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

Current Issue Vol. 15 Issue 1

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Volume 1 Issue 4 June - August 2011

Research Paper

Efficient Image Compression Algorithms Using Evolved Wavelets

Chenchu Krishnaiah G* , T. Jaya Chandra Prasad, M. N. Giri Prasad*

* Associate professor, Department of Electronics and communications Engineering, Gokula Krishna College of Engineering, Sullurpet, Andhra Pradesh, India.

Principal, Rajeev Gandhi Memorial College of Engineering and Technology, Nandyal, Kurnool (Dist), Andhra Pradesh, India.

* Professor in the Department of Electronics and Communication Engineering, J.N.T University College of Engineering, Anantapur, Anantapur(Dist), Andhra Pradesh, India.

G. Chenchu Krishnaiah, T. Jayachandraprasad and M.N. Giri Prasad (2011). Efficient Image Compression Algorithms Using Evolved Wavelets. i-manager’s Journal on Electronics Engineering, 1(4), 1-8. https://doi.org/10.26634/jele.1.4.1504

Abstract

The 9/7 and 5/3 lifting based wavelet filters are widely used in different image compression schemes, such as JPEG 2000 image compression standard. The performance of a hardware implementation of the 9/7 and 5/3 filter banks depends on the accuracy with which filter coefficients are represented. In this paper an attempt has been made to study the performance of 9/7 and 5/3 wavelets on photographic images (monochrome and color) and estimated Peak Signal to Noise Ratio (PSNR), Compression Ratio (CR), Mean Square Error (MSE), Encoding Time, Decoding Time, Transforming Time or Decomposition Time…etc. This study shows that the 5/3 wavelet transform outperform the 9/7 wavelet transform.

References

[1]. M. Vetterli & J. Kovacevic, (1995). Wavelets and Subband Coding, SignalProcessing, Prentice Hall, Englewood, Cliff. NJ.

[2]. D. Taubman, (2000). “High performance scalable image compression with EBCOT”, IEEE Trans. On Image Processing, Vol.9, No.7, pp.1158-1170, Jul.

[3]. A. Said & W.A. Plearlman, (1996). “a new, fast, and efficient image codec based on Set Partitioning In Hierarchical Trees,” IEEE Trans. On Circuits and Systems for Video Technology, Vol.6, No.3, pp.243-250, Jun.

[4]. M. Antonini, M. Barlaud, P. Mathieu, & I. Daubechies, (1992). “Image coading using the wavelet transform,” IEEE Tran. On Image Processing, Vo.1, No.2, ,pp.205-220, Apr.

[5]. J.D. Villasenor, B. Belzer, & J. Liao, (1995). “wavelet filter evaluation for image compression,” IEEE Tran. On Image Processing, Vol.4, No.8, pp.1053-1060, Aug.

[6]. M. Boliek, (2000). “JPEG 2000 Final Committee Draft, http://www.jpeg.org/public/fcd15444-1.pdf,”

[7]. I. Daubechies & W. Sweldens, (1996). “Factoring Wavelet Transforms into Lifting Steps,: Tech.Rep.Bell Laboratories, Lucent Technologies.

[8]. C.T. Haung, P.C. Tseng, & L.G. Chen, (2004). “Flipping Structure: an efficient VLSI architecture for liftingbased discrete wavelet transform,” IEEE Tran. On Signal Processing, Vol.52, No.4, pp.1080-1089, Apr.

[9]. K.A. Kotteri, A.E. Bell, ad J.e. Carletta, (2004). “Design of multiplierless, high-performance, wavelet filter banks with image compression applications,” IEEE Tran. On Circuits and Systems-I, Vol.51, No.3, pp.483-494, Mar.

[10]. G. Strang & T.Q.Nguyen, (1996). Wavelets and Filter Banks, Wellesley, Wellesley-Combridge, MA.

[11]. Sweldens, W (1995). The lifting scheme: A new philosophy in biorthogonal wavelet construction on proc. of SPIEE, Vo.2569, Sar Diego, USA, July, 68-79.

[12]. Calgerbank, A.R., Daubechies, I., Sweledens, W.; Yeo, B.L, (1998). Wavelet Transform that maps integers to integers. Applied Computational and harmonic analysis, Vol.5, No.3, 332-369.

[13]. Strutz, T. Muller, E. (1996). Wavelet filter design for image compression. IEEE-SP Int. Symposium on Time- Frequency and Time-scale analysis, Paris, 18-21 June, 273-276.

[14]. Cohen, A., Daubechies, I., Feauveau, J.-C. (1992). Biorthogonal Bases of compactly supported Wavelets. Comm. On Pure and Applied Mathematics, Vol.45, 485- 560.

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Research Paper

Xilinx System Generator Based Modeling and Simulation of SVPWM

R. Rajendran* , N. Devarajan**

* Assistant Professor, Department of IT, Karpagam College of Engineering, Coimbatore.

** Professor, Department of EEE, Government College of Technology, Coimbatore.

R. Rajendran and N. Devarajan (2011). Xilinx System Generator based Modeling and Simulation of SVPWM. i-manager’s Journal on Electronics Engineering, 1(4), 9-13. https://doi.org/10.26634/jele.1.4.1505

Abstract

This paper is about modeling and simulation of Space Vector Pulse Width Modulation (SVPWM) scheme for a three phase Voltage Source Inverter (VSI). The model was implemented using Xilinx System Generator and MATLAB/Simulink blockset. The most widely used schemes for a three phase VSI are carrier based Sinusoidal PWM (SPWM) and SVPWM. There is an increasing trend of using SVPWM because of their easier digital realization and better DC bus utilization. The novelty of the paper relies on the proposal of the flexible and new model of SVPWM.

References

[1]. B.K.Bose, (1986). Power Electronics and AC Drives, Prentice-Hall,

[2]. B.K.Bose, (1992). Modern Power Electronics: Evolution, Technology, and Applications, IEEE PRESS, New York.

[3]. J.Holtz, (1994).”Pulse Width Modulation for Electronic Power Conversion”, IEEE Proceedings, Vol.82, No.8, pp.1194-1214, Aug.

[4]. S.Ogasawara, H.Akagi, & A.Nabae, (1989). ”A novel PWM scheme of voltage source inverter based on space vector theory”, European Power Electronics Conference Record, Germany, pp.1197-1202.

[5]. H.Van Der Broeck, H.Skudelny, & G.Stanke, (1986).”Analysis and Implementation of a pulse width modulator based on voltage space vectors”, IEEE-IAS Conference Records, pp.244-251.

[6]. S.J.Henriksen, R.E.Betz, & B.J.Cook, (1999).”Digital IM variable speed drive”, IEEE Transaction on Industrial Applications, Vol.35, No.5, Oct.

[7]. S.L.Jung, M.Y.Chang, & Y.Y. Tzou, (1997).”FPGA- based Control IC for a single phase PWM inverter used for UPS”, IEEE PEDS Conf. Rec., pp.344-349, May 26-29.

[8]. S.L.Jung, M.Y.Chang, J.Y.Jyang, L.C.Yeh, & Y.Y.Tzou (1999).,”Design and Implementation of an FPGA- based control IC for AC-voltage regulation”, IEEE Transaction on Power Electronics, Vol.14, No.3, May.

[9]. Y.Y.Tzou, & H.J.Hsu, (1997). ”FPGA based SVPWM control IC for PWM inverters”, IEEE Transaction on Power Electronics, Vol.12, No.6, pp.953-963, Nov.X

[10]. A.V.Peterchev, & S.R.Sanders, (2003). ”Architecture and IC implementation of a Digital VRM Controller”, IEEE Transaction on Power Electronics, Vol.18, No.1,pp 356- 364, Jan.

[11]. W.P.Hew, et al, (2005). “Realization of space vector modulation technique in a single FPGA chip for induction motor drive PWM,” IEEE International Conference on Electron Devices and Solid State Circuits, Hong Kong, pp.817-820, Dec.

[12]. Z.Zhou, et al, (2006). “Realization of an FPGA- based space vector PWM controller,” IEEE International Conference on Power Electronics and Motion Control (IPEMC'2006).

[13]. Bob Zeidman & Robert Zeidman, (2002). Design with FPGA and CPLD, CMP Books, Bekerly.

[14]. Xilinx Application Notes

[15]. Dick, (1999). ”A system level design approach for FPGA based DSP implementations”, in DSP World, Spring.

[16]. www.mathworks.com.

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Research Paper

A New Accordion Based Video Compression Method

Jaya Krishna Sunkara* **

* Assistant Professor, PDCE, Sullurpet, India.

Researcher, Sullurpet, India.

*-**** Bachelor of Technology in ECE, PDCE, Sullurpet, India.

Jaya Krishna Sunkara, Kuruma Purnima, E Navaneetha Sagari and L Rama Subbareddy (2011). A New Accordion Based Video Compression Method. i-manager’s Journal on Electronics Engineering, 1(4), 14-21. https://doi.org/10.26634/jele.1.4.1506

Abstract

Usually, a video signal has high temporal redundancies due to the high correlation between successive frames. This redundancy has not been exploited enough by current video compression techniques. In this pape44grr, we present a new video compression technique which tends to hard exploit the pertinent temporal redundancy in the video to improve processing efficiency with minimum processing complexity. It includes 3D to 2D transformation of the video that allows exploring the temporal redundancy of the video using 2D transforms and avoiding the computationally demanding motion compensation step. This transformation converts the spatial and temporal correlation of the video signal into a high spatial correlation. Indeed, this technique transforms each group of pictures into one picture eventually with high spatial correlation. Thus, the De-correlation of the resulting pictures by the DCT makes efficient energy compaction, and therefore produces a high video compression ratio. Many experimental tests had been conducted to prove the technique efficiency especially in high bit rate and with slow motion video.

References

[1]. E. Q. L. X. Zhou and Y. Chen, (2003). “Implementation of h.264 decoder on general purpose processors with media instructions,” In SPIE Conf. on Image and Video Communications and Processing, (Santa Clara, CA), pp. 224.235, Jan.

[2]. M. B. T. Q. N. A. Molino, F. Vacca, (2004). “Low complexity video codec for mobile video conferencing,” In Eur. Signal Processing Conf. (EUSIPCO), (Vienna, Austria), pp. 665.668, Sept.

[3]. S. B. Gokturk and A. M. Aaron, (2002). “Applying 3d techniques to video for compression”, In Digital Video Processing (EE392J) Projects Winter Quarter..

[4]. T. Fryza, (2002). Compression of Video Signals by 3DDCT Transform. Diploma thesis, Institute of Radio Electronics, FEKT Brno University of Technology, Czech Republic.

[5]. G. M.P. Servais, (1997). “Video compression using the three dimensional discrete cosine transform” In Proc.COMSIG, pp. 27.32.

[6]. R. A.Burg, (2000). “A 3d-dct real-time video compression system for low complexity singlechip vlsi implementation,” in the Mobile Multimedia Conf (MoMuC).

[7].A.N.N.T.R.K.R., (1974). “Discrete cosine transforms,” In IEEE transactions on computing, pp. 90.93.

[8]. T.Fryza and S.Hanus, (2003). “ Video signals transparency in consequence of 3d-dct transform,” In Radioelektronika 2003 Conference Proceedings, (Brno,Czech Republic), pp. 127.130.

[9]. N. Boinovi and J. Konrad, (2005). “Motion analysis in 3d dct domain and its application to video coding,” Vol. 20, pp. 510-528.

[10]. E. Y. Lam and J. W. Goodman, (2000). “A mathematical analysis of the dct coefficient distributions for images,” Vol. 9, pp. 1661-1666.

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Research Paper

Non-linear Analysis of the Cardiovascular System (Vanderpol oscillator) using Simulink

C. Muni Saraswathi* , B. Anuradha, K.Babulu*

* Lecturer, Department of Biomedical Engineering, S.V. Govt. Polytechnic, Tirupati.

Associate Professor of ECE, S.V.U. College of Engineering, Tirupati.

* Associate Professor, Department of ECE, JNTU, Kakinada.

C. Muni Saraswathi, B. Anuradha and K. Babulu (2011). Non-Linear Analysis of the Cardiovascular System (Vanderpol Oscillator) using Simulink. i-manager’s Journal on Electronics Engineering, 1(4), 22-28. https://doi.org/10.26634/jele.1.4.1507

Abstract

The Heart Is The Center Of The Cardiovascular System. Whereas, The Term Cardio Refers To The Heart, The Term Vascular Refers To Blood Vessels. The Heart Acts As A Pump, And Maintains A Constant Circulation Of Blood Throughout The Body. As Blood Flows Through Body Tissues, Nutrients And Oxygen Move From The Blood In To Interstitial Fluid And Then Into Cells. At The Same Time The Blood Picks Up Wastes, Carbondioxide, And Heat. Even At Rest, Our Heart Pumps 30 Times Its Own Weight Each Minute, About 5 Liters To The Lungs And The Same Volume To The Rest Of The Body. The Physiological Oscillators Produce Oscillations, In The Form Of Stable, Closed Trajectory Called A Limit Cycle. These Oscillations Exhibit The Phenomenon Of Frequency Entrainment Or Phase Locking. These Are Analyzed By Presenting Vanderpol Oscillator. Vanderpol Oscillator Is A Non-Linear Oscillator. In The Non-Limit Cycle Oscillator, External Disturbance Move The Phase Trajectory To Different Orbit Around The Centre And Will Not Come To Original Point, Where As In Limit Cycle Oscillator, The Phase Trajectory Returns To Its Original Location. When Vander Pol Oscillator Is Driven By The External Frequency, Which Is Different With Natural Frequency Of The Oscillator, Then The Resultant Output Is Mixture Of Components That Result From The Interaction Of The Driving Periodicity On Natural Oscillations. If The External Frequency Is Equal To Natural Frequency Of Vendor Pol Oscillator, Then Nonlinear System Will Adopt The External Frequency. The Time Courses Of Oscillatory Activity Of Heart And Phase Trajectories Are Generated By Vanderpol Model By Using The Simulink. The Response Of Vander Pol Oscillator For Various External Frequencies Is Also Obtained.

References

[1]. Khandapur, R.S., (1990). Hand Book of Biomedical Engineering, Tata McGraw Hill Publishing Company Ltd., 1990, New Delhi.

[2]. Leslie Cromwell, Fred J. Weibell, Erich A. Pfeiffer, (1999). Biomedical Instrumentation and Measurements, Prentice-Hall India.

[3] . Richard Aston, Principles of Biomedical Instrumentation and Measurements, Merril publishing company, Columbus.

[4].George Alfred, Bait sell (1950). “Human biology”, McGraw Hill Book Company, INC, New York.

[5].Ross & Wilson (2001). “Anatomy and Physiology in Health and Illness”, Anne Waugh, Allison Grant, Churchill Livingstone, London.

[6].Tortora & Grabowski (2003). “Principles of Anatomy and Physiology” John Wiley & Sons, Inc.

[7].Guyton and Hall, “Text book of Medical Physiology” Elsevier.

[8].Michael C.K.KHOO, (2001). Physiology Control Systems, IEEE Press, Prentice-Hall India.

[9].Gerald Harris & Cedric Walker, (1988). Proceedings of the annual international Conference of the IEEE Engineering in Medicine and Biology Society, “Modelling and Biological Interfaces', edited by, Vol. 10.

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Research Paper

Empirical mode decomposition of Atmospheric radar signals

PADMAJA* , S. Varadarajan, G. Madhavi Latha*

* Associate Professor of ECE, C.R Engg College, Tirupati.

Professor of ECE, S.V.U college of Engg, Tirupati.

* Assistant Professor, Sri Vidyanekhethan Engg College, Tirupati.

N. Padmaja, S. Varadarajan and G. Madhavi Latha (2011). Empirical Mode Decomposition Of Atmospheric Radar Signals. i-manager’s Journal on Electronics Engineering, 1(4), 29-37. https://doi.org/10.26634/jele.1.4.1508

Abstract

In this paper, comparison study of wavelet transforms and Empirical mode decomposition (EMD) was performed for two sets of atmospheric radar data. Wavelets and EMD has been applied to the time series data obtained from the mesosphere-stratosphere-troposphere (MST) region near Gadanki, Tirupati. Wavelet analysis is one of the most important methods for removing noise and extracting signal from any data. The de-noising application of the wavelets has been used in spectrum cleaning of the atmospheric signals. EMD is a numerical sifting process to decompose a signal into its fundamental intrinsic oscillatory modes, namely intrinsic mode functions (IMFs). A series of IMFs can be obtained after the application of EMD. The Algorithm is developed and tested using Matlab. Analysis has brought out some of the characteristic features such as Doppler width, SNR of the atmospheric signals. The results showed that the proposed algorithm is efficient for dealing non-linear and non- stationary signals contaminated with noise. SNR using wavelets and EMD has been compared and plotted for validation of the proposed algorithm. EMD is found to be effective in removing the noise embedded in radar echoes.

References

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[2]. Z. Wu & N. E. Huang, (2004). “A study of the characteristics of white noise using the empirical mode decomposition method”. Proc. Roy. Soc London A, Vol. 460, pp. 1597-1611.

[3]. Z.K. Peng, Peter W. Tseb, & F.L. Chu. (2005). An improved Hilbert–Huang transform and its application in Vibration signal analysis. Journal of Sound and Vibration, 286 187–205

[4]. Dejie Yu, Junsheng Cheng, Yu Yang. (2010). Application IEEE transactions on signal processing, Vol. 58, No. 3, March.

[5]. Empirical Mode Decomposition for Trivariate Signals Naveed ur Rehman, Student Member, IEEE, and Danilo P. Mandic, Senior Member, IEEE.

[6] Flandrin P, Rilling G, Goncalves P., “Empirical mode decomposition as a filter bank”. IEEE Signal Processing Letters, Feb. 2004, Vol. 11, Issue 2, Part 1, pp: 112–114.

[7]. N.E Huang, S.R.Long, & Z Shen, (1999). “A new view of nonlinear water waves: The Hilbert spectrum”, Annu. Rev. Fluid Mech, .Vol.31, pp. 417-457.

[8]. Wang Chun, & Peng Dong-ling, (2004). “The Hilbert- Huang Transform and Its Application on Signal Denoising”, China Journal of Scientific Instrument, Vol.25,no.4, pp. 42-45.

[9]. R. Gabriel & F. Patrick. (2008). “The Empirical Mode Decomposition Answers”. IEEE Transactions on Signal Processing, Vol. 56, No. 1.

[10]. Z. K. Penga, Peter W. Tse, & F. L. Chu, (2005). “A comparison study of improved Hilbert–Huang transform and wavelet transform: Application to fault diagnosis for rolling bearing,” Mechanical Systems and Signal Processing, pp. 974–988.

[11]. Yuan Li. (2001). ”Wavelet Analysis for Change Points and Nonlinear Wavelet Estimates in Time Series.” Beijing: China Statistics Press.

[12]. Angkoon Phinyomark, Chusak Limsakul, & Pornchai Phukpattaranont (2010). “Optimal Wavelet Functions in Wavelet Denoising for Multifunction Myoelectric Control” “IEEE Transactions on Signal Processing.

[13]. H. G. R. Tan, A. C. Tan, P. Y. Khong, & V. H. Mok, (2007). “Best wavelet function identification system for ecg signal denoise applications,” in International Conference on Intelligent and Advanced Systems, pp. 631–634.

[14]. M. Kania, M. Fereniec, & R. Maniewski, (2007). “Wavelet denoising for multi-lead high resolution ecg signals,” Measurement Science Review, Vol. 7, No. 4, pp. 30–33,

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Research Paper

Study Of The Voltage Stability In A Multibus Power Network Using AI Technique

G. Prasad* , Vijay Kumar*****

Associate Professor, GITAM University, Visakhapatnam.GITAM University, Visakhapatnam.

Associate Professor, GITAM University, Visakhapatnam.

Associate Professor, GITAM University, Visakhapatnam.

**** Lecturer, Eritrea institute of Technology, Eritrea.

G. Prasad, D. Vijay Kumar, M. Ebraheem and A. Muneiah (2011). Study of the Voltage Stability in a Multibus Power Network using AI Technique. i-manager’s Journal on Electronics Engineering, 1(4), 38-42. https://doi.org/10.26634/jele.1.4.1509

Abstract

Voltage instability is a state of power system encountering an unacceptable voltage level. This paper describes the expressions of Fast Voltage Stability Index (FVSI) and Line Quality Factor (LQF), which may be considered as indication of voltage collapse under constrained condition of an interconnected power system. Artificial Neural Network Technique has been applied to identify the voltage collapse condition. The novelty of this method is that, once the ANN model of the system is developed, through on line checking of the load of the weak bus, the present method can immediately calculate the FVSI and LQF without going through the complex classical calculations. The developed ANN technique has been tested in IEEE 30 bus test system and is found to be in excellent agreement with the result obtained by classical method.

References

[1]. Lei Wang, Meir Kiein, Solomon Yirga, & Prabha Kundur (1997). Dynamic reduction of large power system for stability studies. IEEE Transaction on Power system, Vol- 12, No-2, May Pages 889-895

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[3]. S.Arali, P. kundur, P. Haddink, & D. Mathews (2002). Small Signal stability of a large power system as affected by new generator addition. IEEE Transaction on Power System, Presented at Power Conference, China, pp 812- 816.

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[5]. M. Moghavvemi, Omar, F. M. (1998). Technique for contingency monitoring and voltage collapse prediction. IEE proc. Generator, Transmission and Distribution, Vol- 145, No-6 November.

[6]. Chakrabarty. S, Jeyasurya, (2004). B: Online voltage stability monitoring using Artificial Neural Network- Power Engineering, LESCOPE-04 Large Engineering System Conference pp-71-75.

[7]. Jimenez, C.A. Castro, (2005). C.A: Voltage stability security margin assessment via Artificial Neural Network- Power Tech. June IEEE Russia, pp-27-30

[8]. H. B. Wan, Y. H. Song, A. T. Johns (1995). Artificial Neural Networks for power system Voltage Stability Assessment UPEC, Vol -1.

[9]. Suthar, B. Balasubramanian, (2007). R: Application of an ANN based voltage stability assessment tool to restructured power systems- Bulk Power System Dynamics and Control-VII. 19-24 Aug. pp,1-8.

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Research Paper

In-Vehicle Real-Time Human Emotion Recognition Through Visual, Thermal And Heart Rate Monitoring Using Parallel CPU And GPU Techniques For Driver Safety

Goh Chia Chieh* , Dino Isa**

* Researcher university of nothing Malaysia Campus Department Electrical & Electronics Engineering

** Professor University of Nottingham Malaysia Campus Department Electrical & Electronics Engineering

Goh Chia Chieh and Dino Isa (2011). In-Vehicle Real-Time Human Emotion Recognition Through Visual, Thermal and Heart Rate Monitoring Using Parallel CPU and GPU Techniques for Driver Safety. i-manager’s Journal on Electronics Engineering, 1(4), 43-49. https://doi.org/10.26634/jele.1.4.1510

Abstract

Real-Time Human Emotion Recognition Is A Challenging Task. Speed And Accuracy Has Always Been The Main Concern For Human Emotion Analysis. Such System Poses An Even Greater Challenge For Implementation In A Limited Space Of A Constantly Mobile Environment, For Example, In A Moving Vehicle. On Top Of That, Power Consumption Is Another Major Issue To Be Considered. This Paper Proposes An Efficient Method On How Such System Can Be Implemented In The Mobile Environment By Utilizing Parallel Cpu And Gpu To Achieve Real-Time Emotion Analysis And Judgment. Using Low Power Consumption Single Board Computer (Sbc) With A Dual Graphic Card Installed, The Task Will Be Split Into Multiple Threads Which Process Visual, Thermal And Heart Rate Emotion Monitoring System Simultaneously. Thus, Better Accuracy And Optimal Performance Is Attained Through Parallel Gpu And Cpu Processing. Handling Enormous Of Data Through Parallel Processing Requires Expertise In Combining The Cpu And Gpu Tasks Together By Merging Different Kinds Of Data Competently.

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Research Paper

An Approach For Real Time Audio Watermarking Using Wavetable Alternation In Digital Instruments

M. Vadivel* , V. Saravanan**

*Sathyabama University, Chennai, India.

**SRR Engineering College, Chennai, India.

M. Vadivel and V. Saravanan (2011). An Approach for Real Time Audio Watermarking Using Wave Table Alternation in Digital Instruments. i-manager’s Journal on Electronics Engineering, 1(4), 50-55. https://doi.org/10.26634/jele.1.4.1511

Abstract

Musical performance with digital instruments has become a common practice today and many digital instruments came to be used in Desk Top Music, live performance, etc. However, the spread of digital contents causes the problem of illegal duplication and distribution, so that digital watermark has recently attracted much attention as a technology to solve this problem. In this project we focus on a sound synthesized process in digital instruments and propose a real-time watermark method. Certain watermarks are embedded in wavetables that include in our digital instruments and the insertion of secret messages is actualized with wave table switching additionally embedded watermarks can be extracted from the acoustic signal. The proposed method is able to achieve a real-time watermark.

References

[1]. “A Novel Approach for Genetic Audio Watermarking” Mazdak Zamani1, Azizah Bt Abdul Manaf Rabiah Bt Ahmad3, Farhang Jaryani1, Hamed Taherdoost

[2]. Cox, I. J., Bloom, J. A., & Miller, M. L. (1999). “Digital watermarking”, Morgan Kaufmann Publishers.

[3]. Michael Arnold, Martin Schmucker, & Stephe D.Wolthusen, (2003). “Techniques and Applications of Digital Watermarking and Content Protection”, 1st edition July , Artech.

[4]. Hartung F & Kutter M (1999). Multimedia watermarking techniques Proceedings of the IEEE 87(7): pp 1709–1107.

[5]. Yajima, N., & K. Oishi (2005). “Digital watermarks for audio signals based on amplitude and phase coding,” IEICE technical report. Communication systems, 104(720):53–59, May.

[6]. Ryuki Tachibana (2003). “Audio Watermarking for Live Performance”, in Proc. of Security and Watermarking of Multimedia Contents V, SPIE Vol. 5020, pp. 32-43, Santa Clara, USA, January.

[7]. José García Hernández, Mariko Nakano Miyatake (2006). “Real-time MCLT audio matermarking and comparison of several whitening methods in receptor side.”

[8]. Hartung F & Kutter M (1999). Multimedia watermarking techniques Proceedings of the IEEE 87(7): pp 1709–1107.

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Research Paper

Semiconducting - Carbon Nanotube Field Effect Transistor Based Logic Gates

V. Saravanan* , Kannan .V**

* Research Scholar, Sathyabama University, Chennai, India.

** Professor, Sathyabama University, Chennai, India.

V. Saravanan and V. Kannan (2011). Semiconducting - Carbon Nanotube Field Effect Transistor Based Logic Gates. i-manager’s Journal on Electronics Engineering, 1(4), 56-60. https://doi.org/10.26634/jele.1.4.1512

Abstract

Carbon Nanotube Field Effect Transistors (CNTFET) are new nano-scaled devices for the digital circuits. In MOSFET based digital circuits when the channel length is much smaller than a micrometre is a challenge, and creates the problems in device fabrication, which limits advancing the integrated circuit. Small size of the MOSFET, below a few tens of nanometres creates the low Trans-conductance, gate oxide leakage, low ON-current, Mobility degradation and increased delay. Problems observed in the MOSFET, when size is reduced are avoided in CNTFET, since in case of CNTFET, carbon nanotube is used as channel and high-k material is used as gate dielectric. In this paper, we present the simulation results of semi-conducting Carbon Nanotube Field Effect Transistors based logic gates using HSPICE.

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

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Volume 1 Issue 4 June - August 2011

Chenchu Krishnaiah G* , T. Jaya Chandra Prasad**, M. N. Giri Prasad***

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R. Rajendran* , N. Devarajan**

* Assistant Professor, Department of IT, Karpagam College of Engineering, Coimbatore. ** Professor, Department of EEE, Government College of Technology, Coimbatore.

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Jaya Krishna Sunkara* **

* Assistant Professor, PDCE, Sullurpet, India. ** Researcher, Sullurpet, India. ***-**** Bachelor of Technology in ECE, PDCE, Sullurpet, India.

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C. Muni Saraswathi* , B. Anuradha**, K.Babulu***

* Lecturer, Department of Biomedical Engineering, S.V. Govt. Polytechnic, Tirupati. ** Associate Professor of ECE, S.V.U. College of Engineering, Tirupati. *** Associate Professor, Department of ECE, JNTU, Kakinada.

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PADMAJA* , S. Varadarajan**, G. Madhavi Latha***

* Associate Professor of ECE, C.R Engg College, Tirupati. ** Professor of ECE, S.V.U college of Engg, Tirupati. *** Assistant Professor, Sri Vidyanekhethan Engg College, Tirupati.

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G. Prasad* , Vijay Kumar*****

*Associate Professor, GITAM University, Visakhapatnam.GITAM University, Visakhapatnam. ** Associate Professor, GITAM University, Visakhapatnam. ***Associate Professor, GITAM University, Visakhapatnam. **** Lecturer, Eritrea institute of Technology, Eritrea.

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Goh Chia Chieh* , Dino Isa**

* Researcher university of nothing Malaysia Campus Department Electrical & Electronics Engineering ** Professor University of Nottingham Malaysia Campus Department Electrical & Electronics Engineering

Goh Chia Chieh and Dino Isa (2011). In-Vehicle Real-Time Human Emotion Recognition Through Visual, Thermal and Heart Rate Monitoring Using Parallel CPU and GPU Techniques for Driver Safety. i-manager’s Journal on Electronics Engineering, 1(4), 43-49. https://doi.org/10.26634/jele.1.4.1510

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M. Vadivel* , V. Saravanan**

*Sathyabama University, Chennai, India. **SRR Engineering College, Chennai, India.

M. Vadivel and V. Saravanan (2011). An Approach for Real Time Audio Watermarking Using Wave Table Alternation in Digital Instruments. i-manager’s Journal on Electronics Engineering, 1(4), 50-55. https://doi.org/10.26634/jele.1.4.1511

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V. Saravanan* , Kannan .V**

* Research Scholar, Sathyabama University, Chennai, India. ** Professor, Sathyabama University, Chennai, India.

V. Saravanan and V. Kannan (2011). Semiconducting - Carbon Nanotube Field Effect Transistor Based Logic Gates. i-manager’s Journal on Electronics Engineering, 1(4), 56-60. https://doi.org/10.26634/jele.1.4.1512

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Chenchu Krishnaiah G* , T. Jaya Chandra Prasad, M. N. Giri Prasad*

* Assistant Professor, Department of IT, Karpagam College of Engineering, Coimbatore.

** Professor, Department of EEE, Government College of Technology, Coimbatore.

* Assistant Professor, PDCE, Sullurpet, India.

Researcher, Sullurpet, India.

*-**** Bachelor of Technology in ECE, PDCE, Sullurpet, India.

C. Muni Saraswathi* , B. Anuradha, K.Babulu*

* Lecturer, Department of Biomedical Engineering, S.V. Govt. Polytechnic, Tirupati.

Associate Professor of ECE, S.V.U. College of Engineering, Tirupati.

* Associate Professor, Department of ECE, JNTU, Kakinada.

PADMAJA* , S. Varadarajan, G. Madhavi Latha*

* Associate Professor of ECE, C.R Engg College, Tirupati.

Professor of ECE, S.V.U college of Engg, Tirupati.

* Assistant Professor, Sri Vidyanekhethan Engg College, Tirupati.

Associate Professor, GITAM University, Visakhapatnam.GITAM University, Visakhapatnam.

Associate Professor, GITAM University, Visakhapatnam.

Associate Professor, GITAM University, Visakhapatnam.

**** Lecturer, Eritrea institute of Technology, Eritrea.

* Researcher university of nothing Malaysia Campus Department Electrical & Electronics Engineering

** Professor University of Nottingham Malaysia Campus Department Electrical & Electronics Engineering

*Sathyabama University, Chennai, India.

**SRR Engineering College, Chennai, India.

* Research Scholar, Sathyabama University, Chennai, India.

** Professor, Sathyabama University, Chennai, India.