Study of Wrist Pulse Signals Using a Bi-Modal Gaussian Model

Wrist pulse signals contain important information about the health of a person and hence diagnosis based on pulse signals has assumed great importance. In this paper we demonstrate the efficacy of a two term Gaussian model to extract information from pulse signals. Results have been obtained by conducting experiments on several subjects to record wrist pulse signals for the cases of before exercise and after exercise. Parameters have been extracted from the recorded signals using the model and a paired t-test is performed, which shows that the parameters are significantly different between the two groups. Further, a recursive cluster elimination based support vector machine is used to perform classification between the groups. An average classification accuracy of 99.46% is obtained, along with top classifiers. It is thus shown that the parameters of the Gaussian model show changes across groups and hence the model is effective in distinguishing the changes taking place due to the two different recording conditions. The study has potential applications in healthcare.

Robust Savitzky-Golay Filters

Local polynomial approximation of data is an approach towards signal denoising. Savitzky-Golay (SG) filters are finite-impulse-response kernels, which convolve with the data to result in polynomial approximation for a chosen set of filter parameters. In the case of noise following Gaussian statistics, minimization of mean-squared error (MSE) between noisy signal and its polynomial approximation is optimum in the maximum-likelihood (ML) sense but the MSE criterion is not optimal for non-Gaussian noise conditions. In this paper, we robustify the SG filter for applications involving noise following a heavy-tailed distribution. The optimal filtering criterion is achieved by l(1) norm minimization of error through iteratively reweighted least-squares (IRLS) technique. It is interesting to note that at any stage of the iteration, we solve a weighted SG filter by minimizing l(2) norm but the process converges to l(1) minimized output. The results show consistent improvement over the standard SG filter performance.

Projected climate change impacts on vegetation distribution over Kashmir Himalayas

Despite high vulnerability, the impact of climate change on Himalayan ecosystem has not been properly investigated, primarily due to the inadequacy of observed data and the complex topography. In this study, we mapped the current vegetation distribution in Kashmir Himalayas from NOAA AVHRR and projected it under A1B SRES, RCP-4.5 and RCP-8.5 climate scenarios using the vegetation dynamics model-IBIS at a spatial resolution of 0.5A degrees. The distribution of vegetation under the changing climate was simulated for the 21st century. Climate change projections from the PRECIS experiment using the HADRM3 model, for the Kashmir region, were validated using the observed climate data from two observatories. Both the observed as well as the projected climate data showed statistically significant trends. IBIS was validated for Kashmir Himalayas by comparing the simulated vegetation distribution with the observed distribution. The baseline simulated scenario of vegetation (1960-1990), showed 87.15 % agreement with the observed vegetation distribution, thereby increasing the credibility of the projected vegetation distribution under the changing climate over the region. According to the model projections, grasslands and tropical deciduous forests in the region would be severely affected while as savannah, shrubland, temperate evergreen broadleaf forest, boreal evergreen forest and mixed forest types would colonize the area currently under the cold desert/rock/ice land cover types. The model predicted that a substantial area of land, presently under the permanent snow and ice cover, would disappear by the end of the century which might severely impact stream flows, agriculture productivity and biodiversity in the region.

Fluctuation theory of Rashba Fermi gases: Gaussian and beyond

Fermi gases with generalized Rashba spin-orbit coupling induced by a synthetic gauge field have the potential of realizing many interesting states, such as rashbon condensates and topological phases. Here, we address the key open problem of the fluctuation theory of such systems and demonstrate that beyond-Gaussian effects are essential to capture the finite temperature physics of such systems. We obtain their phase diagram by constructing an approximate non-Gaussian theory. We conclusively establish that spin-orbit coupling can enhance the exponentially small transition temperature (T-c) of a weakly attracting superfluid to the order of the Fermi temperature, paving a pathway towards high T-c superfluids.

Patch-based and multiresolution optimum bilateral filters for denoising images corrupted by Gaussian noise

We propose optimal bilateral filtering techniques for Gaussian noise suppression in images. To achieve maximum denoising performance via optimal filter parameter selection, we adopt Stein's unbiased risk estimate (SURE)-an unbiased estimate of the mean-squared error (MSE). Unlike MSE, SURE is independent of the ground truth and can be used in practical scenarios where the ground truth is unavailable. In our recent work, we derived SURE expressions in the context of the bilateral filter and proposed SURE-optimal bilateral filter (SOBF). We selected the optimal parameters of SOBF using the SURE criterion. To further improve the denoising performance of SOBF, we propose variants of SOBF, namely, SURE-optimal multiresolution bilateral filter (SMBF), which involves optimal bilateral filtering in a wavelet framework, and SURE-optimal patch-based bilateral filter (SPBF), where the bilateral filter parameters are optimized on small image patches. Using SURE guarantees automated parameter selection. The multiresolution and localized denoising in SMBF and SPBF, respectively, yield superior denoising performance when compared with the globally optimal SOBF. Experimental validations and comparisons show that the proposed denoisers perform on par with some state-of-the-art denoising techniques. (C) 2015 SPIE and IS&T

Asymptotic Bounds on the Power-Delay Tradeoff for Fading Point-to-Point Links From Geometric Bounds on the Stationary Distribution of the Queue Length

The optimal power-delay tradeoff is studied for a time-slotted independently and identically distributed fading point-to-point link, with perfect channel state information at both transmitter and receiver, and with random packet arrivals to the transmitter queue. It is assumed that the transmitter can control the number of packets served by controlling the transmit power in the slot. The optimal tradeoff between average power and average delay is analyzed for stationary and monotone transmitter policies. For such policies, an asymptotic lower bound on the minimum average delay of the packets is obtained, when average transmitter power approaches the minimum average power required for transmitter queue stability. The asymptotic lower bound on the minimum average delay is obtained from geometric upper bounds on the stationary distribution of the queue length. This approach, which uses geometric upper bounds, also leads to an intuitive explanation of the asymptotic behavior of average delay. The asymptotic lower bounds, along with previously known asymptotic upper bounds, are used to identify three new cases where the order of the asymptotic behavior differs from that obtained from a previously considered approximate model, in which the transmit power is a strictly convex function of real valued service batch size for every fade state.

Spectral intensity distribution of trapped

To calculate static response properties of a many-body system, local density approximation (LDA) can be safely applied. But, to obtain dynamical response functions, the applicability of LDA is limited bacause dynamics of the system needs to be considered as well. To examine this in the context of cold atoms, we consider a system of non-interacting spin4 fermions confined by a harmonic trapping potential. We have calculated a very important response function, the spectral intensity distribution function (SIDF), both exactly and using LDA at zero temperature and compared with each other for different dimensions, trap frequencies and momenta. The behaviour of the SIDF at a particular momentum can be explained by noting the behaviour of the density of states (DoS) of the free system (without trap) in that particular dimension. The agreement between exact and LDA SIDFs becomes better with increase in dimensions and number of particles.

Strategy for Obtaining Bimodal Bubble Size Distribution in Water Blown Polyurethane Foams

Polyurethane foams with multimodal cell distribution exhibit superior mechanical and thermal properties. A technique for generating bimodal bubble size distribution exists in the literature, but it uses supercritical conditions. In the present work, an alternative based on milder operating conditions is proposed. It is a modification of reaction injection molding (RIM), using reactants already seeded with bubbles. The number density of the seeds determines if two nucleating events can occur. A bimodal bubble size distribution is obtained when this happens A mathematical model is used to test this hypothesis by simulating water blown free rise polyurethane foams. The effects of initial concentration of bubbles, temperature of the reactants, and the weight fraction of water are studied. The study reveals that for certain concentrations of initial number of bubbles, when initial temperature and weight fraction of water are high, it is possible to obtain a second nucleation event, leading to bimodal bubble size distribution.

Tailor-Made Distribution of Nanoparticles in Blend Structure toward Outstanding Electromagnetic Interference Shielding

Engineering blend structure with tailor-made distribution of nanoparticles is the prime requisite to obtain materials with extraordinary properties. Herein, a unique strategy of distributing nanoparticles in different phases of a blend structure has resulted in >99% blocking of incoming electromagnetic (EM) radiation. This is accomplished by designing a ternary polymer blend structure using polycarbonate (PC), poly(vinylidene fluoride) (PVDF), and poly(methyl methacrylate) (PMMA) to simultaneously improve the structural, electrical, and electromagnetic interference shielding (EMI). The blend structure was made conducting by preferentially localizing the multi-wall nanotubes (MWNTs) in the PVDF phase. By taking advantage of pp stacking MWNTs was noncovalently modified with an imidazolium based ionic liquid (IL). Interestingly, the enhanced dispersion of IL-MWNTs in PVDF improved the electrical conductivity of the blends significantly. While one key requisite to attenuate EM radiation (i.e., electrical conductivity) was achieved using MWNTs, the magnetic properties of the blend structure was tuned by introducing barium ferrite (BaFe) nanoparticles, which can interact with the incoming EM radiation. By suitably modifying the surface of BaFe nanoparticles, we can tailor their localization under the macroscopic processing condition. The precise localization of BaFe nanoparticles in the PC phase, due to nucleophilic substitution reaction, and the MWNTs in the PVDF phase not only improved the conductivity but also facilitated in absorption of the incoming microwave radiation due to synergetic effect from MWNT and BaFe. The shielding effectiveness (SE) was measured in X and K-u band, and an enhanced SE of -37 dB was noted at 18 GHz frequency. PMMA, which acted as an interfacial modifier in PC/PVDF blends further, resulting in a significant enhancement in the mechanical properties besides retaining high SE. This study opens a new avenue in designing mechanically strong microwave absorbers with a suitable combination of materials.

Quark and gluon distribution functions in a viscous quark-gluon plasma medium and dilepton production via q(q)over-bar annihilation

Viscous modifications to the thermal distributions of quark-antiquarks and gluons have been studied in a quasiparticle description of the quark-gluon-plasma medium created in relativistic heavy-ion collision experiments. The model is described in terms of quasipartons that encode the hot QCD medium effects in their respective effective fugacities. Both shear and bulk viscosities have been taken in to account in the analysis, and the modifications to thermal distributions have been obtained by modifying the energy-momentum tensor in view of the nontrivial dispersion relations for the gluons and quarks. The interactions encoded in the equation of state induce significant modifications to the thermal distributions. As an implication, the dilepton production rate in the q (q) over bar annihilation process has been investigated. The equation of state is found to have a significant impact on the dilepton production rate along with the viscosities.

The Holy Grail of Polymer Therapeutics for Cancer Therapy: An Overview on the Pharmacokinetics and Bio Distribution

In recent years, multifaceted clinical benefits of polymeric therapeutics have been reported. Over the past decades, cancer has been one of the leading causes of mortality in the world. Many clinically approved chemotherapeutics encounter potential challenges against deadly cancer. Moreover, safety and efficacy of anticancer agents have been limited by undesirable pharmacokinetics and biodistribution. To address these limitations, various polymer drug conjugates are being studied and developed to improve the antitumor efficacy. Among other therapeutics, polymer therapeutics are well established platforms that circumvent anticancer therapeutics from enzymatic metabolism via direct conjugation to therapeutic molecules. Interestingly, polymer therapeutics meets an unmet need of small molecules. Further clinical study showed that polymer-drug conjugation can achieve desired pharmacokinetics and biodistribution properties of several anticancer drugs. The present retrospective review mainly enlightens the most recent preclinical and clinical studies include safety, stability, pharmacokinetic behavior and distribution of polymer therapeutics.

Anomalous lifetime statistics of ligand-receptor binding in a tethered polymer system

In this paper, motivated by observations of non-exponential decay times in the stochastic binding and release of ligand-receptor systems, exemplified by the work of Rogers et al on optically trapped DNA-coated colloids (Rogers et al 2013 Soft Matter 9 6412), we explore the general problem of polymer-mediated surface adhesion using a simplified model of the phenomenon in which a single polymer molecule, fixed at one end, binds through a ligand at its opposite end to a flat surface a fixed distance L away and uniformly covered with receptor sites. Working within the Wilemski-Fixman approximation to diffusion-controlled reactions, we show that for a flexible Gaussian chain, the predicted distribution of times f(t) for which the ligand and receptor are bound is given, for times much shorter than the longest relaxation time of the polymer, by a power law of the form t(-1/4). We also show when the effects of chain stiffness are incorporated into this model (approximately), the structure of f(t) is altered to t(-1/2). These results broadly mirror the experimental trends in the work cited above.

Meridional gradients in aerosol vertical distribution over Indian Mainland: Observations and model simulations

Multi-year observations from the network of ground-based observatories (ARFINET), established under the project `Aerosol Radiative Forcing over India' (ARFI) of Indian Space Research Organization and space-borne lidar `Cloud Aerosol Lidar with Orthogonal Polarization' (CALIOP) along with simulations from the chemical transport model `Goddard Chemistry Aerosol Radiation and Transport' (GOCART), are used to characterize the vertical distribution of atmospheric aerosols over the Indian landmass and its spatial structure. While the vertical distribution of aerosol extinction showed higher values close to the surface followed by a gradual decrease at increasing altitudes, a strong meridional increase is observed in the vertical spread of aerosols across the Indian region in all seasons. It emerges that the strong thermal convections cause deepening of the atmospheric boundary layer, which although reduces the aerosol concentration at lower altitudes, enhances the concentration at higher elevations by pumping up more aerosols from below and also helping the lofted particles to reach higher levels in the atmosphere. Aerosol depolarization ratios derived from CALIPSO as well as the GOCART simulations indicate the dominance of mineral dust aerosols during spring and summer and anthropogenic aerosols in winter. During summer monsoon, though heavy rainfall associated with the Indian monsoon removes large amounts of aerosols, the prevailing southwesterly winds advect more marine aerosols over to landmass (from the adjoining oceans) leading to increase in aerosol loading at lower altitudes than in spring. During spring and summer months, aerosol loading is found to be significant, even at altitudes as high as 4 km, and this is proposed to have significant impacts on the regional climate systems such as Indian monsoon. (C) 2015 Elsevier Ltd. All rights reserved.

MULTI-INSTRUMENT DETECTION IN POLYPHONIC MUSIC USING GAUSSIAN MIXTURE BASED FACTORIAL HMM

We formulate the problem of detecting the constituent instruments in a polyphonic music piece as a joint decoding problem. From monophonic data, parametric Gaussian Mixture Hidden Markov Models (GM-HMM) are obtained for each instrument. We propose a method to use the above models in a factorial framework, termed as Factorial GM-HMM (F-GM-HMM). The states are jointly inferred to explain the evolution of each instrument in the mixture observation sequence. The dependencies are decoupled using variational inference technique. We show that the joint time evolution of all instruments' states can be captured using F-GM-HMM. We compare performance of proposed method with that of Student's-t mixture model (tMM) and GM-HMM in an existing latent variable framework. Experiments on two to five polyphony with 8 instrument models trained on the RWC dataset, tested on RWC and TRIOS datasets show that F-GM-HMM gives an advantage over the other considered models in segments containing co-occurring instruments.