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1

Mixed convection in MHD flow and heat transfer rate near a stagnation-point on a non-linear vertical stretching sheet

Fenuga O. J., Hassan A. R., Olanrewaju P. O.

International Journal of Applied Mechanics and Engineering

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2020

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Vol. 25, no. 1

37--51

EN

This work investigates the mixed convection in a Magnetohydrodynamic (MHD) flow and heat transfer rate near a stagnation-point region over a nonlinear vertical stretching sheet. Using a similarity transformation, the governing equations are transformed into a system of ordinary differential equations which are solved numerically using the fourth order Runge-Kutta method with shooting technique. The influence of pertinent flow parameters on velocity, temperature, surface drag force and heat transfer rate are computed and analyzed. Graphical and tabular results are given to examine the nature of the problem. The heat transfer rate at the surface increases with the mixed convection.

2

Effects of radiation and Eckert number on MHD flow with heat transfer rate near a stagnation point over a non-linear vertical stretching sheet

Fenuga O. J., Hassan A. R., Olanrewaju P. O.

International Journal of Applied Mechanics and Engineering

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2020

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Vol. 25, no. 1

27--36

EN

This work investigates the effects of radiation and Eckert number on an MHD flow with heat transfer rate near a stagnation-point region over a nonlinear vertical stretching sheet. Using a similarity transformation, the governing equations are transformed into a system of ordinary differential equations which are solved numerically using the sixth order Runge-Kutta method with shooting technique. Tabular and graphical results are provided to examine the physical nature of the problem. Heat transfer rate at the surface decreases with radiation, Eckert number and as radiation increases, the flow temperature also increases for velocity ratio parameters […].

3

Computer-aided obstructive sleep apnea screening from single-lead electrocardiogram using statistical and spectral features and bootstrap aggregating

Hassan A. R., Haque M. A.

Biocybernetics and Biomedical Engineering

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2016

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Vol. 36, no. 1

256--266

EN

Automatic sleep apnea screening is important to alleviate the onus of the physicians of analyzing a large volume of data visually. Again, the push towards low-power, portable and wearable sleep quality monitoring systems necessitates the use of minimum number of recording channels to enhance battery life. So, there is a dire need of an automated apnea detection scheme based on single-lead electrocardiogram (ECG). Most of the existing works are based on multiple channels of physiological signals or yield poor performance. The effect of various classification models on algorithmic performance is also poorly explored. In the present work, we propose a statistical and spectral feature based sleep apnea identification scheme that utilizes single-lead ECG signals. Bootstrap aggregating is employed to perform classification. The efficacy of the selected features is demonstrated by intuitive, statistical and graphical analyses. Optimal choices of classifier parameters are also expounded. The performance of the proposed algorithm is evaluated for various classifiers. The performance of our method is also compared to that of the state-of-the-art ones. The proposed method yields accuracy, sensitivity and specificity of 85.97%, 84.14% and 86.83% respectively on a widely used benchmark data-set. Experimental findings backed by statistical and graphical analyses suggest that the proposed method performs better than the existing ones in terms of accuracy, sensitivity, specificity and computational cost.

4

Automatic sleep scoring using statistical features in the EMD domain and ensemble methods

Hassan A. R., Bhuiyan M. I. H.

Biocybernetics and Biomedical Engineering

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2016

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Vol. 36, no. 1

248--255

EN

An automatic sleep scoring method based on single channel electroencephalogram (EEG) is essential not only for alleviating the burden of the clinicians of analyzing a high volume of data but also for making a low-power wearable sleep monitoring system feasible. However, most of the existing works are either multichannel or multiple physiological signal based or yield poor algorithmic performance. In this study, we propound a data-driven and robust automatic sleep staging scheme that uses single channel EEG signal. Decomposing the EEG signal segments using Empirical Mode Decomposition (EMD), we extract various statistical moment based features. The effectiveness of statistical features in the EMD domain is inspected. Statistical analysis is performed for feature selection. We then employ Adaptive Boosting and decision trees to perform classification. The performance of our feature extraction scheme is studied for various choices of classification models. Experimental outcomes manifest that the performance of the proposed sleep staging algorithm is better than that of the state-of-the-art ones. Furthermore, the proposed method's non-REM 1 stage detection accuracy is better than most of the existing works.