Robust simulations of Turing machines with analytic maps and flows

In this paper, we show that closed-form analytic maps and ows can simulate Turing machines in an error-robust manner. The maps and ODEs de ning the ows are explicitly obtained and the simulation is performed in real time

Continuous-time computation with restricted integration capabilities

AbstractRecursion theory on the reals, the analog counterpart of recursive function theory, is an approach to continuous-time computation inspired by the models of Classical Physics. In recursion theory on the reals, the discrete operations of standard recursion theory are replaced by operations on continuous functions such as composition and various forms of differential equations like indefinite integrals, linear differential equations and more general Cauchy problems. We define classes of real recursive functions in a manner similar to the standard recursion theory and we study their complexity. We prove both upper and lower bounds for several classes of real recursive functions, which lie inside the elementary functions, and can be characterized in terms of space complexity. In particular, we show that hierarchies of real recursive classes closed under restricted integration operations are related to the exponential space hierarchy. The results in this paper, combined with earlier results, suggest that there is a close connection between analog complexity classes and subrecursive classes, at least in the region between FLINSPACE and the primitive recursive functions

On estimating the gross primary productivity of Mediterranean grasslands under different fertilization regimes using vegetation indices and hyperspectral reflectance

We applied an empirical modelling approach for gross primary productivity (GPP) estimation from hyperspectral reflectance of Mediterranean grasslands undergoing different fertilization treatments. The objective of the study was to identify combinations of vegetation indices and bands that best represent GPP changes between the annual peak of growth and senescence dry out in Mediterranean grasslands. In situ hyperspectral reflectance of vegetation and CO2 gas exchange measurements were measured concurrently in unfertilized (C) and fertilized plots with added nitrogen (N), phosphorus (P) or the combination of N, P and potassium (NPK). Reflectance values were aggregated according to their similarity (r 90 %) in 26 continuous wavelength intervals (Hyp). In addition, the same reflectance values were resampled by reproducing the spectral bands of both the Sentinel-2A Multispectral Instrument (S2) and Landsat 8 Operational Land Imager (L8) and simulating the signal that would be captured in ideal conditions by either Sentinel-2A or Landsat 8. An optimal procedure for selection of the best subset of predictor variables (LEAPS) was applied to identify the most effective set of vegetation indices or spectral bands for GPP estimation using Hyp, S2 or L8. LEAPS selected vegetation indices according to their explanatory power, showing their importance as indicators of the dynamic changes occurring in community vegetation properties such as canopy water content (NDWI) or chlorophyll and carotenoids = chlorophyll ratio (MTCI, PSRI, GNDVI) and revealing their usefulness for grasslands GPP estimates. For Hyp and S2, bands performed as well as vegetation indices to estimate GPP. To identify spectral bands with a potential for improving GPP estimates based on vegetation indices, we applied a two-step procedure which clearly indicated the short-wave infrared region of the spectra as the most relevant for this purpose. A comparison between S2- and L8-based models showed similar explanatory powers for the two simulated satellite sensors when both vegetation indices and bands were included in the model. Altogether, our results describe the potential of sensors on board Sentinel-2 and Landsat 8 satellites for monitoring grassland phenology and improving GPP estimates in support of a sustainable agriculture managementinfo:eu-repo/semantics/publishedVersio

Proposta de um método para integração de conhecimento em classificação

Instituto Superior de Economia e Gestão.Os problemas considerados são problemas de classificação caracterizados por; 1) os objectos a classificar pertencem a uma de um conjunto de classes prédefinidas; 2) é conhecida a classificação de um conjunto de objectos (amostra); 3) todos os objectos são descritos por um conjunto de atributos. Esta categoria de problemas designa-se por problemas de classificação supervisada. Neste trabalho é proposto um método que permite tomar em consideração a informação incluída na amostra e conhecimento pericial sobre o domínio dos objectos a classificar. Os objectivos são: 1) definir uma regra de decisão para classificar a totalidade dos objectos; 2) tirar partido do conhecimento pericial para definir essa regra; 3) captar, em estruturas que descrevem o domínio dos objectos, o conhecimento envolvido no processo de classificação. A abordagem seguida consiste em desenvolver um método de classificação cujo suporte estrutural é uma árvore de decisão. O método auxilia, no decorrer do processo de classificação, o perito/decisor a formular hipóteses sobre o domínio dos objectos e permite estruturar e integrar o conhecimento pericial e a informação da amostra. O conhecimento resultante é incorporado em duas estruturas, uma árvore de classificação e um grafo bayesiano. O grafo bayesiano é um modelo qualitativo (gráfico) e quantitativo (probabilístico) de relações de causa-efeito entre as variáveis envolvidas. É apresentada uma implementação do método em C-PROLOG e uma aplicação a um problema real.N/

Computability with polynomial differential equations

In this paper, we show that there are Initial Value Problems de ned with polynomial ordinary di erential equations that can simulate univer- sal Turing machines in the presence of bounded noise. The polynomial ODE de ning the IVP is explicitly obtained and the simulation is per- formed in real time

Computational bounds on polynomial differential equations

In this paper we study from a computational perspective some prop-erties of the solutions of polynomial ordinary di erential equations. We consider elementary (in the sense of Analysis) discrete-time dynam-ical systems satisfying certain criteria of robustness. We show that those systems can be simulated with elementary and robust continuous-time dynamical systems which can be expanded into fully polynomial ordinary diferential equations with coe cients in Q[ ]. This sets a computational lower bound on polynomial ODEs since the former class is large enough to include the dynamics of arbitrary Turing machines. We also apply the previous methods to show that the problem of de-termining whether the maximal interval of defnition of an initial-value problem defned with polynomial ODEs is bounded or not is in general undecidable, even if the parameters of the system are computable and comparable and if the degree of the corresponding polynomial is at most 56. Combined with earlier results on the computability of solutions of poly-nomial ODEs, one can conclude that there is from a computational point of view a close connection between these systems and Turing machines

Exploring Spectral Data, Change Detection Information and Trajectories for Land Cover Monitoring over a Fire-Prone Area of Portugal

Alves, A.; Moraes, D.; Barbosa, B.; Costa, H.; Moreira, F.; Benevides, P.; Caetano, M. and Campagnolo, M. (2023). Exploring Spectral Data, Change Detection Information and Trajectories for Land Cover Monitoring over a Fire-Prone Area of Portugal. In Proceedings of the 9th International Conference on Geographical Information Systems Theory, Applications and Management - GISTAM; ISBN 978-989-758-649-1; ISSN 2184-500X, SciTePress, pages 87-97. DOI: 10.5220/0011993100003473---This research was conducted under the collaboration contract DGT-ISA 261/2021 with funding from Compete2020 (POCI-05-5762-FSE-000368), supported by the European Social Fund, and Centro Exploring Spectral Data, Change Detection Information and Trajectories for Land Cover Monitoring over a Fire-Prone Area of Portugal 95 de Investigação em Gestão de Informação (MagIC), Project UIDB/00239/2020 (Forest Research Centre), both supported by the Portuguese Foundation for Science and Technology (FCT)Land use/land cover (LULC) change detection and classification in maps based on automated data processing are becoming increasingly sophisticated in Earth Observation (EO). There is a growing number of annual maps available, with diverse but related production structures consisting primarily of classification and post-classification phases, the latter of which deals with inaccuracies of the first. The methodology production of the “Carta de Ocupação do Solo conjuntural” (COSc), a thematic land cover map of continental Portugal produced by the Directorate-General for Territory (DGT) mostly based on Sentinel-2 images classification, includes a semi-automatic phase of correction that combines expert knowledge and ancillary data in if-then-else rules validated by photointerpretation. Although this approach reduces misclassifications from an initial Random Forest (RF) prediction map, improving consistency between years and compliance with ecological succession, requires a lot of time-consuming semi-automatic procedures. This work evaluates the relevance of exploring an additional set of variables for automatic classification over disturbance-prone areas. A multitemporal dataset with 124 variables was analysed using data dimensionality reduction techniques, resulting in the identification of 35 major explanatory indicators, which were then used as inputs for RF classification with cross-validation. The estimated importance of the explanatory variables shows that composites of spectral bands, which are already included in the current COSc workflow, in conjunction with the inclusion of additional data namely, historical land cover information and change detection coefficients, from the Continuous Change Detection and Classification (CCDC) algorithm, are relevant for predicting land cover classes after disturbance. Since map updating is a more challenging task for disturbed pixels, we focused our analysis on locations where COSc indicated potential land cover change. Nonetheless, the overall classification accuracy for our experiments was 72.34 % which is similar to the accuracy of COSc for this region of Portugal. The findings suggest new variables that could improve future COSc maps.publishersversionpublishe

Uncertainty characterization & validation within ESA Fire-CCI

Uncertainty characterisation and validation are critical phases to generate any Essential Climate Variable (ECV), and therefore both have been included as key deliverables of the ESA CCI programme [1]. All products generated by the CCI are required to have an associated per pixel uncertainty characterisation. This paper describes both the uncertainty characterisation framework and the related uncertainty validation exercise of the Fire-CCI projectinfo:eu-repo/semantics/publishedVersio

Fully automated countrywide monitoring of fuel break maintenance operations

PTDC/CCI-COM/30344/2017 PCIF/SSI/0102/2017 UIDB/00239/2020 UIDB/00066/2020Fuel break (FB) networks are strategic locations for fire control and suppression. In order to be effective for wildfire control, they need to be maintained through regular interventions to reduce fuel loads. In this paper, we describe a monitoring system relying on Earth observations to detect fuel reduction inside the FB network being implemented in Portugal. Two fast automated pixel-based methodologies for monthly monitoring of fuel removals in FB are developed and compared. The first method (M1) is a classical supervised classification using the difference and postdisturbance image of monthly image composites. To take into account the impact of different land cover and phenology in the detection of fuel treatments, a second method (M2) based on an innovative statistical change detection approach was developed. M2 explores time series of vegetation indices and does not require training data or user-defined thresholds. The two algorithms were applied to Sentinel-2 10 m bands and fully processed in the cloud-based platform Google Earth Engine. Overall, the unsupervised M2, which is based on a Welch t-test of two moving window averages, gives better results than the supervised M1 and is suitable for an automated countrywide fuel treatment detection. For both methods, two vegetation indices, the Modified Excess of Green and the Normalized Difference Vegetation Index, were compared and exhibited similar performances.publishersversionpublishe