A. H. M. Zadidul Karim

Work place: Department of EEE, University of Asia Pacific, Dhaka, Bangladesh

E-mail: zadidkarim@gmail.com


Research Interests: Bioinformatics, Computational Biology


A.H.M Zadidul Karim, male, has been serving as an Assistant professor in the Department of Electrical and Electronic Engineering (EEE) Department, University of Asia Pacific (UAP). He joined at UAP in April 2007 after completion his BSc. Engineering. He has completed his MEngg. from BUET. Right now he teaches courses on Digital Electronics, Digital signal processing (DSP), Electrical Machines, Electrical and Electronics Circuits. He is the convener of journal and publication club of EEE department UAP. He has several publications on biomedical signal processing and digital signal processing field.

Author Articles
Successive RR Interval Analysis of PVC With Sinus Rhythm Using Fractal Dimension, Poincaré Plot and Sample Entropy Method

By Md. Meganur Rhaman A. H. M. Zadidul Karim Md. Maksudul Hasan Jarin Sultana

DOI: https://doi.org/10.5815/ijigsp.2013.02.03, Pub. Date: 8 Feb. 2013

Premature ventricular contractions (PVC) are premature heartbeats originating from the ventricles of the heart. These heartbeats occur before the regular heartbeat. The Fractal analysis is most mathematical models produce intractable solutions. Some studies tried to apply the fractal dimension (FD) to calculate of cardiac abnormality. Based on FD change, we can identify different abnormalities present in Electrocardiogram (ECG). Present of the uses of Poincaré plot indexes and the sample entropy (SE) analyses of heart rate variability (HRV) from short term ECG recordings as a screening tool for PVC. Poincaré plot indexes and the SE measure used for analyzing variability and complexity of HRV. A clear reduction of standard deviation (SD) projections in Poincaré plot pattern observed a significant difference of SD between healthy Person and PVC subjects. Finally, a comparison shows for FD, SE and Poincaré plot parameters.

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Effects of Natural Dust on the Performance of PV Panels in Bangladesh

By Md.Mizanur Rahman Md. Aminul Islam A. H. M. Zadidul Karim Asraful Haque Ronee

DOI: https://doi.org/10.5815/ijmecs.2012.10.04, Pub. Date: 8 Oct. 2012

Energy is considered a prime agent in the generation of wealth and a significant factor in economic development. Limited fossil resources and environmental problems associated with them have emphasized the need for new sustainable energy supply options that use renewable energies. Among available technologies for energy production from solar source, photovoltaic system could give a significant contribution to develop a more sustainable energy system. Solar Panel has its wide use starting from a simple 5W diode lamp to a few kW ac drives. A solar panel with a battery and a charge controller and other auxiliary devices like dc to ac converters constitute a Solar Home System (SHS).Solar home system (SHS) is becoming popular day by day and even poor households are now becoming interested to purchase solar home system due to its various advantages. Solar home systems (SHS) have a major problem that is low efficiency. It also decreases output day by day because of improper maintenances, effect of dust and shadow. Accumulation of dust on solar panel of solar photovoltaic (PV) system is a natural process. It was found from the study that the accumulated dust on the surface of photovoltaic solar panel can reduce the system’s efficiency by up to 35% in one month .In this paper we show that the effect of dust accumulation on the solar panel naturally and how it is possible to overcome this problem.

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Classification of ECG Using Chaotic Models

By Khandakar Mohammad Ishtiak A. H. M. Zadidul Karim

DOI: https://doi.org/10.5815/ijmecs.2012.09.04, Pub. Date: 8 Sep. 2012

Chaotic analysis has been shown to be useful in a variety of medical applications, particularly in cardiology. Chaotic parameters have shown potential in the identification of diseases, especially in the analysis of biomedical signals like electrocardiogram (ECG). In this work, underlying chaos in ECG signals has been analyzed using various non-linear techniques. First, the ECG signal is processed through a series of steps to extract the QRS complex. From this extracted feature, bit-to-bit interval (BBI) and instantaneous heart rate (IHR) have been calculated. Then some nonlinear parameters like standard deviation, and coefficient of variation and nonlinear techniques like central tendency measure (CTM), and phase space portrait have been determined from both the BBI and IHR. Standard database of MIT-BIH is used as the reference data where each ECG record contains 650000 samples. CTM is calculated for both BBI and IHR for each ECG record of the database. A much higher value of CTM for IHR is observed for eleven patients with normal beats with a mean of 0.7737 and SD of 0.0946. On the contrary, the CTM for IHR of eleven patients with abnormal rhythm shows low value with a mean of 0.0833 and SD 0.0748. CTM for BBI of the same eleven normal rhythm records also shows high values with a mean of 0.6172 and SD 0.1472. CTM for BBI of eleven abnormal rhythm records show low values with a mean of 0.0478 and SD 0.0308. Phase space portrait also demonstrates visible attractor with little dispersion for a healthy person’s ECG and a widely dispersed plot in 2-D plane for the ailing person’s ECG. These results indicate that ECG can be classified based on this chaotic modeling which works on the nonlinear dynamics of the system.

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