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Title: Application of MANOVA and Mahalanobis Distance in quality control.
Creator: Seth, Mahua., Florida Atlantic University, Mazouz, Abdel Kader, College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
Abstract/Description: Many quality control problems are multivariate in nature since multiple process or product variables are measured and monitored in modern industry. Occasionally a multiple population, multi-variable scenario is encountered in parallel line manufacturing system. This work deals with the analysis of such system using multivariate techniques such as Canonical Analysis, MANOVA and Mahalanobis Distance. Using such techniques, the significant differences between the populations and the magnitude...
Show moreMany quality control problems are multivariate in nature since multiple process or product variables are measured and monitored in modern industry. Occasionally a multiple population, multi-variable scenario is encountered in parallel line manufacturing system. This work deals with the analysis of such system using multivariate techniques such as Canonical Analysis, MANOVA and Mahalanobis Distance. Using such techniques, the significant differences between the populations and the magnitude and directions of the variations within and between the populations were determined. Also, elimination/reduction of such variations using multivariate techniques led to a drastic improvement of the production system. A methodology for application of such multivariate techniques for the purpose of quality assurance and improvement was developed here. The application of this methodology was performed on a pager production line and the results obtained show the benefits and the feasibility of the use of multivariate techniques for on-line monitoring and quality control.
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Date Issued: 1997
PURL: http://purl.flvc.org/fcla/dt/15381
Subject Headings: Quality control, Beepers--Quality control, Process control
Format: Document (PDF)

Title: Quality control and quality assurance manual for the determination of volatile organic contaminants.
Creator: Wang, Tsen C.
Date Issued: 1990-09
PURL: http://purl.flvc.org/fcla/dt/3359103
Subject Headings: Quality control, Quality assurance, Volatile organic compounds
Format: Document (PDF)

Title: Combining decision trees for software quality classification: An empirical study.
Creator: Geleyn, Erik., Florida Atlantic University, Khoshgoftaar, Taghi M.
Abstract/Description: The increased reliance on computer systems in the modern world has created a need for engineering reliability control of computer systems to the highest standards. Software quality classification models are one of the important tools to achieve high reliability. They can be used to calibrate software metrics-based models to predict whether software modules are fault-prone or not. Timely use of such models can aid in detecting faults early in the life cycle. Individual classifiers may be...
Show moreThe increased reliance on computer systems in the modern world has created a need for engineering reliability control of computer systems to the highest standards. Software quality classification models are one of the important tools to achieve high reliability. They can be used to calibrate software metrics-based models to predict whether software modules are fault-prone or not. Timely use of such models can aid in detecting faults early in the life cycle. Individual classifiers may be improved by using the combined decision from multiple classifiers. Several algorithms implement this concept and are investigated in this thesis. These combined learners provide the software quality modeling community with accurate, robust, and goal oriented models. This study presents a comprehensive comparative evaluation of meta learners using a strong and a weak learner, C4.5 and Decision Stump, respectively. Two case studies of industrial software systems are used in our empirical investigations.
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Date Issued: 2002
PURL: http://purl.flvc.org/fcla/dt/12898
Subject Headings: Computer software--Quality control, Software measurement
Format: Document (PDF)

Title: Classification of software quality with tree modeling using C4.5 algorithm.
Creator: Ponnuswamy, Viswanathan Kolathupalayam., Florida Atlantic University, Khoshgoftaar, Taghi M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Developing highly reliable software is a must in today's competitive environment. However quality control is a costly and time consuming process. If the quality of software modules being developed can be predicted early in their life cycle, resources can be effectively allocated improving quality, reducing cost and development time. This study examines the C4.5 algorithm as a tool for building classification trees, classifying software module either as fault-prone or not fault-prone. The...
Show moreDeveloping highly reliable software is a must in today's competitive environment. However quality control is a costly and time consuming process. If the quality of software modules being developed can be predicted early in their life cycle, resources can be effectively allocated improving quality, reducing cost and development time. This study examines the C4.5 algorithm as a tool for building classification trees, classifying software module either as fault-prone or not fault-prone. The classification tree models were developed based on four consecutive releases of a very large legacy telecommunication system. The first two releases were used as training data sets and the subsequent two releases were used as test data sets to evaluate the model. We found out that C4.5 was able to build compact classification trees models with balanced misclassification rates.
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Date Issued: 2001
PURL: http://purl.flvc.org/fcla/dt/12855
Subject Headings: Computer software--Quality control, Software measurement
Format: Document (PDF)

Title: Modeling software quality at system and subsystem level with TREEDISC classification algorithm.
Creator: Liu, Jinxia., Florida Atlantic University, Khoshgoftaar, Taghi M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Software quality models are tools for detecting faults early in the software development process. In this research, the TREEDISC algorithm and a general classification rule were used to create classification tree models and predict software quality by classifying software modules as fault-prone or not fault-prone. Software metrics were collected from four consecutive releases of a very large legacy telecommunications system with six subsystems. Using release 1, four classification tree models...
Show moreSoftware quality models are tools for detecting faults early in the software development process. In this research, the TREEDISC algorithm and a general classification rule were used to create classification tree models and predict software quality by classifying software modules as fault-prone or not fault-prone. Software metrics were collected from four consecutive releases of a very large legacy telecommunications system with six subsystems. Using release 1, four classification tree models were built using raw metrics, and another four tree models were built using PCA metrics. Models were then selected based on release 2. Releases 3 and 4 were used to validate the selected model. Models that used PCA metrics were as good as or better than models that used raw metrics. This study also investigated the performance of classification tree models, when the subsystem identifier was included as a predictor.
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Date Issued: 2001
PURL: http://purl.flvc.org/fcla/dt/12747
Subject Headings: Computer software--Quality control, Software measurement
Format: Document (PDF)

Title: Modeling fault-prone modules of subsystems.
Creator: Thaker, Vishal Kirit., Florida Atlantic University, Khoshgoftaar, Taghi M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: In software engineering software quality has become a topic of major concern. It has also been recognized that the role of maintenance organization is to understand and estimate the cost of maintenance releases of software systems. Planning the next release so as to maximize the increase in functionality and the improvement in quality are essential to successful maintenance management. With the growing collection of software in organizations this cost is becoming substantial. In this research...
Show moreIn software engineering software quality has become a topic of major concern. It has also been recognized that the role of maintenance organization is to understand and estimate the cost of maintenance releases of software systems. Planning the next release so as to maximize the increase in functionality and the improvement in quality are essential to successful maintenance management. With the growing collection of software in organizations this cost is becoming substantial. In this research we have compared two software quality models. We tried to see whether a model built on entire system which predicts subsystem and a model built on subsystem which predicts the same subsystem has similar, better or worst classification results. We used Classification And Regression Tree algorithm (CART) to build classification models. A case study is based on a very large telecommunication system.
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Date Issued: 2000
PURL: http://purl.flvc.org/fcla/dt/12700
Subject Headings: Computer software--Quality control, Software engineering
Format: Document (PDF)

Title: Implementation of a three-group classification model using case-based reasoning.
Creator: Song, Huiming., Florida Atlantic University, Khoshgoftaar, Taghi M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Reliability is becoming a very important and competitive factor for software products. Software quality models based on software metrics provide a systematic and scientific way to detect software faults early and to improve software reliability. Classification models for software quality usually classify observations using two groups. This thesis presents a new algorithm for classification using three groups, i.e., Three-Group Classification Model using Case Based Reasoning. The basic idea...
Show moreReliability is becoming a very important and competitive factor for software products. Software quality models based on software metrics provide a systematic and scientific way to detect software faults early and to improve software reliability. Classification models for software quality usually classify observations using two groups. This thesis presents a new algorithm for classification using three groups, i.e., Three-Group Classification Model using Case Based Reasoning. The basic idea behind the algorithm is that it uses the commonly used two-group classification method three times. This algorithm can be implemented with other techniques such as logistic regression, classification tree models, etc. This work compares its quality with the Discriminant Analysis method. We find that our new method performs much better than Discriminant Analysis. We also show that the addition of object-oriented software measures yielded a model that a practitioner may actually prefer over the simpler procedural measures model.
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Date Issued: 2001
PURL: http://purl.flvc.org/fcla/dt/12816
Subject Headings: Software measurement, Computer software--Quality control
Format: Document (PDF)

Title: An empirical study of a three-group classification model using case-based reasoning.
Creator: Bhupathiraju, Sajan S., Florida Atlantic University, Khoshgoftaar, Taghi M.
Abstract/Description: Reliability is becoming a very important and competitive factor for software-based products. Software metrics-based quality estimation models provide a systematic and scientific approach to detect software faults early in the life cycle, improving software reliability. Classification models for software quality estimation usually classify observations into two groups. This thesis presents an empirical study of an algorithm for software quality classification using three groups: Three-Group...
Show moreReliability is becoming a very important and competitive factor for software-based products. Software metrics-based quality estimation models provide a systematic and scientific approach to detect software faults early in the life cycle, improving software reliability. Classification models for software quality estimation usually classify observations into two groups. This thesis presents an empirical study of an algorithm for software quality classification using three groups: Three-Group Classification Model using Case-Based Reasoning (CBR). The basic idea behind the algorithm is that it uses the commonly used two-group classification technique three times. It can also be implemented with other quality estimation methods, such as Logistic Regression, Regression Trees, etc. This work evaluates the obtained quality with that from the Discriminant Analysis method. Empirical studies were conducted using an inspection data set, collected from a telecommunications system. It was observed that CBR performs better than Discriminant Analysis.
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Date Issued: 2002
PURL: http://purl.flvc.org/fcla/dt/12903
Subject Headings: Software measurement, Computer software--Quality control
Format: Document (PDF)

Title: An empirical study of a three-group software quality classification model.
Creator: Cherukuri, Reena., Florida Atlantic University, Khoshgoftaar, Taghi M.
Abstract/Description: Maintaining superior quality and reliability of software systems is an important issue in software reliability engineering. Software quality estimation models based on software metrics provide a systematic and scientific way to detect fault-prone modules and enable us to achieve high quality in software systems by focusing on high-risk modules within limited resources and budget. In previous works, classification models for software quality usually classified modules into two groups, fault...
Show moreMaintaining superior quality and reliability of software systems is an important issue in software reliability engineering. Software quality estimation models based on software metrics provide a systematic and scientific way to detect fault-prone modules and enable us to achieve high quality in software systems by focusing on high-risk modules within limited resources and budget. In previous works, classification models for software quality usually classified modules into two groups, fault-prone or not fault-prone. This thesis presents a new technique for classifying modules into three groups, i.e., high-risk, medium-risk, and low-risk groups. This new technique calibrates three-group models according to the resources available, which makes it different from other classification techniques. The proposed three-group classification method proved to be efficient and useful for resource utilization in software quality control.
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Date Issued: 2003
PURL: http://purl.flvc.org/fcla/dt/13004
Subject Headings: Software measurement, Computer software--Quality control
Format: Document (PDF)

Title: An empirical study of analogy-based software fault prediction.
Creator: Sundaresh, Nandini., Florida Atlantic University, Khoshgoftaar, Taghi M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Ensuring quality and reliability in software is important with its growing use in day to day life. Having an estimate of the number of faults in software modules early in their life cycles will enable software project managers to direct testing efforts in those considered risky and reduce the waste of resources in testing the entire software system. Case-based reasoning, abbreviated CBR, is one of the methods which predicts the number of faults in a software. The scope of this thesis is two...
Show moreEnsuring quality and reliability in software is important with its growing use in day to day life. Having an estimate of the number of faults in software modules early in their life cycles will enable software project managers to direct testing efforts in those considered risky and reduce the waste of resources in testing the entire software system. Case-based reasoning, abbreviated CBR, is one of the methods which predicts the number of faults in a software. The scope of this thesis is two-fold. First, it empirically investigates the effects of the different factors on the predictive accuracy of CBR. Experiments were done to compare different similarity functions, solution processes, and maximum number of nearest neighbors. Second, it compares the predictive accuracy of CBR models with multiple linear regression and artificial neural network models. The average absolute error and average relative error are used to determine the model with a high accuracy of prediction.
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Date Issued: 2001
PURL: http://purl.flvc.org/fcla/dt/12749
Subject Headings: Computer software--Quality control, Software measurement
Format: Document (PDF)

Title: An empirical study of resource-based selection of rule-based software quality classification models.
Creator: Herzberg, Angela., Florida Atlantic University, Khoshgoftaar, Taghi M.
Abstract/Description: Software managers are under pressure to deliver reliable and high quality software, within a limited time and budget. To achieve this goal, they can be aided by different modeling techniques that allow them to predict the quality of software, so that the improvement efforts can be directed to software modules that are more likely to be fault-prone. Also, different projects have different resource availability constraints, and being able to select a model that is suitable for a specific...
Show moreSoftware managers are under pressure to deliver reliable and high quality software, within a limited time and budget. To achieve this goal, they can be aided by different modeling techniques that allow them to predict the quality of software, so that the improvement efforts can be directed to software modules that are more likely to be fault-prone. Also, different projects have different resource availability constraints, and being able to select a model that is suitable for a specific resource constraint allows software managers to direct enhancement techniques more effectively and efficiently. In our study, we use Rule-Based Modeling ( RBM) to predict the likelihood of a module being fault-prone and the Modified Expected Cost of Misclassification (MECM ) measure to select the models that are suitable, in the context of the given resource constraints. This empirical study validates MECM as a measure to select an appropriate RBM model.
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Date Issued: 2002
PURL: http://purl.flvc.org/fcla/dt/12968
Subject Headings: Software measurement, Computer software--Quality control
Format: Document (PDF)

Title: An empirical study of module order models.
Creator: Adipat, Boonlit., Florida Atlantic University, Khoshgoftaar, Taghi M.
Abstract/Description: Most software reliability approaches classify modules as fault-prone or not fault-prone by way of a predetermined threshold. However, it may not be practical to predefine a threshold because the amount of resources for reliability enhancement may be unknown. Therefore, a module-order model (MOM) predicting the rank order of modules can be used to solve this problem. The objective of this research is to make an empirical study of MOMs based on five different underlying quantitative software...
Show moreMost software reliability approaches classify modules as fault-prone or not fault-prone by way of a predetermined threshold. However, it may not be practical to predefine a threshold because the amount of resources for reliability enhancement may be unknown. Therefore, a module-order model (MOM) predicting the rank order of modules can be used to solve this problem. The objective of this research is to make an empirical study of MOMs based on five different underlying quantitative software quality models. We examine the benefits of principal components analysis with MOM and demonstrate that better accuracy of underlying techniques does not always yield better performance with MOM. Three case studies of large industrial software systems were conducted. The results confirm that MOM can create efficient models using different underlying techniques that provide various accuracy when predicting a quantitative software quality factor over the data sets.
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Date Issued: 2001
PURL: http://purl.flvc.org/fcla/dt/12783
Subject Headings: Computer software--Quality control, Software measurement
Format: Document (PDF)

Title: The role of asset reliability and auditor quality in equity valuation.
Creator: Fallatah, Yaser., Florida Atlantic University, Higgs, Julia
Abstract/Description: This paper brings together the auditor quality, asset reliability and firm valuation literatures by examining the role of auditor quality in equity valuation. The study broadly follows the Richardson et al. (2005) categorization of the reliability of accounting accruals of balance sheet components and conjectures that the role of auditor quality in equity valuation is more pronounced when asset reliability is not high. Auditor quality is measured using reputation, industry specialist and...
Show moreThis paper brings together the auditor quality, asset reliability and firm valuation literatures by examining the role of auditor quality in equity valuation. The study broadly follows the Richardson et al. (2005) categorization of the reliability of accounting accruals of balance sheet components and conjectures that the role of auditor quality in equity valuation is more pronounced when asset reliability is not high. Auditor quality is measured using reputation, industry specialist and tenure metrics. The underlying assumption is that auditor quality enhances the market's perception of firm value; as such, auditor quality may mitigate the cost of security mispricing documented by Richardson et al. (2005) for low or medium reliability accruals. The results of the study provide some support that high quality auditors contribute to the valuation of equity for assets. It is less clear as to whether the value is more pronounced for low or medium reliability assets.
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Date Issued: 2006
PURL: http://purl.flvc.org/fcla/dt/12231
Subject Headings: Auditing--Quality control, Econometrics, Investment analysis
Format: Document (PDF)

Title: Software fault prediction using tree-based models.
Creator: Seliya, Naeem A., Florida Atlantic University, Khoshgoftaar, Taghi M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Maintaining superior quality and reliability in software systems is of utmost importance in today's world. Early fault prediction is a proven method for achieving this. Tree based modelling is a simple and effective method that can be used to predict the number of faults in a software system. In this thesis, we use regression tree based modelling to predict the number of faults in a software module. The goal of this study is four-fold. First, a comparative study of the tree based modelling...
Show moreMaintaining superior quality and reliability in software systems is of utmost importance in today's world. Early fault prediction is a proven method for achieving this. Tree based modelling is a simple and effective method that can be used to predict the number of faults in a software system. In this thesis, we use regression tree based modelling to predict the number of faults in a software module. The goal of this study is four-fold. First, a comparative study of the tree based modelling tools CART and S-PLUS. CART yielded simpler regression trees than those built by S-PLUS. Second, a comparative study of the least squares and the least absolute deviation methods of CART. It is shown that the latter yielded better results than the former. Third, a study of the possible benefits of using principal components analysis when performing regression tree modelling. The fourth and final study is a comparison of tree based modelling with other prediction techniques namely, Case Based Reasoning, Artificial Neural Networks and Multiple Linear Regression.
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Date Issued: 2001
PURL: http://purl.flvc.org/fcla/dt/12782
Subject Headings: Software measurement, Computer software--Quality control
Format: Document (PDF)

Title: Software quality classification using rule-based modeling.
Creator: Mao, Meihui., Florida Atlantic University, Khoshgoftaar, Taghi M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Software-based products are part of our daily life. They can be encountered in most of the systems we interact with. This reliance on software products generates a strong need for better software reliability, reducing the cost associated with potential failures. Reliability in software systems may be achieved by using additional testing. However, extensive software testing is expensive and time consuming. Software quality classification models provide an early prediction of a module's quality...
Show moreSoftware-based products are part of our daily life. They can be encountered in most of the systems we interact with. This reliance on software products generates a strong need for better software reliability, reducing the cost associated with potential failures. Reliability in software systems may be achieved by using additional testing. However, extensive software testing is expensive and time consuming. Software quality classification models provide an early prediction of a module's quality. Boolean Discriminant Function (BDF), Generalized Boolean Discriminant Function (GBDF), and Rule-Based Modeling (RBM) can be used as classification models. This thesis demonstrates the ability of GBDF and RBM to correctly classify modules. The introduction of the AND operator in the GBDF model and the customizable outcomes for the rules in RBM, enhanced the discriminating quality of GBDF and RBM as compared to BDF. Furthermore, they also yielded better balances for the misclassification rates.
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Date Issued: 2002
PURL: http://purl.flvc.org/fcla/dt/12886
Subject Headings: Computer software--Quality control, Software measurement
Format: Document (PDF)

Title: Data Quality in Data Mining and Machine Learning.
Creator: Van Hulse, Jason, Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: With advances in data storage and data transmission technologies, and given the increasing use of computers by both individuals and corporations, organizations are accumulating an ever-increasing amount of information in data warehouses and databases. The huge surge in data, however, has made the process of extracting useful, actionable, and interesting knowled_qe from the data extremely difficult. In response to the challenges posed by operating in a data-intensive environment, the fields of...
Show moreWith advances in data storage and data transmission technologies, and given the increasing use of computers by both individuals and corporations, organizations are accumulating an ever-increasing amount of information in data warehouses and databases. The huge surge in data, however, has made the process of extracting useful, actionable, and interesting knowled_qe from the data extremely difficult. In response to the challenges posed by operating in a data-intensive environment, the fields of data mining and machine learning (DM/ML) have successfully provided solutions to help uncover knowledge buried within data. DM/ML techniques use automated (or semi-automated) procedures to process vast quantities of data in search of interesting patterns. DM/ML techniques do not create knowledge, instead the implicit assumption is that knowledge is present within the data, and these procedures are needed to uncover interesting, important, and previously unknown relationships. Therefore, the quality of the data is absolutely critical in ensuring successful analysis. Having high quality data, i.e., data which is (relatively) free from errors and suitable for use in data mining tasks, is a necessary precondition for extracting useful knowledge. In response to the important role played by data quality, this dissertation investigates data quality and its impact on DM/ML. First, we propose several innovative procedures for coping with low quality data. Another aspect of data quality, the occurrence of missing values, is also explored. Finally, a detailed experimental evaluation on learning from noisy and imbalanced datasets is provided, supplying valuable insight into how class noise in skewed datasets affects learning algorithms.
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Date Issued: 2007
PURL: http://purl.flvc.org/fau/fd/FA00000858
Subject Headings: Data mining--Quality control, Machine learning, Electronic data processing--Quality control
Format: Document (PDF)

Title: Visualization of Impact Analysis on Configuration Management Data for Software Process Improvement.
Creator: Lo, Christopher Hoi-Yin, Huang, Shihong, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: The software development process is an incremental and iterative activity. Source code is constantly altered to reflect changing requirements, to respond to testing results, and to address problem reports. Proper software measurement that derives meaningful numeric values for some attributes of a software product or process can help in identifying problem areas and development bottlenecks. Impact analysis is the evaluation of the risks associated with change requests or problem reports,...
Show moreThe software development process is an incremental and iterative activity. Source code is constantly altered to reflect changing requirements, to respond to testing results, and to address problem reports. Proper software measurement that derives meaningful numeric values for some attributes of a software product or process can help in identifying problem areas and development bottlenecks. Impact analysis is the evaluation of the risks associated with change requests or problem reports, including estimates of effects on resources, effort, and schedule. This thesis presents a methodology called VITA for applying software analysis techniques to configuration management repository data with the aim of identifying the impact on file changes due to change requests and problem reports. The repository data can be analyzed and visualized in a semi-automated manner according to user-selectable criteria. The approach is illustrated with a model problem concerning software process improvement of an embedded software system in the context of performing high-quality software maintenance.
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Date Issued: 2007
PURL: http://purl.flvc.org/fau/fd/FA00012535
Subject Headings: Software mesurement, Software engineering--Quality control, Data mining--Quality control
Format: Document (PDF)

Title: Design of experiment of LCD watch using computer simulation.
Creator: Michalik, Marian., Florida Atlantic University, Mazuoz, Abdel Kader, College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
Abstract/Description: Computer modeling has become indispensable to the engineering process, from initially refining an idea with computer-aided tools to implementing the final steps in manufacturing a product. This thesis addresses the issue of design of a housing of LCD watch. An approach is developed to determine an optimal LCD watch design. An analysis and development of a design of experiment is performed to identify the major controllable variables to performed a statistical significance analysis on...
Show moreComputer modeling has become indispensable to the engineering process, from initially refining an idea with computer-aided tools to implementing the final steps in manufacturing a product. This thesis addresses the issue of design of a housing of LCD watch. An approach is developed to determine an optimal LCD watch design. An analysis and development of a design of experiment is performed to identify the major controllable variables to performed a statistical significance analysis on different shapes for LCD glass. A housing of LCD watch is modeled using Pro/Engineer (a parametric-based solid modeling system), and different shapes of LCD glass are tested using P3/Patran. A non-destructive static experiment is performed on the LCD. This experiment consisted of measuring the maximum displacement and equivalent stress. Taguchi method was used to analyze this experiment.
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Date Issued: 1995
PURL: http://purl.flvc.org/fcla/dt/15149
Subject Headings: Computer simulation, Taguchi methods (Quality control), Engineering design, Liquid crystal displays--Design and construction--Quality control, Liquid crystal devices--Design and construction--Quality control
Format: Document (PDF)

Title: Experiments and modeling on resistivity of multi-layer concrete with and without embedded rebar.
Creator: Liu, Yanbo., College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
Abstract/Description: Factors such as water to cement ratio, moisture, mixture, presence and depth of rebar, and dimension of specimens, all of which affect apparent resistivity of concrete, were analyzed by experimental and modeling methods. Cylinder and rectangular prism concrete specimens were used in the experiments exposed in a high moisture room, laboratory room temperature, high humidity and outdoor weather environments. Single rebar and four rebar specimens were used to study the rebar effect on the...
Show moreFactors such as water to cement ratio, moisture, mixture, presence and depth of rebar, and dimension of specimens, all of which affect apparent resistivity of concrete, were analyzed by experimental and modeling methods. Cylinder and rectangular prism concrete specimens were used in the experiments exposed in a high moisture room, laboratory room temperature, high humidity and outdoor weather environments. Single rebar and four rebar specimens were used to study the rebar effect on the apparent resistivity. Modeling analysis was employed to verify and explain the experimental results. Based on the results, concrete with fly ash showed higher resistivity than concrete with just ordinary Portland cement. Rebar presence had a significant effect on the measured apparent resistivity at some of the locations. The results could be used as a guide for field apparent resistivity measurements and provide a quick, more precise and easy way to estimate the concrete quality.
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Date Issued: 2008
PURL: http://purl.flvc.org/FAU/166452
Subject Headings: Reinforced concrete, Corrosion, Testing, Reinforcing bars, Properties, Concrete, Quality control
Format: Document (PDF)

Title: Development of an Arduino-based 3D printed 6DOF robotic phantom and a MATLAB-based software for Radiation Therapy Quality Assurance.
Creator: Rahman, Md Mushfiqur, Leventouri, Theodora, Shang, Charles, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Physics
Abstract/Description: Quality Assurance (QA) for medical linear accelerators (linac) is the primary concern in external beam radiation therapy. In this research, we have developed a MATLAB-based software named Quality Assurance for Linacs (QALMA), which is unique, due to cost-effectiveness, user friendly interface, and customizability. It includes five modules to perform different QA tests: Star Shot analysis, Picket Fence test, Winston-Lutz test, MLC log file analysis, and verification of light & radiation field...
Show moreQuality Assurance (QA) for medical linear accelerators (linac) is the primary concern in external beam radiation therapy. In this research, we have developed a MATLAB-based software named Quality Assurance for Linacs (QALMA), which is unique, due to cost-effectiveness, user friendly interface, and customizability. It includes five modules to perform different QA tests: Star Shot analysis, Picket Fence test, Winston-Lutz test, MLC log file analysis, and verification of light & radiation field coincidence. We also pay attention to quality assurance of 6DOF treatment couch that plays a very important role in radiation therapy. We developed an Arduino based 3D printed 6DOF robotic phantom to check the accuracy of the treatment couch. This robotic phantom was experimentally validated under clinical standards, and customizable upon requirements of the quality assurance Task. The current features of this robotic phantom open development opportunities beyond the specific couch application, such as organs motion simulation.
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Date Issued: 2018
PURL: http://purl.flvc.org/fau/fd/FA00013165
Subject Headings: Radiation therapy, Radiotherapy--Quality control, Arduino (Computer language), MATLAB
Format: Document (PDF)

FAU Theses and Dissertations (81)	+ -
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Computer software--Quality control (40)	+ -
Software measurement (23)	+ -
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Quality control (14)	+ -
Outcome assessment (Medical care) (4)	+ -

Case-based reasoning (3)	+ -
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article (1)	+ -
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College of Engineering and Computer Science (52)	+ -
Department of Computer and Electrical Engineering and Computer Science (43)	+ -
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