Robotics. Soft Computing

Speech is the most efficient and popular means of human communication Speech is produced as a sequence
of phonemes. Phoneme recognition is the first step performed by automatic speech recognition system. The
state-of-the-art recognizers use mel-frequency cepstral coefficients (MFCC) features derived through short
time analysis, for which the recognition accuracy is limited. Instead of this, here broad phoneme
classification is achieved using features derived directly from the speech at the signal level itself. Broad
phoneme classes include vowels, nasals, fricatives, stops, approximants and silence. The features identified
useful for broad phoneme classification are voiced/unvoiced decision, zero crossing rate (ZCR), short time
energy, most dominant frequency, energy in most dominant frequency, spectral flatness measure and first
three formants. Features derived from short time frames of training speech are used to train a multilayer
feedforward neural network based classifier with manually marked class label as output and classification
accuracy is then tested. Later this broad phoneme classifier is used for broad syllable structure prediction
which is useful for applications such as automatic speech recognition and automatic language
identification.

Epilepsy is one of the pervasive neurological disorders associated with ephemeral and unforeseen
electrical disturbances in the brain. In This paper the EEG signals are resolved into a finite set of band
limited signals termed as Intrinsic mode functions. The Hilbert transform is applied on these IMF’s to
calculate instantaneous frequencies. The 2nd,3rd and 4th IMF's are used to extract features of epileptic
signal. A neural network utilizing back propagation algorithm is implemented for classification of
epilepsy. An overall accuracy of 99.8% is achieved in classification.

A new methodology is developed to analyse existing water quality monitoring networks. This methodology
incorporates different aspects of monitoring, including vulnerability/probability assessment, environmental
health risk, the value of information, and redundancy reduction. The work starts with a formulation of a
conceptual framework for groundwater quality monitoring to represent the methodology’s context. This
work presents the development of Bayesian techniques for the assessment of groundwater quality. The
primary aim is to develop a predictive model and a computer system to assess and predict the impact of
pollutants on the water column. The process of the analysis begins by postulating a model in light of all
available knowledge taken from relevant phenomenon. The previous knowledge as represented by the prior
distribution of the model parameters is then combined with the new data through Bayes’ theorem to yield
the current knowledge represented by the posterior distribution of model parameters. This process of
updating information about the unknown model parameters is then repeated in a sequential manner as
more and more new information becomes available.

This research describes the development of an online thesis database system to assist novice researchers in a Malaysian university to identify research topics in local language major problems have been identified in the current system of keyword search. Firstly, student's lack of experience in querying often results in irrelevant search outcomes. Secondly, language barrier limits students' abilities to conduct keywor semantic search approach that applies ontology system framework design, its ontological development and sample queries are also presented to demonstrate how the system works

This paper is in continuation to what we did previously in the field of Soft Computing. In the previous workthe author proposed several solutions to the XOR problem. In this paper we will see some solutions to theEx-NOR problem using the concept of MLFFN. Ex-NOR problem is a non-linearly separable problem thesolutions of which are discussed in this paper. The Artificial Neural Network proposed is mathematicallyproved to be successful in providing solution to the Ex-NOR problem. The solutions proposed areexplained by means of Architectural Graph and Signal flow graph.

In this work modelling and control of Perfusion system is presented. The Perfusion system simultaneously
controls the partial pressures during Extra Corporeal Membrane Oxygenation (ECMO) support. The
main Problem in ECMO system is exchange of Blood Gases in the Artificial Lung (Oxygenator).It is a
highly Nonlinear Process comprising time-varying parameters, and varying time delays, it is currently
being controlled manually by trained Perfusionist. The new control strategy implemented here has a
feedback linearization routine with time-delay compensation for the Partial pressuresof Oxygen and
Carbon dioxide. The controllers were tuned robustly and tested in simulations with a detailed artificial
Lung (Oxygenator) model in Cardiopulmonary bypass conditions. This Automatic control strategy is
proposed to improve the patient’s safety by fast control reference tracking and good disturbance rejection
under varying conditions.

It is widely acknowledged that in software engineering, the usage of metrics at the initial phases of the object oriented software can help designers to make better decisions. The quality of class diagrams could be a major determinant for the quality of the software product that is finally delivered. Quantitative measurements are useful to assess class diagram quality. Following this innovative thinking, two UML class diagrams are taken to measure the complexity and size. A set of metrics of complexity measures are used to measure the class diagrams. Seven known complexity measures are evaluated and compared for inheritance and interface usage in object oriented programming. Two UML class diagrams are introduced with possible interfaces and measured the complexity metrics and a comparison has been made between the class inheritance and class interface usage through complexity measurements.