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i-manager's Journal on Digital Signal Processing (JDP)

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Volume 4 Issue 1 January - March 2016 [Open Access]

Research Paper

Filtering of ECG Signal Using Adaptive and Non Adaptive Filters

Abhishek Sahu* , Jitendra Kumar**

* PG Scholar, Department of Electronics and Telecommunication, SSTC, Bhilai, India.

** Assistant Professor, Department of Electronics and Instrumentation, SSTC, Bhilai, India.

Sahu,A., and Kumar,J. (2016). Filtering of ECG Signal Using Adaptive and Non Adaptive Filters,i-manager’s Journal on Digital Signal Processing, 4(1), 1-8. https://doi.org/10.26634/jdp.4.1.4855

Abstract

Electrocardiogram (ECG) is an important diagnostic tool for the diagnosis of cardiac abnormalities. In this paper, the authors introduce a study on different types of noises, For example, Power Line Interference (PLI), Motion Artifacts, Electrode Contact Noise, Muscle Contraction, Base Line Drift, Electromyography/noise (EMG), Instrumentation Noise, etc. To eliminate the above mentioned noises, various algorithms of adaptive filter are used and authors also used Discrete Wavelet Transform (DWT) to remove Random Artifacts and filter with constant coefficients as because, hum manner is not accurate. To solve this problem, digital filters are used such as Adaptive filters as Least Mean Square (LMS), Normalized Least Mean Square (NLMS), Recursive Least Square (RLS), sign LMS, sign-sign LMS algorithms and Discrete Wavelet Transform (DWT). The performance of algorithms are evaluated by Signal to Noise Ratio (SNR), Mean Square Error (MSE), Percentage Root Mean Square (%PRD) and Normalized Mean Square (NMSE). In comparison to various adaptive algorithms, SSLMS gives better result for all parameters with MSE = 0.0262, NRMSE = 0.0033 , %PRD = 0.3331, RMSE = 0.331, and SNR = -4.3914 .

References

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[20]. Manikandan MS, and Dandapat S. (2007). “Wavelet energy based diagnostic distortion measure for ECG”. Biomed Signal Process Control, Vol. 2, pp. 80–96.

[21]. R. Sivakumar, R. Tamilselvi and S. Abinaya, (2012). “Noise Analysis & QRS Detection in ECG Signals”. International Conference on Computer Technologyand Science (ICCTS 2012).

[22]. Smita Kasar, Abbhilasha Mishra, and Madhuri Joshi, (2014). “Performance of digital filters for noise removal from ECG signals in time domain”. International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering, Vol. 2, No. 4.

[23]. Uzzal Biswas, and Md. Maniruzzaman, (2014). “ReSmoving power line interference from ECG signal using adaptive filter and notch filter”, International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT) 2014.

[24]. Ashraf A.M. Khalaf, Mostafa. M. Ibrahim, Hesham F, and A. Hamed, (2015). “Performance Study of Adaptive Filtering and Noise Cancellation of Artifacts in ECG Signals”. Department of Electronics & Communications Engineering, Faculty of Engineering, Minia University, Minia, Egypt, ICACT2015, ISBN 978-89-968650-5-6 .

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Research Paper

Feature Extraction of ECG Signal Using Labview

Shubham Mishra* , Shreyash Pandey, Khemraj Deshmukh*, Jitendra Kumar****

- UG Scholar, Department of Electronics and Instrumentation, SSTC, Bhilai, India.

-**** Assistant Professor, Department of Electronics and Instrumentation, SSTC, Bhilai, India.

Mishra,S.,Pandey,S.,Deshmukh,K., and Kumar,J. (2016). Feature Extraction of ECG Signal Using Labview. i-manager’s Journal on Digital Signal Processing, 4(1), 9-15. https://doi.org/10.26634/jdp.4.1.4856

Abstract

In this paper, the authors extracted features of ECG signal using LabVIEW software. The real time ECG signal the authors use, is taken from MIT BIH database in .edf format. The signal is then converted into suitable LabVIEW format using biomedical toolkit provided by NI. The converted signal is then filtered and pre-processed using wavelet transformation technique. ECG features is then extracted which includes P onset, P offset, QRS onset, QRS offset, T onset, T offset, R, P and T wave using the extracted features using which they calculate various parameters like heart rate.

References

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[2]. Guodong Tang and Aina Qin. “ECG Denoising based th on Empirical Mode Decomposition”. 9 International Conference for Young Computer Scientists, pp. 903-906.

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[5]. M. K. Islam, A. N. M. M. Haque, G. Tangim, T. Ahammad, and M. R. H. Khondokar, (2012). “Study and Analysis of ECG Signal Using MATLAB &LABVIEW as Effective Tools”. International Journal of Computer and Electrical Engineering, Vol. 4, No. 3.

[6]. Deepa Annamalai, and S. Muthukrishnan, (2014). “Study and analysis of ECG signal using labview and multisim”. IJPRET, Vol. 2(7), pp. 26-34.

[7]. Juan Pablo Martínez, Rute Almeida,Salvador Olmos, Ana Paula Rocha, and PabloLaguna, (2004). “A Wavelet- Based ECG Delineator:Evaluation on Standard Databases”. IEEE Transactions on Biomedical Engineering, Vol. 51, No. 4.

[8]. Cory L. Clark, (2005). LabVIEW Digital Signal Processing and Digital Communication. Tata McGrawhill Edition.

[9]. ChannappaBhyri, Kalpana. V, S.T. Hamde, and L.M. Waghmare, (2009). “Estimation of ECG features using LabVIEW”. TECHNIA – International Journal of Computing Science and Communication Technologies, Vol. 2, No. 1, (ISSN 0974-3375).

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[15]. B. Castro, D. Kogan, and A. B. Geva, (2000). “ECG st feature extraction using optimal mother wavelet”, The 21 IEEE Convention of the Electrical and Electronic Engineers in Israel, pp. 346-350.

[16]. Emran M. Tamil, Nor HafeezahKamarudin, RosliSalleh, M. Yamani Idna Idris, Noorzaily M. Noor, and AzmiMohd Tamil, “Heartbeat Electrocardiogram (ECG) Signal Feature Extraction Using Discrete Wavelet Transforms (DWT).”

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Research Paper

A Comprehensive Analysis for ECG Classification Using Wavelet Transform

Mayank Kumar Gautam* , V. K. Giri**

* PG Scholar, Department of Electrical Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, India.

** Professor, Department of Electrical Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, India.

Gautham,M,K., and Giri,V,K. (2016). A Comprehensive Analysis for ECG Classification Using Wavelet Transform,i-manager’s Journal on Digital Signal Processing, 4(1), 16-23. https://doi.org/10.26634/jdp.4.1.4858

Abstract

ECG is basically the graphical representation of the electrical activity of cardiac muscles during contraction and release stages. It helps in determination of the cardiac arrhythmias in a well manner. Due to this early detection, arrhythmias can be done properly. In other words, we can say that the bio-potentials generated by the cardiac muscles results in an electrical signal called Electro-cardiogram (ECG). Feature extraction of ECG plays a vital role in manual as well as automatic analysis of ECG for the use in specially designed instruments like ECG monitors, Holter tape recorders and scanners, ambulatory ECG recorders and analyzers. In this paper, the study of pattern recognition of ECG is done. The ECG signal generated waveform gives almost all information about activity of the heart. The feature extraction of ECG is by Wavelet transform. This paper also includes artificial neural network as a classifier for identifying the abnormalities of heart disease.

References

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[2]. Jenisha. J. Hannah and Suja Priyadharsini, (2012). “Patient Adaptive ECG Beat Classifier using Repetition Detection Approach Enhanced by Neural Networks”. International Conference on Computing and Research and Control Engineering (ICCCE 2012), ISBN:978-1-4675- 2248-9.

[3]. K .O. Gupta and P. N. Chatur, (2012). “ECG Signal Analysis and Classification using Data Mining and Artificial Neural Networks”. International Journal of Emerging Technology Advanced Engineering, ISSN:2250-2459, Vol. 2, No. 1, pp. 56-60.

[4]. J. T. Catalano. Guide to ECG Analysis. Lippincott, 2 Edition.

[5]. Bucolo M., Grazia F. D., Sapuppo F., Nikolic D., Vuksanovic B., (2009). “Multidimensional Analysis toward the Identification of ECG Nonlinear Dynamics”. PHYSCON 2009, Catania, Italy.

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[13]. T. M. Nazmy, H. El-Messiry and B. Albokhity, (2009). “Adaptive Neuro-Fuzzy Inference System for Classification of Ecg Signals”. Journal of Theoretical and Applied Information Technology.

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[16]. Philip de Chazal, Maria O'Dwyer and Richard B. Reilly, (2004). “Automatic Classification of Heartbeats Using ECG Morphology and Heartbeat Interval Features”. IEEE Transactions on Biomedical Engineering, Vol. 51, pp. 1196 - 1206.

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Research Paper

Mode-Division Multiplexing over Few-Mode Fiber Using Coherent MIMO Digital Signal Processing

Anuja Mishra* , Sharad Mohan Shrivastava, Pooja Sharma, Prachi Agrawal, Rahul Parganiha

,-* PG Scholar, Department of Communication Engineering, Chhattisgarh Swami Vivekanand Technical University, Bhilai, India.

Assistant Professor, Department of Electronics & Telecommunication, Shri Shankaracharya Technical Campus, Bhilai, India.

Mishra,A., Shrivastava,S,M., Sharma,P., Agrawal,P., and Parganiha,R., (2016). Mode-Division Multiplexed Transmission over Few-Mode Fiber Using Coherent MIMO Digital Signal Processing, i-manager’s Journal on Digital Signal Processing, 4(1), 24-31. https://doi.org/10.26634/jdp.4.1.4860

Abstract

As we know that the capacity limits of single mode fiber has almost reached its maxima, Space Division Multiplexing can be helpful for increasing the data rate requirement. This paper, inferred the transmission of 6 spatial and polarisation modes, each carrying the quadrature-phase-shift-keyed channels over few-mode fiber keeping lower differential group delay. The authors present a Multiple-Input Multiple-Output (MIMO) optical link based on coherent optics and its ability to exploit the inherent information capacity of a multimode fiber. A Coherent implementation differs from previous work in optical MIMO by allowing the system to tolerate smaller modal delay spread yet maintains the necessary diversity needed for MIMO operation. Here in this paper, the authors use Optiwave’s Optisystem software for carrying out required simulations. Optisystem provides Visualizer library consisting of spectrum analyzer, time domain visualizer, power meter, WDM analyzer, Oscilloscope visualizer, etc, which will be helpful in verifying whether the signal has been transmitted efficiently via Few-mode fiber from transmitter section to receiver one with significant flexibility.

References

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[2]. R.-J. Essiambre, G. Kramer, P. J. Winzer, G. J. Foschini, and B. Goebel, (2010). “Capacity limits of optical fiber networks”. J. Lightwave Technol. Vol. 28, No. 4, pp. 662- 701 .

[3]. A. Chraplyvy, (2009). “The coming capacity crunch”. Proc. European Conf. on Opt. Commun. (ECOC).

[4]. P. J. Winzer, (2011). “Energy-efficient optical transport capacity scaling through spatial multiplexing”. Photon. Technol. Lett. Vol. 23, No.13, pp. 851- 853 .

[5]. S. Berdague and P. Facq, (1982). “Mode Division Multiplexing in optical fibers”. Appl. Opt. Vol. 21, No. 11, pp.1950 -1955.

[6]. M.de Boer, C. P. Tsekrekos, A. Martinez, H. Kurniawan, J. W. M. Bergmans, A. M. J. Koonen, H. P. A. Van Den Boom, and F. M. J.Willems, (2005). “A first demonstrator for a mode group diversity multiplexing communication system”. Proc. IEE Seminar, Optical Fibre Communications and Electronic Signal Processing.

[7]. S. Schollmann, S. Soneff, and W. Rosenkranz, (2007). “10.7 Gb/s over 300 m GI-MMF using a 2x2 MIMO system based on mode group diversity multiplexing”. Proc. Conf. Optical Fiber Communication and the National Fiber Optic Engineers Conf. OFC/NFOEC 2007, pp.1-3.

[8]. A.R. Shah, R. C. J. Hsu, A. Tarighat, A. H. Sayed and B. Jalali, (2005). “Coherent Optical MIMO (COMIMO)”. J. Lightwave Technol., Vol. 23, No. 8, pp. 2410-2419.

[9]. A. Li, A. Al Amin, X. Chen and W. Shieh, (2011). ”Reception of mode and polarization multiplexed 107Gb/s CO-OFDM signal over a two-mode fiber”. Proc. Opt. Fiber Commun. Conf. (OFC), PDPB8 .

[10]. M. Salsi, C. Koebele , D. Sperti, P. Tran, P. Brindel, H. Mardoyan, S.Bigo, A. Boutin, F. Verluise, P. Sillard, M. Bigot- Astruc, L. Provost, F. Cerou and G.Charlet, (2011). ”Transmission at 2100Gb/s, over two modes of 40km-long prototype few-mode fiber, using LCOS based mode multiplexer and demultiplexer,” Proc. Opt. Fiber Commun. Conf. (OFC) , PDPB9 .

[11]. N. Hanzawa, K. Saitoh, T. Sakamoto, T. Matsui, S. Tomita, and M. Koshiba, (2011). “Demonstration of Mode- Division Multiplexing transmission over 10 km two-mode fiber with mode coupler”. Proc. Opt. Fiber Commun. Conf. (OFC) , OWA4.

[12]. R. Ryf, S. Randel, A.H. Gnauck, C. Bolle, A. Sierra and S. Mumtaz, M. Esmaeelpour, E. C. Burrows, R.-J. Essiambre, P. J. Winzer, D.W. Peckham, A. McCurdy, and R. Lingle, (2011). “Mode-Division Multiplexing over 96 km of few-mode fiber using coherent 6x6 MIMO processing”. J. Lightwave Technol., Special Issue about OFC/NFOEC

[13]. E. Ip, N. Bai, Y.-K. Huang, E. Mateo, F. Yaman, M.-J. Li, S. Bickham, S. Ten, J. Linares, C. Montero, V. Moreno, X. Prieto, V. Tse, K. M. Chung, A. Lau, H.-Y. Tam, C. Lu, Y. Luo, G.-D. Peng and G. Li (2011). “88x3x112-gb/s WDM transmission over 50 km of three-mode fiber with inline Few-Mode Fiber amplifier”. Proc. European Conf. on Opt. Commun. (ECOC).

[14]. C. Koebele, M. Salsi, L. Milord, R. Ryf, C. Bolle, P. Sillard, S. Bigo and G. Charlet, (2011). “40 km transmission of five mode division multiplexed data streams at 100Gb/s with low MIMO-DSP complexity”. Proc. European Conf. on Opt. Commun. (ECOC).

[15]. P. J. Winzer and C.J. Foschini (2011). “MIMO capacities and outage probabilities in spatially multiplexed optical transport systems”. Optics Express Vol.19, No.17, pp.16680-16696.

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[20]. Y. Awaji, N. Wada, and Y. Toda (2010). “Demonstration of spatial mode division multiplexing using Laguerre Gaussian mode beam in telecom rd wavelength”. Proc. IEEE 23 Annu. Meet. Photon. Soc., pp. 551-552.

[21]. J. Salz, (1985). “Digital transmission over crosscoupled linear channels”. ATnT Tech. J., Vol. 64, pp. 1147 1159.

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[23]. Roland Ryf, Sebastian Randel, Rene- Jean Essiambre and Peter J.Winzer, (2012). “Space-Division Multiplexed Transmission Over Few-Mode- and Coupled- Core Fiber Based on Coherent MIMO Digital Signal Processing”. Bell Labs Technical Planary Paper.

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[25]. Anuja Mishra, Sharad Mohan Shrivastava, Pooja Sharma, Prachi Agrawal and Rahul Parganiha, (2015). ”Space-Division Multiplexed Transmission Over Few-Mode Fiber Based On Coherent MIMO Digital Signal Processing : A Review”, i-managers Journal on Digital Signal Processing, Vol. 3, No. 4 .

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Research Paper

Forecast and Explication of ECG Signal Ongoings Using Soft Computing Techniques

Santosh Kumar Suman* , Mayank Kumar Gautam, V. K. Giri*

- PG Scholar, Department of Electrical Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, India.

Professor, Department of Electrical Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, India.

Suman,S,K.,Gautham,M,K., and Giri, V,K. (2016). Forecast and Explication of ECG Signal Ongoings Using Soft Computing Techniques. i-manager’s Journal on Digital Signal Processing, 4(1), 32-38. https://doi.org/10.26634/jdp.4.1.4861

Abstract

The major cause of human loss in Cardiovascular Disease (CVD) is Cardiac problems, that are increasing day-by-day in the world. In order to achieve a great effort and to diagnose the cardiovascular disease, many people use different types of Mobile Electrocardiogram (ECG) in remote monitoring techniques. ECG Feature Extraction acts as an important role in diagnosing most part of the cardiac diseases. Now it has been comprehensively reviewed all way through for feature extraction of ECG signal analyzing, feature extracting, followed by classifying which has been planned a longtime ago. Here the authors have introduced soft computing techniques. To recognize the present situation of the heart, Electrocardiography and is an essential tool, but it is a time consuming process to analyze a continuous ECG signal as it may hold thousands of nonstop heart beats. At this point, the authors convert analog signal in to a digital one, vice versa, and it helps in accurately diagnosing the signal. Aim of this paper is to present a detection of some heat arrhythmias using soft computing techniques.

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i-manager's Journal on Digital Signal Processing (JDP)

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Volume 4 Issue 1 January - March 2016 [Open Access]

Filtering of ECG Signal Using Adaptive and Non Adaptive Filters

Abhishek Sahu* , Jitendra Kumar**

* PG Scholar, Department of Electronics and Telecommunication, SSTC, Bhilai, India. ** Assistant Professor, Department of Electronics and Instrumentation, SSTC, Bhilai, India.

Sahu,A., and Kumar,J. (2016). Filtering of ECG Signal Using Adaptive and Non Adaptive Filters,i-manager’s Journal on Digital Signal Processing, 4(1), 1-8. https://doi.org/10.26634/jdp.4.1.4855

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Feature Extraction of ECG Signal Using Labview

Shubham Mishra* , Shreyash Pandey**, Khemraj Deshmukh***, Jitendra Kumar****

*-** UG Scholar, Department of Electronics and Instrumentation, SSTC, Bhilai, India. ***-**** Assistant Professor, Department of Electronics and Instrumentation, SSTC, Bhilai, India.

Mishra,S.,Pandey,S.,Deshmukh,K., and Kumar,J. (2016). Feature Extraction of ECG Signal Using Labview. i-manager’s Journal on Digital Signal Processing, 4(1), 9-15. https://doi.org/10.26634/jdp.4.1.4856

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A Comprehensive Analysis for ECG Classification Using Wavelet Transform

Mayank Kumar Gautam* , V. K. Giri**

* PG Scholar, Department of Electrical Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, India. ** Professor, Department of Electrical Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, India.

Gautham,M,K., and Giri,V,K. (2016). A Comprehensive Analysis for ECG Classification Using Wavelet Transform,i-manager’s Journal on Digital Signal Processing, 4(1), 16-23. https://doi.org/10.26634/jdp.4.1.4858

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Mode-Division Multiplexing over Few-Mode Fiber Using Coherent MIMO Digital Signal Processing

Anuja Mishra* , Sharad Mohan Shrivastava**, Pooja Sharma***, Prachi Agrawal****, Rahul Parganiha*****

*,***-***** PG Scholar, Department of Communication Engineering, Chhattisgarh Swami Vivekanand Technical University, Bhilai, India. ** Assistant Professor, Department of Electronics & Telecommunication, Shri Shankaracharya Technical Campus, Bhilai, India.

Mishra,A., Shrivastava,S,M., Sharma,P., Agrawal,P., and Parganiha,R., (2016). Mode-Division Multiplexed Transmission over Few-Mode Fiber Using Coherent MIMO Digital Signal Processing, i-manager’s Journal on Digital Signal Processing, 4(1), 24-31. https://doi.org/10.26634/jdp.4.1.4860

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Forecast and Explication of ECG Signal Ongoings Using Soft Computing Techniques

Santosh Kumar Suman* , Mayank Kumar Gautam**, V. K. Giri***

*-** PG Scholar, Department of Electrical Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, India. *** Professor, Department of Electrical Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, India.

Suman,S,K.,Gautham,M,K., and Giri, V,K. (2016). Forecast and Explication of ECG Signal Ongoings Using Soft Computing Techniques. i-manager’s Journal on Digital Signal Processing, 4(1), 32-38. https://doi.org/10.26634/jdp.4.1.4861

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* PG Scholar, Department of Electronics and Telecommunication, SSTC, Bhilai, India.

** Assistant Professor, Department of Electronics and Instrumentation, SSTC, Bhilai, India.

Shubham Mishra* , Shreyash Pandey, Khemraj Deshmukh*, Jitendra Kumar****

- UG Scholar, Department of Electronics and Instrumentation, SSTC, Bhilai, India.

-**** Assistant Professor, Department of Electronics and Instrumentation, SSTC, Bhilai, India.

* PG Scholar, Department of Electrical Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, India.

** Professor, Department of Electrical Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, India.

Anuja Mishra* , Sharad Mohan Shrivastava, Pooja Sharma, Prachi Agrawal, Rahul Parganiha

,-* PG Scholar, Department of Communication Engineering, Chhattisgarh Swami Vivekanand Technical University, Bhilai, India.

Assistant Professor, Department of Electronics & Telecommunication, Shri Shankaracharya Technical Campus, Bhilai, India.

Santosh Kumar Suman* , Mayank Kumar Gautam, V. K. Giri*

- PG Scholar, Department of Electrical Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, India.

Professor, Department of Electrical Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, India.