1,721,126 research outputs found
A Fast Multilevel Fuzzy Transform Image Compression Method
Full text linkWe present a fast algorithm that improves on the performance of the multilevel fuzzy transform image compression method. The multilevel F-transform (for short, MF-tr) algorithm is an image compression method based on fuzzy transforms that, compared to the classic fuzzy transform (F-transform) image compression method, has the advantage of being able to reconstruct an image with the required quality. However, this method can be computationally expensive in terms of execution time since, based on the compression ratio used, different iterations may be necessary in order to reconstruct the image with the required quality. To solve this problem, we propose a fast variation of the multilevel F-transform algorithm in which the optimal compression ratio is found in order to reconstruct the image in as few iterations as possible. Comparison tests show that our method reconstructs the image in at most half of the CPU time used by the MF-tr algorithm
Vulnerability and climate impacts in urban areas: experimental processes
No full textWe present the results obtained by applying in a GIS platform the hierarchical process developed in the project research Metropolis in order to asses the vulnerability of the physical and social subsystems in which the urban system is decomposed; the model proposed in the research has provided for the construction of specific indicators to assess the climate impact response to the experimented wave and flooding flooding in two areas of study of the city of Naples corresponding to some urban districts in the eastern and western areas of Naples. The model built for the generation of impact scenarios is based on the IPCC AR5 approach; it has been experimented in the course of Metropolis research taking into consideration the value of the resident population in buildings as exposure to the risk generated by heat wave phenomena, and the built-up area of use on the ground floors subject as eposure to the risk generated by pluvial flooding phenomena
The Metropolis research. Experimental models and decision-making processes for the adaptive environmental design in climate change / La ricerca Metropolis. Modelli sperimentali e processi decisionali per il progetto ambientale adattivo in regime di climate change
No full textThe state of knowledge on climate change adaptation at the international level shows several experiences both in the field of research and urban politics that aim at defining methods and procedures, adaptation plans and guidelines for action. However, the specificity of the issues at the local scale requires further experimentations to implement suitable actions based on downscaling processes. Within the Metropolis project, the research group of the Department of Architecture at the Federico II University of Naples has carried out research work aimed at assessing the capacity of the urban system to adapt to the effects of climate change. The research work resulted in the development of a model of knowledge on the vulnerability to heat waves and pluvial flooding based on the study of the information about the features of the social and physical system.
By developing downscaling experimental models, hazard scenarios have been built at the local scale based on the IPCC scenarios on CO2 emissions - Intergovernmental Panel on Climate Change. Finally, a decision-making process has been developed to assess the adaptive capacity of the urban system It allows the making of a long term adaptive design oriented towards processes of urban rehabilitation that are resilient to climate change. The adaptation solutions have been selected grading by their compatibility with the specific context and evaluated for the correspondence to the technological and environmental requirements, to the socio-economic conditions and the effects on the ecosystem
Image autofocusing via direct fuzzy transform
No full textA new method based on the bidimensional direct fuzzy Transform is presented for passive image autofocusing. Comparison results sow that this algorithm has better performance that other passive image autofocusing image contrast algorithms
Fuzzy Transforms Prediction in Spatial Analysis and its Application for Demographic Balance Data in the Municipalities of the "Cilento and Vallo di Diano" National Park
No full textWe present a new prediction algorithm based on fuzzy transforms for forecasting problems in spatial analysis. Our algorithm allows to predict the spatial distribution of assigned parameters of the problem under exam. Here, we test our method by exploring the demographical balance data measured every month in the period 01/01/2003–31/10/2014 in the municipalities of “Cilento and Vallo di Diano” National Park located in the district of Salerno (Italy). We use this method for predicting the value of the parameters “birthrate” and “deathrate” in November 2014. We apply this process in all the municipalities in the area of study; moreover, we present a fuzzification process for establishing the thematic map of the errors calculated between the real data and the predicted data. The thematic maps are constructed in a GIS environment
Atlas of velocity dispersion profiles and rotation curves for elliptical and lenticular galaxies
No full textA Fuzzy Rule-Based GIS Framework to Partition an Urban System Based on Characteristics of Urban Greenery in Relation to the Urban Context
No full textWe present a GIS-based framework implementing a Mamdani fuzzy rule-based system to partition in an unsupervised mode an urban system in urban green areas. The proposed framework is characterized by high usability and flexibility. The study area is partitioned in homogeneous regions regarding the characteristics of public green areas and relations with the residents and buildings. The urban system is initially partitioned in microzones, given the smallest areas in which is taken a census of the urban system in terms of resident population, type and number of buildings and properties, industrial and service activities. During a pre-processing phase, the values of specific indicators defined by a domain expert that characterize the type of urban green and the relationship with the residents and buildings are calculated for each microzone. Subsequently, the fuzzy rule-based system component is executed to classify each microzone based on the fuzzy rule set constructed by the domain expert. Spatially adjoining microzones belonging to the same class are dissolved to form homogeneous areas called Urban Green Contexts. The membership degrees of the microzones to the fuzzy set of their class are used to evaluate the reliability of the classification of the Urban Green Context. We test our framework on the municipality of Pozzuoli (Italy), comparing the results with the ones obtained in a supervised manner by the expert appropriately partitioning and classifying the study area based on his knowledge of the urban study area
Using fuzzy transforms in attribute dependence data analysis
No full textIn this paper, we present a method based on fuzzy transforms to establish dependencies between numerical attributes in datasets. We find the best fuzzy partitions of the attribute domains with respect to which we determine the direct and inverse fuzzy transforms. We use two specific regression indexes (which must be smaller than a threshold deduced experimentally) for evaluating dependency between numerical attributes. The experiments are conducted on two well known datasets: “El Nino” (http://kdd.ics.uci.edu/databases/el_nino/el_nino.data.html) and the remote sensing data determined from US Forest Service (Region 2, Resource Information System, http://kdd.ics.uci.edu/databases/covertype/covertype.data.html). Our results are quite in agreement with the regression analysis of the same data
Extended Gustafson–Kessel granular hotspot detection
No full textA hierarchical granular model that includes a variation of the Extended Gustafson–Kessel (EGK) clustering algorithm for massive event data sets applied in hotspot analysis is proposed. We construct a granular view of the distribution of hotspots on a geographic map related to a phenomenon, whose data are collected in a massive data set. To obtain the final information granules, we partition randomly the data set in s chunks and execute the EGK clustering algorithm separately to each chunk in a distributed architecture. Finally, a weighted EGK clustering algorithm is applied to a data set formed by the centers of all the local hotspots to the elliptical hotspots which constitute the upper level information granules giving a global overview of the spatial distribution of the hotspots on the map. Two indices are calculated to assess how justifiable is this granular view in terms of spatial distribution of the final hotspots on the map. A set of tests on the massive data set Archive Fire from Indonesia are performed by setting a threshold for the above two indexes and by varying the number of chunks. The results show that the proposed algorithm provides a justifiable granular view of the hotspot detected on the map. Comparison tests with respect to other clustering hotspot detection algorithms show that the proposed method is very efficient in terms of execution time and spatial distribution of the final hotspots
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