Refinement and variability techniques in model transformation of software requirements

Tese de Doutoramento em Tecnologias e Sistemas de Informação

The role of liquid-based cytology and ancillary techniques in pleural and pericardic effusions: An institutional experience

BACKGROUND: Fine-needle aspiration cytology (FNAC) of serous membrane effusions may fulfil a challenging role in the diagnostic analysis of both primary and metastatic disease. From this perspective, liquid-based cytology (LBC) represents a feasible and reliable method for empowering the performance of ancillary techniques (ie, immunocytochemistry and molecular testing) with high diagnostic accuracy. METHODS: In total, 3171 LBC pleural and pericardic effusions were appraised between January 2000 and December 2013. They were classified as negative for malignancy (NM), suspicious for malignancy (SM), or positive for malignancy (PM). RESULTS: The cytologic diagnoses included 2721 NM effusions (2505 pleural and 216 pericardic), 104 SM effusions (93 pleural and 11 pericardic), and 346 PM effusions (321 pleural and 25 pericardic). The malignant pleural series included 76 unknown malignancies (36 SM and 40 PM effusions), 174 metastatic lesions (85 SM and 89 PM effusions), 14 lymphomas (3 SM and 11 PM effusions), 16 mesotheliomas (5 SM and 11 SM effusions), and 3 myelomas (all SM effusions). The malignant pericardic category included 20 unknown malignancies (5 SM and 15 PM effusions), 15 metastatic lesions (1 SM and 14 PM effusions), and 1 lymphoma (1 PM effusion). There were 411 conclusive immunocytochemical analyses and 47 molecular analyses, and the authors documented 88% sensitivity, 100% specificity, 98% diagnostic accuracy, 98% negative predictive value, and 100% positive predictive value for FNAC. CONCLUSIONS: FNAC represents a primary diagnostic tool for effusions and a reliable approach with which to determine the correct follow-up. Furthermore, LBC is useful for ancillary techniques, such as immunocytochemistry and molecular analysis, with feasible diagnostic and predictive utility.

Poly lactic acid based biodegradable stents and functionalization techniques: brief review

Commercial stents, especially metallic ones, present several disadvantages, and this gives rise to the necessity of producing or coating stents with different materials, like natural polymers, in order to improve their biocompatibility and minimize the disadvantages of metallic ones. This review paper discusses some applications of natural-based polymers in stents, namely polylactic acid (PLA) for stent development and chitosan for biocompatible coatings of stents . Furthermore, some effective stent functionalization techniques will be discussed, namely Layer by Layer (LBL) technique.

Wireless indoor positioning based on TDOA and DOA estimation techniques using IEEE 802.11 standards

Magdeburg, Univ., Fak. für Elektrotechnik und Informationstechnik, Diss., 2015

Web-based Implementation of Winter Maintenance Decision Support System Using GIS and Remote Sensing, May 2005

Winter maintenance, particularly snow removal and the stress of snow removal materials on public structures, is an enormous budgetary burden on municipalities and nongovernmental maintenance organizations in cold climates. Lately, geospatial technologies such as remote sensing, geographic information systems (GIS), and decision support tools are roviding a valuable tool for planning snow removal operations. A few researchers recently used geospatial technologies to develop winter maintenance tools. However, most of these winter maintenance tools, while having the potential to address some of these information needs, are not typically placed in the hands of planners and other interested stakeholders. Most tools are not constructed with a nontechnical user in mind and lack an easyto-use, easily understood interface. A major goal of this project was to implement a web-based Winter Maintenance Decision Support System (WMDSS) that enhances the capacity of stakeholders (city/county planners, resource managers, transportation personnel, citizens, and policy makers) to evaluate different procedures for managing snow removal assets optimally. This was accomplished by integrating geospatial analytical techniques (GIS and remote sensing), the existing snow removal asset management system, and webbased spatial decision support systems. The web-based system was implemented using the ESRI ArcIMS ActiveX Connector and related web technologies, such as Active Server Pages, JavaScript, HTML, and XML. The expert knowledge on snow removal procedures is gathered and integrated into the system in the form of encoded business rules using Visual Rule Studio. The system developed not only manages the resources but also provides expert advice to assist complex decision making, such as routing, optimal resource allocation, and monitoring live weather information. This system was developed in collaboration with Black Hawk County, IA, the city of Columbia, MO, and the Iowa Department of transportation. This product was also demonstrated for these agencies to improve the usability and applicability of the system.

Identification and quantification techniques in mass spectrometry-based proteomics

Résumé La protéomique basée sur la spectrométrie de masse est l'étude du proteome l'ensemble des protéines exprimées au sein d'une cellule, d'un tissu ou d'un organisme - par cette technique. Les protéines sont coupées à l'aide d'enzymes en plus petits morceaux -les peptides -, et, séparées par différentes techniques. Les différentes fractions contenant quelques centaines de peptides sont ensuite analysées dans un spectromètre de masse. La masse des peptides est enregistrée et chaque peptide est séquentiellement fragmenté pour en obtenir sa séquence. L'information de masse et séquence est ensuite comparée à une base de données de protéines afin d'identifier la protéine d'origine. Dans une première partie, la thèse décrit le développement de méthodes d'identification. Elle montre l'importance de l'enrichissement de protéines comme moyen d'accès à des protéines de moyenne à faible abondance dans le lait humain. Elle utilise des injections répétées pour augmenter la couverture en protéines et la confiance dans l'identification. L'impacte de nouvelle version de base de données sur la liste des protéines identifiées est aussi démontré. De plus, elle utilise avec succès la spectrométrie de masse comme alternative aux anticorps, pour valider la présence de 34 constructions de protéines pathogéniques du staphylocoque doré exprimées dans une souche de lactocoque. Dans une deuxième partie, la thèse décrit le développement de méthodes de quantification. Elle expose de nouvelles approches de marquage des terminus des protéines aux isotopes stables et décrit la première méthode de marquage des groupements carboxyliques au niveau protéine à l'aide de réactifs composé de carbone 13. De plus, une nouvelle méthode, appelée ANIBAL, marquant tous les groupements amines et carboxyliques au niveau de la protéine, est exposée. Summary Mass spectrometry-based proteomics is the study of the proteome -the set of all expressed proteins in a cell, tissue or organism -using mass spectrometry. Proteins are cut into smaller pieces - peptides - using proteolytic enzymes and separated using different separation techniques. The different fractions containing several hundreds of peptides are than analyzed by mass spectrometry. The mass of the peptides entering the instrument are recorded and each peptide is sequentially fragmented to obtain its amino acid sequence. Each peptide sequence with its corresponding mass is then searched against a protein database to identify the protein to which it belongs. This thesis presents new method developments in this field. In a first part, the thesis describes development of identification methods. It shows the importance of protein enrichment methods to gain access to medium-to-low abundant proteins in a human milk sample. It uses repeated injection to increase protein coverage and confidence in identification and demonstrates the impact of new database releases on protein identification lists. In addition, it successfully uses mass spectrometry as an alternative to antibody-based assays to validate the presence of 34 different recombinant constructs of Staphylococcus aureus pathogenic proteins expressed in a Lactococcus lactis strain. In a second part, development of quantification methods is described. It shows new stable isotope labeling approaches based on N- and C-terminus labeling of proteins and describes the first method of labeling of carboxylic groups at the protein level using 13C stable isotopes. In addition, a new quantitative approach called ANIBAL is explained that labels all amino and carboxylic groups at the protein level.

Artificial-Intelligence-Based Optimization of the Management of Snow Removal Assets and Resources Final Report, October 2002

Geographic information systems (GIS) and artificial intelligence (AI) techniques were used to develop an intelligent snow removal asset management system (SRAMS). The system has been evaluated through a case study examining snow removal from the roads in Black Hawk County, Iowa, for which the Iowa Department of Transportation (Iowa DOT) is responsible. The SRAMS is comprised of an expert system that contains the logical rules and expertise of the Iowa DOT’s snow removal experts in Black Hawk County, and a geographic information system to access and manage road data. The system is implemented on a mid-range PC by integrating MapObjects 2.1 (a GIS package), Visual Rule Studio 2.2 (an AI shell), and Visual Basic 6.0 (a programming tool). The system could efficiently be used to generate prioritized snowplowing routes in visual format, to optimize the allocation of assets for plowing, and to track materials (e.g., salt and sand). A test of the system reveals an improvement in snowplowing time by 1.9 percent for moderate snowfall and 9.7 percent for snowstorm conditions over the current manual system.

The Possibilities and Dosimetric Limitations of MLC-Based Intensity-Modulated Radiotherapy Delivery and Optimization Techniques

The use of intensity-modulated radiotherapy (IMRT) has increased extensively in the modern radiotherapy (RT) treatments over the past two decades. Radiation dose distributions can be delivered with higher conformality with IMRT when compared to the conventional 3D-conformal radiotherapy (3D-CRT). Higher conformality and target coverage increases the probability of tumour control and decreases the normal tissue complications. The primary goal of this work is to improve and evaluate the accuracy, efficiency and delivery techniques of RT treatments by using IMRT. This study evaluated the dosimetric limitations and possibilities of IMRT in small (treatments of head-and-neck, prostate and lung cancer) and large volumes (primitive neuroectodermal tumours). The dose coverage of target volumes and the sparing of critical organs were increased with IMRT when compared to 3D-CRT. The developed split field IMRT technique was found to be safe and accurate method in craniospinal irradiations. By using IMRT in simultaneous integrated boosting of biologically defined target volumes of localized prostate cancer high doses were achievable with only small increase in the treatment complexity. Biological plan optimization increased the probability of uncomplicated control on average by 28% when compared to standard IMRT delivery. Unfortunately IMRT carries also some drawbacks. In IMRT the beam modulation is realized by splitting a large radiation field to small apertures. The smaller the beam apertures are the larger the rebuild-up and rebuild-down effects are at the tissue interfaces. The limitations to use IMRT with small apertures in the treatments of small lung tumours were investigated with dosimetric film measurements. The results confirmed that the peripheral doses of the small lung tumours were decreased as the effective field size was decreased. The studied calculation algorithms were not able to model the dose deficiency of the tumours accurately. The use of small sliding window apertures of 2 mm and 4 mm decreased the tumour peripheral dose by 6% when compared to 3D-CRT treatment plan. A direct aperture based optimization (DABO) technique was examined as a solution to decrease the treatment complexity. The DABO IMRT technique was able to achieve treatment plans equivalent with the conventional IMRT fluence based optimization techniques in the concave head-and-neck target volumes. With DABO the effective field sizes were increased and the number of MUs was reduced with a factor of two. The optimality of a treatment plan and the therapeutic ratio can be further enhanced by using dose painting based on regional radiosensitivities imaged with functional imaging methods.

Design Techniques for Sigmadelta based ADC for Wireless Applications

Analog-to digital Converters (ADC) have an important impact on the overall performance of signal processing system. This research is to explore efficient techniques for the design of sigma-delta ADC,specially for multi-standard wireless tranceivers. In particular, the aim is to develop novel models and algorithms to address this problem and to implement software tools which are avle to assist the designer's decisions in the system-level exploration phase. To this end, this thesis presents a framework of techniques to design sigma-delta analog to digital converters.A2-2-2 reconfigurable sigma-delta modulator is proposed which can meet the design specifications of the three wireless communication standards namely GSM,WCDMA and WLAN. A sigma-delta modulator design tool is developed using the Graphical User Interface Development Environment (GUIDE) In MATLAB.Genetic Algorithm(GA) based search method is introduced to find the optimum value of the scaling coefficients and to maximize the dynamic range in a sigma-delta modulator.

Techniques for Enhancing Wind Energy Generation - A CFD Based Multibody Dynamics Approach in Horizontal Axis Wind Turbines

Wind energy has emerged as a major sustainable source of energy.The efficiency of wind power generation by wind mills has improved a lot during the last three decades.There is still further scope for maximising the conversion of wind energy into mechanical energy.In this context,the wind turbine rotor dynamics has great significance.The present work aims at a comprehensive study of the Horizontal Axis Wind Turbine (HAWT) aerodynamics by numerically solving the fluid dynamic equations with the help of a finite-volume Navier-Stokes CFD solver.As a more general goal,the study aims at providing the capabilities of modern numerical techniques for the complex fluid dynamic problems of HAWT.The main purpose is hence to maximize the physics of power extraction by wind turbines.This research demonstrates the potential of an incompressible Navier-Stokes CFD method for the aerodynamic power performance analysis of horizontal axis wind turbine.The National Renewable Energy Laboratory USA-NREL (Technical Report NREL/Cp-500-28589) had carried out an experimental work aimed at the real time performance prediction of horizontal axis wind turbine.In addition to a comparison between the results reported by NREL made and CFD simulations,comparisons are made for the local flow angle at several stations ahead of the wind turbine blades.The comparison has shown that fairly good predictions can be made for pressure distribution and torque.Subsequently, the wind-field effects on the blade aerodynamics,as well as the blade/tower interaction,were investigated.The selected case corresponded to a 12.5 m/s up-wind HAWT at zero degree of yaw angle and a rotational speed of 25 rpm.The results obtained suggest that the present can cope well with the flows encountered around wind turbines.The areodynamic performance of the turbine and the flow details near and off the turbine blades and tower can be analysed using theses results.The aerodynamic performance of airfoils differs from one another.The performance mainly depends on co-efficient of performnace,co-efficient of lift,co-efficient of drag, velocity of fluid and angle of attack.This study shows that the velocity is not constant for all angles of attack of different airfoils.The performance parameters are calculated analytically and are compared with the standardized performance tests.For different angles of ,the velocity stall is determined for the better performance of a system with respect to velocity.The research addresses the effect of surface roughness factor on the blade surface at various sections.The numerical results were found to be in agreement with the experimental data.A relative advantage of the theoretical aerofoil design method is that it allows many different concepts to be explored economically.Such efforts are generally impractical in wind tunnels because of time and money constraints.Thus, the need for a theoretical aerofoil design method is threefold:first for the design of aerofoil that fall outside the range of applicability of existing calalogs:second,for the design of aerofoil that more exactly match the requirements of the intended application:and third,for the economic exploration of many aerofoil concepts.From the results obtained for the different aerofoils,the velocity is not constant for all angles of attack.The results obtained for the aerofoil mainly depend on angle of attack and velocity.The vortex generator technique was meticulously studies with the formulation of the specification for the right angle shaped vortex generators-VG.The results were validated in accordance with the primary analysis phase.The results were found to be in good agreement with the power curve.The introduction of correct size VGs at appropriate locations over the blades of the selected HAWT was found to increase the power generation by about 4%

A Comparative Study of Wavelet Based Feature Extraction Techniques in Recognizing Isolated Spoken Words

Speech is a natural mode of communication for people and speech recognition is an intensive area of research due to its versatile applications. This paper presents a comparative study of various feature extraction methods based on wavelets for recognizing isolated spoken words. Isolated words from Malayalam, one of the four major Dravidian languages of southern India are chosen for recognition. This work includes two speech recognition methods. First one is a hybrid approach with Discrete Wavelet Transforms and Artificial Neural Networks and the second method uses a combination of Wavelet Packet Decomposition and Artificial Neural Networks. Features are extracted by using Discrete Wavelet Transforms (DWT) and Wavelet Packet Decomposition (WPD). Training, testing and pattern recognition are performed using Artificial Neural Networks (ANN). The proposed method is implemented for 50 speakers uttering 20 isolated words each. The experimental results obtained show the efficiency of these techniques in recognizing speech

Development of Hierarchical Clustering Techniques for Gridded Data from Mixed Data Sequences

Knowledge discovery in databases is the non-trivial process of identifying valid, novel potentially useful and ultimately understandable patterns from data. The term Data mining refers to the process which does the exploratory analysis on the data and builds some model on the data. To infer patterns from data, data mining involves different approaches like association rule mining, classification techniques or clustering techniques. Among the many data mining techniques, clustering plays a major role, since it helps to group the related data for assessing properties and drawing conclusions. Most of the clustering algorithms act on a dataset with uniform format, since the similarity or dissimilarity between the data points is a significant factor in finding out the clusters. If a dataset consists of mixed attributes, i.e. a combination of numerical and categorical variables, a preferred approach is to convert different formats into a uniform format. The research study explores the various techniques to convert the mixed data sets to a numerical equivalent, so as to make it equipped for applying the statistical and similar algorithms. The results of clustering mixed category data after conversion to numeric data type have been demonstrated using a crime data set. The thesis also proposes an extension to the well known algorithm for handling mixed data types, to deal with data sets having only categorical data. The proposed conversion has been validated on a data set corresponding to breast cancer. Moreover, another issue with the clustering process is the visualization of output. Different geometric techniques like scatter plot, or projection plots are available, but none of the techniques display the result projecting the whole database but rather demonstrate attribute-pair wise analysis

Development of 2-D MRT based Image Compression Techniques

The thesis explores the area of still image compression. The image compression techniques can be broadly classified into lossless and lossy compression. The most common lossy compression techniques are based on Transform coding, Vector Quantization and Fractals. Transform coding is the simplest of the above and generally employs reversible transforms like, DCT, DWT, etc. Mapped Real Transform (MRT) is an evolving integer transform, based on real additions alone. The present research work aims at developing new image compression techniques based on MRT. Most of the transform coding techniques employ fixed block size image segmentation, usually 8×8. Hence, a fixed block size transform coding is implemented using MRT and the merits and demerits are analyzed for both 8×8 and 4×4 blocks. The N2 unique MRT coefficients, for each block, are computed using templates. Considering the merits and demerits of fixed block size transform coding techniques, a hybrid form of these techniques is implemented to improve the performance of compression. The performance of the hybrid coder is found to be better compared to the fixed block size coders. Thus, if the block size is made adaptive, the performance can be further improved. In adaptive block size coding, the block size may vary from the size of the image to 2×2. Hence, the computation of MRT using templates is impractical due to memory requirements. So, an adaptive transform coder based on Unique MRT (UMRT), a compact form of MRT, is implemented to get better performance in terms of PSNR and HVS The suitability of MRT in vector quantization of images is then experimented. The UMRT based Classified Vector Quantization (CVQ) is implemented subsequently. The edges in the images are identified and classified by employing a UMRT based criteria. Based on the above experiments, a new technique named “MRT based Adaptive Transform Coder with Classified Vector Quantization (MATC-CVQ)”is developed. Its performance is evaluated and compared against existing techniques. A comparison with standard JPEG & the well-known Shapiro’s Embedded Zero-tree Wavelet (EZW) is done and found that the proposed technique gives better performance for majority of images