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Most Cited

Volume 7 Issue 1 September - November 2016

Research Paper

Design of Amplitude Distribution Functions for Desired Shaped Beams

J. Ravindranadh* , A. Sudhakar, K. Padmaraju*

* Associate Professor, Department of Electronics and Communication Engineering, RVR & JC College of Engineering, Andhra Pradesh, India.

Director, RVR & JC College of Engineering, Andhra Pradesh, India.

* Professor & Director, Department of Electronics and Communication Engineering, Jawaharlal Nehru Technology University, Kakinada, Andhra Pradesh, India.

Ravindranadh, J., Sudhakar, A., and Padmaraju, K. (2016). Design of Amplitude Distribution Functions for Desired Shaped Beams. i-manager's Journal on Electronics Engineering , 7(1), 1-8. https://doi.org/10.26634/jele.7.1.8272

Abstract

For the generation of narrow beams and other desired beam shapes, the antenna arrays are widely used. Mostly in high resolution radars and for point to point communication, narrow beams are used. There will be one major lobe followed by a number of minor lobes for any pattern produced by array antenna. For the purpose of covering wide angular regions, flat or sector beams are used. For ground mapping and also for airport surveillance, cosecant beams are used. The width of main beam becomes very small and is mostly a useful point to point communication, when the selected antenna array is sufficiently large enough. Generally, stair-step patterns are employed to identify when there is more than one target moving with both different altitudes and angular regions. However, it is required to design the sufficiently large enough arrays to produce optimized patterns. In this paper, Fourier transform technique is extended to design an aperture distribution for the generation of stair-step patterns and also an amplitude control technique for the generation of sector beams to produce the ramp patterns.

References

[1]. Howard E. Shanks, (1960). “A Geometrical Optics Method of Pattern Synthesis for Linear Arrays”. IRE Transactions on Antennas and Propagations, pp.485-490.

[2]. Victor Galindo, (1964). “Nonlinear Antenna Arrays”. IEEE Transactions on Antennas and Propagation, pp.782- 783.

[3]. M.G. Andreasen, (1974). Theory and Applications of Antenna Arrays. A Wiley Interscience Publication, John Wiley & Sons, U.S.A.

[4]. H. Unz, (1956). “Linear arrays with arbitrarily distributed elements”. Electronics Res. Lab, University of California Berkely, Calif, Vol.60, No.172.

[5]. Warren L. Stutzman, (1971). “Synthesis of Shaped – Beam Radiation Patterns using the Iterative Sampling Method ”. IEEE Transactions on Antennas and Propagation, Vol., No. 1, pp. 36-41.

[6]. Warren L. Stutzman, (1972). “Shaped-Beam Synthesis of Nonuniformly Spaced Linear Arrays”. IEEE Transactions on Antennas and Propagation, pp.499-501.

[7]. H. Schjaer-Jacobsen and K. Madsen, (1976). “Synthesis of Nonuniformly Spaced Arrays Using a General Nonlinear Minimax Optimization Method ”. IEEE Transactions on Antennas and Propagation, pp.501-506.

[8]. A. Chakraborthy, B.N. Das, and G.S. Sanyal, (1981). “Determination of phase functions for a desired one dimensional pattern”. IEEE Trans. on Antennas & Propagation, Vol.29, No.3, pp.502-506.

[9]. Ajoy Chakraborthy, and B.N. Das, (1984). “Scanning of Sector and Cosecant beams generated by a circular aperture”. IEEE Trans. on Antennas and Propagation, Vol.32, No.9, pp. 1000-1003.

[10]. Michael J. Buckley, (1996). “Synthesis of Shaped Beam Antenna Patterns using Implicitly Constrained Current Elements”. IEEE Transactions on Antennas and Propagation, Vol.44, No.2, pp.192-197.

[11]. J.A. Rodriguez, and Ares, (1997). “Synthesis of Shaped Beam Antenna Patterns with Null-Filling in the Sidelobe Region”. Electronics Letters, Vol.33, No.24, pp.2004-2005.

[12]. Akdagli, A. and K. Guney, (2003). “Shaped-beam pattern synthesis of equally and unequally spaced linear arrays using a Modified Tabu Search Algorithm”. Microwave Optical Technology Letters, Vol.36, No.1, pp.16-20.

[13]. Azevedp, J.A.R., (2008). “Shaped beam pattern synthesis with non uniform sample phases”. Progress in Electromagnetics Research B, Vol. 5, pp. 77-90.

[14]. Liu,Y., Z.P. Nie, and Q.H. Liu, (2010). “A new method for the synthesis of non-uniform linear arrays with shaped power patterns”. Prograess in Electromagnetics Research, Vol.107, pp.349-363.

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Research Paper

Voice Conversion using GMM with Minimum Distance Spectral Mapping Plus Amplitude Scaling

Neha Yadav* , Vinay Kumar Jain**

* PG Scholar, Department of Electronics and Telecommunication Engineering, Shri Shankaracharya Technical Campus, Bhilai, India.

** Associate Professor, Department of Electronics and Telecommunication Engineering, Shri Shankaracharya Technical Campus, Bhilai, India.

Yadav, N., and Jain, V.K. (2016). Voice Conversion using GMM with Minimum Distance Spectral Mapping Plus Amplitude Scaling.i-manager's Journal on Electronics Engineering, 7(1), 9-15. https://doi.org/10.26634/jele.7.1.8273

Abstract

VC is one of the fields of the speech processing voice transformation approaches for transforming the characteristics of voice produced by a person speaking, singing or audio samples, transforming voice into simple and flexible ways, so that a listener would be able to identify the speech uttered by the target speaker. Speech processing is widely used in the research for last two decades, with an increasing commercial interest and applications of VC such as Speech-to- Speech Translation (SST), and Text-To-Speech (TTS). More traditional methods are available for voice conversion, but they do not provide better converted speech; such as GMM doesn't generate high quality converted voice because GMM based VC creates over-smoothing. Hence in this research, the authors have proposed a new voice conversion algorithm, Minimum Distance Spectral Mapping (MDSM) based on the idea of Dynamic Time Warping (DTW), where point-to-point mapping is used in the warping function. Also amplitude scaling function is intended to adjust mean source log amplitude spectrum to the mean target log amplitude spectrum, in order to reduce over-smoothing. Since most of the spectral envelopes in amplitude scaling mean log spectral envelopes are smooth, there is no necessity in finding the smoothing factors. The proposed MDSMAS preserves spectral details, provides improved speech quality and identifies the similarity between source and target data and also provides improved result in objective tests.

References

[1]. Helena Duxans, Antonio Bonafonte, Alexender Kain, and Jan van Santen, (2004). “Including Dynamic and Phonetic Information in Voice Conversion System”. In International Conference on Spoken Language Processing.

[2]. D. Silndermann, and H. Hoge, (2003). “VTLN Based cross language voice conversion”. In IEEE Automatic Speech Recognition and Understanding Workshop, pp.676-678.

[3]. M. Mashimo, T. Toda, H. Kawanami, H. Kashioka, K. Shikano, and N. Campbell, (2002). “Evaluation of cross language voice conversion using bilingual and non bilingual databases”. In International Conference on Spoken Language Processing.

[4]. J. Hosom, A. Kain, T. Mishra, J. Van Santen, M. Friedoken, and J. Staehely, (2003). “Ineligibility of modifications to dysarthric speech”. In International Conference on Acoustic Speech and Signal Processing, pp. 924-927.

[5]. O. Turk, and L. Arslan, (2002). “Subband based voice conversion”. In International Conference on Spoken Language Processing, Bogazici University, Istanbul, pp. 289-292.

[6]. R.H. Laskara, D. Chakrabarty, F.A. Taludar, K. Sreenivasa Rao, and K. Banerjee, (2012). “Comparing ANN and GMM in a voice conversion framework”. Elsevier Journal on Applied Soft Computing, Vol. 12, pp. 3332- 3342.

[7]. Kevin D'souza, and K.T.V. Talele, (2015). “Voice conversion using GMM”. In IEEE International Conference on Communication Information & Computing Technology (ICCICT).

[8]. Yannis Stylianou, (2009). “Voice transformation: A survey”. IEEE ICASSP, pp.3585-3588.

[9]. Benesty Jacob, Sondhi, and Huang, (2008). “Voice Transformation”. In Springer Handbook of Speech Processing, pp. 489-503.

[10]. Anderson F. Machado, and Maracelo Queiroz, (2010). “Voice Conversion: A Critical Survey”. SMC 2010 Proceedings, pp. 291-298.

[11]. Tomoki Toda, Hiroshi Saruwatari, and Kiyohiro Shikano, (2001). “Voice Conversion Algorithm on Gaussian Mixture Model with Dynamic Frequency Warping of STRAIGHT Spectrum”. ICASSP, pp.127-130.

[12]. Elizabeth Godoy, Olivier Rosec, and Thierry Chonavel, (2012). “Voice Conversion using Dynamic Frequency Warping with Amplitude Scaling, for Parallel or Nonparallel Corpora”. IEEE Transactions on Audio, Speech, and Language Processing, Vol.20, No.4, pp.1313-1323.

[13]. Daniel Erro, Asunción Moreno, and Antonio Bonafonte, (2010). “Voice Conversion Based on Weighted Frequency Warping”. IEEE Transactions on Audio, Speech, and Language Processing, Vol.18, No.5, pp.922-931.

[14]. Daniel Erro, Eva Navas, and Inma Hernaez, (2013). “Parametric Voice Conversion Based on Bilinear Frequency Warping Plus Amplitude Scaling”. IEEE Transcations on Audio, Speech, and Language Processing, Vol.21, No.3, pp.556-566.

[15]. Xiaohai Tian, Zhizheng Wu, S. W. Lee, and Eng Siong Chng, (2014). “Correlation-based Frequency Warping for th Voice Conversion”. 9 International Symposium on Chinese Spoken Language Processing (ISCSLP), pp.211- 215.

[16]. Jianchun MA, and Wenju LIU, (2005). “Voice Conversion based on Joint Pitch and Spectral Transformation with Component Group-GMM ”. Proceedings of 2005 IEEE International Conference on Natural Language Processing and Knowledge Engineering (IEEE NLP-KE '05).

[17]. Zhihua Jian, and Zhen Yang, (2007). “Voice Conversion Using Canonical Correlation Analysis Based on Gaussian Mixture Model”. Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, pp.210-215.

[18]. Binh Phu Nguyen and Masato Akagi, (2008). “Phoneme-based Spectral Voice Conversion using Temporal Decomposition and Gaussian Mixture Model”. Second International Conference on Communications and Electronics (ICCE 2008). , pp.224-229.

[19]. S. Desai, E. Raghavendra B. Yegnanarayana, A. Black, and K. Prahallad, (2008). “Voice conversion using Artificial Neural Networks”. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2009).

[20]. Daojian Zeng, and Yibiao Yu, (2010). “Voice Conversion using Structured Gaussian Mixture Model”. In Proc. ICSP, pp.541-544.

[21]. Jian Zhihua, and Yang Zhen, (2007). “Voice conversion using Viterbi algorithm based on Gaussian mixture Model”. Proceedings of 2007 International Symposium on Intelligent Signal Processing and Communication Systems, pp.32-35.

[22]. Gui Jin, Michael T. Johnson, Jia Liu, and Xiaokang Lin, (2015). “Voice Conversion based on Gaussian Mixture Modules with Minimum Distance Spectral Mapping”. 5^th International Conference on Information Science and Technology (ICIST).

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Research Paper

Realization of Diode-Free OTRA based Time Marker Generator

Neeta Pandey* , Rajeshwari Pandey, Nikunj Sabharwal*

- Associate Professor, Department of Electronics and Communication Engineering, Delhi Technological University, India.

UG Scholar, Department of Electronics and Communication Engineering, Delhi Technological University, India.

Pandey, N., Pandey, R., and Sabharwal, N. (2016). Realization of Diode-Free OTRA based Time Marker Generator. i-manager's Journal on Electronics Engineering, 7(1), 16-19. https://doi.org/10.26634/jele.7.1.8279

Abstract

This paper proposes a diode-free Operational Transresistance Amplifier (OTRA) based Time Marker Generator (TMG). It comprises of three stages. The first stage uses OTRA in open loop configuration, behaving as a comparator. A RC differentiator is used in the second stage to achieve voltage spikes. The third stage employs a MOS transistor to obtain the desired output. The proposed time marker generates spikes at positive or negative cross points depending upon the type of MOS transistor used. To illustrate the operation of the proposed TMG, SPICE simulations are performed using CMOS based OTRA implementation that uses 0.18 μm CMOS technology parameters. Simulations corroborate with the theoretical predictions.

References

[1]. Yuan, F. (2007). CMOS Current-mode Circuits for Data Communications. Springer Science & Business Media.

[2]. Chen, J.J. , Tsao H.W., and Chen, C. C. (1992). “Operational Transresistance Amplifier using CMOS Technology”. Electronics Letters, Vol.28, No.28, pp.2087- 2088.

[3]. Salama, K. N., and Soliman, A.M. (1999). “CMOS Operational Transresistance Amplifier for analog signal processing”. Microelectronics Journal, Vol.30, No.3, pp.235-245.

[4]. Lo, Y.K., and Chien H.C. (2007). “Single OTRA-based current-mode monostable multivibrator with two triggering modes and a reduced recovery time”. Circuits, Devices & Systems, Vol.1, No.3, pp.257-261.

[5]. Lo, Y.K., and Chien H.C. (2006). “Current-mode monostable multivibrators using OTRAs”. Circuit and Systems II: Express Briefs, IEEE Transactions on Circuits and Systems II: Express Briefs, Vol.53, No.11, pp.1274-1278.

[6]. Lo, Y. K., and Chien H. C. (2010). “Current-input OTRA Schmitt trigger with dual hysteresis modes”. International Journal of Circuit Theory and Applications, Vol.38, No.7, pp.739-746.

[7]. Ghosh, M., Paul, S.K., Ranjan, R. K., and Ranjan, A. (2013). “Third order universal filter using single Operational Transresistance Amplifier”. Journal of Engineering, Article ID 317296, Hindawi Publishing Corporation, pp. 1-6.

[8]. Gokcen, A., and Cam, U. (2009). “MOS-C single amplifier biquads using the Operational Transresistance Amplifier”. AEU-International Journal of Electronics and Communications, Vol.63, No.8, pp.660-664.

[9]. Kilinç, S.,Keskin, A. U., and Çam, U. (2007). “Cascadable voltage-mode multifunction biquad employing single OTRA”. Frequenz, Vol.61, No.3-4, pp.84- 86.

[10]. Soliman, A. M. (2013). “Two integrator loop quadrature oscillators: A review”. Journal of Advanced Research, Vol.4, No.1, pp.1-11.

[11]. Çam, U. (2002). “A novel single-resistance-controlled sinusoidal oscillator employing single Operational Transresistance Amplifier”. Analog Integrated Circuits and Signal Processing, Vol.32, No.2, pp.183-186.

[12]. Chen, H. C. (2014). “New Realizations of Single OTRABased Sinusoidal Oscillators”. Active and Passive Electronic Components, Article ID 938987.

[13]. Nagar, B. C., and Paul, S. K. (2016). “Voltage mode third order quadrature oscillators using OTRAs”. Analog Integrated Circuits and Signal Processing, Vol.88, No.3, pp.517-530.

[14]. Pandey, R., Pandey, N., Sriram, B., and Paul S.K. (2012). “Single OTRA based analog multiplier and its applications”. ISRN Electronics, Article ID 890615.

[15]. Chien, H. C. (2016). “Switch-Controllable Full-Phase Operation Precision Half-Wave Rectifier Using a Single OTRA”. Journal of Circuits, Systems and Computers, Vol.25, No.7.

[16]. Anurag, R., Pandey, N., Pandey, R., and Vijay, R. (2015). “OTRA Based Precision Rectifier”. i-manager's Journal on Electronics Engineering, ISSN Print: 2229-7286 ISSN Online: 2249-0760, Vol.6, No.1, pp.21-27.

[17]. Srinivasulu, A., Tejaswini, V., and Pitchaiah, T. (2015). “ Time Marker Generator using Operational Transconductane Amplifier”. Jurnal Teknologi, Vol.76, No.1, pp.103-105.

[18]. Kafrawy, A. K., and Soliman A. M. (2009). “A modified CMOS differential operational transresistance amplifier (OTRA)”. AEU - International Journal of Electronics and Communications, Vol.63, No.12, pp.1067-1071.

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Research Paper

Effect of Scaling on Phase Frequency Detector

Latika Gulihar* , Bal Krishan**

* Assistant Professor, Department of Electronics Engineering, Satya College of Engineering and Technology, Palwal, Haryana, India.

** Assistant Professor, Department of Electronics Engineering, YMCA University of Science and Technology, Faridabad, Haryana, India.

Gulihar, L., and Krishan, B. (2016). Effect of Scaling on Phase Frequency Detector. i-manager's Journal on Electronics Engineering, 7(1), 20-24. https://doi.org/10.26634/jele.7.1.8280

Abstract

Phase Frequency Detectors (PFD) are the most important components of Phase Locked Loops (PLLs) which gives an output that is linearly proportional to the phase difference of the reference signal (CKR) and output of VCO (CKF) [8]. PFD has a number of applications in communication systems, wireless sensors, and robotics. Some application are focused on the efficiency or bandwidth, some other on low noise and jitters or it can be its size. In this paper, effect of scaling on a latch based PFD is presented. This paper presents the compassion of various technologies using different channel lengths and is useful to determine which is the best suitable channel length having best characteristics. Various technologies of CMOS are used and the performance is considered on the basis of two parameters-power consumed and delay in output when input is already given. The authors have used Tanner EDA 14.11 version on various technologies.

References

[1]. Behzad Razavi, (2010). Design of Analog CMOS Integrated Circuits. McGraw-Hill Higher Education.

[2]. Abishek Mann, Amit Karalkar, Lili He, and Morris Jones, (2010). “The Design of a Low-Power Low-Noise Phase Lock Loop”. IEEE International Synopsys, Quality Electronic Design, pp.528-531.

[3]. W.H. Lee, J.D. Cho, and S.D. Lee, (1999). “A high speed and low power phase- frequency detector and charge-pump”. In Proc. Asia South Pacific Des. Autom. Conf., Vol.1, pp.269-272.

[4]. G.-Y. Tak, S.-B. Hyun, T. Y. Kang, B. G. Choi, and S. S. Park, (2005). “A 6.3-9-GHz CMOS fast settling PLL for MBOFDM UWB applications”. IEEE J. Solid-State Circuits, Vol.40, No.8, pp.1671-1679.

[5]. M. Mansuri, D. Liu, and C.K. Yang, (2002). “Fast frequency acquisition phase-frequency detectors for G samples/s phase-locked loops”. IEEE J. Solid- State Circuits, Vol.37, No.10, pp.1331-1334.

[6]. K. Arshak, O. Abubaker, and E. Jafer, (2004). “Design and Simulation Difference Types CMOS Phase Frequency Detector for High Speed and Low Jitter PLL”. Proceedings th of 5 IEEE International Caracas Conference on Devices, Circuits, and Systems, Dominican Republic, Vol.1, No.3-5, pp.188-191.

[7]. Wu-Hsin Chen, Maciej E. Inerowicz, and Byunghoo Jung, (2010). “Phase Frequency Detector With Minimal Blind Zone for Fast Frequency Acquisition”. IEEE Transaction on Circuit and Systems -II, Vol.57, No.12.

[8]. Pavan Kumar Hanumolu, Merrick Brownlee,Kartikeya Mayara, and Un-Ku Moon, (2004). “Analysis of Charge- Pump Phase-Locked Loops”. IEEE Transaction on Circuit and Systems-I, Vol.51, No.9.

[9]. Behzad Razavi, (2004). Design of Integrated Circuit for Optical Communications. McGraw-Hill Higher Education.

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Research Paper

A GUI based EEG Signal Denoising using Hilbert Huang Transform

N. Padmaja* , M. Bharathi, E. Sujatha*

* Professor, Department of Electronics and Communication Engineering, Sree Vidyanikethan Engineering College (Autonomous), Tirupati, India.

Assistant Professor, Department of Electronics and Communication Engineering, Sree Vidyanikethan Engineering College (Autonomous), Tirupati, India.

* Research Scholar, Department of Electronics and Communication Engineering, JNTUA, Anantapuramu, India.

Padmaja,N., Bharathi, M., and Sujatha, E. (2016). A GUI based EEG Signal Denoising using Hilbert Huang Transform. i-manager's Journal on Electronics Engineering, 7(1), 25-30. https://doi.org/10.26634/jele.7.1.8281

Abstract

The electrical activities of the brain can be recorded using EEG (Electroencephalogram). EEG is often used to diagnose coma, epilepsy, brain death, tumors, stroke, and other brain disorders. In spite of limited spatial resolution, EEG is one of the important and handy tools for research and diagnosis. The EEG records through surface electrodes that are placed onto the scalp of a patient. Unfortunately, EEG data is contaminated by artifacts due to movement of eye related muscles and eyeballs. Thus ocular artifacts make the analysis of neuronal data very complex. EEG signal amplitude is very low in the order of 20-50 μV. These artifacts lead to wrong analysis and interpretation of the disorder. The focus of this work is to develop a novel technique that can detect and remove eye blink artifacts in order to facilitate analysis of EEG recordings using Empirical Mode Decomposition (EMD), and Hilbert Huang Transform (HHT). A GUI (Graphic User Interface) model was designed and developed, so as to enable the users ease in handling data of various patients for medical diagnosis. This project is implemented with the help of GUIDE, a graphical user interface in MatLab. The algorithm developed is applied on the data taken from various patients. The results demonstrate that, HHT is one of the best approaches for analyzing and de-noising EEG Signals.

References

[1]. Jones-Gotman M, Smith ML, and Wieser HG. (1998). “Intraarterial amobarbital procedures”. In: Engel J, Pedley T(Ed.). Epilepsy: A Comprehensive Textbook, Vol. 2, NewYork: Raven Press, pp. 767-75.

[2]. Jung TP, Makeig S, Humphries C, et al. (2000). “Removing electroence phalographic artifacts by blind source separation”. Psychophysiology, Vol. 37, No. 2, pp. 163-78.

[3]. Lagerlund TD, Sharbrough FW, and Busacker NE. (1997). “Spatial filtering of multichannel electroencephalographic recordings through Principal Component Analysis by singular value decomposition”. J. Clin Neurophysiol, Vol. 14, No. 1, pp. 73-82.

[4]. Jung TP, Humphries C, and Lee TW. (1998). “Removing electroencephalographic artifacts comparison between ICA and PCA”. Neural Network Signal Process, Vol. 8, pp. 63-72.

[5]. Padmaja, N., S. Varadarajan, and R. Swathi, (2011). “Signal processing of radar echoes using wavelets and Hilbert Huang transform”. Signal and Image Processing: An International Journal (SIPIJ), Vol.2, No.3.

[6]. Berg P, and Scherg M. (1994). “A multiple source approach to the correction of eye artifacts”. Electroencephalogr Clin. Neurophysiol., Vol. 90, pp. 229- 41.

[7]. T. Zikov, S. Bibian, G. A. Dumont, and M. Huzmezan, (2002). “A wavelet based de-noising technique for ocular th artifact correction of the Electroencephalogram”. In 24 International Conference of the IEEE Engineering in Medicine and Biology Society, Huston, Texas, Vol. 1, pp. 98-105.

[8]. N. E. Huang, Z. Shen, and S. R. Long, (1998). “The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis”. In Proc. Royal. Soc. London. A, Vol. 454, pp. 903-995.

[9]. R. Yan, and R. Gao, (2006). “Hilbert-Huang Transformbased vibration signal analysis for machine health monitoring”. IEEE Trans. Instrum. Meas., Vol. 55, No. 6, pp.2320-2329.

[10]. Tian-yun Li, Yan Zhao, and Nan Li, (2005). “Empirical Mode Decomposition-based Hilbert Transform to Power System Transient Signal Analysis”. Automation of Electric Power Systems, Vol. 29, No. 4, pp. 49-52.

[11]. Mao Wei, Jin Ronghong, Geng Junping, and Li Jiaqiang, (2006). “A Time-Frequency Analysis Method for Non-stationary Signals Based on Improved Hilbert-Huang Transform and Its Application”. Journal of Shanghai Jiaotong University, Vol. 40, No. 5, pp. 724-729.

[12]. Loh CH, et al. (2001). “Application of the Empirical Mode Decomposition- Hilbert Spectrum Method to Identify Near-fault Ground-motion Characteristics and Structural Responses”. Bulletin of the Seismological Society of America, No. 91, pp. 1339-1357.

[13]. A.O. Boudraa, J.C. Cexus, and Z. Saidi, (2004). “EMD-Based signal noise reduction”. International Journal of Signal Processing, Vol. 1, No. 1, pp. 33-37.

[14]. N. Padmaja, and E. Ramyakrishna, (2015). “HHT and DWT Based MIMO-OFDM for Various Modulation Schemes: A Comparative Approach”. i-manager's Journal on Wireless Communication Networks, Vol. 4, No. 2, pp. 26- 31, ISSN Print:2319-4839, ISSN Online: 2320-2351.

[15]. G Ramakrishna, and N. Padmaja, (2016). “Estimation of Teager Energy Using EMD and HHT”. IEEE International Conference on Applied and Theoretical Computing and Communication Technology (ICATCCT - 2016), SJB Institute of Technology, Kengeri, Bengaluru, India.

[16]. Pasala Gowri, P. V. Ramana, and N. Padmaja, (2016). “Independent Component Analysis for Single Channel Source Separation using Wavelet Packet Decomposition”. International Journal of Scientific & Engineering Research, Vol. 7, No. 6, pp. 1210-1221, ISSN 2229-5518.

[17]. Padmaja, N., Varadarajan, S., and Latha, G.M. (2011). “Empirical Mode Decomposition of Atmospheric Radar Signals”. i-manager's Journal on Electronics Engineering, Vol. 1, No. 4, pp. 29, ISSN Print: 2229-7286 ISSN Online: 2249-0760.

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Review Paper

Modeling of Polarization Filter based on Photonic Crystal Fiber using Surface Plasmon Resonance : A Review

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

,- PG Scholar, Department of Electronics and Communication Engineering, Shri Shankaracharya Technical Campus, Bhilai, India.

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

Joshi,A., Shrivastava, S.M., Sahu, V., Sanghvi, A.S., Bhadra, A., Anshu, Tirkey, N. (2016). Modeling of Polarization Filter based on Photonic Crystal Fiber using Surface Plasmon Resonance : A Review. i-manager's Journal on Electronics Engineering, 7(1), 31-36. https://doi.org/10.26634/jele.7.1.8282

Abstract

The polarization effects in photonic crystal fibers is observed by using nanoscale metals films and this effect is calculated by using Finite Element Method. The phenomenon of surface Plasmon resonance has also been observed and it has been observed that the metals which show surface plasmon resonance are generally very good conductors such as gold, silver, Aluminium, etc. In this review paper, various designs have been studied using various metal films, liquid crystals, and so on. It has been observed that resonance wavelengths can be modulated by adjusting the dimensions of air hole as well as the diameter of air hole which is coated by different metal films or metal wires. It has also been observed that the loss in y-mode is very high as compared to x-mode at their respective wavelengths. It is desired to have resonant wavelength in optical window, ie., in the range of 1.31 to 1.55 μm.

References

[1]. Govind P Agrawal, (2007). Nonlinear Fiber Optics. Academic Press.

[2]. Emmanuel K Akowuah, Terry Gorman, Shyqyri Haxha, and Jennifer V Oliver, (2010). “Dual channel planar waveguide surface plasmon resonance biosensor for an aqueous environment”. Optics Express, Vol. 18, No. 24, pp. 24412–24422.

[3]. HL Chen, SG Li, ZK Fan, GW An, JS Li, and Y Han, (2014). “A novel polarization splitter based on dual-core photonic crystal fiber with a liquid crystal modulation core”. IEEE Photonics Journal, Vol. 6, No. 4, pp. 1–9.

[4]. Lei Chen, Weigang Zhang, Zhao Zhang, Yongji Liu, Jonathan Sieg, Liyu Zhang, Quan Zhou, Li Wang, Biao Wang, and Tieyi Yan, (2014). “Design for a singlepolarization photonic crystal fiber wavelength splitter based on hybrid-surface plasmon resonance”. IEEE Photonics Journal, Vol. 6, No. 4, pp. 1–9.

[5]. Jakub Dostalek, J C Tyroky, Jiri Homola, Eduard Brynda, M Skalsky, P Nekvindova, J Spirkova, J Skvor, and J Schrofel, (2001). “Surface plasmon resonance biosensor based on integrated optical waveguide”. Sensors and Actuators B: Chemical, Vol. 76, No. 1, pp. 8–12.

[6]. Y Du, S-G Li, S Liu, X-P Zhu, and X-X Zhang, (2012). “Polarization splitting filter characteristics of Au-filled highbirefringence photonic crystal fiber”. Applied Physics B, Vol. 109, No. 1, pp. 65–74.

[7]. Youfu Geng, Xuejin Li, Xiaoling Tan, Yuanlong Deng, and Xueming Hong, (2014). “Compact and ultrasensitive temperature sensor with a fully liquidfilled photonic crystal fiber mach–zehnder interferometer ”. IEEE Sensors Journal, Vol. 14, No. 1, pp. 167–170.

[8]. HW Lee, MA Schmidt, RF Russell, NY Joly, HK Tyagi, P Uebel, and P St J Russell, (2011). “Pressure-assisted meltfilling and optical characterization of Au nano-wires in microstructured fibers”. Optics Express, Vol. 19, No. 13, pp. 12180–12189.

[9]. HW Lee, MA Schmidt, HK Tyagi, L Prill Sempere, and P St J Russell, (2008). “Polarization-dependent coupling to plasmon modes on submicron gold wire in photonic crystal fiber”. Applied Physics Letters, Vol. 93, No. 11, pp. 111-102.

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i-manager's Journal on Electronics Engineering (JELE)

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Volume 7 Issue 1 September - November 2016

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J. Ravindranadh* , A. Sudhakar, K. Padmaraju*

* PG Scholar, Department of Electronics and Telecommunication Engineering, Shri Shankaracharya Technical Campus, Bhilai, India.

** Associate Professor, Department of Electronics and Telecommunication Engineering, Shri Shankaracharya Technical Campus, Bhilai, India.

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- Associate Professor, Department of Electronics and Communication Engineering, Delhi Technological University, India.

UG Scholar, Department of Electronics and Communication Engineering, Delhi Technological University, India.

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