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

Current Issue Vol. 12 Issue 1

Most Cited

Volume 1 Issue 4 October - December 2013

Research Paper

Analysis of PTS Scheme Used for PAPR Reduction of OFDM Signal

Renu Verma* , Mangal Singh, K. Neelam Dewangan*

* Electronics & Telecommunication Engineering, Chhatrapati Shivaji Institute of Technology, Durg (C.G.) India.

Electronics & Telecommunication Engineering, Chhatrapati Shivaji Institute of Technology, Durg (C.G.) India.

* Electronics & Telecommunication Engineering, Chhatrapati Shivaji Institute of Technology, Durg (C.G.) India.

Verma,R.,Singh,M., and Dewangan,N. (2014). Analysis Of PTS Scheme Used For PAPR Reduction Of OFDM Signal. i-manager’s Journal on Digital Signal Processing, 1(4), 1-7. https://doi.org/10.26634/jdp.1.4.2594

Abstract

For high data rate communication, Orthogonal Frequency Division Multiplexing technique is most effective & it is widely used for various wireless communication for example Digital Video Broadcasting(DVB), Digital Audio Broadcasting (DAB), WiMAX etc. High Peak to Average Power ratio of an OFDM signal is the main drawback of this parallel transmission scheme. Several methods are available to reduce high PAPR of OFDM signal like Clipping, Tone Reservation, Tone Injection, Companding, Partial Transmit Sequence etc. Among these methods PTS is the most powerful method to reduce PAPR of OFDM. In this paper we describe the performance of partial transmit sequence technique applied to the OFDM signal to reduce PAPR, & shows the BER performance improvement after PAPR reduction .This paper also shows that the computational complexity is a drawback of PTS.

References

[1]. Baxley, Robert J., and Guo Tong Zhou. (2007). "Comparing selected mapping and partial transmit sequence for PAR reduction." Broadcasting, IEEE Transactions on 53.4, pp- 797-803.

[2]. Deepan, T. V., (2012). "PAPR reduction performance analysis of Optimized Matrix based Phase Sequences in OFDM systems." International Journal of Electronics and Communications (IJEC) 1.1.

[3]. Hung, Ho-Lung, (2008). "Performance of particle swarm optimization techniques on PAPR reduction for OFDM systems." Systems, Man and Cybernetics, 2008. SMC 2008. IEEE International Conference on. IEEE. [4]. Abolfaz Ghassemi & T. Aaron Gulliver, “A low complexity PTS based radix FFT method for PAPR reduction in OFDM system,” IEEE Transaction on signal processing, Vol. 56, No. 3.

[5]. Yang, L.,K.K Soo. (2011). "PAPR reduction using low complexity PTS to construct of OFDM signals without side information." Broadcasting, IEEE Transactions on 57.2, pp. 284-290.

[6]. Guan, Lili, (2010). "Joint channel estimation and PTS to reduce peak-to-average-power radio in OFDM systems without side information." Signal Processing Letters, IEEE 17.10, pp 883-886.

[7]. Han, Seung Hee, and Jae Hong Lee. (2005). "An overview of peak-to-average power ratio reduction techniques for multicarrier transmission." Wireless Communications, IEEE 12.2.

[8]. Ankita, Er, and Anand Nayyar. (2010). "Review of various PTS (Partial Transmit Sequence) techniques of PAPR (Peak to Average Power Ratio) reduction in MIMO-OFDM." IEEE transactions on wireless communication signal Processing, vol. 9, No. 10.

[9]. Ku, Sheng-Ju, Chin-Liang Wang, and Chiuan-Hsu Chen. (2010). "A reduced-complexity PTS-based PAPR reduction scheme for OFDM systems."Wireless Communications, IEEE Transactions on 9.8, pp. 2455- 2460.

[10]. Vinayaka, V., P. Elavarasan, and G. Nagarajan. (2013). "Reduction of PAPR in OFDM signals using Fountain coded PTS." Communications and Signal Processing (ICCSP), 2013 International Conference on. IEEE.

[11]. Hou, Jun, Jianhua Ge, and Jing Li. (2011). "Peak-toaverage power ratio reduction of OFDM signals using PTS scheme with low computational complexity." Broadcasting, IEEE Transactions on 57.1, pp. 143-148.

[12]. Jiang T., Wu Y., (2008). “An Overview: Peak-to-average power ratio reduction techniques for OFDM signals”, IEEE Trans. Broadcasting, vol. 54, No. 2, pp. 257–268.

[13]. Lingyin Wang & JuLiu, (2011). “PAPR reduction of OFDM signals by PTS with grouping & recursive phase weighting method,” IEEE transactions on broadcasting comm, vol. 57.

[14]. Armstrong, J., (2002). “Peak-to-average power reduction for OFDM by repeated clipping and frequency domain filtering,” Electronics Letters, vol. 38, No. 5, pp. 246–247.

[15]. Wang X. B., Tjhung T. T., Ng C. S., (1999). “Reduction of peak-to-average power ratio of OFDM system using a companding technique”, IEEE Transaction on Broadcasting, vol. 45, No. 3, pp. 303–307.

[16]. Jung Chein Chen, (2009). “Partial transmit sequence for PAPR reduction of OFDM signals with the cross entropy methods,” IEEE signal Processing, vol. 16, no. 6.

[17]. Yajun Wang & Wen Chen, (2010). “PAPR reduction method based on parametric minimum cross entropy for OFDM Signal,” IEEE communication letter, vol. 14, No. 6.

[18]. Yajun Wang & Wen Chen, (2010). “A PAPR reduction method based on Artificial Bee Colony algorithm for OFDM signals,” IEEE transactions on wireless communication signal Processing, vol. 9, No. 10.

[19]. Taspinar, Necmi, "PAPR reduction using artificial bee colony algorithm in OFDM systems." Turk J Electr Eng Comput Sci 19.1 ,2011,pp. 47-58.

[20]. Wen, Jyh-Horng, (2008). "A suboptimal PTS algorithm based on particle swarm optimization technique for PAPR reduction in OFDM systems." EURASIP Journal on Wireless Communications and Networking, vol.144.

[21]. Salehinejad, Hojjat, and Siamak Talebi. (2012). "PAPR Reduction of OFDM Signals by Novel Global Harmony Search in PTS Scheme."International Journal of Digital Multimedia Broadcasting .

[22]. Dhillon, Aman, and Sonia Goyal. (2013). "PAPR Reduction in Multicarrier Modulations Using Firefly Algorithm. “International Journal of Innovative Research in Computer & Communication, Vol.1 issue 5, pp. 271–350

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Research Paper

Performance analysis of HAAR wavelets for segmentation of heart sound

Lekram Premlal Bahekar* , Abhishek Misal**

* Assistant Professor and Research Scholar, Department of Electronics & Telecommunication Engineering, ChhatrapatiShivaji Institute of Technology, Durg, India.

** Sr. Assistant Professor and Research Scholar, Department of Electronics & Telecommunication Engineering, ChhatrapatiShivaji Institute of Technology, Durg, India.

Bahekar,L., and Misal,A. (2013). Performance Analysis Of HAAR Wavelets For Segmentation Of Heart Sound. i-manager’s Journal on Digital Signal Processing, 1(4), 8-13. https://doi.org/10.26634/jdp.1.4.2595

Abstract

The heart is one of the key organs of human bodies and each component of heart sounds reflects important information about the cardiac status. The wavelet shrinkage denosing method can effectively reduce the noise of non-stationary signal but preserve the local regularity and the generalization threshold function is build .We present a method for heart sound segmentation based on the signal’s simplicity and strength .The method has two phases the first phase identifies the timing of s1 and s2 sound limit using simplicity and strength in the wavelet domain. The second phase discripainates among the s1 and s2 using high frequency information. The pre-processing before calculating energy of PCG signal by wavelet ,segment the PCG signal, find different parameters for segmentation .This paper presents an analysis of haar wavelet segmentation of heart sound segmentation techniques and suggested performance measures.

References

[1]. Mawloud Guermoui, Mohamed Lamine Mekhalfi ,karim Ferroudji (2013). Heart sound analysis using wavelets responses and support vector machines,IEEE.

[2]. Nayad kouras, daoud boutana, Messaoud benidir (2012). Wavelet based segmentation and time-frequency caracterisation of some abnormal heart sound signals,24th International Conference on Microelectronics (ICM).

[3]. Girish Gautam,and Deepesh Kumar (2013). Biometric system from heart sound using wavelet based feature set, international conference on communication and signal processing ,April 3-5 ,pp. 551-555.

[4]. AF Quiceno, E Delgado , M Vallverd, AM Matijasevic and G Castellanos-Domnguez (2008). Effective Phonocardiogram Segmentation Using Nonlinear Dynamic Analysis and High-Frequency Decomposition Computers in Cardiology 35 ,pp. 161-164.

[5]. Sabarimalai Manikandan M., & Dandapat S (2007). Wavelet-Based ECG and PCG Signals Compression Technique for Mobile Telemedicine. International Conference Advance computing and communications, Pp. 164-169.

[6]. El. Segaier M, Lilja O, Lukkarinen S, Srnmo L, Sepponen R, Pesonen E (2005). Computer-based detection and analysis of heart sound and murmur. Ann Biomed Eng, 33(7), pp. 937–942.

[7]. Abhishek Misal , G.R.Sinha , R. M. Potdar And M. K. Kowar(2012). Comparison of wavelet transforms for denoising and analysis of PCG signal i-manager's Journal on Communication Engineering and Systems, Vol. 1 l, pp. 49-53.

[8]. Wang haibin, and Hu Yuliang (2008). Heart sound measurement and Analysis system with digital stethoscope, Asia international symposium on mechatronics, pp.26-30

[9]. Abhishek Misal and Sinha G.R (2012). Denoising of PCG signal by using wavelet transforms Advances in Computational Research 4(1), pp 46-49.

[10]. zhi-dong zhao (2005). Wavelet shrinkage denoising by generalized threshold function, proc, 4th, International Conference on Machine Learning and Cybernetics, pp.5501-5506.

[11]. Wang haibin ,and Hu Yuliang (2008 ), Heart sound measurement and Analysis system with digital stethoscope Asia international symposium on mechatronic, pp.26-30.

[12]. Mustafa Yamach, Zumary DokurTamer Olmez(2008). Segmentation on s1-s2 sound in phonocardiogram records using wavelet energies, IEEE.

[13]. Zhihai Tu, and Guitao Cao(2010). Improved methods for detecting main components of heart sounds, Sixth International Conference on Natural Computation, pp.3585-3588.

[14]. Cota Navin Gupta, Ramaswamy Palaniappan and Sundaram Swaminathan(2005). Classification of Homomorphic Segmented honocardiogram Signals Using Grow and Learn Network IEEE Engineering in Medicine and Biology 27th Annual Conference Shanghai, China, 4251- 4254.

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Research Paper

Design Of PID Controller For Boiler Drum Level Control

J. Pavithra* , T.Kalavathi Devi, R. Mouleeshuwarapprabu*

* PG Scholar, Dept. of Electronics and Instrumentation Engineering, Kongu Engineering College, Perundurai, Erode, Tamilnadu, India.

Asst.Professor(Sr.G.), Dept. of Electronics and Instrumentation Engineering, Kongu Engineering College, Perundurai, Erode, Tamilnadu, India.

* Asst.Professor, Dept. of Electronics and Instrumentation Engineering, Kongu Engineering College, Perundurai, Erode, Tamilnadu, India.

Pavithra.J.,Devi,K.T., and Mouleeshuwarapprabu.R. (2013). Design of PID Controller for Boiler Drum Level Control. i-manager’s Journal on Digital Signal Processing, 1(4), 14-18. https://doi.org/10.26634/jdp.1.4.2596

Abstract

In thermal power plant, the use of boiler unit which produces steam helps to rotate the turbine to produce electricity. The boiler unit which consists of boiler drum converts feedwater into steam. The three types of boiler drum system are single element boiler drum, two element boiler drum and three element boiler drum. In thermal power plant the three element boiler drum is used. In the boiler drum, during the conversion of feedwater into steam, the level should be maintained and controlled properly. For controlling purpose the PID controller is used and the tuning of controller parameter controller is done manually. In the proposed method PID controller is replaced by adaptive controller where the tuning of controller will be made automatically. Thus the performance of the boiler drum level control system will be improved by three element method.

References

[1]. Astrom K.J, Eklund K, (1972). “A simplified non-linear model of a drum boiler-turbine unit,” International Journal of Control, Vol. 16, No. 1, pp. 145-169.

[2]. Bell R.D and Astrom K.J, (1987). “Dynamic models for boiler-turbine alternator units: Data loss and parameter estimation for 160 MW unit”, Report TFRT- 3192,Lund inst.Technol., Lund, Sweden.

[3]. Chen C.S, Lee Y.D, Hsu C.T, and Chen C.K (2008). “Transient response of an incinerator plant by considering boiler model with dynamic steam variation”, IEEE Transactions on Power Systems, Vol.23, No.1, pp.92-99.

[4]. Cheres E (1990). “Small and medium size drum boiler models suitable for long term dynamic response ”, IEEE Transaction on Energy Conversion, Vol.5, No.4, pp.686-692.

[5]. Ghousiya Begu k, Mercy D, Kiren Vedi H, (2013). “ An intelligent model based level control of boiler drum”, ISSN, Vol.3, Issue.1, pp. 2250-2459.

[6]. Hugh F. VanLandingham and Nishith D. Tripathi, (1996). “Knowledge-based adaptive fuzzy control of drum level in a boiler system”, Southcon/96, Conference Record, pp. 454 - 459.

[7]. Huang Wei-jian, Wang Yuan, (2010). “Analysis and treatment of drum level regulation abnormality”, Guang Dong Electric Power, Vol.23, No.2, pp.101-102.

[8]. Liu Jing, (2011). “The water level control system of boiler steam drum”, Wide and Heavy Plate, Vol.17, No.1, pp.54-57.

[9]. Preeti Manke and Sharad Tembhurne (2012). “Application of back propagation neural network to drum level control in thermal Power plant”, IJCSI, Vol.9, Issue 2, No.1.

[10]. B.Wayne Beuquette, (2002). “Process control modeling, design and simulation” Prentice Hall PTR.

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Research Paper

HMM Based Privacy Preserving Approach for VOIP Communication

S. Prashantini* , T.J.Jeyaprabha**

* Applied Electronics, Department of ECE, Sri Venkateswara College of Engineering, India.

** Assistant Professor, Department of ECE, Sri Venkateswara College of Engineering, India.

Prashantini.S., and Jeyaprabha.T.J. (2014). HMM Based Privacy Preserving Approach For VOIP Communication. i-manager’s Journal on Digital Signal Processing, 1(4), 19-26. https://doi.org/10.26634/jdp.1.4.2597

Abstract

Pre-processing of Speech Signal serves various purposes in any speech processing application. It includes Noise Removal, Endpoint Detection, Pre-emphasis, Framing, Windowing, Echo Canceling etc. Out of these, silence portion removal along with endpoint detection is the fundamental step for applications like Speech and Speaker Recognition. The proposed method uses multi-layerperceptronalong with hierarchal mixturemodel for classification of voiced part of a speech from silence/unvoiced part. The work shows better end point detection as well as silence removal.The study is based on timing-based traffic analysis attacks thatcan be used to reconstruct the communication on end-to-end voip systems by taking advantage of the reduction or suppression of the generation of traffic whenever the sender detects a voice inactivity. Removal of the silence in between the inter packet time in order to obtain a clear communication. The proposed attacks can detect speakers of encrypted speech communications with high probabilities. With the help of different codecs we are going to detect speakers of speech communications In comparison with traditional traffic analysis attacks, the proposed traffic analysis attacks do not require simultaneous accesses to one traffic flow of interest at both sides.

References

[1]. Y.J. Pyun, Y.H. Park, X. Wang, D.S. Reeves, and P. Ning, (2010). “Tracing Traffic through Intermediate Hosts that Repacketize Flows,” Proc.IEEE INFOCOM '07.

[2]. J.W. Deng and H.T. Tsui, (2000). “An HMM-Based Approach for Gesture Segmentation and Recognition,” Proc. Int'l Conf. Pattern Recognition(ICPR '00), pp. 679-682.

[3]. L.R. Rabiner, “A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition,” Proc. IEEE, Vol. 77, No. 2,pp. 267-296.

[4]. R. Bakis, “Continuous Speech Recognition via Centi second Acoustic states,” J. the Acoustical Soc. of Am.

[5]. G. Danezis and A. Serjantov, (2004). “Statistical Disclosure or Intersection Attacks on Anonymity Systems,” Proc. Sixth Information Hiding Workshop (IH '04), pp. 293-308.

[6]. O. Berthold and H. Langos, (2002). “Dummy Traffic Against Long Term Intersection Attacks,” Proc. Privacy Enhancing Technologies Workshop(PET '02), pp. 110-128.

[7]. X. Wang, S. Chen, and S. Jajodia, (2005). “Tracking Anonymous Peer-to-Peer Voip Calls on the Internet,” Proc. 12th ACM Conf. Computer and Comm. Security (CCS '05), pp. 81-91.

[8]. “Audio Signals Used for Experiments, ”http:// academic. csuohio. edu/zhu _y/ isc 2010/instruction.txt.

[9]. Q. Sun, D.R. Simon, Y.-M. Wang, W. Russell, V.N. Padmanabhan,and L. Qiu, (2002). “Statistical Identification of Encrypted Web Browsing Traffic,” Proc. IEEE Symp. Security and Privacy (SP '02), pp. 19-30.

[10]. D. Herrmann, R. Wendolsky, and H. Federrath, (2009). “Website Fingerprinting: Attacking Popular Privacy Enhancing Technologies with the Multinomial Nai¨ve-Bayes Classifier,” Proc. ACM Workshop Cloud Computing Security (CCSW '09), pp. 31-42.

[11]. L. Lu, E.-C. Chang, and M. Chan, (2010). “Website Fingerprinting and Identification Using Ordered Feature Sequences,” Proc.15th European Conf. Research in Computer Security (ESORICS), D. Gritzalis, B. Preneel, and M. Theoharidou, eds. pp. 199-214.

[12]. C.V. Wright, L. Ballard, S.E. Coull, F. Monrose, and G.M. Masson, (2008).“Spot Me if You Can: Uncovering Spoken Phrases in Encrypted Voip Conversations,” Proc. IEEE Symp. Security and Privacy(SP '08), pp. 35-49.

[13]. C.V. Wright, L. Ballard, F. Monrose, and G.M. Masson, (2007). “Language Identification of Encrypted Voip Traffic: Alejandra y Roberto orAlice and Bob?,” Proc. 16th USENIX Security Symp. USENIX Security Symp., pp. 4:1-4:12, http://portal.acm.org/citationcfm?id=1362903.1362907.

[15]. C. Wright, S. Coull, and F. Monrose, (2009). “Traffic Morphing: An Efficient Defense against Statistical Traffic Analysis,” Proc. Network and Distributed Security Symp. (NDSS '09).

[16]. Network Security M. Backes, G. Doychev, M. Du¨ rmuth, and B. Ko¨ pf, (2010). “Speaker Recognition in Encrypted Voice Streams,” Proc. 15th European Symp. Research in Computer Security (ESORICS '10), pp. 508-523.

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Research Paper

Digital modulation using wavelet shift keying

R.Brindha* , G.A.Sathish kumar**

* Second Year M.E Communication System, Sri Venkateswara College of Engineering, Chennai.

** Professor of ECE, Sri Venkateswara College of Engineering, Chennai.

Brindha.R., and Kumar,S.G.A. (2014). Digital Modulation Using Wavelet Shift Keying. i-manager’s Journal on Digital Signal Processing, 1(4), 27-30. https://doi.org/10.26634/jdp.1.4.2598

Abstract

So for we have seen many modulation techniques in which sine wave (carrier)is used to modulate digital data .In this paper digital data are modulated using wavelets (carrier). Initially only one mother wavelet is created and later scaled version of mother wavelet is derived for each bits in digital data. The scaled wavelets is used to perform digital modulation. The Performance of proposed technique is compared with conventional modulation schemes in terms of bit error rate (BER) and power efficiency (PE).

References

[1]. N. Benvenuto, R. Corvaja, T. Erseghe, and N. Laurenti, (2007). Communication Systems: Fundamentals and Design methods, England John Wiley.

[2]. M. K. Lakshmanan, and N. Nikookar, (2006). “A Review of Wavelet for Digital Wireless Communication”, Springer Wirel. Personal Common., Issue 37, pp. 387–420

[3]. U. A. K. Okonkwo, R. Ngah, and T. A. Rahman, (2009). “Affine Group Linear Operator-based Channel Characterization for Mobile Radio Systems”,WSEAS Trans. on Systems, vol. 8, no. 2, pp. 288-302.

[4]. R. J. Dickenson, and Z. Ghassemlooy, (2003). “A feature extraction and pattern recognition receiver employing wavelet analysis and artificial intelligence for signal detection in diffuse optical wireless communications", IEEE Wireless Communications, Vol. 10, No.2, pp.64-72.

[5]. S. Mallat, (2009). A Wavelet Tour of Signal Processing: rd The Sparse Way, 3 edition Burlington, MA Elsevier.

[6]. P. S. Addison, (2002). The Illustrated Wavelet Transform Handbook, NewYork, Taylor & Francis.

[7]. M. A. Tzannes and M. C. Tzannes, (1992). “Bit-by-bit channel coding using wavelets”, Proc. of GLOBECOM.

[8]. P. Gandhi, S. S. Rao, and R. S. Pappu, (1993). “On Waveform coding using Wavelets”, Twenty-Seventh Asilomar Conference on Signals, Systems and Computers.

[9]. H. M. de Oliveira, H. A. N. Silva, and E. A. Bouton, (2003). “Wavelet Shift-Keying: A New Digital Modulation", XX Simpósio Brasileiro deTelecomunicações-STB, Rio de Janeiro.

[10]. L. Xu, T. Wang, O. Matsuda, M. Cvijetic I. Glesk, and P.R. Prucnal, (2005). “Wavelength-shift-keying (WSK) encoded pulses for opticallabeling applications”, IEEE Photonics Technology Lett, Vol. 17, No. 12, pp. 2775-2777.

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

Current Issue Vol. 12 Issue 1

Most Read

Most Cited

Volume 1 Issue 4 October - December 2013

Analysis of PTS Scheme Used for PAPR Reduction of OFDM Signal

Renu Verma* , Mangal Singh**, K. Neelam Dewangan***

Verma,R.,Singh,M., and Dewangan,N. (2014). Analysis Of PTS Scheme Used For PAPR Reduction Of OFDM Signal. i-manager’s Journal on Digital Signal Processing, 1(4), 1-7. https://doi.org/10.26634/jdp.1.4.2594

Abstract

References

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Performance analysis of HAAR wavelets for segmentation of heart sound

Lekram Premlal Bahekar* , Abhishek Misal**

Bahekar,L., and Misal,A. (2013). Performance Analysis Of HAAR Wavelets For Segmentation Of Heart Sound. i-manager’s Journal on Digital Signal Processing, 1(4), 8-13. https://doi.org/10.26634/jdp.1.4.2595

Abstract

References

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Design Of PID Controller For Boiler Drum Level Control

J. Pavithra* , T.Kalavathi Devi**, R. Mouleeshuwarapprabu***

Pavithra.J.,Devi,K.T., and Mouleeshuwarapprabu.R. (2013). Design of PID Controller for Boiler Drum Level Control. i-manager’s Journal on Digital Signal Processing, 1(4), 14-18. https://doi.org/10.26634/jdp.1.4.2596

Abstract

References

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HMM Based Privacy Preserving Approach for VOIP Communication

S. Prashantini* , T.J.Jeyaprabha**

* Applied Electronics, Department of ECE, Sri Venkateswara College of Engineering, India. ** Assistant Professor, Department of ECE, Sri Venkateswara College of Engineering, India.

Prashantini.S., and Jeyaprabha.T.J. (2014). HMM Based Privacy Preserving Approach For VOIP Communication. i-manager’s Journal on Digital Signal Processing, 1(4), 19-26. https://doi.org/10.26634/jdp.1.4.2597

Abstract

References

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Digital modulation using wavelet shift keying

R.Brindha* , G.A.Sathish kumar**

* Second Year M.E Communication System, Sri Venkateswara College of Engineering, Chennai. ** Professor of ECE, Sri Venkateswara College of Engineering, Chennai.

Brindha.R., and Kumar,S.G.A. (2014). Digital Modulation Using Wavelet Shift Keying. i-manager’s Journal on Digital Signal Processing, 1(4), 27-30. https://doi.org/10.26634/jdp.1.4.2598

Abstract

References

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Renu Verma* , Mangal Singh, K. Neelam Dewangan*

J. Pavithra* , T.Kalavathi Devi, R. Mouleeshuwarapprabu*

* Applied Electronics, Department of ECE, Sri Venkateswara College of Engineering, India.

** Assistant Professor, Department of ECE, Sri Venkateswara College of Engineering, India.

* Second Year M.E Communication System, Sri Venkateswara College of Engineering, Chennai.

** Professor of ECE, Sri Venkateswara College of Engineering, Chennai.