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

Current Issue Vol. 11 Issue 2

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Volume 5 Issue 3 July - September 2017

Article

A Hamming Code Based Single Bit Error Detection and Correction for 128 or 256 Bits

Vinita Malviya* , Soumya Gupta, Hardik Thakkar*, Dharmendra Singh****

- UG Scholar, Department of Electronics and Telecommunication, SSIPMT, Raipur, India.

-**** Assistant Professor, Department of Electronics and Telecommunication, SSIPMT, Raipur, India.

Malviya, V., Gupta, S., Thakkar, H., and Singh, D. (2017). A Hamming Code Based Single Bit Error Detection and Correction for 128 or 256 Bits. i-manager's Journal on Digital Signal Processing, 5(3), 1-4. https://doi.org/10.26634/jdp.5.3.13928

Abstract

An error in data communication is very common and it cannot be avoided. The error is of two types, such as single bit and burst error, which is depends upon noise present in the path via the data, is transferred. There are various method used for the detection of this error, and this is done on the basis of even or odd parity. In this paper, a hamming code technique is used to detect and correct the single bit error. An extra bit is sent to receive along with the original data known as a parity bit, this is also done on the basis of even or odd parity. This extra bit will enhance the security of the data being transferred.

References

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[4]. Gupta, B. K. (2013). Novel Hamming code for error correction and detection of higher data bits using VHDL. International Journal of Scientific & Engineering Research, 4(4), 272-275.

[5]. Gupta, B. K., & Dua, R. L. (2012a). Communication by 31 Bit Hamming Code Transceiver with even parity and odd parity check method by using VHDL. International Journal of Computational Engineering Research, 2(2), 278-288.

[6]. Gupta, B. K., & Dua, R. L. (2012b). Various Methodologies used for 25 Bit Information Data String Communication through Hamming Code. International Journal of Applied Information Systems, 2(2), 57-65.

[7]. Gupta, B. K., & Dua, R. L. (2011). 30 bit hamming code for error detection and correction with even parity and odd parity check method by using VHDL. International Journal of Computer Applications, 35, 31-38.

[8]. Kumar, U. K., & Umashankar, B. S. (2007, February). Improved hamming code for error detection and correction. In Wireless Pervasive Computing, 2007. ISWPC'07. 2^nd International Symposium on IEEE.

[9]. Saleh, A. H. (2015). Design of Hamming Code for 64 Bit Single Error Detection and Correction using VHDL. Diyala Journal of Engineering Sciences, 8(3), 22-37.

[10]. Singh, J., & Singh, J. (2012, January). A comparative study of error detection and correction coding techniques. In Advanced Computing & Communication Technologies (ACCT), 2012 Second International Conference (pp. 187-189).

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Research Paper

Control of Multiple Renewable Sources with Fuzzy Logic VSI Controller for Power Quality Improvement

Srinivas Singirikonda* , V. Mounika**

* IEEE Member, Department of Electrical and Electronics Engineering, TKR College of Engineering and Technology, JNTUH, Hyderabad, India.

** IEEE Member, Department of Electrical and Electronics Engineering, TKR College of Engineering and Technology, JNTUH, Hyderabad, India.

Singirikonda, S., and Mounika, V. (2017). Control of Multiple Renewable Sources with Fuzzy Logic VSI Controller for Power Quality Improvement. i-manager's Journal on Digital Signal Processing, 5(3), 5-19. https://doi.org/10.26634/jdp.5.3.13929

Abstract

Dynamic Stability Analysis of a Connected Wind and Photovoltaic Micro Grid System is proposed in this research work using the Voltage Source Converter (VSC) and the Fuzzy Control VSI (Voltage Source Inverter) integrated into an electrical grid. The wind farm consist of a Permanent Magnet Synchronous Generator (PMSG) powered by a wind turbine. The power outputs of PMSG and the photovoltaic system are fed to a common DC link from a Voltage -Source Converter (VSC) and DC-DC boost converter together with a supercapacitor, to soften the generated energy delivered to the micro-grid. This work proposes a control scheme with a fuzzy logic structure to achieve the stability of the proposed system while obtaining maximum power extractions for the wind and the photovoltaic system. Along with these, renewable sources such as the battery and fuel cell are also integrated into DC link where the actions are observed. The analysis and total design is done in the MATLAB software with graphic representations of dynamic performance.

References

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[3]. Cao, J., & Emadi, A. (2012). A new batter y/ ultracapacitor hybrid energy storage system for electric, hybrid, and plug-in hybrid electric vehicles. IEEE Transactions on Power Electronics, 27(1), 122-132.

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[6]. Khanh, L. N., Seo, J. J., Kim, Y. S., & Won, D. J. (2010). Power-management strategies for a grid-connected PVFC hybrid system. IEEE Transactions on Power Delivery, 25(3), 1874-1882.

[7]. Kim, S. K., Jeon, J. H., Cho, C. H., Ahn, J. B., & Kwon, S. H. (2008). Dynamic modeling and control of a gridconnected hybrid generation system with versatile power transfer. IEEE Transactions on Industrial Electronics, 55(4), 1677-1688.

[8]. Li, W., & Joós, G. (2007). Comparison of energy storage system technologies and configurations in a wind farm. In Power Electronics Specialists Conference (pp. 1280-1285). IEEE.

[9]. Li, X., Hui, D., & Lai, X. (2013). Battery Energy Storage Station (BESS)-based smoothing control of photovoltaic (PV) and wind power generation fluctuations. IEEE Transactions on Sustainable Energy, 4(2), 464-473.

[10]. Liu, F., Duan, S., Liu, F., Liu, B., & Kang, Y. (2008). A variable step size INC MPPT method for PV systems. IEEE Transactions on Industrial Electronics, 55(7), 2622-2628.

[11]. Liu, X., Wang, P., & Loh, P. C. (2011). A hybrid AC/DC microgrid and its coordination control. IEEE Transactions on Smart Grid, 2(2), 278-286.

[12]. Lu, S. Y., Wang, L., Lo, T. M., & Prokhorov, A. V. (2015). Integration of wind power and wave power generation systems using a DC microgrid. IEEE Transactions on Industry Applications, 51(4), 2753-2761.

[13]. Mutoh, N., & Inoue, T. (2007). A control method to charge series-connected ultraelectric double-layer capacitors suitable for photovoltaic generation systems combining MPPT control method. IEEE Transactions on Industrial Electronics, 54(1), 374-383.

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[15]. Nelson, D. B., Nehrir, M. H., & Wang, C. (2006). Unit sizing and cost analysis of stand-alone hybrid wind/PV/fuel cell power generation systems. Renewable Energy, 31(10), 1641-1656.

[16]. Polinder, H., Van der Pijl, F. F., De Vilder, G. J., & Tavner, P. J. (2006). Comparison of direct-drive and geared generator concepts for wind turbines. IEEE Transactions on Energy Conversion, 21(3), 725-733.

[17]. Sandhu, M., & Thakur, T. (2014). Issues, challenges, causes, impacts and utilization of renewable energy sources-Grid Integration. Int. Journal of Engineering Research and Applications, 4(3), 636-643.

[18]. Shadmand, M. B., & Balog, R. S. (2014). Multiobjective optimization and design of photovoltaic-wind hybrid system for community smart DC microgrid. IEEE Transactions on Smart Grid, 5(5), 2635-2643.

[19]. Singaravel, M. R., & Daniel, S. A. (2015). MPPT with single DC–DC converter and inverter for grid-connected hybrid wind-driven PMSG–PV system. IEEE Transactions on Industrial Electronics, 62(8), 4849-4857.

[20]. Tara, E., Filizadeh, S., Jatskevich, J., Dirks, E., Davoudi, A., Saeedifard, M., Strunz,, K. & Sood, V. K. (2012). Dynamic average-value modeling of hybrid-electric vehicular power systems. IEEE Transactions on Power Delivery, 27(1), 430-438.

[21]. Wang, C., & Nehrir, M. H. (2008). Power management of a stand-alone wind/photovoltaic/fuel cell energy system. IEEE Transactions on Energy Conversion, 23(3), 957- 967.

[22]. Yoshimoto, K., Nanahara, T., Koshimizu, G., & Uchida, Y. (2006). New control method for regulating state-ofcharge of a battery in hybrid wind power/Battery Energy Storage System. In Power Systems Conference and Exposition (pp. 1244-125). IEEE.

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Research Paper

HSI Color Conversion and Hog Feature Extraction in Traffic Light Detection using AdaBSF

J. Jebisha* , V. Monisha, B. Femila Jemi*

*-*** Department of Electronics and Communication Engineering, Arunachala College of Engineering for Women, TamilNadu, India.

Jebisha, J., Monisha, V., and Jemi, F, B. (2017). HSI Color Conversion and Hog Feature Extraction in Traffic Light Detection using AdaBSF. i-manager's Journal on Digital Signal Processing, 5(3), 20-26. https://doi.org/10.26634/jdp.5.3.13930

Abstract

Real time traffic light detection system is an important application of intelligent transportation system. In different driving environment, traffic light is regarded as a difficult one for various illuminations due to the distance measurements. Adaptive Background Suppresion Filter (AdaBSF) is used for background suppression of the images. In this proposed work, the authors include HSI image conversion and HOG feature extraction methods along with the conventional AdaBSF algorithm. Linear cascaded SVM is used for the classification of SVM based image. The enhanced method has been finally considered to be better for finding the traffic light even under strong sunlight, cloudy seasons, and during night times. The authors have performed traffic light detection in real time and they consider that this technique is efficient than other conventional traffic light detection systems.

References

[1]. Cai, Z., Li, Y., & Gu, M. (2012, July). Real-time recognition system of traffic light in urban environment. In Computational Intelligence for Security and Defence Applications (CISDA), 2012 IEEE Symposium on (pp. 1-6). IEEE.

[2]. Davis, M., & Sahin, F. (2016, October). HOG feature human detection system. In Systems, Man, and Cybernetics (SMC), 2016 IEEE International Conference on (pp. 002878-002883). IEEE.

[3]. de Charette, R., & Nashashibi, F. (2009, October). Traffic light recognition using image processing compared to learning processes. In Intelligent Robots and Systems, 2009. IROS 2009. IEEE/RSJ International Conference on (pp. 333-338). IEEE.

[4]. Devika, R., Prabhu, P., Naveen, B., & Kumar, P. S. (2017). Real time Traffic Light and Brake Light Detection Using ANN Classifier. SSRG International Journal of Computer Science and Engineering, 197-203.

[5]. Diaz-Cabrera, M., Cerri, P., & Sanchez-Medina, J. (2012, September). Suspended traffic lights detection and distance estimation using color features. In Intelligent Transportation Systems (ITSC), 2012 15^th International IEEE Conference on (pp. 1315-1320). IEEE.

[6]. Fairfield, N., & Urmson, C. (2011, May). Traffic light mapping and detection. In Robotics and Automation (ICRA), 2011 IEEE International Conference on (pp. 5421- 5426). IEEE.

[7]. Gong, J., Jiang, Y., Xiong, G., Guan, C., Tao, G., & Chen, H. (2010, June). The recognition and tracking of traffic lights based on color segmentation and camshift for intelligent vehicles. In Intelligent Vehicles Symposium (IV), 2010 IEEE (pp. 431-435). IEEE.

[8]. Jang, C., Kim, C., Kim, D., Lee, M., & Sunwoo, M. (2014, June). Multiple exposure images based traffic light recognition. In Intelligent Vehicles Symposium Proceedings, 2014 IEEE (pp. 1313-1318). IEEE.

[9]. John, V., Yoneda, K., Qi, B., Liu, Z., & Mita, S. (2014, October). Traffic light recognition in varying illumination using deep learning and saliency map. In Intelligent Transportation Systems (ITSC), 2014 IEEE 17^th International Conference on (pp. 2286-2291). IEEE.

[10]. Kantorov, V., & Laptev, I. (2014). Efficient feature extraction, encoding, and classification for action recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2593- 2600).

[11]. Kramer, O. (2015). Cascade support vector machines with dimensionality reduction. Applied Computational Intelligence and Soft Computing, Article ID 216132. Retrieved from http://dx.doi.org/10.1155/2015/216132

[12]. Lindner, F., Kressel, U., & Kaelberer, S. (2004, June). Robust recognition of traffic signals. In Intelligent Vehicles Symposium, 2004 IEEE (pp. 49-53). IEEE.

[13]. Omachi, M., & Omachi, S. (2009, August). Traffic light detection with color and edge information. In Computer Science and Information Technology, 2009. ICCSIT 2009. 2^nd IEEE International Conference on (pp. 284-287). IEEE.

[14]. Omachi, M., & Omachi, S. (2010, October). Detection of traffic light using structural information. In Signal Processing (ICSP), 2010 IEEE 10^th International Conference on (pp. 809-812). IEEE.

[15]. Shen, Y., Ozguner, U., Redmill, K., & Liu, J. (2009, June). A robust video based traffic light detection algorithm for intelligent vehicles. In Intelligent Vehicles Symposium, 2009 IEEE (pp. 521-526). IEEE.

[16]. Shi, Z., Zou, Z., & Zhang, C. (2016). Real-time traffic light detection with Adaptive Background Suppression Filter. IEEE Transactions on Intelligent Transportation Systems, 17(3), 690-700.

[17]. Shioyama, T., Wu, H., Nakamura, N., & Kitawaki, S. (2002). Measurement of the length of pedestrian crossings and detection of traffic lights from image data. Measurement Science and Technology, 13(9), 1450.

[18]. Siogkas, G., Skodras, E., & Dermatas, E. (2012, February). Traffic Lights detection in adverse conditions using Color, Symmetry and Spatiotemporal Information. In VISAPP (Vol. 1, pp. 620-627).

[19]. Tae-Hyun, H., In-Hak, J., & Seong-Ik, C. (2006, December). Detection of traffic lights for vision-based car navigation system. In Pacific-Rim Symposium on Image and Video Technology (pp. 682-691). Springer, Berlin, Heidelberg.

[20]. Wang, C., Jin, T., Yang, M., & Wang, B. (2011). Robust and real-time traffic lights recognition in complex urban environments. International Journal of Computational Intelligence Systems, 4(6), 1383-1390.

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Research Paper

Fusion Based Integrated Advance Magnetic Visualization of MRI 3D Images Using Advance Matlab Tools

Padmaja Grandhe* , E. Sreenivasa Reddy, D. Vasumathi*

* Research Scholar, Department of Computer Science and Engineering, JNTUK, Kakinada, A.P, India.

Dean & Professor, Department of Computer Science and Engineering, Acharya Nagarjuna University, Guntur, A.P, India.

* Professor, Department of Computer Science and Engineering, JNTUH, Hyderabad, Telangana, India.

Grandhe, P., Reddy, S, E., and Vasumathi, D. (2017). Fusion Based Integrated Advance Magnetic Visualization of MRI 3D Images Using Advance Matlab Tools. i-manager's Journal on Digital Signal Processing, 5(3), 27-39. https://doi.org/10.26634/jdp.5.3.13932

Abstract

The fusion expression means to extract a sequence which is acquired in several domains. The three-dimensional (3D) images have the deep information, which is not available in the conventional 2D images. The image fusion procedure of two images aim to get a more in-depth examination of the picture. 3D Fusion of medical images are found to be useful that they are medical images containing the data with significant scientific information for doctors during their analysis. The objective of this work is to examine the subsections of the obtained 3D structure along three axes. The paper deals with the DICOM (Digital Imaging and Communication in Medicine) images restoration, which is initially extremely useful for production of customized data that are atomically implemented by using a fast prototyping technology. MRI Images provide better contrast of soft tissues than CT images. Hence it provides better results in image fusion of MRI and CT images is done by using Wavelet Transforms in MATLAB. The researchers are forever focusing on biomedical 3D imaging configuration. The image slices of the involved region in the modified image in DICOM format are preprocessed first using developed a Matlab code, which is an open source medical software used to reconstruct structures of the human body based on three-dimensional images which are acquired using CT or MRI images. The proposed algorithm FBIAMV (Fusion Based Integrated Advance Magnetic Visualization) of MRI 3D images generates the three-dimensional models equivalent to different parts of the human body. The proposed multiform method help doctors and other clinicians in diagnosis of diseases leading to a better treatment.

References

[1]. Abood, Z. M. (2013). Edges enhancement of medical color images using add images. IOSR Journal of Research & Method in Education, 2(4), 52-60.

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[8]. El Naqa, I., Yang, D., Apte, A., Khullar, D., Mutic, S., Zheng , J .,&D easy,J.O.(2007).Concurrent multimodality image segmentation by active contours for radiotherapy treatment planning. Medical Physics, 34(12), 4738-4749.

[9]. Hsu, A. R., Cai, W., Veeravagu, A., Mohamedali, K. A., Chen, K., Kim, S., & Chen, X. (2007). Multimodality molecular imaging of glioblastoma growth inhibition with vasculature-targeting fusion toxin VEGF121/rGel. Journal of Nuclear Medicine, 48(3), 445-454.

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[16]. Milker-Zabel, S., Zabel-du Bois, A., Henze, M., Huber, P., Schulz-Ertner, D., Hoess, A., ... & Debus, J. (2006). Improved target volume definition for fractionated stereotactic radiotherapy in patients with intracranial meningiomas by correlation of CT, MRI, and [68Ga]- DOTATOC-PET. International Journal of Radiation Oncology Biology Physics, 65(1), 222-227.

[17]. Nestle, U., Kremp, S., Schaefer-Schuler, A., Sebastian-Welsch, C., Hellwig, D., Rübe, C., & Kirsch, C. M. (2005). Comparison of different methods for delineation of 18F-FDG PET–positive tissue for target volume definition in radiotherapy of patients with non–small cell lung cancer. Journal of Nuclear Medicine, 46(8), 1342-1348.

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Review Paper

Algorithms for the Analysis of MST Radar Signals - A Survey

P. Suresh Babu* , G.Sreenivasulu**

* Research Scholar, Sri Venkateswara University College of Engineering, Sri Venkateswara University, Tirupati, India.

** Professor and Head, Department of Electronics and Communication Engineering, Sri Venkateswara University College of Engineering, Sri Venkateswara University, Tirupati, India.

Babu, S, P., and Sreenivasulu, G. (2017). Algorithms for the Analysis of MST Radar Signals - A Survey. i-manager's Journal on Digital Signal Processing, 5(3), 40-44. https://doi.org/10.26634/jdp.5.3.13933

Abstract

The analysis of the MST Radar Data has become more predominant in the present world. A major research on MST Radar Data was going on in NARL, Gadanki, which provides the data regarding the atmospheric movements. In order to obtain the information on the wind parameters, the signals collected from the radar are analyzed, which mainly involves the estimation of power spectrum. Analysis of the MST Radar Data involves the estimation of the Signal to Noise Ratio (SNR) of the Doppler profiles, Wind Profiles and Parameters, Temperature Profiles, etc. In NARL, the weather data or the Meteor data was collected and the data was converted into raw format. This raw data was used for processing either in time domain or frequency domain. Many techniques have been employed for processing the MST Radar data, which shows better results when compared to the GPS Data (Data Processed in NARL). This paper mainly gives survey of papers in which various methods are used for the analysis of the MST Radar data.

References

[1]. Eappen, N. I., Reddy, T. S., & Reddy, G. R. (2016). Semiparametric Algorithm for processing MST Radar Data. IEEE Transactions on Geoscience and Remote Sensing, 54(5), 2713-2721.

[2]. Madhu, C., Babu, P. S., & Jayachandranath, S. (2012). Denoising of Spectral Data using Complex Wavelets. International Journal of Engineering Research and Applications, 2(5), 1352-1357.

[3]. Rao, D. U. M., Reddy, T. S., & Reddy, G. R. (2014). Atmospheric Radar Signal Processing using Principal Component Analysis. Digital Signal Processing, 32, 79-84.

[4]. Reddy, D. R. K., & Anuradha, B. (2014). SNR Improvement of MST Radar Signals by 3COSH Window over Kaiser Window. International Journal of Advanced Research in Computer and Communication Engineering, 3(1), 4959-4966.

[5]. Reddy, T. S., & Reddy, G. R. (2010a). MST Radar Signal Processing Using Cepstral Thresholding. IEEE Transactions on Geoscience and Remote Sensing, 48(6), 2704-2710.

[6]. Reddy, T. S., & Reddy, G. R. (2010b). Spectral Analysis of Atmospheric Radar Signal using Filter Banks-polyphase Approach. Digital Signal Processing, 20(4), 1061-1071.

[7]. Reddy, T. S., Reddy, G. R., & Varadarajan, S. (2009). MST Radar Signal Processing using Wavelet-based Denoising. IEEE Geoscience and Remote Sensing Letters, 6(4), 752-756.

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

Current Issue Vol. 11 Issue 2

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Most Cited

Volume 5 Issue 3 July - September 2017

A Hamming Code Based Single Bit Error Detection and Correction for 128 or 256 Bits

Vinita Malviya* , Soumya Gupta**, Hardik Thakkar***, Dharmendra Singh****

*-** UG Scholar, Department of Electronics and Telecommunication, SSIPMT, Raipur, India. ***-**** Assistant Professor, Department of Electronics and Telecommunication, SSIPMT, Raipur, India.

Malviya, V., Gupta, S., Thakkar, H., and Singh, D. (2017). A Hamming Code Based Single Bit Error Detection and Correction for 128 or 256 Bits. i-manager's Journal on Digital Signal Processing, 5(3), 1-4. https://doi.org/10.26634/jdp.5.3.13928

Abstract

References

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Control of Multiple Renewable Sources with Fuzzy Logic VSI Controller for Power Quality Improvement

Srinivas Singirikonda* , V. Mounika**

* IEEE Member, Department of Electrical and Electronics Engineering, TKR College of Engineering and Technology, JNTUH, Hyderabad, India. ** IEEE Member, Department of Electrical and Electronics Engineering, TKR College of Engineering and Technology, JNTUH, Hyderabad, India.

Singirikonda, S., and Mounika, V. (2017). Control of Multiple Renewable Sources with Fuzzy Logic VSI Controller for Power Quality Improvement. i-manager's Journal on Digital Signal Processing, 5(3), 5-19. https://doi.org/10.26634/jdp.5.3.13929

Abstract

References

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HSI Color Conversion and Hog Feature Extraction in Traffic Light Detection using AdaBSF

J. Jebisha* , V. Monisha**, B. Femila Jemi***

*-*** Department of Electronics and Communication Engineering, Arunachala College of Engineering for Women, TamilNadu, India.

Jebisha, J., Monisha, V., and Jemi, F, B. (2017). HSI Color Conversion and Hog Feature Extraction in Traffic Light Detection using AdaBSF. i-manager's Journal on Digital Signal Processing, 5(3), 20-26. https://doi.org/10.26634/jdp.5.3.13930

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Fusion Based Integrated Advance Magnetic Visualization of MRI 3D Images Using Advance Matlab Tools

Padmaja Grandhe* , E. Sreenivasa Reddy**, D. Vasumathi***

Grandhe, P., Reddy, S, E., and Vasumathi, D. (2017). Fusion Based Integrated Advance Magnetic Visualization of MRI 3D Images Using Advance Matlab Tools. i-manager's Journal on Digital Signal Processing, 5(3), 27-39. https://doi.org/10.26634/jdp.5.3.13932

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Algorithms for the Analysis of MST Radar Signals - A Survey

P. Suresh Babu* , G.Sreenivasulu**

* Research Scholar, Sri Venkateswara University College of Engineering, Sri Venkateswara University, Tirupati, India. ** Professor and Head, Department of Electronics and Communication Engineering, Sri Venkateswara University College of Engineering, Sri Venkateswara University, Tirupati, India.

Babu, S, P., and Sreenivasulu, G. (2017). Algorithms for the Analysis of MST Radar Signals - A Survey. i-manager's Journal on Digital Signal Processing, 5(3), 40-44. https://doi.org/10.26634/jdp.5.3.13933

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Vinita Malviya* , Soumya Gupta, Hardik Thakkar*, Dharmendra Singh****

- UG Scholar, Department of Electronics and Telecommunication, SSIPMT, Raipur, India.

-**** Assistant Professor, Department of Electronics and Telecommunication, SSIPMT, Raipur, India.

* IEEE Member, Department of Electrical and Electronics Engineering, TKR College of Engineering and Technology, JNTUH, Hyderabad, India.

** IEEE Member, Department of Electrical and Electronics Engineering, TKR College of Engineering and Technology, JNTUH, Hyderabad, India.

J. Jebisha* , V. Monisha, B. Femila Jemi*

Padmaja Grandhe* , E. Sreenivasa Reddy, D. Vasumathi*

* Research Scholar, Sri Venkateswara University College of Engineering, Sri Venkateswara University, Tirupati, India.

** Professor and Head, Department of Electronics and Communication Engineering, Sri Venkateswara University College of Engineering, Sri Venkateswara University, Tirupati, India.