Analysis of large-amplitude stratospheric mountain wave event observed from the AIRS and MLS sounders over the western Himalayan region

Mountain waves in the stratosphere have been observed over elevated topographies using both nadir-looking and limb-viewing satellites. However, the characteristics of mountain waves generated over the Himalayan Mountain range and the adjacent Tibetan Plateau are relatively less explored. The present study reports on three-dimensional (3-D) properties of a mountain wave event that occurred over the western Himalayan region on 9 December 2008. Observations made by the Atmospheric Infrared Sounder on board the Aqua and Microwave Limb Sounder on board the Aura satellites are used to delineate the wave properties. The observed wave properties such as horizontal (lambda(x), lambda(y)) and vertical (lambda(z)) wavelengths are 276 km (zonal), 289 km (meridional), and 25 km, respectively. A good agreement is found between the observed and modeled/analyzed vertical wavelength for a stationary gravity wave determined using the Modern Era Retrospective Analysis for Research and Applications (MERRA) reanalysis winds. The analysis of both the National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis and MERRA winds shows that the waves are primarily forced by strong flow across the topography. Using the 3-D properties of waves and the corrected temperature amplitudes, we estimated wave momentum fluxes of the order of similar to 0.05 Pa, which is in agreement with large-amplitude mountain wave events reported elsewhere. In this regard, the present study is considered to be very much informative to the gravity wave drag schemes employed in current general circulation models for this region.

Perception of time-varying signals: timbre and phonetic JND of diphthong

In this paper we propose a linear time-varying model for diphthong synthesis based on linear interpolation of formant frequencies. We, thence, determine the timbre just-noticeable difference (JND) for diphthong /a I/ (as in ‘buy’) with a constant pitch excitation through perception experiment involving four listeners and explore the phonetic JND of the diphthong. Their JND responses are determined using 1-up-3-down procedure. Using the experimental data, we map the timbre JND and phonetic JND onto a 2-D region of percentage change of formant glides. The timbre and phonetic JND contours for constant pitch show that the phonetic JND region encloses timbre JND region and also varies across listeners. The JND is observed to be more sensitive to ending vowel /I/ than starting vowel /a/ in some listeners and dependent on the direction of perturbation of starting and ending vowels.

Texture discriminability in monkey inferotemporal cortex predicts human texture perception

Shape and texture are both important properties of visual objects, but texture is relatively less understood. Here, we characterized neuronal responses to discrete textures in monkey inferotemporal (IT) cortex and asked whether they can explain classic findings in human texture perception. We focused on three classic findings on texture discrimination: 1) it can be easy or hard depending on the constituent elements; 2) it can have asymmetries, and 3) it is reduced for textures with randomly oriented elements. We recorded neuronal activity from monkey inferotemporal (IT) cortex and measured texture perception in humans for a variety of textures. Our main findings are as follows: 1) IT neurons show congruent selectivity for textures across array size; 2) textures that were easy for humans to discriminate also elicited distinct patterns of neuronal activity in monkey IT; 3) texture pairs with asymmetries in humans also exhibited asymmetric variation in firing rate across monkey IT; and 4) neuronal responses to randomly oriented textures were explained by an average of responses to homogeneous textures, which rendered them less discriminable. The reduction in discriminability of monkey IT neurons predicted the reduced discriminability in humans during texture discrimination. Taken together, our results suggest that texture perception in humans is likely based on neuronal representations similar to those in monkey IT.

Inhibition of protein synthesis leading to unfolded protein response is the major event in abrin-mediated apoptosis

Abrin obtained from the plant Abrus precatorius inhibits protein synthesis and also triggers apoptosis in cells. Previous studies from our laboratory suggested a link between these two events. Using an active site mutant of abrin A-chain which exhibits 225-fold lower protein synthesis inhibitory activity than the wild-type abrin A-chain, we demonstrate in this study that inhibition of protein synthesis induced by abrin is the major factor triggering unfolded protein response leading to apoptosis. Since abrin A-chain requires the B-chain for internalization into cells, the wild-type and mutant recombinant abrin A-chains were conjugated to native ricin B-chain to generate hybrid toxins, and the toxic effects of the two conjugates were compared. The rate of inhibition of protein synthesis mediated by the mutant ricin B-rABRA (R167L) conjugate was slower than that of the wild-type ricin B-rABRA conjugate as expected. The mutant conjugate activated p38MAPK and caspase-3 similar to its wild-type counterpart although at later time points. Overall, these results confirm that inhibition of protein synthesis is the major event contributing to abrin-mediated apoptosis.

Event-triggered sampling using signal extrema for instantaneous amplitude and instantaneous frequency estimation

Event-triggered sampling (ETS) is a new approach towards efficient signal analysis. The goal of ETS need not be only signal reconstruction, but also direct estimation of desired information in the signal by skillful design of event. We show a promise of ETS approach towards better analysis of oscillatory non-stationary signals modeled by a time-varying sinusoid, when compared to existing uniform Nyquist-rate sampling based signal processing. We examine samples drawn using ETS, with events as zero-crossing (ZC), level-crossing (LC), and extrema, for additive in-band noise and jitter in detection instant. We find that extrema samples are robust, and also facilitate instantaneous amplitude (IA), and instantaneous frequency (IF) estimation in a time-varying sinusoid. The estimation is proposed solely using extrema samples, and a local polynomial regression based least-squares fitting approach. The proposed approach shows improvement, for noisy signals, over widely used analytic signal, energy separation, and ZC based approaches (which are based on uniform Nyquist-rate sampling based data-acquisition and processing). Further, extrema based ETS in general gives a sub-sampled representation (relative to Nyquistrate) of a time-varying sinusoid. For the same data-set size captured with extrema based ETS, and uniform sampling, the former gives much better IA and IF estimation. (C) 2015 Elsevier B.V. All rights reserved.

Event-triggered Sampling and Reconstruction of Sparse Real-valued Trigonometric Polynomials

We propose data acquisition from continuous-time signals belonging to the class of real-valued trigonometric polynomials using an event-triggered sampling paradigm. The sampling schemes proposed are: level crossing (LC), close to extrema LC, and extrema sampling. Analysis of robustness of these schemes to jitter, and bandpass additive gaussian noise is presented. In general these sampling schemes will result in non-uniformly spaced sample instants. We address the issue of signal reconstruction from the acquired data-set by imposing structure of sparsity on the signal model to circumvent the problem of gap and density constraints. The recovery performance is contrasted amongst the various schemes and with random sampling scheme. In the proposed approach, both sampling and reconstruction are non-linear operations, and in contrast to random sampling methodologies proposed in compressive sensing these techniques may be implemented in practice with low-power circuitry.

Discovering compressing serial episodes from event sequences

Most pattern mining methods yield a large number of frequent patterns, and isolating a small relevant subset of patterns is a challenging problem of current interest. In this paper, we address this problem in the context of discovering frequent episodes from symbolic time-series data. Motivated by the Minimum Description Length principle, we formulate the problem of selecting relevant subset of patterns as one of searching for a subset of patterns that achieves best data compression. We present algorithms for discovering small sets of relevant non-redundant episodes that achieve good data compression. The algorithms employ a novel encoding scheme and use serial episodes with inter-event constraints as the patterns. We present extensive simulation studies with both synthetic and real data, comparing our method with the existing schemes such as GoKrimp and SQS. We also demonstrate the effectiveness of these algorithms on event sequences from a composable conveyor system; this system represents a new application area where use of frequent patterns for compressing the event sequence is likely to be important for decision support and control.

Burst event detection in wall turbulence by WVITA method

Wavelet Variable Interval Time Average (WVITA) is introduced as a method incorporating burst event detection in wall turbulence. Wavelet transform is performed to unfold the longitudinal fluctuating velocity time series measured in the near wall region of a turbulent boundary layer using hot-film anemometer. This unfolding is both in time and in space simultaneously. The splitted kinetic of the longitudinal fluctuating velocity time series among different scales is obtained by integrating the square of wavelet coefficient modulus over temporal space. The time scale that related to burst events in wall turbulence passing through the fixed probe is ascertained by maximum criterion of the kinetic energy evolution across scales. Wavelet transformed localized variance of the fluctuating velocity time series at the maximum kinetic scale is put forward instead of localized short time average variance in Variable Interval Time Average (VITA) scheme. The burst event detection result shows that WVITA scheme can avoid erroneous judgement and solve the grouping problem more effectively which is caused by VITA scheme itself and can not be avoided by adjusting the threshold level or changing the short time average interval.