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In the current scenario, learning has taken a completely new dimension. New methods of teaching like E-learning and collaborative learning etc have computerized the learning process to a large extent. But the teachers' presence is still mandatory at all times. Also the current systems have low reliability so the teacher has to monitor them constantly. To solve this problem this paper introduces an automated question answering system. This proposed system answers the queries posted by the student in a more interactive way like a virtual teacher (chatbot system). The Q&A knowledge base can be accessed and modified by the instructor who could also know the areas where the students are more prone to doubts thus helping the student as well as the instructor.

Multicarrier modulations like OFDM suffer from large Peak-to-Average-Power Ratio (PAPR) which causes challenges to the RF front-end. Large PAPR lead to inefficient use of High Power Amplifiers (HPAs) or decreases the system performance. A large number of PAPR reduction methods have been proposed in literature like coding, clipping, tone reservation, tone injection, symbol phase manipulation and constellation adjustment. Active constellation extension (ACE) is a simple method that extends the outer constellation points away such that the minimum distance between the constellation points is not reduced. In this paper adaptive ACE for higher order constellation is simulated. The results are conduced by the power spectral density plots, CCDF and BER plots.

This paper is about the application of statistical pattern recognition techniques to the classification of text with the objective of retrieving documents relevant for the construction of gene networks. We start from the usual practice of representing a document, electronically available abstracts of scientific papers in this case, as a high dimensional vector of term of occurrences. We consider the problem of retrieving documents corresponding to the metabolic pathway of the organism yeast, Saccharomyces Cerevisiae, using a trained classifier as filter. We use support vector machines (SVMs) as classifiers and compare techniques for reducing the dimensionality of the problem: latent semantic kernels (LSK) and sequential forward selection (SFS). In order to deal with the issue of having only a small set of accurately labelled documents, we used the approach of transductive inference. In this case, LSK leads to a subspace formed as a linear combination of features (terms in the lexicon) while SFS selects a subset of the dimension. We find, for this problem, that the discriminant information appears to lie in a subspace, which is very small in dimensionality compared to that of the original formulation. By matching against the gene ontology (GO) database, we further find that the selection process (SFS) picks out the discriminant terms that are of biological significance for this problem.

The nonlinear prediction capability of neural nets is applied to the design of improved predictive speech coders. Performance evaluations and comparisons with linear predictive speech coding are presented. These tests show the applicability of nonlinear prediction to speech coding and the improvement in coding performance. >

We present a technique to determine the embedding dimension of deterministic time series. The technique is based mainly on the assumption that if a continuous deterministic time series, with first derivative and underlying model f/sub S/(x/sub t-1/, ..., x/sub t-m/)=x/sub t/ is represented on its convenient embedding dimension /spl Rfr//sup m/, then any two neighbouring input observations x/sub m//sup i/ and x/sub m//sup j/, should correspond to similar output observations x/sub t//sup i/ and x/sub t//sup j/. Thus the ratio between the input and output distances of neighbouring observations is used as a criterion to determine the time series embedding dimension. The performance of this technique is illustrated and compared using synthetic time series (Logistic map, Henon map and Mackey Glass function), as well as the Laser data from the Santa Fe competition. Our technique applied to the Laser data, in combination with a Limited Resource Allocating Neural Network (LRAN), has proved to be as successful as the techniques proposed by the winners of the competition on the 100-step prediction of this chaotic time series.

We present an approach for the enhancement of speech signals corrupted by additive white noise of Gaussian statistics. The speech enhancement problem is treated as a signal estimation problem within a Bayesian framework. The conventional all-pole speech production model is assumed to govern the behaviour of the clean speech signal. The additive noise level and all-pole model gain are automatically inferred during the speech enhancement process. The strength of the Bayesian approach developed in this paper lies in its ability to perform speech enhancement without the usual requirement of estimating the level of the corrupting noise from "silence" segments of the corrupted signal. The performance of the Bayesian approach is compared to that of the Lim & Oppenheim (1978) framework, to which it follows a similar iterative nature. A significant quality improvement is obtained over the Lim & Oppenheim framework.

This paper investigates a Bayesian approach to the enhancement of speech signals corrupted by additive white Gaussian noise. Parametric models for the speech and noise processes are constructed, leading to a posterior distribution for the model parameters and uncorrupted speech samples given the observed noisy speech samples. Being analytically intractable, inferences concerning these variables are performed using Markov chain Monte Carlo (MCMC) methods. The efficiency of the sampling scheme within this framework is further improved by employing state-space techniques based on the Kalman filter.

Considering the perceptual importance of phonetic transitions as minimal contextual variant units, this paper addresses the problem by modelling explicitly interphone dynamics covered in diphones. Subspace projections based on a time-constrained PCA (TC-PCA) are developed which focus on the temporal evolution. They reveal characteristic trajectories present in a low-dimensional spectral representation facilitating robust parameter estimation and simultaneously optimise the discriminant information. A matched filter design is applied to a multiple hypotheses rescoring scheme which enables operating in very low-dimensional parameter space. Using such multiple hypotheses paradigm the complementary information effectiveness of modelling explicitly inter-phone dynamics covered in diphones can be shown using the TIMIT database, resulting in improved phone error rates.