Automated Generation of Indoor Accessibility Information for Mobility-Impaired Individuals

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

A semi-automatic approach to code smells detection

Eradication of code smells is often pointed out as a way to improve readability, extensibility and design in existing software. However, code smell detection remains time consuming and error-prone, partly due to the inherent subjectivity of the detection processes presently available. In view of mitigating the subjectivity problem, this dissertation presents a tool that automates a technique for the detection and assessment of code smells in Java source code, developed as an Eclipse plugin. The technique is based upon a Binary Logistic Regression model that uses complexity metrics as independent variables and is calibrated by expert‟s knowledge. An overview of the technique is provided, the tool is described and validated by an example case study.

Semantic relations extraction from unstructured information for domain ontologies enrichment

Based in internet growth, through semantic web, together with communication speed improvement and fast development of storage device sizes, data and information volume rises considerably every day. Because of this, in the last few years there has been a growing interest in structures for formal representation with suitable characteristics, such as the possibility to organize data and information, as well as the reuse of its contents aimed for the generation of new knowledge. Controlled Vocabulary, specifically Ontologies, present themselves in the lead as one of such structures of representation with high potential. Not only allow for data representation, as well as the reuse of such data for knowledge extraction, coupled with its subsequent storage through not so complex formalisms. However, for the purpose of assuring that ontology knowledge is always up to date, they need maintenance. Ontology Learning is an area which studies the details of update and maintenance of ontologies. It is worth noting that relevant literature already presents first results on automatic maintenance of ontologies, but still in a very early stage. Human-based processes are still the current way to update and maintain an ontology, which turns this into a cumbersome task. The generation of new knowledge aimed for ontology growth can be done based in Data Mining techniques, which is an area that studies techniques for data processing, pattern discovery and knowledge extraction in IT systems. This work aims at proposing a novel semi-automatic method for knowledge extraction from unstructured data sources, using Data Mining techniques, namely through pattern discovery, focused in improving the precision of concept and its semantic relations present in an ontology. In order to verify the applicability of the proposed method, a proof of concept was developed, presenting its results, which were applied in building and construction sector.

Bioethanol production by Saccharomyces cerevisiae, Pichia stipitis and Zymomonas mobilis from delignified coconut fibre mature and lignin extraction according to biorefinery concept

In search to increase the offer of liquid, clean, renewable and sustainable energy in the world energy matrix, the use of lignocellulosic materials (LCMs) for bioethanol production arises as a valuable alternative. The objective of this work was to analyze and compare the performance of Saccharomyces cerevisiae, Pichia stipitis and Zymomonas mobilis in the production of bioethanol from coconut fibre mature (CFM) using different strategies: simultaneous saccharification and fermentation (SSF) and semi-simultaneous saccharification and fermentation (SSSF). The CFM was pretreated by hydrothermal pretreatment catalyzed with sodium hydroxide (HPCSH). The pretreated CFM was characterized by X-ray diffractometry and SEM, and the lignin recovered in the liquid phase by FTIR and TGA. After the HPCSH pretreatment (2.5% (v/v) sodium hydroxide at 180 °C for 30 min), the cellulose content was 56.44%, while the hemicellulose and lignin were reduced 69.04% and 89.13%, respectively. Following pretreatment, the obtained cellulosic fraction was submitted to SSF and SSSF. Pichia stipitis allowed for the highest ethanol yield 90.18% in SSSF, 91.17% and 91.03% were obtained with Saccharomyces cerevisiae and Zymomonas mobilis, respectively. It may be concluded that the selection of the most efficient microorganism for the obtention of high bioethanol production yields from cellulose pretreated by HPCSH depends on the operational strategy used and this pretreatment is an interesting alternative for add value of coconut fibre mature compounds (lignin, phenolics) being in accordance with the biorefinery concept.

Laboratory detection of respiratory viruses by automated techniques.

Advances in clinical virology for detecting respiratory viruses have been focused on nucleic acids amplification techniques, which have converted in the reference method for the diagnosis of acute respiratory infections of viral aetiology. Improvements of current commercial molecular assays to reduce hands-on-time rely on two strategies, a stepwise automation (semi-automation) and the complete automation of the whole procedure. Contributions to the former strategy have been the use of automated nucleic acids extractors, multiplex PCR, real-time PCR and/or DNA arrays for detection of amplicons. Commercial fully-automated molecular systems are now available for the detection of respiratory viruses. Some of them could convert in point-of-care methods substituting antigen tests for detection of respiratory syncytial virus and influenza A and B viruses. This article describes laboratory methods for detection of respiratory viruses. A cost-effective and rational diagnostic algorithm is proposed, considering technical aspects of the available assays, infrastructure possibilities of each laboratory and clinic-epidemiologic factors of the infection.

A novel method for automated classification of epileptiform activity in the human electroencephalogram-based on independent component analysis.

Diagnosis of several neurological disorders is based on the detection of typical pathological patterns in the electroencephalogram (EEG). This is a time-consuming task requiring significant training and experience. Automatic detection of these EEG patterns would greatly assist in quantitative analysis and interpretation. We present a method, which allows automatic detection of epileptiform events and discrimination of them from eye blinks, and is based on features derived using a novel application of independent component analysis. The algorithm was trained and cross validated using seven EEGs with epileptiform activity. For epileptiform events with compensation for eyeblinks, the sensitivity was 65 +/- 22% at a specificity of 86 +/- 7% (mean +/- SD). With feature extraction by PCA or classification of raw data, specificity reduced to 76 and 74%, respectively, for the same sensitivity. On exactly the same data, the commercially available software Reveal had a maximum sensitivity of 30% and concurrent specificity of 77%. Our algorithm performed well at detecting epileptiform events in this preliminary test and offers a flexible tool that is intended to be generalized to the simultaneous classification of many waveforms in the EEG.

Automated Segmentation of Cerebral Vasculature with Aneurysms in 3DRA and TOF-MRA using Geodesic Active Regions: an Evaluation Study

Purpose: To evaluate the suitability of an improved version of an automatic segmentation method based on geodesic active regions (GAR) for segmenting cerebral vasculature with aneurysms from 3D X-ray reconstruc-tion angiography (3DRA) and time of °ight magnetic resonance angiography (TOF-MRA) images available in the clinical routine.Methods: Three aspects of the GAR method have been improved: execution time, robustness to variability in imaging protocols and robustness to variability in image spatial resolutions. The improved GAR was retrospectively evaluated on images from patients containing intracranial aneurysms in the area of the Circle of Willis and imaged with two modalities: 3DRA and TOF-MRA. Images were obtained from two clinical centers, each using di®erent imaging equipment. Evaluation included qualitative and quantitative analyses ofthe segmentation results on 20 images from 10 patients. The gold standard was built from 660 cross-sections (33 per image) of vessels and aneurysms, manually measured by interventional neuroradiologists. GAR has also been compared to an interactive segmentation method: iso-intensity surface extraction (ISE). In addition, since patients had been imaged with the two modalities, we performed an inter-modality agreement analysis with respect to both the manual measurements and each of the two segmentation methods. Results: Both GAR and ISE di®ered from the gold standard within acceptable limits compared to the imaging resolution. GAR (ISE, respectively) had an average accuracy of 0.20 (0.24) mm for 3DRA and 0.27 (0.30) mm for TOF-MRA, and had a repeatability of 0.05 (0.20) mm. Compared to ISE, GAR had a lower qualitative error in the vessel region and a lower quantitative error in the aneurysm region. The repeatabilityof GAR was superior to manual measurements and ISE. The inter-modality agreement was similar between GAR and the manual measurements. Conclusions: The improved GAR method outperformed ISE qualitatively as well as quantitatively and is suitable for segmenting 3DRA and TOF-MRA images from clinical routine.

Visualization Resources for Iowa State University and the Iowa DOT: An Automated Design Model to Simulator Converter, November 2012

This project developed an automatic conversion software tool that takes input a from an Iowa Department of Transportation (DOT) MicroStation three-dimensional (3D) design file and converts it into a form that can be used by the University of Iowa’s National Advanced Driving Simulator (NADS) MiniSim. Once imported into the simulator, the new roadway has the identical geometric design features as in the Iowa DOT design file. The base roadway appears as a wireframe in the simulator software. Through additional software tools, textures and shading can be applied to the roadway surface and surrounding terrain to produce the visual appearance of an actual road. This tool enables Iowa DOT engineers to work with the universities to create drivable versions of prospective roadway designs. By driving the designs in the simulator, problems can be identified early in the design process. The simulated drives can also be used for public outreach and human factors driving research.

Feasibility of Using Automated Distress Data in the County Need Study, TR-418, 1998

The purpose of this project was to determine the feasibility of using pavement condition data collected for the Iowa Pavement Management Program (IPMP) as input to the Iowa Quadrennial Need Study. The need study, conducted by the Iowa Department of Transportation (Iowa DOT) every four years, currently uses manually collected highway infrastructure condition data (roughness, rutting, cracking, etc.). Because of the Iowa DOT's 10-year data collection cycles, condition data for a given highway segment may be up to 10 years old. In some cases, the need study process has resulted in wide fluctuations in funding allocated to individual Iowa counties from one study to the next. This volatility in funding levels makes it difficult for county engineers to plan and program road maintenance and improvements. One possible remedy is to input more current and less subjective infrastructure condition data. The IPMP was initially developed to satisfy the Intermodal Surface Transportation Efficiency Act (ISTEA) requirement that federal-aid-eligible highways be managed through a pavement management system. Currently all metropolitan planning organizations (MPOs) in Iowa and 15 of Iowa's 18 RPAs participate in the IPMP. The core of this program is a statewide data base of pavement condition and construction history information. The pavement data are collected by machine in two-year cycles. Using pilot areas, researchers examined the implications of using the automated data collected for the IPMP as input to the need study computer program, HWYNEEDS. The results show that using the IPMP automated data in HWYNEEDS is feasible and beneficial, resulting in less volatility in the level of total need between successive quadrennial need studies. In other words, the more current the data, the smaller the shift in total need.

Iowa DOT Evaluation of the PASCO Road Survey System, HR-539, 1987

The report compares and contrasts the automated PASCO method of pavement evaluation to the manual procedures used by the Iowa Department of Transportation (DOT) to evaluate pavement condition. Iowa DOT's use of IJK and BPR roadmeters and manual crack and patch surveys are compared to PASCO's use of 35-mm photography, artificial lighting and hairline projection, tracking wheels and lasers to measure ride, cracking and patching, rut depths, and roughness. The Iowa DOT method provides a Present Serviceability Index (PSI) value and PASCO provides a Maintenance Control Index (MCI). Seven sections of Interstate Highway, county roads and city streets, and one shoulder section were tested with different speeds of data collection, surface types and textures, and stop and start conditions. High correlation of results between the two methods in the measurement of roughness (0.93 for the tracking wheel and 0.84 for the laser method) were recorded. Rut depth correlations of 0.61 and cracking of 0.32 are attributed to PASCO's more comprehensive measurement techniques. A cost analysis of the data provided by both systems indicates that PASCO is capable of providing a comparable result with improved accuracy at a cost of $125-$150 or less per two-lane mile depending on survey mileage. Improved data collection speed, accuracy, and reliability, and a visible record of pavement condition for comparable costs are available. The PASCO system's ability to provide the data required in the Highway Pavement Distress Identification Manual, the Pavement Condition Rating Guide, and the Strategic Highway Research Program Long Term Pavement Performance (LTPP) Studies, is also outlined in the report.

One-step nucleic acid amplification (OSNA): a new road to better staging in colon cancer patients?

Introduction: Approximately one fifth of stage I and II colon cancer patients will suffer from recurrent disease. This is partly due to the presence of small nodal tumour infiltrates, which are undetected by standard histopathology using Haematoxylin & Eosin (H&E) staining on one slice and thus may not receive beneficial adjuvant therapy. A new diagnostic, semi-automatic system, called one-step nucleic acid amplification (OSNA), was recently designed for the detection of cytokeratin 19 (CK19) mRNA as a surrogate for lymph node metastases. The objective of the present investigation was to compare the performance of OSNA with both standard H&E as well as intensive histopathologic analyses in the detection of colon cancer lymph node micro- and macro-metastases.Methods: In this prospective study 313 lymph nodes from 22 consecutive stage I - III colon cancer patients were assessed. Half of each lymph node was analysed initially based on one slice of H&E followed by an intensive histologic work-up (5 levels of H&E and immuno-histochemistry staining for each slice), the other half was analysed using OSNA.Results: All OSNA results were available after less than 40 minutes. Fifty-one lymph nodes were positive and 246 lymph nodes negative with both OSNA and standard H&E. OSNA was more sensitive to detect small nodal tumor infiltrates compared to H&E (11 OSNA pos. /H&E neg.). Compared to intensive histopathologic analyses, OSNA had a sensitivity of 94.5% and a specificity of 97.6% to detect lymph node micro- and macro-metastases with a concordance rate of 97.1%. An upstaging due to OSNA was found in 2/13 (15.3%) initially node negative colon cancer patients.Conclusion: OSNA appears to be a powerful and promising molecular tool for the detection of lymph node macro- and micro-metastases in colon cancer patients. OSNA has a similar performance in the detection of micro- and macro-metastases compared to intensive histopathologic investigations and appears to be superior to standard histology with H&E. Since the use of OSNA allows the analysis of the whole lymph node, the problem of sampling bias and undetected tumor deposits due to uninvestigated material will be overcome in the future and OSNA may thus improve staging in colon cancer patients. It is hoped that this improved staging will lead to better patient selection for adjuvant therapy and consecutively improved local and distant control as well as better overall survival.

Effectiveness of Dynamic Messaging on Driver Behavior for Late Merge Lane Road Closures, 2009

Efforts to improve safety and traffic flow through merge areas on high volume/high speed roadways have included early merge and late merge concepts and several studies of the effectiveness of these concepts, many using Intelligent Transportation Systems for implementation. The Iowa Department of Transportation (Iowa DOT) planned to employ a system of dynamic message signs (DMS) to enhance standard temporary traffic control for lane closures and traffic merges at two bridge construction projects in western Iowa (Adair County and Cass County counties) on I-80 during the 2008 construction season. To evaluate the DMS system’s effectiveness for impacting driver merging actions, the Iowa DOT contracted with Iowa State University’s Center for Transportation Research and Education to perform the evaluation and make recommendations for future use of this system based on the results. Data were collected over four weekends, beginning August 1–4 and ending October 16–20, 2008. Two weekends yielded sufficient data for evaluation, one of transition traffic flow and the other with a period of congestion. For both of these periods, a statistical review of collected data did not indicate a significant impact on driver merging actions when the DMS messaging was activated as compared to free flow conditions with no messaging. Collection of relevant project data proved to be problematic for several reasons. In addition to personnel safety issues associated with the placement and retrieval of counting devices on a high speed roadway, unsatisfactory equipment performance and insufficient congestion to activate the DMS messaging hampered efforts. A review of the data that was collected revealed different results taken by the tube counters compared to the older model plate counters. Although variations were not significant from a practical standpoint, a statistical evaluation showed that the data, including volumes, speeds, and classifications from the two sources were not comparable at a 95% level of confidence. Comparison of data from the Iowa DOT’s automated traffic recorders (ATRs) in the area also suggested variations in results from these data collection systems. Additional comparison studies were recommended.

Automated monitoring of medical protocols: a secure and distributed architecture

The control of the right application of medical protocols is a key issue in hospital environments. For the automated monitoring of medical protocols, we need a domain-independent language for their representation and a fully, or semi, autonomous system that understands the protocols and supervises their application. In this paper we describe a specification language and a multi-agent system architecture for monitoring medical protocols. We model medical services in hospital environments as specialized domain agents and interpret a medical protocol as a negotiation process between agents. A medical service can be involved in multiple medical protocols, and so specialized domain agents are independent of negotiation processes and autonomous system agents perform monitoring tasks. We present the detailed architecture of the system agents and of an important domain agent, the database broker agent, that is responsible of obtaining relevant information about the clinical history of patients. We also describe how we tackle the problems of privacy, integrity and authentication during the process of exchanging information between agents.

Social sensing for psychology: Automated interpersonal behavior assessment

In this article, we show how the use of state-of-the-art methods in computer science based on machine perception and learning allows the unobtrusive capture and automated analysis of interpersonal behavior in real time (social sensing). Given the high ecological validity of the behavioral sensing, the ease of behavioral-cue extraction for large groups over long observation periods in the field, the possibility of investigating completely new research questions, and the ability to provide people with immediate feedback on behavior, social sensing will fundamentally impact psychology.

An ontology to automated learning scenarios? an approach to its knowledge domain

Automation or semi-automation of learning scenariospecifications is one of the least exploredsubjects in the e-learning research area. There isa need for a catalogue of learning scenarios and atechnique to facilitate automated retrieval of stored specifications. This requires constructing anontology with this goal and is justified inthis paper. This ontology must mainlysupport a specification technique for learning scenarios. This ontology should also be useful in the creation and validation of new scenarios as well as in the personalization of learning scenarios or their monitoring. Thus, after justifying the need for this ontology, a first approach of a possible knowledge domain is presented. An example of a concrete learning scenario illustrates some relevant concepts supported by this ontology in order to define the scenario in such a way that it could be easy to automate.