Mechanism of the addition of alcohols to substituted phenylisothiocyanates : Electrical effects of the substituents on the reaction

The addition reaction of alcohols to substituted phenylisothiocyanates is found to be a second-order reaction. The reaction is catalysed by triethylamine. First-order rate constants of the addition reaction have been determined in excess of ethanol, for a number of substituted phenylisothiocyanates and the rate data give a satisfactory linear correlation with Hammett σ constants of groups. While the energies of activation vary randomly with substitution, the entropies of activation bear a linear relationship to the energies of activation. Infra-red spectra indicate that the thiourethanes which are the products of the addition reaction exist in the thioamide form. The most prominent resonance form which can satisfactorily explain both the kinetic and infrared data, has been suggested.

The Line Spectral Frequency Model of a Finite-Length Sequence

The line spectral frequency (LSF) of a causal finite length sequence is a frequency at which the spectrum of the sequence annihilates or the magnitude spectrum has a spectral null. A causal finite-length sequencewith (L + 1) samples having exactly L-LSFs, is referred as an Annihilating (AH) sequence. Using some spectral properties of finite-length sequences, and some model parameters, we develop spectral decomposition structures, which are used to translate any finite-length sequence to an equivalent set of AH-sequences defined by LSFs and some complex constants. This alternate representation format of any finite-length sequence is referred as its LSF-Model. For a finite-length sequence, one can obtain multiple LSF-Models by varying the model parameters. The LSF-Model, in time domain can be used to synthesize any arbitrary causal finite-length sequence in terms of its characteristic AH-sequences. In the frequency domain, the LSF-Model can be used to obtain the spectral samples of the sequence as a linear combination of spectra of its characteristic AH-sequences. We also summarize the utility of the LSF-Model in practical discrete signal processing systems.

Dielectric relaxation and electrical conductivity in Bi5NbO10 oxygen ion conductors prepared by a modified sol–gel process

Crystalline Bi5NbO10 nanoparticles have been achieved through a modified sol–gel process using a mixture of ethylenediamine and ethanolamine as a solvent. The Bi5NbO10 nanoparticles were characterized by X-ray diffraction (XRD), differential scanning calorimetry/thermogravimetry (DSC/TG), Fourier transform infrared spectroscopy (FT-IR), transmission electron microscopy (TEM) and Raman spectroscopy. The results showed that well-dispersed 5–60 nm Bi5NbO10 nanoparticles were prepared through heat-treating the precursor at 650 °C and the high density pellets were obtained at temperatures lower than those commonly employed. The frequency and temperature dependence of the dielectric constant and the electrical conductivity of the Bi5NbO10 solid solutions were investigated in the 0.1 Hz to 1 MHz frequency range. Two distinct relaxation mechanisms were observed in the plots of dielectric loss and the imaginary part of impedance (Z″) versus frequency in the temperature range of 200–350 °C. The dielectric constant and the loss in the low frequency regime were electrode dependent. The ionic conductivity of Bi5NbO10 solid solutions at 700 °C is 2.86 Ω−1 m−1 which is in same order of magnitude for Y2O3-stabilized ZrO2 ceramics at same temperature. These results suggest that Bi5NbO10 is a promising material for an oxygen ion conductor.

Denoising signals corrupted by chaotic noise

We consider the problem of signal estimation where the observed time series is modeled as y(i) = x(i) + s(i) with {x(i)} being an orbit of a chaotic self-map on a compact subset of R-d and {s(i)} a sequence in R-d converging to zero. This model is motivated by experimental results in the literature where the ocean ambient noise and the ocean clutter are found to be chaotic. Making use of observations up to time n, we propose an estimate of s(i) for i < n and show that it approaches s(i) as n -> infinity for typical asymptotic behaviors of orbits. (C) 2010 Elsevier B.V. All rights reserved.

A Wavelet based Teager Energy Operator for Spike Detection in Microelectrode Array Recordings

Spike detection in neural recordings is the initial step in the creation of brain machine interfaces. The Teager energy operator (TEO) treats a spike as an increase in the `local' energy and detects this increase. The performance of TEO in detecting action potential spikes suffers due to its sensitivity to the frequency of spikes in the presence of noise which is present in microelectrode array (MEA) recordings. The multiresolution TEO (mTEO) method overcomes this shortcoming of the TEO by tuning the parameter k to an optimal value m so as to match to frequency of the spike. In this paper, we present an algorithm for the mTEO using the multiresolution structure of wavelets along with inbuilt lowpass filtering of the subband signals. The algorithm is efficient and can be implemented for real-time processing of neural signals for spike detection. The performance of the algorithm is tested on a simulated neural signal with 10 spike templates obtained from [14]. The background noise is modeled as a colored Gaussian random process. Using the noise standard deviation and autocorrelation functions obtained from recorded data, background noise was simulated by an autoregressive (AR(5)) filter. The simulations show a spike detection accuracy of 90%and above with less than 5% false positives at an SNR of 2.35 dB as compared to 80% accuracy and 10% false positives reported [6] on simulated neural signals.

Real-Time Simulation of Electrical Machines on FPGA Platform

This paper presents real-time simulation models of electrical machines on FPGA platform. Implementation of the real-time numerical integration methods with digital logic elements is discussed. Several numerical integrations are presented. A real-time simulation of DC machine is carried out on this FPGA platform and important transient results are presented. These results are compared to simulation results obtained through a commercial off-line simulation software

Performance analysis of a suprathreshold, stochastic resonance based nonlinear detector

In this paper we introduce a nonlinear detector based on the phenomenon of suprathreshold stochastic resonance (SSR). We first present a model (an array of 1-bit quantizers) that demonstrates the SSR phenomenon. We then use this as a pre-processor to the conventional matched filter. We employ the Neyman-Pearson(NP) detection strategy and compare the performances of the matched filter, the SSR-based detector and the optimal detector. Although the proposed detector is non-optimal, for non-Gaussian noises with heavy tails (leptokurtic) it shows better performance than the matched filter. In situations where the noise is known to be leptokurtic without the availability of the exact knowledge of its distribution, the proposed detector turns out to be a better choice than the matched filter.

Estimation of signals in colored non gaussian noise based on Gaussian Mixture models

Non-Gaussianity of signals/noise often results in significant performance degradation for systems, which are designed using the Gaussian assumption. So non-Gaussian signals/noise require a different modelling and processing approach. In this paper, we discuss a new Bayesian estimation technique for non-Gaussian signals corrupted by colored non Gaussian noise. The method is based on using zero mean finite Gaussian Mixture Models (GMMs) for signal and noise. The estimation is done using an adaptive non-causal nonlinear filtering technique. The method involves deriving an estimator in terms of the GMM parameters, which are in turn estimated using the EM algorithm. The proposed filter is of finite length and offers computational feasibility. The simulations show that the proposed method gives a significant improvement compared to the linear filter for a wide variety of noise conditions, including impulsive noise. We also claim that the estimation of signal using the correlation with past and future samples leads to reduced mean squared error as compared to signal estimation based on past samples only.

Polymer composite/nanocomposite processing and its effect on the electrical properties

One of the biggest challenges when considering polymer nanocomposites for electrical insulation applications lies in determining their electrical properties accurately, which in turn depend on several factors, primary being dispersion of particles in the polymer matrix. With this background, this paper reports an experimental study to understand the effects of different processing techniques on the dispersion of filler particles in the polymer matrix and their related effect on the dielectric properties of the composites. Polymer composite and nanocomposite samples for the study were prepared by mixing 10% by weight of commercially available TiO2 particles of two different sizes in epoxy using different processing methods. A considerable effect of the composite processing method could be seen in the dielectric properties of nanocomposites.

A speech-music discriminator using HILN model based features

We propose a simple speech music discriminator that uses features based on HILN(Harmonics, Individual Lines and Noise) model. We have been able to test the strength of the feature set on a standard database of 66 files and get an accuracy of around 97%. We also have tested on sung queries and polyphonic music and have got very good results. The current algorithm is being used to discriminate between sung queries and played (using an instrument like flute) queries for a Query by Humming(QBH) system currently under development in the lab.

Dynamic programming based optimum non-uniform samples for speech reconstruction and coding

Non-uniform sampling of a signal is formulated as an optimization problem which minimizes the reconstruction signal error. Dynamic programming (DP) has been used to solve this problem efficiently for a finite duration signal. Further, the optimum samples are quantized to realize a speech coder. The quantizer and the DP based optimum search for non-uniform samples (DP-NUS) can be combined in a closed-loop manner, which provides distinct advantage over the open-loop formulation. The DP-NUS formulation provides a useful control over the trade-off between bitrate and performance (reconstruction error). It is shown that 5-10 dB SNR improvement is possible using DP-NUS compared to extrema sampling approach. In addition, the close-loop DP-NUS gives a 4-5 dB improvement in reconstruction error.

A unified model for auxiliary switch commutated DC-DC converters

A novel ZVS auxiliary switch commutated variation for all DGDC converter topologies has been proposed in 2006. With proper designation of the circuit variables (throw current I and the pole voltage V), all these converters are seen to be governed by an identical set of equations. With idealized switches, the steady-state performance is obtainable in an analytical form. The conversion ratio of the converter topologies is obtained. A generalized equivalent circuit emerges for all these converters from the steady-state conversion ratio. It also provides a dynamic model as well. With these generalized steady-state equivalent circuits, small signal analysis of these converters may be carried out readily. It enables one to use the familiar state space averaged results of the standard PWM DGDC converters for the resonant counterparts. Th dc and ac models reveals that dc and low frequency behaviour of the proposed family of converters is similiar to that of its PWM parent

Minimum-Decoding-Complexity, maximum-rate Space-Time Block Codes from Clifford algebras

It is well known that Alamouti code and, in general, Space-Time Block Codes (STBCs) from complex orthogonal designs (CODs) are single-symbol decodable/symbolby-symbol decodable (SSD) and are obtainable from unitary matrix representations of Clifford algebras. However, SSD codes are obtainable from designs that are not CODs. Recently, two such classes of SSD codes have been studied: (i) Coordinate Interleaved Orthogonal Designs (CIODs) and (ii) Minimum-Decoding-Complexity (MDC) STBCs from Quasi-ODs (QODs). In this paper, we obtain SSD codes with unitary weight matrices (but not CON) from matrix representations of Clifford algebras. Moreover, we derive an upper bound on the rate of SSD codes with unitary weight matrices and show that our codes meet this bound. Also, we present conditions on the signal sets which ensure full-diversity and give expressions for the coding gain.

Comparative study of filter-bank mean-energy distance for automated segmentation of speech signals

This paper describes a method of automated segmentation of speech assuming the signal is continuously time varying rather than the traditional short time stationary model. It has been shown that this representation gives comparable if not marginally better results than the other techniques for automated segmentation. A formulation of the 'Bach' (music semitonal) frequency scale filter-bank is proposed. A comparative study has been made of the performances using Mel, Bark and Bach scale filter banks considering this model. The preliminary results show up to 80 % matches within 20 ms of the manually segmented data, without any information of the content of the text and without any language dependence. 'Bach' filters are seen to marginally outperform the other filters.