9,308 research outputs found

    Visualizing Magnitude: Graphical Number Representations Help Users Detect Large Number Entry Errors

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    Nurses frequently have to program infusion pumps to deliver a prescribed quantity of drug over time. Occasional errors are made in the performance of this routine number entry task, resulting in patients receiving the incorrect dose of a drug. While many of these number entry errors are inconsequential, others are not; infusing 100 ml of a drug instead of 10 ml can be fatal. This paper investigates whether a supplementary graphical number representation, depicting the magnitude of a number, can help people detect number entry errors. An experiment was conducted in which 48 participants had to enter numbers from a ‘prescription sheet’ to a computer interface using a keyboard. The graphical representation was supplementary and was shown both on the ‘prescription sheet’ and the device interface. Results show that while overall more errors were made when the graphical representation was visible, the graphical representation helped participants to detect larger number entry errors (i.e., those that were out by at least an order of magnitude). This work suggests that a graphical number entry system that visualizes magnitude of number can help people detect serious number entry errors

    Supervised learning and inference of semantic information from road scene images

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    Premio Extraordinario de Doctorado de la UAH en el año académico 2013-2014Nowadays, vision sensors are employed in automotive industry to integrate advanced functionalities that assist humans while driving. However, autonomous vehicles is a hot field of research both in academic and industrial sectors and entails a step beyond ADAS. Particularly, several challenges arise from autonomous navigation in urban scenarios due to their naturalistic complexity in terms of structure and dynamic participants (e.g. pedestrians, vehicles, vegetation, etc.). Hence, providing image understanding capabilities to autonomous robotics platforms is an essential target because cameras can capture the 3D scene as perceived by a human. In fact, given this need for 3D scene understanding, there is an increasing interest on joint objects and scene labeling in the form of geometry and semantic inference of the relevant entities contained in urban environments. In this regard, this Thesis tackles two challenges: 1) the prediction of road intersections geometry and, 2) the detection and orientation estimation of cars, pedestrians and cyclists. Different features extracted from stereo images of the KITTI public urban dataset are employed. This Thesis proposes a supervised learning of discriminative models that rely on strong machine learning techniques for data mining visual features. For the first task, we use 2D occupancy grid maps that are built from the stereo sequences captured by a moving vehicle in a mid-sized city. Based on these bird?s eye view images, we propose a smart parameterization of the layout of straight roads and 4 intersecting roads. The dependencies between the proposed discrete random variables that define the layouts are represented with Probabilistic Graphical Models. Then, the problem is formulated as a structured prediction, in which we employ Conditional Random Fields (CRF) for learning and convex Belief Propagation (dcBP) and Branch and Bound (BB) for inference. For the validation of the proposed methodology, a set of tests are carried out, which are based on real images and synthetic images with varying levels of random noise. In relation to the object detection and orientation estimation challenge in road scenes, this Thesis goal is to compete in the international challenge known as KITTI evaluation benchmark, which encourages researchers to push forward the current state of the art on visual recognition methods, particularized for 3D urban scene understanding. This Thesis proposes to modify the successful part-based object detector known as DPM in order to learn richer models from 2.5D data (color and disparity). Therefore, we revisit the DPM framework, which is based on HOG features and mixture models trained with a latent SVM formulation. Next, this Thesis performs a set of modifications on top of DPM: I) An extension to the DPM training pipeline that accounts for 3D-aware features. II) A detailed analysis of the supervised parameter learning. III) Two additional approaches: "feature whitening" and "stereo consistency check". Additionally, a) we analyze the KITTI dataset and several subtleties regarding to the evaluation protocol; b) a large set of cross-validated experiments show the performance of our contributions and, c) finally, our best performing approach is publicly ranked on the KITTI website, being the first one that reports results with stereo data, yielding an increased object detection precision (3%-6%) for the class 'car' and ranking first for the class cyclist

    Supervised learning and inference of semantic information from road scene images

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    Premio Extraordinario de Doctorado de la UAH en el año académico 2013-2014Nowadays, vision sensors are employed in automotive industry to integrate advanced functionalities that assist humans while driving. However, autonomous vehicles is a hot field of research both in academic and industrial sectors and entails a step beyond ADAS. Particularly, several challenges arise from autonomous navigation in urban scenarios due to their naturalistic complexity in terms of structure and dynamic participants (e.g. pedestrians, vehicles, vegetation, etc.). Hence, providing image understanding capabilities to autonomous robotics platforms is an essential target because cameras can capture the 3D scene as perceived by a human. In fact, given this need for 3D scene understanding, there is an increasing interest on joint objects and scene labeling in the form of geometry and semantic inference of the relevant entities contained in urban environments. In this regard, this Thesis tackles two challenges: 1) the prediction of road intersections geometry and, 2) the detection and orientation estimation of cars, pedestrians and cyclists. Different features extracted from stereo images of the KITTI public urban dataset are employed. This Thesis proposes a supervised learning of discriminative models that rely on strong machine learning techniques for data mining visual features. For the first task, we use 2D occupancy grid maps that are built from the stereo sequences captured by a moving vehicle in a mid-sized city. Based on these bird?s eye view images, we propose a smart parameterization of the layout of straight roads and 4 intersecting roads. The dependencies between the proposed discrete random variables that define the layouts are represented with Probabilistic Graphical Models. Then, the problem is formulated as a structured prediction, in which we employ Conditional Random Fields (CRF) for learning and convex Belief Propagation (dcBP) and Branch and Bound (BB) for inference. For the validation of the proposed methodology, a set of tests are carried out, which are based on real images and synthetic images with varying levels of random noise. In relation to the object detection and orientation estimation challenge in road scenes, this Thesis goal is to compete in the international challenge known as KITTI evaluation benchmark, which encourages researchers to push forward the current state of the art on visual recognition methods, particularized for 3D urban scene understanding. This Thesis proposes to modify the successful part-based object detector known as DPM in order to learn richer models from 2.5D data (color and disparity). Therefore, we revisit the DPM framework, which is based on HOG features and mixture models trained with a latent SVM formulation. Next, this Thesis performs a set of modifications on top of DPM: I) An extension to the DPM training pipeline that accounts for 3D-aware features. II) A detailed analysis of the supervised parameter learning. III) Two additional approaches: "feature whitening" and "stereo consistency check". Additionally, a) we analyze the KITTI dataset and several subtleties regarding to the evaluation protocol; b) a large set of cross-validated experiments show the performance of our contributions and, c) finally, our best performing approach is publicly ranked on the KITTI website, being the first one that reports results with stereo data, yielding an increased object detection precision (3%-6%) for the class 'car' and ranking first for the class cyclist

    NASA SBIR abstracts of 1991 phase 1 projects

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    The objectives of 301 projects placed under contract by the Small Business Innovation Research (SBIR) program of the National Aeronautics and Space Administration (NASA) are described. These projects were selected competitively from among proposals submitted to NASA in response to the 1991 SBIR Program Solicitation. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 301, in order of its appearance in the body of the report. Appendixes to provide additional information about the SBIR program and permit cross-reference of the 1991 Phase 1 projects by company name, location by state, principal investigator, NASA Field Center responsible for management of each project, and NASA contract number are included

    Indoor navigation for the visually impaired : enhancements through utilisation of the Internet of Things and deep learning

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    Wayfinding and navigation are essential aspects of independent living that heavily rely on the sense of vision. Walking in a complex building requires knowing exact location to find a suitable path to the desired destination, avoiding obstacles and monitoring orientation and movement along the route. People who do not have access to sight-dependent information, such as that provided by signage, maps and environmental cues, can encounter challenges in achieving these tasks independently. They can rely on assistance from others or maintain their independence by using assistive technologies and the resources provided by smart environments. Several solutions have adapted technological innovations to combat navigation in an indoor environment over the last few years. However, there remains a significant lack of a complete solution to aid the navigation requirements of visually impaired (VI) people. The use of a single technology cannot provide a solution to fulfil all the navigation difficulties faced. A hybrid solution using Internet of Things (IoT) devices and deep learning techniques to discern the patterns of an indoor environment may help VI people gain confidence to travel independently. This thesis aims to improve the independence and enhance the journey of VI people in an indoor setting with the proposed framework, using a smartphone. The thesis proposes a novel framework, Indoor-Nav, to provide a VI-friendly path to avoid obstacles and predict the user s position. The components include Ortho-PATH, Blue Dot for VI People (BVIP), and a deep learning-based indoor positioning model. The work establishes a novel collision-free pathfinding algorithm, Orth-PATH, to generate a VI-friendly path via sensing a grid-based indoor space. Further, to ensure correct movement, with the use of beacons and a smartphone, BVIP monitors the movements and relative position of the moving user. In dark areas without external devices, the research tests the feasibility of using sensory information from a smartphone with a pre-trained regression-based deep learning model to predict the user s absolute position. The work accomplishes a diverse range of simulations and experiments to confirm the performance and effectiveness of the proposed framework and its components. The results show that Indoor-Nav is the first type of pathfinding algorithm to provide a novel path to reflect the needs of VI people. The approach designs a path alongside walls, avoiding obstacles, and this research benchmarks the approach with other popular pathfinding algorithms. Further, this research develops a smartphone-based application to test the trajectories of a moving user in an indoor environment

    Building and evaluating an inconspicuous smartphone authentication method

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    Tese de mestrado em Engenharia Informática, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2013Os smartphones que trazemos connosco estão cada vez mais entranhados nas nossas vidas intimas. Estes dispositivos possibilitam novas formas de trabalhar, de socializar, e ate de nos divertirmos. No entanto, também criaram novos riscos a nossa privacidade. Uma forma comum de mitigar estes riscos e configurar o dispositivo para bloquear apos um período de inatividade. Para voltar a utiliza-lo, e então necessário superar uma barreira de autenticação. Desta forma, se o aparelho cair das mãos de outra pessoa, esta não poderá utiliza-lo de forma a que tal constitua uma ameaça. O desbloqueio com autenticação e, assim, o mecanismo que comummente guarda a privacidade dos utilizadores de smartphones. Porem, os métodos de autenticação atualmente utilizados são maioritariamente um legado dos computadores de mesa. As palavras-passe e códigos de identificação pessoal são tornados menos seguros pelo facto de as pessoas criarem mecanismos para os memorizarem mais facilmente. Alem disso, introduzir estes códigos e inconveniente, especialmente no contexto móvel, em que as interações tendem a ser curtas e a necessidade de autenticação atrapalha a prossecução de outras tarefas. Recentemente, os smartphones Android passaram a oferecer outro método de autenticação, que ganhou um grau de adoção assinalável. Neste método, o código secreto do utilizador e uma sucessão de traços desenhados sobre uma grelha de 3 por 3 pontos apresentada no ecrã táctil. Contudo, quer os códigos textuais/numéricos, quer os padrões Android, são suscetíveis a ataques rudimentares. Em ambos os casos, o canal de entrada e o toque no ecrã táctil; e o canal de saída e o visual. Tal permite que outras pessoas possam observar diretamente a introdução da chave; ou que mais tarde consigam distinguir as marcas deixadas pelos dedos na superfície de toque. Alem disso, estes métodos não são acessíveis a algumas classes de utilizadores, nomeadamente os cegos. Nesta dissertação propõe-se que os métodos de autenticação em smartphones podem ser melhor adaptados ao contexto móvel. Nomeadamente, que a possibilidade de interagir com o dispositivo de forma inconspícua poderá oferecer aos utilizadores um maior grau de controlo e a capacidade de se auto-protegerem contra a observação do seu código secreto. Nesse sentido, foi identificada uma modalidade de entrada que não requer o canal visual: sucessões de toques independentes de localização no ecrã táctil. Estes padrões podem assemelhar-se (mas não estão limitados) a ritmos ou código Morse. A primeira contribuição deste trabalho e uma técnica algorítmica para a deteção destas sucessões de toques, ou frases de toque, como chaves de autenticação. Este reconhecedor requer apenas uma demonstração para configuração, o que o distingue de outras abordagens que necessitam de vários exemplos para treinar o algoritmo. O reconhecedor foi avaliado e demonstrou ser preciso e computacionalmente eficiente. Esta contribuição foi enriquecida com o desenvolvimento de uma aplicação Android que demonstra o conceito. A segunda contribuição e uma exploração de fatores humanos envolvidos no uso de frases de toque para autenticação. E consubstanciada em três estudos com utilizadores, em que o método de autenticação proposto e comparado com as alternativas mais comuns: PIN e o padrão Android. O primeiro estudo (N=30) compara os três métodos no que que diz respeito a resistência a observação e à usabilidade, entendida num sentido lato, que inclui a experiencia de utilização (UX). Os resultados sugerem que a usabilidade das três abordagens e comparável, e que em condições de observação perfeitas, nos três casos existe grande viabilidade de sucesso para um atacante. O segundo estudo (N=19) compara novamente os três métodos mas, desta feita, num cenário de autenticação inconspícua. Com efeito, os participantes tentaram introduzir os códigos com o dispositivo situado por baixo de uma mesa, fora do alcance visual. Neste caso, demonstra-se que a autenticação com frases de toque continua a ser usável. Já com as restantes alternativas existe uma diminuição substancial das medidas de usabilidade. Tal sugere que a autenticação por frases de toque suporta a capacidade de interação inconspícua, criando assim a possibilidade de os utilizadores se protegerem contra possíveis atacantes. O terceiro estudo (N=16) e uma avaliação de usabilidade e aceitação do método de autenticação com utilizadores cegos. Neste estudo, são também elicitadas estratégias de ocultação suportadas pela autenticação por frases de toque. Os resultados sugerem que a técnica e também adequada a estes utilizadores.As our intimate lives become more tangled with the smartphones we carry, privacy has become an increasing concern. A widely available option to mitigate security risks is to set a device so that it locks after a period of inactivity, requiring users to authenticate for subsequent use. Current methods for establishing one's identity are known to be susceptible to even rudimentary observation attacks. The mobile context in which interactions with smartphones are prone to occur further facilitates shoulder-surfing. We submit that smartphone authentication methods can be better adapted to the mobile context. Namely, the ability to interact with the device in an inconspicuous manner could offer users more control and the ability to self-protect against observation. Tapping is a communication modality between a user and a device that can be appropriated for that purpose. This work presents a technique for employing sequences of taps, or tap phrases, as authentication codes. An efficient and accurate tap phrase recognizer, that does not require training, is presented. Three user studies were conducted to compare this approach to the current leading methods. Results indicate that the tapping method remains usable even under inconspicuous authentications scenarios. Furthermore, we found that it is appropriate for blind users, to whom usability barriers and security risks are of special concern

    Plant-Wide Diagnosis: Cause-and-Effect Analysis Using Process Connectivity and Directionality Information

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    Production plants used in modern process industry must produce products that meet stringent environmental, quality and profitability constraints. In such integrated plants, non-linearity and strong process dynamic interactions among process units complicate root-cause diagnosis of plant-wide disturbances because disturbances may propagate to units at some distance away from the primary source of the upset. Similarly, implemented advanced process control strategies, backup and recovery systems, use of recycle streams and heat integration may hamper detection and diagnostic efforts. It is important to track down the root-cause of a plant-wide disturbance because once corrective action is taken at the source, secondary propagated effects can be quickly eliminated with minimum effort and reduced down time with the resultant positive impact on process efficiency, productivity and profitability. In order to diagnose the root-cause of disturbances that manifest plant-wide, it is crucial to incorporate and utilize knowledge about the overall process topology or interrelated physical structure of the plant, such as is contained in Piping and Instrumentation Diagrams (P&IDs). Traditionally, process control engineers have intuitively referred to the physical structure of the plant by visual inspection and manual tracing of fault propagation paths within the process structures, such as the process drawings on printed P&IDs, in order to make logical conclusions based on the results from data-driven analysis. This manual approach, however, is prone to various sources of errors and can quickly become complicated in real processes. The aim of this thesis, therefore, is to establish innovative techniques for the electronic capture and manipulation of process schematic information from large plants such as refineries in order to provide an automated means of diagnosing plant-wide performance problems. This report also describes the design and implementation of a computer application program that integrates: (i) process connectivity and directionality information from intelligent P&IDs (ii) results from data-driven cause-and-effect analysis of process measurements and (iii) process know-how to aid process control engineers and plant operators gain process insight. This work explored process intelligent P&IDs, created with AVEVA® P&ID, a Computer Aided Design (CAD) tool, and exported as an ISO 15926 compliant platform and vendor independent text-based XML description of the plant. The XML output was processed by a software tool developed in Microsoft® .NET environment in this research project to computationally generate connectivity matrix that shows plant items and their connections. The connectivity matrix produced can be exported to Excel® spreadsheet application as a basis for other application and has served as precursor to other research work. The final version of the developed software tool links statistical results of cause-and-effect analysis of process data with the connectivity matrix to simplify and gain insights into the cause and effect analysis using the connectivity information. Process knowhow and understanding is incorporated to generate logical conclusions. The thesis presents a case study in an atmospheric crude heating unit as an illustrative example to drive home key concepts and also describes an industrial case study involving refinery operations. In the industrial case study, in addition to confirming the root-cause candidate, the developed software tool was set the task to determine the physical sequence of fault propagation path within the plant. This was then compared with the hypothesis about disturbance propagation sequence generated by pure data-driven method. The results show a high degree of overlap which helps to validate statistical data-driven technique and easily identify any spurious results from the data-driven multivariable analysis. This significantly increase control engineers confidence in data-driven method being used for root-cause diagnosis. The thesis concludes with a discussion of the approach and presents ideas for further development of the methods

    Aviation Safety/Automation Program Conference

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    The Aviation Safety/Automation Program Conference - 1989 was sponsored by the NASA Langley Research Center on 11 to 12 October 1989. The conference, held at the Sheraton Beach Inn and Conference Center, Virginia Beach, Virginia, was chaired by Samuel A. Morello. The primary objective of the conference was to ensure effective communication and technology transfer by providing a forum for technical interchange of current operational problems and program results to date. The Aviation Safety/Automation Program has as its primary goal to improve the safety of the national airspace system through the development and integration of human-centered automation technologies for aircraft crews and air traffic controllers

    Toward New Ecologies of Cyberphysical Representational Forms, Scales, and Modalities

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    Research on tangible user interfaces commonly focuses on tangible interfaces acting alone or in comparison with screen-based multi-touch or graphical interfaces. In contrast, hybrid approaches can be seen as the norm for established mainstream interaction paradigms. This dissertation describes interfaces that support complementary information mediations, representational forms, and scales toward an ecology of systems embodying hybrid interaction modalities. I investigate systems combining tangible and multi-touch, as well as systems combining tangible and virtual reality interaction. For each of them, I describe work focusing on design and fabrication aspects, as well as work focusing on reproducibility, engagement, legibility, and perception aspects
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