Communicating neurons: a connectionist spiking neuron implementation of stochastic diffusion search

An information processing paradigm in the brain is proposed, instantiated in an artificial neural network using biologically motivated temporal encoding. The network will locate within the external world stimulus, the target memory, defined by a specific pattern of micro-features. The proposed network is robust and efficient. Akin in operation to the swarm intelligence paradigm, stochastic diffusion search, it will find the best-fit to the memory with linear time complexity. information multiplexing enables neurons to process knowledge as 'tokens' rather than 'types'. The network illustrates possible emergence of cognitive processing from low level interactions such as memory retrieval based on partial matching. (C) 2007 Elsevier B.V. All rights reserved.

A combined engineering and statistical model of UK domestic appliance electrical load profiles

The development of a combined engineering and statistical Artificial Neural Network model of UK domestic appliance load profiles is presented. The model uses diary-style appliance use data and a survey questionnaire collected from 51 suburban households and 46 rural households during the summer of 2010 and2011 respectively. It also incorporates measured energy data and is sensitive to socioeconomic, physical dwelling and temperature variables. A prototype model is constructed in MATLAB using a two layer feed forward network with back propagation training which has a 12:10:24 architecture. Model outputs include appliance load profiles which can be applied to the fields of energy planning (microrenewables and smart grids), building simulation tools and energy policy.

Predicting stock index volatility: can market volume help?

This paper explores a number of statistical models for predicting the daily stock return volatility of an aggregate of all stocks traded on the NYSE. An application of linear and non-linear Granger causality tests highlights evidence of bidirectional causality, although the relationship is stronger from volatility to volume than the other way around. The out-of-sample forecasting performance of various linear, GARCH, EGARCH, GJR and neural network models of volatility are evaluated and compared. The models are also augmented by the addition of a measure of lagged volume to form more general ex-ante forecasting models. The results indicate that augmenting models of volatility with measures of lagged volume leads only to very modest improvements, if any, in forecasting performance.

The climate of Europe 6000 years ago

Palaeoclimates across Europe for 6000 y BP were estimated from pollen data using the modern pollen analogue technique constrained with lake-level data. The constraint consists of restricting the set of modern pollen samples considered as analogues of the fossil samples to those locations where the implied change in annual precipitation minus evapotranspiration (P–E) is consistent with the regional change in moisture balance as indicated by lakes. An artificial neural network was used for the spatial interpolation of lake-level changes to the pollen sites, and for mapping palaeoclimate anomalies. The climate variables reconstructed were mean temperature of the coldest month (T c ), growing degree days above 5  °C (GDD), moisture availability expressed as the ratio of actual to equilibrium evapotranspiration (α), and P–E. The constraint improved the spatial coherency of the reconstructed palaeoclimate anomalies, especially for P–E. The reconstructions indicate clear spatial and seasonal patterns of Holocene climate change, which can provide a quantitative benchmark for the evaluation of palaeoclimate model simulations. Winter temperatures (T c ) were 1–3 K greater than present in the far N and NE of Europe, but 2–4 K less than present in the Mediterranean region. Summer warmth (GDD) was greater than present in NW Europe (by 400–800 K day at the highest elevations) and in the Alps, but >400 K day less than present at lower elevations in S Europe. P–E was 50–250 mm less than present in NW Europe and the Alps, but α was 10–15% greater than present in S Europe and P–E was 50–200 mm greater than present in S and E Europe.

Collocating satellite-based radar and radiometer measurements — methodology and usage examples

Collocations between two satellite sensors are occasions where both sensors observe the same place at roughly the same time. We study collocations between the Microwave Humidity Sounder (MHS) on-board NOAA-18 and the Cloud Profiling Radar (CPR) on-board CloudSat. First, a simple method is presented to obtain those collocations and this method is compared with a more complicated approach found in literature. We present the statistical properties of the collocations, with particular attention to the effects of the differences in footprint size. For 2007, we find approximately two and a half million MHS measurements with CPR pixels close to their centrepoints. Most of those collocations contain at least ten CloudSat pixels and image relatively homogeneous scenes. In the second part, we present three possible applications for the collocations. Firstly, we use the collocations to validate an operational Ice Water Path (IWP) product from MHS measurements, produced by the National Environment Satellite, Data and Information System (NESDIS) in the Microwave Surface and Precipitation Products System (MSPPS). IWP values from the CloudSat CPR are found to be significantly larger than those from the MSPPS. Secondly, we compare the relation between IWP and MHS channel 5 (190.311 GHz) brightness temperature for two datasets: the collocated dataset, and an artificial dataset. We find a larger variability in the collocated dataset. Finally, we use the collocations to train an Artificial Neural Network and describe how we can use it to develop a new MHS-based IWP product. We also study the effect of adding measurements from the High Resolution Infrared Radiation Sounder (HIRS), channels 8 (11.11 μm) and 11 (8.33 μm). This shows a small improvement in the retrieval quality. The collocations described in the article are available for public use.

Photometric redshifts and k-corrections for the Sloan Digital Sky Survey Data Release 7

We present a catalogue of galaxy photometric redshifts and k-corrections for the Sloan Digital Sky Survey Data Release 7 (SDSS-DR7), available on the World Wide Web. The photometric redshifts were estimated with an artificial neural network using five ugriz bands, concentration indices and Petrosian radii in the g and r bands. We have explored our redshift estimates with different training sets, thus concluding that the best choice for improving redshift accuracy comprises the main galaxy sample (MGS), the luminous red galaxies and the galaxies of active galactic nuclei covering the redshift range 0 < z < 0.3. For the MGS, the photometric redshift estimates agree with the spectroscopic values within rms = 0.0227. The distribution of photometric redshifts derived in the range 0 < z(phot) < 0.6 agrees well with the model predictions. k-corrections were derived by calibration of the k-correct_v4.2 code results for the MGS with the reference-frame (z = 0.1) (g - r) colours. We adopt a linear dependence of k-corrections on redshift and (g - r) colours that provide suitable distributions of luminosity and colours for galaxies up to redshift z(phot) = 0.6 comparable to the results in the literature. Thus, our k-correction estimate procedure is a powerful, low computational time algorithm capable of reproducing suitable results that can be used for testing galaxy properties at intermediate redshifts using the large SDSS data base.

Searching for s-Process-Enhanced Metal-Poor Stars

We present preliminary results for the estimation of barium [Ba/Fe], and strontium [Sr/Fe], abundances ratios using medium-resolution spectra (1-2 angstrom). We established a calibration between the abundance ratios and line indices for Ba and Sr, using multiple regression and artificial neural network techniques. A comparison between the two techniques (showing the advantage of the latter), as well as a discussion of future work, is presented.

A psychophysical and computational analysis of the spatio-temporal mechanisms underlying the flash-lag effect

Several accounts put forth to explain the flash-lag effect (FLE) rely mainly on either spatial or temporal mechanisms. Here we investigated the relationship between these mechanisms by psychophysical and theoretical approaches. In a first experiment we assessed the magnitudes of the FLE and temporal-order judgments performed under identical visual stimulation. The results were interpreted by means of simulations of an artificial neural network, that wits also employed to make predictions concerning the F LE. The model predicted that a spatio-temporal mislocalisation would emerge from two, continuous and abrupt-onset, moving stimuli. Additionally, a straightforward prediction of the model revealed that the magnitude of this mislocalisation should be task-dependent, increasing when the use of the abrupt-onset moving stimulus switches from a temporal marker only to both temporal and spatial markers. Our findings confirmed the model`s predictions and point to an indissoluble interplay between spatial facilitation and processing delays in the FLE.

A WAVELET-BASED SPEAKER VERIFICATION ALGORITHM

This paper presents a study on wavelets and their characteristics for the specific purpose of serving as a feature extraction tool for speaker verification (SV), considering a Radial Basis Function (RBF) classifier, which is a particular type of Artificial Neural Network (ANN). Examining characteristics such as support-size, frequency and phase responses, amongst others, we show how Discrete Wavelet Transforms (DWTs), particularly the ones which derive from Finite Impulse Response (FIR) filters, can be used to extract important features from a speech signal which are useful for SV. Lastly, an SV algorithm based on the concepts presented is described.

Development Of Water Pollution Early Warning System For Oyster Harvesting Areas

The objective of this study is to develop a Pollution Early Warning System (PEWS) for efficient management of water quality in oyster harvesting areas. To that end, this paper presents a web-enabled, user-friendly PEWS for managing water quality in oyster harvesting areas along Louisiana Gulf Coast, USA. The PEWS consists of (1) an Integrated Space-Ground Sensing System (ISGSS) gathering data for environmental factors influencing water quality, (2) an Artificial Neural Network (ANN) model for predicting the level of fecal coliform bacteria, and (3) a web-enabled, user-friendly Geographic Information System (GIS) platform for issuing water pollution advisories and managing oyster harvesting waters. The ISGSS (data acquisition system) collects near real-time environmental data from various sources, including NASA MODIS Terra and Aqua satellites and in-situ sensing stations managed by the USGS and the NOAA. The ANN model is developed using the ANN program in MATLAB Toolbox. The ANN model involves a total of 6 independent environmental variables, including rainfall, tide, wind, salinity, temperature, and weather type along with 8 different combinations of the independent variables. The ANN model is constructed and tested using environmental and bacteriological data collected monthly from 2001 – 2011 by Louisiana Molluscan Shellfish Program at seven oyster harvesting areas in Louisiana Coast, USA. The ANN model is capable of explaining about 76% of variation in fecal coliform levels for model training data and 44% for independent data. The web-based GIS platform is developed using ArcView GIS and ArcIMS. The web-based GIS system can be employed for mapping fecal coliform levels, predicted by the ANN model, and potential risks of norovirus outbreaks in oyster harvesting waters. The PEWS is able to inform decision-makers of potential risks of fecal pollution and virus outbreak on a daily basis, greatly reducing the risk of contaminated oysters to human health.

Implementação de classificador de tarefas mentais baseado em EEG

BARBOSA, André F. ; SOUZA, Bryan C. ; PEREIRA JUNIOR, Antônio ; MEDEIROS, Adelardo A. D.de, . Implementação de Classificador de Tarefas Mentais Baseado em EEG. In: CONGRESSO BRASILEIRO DE REDES NEURAIS, 9., 2009, Ouro Preto, MG. Anais... Ouro Preto, MG, 2009