Spectral zero-crossings: localization properties and application to epoch extraction in speech signals

We analyze the spectral zero-crossing rate (SZCR) properties of transient signals and show that SZCR contains accurate localization information about the transient. For a train of pulses containing transient events, the SZCR computed on a sliding window basis is useful in locating the impulse locations accurately. We present the properties of SZCR on standard stylized signal models and then show how it may be used to estimate the epochs in speech signals. We also present comparisons with some state-of-the-art techniques that are based on the group-delay function. Experiments on real speech show that the proposed SZCR technique is better than other group-delay-based epoch detectors. In the presence of noise, a comparison with the zero-frequency filtering technique (ZFF) and Dynamic programming projected Phase-Slope Algorithm (DYPSA) showed that performance of the SZCR technique is better than DYPSA and inferior to that of ZFF. For highpass-filtered speech, where ZFF performance suffers drastically, the identification rates of SZCR are better than those of DYPSA.

A risk-estimation-based formulation for speech enhancement and its relation to Wiener filtering

The goal of speech enhancement algorithms is to provide an estimate of clean speech starting from noisy observations. The often-employed cost function is the mean square error (MSE). However, the MSE can never be computed in practice. Therefore, it becomes necessary to find practical alternatives to the MSE. In image denoising problems, the cost function (also referred to as risk) is often replaced by an unbiased estimator. Motivated by this approach, we reformulate the problem of speech enhancement from the perspective of risk minimization. Some recent contributions in risk estimation have employed Stein's unbiased risk estimator (SURE) together with a parametric denoising function, which is a linear expansion of threshold/bases (LET). We show that the first-order case of SURE-LET results in a Wiener-filter type solution if the denoising function is made frequency-dependent. We also provide enhancement results obtained with both techniques and characterize the improvement by means of local as well as global SNR calculations.

Tensile flow and work hardening behavior of hot cross-rolled AA7010 aluminum alloy sheets

The present work describes the tensile flow and work hardening behavior of a high strength 7010 aluminum alloy by constitutive relations. The alloy has been hot rolled by three different cross-rolling schedules. Room temperature tensile properties have been evaluated as a function of tensile axis orientation in the as-hot rolled as well as peak aged conditions. It is found that both the Ludwigson and a generalized Voce-Bergstrom relation adequately describe the tensile flow behavior of the present alloy in all conditions compared to the Hollomon relation. The variation in the Ludwigson fitting parameter could be correlated well with the microstructural features and anisotropic contribution of strengthening precipitates in the as-rolled and peak aged conditions, respectively. The hardening rate and the saturation stress of the first Voce-Bergstrom parameter, on the other hand, depend mainly on the crystallographic texture of the specimens. It is further shown that for the peak aged specimens the uniform elongation (epsilon(u)) derived from the Ludwigson relation matches well with the measured epsilon(u) irrespective of processing and loading directions. However, the Ludwigson fit overestimates the epsilon(u) in case of the as-rolled specimens. The Hollomon fit, on the other hand, predicts well the measured epsilon(u), of the as-rolled specimens but severely underestimates the epsilon(u), for the peak aged specimens. Contrarily, both the relations significantly overestimate the UTS of the as-rolled and the peak aged specimens. The Voce-Bergstrom parameters define the slope of e Theta-sigma plots in the stage-III regime when the specimens show a classical linear decrease in hardening rate in stage-III. Further analysis of work hardening behavior throws some light on the effect of texture on the dislocation storage and dynamic recovery.

Nitric oxide and KLF4 protein epigenetically modify class II transactivator to repress Major histocompatibility complex II expression during Mycobacterium bovis Bacillus Calmette-Guerin infection

Pathogenic mycobacteria employ several immune evasion strategies such as inhibition of class II transactivator (CIITA) and MHC-II expression, to survive and persist in host macrophages. However, precise roles for specific signaling components executing down-regulation of CIITA/MHC-II have not been adequately addressed. Here, we demonstrate that Mycobacterium bovis bacillus Calmette-Guerin (BCG)-mediated TLR2 signaling-induced iNOS/NO expression is obligatory for the suppression of IFN-gamma-induced CIITA/MHC-II functions. Significantly, NOTCH/PKC/MAPK-triggered signaling cross-talk was found critical for iNOS/NO production. NO responsive recruitment of a bifunctional transcription factor, KLF4, to the promoter of CIITA during M. bovis BCG infection of macrophages was essential to orchestrate the epigenetic modifications mediated by histone methyltransferase EZH2 or miR-150 and thus calibrate CIITA/MHC-II expression. NO-dependent KLF4 regulated the processing and presentation of ovalbumin by infected macrophages to reactive T cells. Altogether, our study delineates a novel role for iNOS/NO/KLF4 in dictating the mycobacterial capacity to inhibit CIITA/MHC-II-mediated antigen presentation by infected macrophages and thereby elude immune surveillance.

Information and Efficiency in the Nervous System-A Synthesis

In systems biology, questions concerning the molecular and cellular makeup of an organism are of utmost importance, especially when trying to understand how unreliable components-like genetic circuits, biochemical cascades, and ion channels, among others-enable reliable and adaptive behaviour. The repertoire and speed of biological computations are limited by thermodynamic or metabolic constraints: an example can be found in neurons, where fluctuations in biophysical states limit the information they can encode-with almost 20-60% of the total energy allocated for the brain used for signalling purposes, either via action potentials or by synaptic transmission. Here, we consider the imperatives for neurons to optimise computational and metabolic efficiency, wherein benefits and costs trade-off against each other in the context of self-organised and adaptive behaviour. In particular, we try to link information theoretic (variational) and thermodynamic (Helmholtz) free-energy formulations of neuronal processing and show how they are related in a fundamental way through a complexity minimisation lemma.

Achievable secrecy sum-rate in a fading MAC-WT with power control and without CSI of eavesdropper

We consider a two user fading Multiple Access Channel with a wire-tapper (MAC-WT) where the transmitter has the channel state information (CSI) to the intended receiver but not to the eavesdropper (eve). We provide an achievable secrecy sum-rate with optimal power control. We next provide a secrecy sum-rate with optimal power control and cooperative jamming (CJ). We then study an achievable secrecy sum rate by employing an ON/OFF power control scheme which is more easily computable. We also employ CJ over this power control scheme. Results show that CJ boosts the secrecy sum-rate significantly even if we do not know the CSI of the eve's channel. At high SNR, the secrecy sum-rate (with CJ) without CSI of the eve exceeds the secrecy sum-rate (without CJ) with full CSI of the eve.

Grassmannian fusion frames and its use in block sparse recovery

Tight fusion frames which form optimal packings in Grassmannian manifolds are of interest in signal processing and communication applications. In this paper, we study optimal packings and fusion frames having a specific structure for use in block sparse recovery problems. The paper starts with a sufficient condition for a set of subspaces to be an optimal packing. Further, a method of using optimal Grassmannian frames to construct tight fusion frames which form optimal packings is given. Then, we derive a lower bound on the block coherence of dictionaries used in block sparse recovery. From this result, we conclude that the Grassmannian fusion frames considered in this paper are optimal from the block coherence point of view. (C) 2013 Elsevier B.V. All rights reserved.

Fabrication of a novel biomaterial with enhanced mechanical and conducting properties

Conducting polymers have the combined advantages of metal conductivity with ease in processing and biocompatibility; making them extremely versatile for biosensor and tissue engineering applications. However, the inherent brittle property of conducting polymers limits their direct use in such applications which generally warrant soft and flexible material responses. Addition of fillers increases the material compliance, but is achieved at the cost of reduced electrical conductivity. To retain suitable conductivity without compromising the mechanical properties, we fabricate an electroactive blend (dPEDOT) using low grade PEDOT: PSS as the base conducting polymer with polyvinyl alcohol as filler and glycerol as a dopant. Bulk dPEDOT films show a thermally stable response till 110 degrees C with over seven fold increase in room temperature conductivity as compared to 0.002 S cm(-1) for pristine PEDOT: PSS. We characterize the nonlinear stress-strain response of dPEDOT, well described using a Mooney-Rivlin hyperelastic model, and report elastomer-like moduli with ductility similar to fives times its original length. Dynamic mechanical analysis shows constant storage moduli over a large range of frequencies with corresponding linear increase in tan(delta). We relate the enhanced performance of dPEDOT with the underlying structural constituents using FTIR and AFM microscopy. These data demonstrate specific interactions between individual components of dPEDOT, and their effect on surface topography and material properties. Finally, we show biocompatibility of dPEDOT using fibroblasts that have comparable cell morphologies and viability as the control, which make dPEDOT attractive as a biomaterial.

Mechanical properties of CNT-Bisphenol E cyanate ester-based CFRP nanocomposite developed through VARTM process

This paper reports on the effect of multiwall carbon nanotubes (CNTs) without and with chemical functionalization on the mechanical properties of Bisphenol E cyanate ester resin (BECy) based carbon fibre reinforced plastic (CFRP) laminated composites. BECy with its low viscosity, low moisture uptake and superior mechanical properties is selected for its application in CFRP laminates through the cost-effective Vacuum Assisted Resin Transfer Moulding (VARTM) process. However, unlike CNT-epoxy-CFRP composites, processing and performance issues such as dispersion of CNTs, chemical bonding with resin, functionalization effects, effects on mechanical properties, etc. for BECy-CNT-CFRP composite system are not well reported. The objective of this study is to improve the mechanical properties of BECy resin with small additions of CNTs and functionalized CNTs in CFRP laminates. CNTs and fCNTs are infused into BECy using ultrasonication and standard mixing methods. Improvements in Young's modulus and strength in tension, compression, shear, flexure and interlaminar shear strength are analysed. It is observed that addition of 0.5wt% CNTs effected for maximum mechanical properties of the resin and 1wt% CNTs for the mechanical properties of CNT-CFRP nanocomposite. Further, improvements obtained with fCNTs are marginal. Dispersion behaviour and effect of CNTs/fCNTs in load transfer corroborated with SEM pictures are presented. The enhanced mechanical properties realized in VARTM processing of BECy-CFRP laminate indicate clear advantage of CNT based modification of the process.

Simvastatin Induced Neurite Outgrowth Unveils Role of Cell Surface Cholesterol and Acetyl CoA Carboxylase in SH-SY5Y Cells

Statins are known to modulate cell surface cholesterol (CSC) and AMP-activated protein kinase (AMPK) in nonneural cells; however no study demonstrates whether CSC and AMPK may regulate simvastatin induced neuritogenesis (SIN). We found that simvastatin (SIM) maintains CSC as shown by Fillipin III staining, Flotillin-2 protein expression / localization and phosphorylation of various receptor tyrosine kinases (RTKs) in the plasma membrane. Modulation of CSC revealed that SIN is critically dependent on this CSC. Simultaneously, phospho array for mitogen activated protein kinases (MAPKs) revealed PI3K / Akt as intracellular pathway which modulates lipid pathway by inhibiting AMPK activation. Though, SIM led to a transient increase in AMPK phosphorylation followed by a sudden decline; the effect was independent of PI3K. Strikingly, AMPK phosphorylation was regulated by protein phosphatase 2A (PP2A) activity which was enhanced upon SIM treatment as evidenced by increase in threonine phosphorylation. Moreover, it was observed that addition of AMP analogue and PP2A inhibitor inhibited SIN. Biocomposition of neurites shows that lipids form a major part of neurites and AMPK is known to regulate lipid metabolism majorly through acetyl CoA carboxylase (ACC). AMPK activity is negative regulator of ACC activity and we found that phosphorylation of ACC started to decrease after 6 hrs which becomes more pronounced at 12 hrs. Addition of ACC inhibitor showed that SIN is dependent on ACC activity. Simultaneously, addition of Fatty acid synthase (FAS) inhibitor confirmed that endogenous lipid pathway is important for SIN. We further investigated SREBP-1 pathway activation which controls ACC and FAS at transcriptional level. However, SIM did not affect SREBP-1 processing and transcription of its target genes likes ACC1 and FAS. In conclusion, this study highlights a distinct role of CSC and ACC in SIN which might have implication in process of neuronal differentiation induced by other agents.

QoS Constrained Optimal Sink and Relay Placement in Planned Wireless Sensor Networks

We are given a set of sensors at given locations, a set of potential locations for placing base stations (BSs, or sinks), and another set of potential locations for placing wireless relay nodes. There is a cost for placing a BS and a cost for placing a relay. The problem we consider is to select a set of BS locations, a set of relay locations, and an association of sensor nodes with the selected BS locations, so that the number of hops in the path from each sensor to its BS is bounded by h(max), and among all such feasible networks, the cost of the selected network is the minimum. The hop count bound suffices to ensure a certain probability of the data being delivered to the BS within a given maximum delay under a light traffic model. We observe that the problem is NP-Hard, and is hard to even approximate within a constant factor. For this problem, we propose a polynomial time approximation algorithm (SmartSelect) based on a relay placement algorithm proposed in our earlier work, along with a modification of the greedy algorithm for weighted set cover. We have analyzed the worst case approximation guarantee for this algorithm. We have also proposed a polynomial time heuristic to improve upon the solution provided by SmartSelect. Our numerical results demonstrate that the algorithms provide good quality solutions using very little computation time in various randomly generated network scenarios.

A Bacterial Foraging Optimization and Learning Automata Based Feature Selection for Motor Imagery EEG Classification

Selection of relevant features is an open problem in Brain-computer interfacing (BCI) research. Sometimes, features extracted from brain signals are high dimensional which in turn affects the accuracy of the classifier. Selection of the most relevant features improves the performance of the classifier and reduces the computational cost of the system. In this study, we have used a combination of Bacterial Foraging Optimization and Learning Automata to determine the best subset of features from a given motor imagery electroencephalography (EEG) based BCI dataset. Here, we have employed Discrete Wavelet Transform to obtain a high dimensional feature set and classified it by Distance Likelihood Ratio Test. Our proposed feature selector produced an accuracy of 80.291% in 216 seconds.

Pricing and Power allocation in Femtocells using Stackelberg Game Theory

We consider a system with multiple Femtocells operating in a Macrocell. The transmissions in one Femtocell interfere with its neighboring Femtocells as well as with the Macrocell Base Station. We model Femtocells as selfish nodes and the Macrocell Base Station protects itself by pricing subchannels for each usage. We use Stackelberg game model to study this scenario and obtain equilibrium policies that satisfy certain quality of service.

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.

Power Allocation in MIMO Wiretap Channel with Statistical CSI and Finite-Alphabet Input

In this paper, we consider the problem of power allocation in MIMO wiretap channel for secrecy in the presence of multiple eavesdroppers. Perfect knowledge of the destination channel state information (CSI) and only the statistical knowledge of the eavesdroppers CSI are assumed. We first consider the MIMO wiretap channel with Gaussian input. Using Jensen's inequality, we transform the secrecy rate max-min optimization problem to a single maximization problem. We use generalized singular value decomposition and transform the problem to a concave maximization problem which maximizes the sum secrecy rate of scalar wiretap channels subject to linear constraints on the transmit covariance matrix. We then consider the MIMO wiretap channel with finite-alphabet input. We show that the transmit covariance matrix obtained for the case of Gaussian input, when used in the MIMO wiretap channel with finite-alphabet input, can lead to zero secrecy rate at high transmit powers. We then propose a power allocation scheme with an additional power constraint which alleviates this secrecy rate loss problem, and gives non-zero secrecy rates at high transmit powers.