AR modelling and DDS for in situ surveillance of capacitive pressure transmitters

In this work, a methodology is proposed to find the dynamics poles of a capacitive pressure transmitter in order to enhance and extend the on line surveillance of this type of sensors based on the response time measurement by applying noise analysis techniques and the Dynamic Data System. Several measurements have been analyzed taken from a Pressurized Water Reactor. The methodology proposes an autoregressive fit whose order is determined by the sensor dynamics poles. Nevertheless, the signals that have been analyzed, could not be filtered properly in order to remove the plant noise, thus, this was considered as an additional pair of complex conjugate poles. With this methodology we have come up with the numerical value of the sensor second real pole in spite of its low influence on the sensor dynamic response. This opens up a more accurate on line sensor surveillance since the previous methods were achieved by considering one real pole only.

Surveillance of habitats and plant diversity indicators across a regional gradient in the Iberian Peninsula

Dentro del Proyecto EBONE (Red de Observación de la Biodiversidad Europea) se analizan diferentes tipos de paisaje en varias zonas de Madrid y norte de Portugal. Se realiza un estudio de los habitats y la diversidad de especies con el objetivo principal de preservarlos y conservalos.

Real-time Surveillance Application by Multiple Detectors and Compressive Trackers

A real-time surveillance system for IP network cameras is presented. Motion, part-body, and whole-body detectors are efficiently combined to generate robust and fast detections, which feed multiple compressive trackers. The generated trajectories are then improved using a reidentification strategy for long term operation.

Foreground segmentation in depth imagery using depth and spatial dynamic models for video surveillance applications

Low-cost systems that can obtain a high-quality foreground segmentation almostindependently of the existing illumination conditions for indoor environments are verydesirable, especially for security and surveillance applications. In this paper, a novelforeground segmentation algorithm that uses only a Kinect depth sensor is proposedto satisfy the aforementioned system characteristics. This is achieved by combininga mixture of Gaussians-based background subtraction algorithm with a new Bayesiannetwork that robustly predicts the foreground/background regions between consecutivetime steps. The Bayesian network explicitly exploits the intrinsic characteristics ofthe depth data by means of two dynamic models that estimate the spatial and depthevolution of the foreground/background regions. The most remarkable contribution is thedepth-based dynamic model that predicts the changes in the foreground depth distributionbetween consecutive time steps. This is a key difference with regard to visible imagery,where the color/gray distribution of the foreground is typically assumed to be constant.Experiments carried out on two different depth-based databases demonstrate that theproposed combination of algorithms is able to obtain a more accurate segmentation of theforeground/background than other state-of-the art approaches.

Vigilancia tecnológica: un aprendizaje adaptativo a través del pensamiento divergente Technological surveillance: an adaptive learning through divergent thinking

El estudio de la evolución tecnológica a partir de logros concretos de la humanidad es una forma de aproximación a la enseñanza de la ingeniería. Comprender que las tecnologías existentes son el resultado de un depurado proceso de creación/selección no es trivial. Por ello combinar un conocimiento deductivo profundo con un pensamiento divergente que favorezca la obtención de soluciones viables y creativas es fundamental en el contexto de incertidumbre económica actual. En este ensayo se comparan siete técnicas de enseñanza que pretenden favorecer el PD en una estrategia adaptativa para alumnos con distintos estilos cognitivos (AA) sobre la base de un proceso de vigilancia tecnológica (VT) aplicada a algunos objetos cotidianos y otros muy especializados.

Cognitive Surveillance Architecture for Scenario Understanding

Uno de los mayores retos para la comunidad científica es conseguir que las máquinas posean en un futuro la capacidad del sistema visual y cognitivo humanos, de forma que, por ejemplo, en entornos de video vigilancia, puedan llegar a proporcionar de manera automática una descripción fiable de lo que está ocurriendo en la escena. En la presente tesis, mediante la propuesta de un marco de trabajo de referencia, se discuten y plantean los pasos necesarios para el desarrollo de sistemas más inteligentes capaces de extraer y analizar, a diferentes niveles de abstracción y mediante distintos módulos de procesamiento independientes, la información necesaria para comprender qué está sucediendo en un conjunto amplio de escenarios de distinta naturaleza. Se parte de un análisis de requisitos y se identifican los retos para este tipo de sistemas en la actualidad, lo que constituye en sí mismo los objetivos de esta tesis, contribuyendo así a un modelo de datos basado en el conocimiento que permitirá analizar distintas situaciones en las que personas y vehículos son los actores principales, dejando no obstante la puerta abierta a la adaptación a otros dominios. Así mismo, se estudian los distintos procesos que se pueden lanzar a nivel interno así como la necesidad de integrar mecanismos de realimentación a distintos niveles que permitan al sistema adaptarse mejor a cambios en el entorno. Como resultado, se propone un marco de referencia jerárquico que integra las capacidades de percepción, interpretación y aprendizaje para superar los retos identificados en este ámbito; y así poder desarrollar sistemas de vigilancia más robustos, flexibles e inteligentes, capaces de operar en una variedad de entornos. Resultados experimentales ejecutados sobre distintas muestras de datos (secuencias de vídeo principalmente) demuestran la efectividad del marco de trabajo propuesto respecto a otros propuestos en el pasado. Un primer caso de estudio, permite demostrar la creación de un sistema de monitorización de entornos de parking en exteriores para la detección de vehículos y el análisis de plazas libres de aparcamiento. Un segundo caso de estudio, permite demostrar la flexibilidad del marco de referencia propuesto para adaptarse a los requisitos de un entorno de vigilancia completamente distinto, como es un hogar inteligente donde el análisis automático de actividades de la vida cotidiana centra la atención del estudio. ABSTRACT One of the most ambitious objectives for the Computer Vision and Pattern Recognition research community is that machines can achieve similar capacities to the human's visual and cognitive system, and thus provide a trustworthy description of what is happening in the scene under surveillance. Thus, a number of well-established scenario understanding architectural frameworks to develop applications working on a variety of environments can be found in the literature. In this Thesis, a highly descriptive methodology for the development of scene understanding applications is presented. It consists of a set of formal guidelines to let machines extract and analyse, at different levels of abstraction and by means of independent processing modules that interact with each other, the necessary information to understand a broad set of different real World surveillance scenarios. Taking into account the challenges that working at both low and high levels offer, we contribute with a highly descriptive knowledge-based data model for the analysis of different situations in which people and vehicles are the main actors, leaving the door open for the development of interesting applications in diverse smart domains. Recommendations to let systems achieve high-level behaviour understanding will be also provided. Furthermore, feedback mechanisms are proposed to be integrated in order to let any system to understand better the environment and the logical context around, reducing thus the uncertainty and noise, and increasing its robustness and precision in front of low-level or high-level errors. As a result, a hierarchical cognitive architecture of reference which integrates the necessary perception, interpretation, attention and learning capabilities to overcome main challenges identified in this area of research is proposed; thus allowing to develop more robust, flexible and smart surveillance systems to cope with the different requirements of a variety of environments. Once crucial issues that should be treated explicitly in the design of this kind of systems have been formulated and discussed, experimental results shows the effectiveness of the proposed framework compared with other proposed in the past. Two case studies were implemented to test the capabilities of the framework. The first case study presents how the proposed framework can be used to create intelligent parking monitoring systems. The second case study demonstrates the flexibility of the system to cope with the requirements of a completely different environment, a smart home where activities of daily living are performed. Finally, general conclusions and future work lines to further enhancing the capabilities of the proposed framework are presented.

Demonstration of an interferon γ-dependent tumor surveillance system in immunocompetent mice

This study demonstrates that endogenously produced interferon γ (IFN-γ) forms the basis of a tumor surveillance system that controls development of both chemically induced and spontaneously arising tumors in mice. Compared with wild-type mice, mice lacking sensitivity to either IFN-γ (i.e., IFN-γ receptor-deficient mice) or all IFN family members (i.e., Stat1-deficient mice) developed tumors more rapidly and with greater frequency when challenged with different doses of the chemical carcinogen methylcholanthrene. In addition, IFN-γ-insensitive mice developed tumors more rapidly than wild-type mice when bred onto a background deficient in the p53 tumor-suppressor gene. IFN-γ-insensitive p53−/− mice also developed a broader spectrum of tumors compared with mice lacking p53 alone. Using tumor cells derived from methylcholanthrene-treated IFN-γ-insensitive mice, we found IFN-γ’s actions to be mediated at least partly through its direct effects on the tumor cell leading to enhanced tumor cell immunogenicity. The importance and generality of this system is evidenced by the finding that certain types of human tumors become selectively unresponsive to IFN-γ. Thus, IFN-γ forms the basis of an extrinsic tumor-suppressor mechanism in immunocompetent hosts.

A Prospective Surveillance Study of Candidaemia : Epidemiology, Risk Factors, Antifungal Treatment and Outcome in Hospitalized Patients

Funding This work was supported by the Wellcome Trust Strategic Award for Medical Mycology and Fungal Immunology 097377/Z/11/Z. Data collection was supported by a grant from Pfizer. GR was also supported by a research fellowship grant from Gilead Sciences. The collection of the isolates was funded by a Gilead Fellowship to GR.

Immune surveillance against a solid tumor fails because of immunological ignorance

Many peripheral solid tumors such as sarcomas and carcinomas express tumor-specific antigens that can serve as targets for immune effector T cells. Nevertheless, overall immune surveillance against such tumors seems relatively inefficient. We studied immune surveillance against a s.c. sarcoma expressing a characterized viral tumor antigen. Surprisingly, the tumor cells were capable of inducing a protective cytotoxic T cell response if transferred as a single-cell suspension. However, if they were transplanted as small tumor pieces, tumors readily grew. Tumor growth correlated strictly with (i) failure of tumor cells to reach the draining lymph nodes and (ii) absence of primed cytotoxic T cells. Cytotoxic T cells were not tolerant or deleted because a tumor antigen-specific cytotoxic T cell response was readily induced in lymphoid tissue by immunization with virus or with tumor cells even in the presence of large tumors. Established tumors were rejected by vaccine-induced effector T cells if effector T cells were maintained by prolonged or repetitive vaccination, but not by single-dose vaccination. Thus, in addition to several other tumor-promoting parameters, some antigenic peripheral sarcomas—and probably carcinomas—may grow not because they anergize or tolerize tumor-specific T cells, but because such tumors are immunologically dealt with as if they were in a so-called immunologically privileged site and are ignored for too long.