Optical, Electrochemical, and Structural Properties of Spray Coated Dihexyl Substituted Poly (3,4 propylene dioxythiophene) Film for Optoelectronics Devices

The dihexyl substituted poly (3,4-propylenedioxythiophene) (PProDOT-Hx(2)) thin films uniformly deposited by cost effective spray coating technique on transparent conducting oxide coated substrates. The electro-optical properties of PProDOT-Hx(2) films were studied by UV-Vis spectroscopy that shows the color contrast about 45% with coloration efficiency of approximate to 185cm(2)/C. The electrochemical properties of PProDOT-Hx(2) films were studied by cyclic voltammetry and AC impedance techniques. The cyclic voltammogram shows that redox reaction of films are diffusion controlled and ions transportation will be faster on the polymer film at higher scan rate. Impedance spectra indicate that polymer films are showing interface charge transfer process as well as capacitive behavior between the electrode and electrolyte. The XRD of the PProDOT-Hx(2) thin films revealed that the films are in amorphous nature, which accelerates the transportation of ions during redox process.

Electrothermally tunable MEMS filters

MEMS resonators have potential application in the area of frequency selective devices (e.g., gyroscopes, mass sensors, etc.). In this paper, design of electro thermally tunable resonators is presented. SOIMUMPs process is used to fabricate resonators with springs (beams) and a central mass. When voltage is applied, due to joule heating, temperature of the conducting beams goes up. This results in increase of electrical resistance due to mobility degradation. Due to increase in the temperature, springs start softening and therefore the fundamental frequency decreases. So for a given structure, one can modify the original fundamental frequency by changing the applied voltage. Coupled thermal effects result in non-uniform heating. It is observed from measurements and simulations that some parts of the beam become very hot and therefore soften more. Consequently, at higher voltages, the structure (equivalent to a single resonator) behaves like coupled resonators and exhibits peak splitting. In this mode, the given resonator can be used as a band rejection filter. This process is reversible and repeatable. For the designed structure, it is experimentally shown that by varying the voltage from 1 to 16V, the resonant frequency could be changed by 28%.

Switching and Release Time Analysis of Electrostatically Actuated Capacitive RF MEMS Switches

This paper explains the reason behind pull-in time being more than pull-up time of many Radio Frequency Micro-Electro-Mechanical Systems (RF MEMS) switches at actuation voltages comparable to the pull-in voltage. Analytical expressions for pull-in and pull-up time are also presented. Experimental data as well as finite element simulations of electrostatically actuated beams used in RF-MEMS switches show that the pull-in time is generally more than the pull-up time. Pull-in time being more than pull-up time is somewhat counter-intuitive because there is a much larger electrostatic force during pull-in than the restoring mechanical force during the release. We investigated this issue analytically and numerically using a 1D model for various applied voltages and attribute this to energetics, the rate at which the forces change with time, and softening of the overall effective stiffness of the electromechanical system. 3D finite element analysis is also done to support the 1D model-based analyses.

Design and analysis of integrated optic quadrature phase shift keying modulator

Quadrature phase shift keying (QPSK) is one of the most popular modulation schemes in coherent optical communication systems for data rates in excess of 40 Gbps because of its high spectral efficiency. This paper proposes a simple method of implementing a QPSK modulator in integrated optic (IO) domain. The QPSK modulator is realized using standard IO components, such as Y-branches and electro-optic modulators (EOMs). Design optimization of EOM is carried out considering the fabrication constraints, miniaturization aspects, and simplicity. Also, the interdependency between electrode length, operating voltage, and electrode gap of an EOM has been captured in the form of a family of curves. These plots enable designing of EOMs for custom requirements. An innovative approach has been adopted in demonstrating the operation of IO QPSK modulator in terms of phase data extracted from beam propagation model. The results obtained by this approach have been verified using the conventional interferometric approach. The operation of the proposed IO QPSK modulator is experimentally demonstrated. The design of IO QPSK modulator is taken up as a part of a broader scheme that aims at generation of QPSK modulated microwave signal based on optical heterodyning. (C) 2014 Society of Photo-Optical Instrumentation Engineers (SPIE)

Reduced graphene oxide-supported nickel oxide catalyst with improved CO tolerance for formic acid electrooxidation

The superior catalytic activity along with improved CO tolerance for formic acid electro-oxidation has been demonstrated on a NiO-decorated reduced graphene oxide (rGO) catalyst. The cyclic voltammetry response of rGO-NiO/Pt catalyst elucidates improved CO tolerance and follows direct oxidation pathway. It is probably due to the beneficial effect of residual oxygen groups on rGO support which is supported by FT-IR spectrum. A strong interaction of rGO support with NiO nanoparticles facilitates the removal of CO from the catalyst surface. The chronoamperometric response indicates a higher catalytic activity and stability of rGO-NiO/Pt catalyst than the NiO/Pt and unmodified Pt electrode catalyst for a prolonged time of continuous oxidation of formic acid. Copyright (C) 2014, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.

Effect of Process Deviations on Performance of Piezoresistive Pressure Sensors

Diaphragm thickness and the corresponding piezoresistor locations change due to over or under etching in bulk micromachined piezoresistive pressure sensor which intern influences the device performance. In the present work, variation of sensitivity and nonlinearity of a micro electro mechanical system low pressure sensor is investigated. The sensor is modeled using finite element method to analyze the variation of sensitivity and nonlinearity with diaphragm thickness. To verify the simulated results, the sensors with different diaphragm thicknesses are fabricated. The models are verified by comparing the calculated results with experimental data. This study is potentially useful for the researchers as most of the times the diaphragm is either over-etched or under-etched due to inherent variation in wafer thickness and involving manual operations.

MULTI-PITCH TRACKING USING GAUSSIAN MIXTURE MODEL WITH TIME VARYING PARAMETERS AND GRATING COMPRESSION TRANSFORM

Grating Compression Transform (GCT) is a two-dimensional analysis of speech signal which has been shown to be effective in multi-pitch tracking in speech mixtures. Multi-pitch tracking methods using GCT apply Kalman filter framework to obtain pitch tracks which requires training of the filter parameters using true pitch tracks. We propose an unsupervised method for obtaining multiple pitch tracks. In the proposed method, multiple pitch tracks are modeled using time-varying means of a Gaussian mixture model (GMM), referred to as TVGMM. The TVGMM parameters are estimated using multiple pitch values at each frame in a given utterance obtained from different patches of the spectrogram using GCT. We evaluate the performance of the proposed method on all voiced speech mixtures as well as random speech mixtures having well separated and close pitch tracks. TVGMM achieves multi-pitch tracking with 51% and 53% multi-pitch estimates having error <= 20% for random mixtures and all-voiced mixtures respectively. TVGMM also results in lower root mean squared error in pitch track estimation compared to that by Kalman filtering.

VISUAL OBJECT TRACKING VIA RANDOM FERNS BASED CLASSIFICATION

Designing a robust algorithm for visual object tracking has been a challenging task since many years. There are trackers in the literature that are reasonably accurate for many tracking scenarios but most of them are computationally expensive. This narrows down their applicability as many tracking applications demand real time response. In this paper, we present a tracker based on random ferns. Tracking is posed as a classification problem and classification is done using ferns. We used ferns as they rely on binary features and are extremely fast at both training and classification as compared to other classification algorithms. Our experiments show that the proposed tracker performs well on some of the most challenging tracking datasets and executes much faster than one of the state-of-the-art trackers, without much difference in tracking accuracy.

TIME VARYING LINEAR PREDICTION USING SPARSITY CONSTRAINTS

Time-varying linear prediction has been studied in the context of speech signals, in which the auto-regressive (AR) coefficients of the system function are modeled as a linear combination of a set of known bases. Traditionally, least squares minimization is used for the estimation of model parameters of the system. Motivated by the sparse nature of the excitation signal for voiced sounds, we explore the time-varying linear prediction modeling of speech signals using sparsity constraints. Parameter estimation is posed as a 0-norm minimization problem. The re-weighted 1-norm minimization technique is used to estimate the model parameters. We show that for sparsely excited time-varying systems, the formulation models the underlying system function better than the least squares error minimization approach. Evaluation with synthetic and real speech examples show that the estimated model parameters track the formant trajectories closer than the least squares approach.

ELLIPSE FITTING USING FINITE RATE OF INNOVATION PRINCIPLES

We address the problem of parameter estimation of an ellipse from a limited number of samples. We develop a new approach for solving the ellipse fitting problem by showing that the x and y coordinate functions of an ellipse are finite-rate-of-innovation (FRI) signals. Uniform samples of x and y coordinate functions of the ellipse are modeled as a sum of weighted complex exponentials, for which we propose an efficient annihilating filter technique to estimate the ellipse parameters from the samples. The FRI framework allows for estimating the ellipse parameters reliably from partial or incomplete measurements even in the presence of noise. The efficiency and robustness of the proposed method is compared with state-of-art direct method. The experimental results show that the estimated parameters have lesser bias compared with the direct method and the estimation error is reduced by 5-10 dB relative to the direct method.

ON THE ROLE OF THE HILBERT TRANSFORM IN BOOSTING THE PERFORMANCE OF THE ANNIHILATING FILTER

We consider the problem of parameter estimation from real-valued multi-tone signals. Such problems arise frequently in spectral estimation. More recently, they have gained new importance in finite-rate-of-innovation signal sampling and reconstruction. The annihilating filter is a key tool for parameter estimation in these problems. The standard annihilating filter design has to be modified to result in accurate estimation when dealing with real sinusoids, particularly because the real-valued nature of the sinusoids must be factored into the annihilating filter design. We show that the constraint on the annihilating filter can be relaxed by making use of the Hilbert transform. We refer to this approach as the Hilbert annihilating filter approach. We show that accurate parameter estimation is possible by this approach. In the single-tone case, the mean-square error performance increases by 6 dB for signal-to-noise ratio (SNR) greater than 0 dB. We also present experimental results in the multi-tone case, which show that a significant improvement (about 6 dB) is obtained when the parameters are close to 0 or pi. In the mid-frequency range, the improvement is about 2 to 3 dB.

NESTED SPARSE BAYESIAN LEARNING FOR BLOCK-SPARSE SIGNALS WITH INTRA-BLOCK CORRELATION

In this work, we address the recovery of block sparse vectors with intra-block correlation, i.e., the recovery of vectors in which the correlated nonzero entries are constrained to lie in a few clusters, from noisy underdetermined linear measurements. Among Bayesian sparse recovery techniques, the cluster Sparse Bayesian Learning (SBL) is an efficient tool for block-sparse vector recovery, with intra-block correlation. However, this technique uses a heuristic method to estimate the intra-block correlation. In this paper, we propose the Nested SBL (NSBL) algorithm, which we derive using a novel Bayesian formulation that facilitates the use of the monotonically convergent nested Expectation Maximization (EM) and a Kalman filtering based learning framework. Unlike the cluster-SBL algorithm, this formulation leads to closed-form EMupdates for estimating the correlation coefficient. We demonstrate the efficacy of the proposed NSBL algorithm using Monte Carlo simulations.

AN OPTIMUM SHRINKAGE ESTIMATOR BASED ON MINIMUM-PROBABILITY-OF-ERROR CRITERION AND APPLICATION TO SIGNAL DENOISING

We address the problem of designing an optimal pointwise shrinkage estimator in the transform domain, based on the minimum probability of error (MPE) criterion. We assume an additive model for the noise corrupting the clean signal. The proposed formulation is general in the sense that it can handle various noise distributions. We consider various noise distributions (Gaussian, Student's-t, and Laplacian) and compare the denoising performance of the estimator obtained with the mean-squared error (MSE)-based estimators. The MSE optimization is carried out using an unbiased estimator of the MSE, namely Stein's Unbiased Risk Estimate (SURE). Experimental results show that the MPE estimator outperforms the SURE estimator in terms of SNR of the denoised output, for low (0 -10 dB) and medium values (10 - 20 dB) of the input SNR.

MAXIMUM A-POSTERIORI ESTIMATION OF MISSING SAMPLES WITH CONTINUITY CONSTRAINT IN ELECTROMAGNETIC ARTICULOGRAPHY DATA

Electromagnetic Articulography (EMA) technique is used to record the kinematics of different articulators while one speaks. EMA data often contains missing segments due to sensor failure. In this work, we propose a maximum a-posteriori (MAP) estimation with continuity constraint to recover the missing samples in the articulatory trajectories recorded using EMA. In this approach, we combine the benefits of statistical MAP estimation as well as the temporal continuity of the articulatory trajectories. Experiments on articulatory corpus using different missing segment durations show that the proposed continuity constraint results in a 30% reduction in average root mean squared error in estimation over statistical estimation of missing segments without any continuity constraint.

Efficient Adaptive Mesh Refinement for MoM-based Package-Board 3D Full-wave Extraction

The accurate solution of 3D full-wave Method of Moments (MoM) on an arbitrary mesh of a package-board structure does not guarantee accuracy, since the discretizations may not be fine enough to capture rapid spatial changes in the solution variable. At the same time, uniform over-meshing on the entire structure generates large number of solution variables and therefore requires an unnecessarily large matrix solution. In this work, a suitable refinement criterion for MoM based electromagnetic package-board extraction is proposed and the advantages of the adaptive strategy are demonstrated from both accuracy and speed perspectives.