTH DATA ANALYTICS.
Creator: Wang, Shuwen, Zhu, Xingquan, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Health data analysis has emerged as a critical domain with immense potential to revolutionize healthcare delivery, disease management, and medical research. However, it is confronted by formidable challenges, including sample bias, data privacy concerns, and the cost and scarcity of labeled data. These challenges collectively impede the development of accurate and robust machine learning models for various healthcare applications, from disease diagnosis to treatment recommendations. Sample...
Show moreHealth data analysis has emerged as a critical domain with immense potential to revolutionize healthcare delivery, disease management, and medical research. However, it is confronted by formidable challenges, including sample bias, data privacy concerns, and the cost and scarcity of labeled data. These challenges collectively impede the development of accurate and robust machine learning models for various healthcare applications, from disease diagnosis to treatment recommendations. Sample bias and specificity refer to the inherent challenges in working with health datasets that may not be representative of the broader population or may exhibit disparities in their distributions. These biases can significantly impact the generalizability and effectiveness of machine learning models in healthcare, potentially leading to suboptimal outcomes for certain patient groups. Data privacy and locality are paramount concerns in the era of digital health records and wearable devices. The need to protect sensitive patient information while still extracting valuable insights from these data sources poses a delicate balancing act. Moreover, the geographic and jurisdictional differences in data regulations further complicate the use of health data in a global context. Label cost and scarcity pertain to the often labor-intensive and expensive process of obtaining ground-truth labels for supervised learning tasks in healthcare. The limited availability of labeled data can hinder the development and deployment of machine learning models, particularly in specialized medical domains.
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Date Issued: 2023
PURL: http://purl.flvc.org/fau/fd/FA00014336
Subject Headings: Data analytics, Data mining, Ensemble learning (Machine learning), Machine learning, Health
Format: Document (PDF)

Title: An Algorithm for the Automated Interpretation of Cardiac Auscultation.
Creator: Lieber, Claude, Erdol, Nurgun, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Cardiac auscultation, an important part of the physical examination, is difficult for many primary care providers. As a result, diagnoses are missed or auscultatory signs misinterpreted. A reliable, automated means of interpreting cardiac auscultation should be of benefit to both the primary care provider and to patients. This paper explores a novel approach to this problem and develops an algorithm that can be expanded to include all the necessary electronics and programming to develop such...
Show moreCardiac auscultation, an important part of the physical examination, is difficult for many primary care providers. As a result, diagnoses are missed or auscultatory signs misinterpreted. A reliable, automated means of interpreting cardiac auscultation should be of benefit to both the primary care provider and to patients. This paper explores a novel approach to this problem and develops an algorithm that can be expanded to include all the necessary electronics and programming to develop such a device. The algorithm is explained and its shortcomings exposed. The potential for further development is also expounded.
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Date Issued: 2016
PURL: http://purl.flvc.org/fau/fd/FA00004609, http://purl.flvc.org/fau/fd/FA00004609
Subject Headings: Phonocardiography., Signal processing., Pattern recognition systems., Imaging systems in medicine., Decision support systems., Medicine--Data processing.
Format: Document (PDF)

Title: Applications of pulse width modulation to LEDs, fuel cells and battery technology.
Creator: Watt, Wayne W., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: It has become a case of great desire and, in some instances, a requirement to have systems in engineering be energy efficient, in addition to being effectively powerful. It is rare that there is a single technique that has the range to make this possible in a wide collection of areas in the field. The work done in this thesis exhibits how Pulse Width Modulation (PWM) bridges LEDs, plug in vehicles, fuel cells and batteries, all seemingly different sub categories of electrical engineering. It...
Show moreIt has become a case of great desire and, in some instances, a requirement to have systems in engineering be energy efficient, in addition to being effectively powerful. It is rare that there is a single technique that has the range to make this possible in a wide collection of areas in the field. The work done in this thesis exhibits how Pulse Width Modulation (PWM) bridges LEDs, plug in vehicles, fuel cells and batteries, all seemingly different sub categories of electrical engineering. It stems from an undergraduate directed independent study supervised by Dr. Zilouchian that encircled LEDs and electric vehicles and how they contribute to a smart electric grid. This thesis covers the design and development of a prototype board that test how PWM saves energy, prolongs lifespan and provides a host of customizable features in manufactured LED lights that are used in the marine industry. Additionally, the concept of charging batteries that provide power to electric vehicles was explored. It is stressed that consumers who are interested in electric vehicles are concerned about refueling and recharge times. It is natural that a competing product, such as the electric vehicle in a world dominated by internal combustion engines, will perform on par if not better than existing choices. Tests are conducted to investigate the methods of fast battery charging and the challenges this technique creates. Attention is also given to the development of a pulsed Proton Exchange Membrane (PEM) fuel cell, specifically to prove whether pulse modulation is more efficient in a hydrogen producing fuel cell as opposed to direct-driven voltage and current alternatives.
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Date Issued: 2011
PURL: http://purl.flvc.org/FAU/3174313
Subject Headings: Power electronics, Digital control systems, Electric current converters, Fuel cells, Economic aspects
Format: Document (PDF)

Title: Asset identification using image descriptors.
Creator: Friedel, Reena Ursula., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Asset management is a time consuming and error prone process. Information Technology (IT) personnel typically perform this task manually by visually inspecting assets to identify misplaced assets. If this process is automated and provided to IT personnel it would prove very useful in keeping track of assets in a server rack. A mobile based solution is proposed to automate this process. The asset management application on the tablet captures images of assets and searches an annotated database...
Show moreAsset management is a time consuming and error prone process. Information Technology (IT) personnel typically perform this task manually by visually inspecting assets to identify misplaced assets. If this process is automated and provided to IT personnel it would prove very useful in keeping track of assets in a server rack. A mobile based solution is proposed to automate this process. The asset management application on the tablet captures images of assets and searches an annotated database to identify the asset. We evaluate the matching performance and speed of asset matching using three different image feature descriptors. Methods to reduce feature extraction and matching complexity were developed. Performance and accuracy tradeoffs were studied, domain specific problems were identified, and optimizations for mobile platforms were made. The results show that the proposed methods reduce complexity of asset matching by 67% when compared to the matching process using unmodified image feature descriptors.
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Date Issued: 2012
PURL: http://purl.flvc.org/FAU/3342051
Subject Headings: Data mining, Technological innovations, Mobile computing, User-centered system design, Application software, Development
Format: Document (PDF)

Title: Barometric distillation and the problem of non-condensable gases.
Creator: Martinson, Eiki., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Barometric distillation is an alternative method of producing fresh water by desalination. This proposed process evaporates saline water at low pressure and consequently low temperature; low pressure conditions are achieved by use of barometric columns and condensation is by direct contact with a supply of fresh water that will be augmented by the distillate. Low-temperature sources of heat, such as the cooling water rejected by electrical power generating facilities, can supply this system...
Show moreBarometric distillation is an alternative method of producing fresh water by desalination. This proposed process evaporates saline water at low pressure and consequently low temperature; low pressure conditions are achieved by use of barometric columns and condensation is by direct contact with a supply of fresh water that will be augmented by the distillate. Low-temperature sources of heat, such as the cooling water rejected by electrical power generating facilities, can supply this system with the latent heat of evaporation. Experiments are presented that show successful distillation with a temperature difference between evaporator and condenser smaller than 10ê C. Accumulation of dissolved gases coming out of solution, a classic problem in lowpressure distillation, is indirectly measured using a gas-tension sensor. The results of these experiments are used in an analysis of the specific energy required by a production process capable of producing 15 liters per hour. With a 20ê C difference, and neglecting latent heat, this analysis yields a specific energy of 1.85 kilowatt-hour per cubic meter, consumed by water pumping and by removal of non-condensable gases.
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Date Issued: 2010
PURL: http://purl.flvc.org/FAU/2978949
Subject Headings: Chemistry, Physical and theoretical, Fluid mechanics, Saline water conversion, Renewable energy sources, Groundwater, Purification
Format: Document (PDF)

Title: Automated biometrics of audio-visual multiple modals.
Creator: Huang, Lin, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Biometrics is the science and technology of measuring and analyzing biological data for authentication purposes. Its progress has brought in a large number of civilian and government applications. The candidate modalities used in biometrics include retinas, fingerprints, signatures, audio, faces, etc. There are two types of biometric system: single modal systems and multiple modal systems. Single modal systems perform person recognition based on a single biometric modality and are affected by...
Show moreBiometrics is the science and technology of measuring and analyzing biological data for authentication purposes. Its progress has brought in a large number of civilian and government applications. The candidate modalities used in biometrics include retinas, fingerprints, signatures, audio, faces, etc. There are two types of biometric system: single modal systems and multiple modal systems. Single modal systems perform person recognition based on a single biometric modality and are affected by problems like noisy sensor data, intra-class variations, distinctiveness and non-universality. Applying multiple modal systems that consolidate evidence from multiple biometric modalities can alleviate those problems of single modal ones. Integration of evidence obtained from multiple cues, also known as fusion, is a critical part in multiple modal systems, and it may be consolidated at several levels like feature fusion level, matching score fusion level and decision fusion level. Among biometric modalities, both audio and face modalities are easy to use and generally acceptable by users. Furthermore, the increasing availability and the low cost of audio and visual instruments make it feasible to apply such Audio-Visual (AV) systems for security applications. Therefore, this dissertation proposes an algorithm of face recognition. In addition, it has developed some novel algorithms of fusion in different levels for multiple modal biometrics, which have been tested by a virtual database and proved to be more reliable and robust than systems that rely on a single modality.
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Date Issued: 2010
PURL: http://purl.flvc.org/FAU/1927864
Subject Headings: Pattern recognition systems, Optical pattern recognition, Biometric identification, Identification, Automation, Automatic speech recognition
Format: Document (PDF)

Title: Automated control of microfluidics devices.
Creator: Gerstel, Ian., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: In order for microfluidics devices to be marketable, they must be inexpensive and easy to use. Two projects were pursued in this study for this purpose. The first was the design of a chip alignment system for visual feedback, in which a two-layer microfluidic chip was placed under a camera and an image processing and linear algebra program aligned a computer model to it. The system then translated the new locations of air valves and could detect valve activation in a chip filled with food...
Show moreIn order for microfluidics devices to be marketable, they must be inexpensive and easy to use. Two projects were pursued in this study for this purpose. The first was the design of a chip alignment system for visual feedback, in which a two-layer microfluidic chip was placed under a camera and an image processing and linear algebra program aligned a computer model to it. The system then translated the new locations of air valves and could detect valve activation in a chip filled with food coloring. The second was the design of a cheap, portable system to detect phosphorus in water. This system could not be completed due to time constraints, but the methods were detailed, and design ideas were laid out for future work.
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Date Issued: 2010
PURL: http://purl.flvc.org/FAU/3158764
Subject Headings: Microfluidics, Design, Microelectromagnetical systems, Design, Fluidic devices, Design, Micromechanics
Format: Document (PDF)

Title: Automated nursing knowledge classification using indexing.
Creator: Chinchanikar, Sucharita Vijay., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Promoting healthcare and wellbeing requires the dedication of a multi-tiered health service delivery system, which is comprised of specialists, medical doctors and nurses. A holistic view to a patient care perspective involves emotional, mental and physical healthcare needs, in which caring is understood as the essence of nursing. Properly and efficiently capturing and managing nursing knowledge is essential to advocating health promotion and illness prevention. This thesis proposes a...
Show morePromoting healthcare and wellbeing requires the dedication of a multi-tiered health service delivery system, which is comprised of specialists, medical doctors and nurses. A holistic view to a patient care perspective involves emotional, mental and physical healthcare needs, in which caring is understood as the essence of nursing. Properly and efficiently capturing and managing nursing knowledge is essential to advocating health promotion and illness prevention. This thesis proposes a document-indexing framework for automating classification of nursing knowledge based on nursing theory and practice model. The documents defining the numerous categories in nursing care model are structured with the help of expert nurse practitioners and professionals. These documents are indexed and used as a benchmark for the process of automatic mapping of each expression in the assessment form of a patient to the corresponding category in the nursing theory model. As an illustration of the proposed methodology, a prototype application is developed using the Latent Semantic Indexing (LSI) technique. The prototype application is tested in a nursing practice environment to validate the accuracy of the proposed algorithm. The simulation results are also compared with an application using Lucene indexing technique that internally uses modified vector space model for indexing. The result comparison showed that the LSI strategy gives 87.5% accurate results compared to the Lucene indexing technique that gives 80% accuracy. Both indexing methods maintain 100% consistency in the results.
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Date Issued: 2009
PURL: http://purl.flvc.org/FAU/186677
Subject Headings: Nursing, Computer-assisted instruction, Data transmission systems, Outcome assessment (Medical care), Nursing assessment, Digital techniques
Format: Document (PDF)

Title: A BCU scalable sensory acquisition system for EEG embedded applications.
Creator: Fathalla, Sherif S., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Electroencephalogram (EEG) Recording has been through a lot of changes and modification since it was first introduced in 1929 due to rising technologies and signal processing advancements. The EEG Data acquisition stage is the first and most valuable component in any EEG recording System, it has the role of gathering and conditioning its input and outputting reliable data to be effectively analyzed and studied by digital signal processors using sophisticated and advanced algorithms which help...
Show moreElectroencephalogram (EEG) Recording has been through a lot of changes and modification since it was first introduced in 1929 due to rising technologies and signal processing advancements. The EEG Data acquisition stage is the first and most valuable component in any EEG recording System, it has the role of gathering and conditioning its input and outputting reliable data to be effectively analyzed and studied by digital signal processors using sophisticated and advanced algorithms which help in numerous medical and consumer applications. We have designed a low noise low power EEG data acquisition system that can be set to act as a standalone mobile EEG data processing unit providing data preprocessing functions; it can also be a very reliable high speed data acquisition interface to an EEG processing unit.
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Date Issued: 2010
PURL: http://purl.flvc.org/FAU/3164095
Subject Headings: Brain-computer interfaces, Computational neuroscience, Neural networks (Computer science)
Format: Document (PDF)

Title: Automatic parking lot occupancy computation using motion tracking.
Creator: Justo Torres, Francisco Alberto, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Nowadays it is very hard to find available spots in public parking lots and even harder at facilities such as universities and sports venues. A system that provides drivers with parking availability and parking lot occupancy will allow users find a parking space much easier and faster. This thesis presents a system for automatic parking lot occupancy computation using motion tracking. The use of computer vision techniques and low cost video sensors makes it possible to have an accurate system...
Show moreNowadays it is very hard to find available spots in public parking lots and even harder at facilities such as universities and sports venues. A system that provides drivers with parking availability and parking lot occupancy will allow users find a parking space much easier and faster. This thesis presents a system for automatic parking lot occupancy computation using motion tracking. The use of computer vision techniques and low cost video sensors makes it possible to have an accurate system that allows drivers to find a parking spot. Video bitrate and quality reduction and its impact on performance were studied. It was concluded that high quality video is not necessary for the proposed algorithm to obtain accurate results. The results show that relatively inexpensive and low bandwidth networks can be used to develop large scale parking occupancy applications.
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Date Issued: 2013
PURL: http://purl.flvc.org/fcla/dt/3362483
Subject Headings: Traffic estimation, Automobile parking, Transportation engineering, Transportation demand management, Electronics in transportation, Computer vision
Format: Document (PDF)

Title: Analysis of Eye Response to Video Quality and Structure.
Creator: Pappusetty, Deepti, Kalva, Hari, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Real-time eye tracking systems with human-computer interaction mechanism are being adopted to advance user experience in smart devices and consumer electronic systems. Eye tracking systems measure eye gaze and pupil response non-intrusively. This research presents an analysis of eye pupil and gaze response to video structure and content. The set of experiments for this study involved presenting different video content to subjects and measuring eye response with an eye tracker. Results show...
Show moreReal-time eye tracking systems with human-computer interaction mechanism are being adopted to advance user experience in smart devices and consumer electronic systems. Eye tracking systems measure eye gaze and pupil response non-intrusively. This research presents an analysis of eye pupil and gaze response to video structure and content. The set of experiments for this study involved presenting different video content to subjects and measuring eye response with an eye tracker. Results show significant changes in video and scene cuts led to sharp constrictions. User response to videos can provide insights that can improve subjective quality assessment metrics. This research also presents an analysis of the pupil and gaze response to quality changes in videos. The results show pupil constrictions for noticeable changes in perceived quality and higher fixations/saccades ratios with lower quality. Using real-time eye tracking systems for video analysis and quality evaluation can open a new class of applications for consumer electronic systems.
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Date Issued: 2017
PURL: http://purl.flvc.org/fau/fd/FA00005940
Subject Headings: Dissertations, Academic -- Florida Atlantic University, Eye tracking., Video., Quality (Aesthetics)
Format: Document (PDF)

Title: An implementation of the IEEE 1609.4 wave standard for use in a vehicular networking testbed.
Creator: Kuffermann, Kyle, Mahgoub, Imad, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: We present an implementation of the IEEE WAVE (Wireless Access in Vehicular Environments) 1609.4 standard, Multichannel Operation. This implementation provides concurrent access to a control channel and one or more service channels, enabling vehicles to communicate among each other on multiple service channels while still being able to receive urgent and control information on the control channel. Also included is functionality that provides over-the-air timing synchronization, allowing...
Show moreWe present an implementation of the IEEE WAVE (Wireless Access in Vehicular Environments) 1609.4 standard, Multichannel Operation. This implementation provides concurrent access to a control channel and one or more service channels, enabling vehicles to communicate among each other on multiple service channels while still being able to receive urgent and control information on the control channel. Also included is functionality that provides over-the-air timing synchronization, allowing participation in alternating channel access in the absence of a reliable time source. Our implementation runs on embedded Linux and is built on top of IEEE 802.11p, as well as a customized device driver. This implementation will serve as a key compo- nent in our IEEE 1609-compliant Vehicular Multi-technology Communication Device (VMCD) that is being developed for a VANET testbed under the Smart Drive initiative, supported by the National Science Foundation.
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Date Issued: 2014
PURL: http://purl.flvc.org/fau/fd/FA00004299, http://purl.flvc.org/fau/fd/FA00004299
Subject Headings: Vehicular ad hoc networks (Computer networks)., Wireless sensor networks., Wireless communication systems., Wireless LANs., Linux., Expert systems (Computer science), Operating systems (Computers)
Format: Document (PDF)

Title: An interactive system to enhance social and verbal communication skills of children withautism spectrum disorders.
Creator: Minan, Maria Jose, Huang, Shihong, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Affecting one in every 68 children, Autism Spectrum Disorder (ASD) is one of the fastest growing developmental disabilities. Scientific research has proven that early behavioral intervention can improve learning, communication, and social skills. Similarly, studies have shown that the usage of of-the-shelf technology boosts motivation in children diagnosed with ASD while increasing their attention span and ability to interact socially. Embracing perspectives from different fields of study can...
Show moreAffecting one in every 68 children, Autism Spectrum Disorder (ASD) is one of the fastest growing developmental disabilities. Scientific research has proven that early behavioral intervention can improve learning, communication, and social skills. Similarly, studies have shown that the usage of of-the-shelf technology boosts motivation in children diagnosed with ASD while increasing their attention span and ability to interact socially. Embracing perspectives from different fields of study can lead to the development of an effective tool to complement traditional treatment of those with ASD. This thesis documents the re-engineering, extension, and evolu- tion of Ying, an existing web application designed to aid in the learning of autistic children. The original methodology of Ying combines expertise from other research areas including developmental psychology, semantic learning, and computer science. In this work, Ying is modifed to incorporate aspects of traditional treatment, such as Applied Behavior Analysis. Using cutting-edge software technology in areas like voice recognition and mobile device applications, this project aspires to use software engineering approaches and audio-visual interaction with the learner to enhance social behavior and reinforce verbal communication skills in children with ASD, while detecting and storing learning patterns for later study.
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Date Issued: 2014
PURL: http://purl.flvc.org/fau/fd/FA00004306, http://purl.flvc.org/fau/fd/FA00004306
Subject Headings: Autism spectrum disorders in children -- Treatment -- Technological innovations, Children with autism spectrum disorders -- Education -- Technological innovations, Communication disorders in children -- Treatment -- Technological innovations, Computers and people with disabilities, Learning, Psychology of, Optical pattern recognition
Format: Document (PDF)

Title: An empirical methodology for foundry specific submicroncmos analog circuit design.
Creator: Rivas-Torres, Wilfredo, Roth, Zvi S., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Analog CMOS amplifiers are the building blocks for many analog circuit applications such as Operational Amplifiers, Comparators, Analog to Digital converters and others. This dissertation presents empirical design methodologies that are both intuitive and easy to follow on how to design these basic building blocks. The design method involves two main phases. In the first phase NMOS and PMOS transistor design kits, provided by a semiconductor foundry, are fully characterized using a set of...
Show moreAnalog CMOS amplifiers are the building blocks for many analog circuit applications such as Operational Amplifiers, Comparators, Analog to Digital converters and others. This dissertation presents empirical design methodologies that are both intuitive and easy to follow on how to design these basic building blocks. The design method involves two main phases. In the first phase NMOS and PMOS transistor design kits, provided by a semiconductor foundry, are fully characterized using a set of simulation experiments. In the second phase the user is capable of modifying all the relevant circuit design parameters while directly observing the tradeoffs in the circuit performance specifications. The final design is a circuit that very closely meets a set of desired design specifications for the design parameters selected. That second phase of the proposed design methodology utilizes a graphical user interface in which the designer moves a series of sliders allowing assessment of various design tradeoffs. The theoretical basis for this design methodology involves the transconductance efficiency and inversion coefficient parameters. In this dissertation there are no restrictive assumptions about the MOS transistor models. The design methodology can be used with any submicron model supported by the foundry process and in this sense the methods included within are general and non-dependent on any specific MOSFET model (e.g. EKV or BSIM3). As part of the design tradeoffs assessment process variations are included during the design process rather than as part of some post-nominal-design analysis. One of the central design parameters of each transistor in the circuit is the MOSFET inversion coefficient. The calculation of the inversion coefficient necessitates the determination of an important process parameter known as the Technology Current. In this dissertation a new method to determine the technology current is developed. Y Parameters are used to characterize the CMOS process and this also helps in improving the technology current determination method. A study of the properties of the technology current proves that indeed a single long channel saturated MOS transistor can be used to determine a fixed technology current value that is used in subsequent submicron CMOS design. Process corners and the variability of the technology current are also studied and the universality of the transconductance efficiency versus inversion coefficient response is shown to be true even in the presence of process variability.
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Date Issued: 2013
PURL: http://purl.flvc.org/fau/fd/FA0004050
Subject Headings: Electron transport, Integrated circuits -- Design and construction, Metal oxide semiconductors, Complementary -- Mathematical models
Format: Document (PDF)

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