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.     

SHELF LIFE OF SPRAY-DRIED MILK FORMULATIONS SUPPLEMENTED WITH N-3 FATTY ACIDS

Studies were carried out to assess the shelf life of spray‐dried milk formulation containing linseed oil (LSO) or fish oil (FO) as a source of n‐3 polyunsaturated fatty acids (PUFA) in comparison with a milk formulation containing groundnut oil (GNO) stored at 4, 27 and 37C in tin cans for a period of up to 6 months. Total protein, fat, carbohydrate, moisture and solubility index of formulations stored for 6 months were comparable with fresh samples. The fresh samples containing LSO contained 20% ∝ linolenic acid (LNA). At the end of 6 months, the ∝ LNA levels were found to be 18.4, 18.0 and 16.2% in samples stored at 4, 27 and 37C, respectively. The fresh samples containing FO had 2.4% eicosapentaenoic acid (EPA). At the end of 6 months, the EPA levels were found to be 2.2, 2.0 and 1.6% in samples stored at 4, 27 and 37C, respectively. The docosahexaenoic acid (DHA) level in the freshly prepared sample containing FO was 2.1%. At the end of 6 months, the DHA levels were found to be 1.7, 1.6 and 1.3% in samples stored at 4, 27 and 37C, respectively. The whiteness of the formulation containing GNO, LSO or FO remained unaltered for a period of 5 months, but dropped marginally when n‐3 PUFA‐containing formulations were stored at 37C for 6 months. Acceptability scores of formulations were similar for 5 months storage but declined significantly in the FO formulation at the end of 6 months of storage. All n‐3 PUFA‐enriched milk formulations were stable and accepted well for up to 5 months when stored at different temperatures.     

Unavoidable front contact model of Si solar cell through a generalized effective medium approximation approach

Fill factor of the solar cell mainly depends on series resistance and contact resistance, which are the most effective parameters to collect carriers (electrons and holes) from both electrodes of C-Si solar cells. We have used both mathematical and experimental approaches to reduce these resistances for enhancement of power conversion efficiency (PCE) by increasing fill factor. After processing by light-induced plating (LIP) for metal contact, the PCE of solar cell is obtained as 14.43%, which is 8.8% more than that before LIP processing.     

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.