Algoritmos e Arquiteturas VLSI para Detectores MIMO com Decisão Suave

The use of Multiple Input Multiple Output (MIMO) systems has permitted the recent evolution of wireless communication standards. The Spatial Multiplexing MIMO technique, in particular, provides a linear gain at the transmission capacity with the minimum between the numbers of transmit and receive antennas. To obtain a near capacity performance in SM-MIMO systems a soft decision Maximum A Posteriori Probability MIMO detector is necessary. However, such detector is too complex for practical solutions. Hence, the goal of a MIMO detector algorithm aimed for implementation is to get a good approximation of the ideal detector while keeping an acceptable complexity. Moreover, the algorithm needs to be mapped to a VLSI architecture with small area and high data rate. Since Spatial Multiplexing is a recent technique, it is argued that there is still much room for development of related algorithms and architectures. Therefore, this thesis focused on the study of sub optimum algorithms and VLSI architectures for broadband MIMO detector with soft decision. As a result, novel algorithms have been developed starting from proposals of optimizations for already established algorithms. Based on these results, new MIMO detector architectures with configurable modulation and competitive area, performance and data rate parameters are here proposed. The developed algorithms have been extensively simulated and the architectures were synthesized so that the results can serve as a reference for other works in the area

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.

Boto cinza (Sotalia guianensis, Van Benédén, 1864) (Cetacea, Delphinidae): Atividade aérea, forrageio e interações inter-específicas, na Praia de Pipa (Tibau do Sul RN) e estudo comparativo entre duas populações do Nordeste do Brasil

Sotalia guianensis is a small cetacean of the Delphinidae family, with coastal habits and whose area of distribution ranges from Florianópolis (27º35'S, 48º34'W), in Brazil, to Honduras (15º58'N, 85º42'W). At Pipa beach, on the south coast of RN state, in Brazil, the species occur throughout the year. The present study was carried out in two bays, which are bordered by cliffs. The animals were monitored from vantage points, using the "Ad libitum" and "all the occurrences" methods; during the years of 1999 and 2004. The study was divided in 4 chapters: Behavioral standards of two populations of gray dolphin, (Sotalia guianensis, Van Benédén, 1864) in the northeast of Brazil; Aerial activity of the gray dolphin: its possible function and the influence of environmental and behavioral factors; The influence of daily and monthly variation of the tides, of the period of the day and group size on the gray dolphin forage activity; kleptoparasitism interactions of frigatebird (Fregata magnificens, Mattheus, 1914) during the gray dolphin forage activity. The results have shown that the gray dolphin has a varied and complex behavioral repertoire. The leap is the most frequent behavior; the aerial activity is diffuse during daylight and is influenced by some factors, such as the level of the tide and social factors. The gray dolphin, when in the bay, most frequently feeds isolate or in small groups. The forage is diffuse during daylight; however, being more frequent in the morning and is influenced by the daily and monthly variation of the tide. At Pipa beach, kleptoparasitarian interactions were registered between the gray dolphin and the frigatebird (Fregata magnificens). The frigatebird forage strategy consists basically of two ways: to fly over great extensions searching for dead fish and to steal food (kleptoparasitism). These interactions were predominantly carried out between immature and female adult birds and adult and immature dolphins, and occurred during daylight. The present study can be considered an initial landmark to a better knowledge on the gray dolphin surface behavior, especially regarding the aerial behavioral repertoire and forage strategy of this species. However, it is necessary to continue these studies, so that we can understand better the complex social life of these animals and thus create effective measures for its conservation

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.