Every year, forest fires burn thousands of hectares of forest around the world and cause significant damage to the economy and people from the affected zone. For that reason, computational fire spread models arise as ...
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Away to improve air quality (AQ) in urban areas consists of including more green infrastructures. To evaluate their effects, air quality simulations are employed to examine the behavior of pollutants and their dispers...
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Agent-based modeling is a powerful computational tool that can simulate complex behavior based on rules in both micro and macro scales. Defining the constant agent behavior patterns rules is uncertain since it depends...
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Away to improve air quality (AQ) in urban areas consists of including more green infrastructures. To evaluate their effects, air quality simulations are employed to examine the behavior of pollutants and their dispers...
Away to improve air quality (AQ) in urban areas consists of including more green infrastructures. To evaluate their effects, air quality simulations are employed to examine the behavior of pollutants and their dispersion patterns influenced by meteorological conditions. To study either the advantages or the drawbacks of modifying the green morphology of a city, the first step is to create a set of hypothetical green land-use maps that will later be used as input for air quality simulations. In this paper, we present an automatic green land-use generator, which uses the Monte Carlo method and Moore Neighborhood to create coherent land-use maps. These maps will be later on used to run simulations that show the impact on AQ of adding more green space to our cities.
Parallel distributed architectures are essential for solving large-scale scientific and engineering problems. Its increasing use has generated the need for performance prediction for both deterministic applications an...
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The increasing demand for cloud computing drives the expansion in scale of datacenters and their internal optical network, in a strive for increasing bandwidth, high reliability, and lower latency. Optical transceiver...
The increasing demand for cloud computing drives the expansion in scale of datacenters and their internal optical network, in a strive for increasing bandwidth, high reliability, and lower latency. Optical transceivers are essential elements of optical networks, whose reliability has not been well-studied compared to other hardware components. In this paper, we leverage high quantities of monitoring data from optical transceivers and OS-level metrics to provide statistical insights about the occurrence of optical transceiver failures. We estimate transceiver failure rates and normal operating ranges for monitored attributes, correlate early-observable patterns to known failure symptoms, and finally develop failure prediction models based on our analyses. Our results enable network administrators to deploy early-warning systems and enact predictive maintenance strategies, such as replacement or traffic re-routing, reducing the number of incidents and their associated costs.
The ability of CNNs to efficiently and accurately perform complex functions, such as object detection, has fostered their adoption in safety-related autonomous systems. These algorithms require high computational perf...
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The ability of CNNs to efficiently and accurately perform complex functions, such as object detection, has fostered their adoption in safety-related autonomous systems. These algorithms require high computational performance platforms that exploit high levels of parallelism. The detection, control and mitigation of random errors in these underlying high computational platforms become a must according to functional safety standards. In this paper, we propose protecting, with a catalog of diagnostic techniques, the most computationally expensive operation of the CNNs, the matrix multiplication. However, this protection entails a performance penalty, and the complete CNN protection may be unaffordable for those systemsoperating with strict real-time constraints. This paper proposes a three-stage methodology to selectively protect CNN layers to achieve the required diagnostic coverage and performance trade-off: i) sensitivity analysis to misclassification per CNN layers using a statistical fault injection campaign, ii) layer-by- layer performance impact and diagnostic coverage analysis, and iii) selective layer protection. Furthermore, we propose a strategy to effectively compute the achievable diagnostic coverage of large matrices implemented on GPUs. Finally, we apply the proposed methodology and strategy in Tiny YOLO-v3, an object detector based on CNNs.
The use of drones for routing and monitoring tasks has grown significantly, with applications such as traffic surveillance and road inspections gaining prominence . These real-world scenarios often involve unpredictab...
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The Hospital Emergency Department (ED) provides critical care for acute and urgent conditions, making it one of the most complex areas in healthcare. Optimizing staff configurations to reduce patient waiting times and...
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