Objective. To investigate the effect of yoga practice on cognitive skills, autonomic nervous system, and heart rate variability by analyzing physiological parameters. Methods. The study was conducted on 30 normal young healthy engineering students. They were randomly selected into two groups: yoga group and control group. The yoga group practiced yoga one and half hour per day for six days in a week, for a period of five months. Results. The yoga practising group showed increased α, β, and δ EEG band powers and significant reduction in θ and γ band powers. The increased α and β power can represent enhanced cognitive functions such as memory and concentration, and that of δ signifies synchronization of brain activity. The heart rate index θ/α decreased, neural activity β/θ increased, attention resource index β/(α + θ) increased, executive load index (δ + θ)/α decreased, and the ratio (δ + θ)/(α + β) decreased. The yoga practice group showed improvement in heart rate variability, increased SDNN/RMSSD, and reduction in LF/HF ratio. Conclusion. Yoga practising group showed significant improvement in various cognitive functions, such as performance enhancement, neural activity, attention, and executive function. It also resulted in increase in the heart rate variability, parasympathetic nervous system activity, and balanced autonomic nervous system reactivity.

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In this paper, an algorithm for order reduction of linear multivariable systems is proposed using the combined advantages of the dominant pole retention method and the error minimization by Genetic algorithm. The denominator of the reduced order transfer function matrix is obtained by retaining the dominant poles of the original system while the numerator terms of the lower order transfer matrix are determined by minimizing the integral square error in between the transient responses of original and reduced order models using Genetic algorithm. Each element of the transfer function matrix of the original system is considered separately. The reduction procedure is simple and computer oriented. The proposed algorithm guarantees stability of the reduced order transfer function matrix if the original high order system is stable and is having superior features, including easy implementation and good computational efficiency. The proposed algorithm has been applied successfully to the transfer function matrix of a 10 th order two-input two-output linear time invariant model of a practical power system. The performance of the algorithm is tested by comparing the relevant computer simulation results.

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A mixed method is proposed for deriving reduced-order models of high-order linear time invariant systems using the combined advantages of eigen spectrum analysis and the Padé approximation technique. The denominator of the reduced-order model is found by eigen spectrum analysis, and the dynamics of the numerator are chosen using the Padé approximation technique. This method guarantees stability of the reduced model if the original high-order system is stable. The method is illustrated by three numerical examples.