Peninsular India (PI), which lies south of 24 • N latitude, has experienced several devastating earthquakes in the past. However, very few strong motion records are available for developing attenuation relations for ground acceleration, required by engineers to arrive at rational design response spectra for construction sites and cities in PI. Based on a well-known seismological model, the present paper statistically simulates ground motion in PI to arrive at an empirical relation for estimating 5% damped response spectra, as a function of magnitude and source to site distance, covering bedrock and soil conditions. The standard error in the proposed relationship is reported as a function of the frequency, for further use of the results in probabilistic seismic hazard analysis.

Indian monsoon rainfall data is shown to be decomposable into six empirical time series, called intrinsic mode functions. This helps one to identify the first empirical mode as a nonlinear part and the remaining as the linear part of the data. The nonlinear part is handled by artificial neural network (ANN) techniques, whereas the linear part is amenable for modeling through simple regression concepts. It is found that the proposed model explains between 75 to 80% of the interannual variability (IAV) of eight regional rainfall series considered here. The model is efficient in statistical forecasting of rainfall as verified on an independent subset of the data series. It is demonstrated that the model is capable of foreshadowing the drought of 2002, with the help of only antecedent data. The statistical forecast of All India rainfall for the year of 2004 is 80.34 cms with a standard deviation of 3.3 cms. This expected value is 94.25% of the longterm climatic average.

Neural signals recorded at different scales contain information about environment and behavior and have been used to control Brain Machine Interfaces with varying degrees of success. However, a direct comparison of their efficacy has not been possible due to different recording setups, tasks, species, etc. To address this, we implanted customized arrays having both microelectrodes and electrocorticogram (ECoG) electrodes in the primary visual cortex of 2 female macaque monkeys, and also recorded electroencephalogram (EEG), while they viewed a variety of naturalistic images and parametric gratings. Surprisingly, ECoG had higher information and decodability than all other signals. Combining a few ECoG electrodes allowed more accurate decoding than combining a much larger number of microelectrodes. Control analyses showed that higher decoding accuracy of ECoG compared with local field potential was not because of differences in low-level visual features captured by them but instead because of larger spatial summation of the ECoG. Information was high in the 30-80 Hz range and at lower frequencies. Information in different frequencies and scales was nonredundant. These results have strong implications for Brain Machine Interface applications and for study of population representation of visual stimuli.

Local field potentials (LFPs) in visual cortex are reliably modulated when the subject’s focus of attention is cued into versus out of the receptive field of the recorded sites, similar to modulation of spikes. However, human psychophysics studies have used an additional attention condition, neutral cueing, for decades. The effect of neutral cueing on spikes was examined recently and found to be intermediate between cued and uncued conditions. However, whether LFPs are also precise enough to represent graded states of attention is unknown. We found in rhesus monkeys that LFPs during neutral cueing were also intermediate between cued and uncued conditions. For a single electrode, attention was more discriminable using high frequency (>30 Hz) LFP power than spikes, which is expected because LFP represents a population signal and therefore is expected to be less noisy than spikes. However, previous studies have shown that when multiple electrodes are used, spikes can outperform LFPs. Surprisingly, in our study, spikes did not outperform LFPs when discriminability was computed using multiple electrodes, even though the LFP activity was highly correlated across electrodes compared with spikes. These results constrain the spatial scale over which attention operates and highlight the usefulness of LFPs in studying attention.

In the absence of strong motion records, ground motion during the 26 th January, 2001 Kutch, India earthquake, has been estimated by analytical methods. A contour map of peak ground acceleration (PGA) values in the near source region is provided. These results are validated by comparing them with spectral response recorder data and field observations. It is found that very near the epicenter, PGA would have exceeded 0.6 g. A set of three aftershock records have been used as empirical Green's functions to simulate ground acceleration time history and 5% damped response spectrum at Bhuj City. It is found that at Bhuj, PGA would have been 0.31 g-0.37 g. It is demonstrated that source mechanism models can be effectively used to understand spatial variability of large-scale ground movements near urban areas due to the rupture of active faults.

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Great earthquakes in the past (e.g. 1869 Cachar earthquake, 1897 great Assam earthquake) have caused large scale damage and ground liquefaction in the Guwahati city. Moreover, seismologists are of opinion that a great earthquake might occur in the unruptured segment of the North-East Himalaya that is near to Guwahati city. In this paper, the liquefaction hazard due to these events have been simulated. The obtained results are in general agreement with the reported damages due to the past earthquakes. The central part of the city (i.e. Dispur, GS road), that has large thickness of soft soil deposit and shallow ground water table, is highly vulnerable to liquefaction.

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