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

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Volume 4 Issue 4 October - December 2016

Research Paper

Comparative Study of Induction Motor Stator Current Using FFT and Wavelet

Jayanta Kumar Sahni* , Kuldeep Sahay, Satyendra Singh*

* Senior Research Assistant, Department of Electrical Engineering, Institute of Engineering & Technology, Lucknow, U.P, India.

Professor & Head, Department of Electrical Engineering, Institute of Engineering & Technology, Lucknow, U.P, India.

* Associate Professor, Department of Electrical Engineering, Institute of Engineering & Technology, Lucknow, U.P, India.

Sahni, J, K, Sahay, K, and Singh, S (2016). Comparative Study of Induction Motor Stator Current Using FFT and Wavelet. i-manager’s Journal on Digital Signal Processing, 4(4), 1-7. https://doi.org/10.26634/jdp.4.4.8310

Abstract

This paper draws a comparative study of the induction motor stator current using Fast Fourier Transform (FFT) and Wavelet transform. The FFT is one of the most popular and oldest techniques used in signal processing to extract the frequency contents of the signal in the steady state. However, it suffers a drawback that in the process of extracting frequency information; the time information gets partially lost. This drawback is compensated by the wavelet transform. This is a new technique which is gaining popularity in many fields of science and engineering. In this paper, a single phase induction motor stator current signal data has been analyzed using FFT and Wavelet in the latest MATLAB environment. It has been shown that these two transform techniques reveals different aspects of a same stator current signal.

References

[1]. Jee-Hoong Jung, Jong-Jae Lee, and Bong-Hwan Kwon, (2006). "Online diagnosis of Induction Motors using MCSA”. IEEE Transactions on Industrial Electronics, Vol.53, No.6.

[2]. Zhongming Ye, Bin Wu and Alireza Sadeghian (2003). "Current Signature Analysis of Induction Motor Mechanical Faults by Wavelet Packet Decomposition”. IEEE Transactions on Industrial Electronics, Vol.50, No.6,

[3]. Bashir Mahdi Ebrahimi, Jawad Faiz, Lofti-Farad, and P Pillay, (2012). "Novel Indices for Broken Rotor Bar Faults Diagnosis in Induction Motors using Wavelet Transform”. Mechanical System and Signal Processing, Elsevier, Vol.30, pp.131-145.

[4]. S. Bakhri, N. Ertugrul, W.L. Soong, and S. Al-Sarawi, (2007). "Investigation and Development of a Real-Time on-line Condition Monitoring System for Induction Motor”. Australasian University Power Engineering Conference, Perth, Australia, pp.9-12.

[5]. D.K Chaturvedi, Md. Sharif Iqbal, and Mayank Pratap Singh, (2015). "Condition Monitoring of Induction motor”. International Conference on Recent Development in Control, Automation and Power Engineering (RDCAPE), pp.135-140.

[6]. William T. Thomson and Ronald J. Gilmore, (2003). "Motor Current Signature analysis to Detect Faults in Induction Motor Drives-Fundamentals, data Interpretation, and Industrial Case Histories”. Proceedings of the Thirtysecond Turbo Machinery Symposium, pp. 149-156.

[7]. Neelam Mehala, and Ratna Dahiya, (2007). "Motor Current Signature Analysis and its Application In Induction Motor Fault Diagnosis”. International Journal of System Applications, Engineering & Development, Vol.2, No. 1.

[8]. B. Hulugappa, Tajmul Pashab, and K.M Ramakrishnan, (2012). "Condition Monitoring of Induction Motor Ball Bearing using Monitoring Techniques”. International Journal of Scientific and Research Publication, Vol.2, No. 11.

[9]. Khadim Moin Siddiqui, Kuldeep Sahay, and V.K. Giri, (2014). “Health Monitoring and Fault Diagnosis in Induction Motor-A Review”. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol.3, No.1, pp.6549-6565.

[10]. Ravi C Bhavsar and R.A Patel, (2013). “Various Techniques for Condition Monitoring of Three Phase Induction Motor”. International Journal of Engineering Inventions, Vol. 3, No. 4, pp.22-26.

[11]. Tommy WS Chow and Shi Hai, (2004). “Induction Machine Fault Diagnostic Analysis with Wavelet Technique”. IEEE Transactions on Industrial Electronics, Vol.51, No.3.

[12]. Robi Polikar, (1996). The Wavelet Tutorial, Second Edition.

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Research Paper

Wavelet-Packet-Transform Based Differential Protection of Power Transformer

Suribabu* , Sanker Ram**

* Research Scholar, Department of Electrical and Electronics Engineering, JNTUH, Telangana, India.

** Professor, Department of Electrical and Electronics Engineering, JNTUH, Telangana, India.

Suribabu, and Ram,S (2016). Wavelet-Packet-Transform Based Differential Protection of Power Transformer. i-manager’s Journal on Digital Signal Processing, 4(4), 8-16. https://doi.org/10.26634/jdp.4.4.8311

Abstract

One of the most expensive apparatus in a power system network is the Power Transformer, which needs a continuous monitoring of its health such that the system performs efficiently. Modern power transformer is one of the most vital devices of the electric power system and its protection is critical. For this reason, the protection of power transformers has taken an important consideration by the researchers. This paper details a unique algorithm adopting wavelet packet transform method which separates the special features between magnetizing inrush and fault current, of differential current in a differential protection of a three phase transformer The performance of the proposed technique is verified under a variety of fault conditions on a typical IEEE system which is used for testing and verifying the results. Thus the proposed method is found to be fast and accurate.

References

[1]. Suribabu Miriyala, and Sanker Ram, (2016). “Fuzzy Logic Techniques and Wavelet Transform Based Differential Protection for Power Transformer”. International Journal of Innovative Research in Science, Engineering, and Technology, Vol.5, No.12, pp. 20775 – 20785.

[2]. P. Liu, O.P. Malik, D. Chen, G. Hope, and Y. Guo, (1992). “Improved Operation of Differential Protection of Power Transformers for Internal Faults”. IEEE Trans. Power Deliv., Vol.7, No.4, pp. 1912–1919.

[3]. S.A. Salehand and M.A. Rahman, (2005). "Modeling and Protection of a Three-phase Power Transformer using Wavelet Packet Transform”. IEEE Trans. Power Del., Vol.20, No.2, pp. 1273–1282.

[4]. R. Naresh, V. Sharma, and M. Vashisth, (2008). “An Integrated Neural Fuzzy Approach for Fault Diagnosis of Transformers”. IEEE Trans. Power Del., Vol.23, No.4, pp.2017–2024.

[5]. X. Hao and S. Cai-xin, (2007). “Artificial immune Network Classification Algorithm for Fault Diagnosis of Power Transformer”. IEEE Trans. Power Del., Vol.22, No.2, pp.930–935.

[6]. T. M. Lai, L. A. Snider, E. Lo, and D. Sutanto, (2005). “High-impedance Fault Detection using Discrete Wavelet Transform and Frequency Range and RMS Conversion”. IEEE Trans. Power Del., Vol. 20, No.1, pp.397–407.

[7]. M. Geethanjali and S. Mary Raja Slochanal, (2008). “A Combined Adaptive Network and Fuzzy Inference System (ANFIS) Approach for Over current Relay System”. Proceedings of Neuro Computing, Vol.7.1, pp.895-903.

[8]. M. Oliveira and A. Bretas, (2009). “Application of Discrete Wavelet Transform for differential protection of power transformers”. Power Tech. IEEE Bucharest, pp. 1–8.

[9]. X. Dong and S. Shi, (2008). “Identifying single-phase-toground fault feeder in neutral non effectively grounded distribution system using wavelet transform”. IEEE Trans. Power Del., Vol. 23, No. 4, pp.1829–1837.

[10]. S. A. Saleh and M. A. Rahman, (2005). “Testing of a Wavelet Packet Transform based Differential Protection for Resistance-grounded Three-phase Transformers”. IEEE IAS Annu., Vol.2, pp.852–859.

[11]. R. Naresh, V. Sharma, and M. Vashisth, (2008). “An Integrated Neural Fuzzy Approach for Fault Diagnosis of Transformers”. IEEE Trans. Power Del., Vol.23, No.4, pp.2017–2024.

[12]. X. Hao and S. Cai-Xin, (2007). “Artificial Immune Network Classification Algorithm for Fault Diagnosis of Power Transformer”. IEEE Trans. Power Del., Vol.22, No.2, pp.930–935.

[13]. Feng X.L., Tan J.C., and Bo Z.Q. (2006). "A New Wavelet Transform Approach to Discriminate Magnetizing st Inrush Current and Fault Current”. Proceedings of the 41 International Universities Power Engineering Conference, Vol. 3, pp. 876-880.

[14]. Guzman A., Stanley Z. and Benmouyal G. (2001). "A Current based Solution for Transformer Differential Protection - Part II: Relay Description and Evaluation". IEEE Transactions on Power Delivery, Vol. 17, No. 4, pp. 886-893.

[15]. G. Hosemann, and H. M. Steigerwald, (1993). “Model Saturation Detector for Digital Differential Protection”. IEEE Trans. Power Del., Vol.8, No 3, pp.933–940.

[16]. Inagaki K., Higaki M., Matsui Y., Kurita K., Suzuki M., Yoshida K. and Maeda T. (1988). "Digital Protection Method of Power Transformers Based on an Equivalent Circuit Composed of Inverse Inductance". IEEE Transactions on Power Delivery, Vol. 3, No. 4, pp.1501-1510.

[17]. M. G. Morante and D. W. Nicoletti, (1999). “A Waveletbased Differential Transformer Protection”. IEEE Trans. Power Del., Vol.14, No.4, pp.1351–1358.

[18]. G. Stang and T. Nguyen, (1996). Wavelets and Wavelet Filter Banks. Wellesley, MA: Wellesley-Cambridge.

[19]. Y. Sheng and M. Steven, (2002). “Decision trees and wavelet analysis for power transformer protection”. IEEE Trans. Power Del., Vol.17, No.2, pp.429–433.

[20]. Jiang F., Bo Z.Q., Chin P.S.M., Redfern M.A., and Chen Z. (2000). “Power transformer protection based on transient detection using Discrete Wavelet Transform (DWT)”. IEEE Power Engineering Society Winter Meeting, Vol.3, pp.1856- 1861.

[21]. O. S. Youssef, (2003). “A Wavelet-based Technique for Discrimination between Faults and Inrush Currents in Transformers”. IEEE Trans. Power Del., Vol.18, No.1, pp.170–176.

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Research Paper

Parallel Normalized Filtered X-LMS Algorithm for Noise Cancellation Using Multiple Sub-Filters Approach

B.Anitha* , C.Sailaja**

* Associate Professor, Department of Electronics and Communication Engineering, Guru Nanak Institutions Technical Campus, Telangana, India.

** Assistant Professor, Department of Electronics and Communication Engineering, Guru Nanak Institutions Technical Campus, Telangana, India.

Anitha. B, and Sailaja. C (2016). Parallel Normalized Filtered X-LMS Algorithm for Noise Cancellation Using Multiple Sub-Filters Approach. i-manager’s Journal on Digital Signal Processing, 4(4), 17-21. https://doi.org/10.26634/jdp.4.4.8312

Abstract

In this paper, a novel approach of noise cancellation algorithm using Parallel Normalized Filtered x-LMS (P-NFxLMS) is proposed. The degraded audio signal quality can be improved by using adaptive filters. In general, Least Mean Square (LMS) adaptive filtering algorithms are used to recover corrupted signal. The implementation of LMS algorithm is simple. LMS algorithm exhibits degraded performance if the desired signal has large power fluctuations. The Normalized Fx LMS is also computationally simple and improves the performance of LMS algorithm. In this paper, an algorithm is proposed to decompose a long adaptive filter into multiple sub-filters with lower order, and are implemented in parallel to increase the convergence speed. Finally, the proposed Parallel Normalized Filtered x-LMS (P-NFxLMS) algorithm yields faster convergence with minimum Mean Square Error.

References

[1]. Shing-Chow Chan, and Yijing Chu, (2011). "Performance analysis and design of FxLMS Algorithm in Broadband ANC System with Online Secondary-Path Modeling". IEEE Transaction on Audio Speech and Language Processing, Vol.20, No.3, pp.982-993.

[2]. Muhammad Tahir Akhtar, and Wataru Mitsuhashi, (2009). “Improving performance of FxLMS algorithm for active noise control of impulsive noise”. Journal of Sound and Vibration, Vol.327, No.3-5, pp.647-656.

[3]. Amira Mohammed, Waleed M. El-Nahal, and Adel E. El- Hannawy, (2014). "Adaptive Variable step-size Algorithm for Acoustic Noise Cancellation by using Multiple sub-filters Approach''. IJCA, Vol.107, No.8.

[4]. Muhammad Tahir Akhtar, and Wataru Mitsuhashi, (2010). “A Modified Normalized FxLMS Algorithm for Active Control of Impulsive Noise”. 18^th European Signal Processing Conference, IEEE.

[5]. Alaka Barik, Govind Murmu, Tarkeshwar Prasad Bhardwaj and Ravinder Nath, (2010). “LMS adaptive Multiple Sub-Filters Based Acoustic Echo Cancellation". Computer and Communication Technology (ICCCT), 2010 International Conference.

[6]. K. Wang, and W. Ren, (1999). “Convergence analysis of the multi-variable filtered-X LMS Algorithm with Application to Active Noise Control”. IEEE Transactions on Signal Processing, Vol.47, No.4, pp.1166-1170.

[7]. B. Huang, Y. G. Xiao, J. W. Sun, and G. Wei, (2013). “A variable step-size FXLMS algorithm for narrowband active noise control”. IEEE Transactions on Audio, Speech, and Language Processing, Vol.21, No.2, pp.301-312.

[8]. D. C. Chang and F. T. Chu, (2013). “A New Variable Taplength and Step Size FxLMS Algorithm”. IEEE Signal Processing Letters, Vol. 20, No.11, pp.1122-1125.

[9]. B. Anitha, Srinivas Bachu, and C. Sailaja, (2015). ''System Identification and Echo Canceller With Adaptive Filtering Algorithms”. i-manager’s Journal on Digital Signal Processing, Print ISSN: 2321-7480, E-ISSN: 2322-0368, Vol.3, No.3, pp.30-34.

[10]. M.A. Raja and A. Shanmugam, (2013). "Performance Analysis of Adaptive Algorithms for Noise Cancellation in Speech Processing''. Journal of Theoretical and Applied Information Technology, Vol.48, No.2, pp.704-707.

[11]. Gurjinder Singh, ManinderKaur, KVP. Singh, and Rashpinder Kaur, (2013). ''Performance Analysis of CPVSSLMS and CP-LMS Algorithms in Lab View''. International Journal of Current Engineering and Technology, Vol.3, No.4, pp.1238-1241.

[12]. P. Babu and A. Krishnan, (2009). ''Modified FxAFA Algorithm using Dynamic step size for Active Noise Control systems". International Journal of Recent Trends in Engineering, Vol.2, No.7, pp.37-39.

[13]. Sakshi Gaur and V. K. Gupta,“A Review on Filtered-X LMS Algorithm”. International Journal of Signal Processing System, Vol.4, No.2, pp.172-176.

[14]. Soria, E., Calpe, J., Chambers, J., Martinez, M., Camps, G., and Guerrero, J.D.M. (2008). “A novel approach to introducing adaptive filters based on the LMS algorithm and its variants”. IEEE Transactions on Education, Vol. 47, No.1, pp.127-133.

[15]. Ahmed I. Sulyman and Azzedine Zerguine, (2004). "Echo Cancellation using a Variable Step-Size NLMS Algorithm". 12^th European Signal Processing Conference, IEEE.

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Research Paper

A Novel Approach to Improve the Wind Profiler Doppler Spectra Using Wavelets

P. Krishna Murthy* , S. Narayana Reddy**

* Research Scholar, Department of Electronics & Communication Engineering, SVU College of Engineering, Tirupati. A.P, India.

** Professor, Department of Electronics & Communication Engineering, SVU College of Engineering, Tirupati, A.P, India.

Murthy, K. P, and Reddy, N.S. (2016). A Novel Approach to Improve the Wind Profiler Doppler Spectra Using Wavelets. i-manager’s Journal on Digital Signal Processing, 4(4), 22-28. https://doi.org/10.26634/jdp.4.4.8313

Abstract

The dedicated VHF clear-air atmospheric radars built in the late 70’s and early 80’s were found to be capable of observing atmospheric parameters such as the three-dimensional wind vector. These radars measure wind continuously. Wind profiling radars yield real-time lower atmospheric wind profiles in continuous unattended operation. Wind profilers are necessary to measure the height profile of wind vector and Signal to Noise Ratio (SNR) by detecting the Doppler shift of echoes. The lower atmospheric signals, which are processed in the present work has been obtained from the LAWP radar at National Atmospheric Research Laboratory (NARL), Gadanki, India. This paper discusses estimation of the wind profile using Peak Detection Technique (PDT) and Db11 Wavelet. The fake peaks which are adjacent to the real in the radar data can be detected by applying the threshold. In order to get the true position of the object, those fake peaks should be removed and this is possible by using Peak Detection Technique. Effective Doppler shift for LAWP data is obtained by using Peak Detection Technique and compared with the doppler obtained for LAWP data using Daubechies Wavelet. Results shows that there is an effective doppler shift after using Db11 Wavelet.

References

[1]. P. Srinivasulu, P. Yasodha, S. N. Reddy, S.Satyanarayana and A. Jayaraman, (2011) “Simplified Active Array L-Band Radar for Atmospheric Wind Profiling: Initial Results”. Journal of Atmospheric and Oceanic Technology, Vol. 28, pp.1436- 1447.

[2]. P. Srinivasulu, P. Yasodha, S. N. Reddy, P. Kamaraj, T. N. Rao, S. Satyanarayana, and A. Jayaraman, (2012). “1280 MHz Active Array Radar Wind Profiler for Lower Atmosphere: System Description and Data Validation”. Journal of Atmospheric and Oceanic Technology, Vol.29, pp.1455-1470.

[3]. Teter H. Hildebrand and R. S. Sekhon, (1974). “Objective Determination of the Noise Level in Doppler Spectra”. Journal of Applied Meteorology, Vol.13, pp 808-811.

[4]. Palshikar, G. (2009). “Simple Algorithms for Peak st Detection in Time-Series”. 1 IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence.

[5]. Daubechies I. (1990). “The Wavelet Transform, Time- Frequency Localization and Signal Analysis”. IEEE Transformation and Information Theory, Vol. 36, pp. 961-1005.

[6]. P. Krishna Murthy and S. Narayana Reddy, (2016). “Wavelet Transform Based Method to Improve the Signal to Noise Ratio for Lower Atmospheric Wind Profiler Data”. International Journal of Modern Electronics and Communication Engineering, Vol.4, No. 4, pp. 58-62.

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Research Paper

An Improved Invisible Watermarking Technique for Video Authentication Using DT-CWT with SVD Technique for Copyright Video

Astha Sharma* , Ravi Mishra**

* Research Scholar, Department of Electronics and Telecommunication Engineering, Faculty of Engineering and Technology, Shri Shankaracharya Technical Campus, Bhilai, Chhattishgarh, india.

** Senior Associate Professor, Department of Electrical and Electronics Engineering, Faculty of Engineering and Technology, Shri Shankaracharya Technical Campus, Bhilai, Chhattishgarh, India.

Sharma, A, and Mishra, R (2016). An Improved Invisible Watermarking Technique for Video Authentication Using DT-CWT with SVD Technique for Copyright Video. i-manager’s Journal on Digital Signal Processing, 4(4), 29-34. https://doi.org/10.26634/jdp.4.4.8314

Abstract

In the modern digital life, the increased important roles of digital content invites new challenges for securing the exchange of digital media. This paper addresses a unique, best copyright protection and watermarking scheme for videos. In the video watermarking chrominance, channel of the selected frames is decomposed into two types of even and odd shares of video frames. For embedding, if odd share is selected, then DT-CWT (Two dimensional Dual Tree Complex Wavelet Transform) is applied on it. Video watermarking has two main process - watermark embedding and watermark extraction process. Before the watermarking embedding process, the input video sequence is converted into a number of single frames. Here the authors have applied Singular Value Decomposition and Dual Tree Complex Wavelets Transform on watermark image.

References

[1]. Rini T Paul, (2014). “Video Watermarking Based on DWTSVD Techniques”. International Journal of Science and Research (IJSR), ISSN (Online): 2319-7064, Vol.3, No.11.

[2]. Raymond K.W. Chan, and M. C. Lee (1997), “3D-DCT Quantization as a Compression Technique for Video sequences”. In Proc. of Intl. Conf. on Virtual System and Multimedia, Geneva, Switzerland .

[3]. F. Hartung and B. Girod (1998), “Watermarking of Uncompressed and Compressed Video”. Signal Processing, Vol.66, No.3 (Special issue on Watermarking), pp. 283-301.

[4]. H. Kinoshita (1996), “An image digital signature system with ZKIP for the graph isomorphism”. In Proc. IEEE Int. Conf. on Image Processing, Lussane, Switzerland, Vol.3, pp.16-19.

[5]. G. Langelaar, I. Setyawan, and R. Lagendijk, (2000), “Watermarking Digital Image and Video Data”. In IEEE Signal Processing Magazine, Vol.17, pp. 20-43.

[6]. Mandeep Singh Saini, VenkataKranthi B, and Gursharanjeet Singh Kalra (2012), “Comparative Analysis of Digital Image Watermarking Techniques in Frequency Domain using Matlab Simulink”. International Journal of Engineering Research and Applications (IJERA), ISSN: 2248- 9622, Vol.2, No. 4.

[7]. Keshav S Rawat, and Dheerendra S Tomar (2010), “Digital watermarking schemes for authorization against copying or piracy of color images”. Indian Journal of Computer Science and Engineering, Vol.1, No. 4, pp. 295- 300.

[8]. Hanane Mirza, Hien Thai, and Zensho Nakao, (2008), “Digital Video Watermarking Based on RGB Color Channels and Principal Component Analysis”. KES, pp. 125–132.

[9]. Yavuz E., and Telatar Z., (2007). “Digital Watermarking with PCA Based Reference Images”. ACIVS, Springer-Verlag Lecture Notes in Computer Science, 4678, pp.1014- 1023.

[10].Saraju P. Mohanty, Renuka Kumara C, and Sridhara Nayak, (2004). “FPGA Based Implementation of an Invisible-Robust Image Watermarking”. CIT, pp. 344

[11]. H. Andrews and C. Patterson, [1976]. "Singular Value Decompositions and Digital Image Processing". IEEE Trans. on Acoustics, Speech, and Signal Processing, Vol. 24, No. 1, pp. 26-53.

[12]. Yui-Lam and Wan-Chi Siu, [1997]. “Variable temporal- Length 3D Discrete Cosine Transform”. IEEE Transactions on Image Processing, Vol. 6.

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Review Paper

Modeling of Capacity Enhancement of Heterogeneous Few Mode Multi-Core Fiber: A Review

Anshu* , Sharad Mohan Shrivastava, Vikas Sahu*, Aakash Joshi**, Anamika Bhadra*, Anjali S. Sanghvi, Nirmala Tirkey***

,- PG Scholar, Department of Electronics & Telecommunication Engineering, Shri Shankaracharya Technical Campus, Bhilai, India.

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

Anshu, Shrivastava, S, M, Sahu, V, Joshi, A, Bhadra, A, Sanghvi, A. S, and Tirkey, N (2016). Modeling Of Capacity Enhancement of Heterogeneous Few Mode Multi-Core Fiber: A Review. i-manager’s Journal on Digital Signal Processing, 4(4), 35-46. https://doi.org/10.26634/jdp.4.4.8315

Abstract

This paper aims to make understand the fundamentals and recent advancement in Multi-core Fiber Technology using Space Division Multiplexing. Few Mode Multi-core Fiber (FM-MCF) that enable Space Division Multiplexing (SDM) have greater potential to improve the transmission capacity compared to Single Spatial Mode Fiber (SSMF). The concept of Heterogeneous Few Mode Multi-core Fibers has paved its way in optical communication system by replacing Homogeneous Few Mode Multi-core Fibers which were previously opted. The uncoupled Multi-core Fibers (MCFs) which can utilize multiple cores are arranged in a fiber as spatial transmission channels and then is used for the SDM transmission. Design of 36 core and 3 mode is also demonstrated. Measurement of Inter-core XT for different bending radius is studied. And to give the readers a glimpse of recent development in Multi-core Fiber (MCF) technology, some noticeable research papers have also been discussed. System implementations based on MCF are mentioned along with future research directions.

References

[1]. R. G. H. Uden, R. A. Correa, E. A. Lopez, F. M. Huijskens, C. Xia, G. Li, A. Schulzgen, H. Waardt, A. M. J. Koonen and C. M. Okonkwo, (2014). “Ultra and high-density spatial division multiplexing with a few-mode multicore fibre”. Nature Photonics, Vol.8, pp. 865-870.

[2]. H. Takara, T. Mizuno, H. Kawakami, Y. Miyamoto, H. Matsuda, K. Kitamura, H. Ono, S. Asakawa, Y. Amma, K. Hirakawa, S. Matsuo, K. Tsujikawa, and M. Yamada, (2014). “120.7-Tb/s (7 SDM/180 WDM/95.8 Gb/s) MCF-ROPA Unrepeatered Transmission of PDM-32QAM Channels over 2 0 4 km” .European Conference on Optical Communication (ECOC), Cannes, PD.3.1.

[3]. Saitoh, Kunimasa, and Shoichiro Matsuo, (2013). ”Multicore fibers for large capacity transmission”. Nanophotonics, Vol.2, No.5-6, pp.441 - 454.

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[5]. K. Shibahara, T. Mizuno, H. Takara, A. Sano, H. Kawakami, D. Lee, Y. Miyamoto, H. Ono, M. Oguma, Y. Abe, T. Kobayashi, T. Matsui, R. Fukumoto, Y. Amma, T. Hosokawa, S. Matsuo, K. Saito, H. Nasu, and T. Morioka, (2015). “Dense SDM (12 core3 mode) Transmission over 527 km with 33.2-ns Mode-Dispersion employing Low Complexity Parallel MIMO Frequency- Domain Equalization ” . Conference on Optical Fiber Communication (OFC), Los Angeles, Th5C.2.

[6]. K. Igarashi, D. Souma, Y. Wakayama, K. Takeshima, Y. Kawaguchi, T. Tsuritani, I. Morita, and M. Suzuki, (2015). “114 Space-Division-Multiplexed Transmission over 9.8-km Weakly Coupled 6 Mode Uncoupled 19 Core Fibers”. Conference on Optical Fiber Communication (OFC), Los Angeles, Th5C.4.

[7]. R. Ryf, H. Chen, N. K. Fontaine, A. M. V. Benftez, J. A. Lopez, C. Jin, B. Huang, M.B. Astruc, D. Molin F. Achten, P. Sillard, and R. A. Correa, (2015). “10-Mode Mode- Multiplexed Transmission over 125-km Single-Span Multimode Fiber”. European Conference on Optical Communication (ECOC), Valencia, PDP 3.2.

[8]. Shieh, W, Hongchun Bao, and Y. Tang, (2008). “Coherent optical OFDM: Theory and Design”. Optics Express, Vol. 16, No.2, pp. 841-859.

[9]. Ryf, Roland, et al. (2014). “Space-division Multiplexed Transmission Over 33 Coupled-core Multi-core Fiber”. Optical Fiber Communication Conference. Optical Society of America.

[10]. Matsuo, Shoichiro, et al., (2011). “Large-effectivearea ten-core fiber with cladding diameter of about 200 mm”. Optics Letters, Vo. 36, No.23, pp. 4626 - 4628.

[11]. Takeshima, K. et al., (2015). “51.1-Tbit/s MCF transmission over 2,520 km using cladding pumped 7-core EDFAs”. Optical Fiber Communication Conference (OFC), Los Angeles (California), W3G.1, doi:10.1364/OFC.2015. W3G.1

[12]. Takara, A. et al., (2015). “Dense SDM (12-core3- mode) transmission over 527 km with 33.2-ns mode dispersion employing low complexity parallel MIMO frequency domain equalization”. Optical Fiber Communication Conference (OFC), Los Angeles (California), ThC.3, doi: 10.1364/OFC.2015.Th5C.3

[13]. Soma, D. et al., (2015). “2.05 Pbit/s super nyquist WDM SDM transmission using 9.8-km 6-mode 19-core fiber in full Cband”. European Conference on Optical Communications (ECOC), Valencia (Spain), Paper 3.2, doi: 10.1109/ECOC. 2015.7341686.

[14]. Puttnam, B. J. et al. (2015). “2.15 Pb/s transmission using a 22-core homogeneous single-mode multi-core fiber and wideband optical comb”. European Conference on Optical Communications (ECOC), Valencia (Spain), doi: 10.1109/ ECOC. 2015. 7341685

[15]. Ye, F. et al., (2015). “High-count multi-core fibers for space division multiplexing with propagation direction interleaving”. Optical Fiber Communication Conference (OFC), Los Angeles (California), doi: 10.1364/OFC. 2015. Th4C.3.

[16]. Ye, F. et al., (2016). Wavelength Dependence of Inter Core Crosstalk in Homogeneous Multi-core Fibers”. IEEE Photon. Technol. Lett., Vol. 28, pp.2730.

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[18]. Kunimasa Saitoh, and Shoichiro Matsuo, (2016). “Multicore Fiber Technology”. Journal of Lightwave Technology (JLT), Vol.34, No.11,pp.55 - 66.

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

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Volume 4 Issue 4 October - December 2016

Comparative Study of Induction Motor Stator Current Using FFT and Wavelet

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Sahni, J, K, Sahay, K, and Singh, S (2016). Comparative Study of Induction Motor Stator Current Using FFT and Wavelet. i-manager’s Journal on Digital Signal Processing, 4(4), 1-7. https://doi.org/10.26634/jdp.4.4.8310

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Wavelet-Packet-Transform Based Differential Protection of Power Transformer

Suribabu* , Sanker Ram**

* Research Scholar, Department of Electrical and Electronics Engineering, JNTUH, Telangana, India. ** Professor, Department of Electrical and Electronics Engineering, JNTUH, Telangana, India.

Suribabu, and Ram,S (2016). Wavelet-Packet-Transform Based Differential Protection of Power Transformer. i-manager’s Journal on Digital Signal Processing, 4(4), 8-16. https://doi.org/10.26634/jdp.4.4.8311

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Parallel Normalized Filtered X-LMS Algorithm for Noise Cancellation Using Multiple Sub-Filters Approach

B.Anitha* , C.Sailaja**

* Associate Professor, Department of Electronics and Communication Engineering, Guru Nanak Institutions Technical Campus, Telangana, India. ** Assistant Professor, Department of Electronics and Communication Engineering, Guru Nanak Institutions Technical Campus, Telangana, India.

Anitha. B, and Sailaja. C (2016). Parallel Normalized Filtered X-LMS Algorithm for Noise Cancellation Using Multiple Sub-Filters Approach. i-manager’s Journal on Digital Signal Processing, 4(4), 17-21. https://doi.org/10.26634/jdp.4.4.8312

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A Novel Approach to Improve the Wind Profiler Doppler Spectra Using Wavelets

P. Krishna Murthy* , S. Narayana Reddy**

* Research Scholar, Department of Electronics & Communication Engineering, SVU College of Engineering, Tirupati. A.P, India. ** Professor, Department of Electronics & Communication Engineering, SVU College of Engineering, Tirupati, A.P, India.

Murthy, K. P, and Reddy, N.S. (2016). A Novel Approach to Improve the Wind Profiler Doppler Spectra Using Wavelets. i-manager’s Journal on Digital Signal Processing, 4(4), 22-28. https://doi.org/10.26634/jdp.4.4.8313

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An Improved Invisible Watermarking Technique for Video Authentication Using DT-CWT with SVD Technique for Copyright Video

Astha Sharma* , Ravi Mishra**

Sharma, A, and Mishra, R (2016). An Improved Invisible Watermarking Technique for Video Authentication Using DT-CWT with SVD Technique for Copyright Video. i-manager’s Journal on Digital Signal Processing, 4(4), 29-34. https://doi.org/10.26634/jdp.4.4.8314

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Modeling of Capacity Enhancement of Heterogeneous Few Mode Multi-Core Fiber: A Review

Anshu* , Sharad Mohan Shrivastava**, Vikas Sahu***, Aakash Joshi****, Anamika Bhadra*****, Anjali S. Sanghvi******, Nirmala Tirkey*******

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

Anshu, Shrivastava, S, M, Sahu, V, Joshi, A, Bhadra, A, Sanghvi, A. S, and Tirkey, N (2016). Modeling Of Capacity Enhancement of Heterogeneous Few Mode Multi-Core Fiber: A Review. i-manager’s Journal on Digital Signal Processing, 4(4), 35-46. https://doi.org/10.26634/jdp.4.4.8315

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Jayanta Kumar Sahni* , Kuldeep Sahay, Satyendra Singh*

* Research Scholar, Department of Electrical and Electronics Engineering, JNTUH, Telangana, India.

** Professor, Department of Electrical and Electronics Engineering, JNTUH, Telangana, India.

* Associate Professor, Department of Electronics and Communication Engineering, Guru Nanak Institutions Technical Campus, Telangana, India.

** Assistant Professor, Department of Electronics and Communication Engineering, Guru Nanak Institutions Technical Campus, Telangana, India.

* Research Scholar, Department of Electronics & Communication Engineering, SVU College of Engineering, Tirupati. A.P, India.

** Professor, Department of Electronics & Communication Engineering, SVU College of Engineering, Tirupati, A.P, India.

Anshu* , Sharad Mohan Shrivastava, Vikas Sahu*, Aakash Joshi**, Anamika Bhadra*, Anjali S. Sanghvi, Nirmala Tirkey***

,- PG Scholar, Department of Electronics & Telecommunication Engineering, Shri Shankaracharya Technical Campus, Bhilai, India.

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