Undersampled Critical Branching Processes on Small-World and Random Networks Fail to Reproduce the Statistics of Spike Avalanches

The power-law size distributions obtained experimentally for neuronal avalanches are an important evidence of criticality in the brain. This evidence is supported by the fact that a critical branching process exhibits the same exponent t~3=2. Models at criticality have been employed to mimic avalanche propagation and explain the statistics observed experimentally. However, a crucial aspect of neuronal recordings has been almost completely neglected in the models: undersampling. While in a typical multielectrode array hundreds of neurons are recorded, in the same area of neuronal tissue tens of thousands of neurons can be found. Here we investigate the consequences of undersampling in models with three different topologies (two-dimensional, small-world and random network) and three different dynamical regimes (subcritical, critical and supercritical). We found that undersampling modifies avalanche size distributions, extinguishing the power laws observed in critical systems. Distributions from subcritical systems are also modified, but the shape of the undersampled distributions is more similar to that of a fully sampled system. Undersampled supercritical systems can recover the general characteristics of the fully sampled version, provided that enough neurons are measured. Undersampling in two-dimensional and small-world networks leads to similar effects, while the random network is insensitive to sampling density due to the lack of a well-defined neighborhood. We conjecture that neuronal avalanches recorded from local field potentials avoid undersampling effects due to the nature of this signal, but the same does not hold for spike avalanches. We conclude that undersampled branching-process-like models in these topologies fail to reproduce the statistics of spike avalanches.

Classificação Automática de Modulação Digital com uso de Correntropia para Ambientes de Rádio Cognitivo

Modern wireless systems employ adaptive techniques to provide high throughput while observing desired coverage, Quality of Service (QoS) and capacity. An alternative to further enhance data rate is to apply cognitive radio concepts, where a system is able to exploit unused spectrum on existing licensed bands by sensing the spectrum and opportunistically access unused portions. Techniques like Automatic Modulation Classification (AMC) could help or be vital for such scenarios. Usually, AMC implementations rely on some form of signal pre-processing, which may introduce a high computational cost or make assumptions about the received signal which may not hold (e.g. Gaussianity of noise). This work proposes a new method to perform AMC which uses a similarity measure from the Information Theoretic Learning (ITL) framework, known as correntropy coefficient. It is capable of extracting similarity measurements over a pair of random processes using higher order statistics, yielding in better similarity estimations than by using e.g. correlation coefficient. Experiments carried out by means of computer simulation show that the technique proposed in this paper presents a high rate success in classification of digital modulation, even in the presence of additive white gaussian noise (AWGN)

Modelagem do clima de ondas e seus efeitos sobre as feições morfológicas costeiras no litoral setentrional do Rio Grande do Norte

This thesis presents the results of application of SWAN Simulating WAves Nearshore numerical model, OF third generation, which simulates the propagation and dissipation of energy from sea waves, on the north continental shelf at Rio Grande do Norte, to determine the wave climate, calibrate and validate the model, and assess their potential and limitations for the region of interest. After validation of the wave climate, the results were integrated with information from the submarine relief, and plant morphology of beaches and barrier islands systems. On the second phase, the objective was to analyze the evolution of the wave and its interaction with the shallow seabed, from three transverse profiles orientation from N to S, distributed according to the parallel longitudinal, X = 774000-W, 783000-W e 800000-W. Subsequently, it was were extracted the values of directional waves and winds through all the months between november 2010 to november 2012, to analyze the impact of these forces on the movement area, and then understand the behavior of the morphological variations according to temporal year variability. Based on the results of modeling and its integration with correlated data, and planimetric variations of Soledade and Minhoto beach systems and Ponta do Tubarão and Barra do Fernandes barrier islands systems, it was obtained the following conclusions: SWAN could reproduce and determine the wave climate on the north continental shelf at RN, the results show a similar trend for the measurements of temporal variations of significant height (HS, m) and the mean wave period (Tmed, s); however, the results of parametric statistics were low for the estimates of the maximum values in most of the analyzed periods compared data of PT 1 and PT 2 (measurement points), with alternation of significant wave heights, at times overrated with occasional overlap of swell episodes. By analyzing the spatial distribution of the wave climate and its interaction with the underwater compartmentalization, it was concluded that there is interaction of wave propagation with the seafloor, showing change in significant heights whenever it interacts with the seafloor features (beachrocks, symmetric and asymmetric longitudinal dunes, paleochannel, among others) in the regions of outer, middle and inner shelf. And finally, it is concluded that the study of the stability areas allows identifications of the most unstable regions, confirming that the greatest range of variation indicates greater instability and consequent sensitivity to hydrodynamic processes operating in the coastal region, with positive or negative variation, especially at Ponta do Tubarão and Barra do Fernandes barrier islands systems, where they are more susceptible to waves impacts, as evidenced in retreat of the shoreline

Aplicação do Sensoriamento Remoto e do Sistema de Informações Geográficas na Detecção de Manchas de Óleo na Região do Pólo de Exploração de Guamaré, R.N.

Objective to establish a methodology for the oil spill monitoring on the sea surface, located at the Submerged Exploration Area of the Polo Region of Guamaré, in the State of Rio Grande do Norte, using orbital images of Synthetic Aperture Radar (SAR integrated with meteoceanographycs products. This methodology was applied in the following stages: (1) the creation of a base map of the Exploration Area; (2) the processing of NOAA/AVHRR and ERS-2 images for generation of meteoceanographycs products; (3) the processing of RADARSAT-1 images for monitoring of oil spills; (4) the integration of RADARSAT-1 images with NOAA/AVHRR and ERS-2 image products; and (5) the structuring of a data base. The Integration of RADARSAT-1 image of the Potiguar Basin of day 21.05.99 with the base map of the Exploration Area of the Polo Region of Guamaré for the identification of the probable sources of the oil spots, was used successfully in the detention of the probable spot of oil detected next to the exit to the submarine emissary in the Exploration Area of the Polo Region of Guamaré. To support the integration of RADARSAT-1 images with NOAA/AVHRR and ERS-2 image products, a methodology was developed for the classification of oil spills identified by RADARSAT-1 images. For this, the following algorithms of classification not supervised were tested: K-means, Fuzzy k-means and Isodata. These algorithms are part of the PCI Geomatics software, which was used for the filtering of RADARSAT-1 images. For validation of the results, the oil spills submitted to the unsupervised classification were compared to the results of the Semivariogram Textural Classifier (STC). The mentioned classifier was developed especially for oil spill classification purposes and requires PCI software for the whole processing of RADARSAT-1 images. After all, the results of the classifications were analyzed through Visual Analysis; Calculation of Proportionality of Largeness and Analysis Statistics. Amongst the three algorithms of classifications tested, it was noted that there were no significant alterations in relation to the spills classified with the STC, in all of the analyses taken into consideration. Therefore, considering all the procedures, it has been shown that the described methodology can be successfully applied using the unsupervised classifiers tested, resulting in a decrease of time in the identification and classification processing of oil spills, if compared with the utilization of the STC classifier