AIGO: a southern hemisphere detector for the worldwide array of ground-based interferometric gravitational wave detectors

This paper describes the proposed AIGO detector for the worldwide array of interferometric gravitational wave detectors. The first part of the paper summarizes the benefits that AIGO provides to the worldwide array of detectors. The second part gives a technical description of the detector, which will follow closely the Advanced LIGO design. Possible technical variations in the design are discussed

Information content in Gaussian noise: optimal compression rates

We approach the theoretical problem of compressing a signal dominated by Gaussian noise. We present expressions for the compression ratio which can be reached, under the light of Shannon's noiseless coding theorem, for a linearly quantized stochastic Gaussian signal (noise). The compression ratio decreases logarithmically with the amplitude of the frequency spectrum $P(f)$ of the noise. Entropy values and compression rates are shown to depend on the shape of this power spectrum, given different normalizations. The cases of white noise (w.n.), $f^{n_p}$ power-law noise ---including $1/f$ noise---, (w.n.$+1/f$) noise, and piecewise (w.n.+$1/f |$ w.n.$+1/f^2$) noise are discussed, while quantitative behaviours and useful approximations are provided.Comment: 28 LateX pages and 6 Fig, replaced with minor changes to match published versio

Autoregressive time series prediction by means of fuzzy inference systems using nonparametric residual variance estimation

We propose an automatic methodology framework for short- and long-term prediction of time series by means of fuzzy inference systems. In this methodology, fuzzy techniques and statistical techniques for nonparametric residual variance estimation are combined in order to build autoregressive predictive models implemented as fuzzy inference systems. Nonparametric residual variance estimation plays a key role in driving the identification and learning procedures. Concrete criteria and procedures within the proposed methodology framework are applied to a number of time series prediction problems. The learn from examples method introduced by Wang and Mendel (W&M) is used for identification. The Levenberg–Marquardt (L–M) optimization method is then applied for tuning. The W&M method produces compact and potentially accurate inference systems when applied after a proper variable selection stage. The L–M method yields the best compromise between accuracy and interpretability of results, among a set of alternatives. Delta test based residual variance estimations are used in order to select the best subset of inputs to the fuzzy inference systems as well as the number of linguistic labels for the inputs. Experiments on a diverse set of time series prediction benchmarks are compared against least-squares support vector machines (LS-SVM), optimally pruned extreme learning machine (OP-ELM), and k-NN based autoregressors. The advantages of the proposed methodology are shown in terms of linguistic interpretability, generalization capability and computational cost. Furthermore, fuzzy models are shown to be consistently more accurate for prediction in the case of time series coming from real-world applications.Ministerio de Ciencia e Innovación TEC2008-04920Junta de Andalucía P08-TIC-03674, IAC07-I-0205:33080, IAC08-II-3347:5626

Role of the sediments of two tropical dam reservoirs in the flux of metallic elements to the water column

In tropical climates, the high rainfall and temperature, throughout the annual cycle, allow high leaching rates of metallic elements from the basin upstream, which accumulate in the reservoirs. However, the concentration of these elements in natural waters is usually lower than expected, due to the ease of adsorption and co-precipitation in solid phases. We have studied two tropical dam reservoirs in Brazil, Três Marias (Minas Gerais) and Tucuruí (Pará), with the aim of understanding the correlation between physical–chemical parameters of the water column, chemical and mineralogical characteristics of the accumulated material and the solubility, mobilization and precipitation of metals in reservoirs. Metals speciation performed in selected samples determined that metallic micronutrients are preferentially adsorbed or retained through precipitation/co-precipitation onto fine-size charged crystalline/amorphous Fe-oxides. Under the prevailing reducing and low pH conditions of the bottom reservoirs, some adsorbed metals (particularly Fe and Mn) are easily released from their metal bearing-phases and mobilized to the aqueous phase of sediments, which show high levels of soluble forms of these elements. However, the solubilization process and the release to the water column are not very extensive, as abundances of metals such as Fe, Mn, Zn and Cu in water are low, although increasing with dept