Efficient adaptive algorithm for modeling a speech signal
This paper presents a time-domain prediction error method based on Newton algorithm identification for the identification of auto regressive moving average (ARMA) models of linear structural systems. The proposed structural identification method can incorporate as much as possible structural information known a priori into the structural identification process to improve the identification accuracy. This method incorporates a recursive procedure that makes successive corrections in determining, a linear mathematical model, based on the data of observation, to represent the system considered. To avoid the practical difficulty often associated with bias prediction error measurement, a stochastic Newton method is proposed. The numerical results show that the proposed method is capable to obtain the minimal standard deviation of error prediction and the estimated parameters are closer with actual values. An application of a speech signal is provided then to identify the parameters of AR model corresponding to the speech signal.
A Comparative Study of Speech Modeling Methods
In this paper, we present two stochastic methods, to identify the parameters of a physical process with unbiased estimates based on whitening error of prediction. These methods incorporate a recursive procedure that makes successive corrections in determining, a linear mathematical model, based on the data of observation, to represent the system considered. The ARMA model is a typical example. Several tests of simulation were carried out to show the abilities of the least mean squares algorithm LMS and stochastic Newton algorithm. An application is provided then to identify the parameters of AR model corresponding to the speech signal. Key-Words: Modeling, Identification, Unbiased estimator, Newton method, Gradient method, Speech signal.
Applications of nanocomposite hydrogels for biomedical engineering and environmental protection
Nanocomposite hydrogels are polymeric networks that possess a unique property of hydration. The presence of alcohols, carboxylic acids and amides as hydrophilic moieties in structure of nanocomposite hydrogels enhances their stiffness and water-absorbing capacity. Addition of cross-linker in the synthesis of hydrogels increases their stability under extreme conditions of temperature, pH and pressure. Natural polymer-based nanocomposite hydrogels are biodegradable, highly hydrophilic and possess good mechanical strength. Gelatin, chitin, cellulose, pectin, carrageenan, starch and alginate are natural polymers commonly used to fabricate nanocomposite hydrogels. Nanocomposite hydrogels have special characteristics such as high swelling rate, selectivity and stimuli-sensitive nature. Here we review nanocomposite hydrogels for environmental protection and biomedical engineering. Applications in biomedical engineering include drug delivery agents, wound dressing, tissue engineering and antibacterials. Applications in environmental protection include ion exchangers, adsorption, photocatalysis and soil conditioning. Many nanocomposite hydrogels show excellent adsorption selectivity for heavy metal ions: Cu2+ up to 30.35 mg/g, Pb2+ up to 35.94 mg/g, and Zn2+ and Fe3+ up to 94.34 mg/g. Xanthan gum-based nanocomposite hydrogel has removed 96% dye from industrial effluent as reported. In addition, most of the nanocomposite hydrogels showed better adsorption capacity for pollutants in the pH range from 5 to 7. The nanocomposite hydrogels could also be regenerated and successfully utilized for several times. Nanocomposite hydrogels are therefore good bio-absorbent materials for environmental detoxification.