Harmonic modification and data adaptive filtering based approach to robust pitch estimation

A novel and robust pitch estimation method is presented in this paper. The basic idea is to reshape the speech signal using a combination of the dominant harmonic modification (DHM) and data adaptive time domain filtering techniques. The noisy speech signal is filtered within the ranges of fundamental frequencies to obtain the pre-filtered signal (PFS). The dominant harmonic (DH) of the PFS is determined and enhanced its amplitude. Normalized autocorrelation function (NACF) is applied to that modified signal. Then empirical mode decomposition (EMD) based data adaptive time domain filtering is applied to the NACF signal. Partial reconstruction is performed in EMD domain. The pitch period is determined from the partially reconstructed signal. The experimental results show that the proposed method performs better than the other recently developed methods for noisy and clean speech signals in terms of gross and fine pitch errors.     

Estimation of gross calorific value of coals using artificial neural networks

The gross calorific value (GCV) is an important property defining the energy content and thereby efficiency of fuels, such as coals. There exist a number of correlations for estimating the GCV of a coal sample based upon its proximate and/or ultimate analyses. These correlations are mainly linear in character although there are indications that the relationship between the GCV and a few constituents of the proximate and ultimate analyses could be nonlinear. Accordingly, in this paper a total of seven nonlinear models have been developed using the artificial neural networks (ANN) methodology for the estimation of GCV with a special focus on Indian coals. The comprehensive ANN model developed here uses all the major constituents of the proximate and ultimate analyses as inputs while the remaining six sub-models use different combinations of the constituents of the stated analyses. It has been found that the GCV prediction accuracy of all the models is excellent with the comprehensive model being the most accurate GCV predictor. Also, the performance of the ANN models has been found to be consistently better than that of their linear counterparts. Additionally, a sensitivity analysis of the comprehensive ANN model has been performed to identify the important model inputs, which significantly affect the GCV. The ANN-based modeling approach illustrated in this paper is sufficiently general and thus can be gainfully extended for estimating the GCV of a wide spectrum of solid, liquid and gaseous fuels.     

Bed expansion ratio of mono-sized and binary mixtures in fluidized,spouted, and spout-fluid beds

Studies on bed expansion ratio were carried out in fluidized, spouted, and spout-fluid beds. A single column has been used to compare the characteristics of fluidized, spouted, and spout-fluid beds. Experiments were carried out using air and glass beads under fluidized, spouted, and spout-fluid bed conditions separately to study the effect of gas velocity, bed mass, and particle size on bed expansion ratio. Glass beads of different sizes (0.75, 1.2, 1.7, and 3.075?mm) have been used as solid bed material. Bed expansion ratio was determined for mono-size particles and binary mixtures (different diameter ratios and composition). It was found that the bed expansion ratio decreases with increase in bed mass for only spouting condition and spout-fluidization conditions. The bed expansion ratio increases with increase in bed mass for only fluidization condition.     

Intelligent and Efficient Strategy for Unstructured Environment Sensing Using Mobile Robot Agents

In field environments it is not usually possible to provide robots in advance with valid geometric models of its task and environment. The robot or robot teams need to create these models by scanning the environment with its sensors. Here, an information-based iterative algorithm to plan the robot's visual exploration strategy is proposed to enable it to most efficiently build 3D models of its environment and task. The method assumes mobile robot (or vehicle) with vision sensors mounted at a manipulator end-effector (eye-in-hand system). This algorithm efficiently repositions the systems' sensing agents using an information theoretic approach and fuses sensory information using physical models to yield a geometrically consistent environment map. This is achieved by utilizing a metric derived from Shannon's information theory to determine optimal sensing poses for the agent(s) mapping a highly unstructured environment. This map is then distributed among the agents using an information-based relevant data reduction scheme. This method is particularly well suited to unstructured environments, where sensor uncertainty is significant. Issues addressed include model-based multiple sensor data fusion, and uncertainty and vehicle suspension motion compensation. Simulation results show the effectiveness of this algorithm.     

Tribological Performance Optimization of Electroless Ni–P Coatings Using the Taguchi Method and Grey Relational Analysis

The present article considers an experimental study of tribological performance of electroless Ni–P coatings and optimization of tribological test parameters based on the Taguchi method coupled with grey relational analysis. A grey relational grade obtained from the grey relational analysis is used as performance index to study the behaviour of electroless Ni–P coating with respect to friction and wear characteristics. Experiments are carried out by utilizing the combination of tribological test parameters based on L₂₇ Taguchi orthogonal design with three test parameters, viz., load, speed and time. It is observed that all the three test parameters have significant contribution in controlling the friction and wear behaviour of electroless Ni–P coating. In addition, the interaction of load and time has significant influence on tribological performance. The surface morphology, composition and wear mechanism of the coatings are studied with the help of scanning electron microscopy, X-ray diffraction analysis and energy dispersed X-ray analysis.     

Characterisation of a Schottky ISFET as Hg-MOSFET and as cytochrome P450-ENFET

A cost-effective and simple method is proposed wherein a Schottky ion sensitive field effect transistor (Schottky ISFET)-based sensor is characterised as metal oxide semiconductor and enzyme field effect transistor (ENFET). This technique involves deposition of mercury (Hg) as gate material over the sensing layer mitigating the complexity of fabrication process, thereby eliminating the need of refabricating an identical device. A Schottky-based ISFET simplifies the fabrication process as the requisite for doping of source and drain regions becomes redundant. Steps involved in lithography process for fabricating metal oxide semiconductor field effect transistor (MOSFET) are reduced with the use of liquid metal Hg as gate over layer. Such a device can be transformed back to an ISFET without any additional etching process. Furthermore, the same ISFET device can be utilised as an ENFET when the former is used in conjunction with a biological element. In this work, a Schottky-based ISFET has been characterised as Hg-MOSFET and as cytochrome P450-ENFET. Multiple tests on the device exhibit that the same ISFET sensor can be used both as a MOSFET and an ENFET with good repeatability and versatility without losing its sensitivity.     

ESR study of intramolecular energy transfer in the radiolysis of cellulose thenoates

The substitution of 2-thenoyl, 5-methyl-2-thenoyl, 2-thiopheneacryloyl, 5-bromo-2-thenoyl, and 5-bromo-2-thiopheneacryloyl groups on fibrous cotton cellulose increased the radiation resistance of cellulose, as indicated by the retention of the breaking strengths of the modified fibrous celluloses at high dosages of γ-radiation, as compared with that of irradiated, unmodified fibrous cellulose. The presence of electropositive or electronegative substituents on the thiophene groups did not reduce the radioprotective effects of these groups for cellulose. Crosslinking of the cellulose thenoates in 1,3-di(4-pyridyl)-propane did not significantly reduce the radiation resistance of the thenoates. Examination of the ESR spectra of irradiated cellulose and cellulose thenoates indicated that the site of the long-lived free radicals on the irradiated cellulose molecules was not changed by the chemical modification. However, the concentration of long-lived free radicals in irradiated cellulose thenoates, at a given radiation dosage, was less than that in irradiated cellulose. The localization of energy on carbon C₁ or C₄ of the cellulose molecule, which leads to depolymerization and loss in breaking strength of fibrous cellulose, was decreased. The radioprotective effects of thiophene groups for cellulose were similar to those of furan and benzenoid groups.     

Synthesis, characterization, and application of 1-d cerium oxide nanomaterials: a review

The present work provides a comprehensive overview of the recent progress of research work toward developing new one dimensional (1-D) ceria (CeO(2)) nanomaterials. The review has been classified into three parts: the preparation procedures with identification of the existing different dimensional ceria nanomaterials, the formation mechanisms, and an analysis of their applications. From literature survey, it is inaugurated that the fundamental structures of the ceria nanomaterials constructively dominate their properties and applications. In addition, this work will also provide a perspective on the future technical trends for the development of different dimensional CeO(2) nanomaterials.