There exist several image-enhancement algorithms and tasks associated with imaging through turbulence that depend on defining the quality of an image. Examples include: "lucky imaging", choosing the width of the inverse filter for image reconstruction, or stopping iterative deconvolution. We collected a number of image quality metrics found in the literature. Particularly interesting are the blind, "no-reference" metrics. We discuss ways of evaluating the usefulness of these metrics, even when a fully objective comparison is impossible because of the lack of a reference image. Metrics are tested on simulated and real data. Field data comes from experiments performed by the NATO SET 165 research group over a 7 km distance in Dayton, Ohio

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Location-based recommender systems (LBRSs) are gaining importance with the proliferation of location-based services provided by mobile devices as well as user-generated content in social networks. Collaborative approaches for recommendation rely on the opinions of like-minded people, so-called neighbours, for prediction. Thus, an adequate selection of such neighbours becomes essential for achieving good prediction results. The aim of this work is to explore different strategies to select neighbours in the context of a collaborative filtering–based recommender system for POI (places of interest) recommendations. Whereas standard methods are based on user similarity to delimit a neighbourhood, in this work several strategies are proposed based on direct social relationships and geographical information extracted from location-based social networks (LBSNs). The impact of the different strategies proposed has been evaluated and compared against the traditional collaborative filtering approach using a dataset from a popular network as Foursquare. In general terms, the proposed strategies for selecting neighbours based on the different elements available in a LBSN achieve better results than the traditional collaborative filtering approach. Our findings can be helpful both to researchers in the recommender systems area and to recommender system developers in the context of LBSNs, since they can take into account our results to design and provide more effective services considering the huge amount of knowledge produced in LBSNs.

El artículo tiene como objetivo principalevaluar dos plataformas digitales de redes sociales, el Blog y el Whatsapp, para reducir la reprobación en la educación superior. Esta es un grave problema para alumnos, docentes e instituciones que implica una serie de recursos humanos y materiales perdidos. De ello, se requiere cambiar los estilos de aprendizaje tradicional a nuevas herramientas tecnológicas aplicables al aprendizaje en línea y activo; algunas de ellas, en particular, pueden utilizarse para reducir la reprobación y deserción de alumnos (Cabrero y Llorente, 2005). El diseño metodológico del presente documento tiene un enfoque mixto. Por su finalidad es una investigación exploratoria, descriptiva y correlacional. En ella, se comparan 2 plataformas digitales para determinar cuál de ellas mejora la transferencia de conocimientos a través del trabajo colaborativo y reduce la reprobación. Los resultados de este trabajo muestran que el Blog ofrece mejores resultados que el Whatsapp para disminuir la reprobación en la carrera de Ingeniería de Software impartida en la Universidad Politécnica de Santa Rosa Jáuregui. La plataforma digital del Blog representa la mejor alternativa como una plataforma digital que incide en el aprendizaje activo que disminuye la reprobación de los alumnos y mejora el rendimiento escolar.

Higher education institutions' wireless networks have different roles and network requirements, ranging from educational platforms and informative consultations. Currently, the inefficient use of network resources, poor wireless planning, and other factors, affect having a robust and stable network platform. Different authors have investigated the various strategies for the optimization of wireless infrastructures. Still, most of the cases studied aim to improve traditional performance variables without considering maximizing the level of user satisfaction, which represents a flaw that this research paper hopes to solve through SDWN and a predictive model. The authors will determine an appropriate methodology to estimate the user's level of satisfaction through an algorithm or predictive model based on nonlinear multiple regression supported on network performance variables, making a characterization of the project's environment analyzing the wireless conditions. The investigation phases will follow the life cycle guidelines defined by the Cisco PPDIOO methodology (Prepare, Plan, Design, Implement, Operate, Optimize). As a result, it is expected that the project will be the beginning of academic research that will help create strategies to optimize the WiFi network of any educational institution to maximize user satisfaction. In short, the optimization process provides the network with differentiating factors through a modular design with variable modification of parameters according to the users' requirements and needs.