Clinical decision support system for point of care use: ontology driven design and software implementation

OBJECTIVES: The objective of this research was to design a clinical decision support system (CDSS) that supports heterogeneous clinical decision problems and runs on multiple computing platforms. Meeting this objective required a novel design to create an extendable and easy to maintain clinical CDSS for point of care support. The proposed solution was evaluated in a proof of concept implementation. METHODS: Based on our earlier research with the design of a mobile CDSS for emergency triage we used ontology-driven design to represent essential components of a CDSS. Models of clinical decision problems were derived from the ontology and they were processed into executable applications during runtime. This allowed scaling applications' functionality to the capabilities of computing platforms. A prototype of the system was implemented using the extended client-server architecture and Web services to distribute the functions of the system and to make it operational in limited connectivity conditions. RESULTS: The proposed design provided a common framework that facilitated development of diversified clinical applications running seamlessly on a variety of computing platforms. It was prototyped for two clinical decision problems and settings (triage of acute pain in the emergency department and postoperative management of radical prostatectomy on the hospital ward) and implemented on two computing platforms-desktop and handheld computers. CONCLUSIONS: The requirement of the CDSS heterogeneity was satisfied with ontology-driven design. Processing of application models described with the help of ontological models allowed having a complex system running on multiple computing platforms with different capabilities. Finally, separation of models and runtime components contributed to improved extensibility and maintainability of the system.

RKHS-Methods at Series Summation for Software Implementation

Reproducing Kernel Hilbert Space (RKHS) and Reproducing Transformation Methods for Series Summation that allow analytically obtaining alternative representations for series in the finite form are developed.

Online Alpha Wave detector: an Embedded hardware-software implementation

The recent trend on embedded system development opens a new prospect for applications that in the past were not possible. The eye tracking for sleep and fatigue detection has become an important and useful application in industrial and automotive scenarios since fatigue is one of the most prevalent causes of earth-moving equipment accidents. Typical applications such as cameras, accelerometers and dermal analyzers are present on the market but have some inconvenient. This thesis project has used EEG signal, particularly, alpha waves, to overcome them by using an embedded software-hardware implementation to detect these signals in real time

Unibo-BP: an innovative free software implementation of Bundle Protocol Version 7 (RFC 9171)

The BP (Bundle Protocol) version 7 has been recently standardized by IETF in RFC 9171, but it is the whole DTN (Delay-/Disruption-Tolerant Networking) architecture, of which BP is the core, that is gaining a renewed interest, thanks to its planned adoption in future space missions. This is obviously positive, but at the same time it seems to make space agencies more interested in deployment than in research, with new BP implementations that may challenge the central role played until now by the historical BP reference implementations, such as ION and DTNME. To make Unibo research on DTN independent of space agency decisions, the development of an internal BP implementation was in order. This is the goal of this thesis, which deals with the design and implementation of Unibo-BP: a novel, research-driven BP implementation, to be released as Free Software. Unibo-BP is fully compliant with RFC 9171, as demonstrated by a series of interoperability tests with ION and DTNME, and presents a few innovations, such as the ability to manage remote DTN nodes by means of the BP itself. Unibo-BP is compatible with pre-existing Unibo implementations of CGR (Contact Graph Routing) and LTP (Licklider Transmission Protocol) thanks to interfaces designed during the thesis. The thesis project also includes an implementation of TCPCLv3 (TCP Convergence Layer version 3, RFC 7242), which can be used as an alternative to LTPCL to connect with proximate nodes, especially in terrestrial networks. Summarizing, Unibo-BP is at the heart of a larger project, Unibo-DTN, which aims to implement the main components of a complete DTN stack (BP, TCPCL, LTP, CGR). Moreover, Unibo-BP is compatible with all DTNsuite applications, thanks to an extension of the Unified API library on which DTNsuite applications are based. The hope is that Unibo-BP and all the ancillary programs developed during this thesis will contribute to the growth of DTN popularity in academia and among space agencies.

Software methods for HTML files protection in local / corporate networks: review and implementation

Tässä luomistyössä on esitetty tutkimus informaation suojaamisen menetelmien osalta paikallisissa ja ryhmäkuntaisissa verkoissa. Tutkimukseen kuuluu nykyaikaisten kryptagraafisten järjestelmien, Internetin/Intranetin ohjelmointikeinojen ja pääsyoikeuksien jakelumenetelmien analyysi. Tutkimusten perusteella on laadittu ohjelmiston prototyyppi HTML-tiedostojen suojaamista varten. Ohjelmiston laatimisprosessi on sisältänyt vaatimusten, järjestelmän ja suojelukomponenttien suunnittelun ja protytyypin testauksen. Ohjelmiston realisoinnin jälkeen kirjoitettiin käyttöohjeet. Ohjelmiston prototyyppi suojaa informaatiota HTML-tiedoston koko käytön aikana ja eri yrityksissä voidaan käyttää sitä pienien laajennuksien jälkeen.

A family of implementation-friendly BN elliptic curves

For the last decade, elliptic curve cryptography has gained increasing interest in industry and in the academic community. This is especially due to the high level of security it provides with relatively small keys and to its ability to create very efficient and multifunctional cryptographic schemes by means of bilinear pairings. Pairings require pairing-friendly elliptic curves and among the possible choices, Barreto-Naehrig (BN) curves arguably constitute one of the most versatile families. In this paper, we further expand the potential of the BN curve family. We describe BN curves that are not only computationally very simple to generate, but also specially suitable for efficient implementation on a very broad range of scenarios. We also present implementation results of the optimal ate pairing using such a curve defined over a 254-bit prime field. (C) 2001 Elsevier Inc. All rights reserved.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

Software-based study of a non-sorting method for median calculation on a set of integer numbers

This paper presents a software-based study of a hardware-based non-sorting median calculation method on a set of integer numbers. The method divides the binary representation of each integer element in the set into bit slices in order to find the element located in the middle position. The method exhibits a linear complexity order and our analysis shows that the best performance in execution time is obtained when slices of 4-bit in size are used for 8-bit and 16-bit integers, in mostly any data set size. Results suggest that software implementation of bit slice method for median calculation outperforms sorting-based methods with increasing improvement for larger data set size. For data set sizes of N > 5, our simulations show an improvement of at least 40%.

Energy-performance tradeoffs in software transactional memory

Transactional memory (TM) is a new synchronization mechanism devised to simplify parallel programming, thereby helping programmers to unleash the power of current multicore processors. Although software implementations of TM (STM) have been extensively analyzed in terms of runtime performance, little attention has been paid to an equally important constraint faced by nearly all computer systems: energy consumption. In this work we conduct a comprehensive study of energy and runtime tradeoff sin software transactional memory systems. We characterize the behavior of three state-of-the-art lock-based STM algorithms, along with three different conflict resolution schemes. As a result of this characterization, we propose a DVFS-based technique that can be integrated into the resolution policies so as to improve the energy-delay product (EDP). Experimental results show that our DVFS-enhanced policies are indeed beneficial for applications with high contention levels. Improvements of up to 59% in EDP can be observed in this scenario, with an average EDP reduction of 16% across the STAMP workloads. © 2012 IEEE.

Desenvolvimento e aplicação de um software cristalográfico com protocolo de acesso a um banco de dados distribuído

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Real-Time Localization Using Software Defined Radio

Service providers make use of cost-effective wireless solutions to identify, localize, and possibly track users using their carried MDs to support added services, such as geo-advertisement, security, and management. Indoor and outdoor hotspot areas play a significant role for such services. However, GPS does not work in many of these areas. To solve this problem, service providers leverage available indoor radio technologies, such as WiFi, GSM, and LTE, to identify and localize users. We focus our research on passive services provided by third parties, which are responsible for (i) data acquisition and (ii) processing, and network-based services, where (i) and (ii) are done inside the serving network. For better understanding of parameters that affect indoor localization, we investigate several factors that affect indoor signal propagation for both Bluetooth and WiFi technologies. For GSM-based passive services, we developed first a data acquisition module: a GSM receiver that can overhear GSM uplink messages transmitted by MDs while being invisible. A set of optimizations were made for the receiver components to support wideband capturing of the GSM spectrum while operating in real-time. Processing the wide-spectrum of the GSM is possible using a proposed distributed processing approach over an IP network. Then, to overcome the lack of information about tracked devices’ radio settings, we developed two novel localization algorithms that rely on proximity-based solutions to estimate in real environments devices’ locations. Given the challenging indoor environment on radio signals, such as NLOS reception and multipath propagation, we developed an original algorithm to detect and remove contaminated radio signals before being fed to the localization algorithm. To improve the localization algorithm, we extended our work with a hybrid based approach that uses both WiFi and GSM interfaces to localize users. For network-based services, we used a software implementation of a LTE base station to develop our algorithms, which characterize the indoor environment before applying the localization algorithm. Experiments were conducted without any special hardware, any prior knowledge of the indoor layout or any offline calibration of the system.

Real-time localization using software defined radio

Service providers make use of cost-effective wireless solutions to identify, localize, and possibly track users using their carried MDs to support added services, such as geo-advertisement, security, and management. Indoor and outdoor hotspot areas play a significant role for such services. However, GPS does not work in many of these areas. To solve this problem, service providers leverage available indoor radio technologies, such as WiFi, GSM, and LTE, to identify and localize users. We focus our research on passive services provided by third parties, which are responsible for (i) data acquisition and (ii) processing, and network-based services, where (i) and (ii) are done inside the serving network. For better understanding of parameters that affect indoor localization, we investigate several factors that affect indoor signal propagation for both Bluetooth and WiFi technologies. For GSM-based passive services, we developed first a data acquisition module: a GSM receiver that can overhear GSM uplink messages transmitted by MDs while being invisible. A set of optimizations were made for the receiver components to support wideband capturing of the GSM spectrum while operating in real-time. Processing the wide-spectrum of the GSM is possible using a proposed distributed processing approach over an IP network. Then, to overcome the lack of information about tracked devices’ radio settings, we developed two novel localization algorithms that rely on proximity-based solutions to estimate in real environments devices’ locations. Given the challenging indoor environment on radio signals, such as NLOS reception and multipath propagation, we developed an original algorithm to detect and remove contaminated radio signals before being fed to the localization algorithm. To improve the localization algorithm, we extended our work with a hybrid based approach that uses both WiFi and GSM interfaces to localize users. For network-based services, we used a software implementation of a LTE base station to develop our algorithms, which characterize the indoor environment before applying the localization algorithm. Experiments were conducted without any special hardware, any prior knowledge of the indoor layout or any offline calibration of the system.

Usability-Oriented Software Development Process

Usability is the capability of the software product to be understood, learned, used and attractive to the user, when used under specified conditions. Many studies demonstrate the benefits of usability, yet to this day software products continue to exhibit consistently low levels of this quality attribute. Furthermore, poor usability in software systems contributes largely to software failing in actual use. One of the main disciplines involved in usability is that of Human-Computer Interaction (HCI). Over the past two decades the HCI community has proposed specific features that should be present in applications to improve their usability, yet incorporating them into software continues to be far from trivial for software developers. These difficulties are due to multiple factors, including the high level of abstraction at which these HCI recommendations are made and how far removed they are from actual software implementation. In order to bridge this gap, the Software Engineering community has long proposed software design solutions to help developers include usability features into software, however, the problem remains an open research question. This doctoral thesis addresses the problem of helping software developers include specific usability features into their applications by providing them with a structured and tangible guidance in the form of a process, which we have termed the Usability-Oriented Software Development Process. This process is supported by a set of Software Usability Guidelines that help developers to incorporate a set of eleven usability features with high impact on software design. After developing the Usability-oriented Software Development Process and the Software Usability Guidelines, they have been validated across multiple academic projects and proven to help software developers to include such usability features into their software applications. In doing so, their use significantly reduced development time and improved the quality of the resulting designs of these projects. Furthermore, in this work we propose a software tool to automate the application of the proposed process. In sum, this work contributes to the integration of the Software Engineering and HCI disciplines providing a framework that helps software developers to create usable applications in an efficient way.

Software para análisis automático de ficheros de imagen

El presente proyecto fin de carrera consiste en el diseño, desarrollo e implementación de una aplicación informática cuya función sea la identificación de distintos ficheros de imagen, audio y video y la interpretación y presentación de los metadatos asociados a los mismos. El software desarrollado, EXTRACTORDATOS_LBS, reconocerá el tipo de formato del fichero bajo estudio a partir del análisis de los bytes de identificación contenidos en la cabecera del archivo. En base a la información registrada en dicha cabecera, la aplicación interpretará el contenido de los metadatos asociados al fichero, mostrando por pantalla aquellos que resulten de interés para el análisis de los mismos. Previamente a la implementación del software se acomete el análisis teórico de los formatos de diversos archivos multimedia, recogidos en múltiples normas y recomendaciones. Tras esa identificación, se procede al desarrollo de la aplicación EXTRACTORDATOS_LBS , que informa de los parámetros de interés contenidos en las cabeceras de los archivos. El desarrollo se ilustra con los diagramas conceptuales asociados a la arquitectura del software implementado. De igual forma, se muestran las salidas por pantalla de una serie de ficheros de muestra, y se presenta el manual de usuario de la aplicación. La versión electrónica de este documento acompaña el ejecutable que permite el análisis de los archivos. This final project consists in the design, development and implementation of a computer application whose function is the identification of different image, audio and video files and the interpretation and presentation of their metadata. The software developed, EXTRACTORDATOS_LBS, will recognize the type of the file under study through the analysis of the identification bytes contained on the file’s header. Based on information registered in this header, the application will interpret the metadata content associated to file, displaying the most interesting ones for their analysis. Prior to the software implementation, a theoretical analysis of the different formats of media files is undertaken. After this identification, the application EXTRACTORDATOS_LBS is developed. This software analyzes and displays the most interesting parameters contained in multimedia file’s header. The development of the application is illustrated with flow charts associated to the architecture of the software. Furthermore, some graphic examples of use of the program are included, as well as the user’s manual. The electronic version of this document attaches the executable file that permits file analysis.

Architecture and Implementation of Reporting Means in Adaptive Dynamically Extended Information Systems

This article describes architecture and implementation of subsystem intended for working with queries and reports in adaptive dynamically extended information systems able to dynamically extending. The main features of developed approach are application universality, user orientation and opportunity to integrate with external information systems. Software implementation is based on multilevel metadata approach.