Academic literature on the topic 'Automates à registres'

Published Article<br>The purpose of this project is to investigate the application of Radio Frequency Identification, RFID, to automatic student attendance register. The aim is that the students in any class can be recorded when they carry their student cards with them without having to individually swipe the card or allocate special interaction time. The successful implementation of this proposal will facilitate such record keeping in a non-intrusive and efficient manner and will provide the platform for further research on the correlation between attendance and performance of the students. The opportunity for related research is identified regarding the range of the parameters involved, ensuring that individual identifications do not clash and interfacing challenges with the central record keeping are overcome.

Processors are a basic unit of the computer which accomplish the mission of processing data stored in the memory. Large memories are required to process a big amount of data. Not all data is required at the same time, few data is required faster than other. For this reason, the memory is structured  in a hierarchy, from smaller and faster to bigger and slower. The cache memory is one of the fastest elements and closest to the processor in the memory hierarchy. The processor design companies hides its characteristics, usually under a confidential documentation that can not be accessed by the software developers. One of the most important characteristics kept in secret in this documentation is the replacement policy. The most famous replacement policies are known but the hardware designers can apply modifications for performance, cost or design reasons. The obfuscation of a part of the processor implies many developers to avoid problems with, for example, the runtime. If a task must be executed always in a certain time, the developer will take always the case requiring more time to execute (also called "Worst Case Execution Time") implying an underutilisation of the processor. This project will be focused on a new method to represent  and infer the replacement policy: modelling the replacement policies with automaton and using a learning process framework called LearnLib to guess them. This is not the first project trying to match the cache memory characteristics, actually a previous project is the basis to find a more general model to define the replacement policies. The results of LearnLib are modelled as an automaton. In order to test the effectiveness of this framework, different replacement policies will be simulated and verified. To provide a interface with a real cache memories is developed a program called hwquery. This program will interface a real cache request for using it in Learnlib.

Avec la montée en charge de l’informatisation des systèmes d’information hospitaliers, une quantité croissante de données est produite tout au long de la prise en charge des patients. L’utilisation secondaire de ces données constitue un enjeu essentiel pour la recherche ou l’évaluation en santé. Dans le cadre de cette thèse, nous discutons les verrous liés à la représentation et à la sémantique des données, qui limitent leur utilisation secondaire en cancérologie. Nous proposons des méthodes basées sur des ontologies pour l’intégration sémantique des données de diagnostics. En effet, ces données sont représentées par des terminologies hétérogènes. Nous étendons les modèles obtenus pour la représentation de la maladie tumorale, et les liens qui existent avec les diagnostics. Enfin, nous proposons une architecture combinant entrepôts de données, registres de métadonnées et web sémantique. L’architecture proposée permet l’intégration syntaxique et sémantique d’un grand nombre d’observations. Par ailleurs, l’intégration de données et de connaissances (sous la forme d’ontologies) a été utilisée pour construire un algorithme d’identification de la maladie tumorale en fonction des diagnostics présents dans les données de prise en charge. Cet algorithme basé sur les classes de l’ontologie est indépendant des données effectivement enregistrées. Ainsi, il fait abstraction du caractère hétérogène des données diagnostiques initialement disponibles. L’approche basée sur une ontologie pour l’identification de la maladie tumorale, permet une adaptation rapide des règles d’agrégation en fonction des besoins spécifiques d’identification. Ainsi, plusieurs versions du modèle d’identification peuvent être utilisées avec des granularités différentes<br>With the increasing adoption of Electronic Health Records (EHR), the amount of data produced at the patient bedside is rapidly increasing. Secondary use is there by an important field to investigate in order facilitate research and evaluation. In these work we discussed issues related to data representation and semantics within EHR that need to be address in order to facilitate secondary of structured data in oncology. We propose and evaluate ontology based methods for heterogeneous diagnosis terminologies integration in oncology. We then extend obtained model to enable tumoral disease representation and links with diagnosis as recorded in EHR. We then propose and implement a complete architecture combining a clinical data warehouse, a metadata registry and web semantic technologies and standards. This architecture enables syntactic and semantic integration of a broad range of hospital information System observation. Our approach links data with external knowledge (ontology), in order to provide a knowledge resource for an algorithm for tumoral disease identification based on diagnosis recorded within EHRs. As it based on the ontology classes, the identification algorithm is uses an integrated view of diagnosis (avoiding semantic heterogeneity). The proposed architecture leading to algorithm on the top of an ontology offers a flexible solution. Adapting the ontology, modifying for instance the granularity provide a way for adapting aggregation depending on specific needs

Introduction: Lay use of automated external defibrillators (AEDs) before the arrival of emergency medical services (EMS) providers on scene increases survival after out-of-hospital cardiac arrest (OHCA). AEDs have been placed in public locations may be not ready for use when needed. We describe a protocol for AED surveillance that tracks these devices through time and space to improve public health, and survival as well as facilitate research. Methods and analysis: Included AEDs are installed in public locations for use by laypersons to treat patients with OHCA before the arrival of EMS providers on scene. Included cases of OHCA are patients evaluated by organised EMS personnel and treated for OHCA. Enrolment of 10 000 AEDs annually will yield precision of 0.4% in the estimate of readiness for use. Enrolment of 2500 patients annually will yield precision of 1.9% in the estimate of survival to hospital discharge. Recruitment began on 21 Mar 2014 and is ongoing. AEDs are found by using multiple methods. Each AED is then tagged with a label which is a unique two-dimensional (2D) matrix code; the 2D matrix code is recorded and the location and status of the AED tracked using a smartphone; these elements are automatically passed via the internet to a secure and confidential database in real time. Whenever the 2D matrix code is rescanned for any non-clinical or clinical use of an AED, the user is queried to answer a finite set of questions about the device status. The primary outcome of any clinical use of an AED is survival to hospital discharge. Results are summarised descriptively. Ethics and dissemination: These activities are conducted under a grant of authority for public health surveillance from the Food and Drug Administration. Results are provided periodically to participating sites and sponsors to improve public health and quality of care.

Although offset printing has been and still is the most common printing technology for color print productions, its print productions are subject to variations due to environmental and process parameters. Therefore, it is very important to frequently control the print production quality criteria in order to make the process predictable, reproducible and stable. One of the most important parts in a modern industrial offset printing is Computer to Plate (CtP), which exposes the printing plate. One of the most important quality criteria for printing is to control the dot gain level. Dot gain refers to an important phenomenon that causes the printed elements to appear larger than their reference size sent to the CtP. It is crucial to have the dot gain level within an acceptable range, defined by ISO 12647-2 for offset printing. This is done by dot gain compensation methods in the Raster Image Processor (RIP). Dot gain compensation is however a complicated task in offset printing because of the huge number of parameters affecting dot gain. Another important quality criterion affecting the print quality in offset is the register variation caused by the misplacement of printing sheet in the printing unit. Register variation causes tone value variations, gray balance variation and blurred image details. Trapping is another important print quality criterion that should be measured in an offset printing process. Trapping occurs when the inks in different printing units are printed wet-on-wet in a multi-color offset printing machine. Trapping affects the gray balance and makes the resulting colors of overlapped inks pale. In this dissertation three different dot gain compensation methods are discussed. The most accurate and efficient dot gain compensation method, which is noniterative, has been tested, evaluated and applied using many offset printing workflows. To further increase the accuracy of this method, an approach to effectively select the correction points of a RIP with limited number of correction points, has also been proposed. Correction points are the tone values needed to be set in the RIP to define a dot gain compensation curve. To fulfill the requirement of having the register variation within the allowed range, it has to be measured and quantified. There have been two novel models proposed in this dissertation that determine the register variation value. One of the models is based on spectrophotometry and the other one on densitometry. The proposed methods have been evaluated by comparison to the industrial image processing based register variation model, which is expensive and not available in most printing companies. The results of all models were comparable, verifying that the proposed models are good  alternatives to the image processing based model. The existing models determining the trapping values are based on densitometric measurements and quantify the trapping effect by a percentage value. In this dissertation, a novel trapping model is proposed that quantifies the trapping effect by a color difference metric, i.e. <img src="http://www.diva-portal.org/cgi-bin/mimetex.cgi?%5Csmall%5CDelta%20E_a_b%5E%5Cast" />, which is more useful and understandable for print machine operators. The comparison between the proposed trapping model and the existing models has shown very good correlations and verified that the proposed model has a bigger dynamic range. The proposed trapping model has also been extended to take into account the effect of ink penetration and gloss. The extended model has been tested using a  high glossy coated paper and the results have shown that the gloss and ink penetration can be neglected for this type of paper. An automated CtP calibration system for offset printing workflow has been introduced and described in this dissertation. This method is a good solution to generate the needed huge numbers of dot gain compensation curves to have an accurate CtP calibration.

Automatic Speech Recognition (ASR) is one of the most important applications in the area of cognitive computing. Fast and accurate ASR is emerging as a key application for mobile and wearable devices. These devices, such as smartphones, have incorporated speech recognition as one of the main interfaces for user interaction. This trend towards voice-based user interfaces is likely to continue in the next years which is changing the way of human-machine interaction. Effective speech recognition systems require real-time recognition, which is challenging for mobile devices due to the compute-intensive nature of the problem and the power constraints of such systems and involves a huge effort for CPU architectures to reach it. GPU architectures offer parallelization capabilities which can be exploited to increase the performance of speech recognition systems. However, efficiently utilizing the GPU resources for speech recognition is also challenging, as the software implementations exhibit irregular and unpredictable memory accesses and poor temporal locality. The purpose of this thesis is to study the characteristics of ASR systems running on low-power mobile devices in order to propose different techniques to improve performance and energy consumption. We propose several software-level optimizations driven by the power/performance analysis. Unlike previous proposals that trade accuracy for performance by reducing the number of Gaussians evaluated, we maintain accuracy and improve performance by effectively using the underlying CPU microarchitecture. We use a refactored implementation of the GMM evaluation code to ameliorate the impact of branches. Then, we exploit the vector unit available on most modern CPUs to boost GMM computation, introducing a novel memory layout for storing the means and variances of the Gaussians in order to maximize the effectiveness of vectorization. In addition, we compute the Gaussians for multiple frames in parallel, significantly reducing memory bandwidth usage. Our experimental results show that the proposed optimizations provide 2.68x speedup over the baseline Pocketsphinx decoder on a high-end Intel Skylake CPU, while achieving 61% energy savings. On a modern ARM Cortex-A57 mobile processor our techniques improve performance by 1.85x, while providing 59% energy savings without any loss in the accuracy of the ASR system. Secondly, we propose a register renaming technique that exploits register reuse to reduce the pressure on the register file. Our technique leverages physical register sharing by introducing minor changes in the register map table and the issue queue. We evaluated our renaming technique on top of a modern out-of-order processor. The proposed scheme supports precise exceptions and we show that it results in 9.5% performance improvements for GMM evaluation. Our experimental results show that the proposed register renaming scheme provides 6% speedup on average for the SPEC2006 benchmarks. Alternatively, our renaming scheme achieves the same performance while reducing the number of physical registers by 10.5%. Finally, we propose a hardware accelerator for GMM evaluation that reduces the energy consumption by three orders of magnitude compared to solutions based on CPUs and GPUs. The proposed accelerator implements a lazy evaluation scheme where Gaussians are computed on demand, avoiding 50% of the computations. Furthermore, it employs a novel clustering scheme to reduce the size of the GMM parameters, which results in 8x memory bandwidth savings with a negligible impact on accuracy. Finally, it includes a novel memoization scheme that avoids 74.88% of floating-point operations. The end design provides a 164x speedup and 3532x energy reduction when compared with a highly-tuned implementation running on a modern mobile CPU. Compared to a state-of-the-art mobile GPU, the GMM accelerator achieves 5.89x speedup over a highly optimized CUDA implementation, while reducing energy by 241x.<br>El reconocimiento automático de voz (ASR) es una de las aplicaciones más importantes en el área de la computación cognitiva. ASR rápido y preciso se está convirtiendo en una aplicación clave para dispositivos móviles y portátiles. Estos dispositivos, como los Smartphones, han incorporado el reconocimiento de voz como una de las principales interfaces de usuario. Es probable que esta tendencia hacia las interfaces de usuario basadas en voz continúe en los próximos años, lo que está cambiando la forma de interacción humano-máquina. Los sistemas de reconocimiento de voz efectivos requieren un reconocimiento en tiempo real, que es un desafío para los dispositivos móviles debido a la naturaleza de cálculo intensivo del problema y las limitaciones de potencia de dichos sistemas y supone un gran esfuerzo para las arquitecturas de CPU. Las arquitecturas GPU ofrecen capacidades de paralelización que pueden aprovecharse para aumentar el rendimiento de los sistemas de reconocimiento de voz. Sin embargo, la utilización eficiente de los recursos de la GPU para el reconocimiento de voz también es un desafío, ya que las implementaciones de software presentan accesos de memoria irregulares e impredecibles y una localidad temporal deficiente. El propósito de esta tesis es estudiar las características de los sistemas ASR que se ejecutan en dispositivos móviles de baja potencia para proponer diferentes técnicas para mejorar el rendimiento y el consumo de energía. Proponemos varias optimizaciones a nivel de software impulsadas por el análisis de potencia y rendimiento. A diferencia de las propuestas anteriores que intercambian precisión por el rendimiento al reducir el número de gaussianas evaluadas, mantenemos la precisión y mejoramos el rendimiento mediante el uso efectivo de la microarquitectura subyacente de la CPU. Usamos una implementación refactorizada del código de evaluación de GMM para reducir el impacto de las instrucciones de salto. Explotamos la unidad vectorial disponible en la mayoría de las CPU modernas para impulsar el cálculo de GMM. Además, calculamos las gaussianas para múltiples frames en paralelo, lo que reduce significativamente el uso de ancho de banda de memoria. Nuestros resultados experimentales muestran que las optimizaciones propuestas proporcionan un speedup de 2.68x sobre el decodificador Pocketsphinx en una CPU Intel Skylake de alta gama, mientras que logra un ahorro de energía del 61%. En segundo lugar, proponemos una técnica de renombrado de registros que explota la reutilización de registros físicos para reducir la presión sobre el banco de registros. Nuestra técnica aprovecha el uso compartido de registros físicos mediante la introducción de cambios en la tabla de renombrado de registros y la issue queue. Evaluamos nuestra técnica de renombrado sobre un procesador moderno. El esquema propuesto admite excepciones precisas y da como resultado mejoras de rendimiento del 9.5% para la evaluación GMM. Nuestros resultados experimentales muestran que el esquema de renombrado de registros propuesto proporciona un 6% de aceleración en promedio para SPEC2006. Finalmente, proponemos un acelerador para la evaluación de GMM que reduce el consumo de energía en tres órdenes de magnitud en comparación con soluciones basadas en CPU y GPU. El acelerador propuesto implementa un esquema de evaluación perezosa donde las GMMs se calculan bajo demanda, evitando el 50% de los cálculos. Finalmente, incluye un esquema de memorización que evita el 74.88% de las operaciones de coma flotante. El diseño final proporciona una aceleración de 164x y una reducción de energía de 3532x en comparación con una implementación altamente optimizada que se ejecuta en una CPU móvil moderna. Comparado con una GPU móvil de última generación, el acelerador de GMM logra un speedup de 5.89x sobre una implementación CUDA optimizada, mientras que reduce la energía en 241x.

Digital urban tree registers serve many porposes and facilitate the administration, care and management of urban trees within a city or municipality. Currently, mapping of urban tree stands is carried out manually with methods which are both laborious and time consuming. The aim of this study is to establish a way of operation based on the use of existing LiDAR data and othophotos to automatically detect individual trees. By using the extensions LIDAR Analyst and Feature Analyst for ArcMap a tree extraction was performed. This was carried out over the extent of the city district committee area of Östermalm in the city of Stockholm, Sweden. The results were compared to the city’s urban tree register and validated by calculating its Precision and Recall. This showed that FeatureAnalyst generated the result with the highest accuracy. The derived trees were represented by polygons which despite their high accuracy make the result unsuitable for detecting individual tree positions. Even though the use of LIDAR Analyst resulted in a less precise tree mapping result, individual tree positions were detected satisfactory. This especially in areas with more sparse, regular tree stands. The study concludes that the use of both tools complement each other and compensate the shortcomings of the other. FeatureAnalyst maps an acceptable tree coverage while LIDAR Analyst more accurately identifies individual tree positions. Thus, a combination of the two results could be used for individual tree mapping.<br>Digitala urbana trädregister tjänar många syften och underlättar för städer och kommuner att administrera, sköta och hantera sina park- och gatuträd. Dagens kartering av urbana trädbestånd sker ofta manuellt med metoder vilka är både arbetsintensiva och tidskrävande. Denna studie syftar till att utveckla ett arbetssätt för att med hjälp av befintliga LiDAR-data och ortofoton automatiskt kartera individuella träd. Med hjälp av tilläggen LIDAR Analyst och FeatureAnalyst för ArcMap utfördes en trädkartering över Östermalms stadsdelsnämndsområde i Stockholms stad. Efter kontroll mot stadens träddatabas och validering av resultatet genom beräknandet av Precision och Recall konstaterades att användningen av FeatureAnalyst resulterade i det bästa trädkarteringsresultatet. Dessa träd representeras av polygoner vilket medför att resultatet trots sin goda täckning inte lämpar sig för identifierandet av enskilda trädpositioner. Även om användningen av LIDAR Analyst resulterade i ett mindre precist karteringsresultat erhölls goda positionsbestämmelser för enskilda träd, främst i områden med jämna, glesa trädbestånd. Slutsatsen av detta är att användandet av de båda verktygen kompenserar varandras tillkortakommanden där FeatureAnalyst ger en godtagbar trädtäckning medan LIDAR Analyst bättre identifierar enskilda trädpositioner. En kombination av de båda resultaten skulle alltså kunna användas i trädkarteringssyfte.