We present a robust and automatic method to generate an idealized surface geometry of a city landscape ready to be meshed for computer simulations. The city geometry is idealized for non viscous flow simulations and t...
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We present a robust and automatic method to generate an idealized surface geometry of a city landscape ready to be meshed for computer simulations. The city geometry is idealized for non viscous flow simulations and targets two main geometrical features: the topography and the city blocks. The procedure is fully automatic and demands no human interaction given the following source data: the city cadastre, a Digital Elevation Model (DEM) of all the target domain, and Light Detection And Ranging (LiDAR) data of the domain region covered by the cadastre. The geometry representation takes three main steps. First, a 2D mesh of the cadastre is generated, where the elements are marked according to street and block regions. Second, using a DEM of the city landscape the topography surface mesh is generated by finding the best surface mesh in the least-squares sense obtained by deforming the previous 2D mesh. Third, we extrude the block facades and we compute a planar ceiling taking into account all the buildings belonging to that city block. We describe the applicability of the geometry representation by presenting the work-flow required to generate an unstructured mesh valid for non-viscous flow or transport simulations. Finally, we illustrate the main application by obtaining a surface and tetrahedral mesh for the city of Barcelona in Spain.
In multi-room environments, modelling the sound propagation is complex due to the coupling of rooms and diverse source-receiver positions. A common scenario is when the source and the receiver are in different rooms w...
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The results of a listening test conducted to evaluate the perceived similarity of the five metric patterns used in flamenco music are presented. The objective of this experiment includes testing out the validity of th...
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The results of a listening test conducted to evaluate the perceived similarity of the five metric patterns used in flamenco music are presented. The objective of this experiment includes testing out the validity of those measures from a perceptual point of view. Dissimilarity ratings were analyzed using multi-dimensional scaling and phylogenetic analysis, and the perceptual measures of similarity were then compared with mathematical measures of rhythmic similarity used in to determine which measure best matches human judgments. The graphical interface was programmed in Java on a MacPro computer, and sounds were presented over headphones. A dissimilarity matrix was created for each participant based on their ratings, and these values were averaged for every participant to symmetrize the matrix.
Service quality commitments in cloud service provisioning are typically described in Service Level Agreements (SLA). Service availability is always a major parameter to be included in such SLAs. and the cloud provider...
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Service quality commitments in cloud service provisioning are typically described in Service Level Agreements (SLA). Service availability is always a major parameter to be included in such SLAs. and the cloud provider is bounded to guarantee a minimum availability value, for which current cloud monitoring systems employ a naive estimator. In this paper a new estimation method is proposed for service availability, which is based on the bootstrap technique and employs a non-parametric statistical hypothesis test. Through Monte Carlo simulation, the method is shown to be much more accurate than the naive one under three stochastic models for the durations of operating and outage periods, exhibiting a Type I error probability lower than 1 % in most cases, while the naive estimator yields error probabilities around 40%.
One of the biggest challenges in learning from data streams is adapting the classification model to new data. Due to the evolving nature of data streams, they are subject to a phenomenon known as concept drift that ma...
One of the biggest challenges in learning from data streams is adapting the classification model to new data. Due to the evolving nature of data streams, they are subject to a phenomenon known as concept drift that makes previously learned knowledge and model outdated. Therefore, concept drift must be efficiently detected in order to adapt the classification model. While there exists a plethora of drift detectors, with different mechanisms, selecting the most suitable for a new stream is a difficult task, since apriori knowledge may not be available and changes over time can affect the performance of the detector. This paper proposes a framework that exploits statistical and temporal meta-features from sliding windows to dynamically recommend a suitable drift detector in real-time for unseen chunks of streams according to its properties using Meta-Learning. We performed experiments on 10 real-world data streams and 18 synthetic generated data streams that were subject to concept drift and class imbalance in order to evaluate the performance of the proposed framework. Experiments exposed that the proposed approach was able to enhance the concept drift detection in a variety of scenarios demonstrating robustness to class imbalance and the advantages of dynamically selecting the drift detector.
Power Saving Class (PSC) is an essential issue on IEEE 802.16-2009. In previous research, many algorithms had been proposed to reduce the consumption of power, but most of them only considered multiple connections in ...
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Smart cities have been a very active research area in the past 20 years, while continuously adapting to new technological advancements and keeping up with the times regarding sustainability and climate change. In this...
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ISBN:
(数字)9798350364316
ISBN:
(纸本)9798350364323
Smart cities have been a very active research area in the past 20 years, while continuously adapting to new technological advancements and keeping up with the times regarding sustainability and climate change. In this context, there have been numerous proposals to expand the scope of smart cities, focusing on resilience and sustainability, among other aspects, resulting in terms like smart sustainable cities. At the same time, there is an ongoing discussion regarding the degree in which smart cities put people at their centre. In this work, we argue toward expanding the current smart city definition by integrating the circular economy as one of its central pillars and adopting the term smart (and) circular city. We discuss the ways a smart and circular city encompasses both sustainability and smartness in an integral manner, while also being well-positioned to foster novel business activity and models and helping to place citizens at the heart of the smart city. In this sense, we also argue that previous research in smart cities and technologies, such as those related to Industry 4.0, can serve as a cornerstone to implement circular economy activities within cities, at a scale that exceeds current activities that are based on more conventional approaches. We also outline current open challenges in this domain and research questions that still need to be addressed.
Trust and security are critical deployment require-ments for Industrial Internet of Things (IIoT) networks. A recent protocol, called TRUTH, integrates security mechanisms for authentication and privacy alongside a De...
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In this work, we present a method for automatic colorization of grayscale videos. The core of the method is a Generative Adversarial Network that is trained and tested on sequences of frames in a sliding window manner...
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Smart cities have been a very active research area in the past 20 years, while continuously adapting to new technological advancements and keeping up with the times regarding sustainability and climate change. In this...
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