Refine your search
Collections
Co-Authors
Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Kannan, P.
- Performance Comparison of Various Noisy Audio Signals Analysis Using Different Sampling Rates
Abstract Views :216 |
PDF Views:0
Authors
Affiliations
1 ECE Department, PET Engineering College, Vallioor, Tamilnadu, IN
2 Communication Systems, PET Engineering College, Vallioor, Tamilnadu, IN
1 ECE Department, PET Engineering College, Vallioor, Tamilnadu, IN
2 Communication Systems, PET Engineering College, Vallioor, Tamilnadu, IN
Source
International Journal of Advanced Networking and Applications, Vol 9, No 5 (2018), Pagination: 3545-3556Abstract
The discrete time systems that process data at more than one sampling rate are known as multirate systems. The two basic operations in multirate signal processing are decimation and interpolation.One of the important applications of multirate signal processing is sub-band coding of speech signal. In the proposed system, speech signal is taken as input signal. Additive White Gaussian Noise is added with the input speech signal. The input speech signal spectrum is divided into frequency sub-bands using a bank of finite response filters. Hamming, Hanning, Blackman, Rectangular and Kaiser windowing methods are used to implement the low pass and high pass filters. Finally performance of the proposed system is evaluated on the TIMIT data base using the parameters like leakage factor, main lobe width, side lobe attenuation, peak amplitude of side lobe and signal to noise ratio. The performance evaluation shows which window is suitable for designing the finite impulse response filters and sub-band coding system.Keywords
Windowing, Signal to Noise Ratio, FIR Filter, Multirate.Full Text
References
- Ashraf M. Aziz, Subband Coding of Speech Signals Using Decimation and Interpolation, 13th International Conference on Aerospace Sciences & Aviation Technology, ASAT- 13, 2009, pp.116.
- Christian Feldbauer, Marian Kepesi, Klaus Witrisal, Multirate Signal Processing, Signal Processing and Speech Communication Laboratory, V 1.3.3, 2005,pp.1-10.
- Dolly Agrawal, Divya Kumud, A Review On Multirate Digital Signal Processing, International Journal of Electrical and Electronics Engineers (IJEEE), Vol. No.6, Issue No. 02, 2014,pp.324328.
- Jagriti Saini and Rajesh Mehra, Power Spectral Density Analysis of Speech Signal using Window Techniques, International Journal of Computer Applications, Volume 131 – No.14, 2015, pp.3336.
- John G. Proakis and Dimitris G. Manolakis, Digital Signal Processing: Principles, Algorithms and Applications, Third Edition.
- Komal Jindal, Analysis of Speech Signals, International Journal of Computer Science and Mobile Computing (IJCSMC), Vol.3, Issue 3, 2014, pp.795-800.
- Lalima Singh, Speech Signal Analysis using FFT and LPC, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), Volume 4, Issue 4, 2015, pp.1658-1660.
- Lalitha R Naik, Devaraja Naik R L, Sub-band Coding Of Noisy Speech Signals Using Digital Signal Processing, International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE), Volume 4, Issue 4, 2015,pp.901-904.
- Lalitha R Naik, Devaraja Naik R L, Sub-band Coding of Speech Signals using Multirate Signal Processing and comparing the various parameter of different speech signals by corrupting the same speech signal, International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), Volume 4, Issue 2, 2015,pp.217-221.
- Ljiljana D. Milic, Tapio Saramaki and Robert Bregovic, Multirate Filters: An Overiew, Proc.
- IEEE Asia Pacific Conf. on Circuits, Syst., 2006, pp.914-917.
- Maurya A.K. and Dr.Deepak Nagarai, Multirate Signal Processing: Graphical Representation & Comparison of Decimation and Interpolation Identities using MATLAB, International Journal of Electronics and Communication Engineering, Volume 4, 2011, pp.443-452.
- Mohammed Mynuddin, Md. Tanjimuddin, Md. Masud Rana, Abdullah, Designing a Low- Pass Fir Digital Filter by Using Hamming Window and Blackman Window Technique, Science Journal of Circuits, Systems and Signal Processing , Vol. 4, No. 2, 2015, pp.9-13.
- Prajoy Podder, Tanvir Zaman Khan, Mamdudul Haque Khan and M.Muktadir Rahman, Comparative Performance Analysis of Hamming, Hanning and Blackman Window, International Journal of Computer Applications, Volume 96– No.18, 2014,pp.1-7.
- Ramya.M, Sathyamoorthy.M, Speech Coding by using Sub band Coding, International Conference on Computing and Control Engineering (ICCCE), 2012.
- Raymond N.J. Veldhuis, Marcel Breeuwer, Robbert van der Waal, Subband Coding of Digital Audio Signals Without Loss of Quality, Philips Research Laboratories, IEEE., vol. A1a.8, 1989, pp. 2009-2012.
- Ronald E. Crochiere and Lawrence R. Rabiner, Optimum FIR Digital Filter Implementations for Decimation, Interpolation and Narrow-Band Filtering, IEEE Transactions on Acoustics, Speech, And Signal Processing, Vol. Assp-23, No.5, 1975,pp.444-455.
- Saurabh Singh Rajput, Dr.S.S. Bhadauria, Implementation of FIR Filter using Adjustable Window Function and Its Application in Speech Signal Processing International Journal of Advances in Electrical and Electronics Engineering, Volume1, Issue 2, pp.158-164.
- Saurabh Singh Rajput, S.S. Bhadauria, Implementation of FIR Filter using Efficient Window Function and Its Application In Filtering a Speech Signal, International Journal of Electrical, Electronic and Mechanical Controls, Volume 1, Issue 1, 2012.
- Shikha Shukla, Kamal Prakash Pandev, Rakesh Kumar Singh, Implementation and Simulation of Low Pass Finite Impulse Response Filter Using Different Window Method, International Journal of Emerging Technology and Advanced Engineering, Volume 5, Issue 1, 2015,pp.88-93.
- Suraj R. Gaikwad and Gopal S. Gawande, Implementation of Efficient Multirate Filter Structure for Decimation, International Journal of Current Engineering and Technology, Vol.4, No.2, 2014, pp.1008-1010.
- Suraj R. Gaikwad, Prof. Gopal Gawande, Review: Design of Highly Efficient Multirate Digital Filters, International Journal of Engineering Research and Applications, Vol. 3, Issue 6, 2013,pp.560-564.
- Suresh Babu P., Dr.D.Srinivasulu Reddy and Dr.P.V.N.Reddy, Speech Signal Analysis Using Windowing Techniques, International Journal of Emerging Trends in Engineering Research (IJETER), Vol. 3 No.6, 2015,pp.257-263.
- Suverna Sengar and Partha Pratim Bhattacharya , Multirate Filtering for Digital Signal Processing and its Applications, ARPN Journal of Science and Technology, VOL. 2, NO.3, 2012, pp.228237.
- Vijayakumar Majjagi, Sub Band Coding of Speech Signal by using Multi-Rate Signal Processing, International Journal of Engineering Research & Technology (IJERT), Vol. 2 Issue 9, 2013, pp.45-49.
- Vishv Mohan , Analysis And Synthesis of Speech Using Matlab, International Journal of Advancements in Research & Technology, Volume 2, Issue 5, 2013,pp.373-382.
- Design of Low Noise Amplifier using Positive Feedback Gain Enhancement Technique
Abstract Views :198 |
PDF Views:0
Authors
Affiliations
1 Department of ECE, PET Engineering College,Vallioor, IN
2 Department of ECE, PET Engineering College, Vallioor, IN
3 Department of ECE, MEPCO Schlenk Engineering College, Sivakasi, IN
1 Department of ECE, PET Engineering College,Vallioor, IN
2 Department of ECE, PET Engineering College, Vallioor, IN
3 Department of ECE, MEPCO Schlenk Engineering College, Sivakasi, IN
Source
International Journal of Advanced Networking and Applications, Vol 9, No 5 (2018), Pagination: 3563-3570Abstract
Low Noise Amplifier (LNA) is a digital amplifier that amplifies a completely low strength signal without drastically degrading its sign to the noise ratio. It's far in any other case known as block down converter. Its miles designed to decrease the additional noise. Here, the low noise amplifier (LNA) is designed the use of a new configuration of high-quality remarks advantage enhancement technique that's suitable for low energy and coffee noise packages. On this proposed technique, additional wonderful comments capacitor is attached to any of the transistor terminal, which will increase voltage benefit due to reducing the entire transconductance. The everyday amplifier circuit is designed the use of energy constrained simultaneous noise and input matching (PCSNIM) method. This technique is used to gain simultaneous enter impedance and minimal noise matching. By means of using this advantage stronger method, the gain of the LNA has been elevated and noise parent is reduced without sacrificing bandwidth, linearity and electricity intake. In this paper we attain 8.620 dB of gain at 4GHz frequency by way of the usage of superb feedback capacitor and interstage matching inductor and also the design of Ultra Wide Band (UWB) LNA provides 14.62dB gain by using inductive source degeneration topology.Keywords
Block Down Converter, Gain, Positive Feedback, Transconductance.Full Text
References
- Ali Hajimiri and Thomas H.Lee, ‘Design Issues in CMOS Differential LC Oscillators’, IEEE Journal of Solid State Circuits, May 1999, vol.34,no. 5.
- Andrew N. Karanicolas,’ A 2.7 V 900 MHz CMOS LNA and Mixer’, IEEEJ. Solid state circuits, vol. 31, no.12, Dec.1996, pp. 1939-1944.
- Derek K. Shaeffer, and Thomas H. Lee.: ‘Corrections to A 1.5-V, 1.5-GHz CMOS Low Noise Amplifier’, IEEE J. Solid-State Circuit, 2005, 40, (6), pp. 1397–1398.
- Md Rahan Chowdhury, Malik Quamrus Samawat, Irtiza Ahmed Salman,’ An Ultra-Wide-Band 2.66 - 3.75 GHz LNA in 0.18-μm CMOS Radio Frequency’, International Journal of Engineering Research and General Science Volume 5, Issue 3, May-June, 2017 ,ISSN 2091-2730
- Ming – Dou Ker and Shue-Chang Liu, ‘Whole – Chip ESD Protection Design for Submicron CMOS VLSI’, IEEE International Symposium on Circuits and Systems, June 9-12,1997.
- M.Ramana Reddy, Dr. N.S Murthy Sharma, Dr. P. Chandra Sekhar,’ A 3.5 GHz Low Noise, High Gain Narrow Band Differential Low Noise Amplifier Design for Wi-MAX Applications’, International Journal of Electronics Engineering Research. ISSN 0975-6450 Volume 9, Number 4 (2017) pp. 505-516
- Nikola Petrović, Radivoje Djurić,’ A 94GHz low power UWB LNA for passive radiometer’, INFOTEHJAHORINA Vol. 16, March 2017.
- Pramod K. B., Kumaraswamy H. V.,’ The Linear, Nonlinear Measurements, Analysis and Evaluation for the Design of Ultra-Wideband Low Noise Amplifier’, International Journal of Computer Applications (0975 – 8887) Volume 158 – No 6, January 2017
- Sam HamidonJ.,et al., ‘Design of Single Stage LNA using L-Matching Network for WIMAX Applications’, ARPN Journal of Engineering and Applied Sciences,vol. 9,no.10,Oct.2014,ISSN 1819-6608.
- Vaithinathan.V, Raja.J and Srinivasan .R, ‘A Low Power ,High gain, Low Noise Amplifier with improved noise figure and input matching for Ultra Wide Band Applications’, IJST ,Transactions of Electrical Engineering,vol.36,no,E2,pp 163-174,2012.
- Yu-Da Shiu et al., ‘CMOS Power Amplifier with ESD Protection Design Merged in Matching Network’, IEEE Journal of Solid State Circuits,2007.
- Zaid Albataineha, Yazan Hamadeh, Jafar Moheidat, Ahmad Dagamseh, Idrees Al-Kofahi, Mohammed Alsumady,’ A High-Gain Low Noise Amplifier for RFID Front-Ends Reader’, Jordan Journal of Electrical Engineering ISSN (Print): 2409-9600, ISSN (Online): 2409-9619
- Classification of Remote Sensing Images using Wavelet Based Contourlet Transform and Accuracy Analysis of Classified Images
Abstract Views :211 |
PDF Views:0
Authors
Affiliations
1 Department of Electronics and Communication Engineering , PET Engineering College, Anna University, IN
2 Department of Electronics and Communication Engineering, PET Engineering College, Anna University, IN
1 Department of Electronics and Communication Engineering , PET Engineering College, Anna University, IN
2 Department of Electronics and Communication Engineering, PET Engineering College, Anna University, IN
Source
International Journal of Advanced Networking and Applications, Vol 9, No 5 (2018), Pagination: 3601-3605Abstract
In remote sensing classification of spatial and spectral feature of multispectral images with high accuracy provide greater performance analysis. Wavelet transform is the most preferred transform for classification of both spectral and spatial features. The difficulty present here is the non-availability of directional features. The proposed wavelet based contourlet transform provide salient feature extraction using laplacian pyramid followed by directional filter banks. The extracted features were greatly reduced by using principle component analysis method. By that features the multispectral image has been classified into urban, wasteland, waterbody, hilly region by using fuzzy-c-means clustering algorithm. The wavelet transform is used for low frequency component classification and contourlet transform is used for high frequency component classification on the remote sensing images in the existing method. The aforesaid transforms provide less classification accuracy for remote sensing images. So it is proposed that the wavelet based contourlet transform is to be used for the analysis of both high frequency and low frequency component classification. Hence the proposed method shows that classification accuracy is higher than the existing method.Keywords
Multispectral Image, Contourlet Transform, Wavelet Transfor, Fuzzy-C-Means Clustering Algorithm &PCA.Full Text
References
- Aditsharma,Khunteta (2016),” Satellite Image Contrast and Resolution Enhancement using Discrete Wavelet Transform and Singular Value Decomposition”, International Conference on Emerging Trends in Electrical, Electronics and Sustainable Energy Systems (ICETEESES–16)
- Ahmad, F., Ahmed, Z., & Najam, A. (2013), “Soft biometric gender classification using face for real time surveillance in cross dataset environment”. IEEE International Conference on Multi Topic, pp. 131–135.
- A. Alaguraja , K. Venkateswaran, N. Kasthuri1, R. (2015), “Performance Comparison of Wavelet and Contourlet Frame Based Features for Improving Classification Accuracy in Remote Sensing Images”, Journal Indian Social Remote Sensing.
- Alagu Raja, R. A., Anand, V., Maithani, S., SenthilKumar, A., & AbhaiKumar, V. (2009), “Wavelet frame based feature extraction technique for improving classification accuracy”, Journal of the Indian Society of Remote Sensing, 37(3), 423–443.
- Amelard, R., Wong A., & Clausi, D.A. (2013), “Unsupervised classification of agricultural land cover using polarimetric synthetic aperture radar via a sparse texture dictionary model”, IEEE International Conference on Geoscience and Remote Sensing symposium, pp. 4383–4386.
- Asli Ozdarici Ok, Ozlem Akar and Oguz Gungor (2012)” Evaluation of random forest method for agricultural crop classification”, European Journal of Remote Sensing - 2012, 45: 421-432 doi: 10.5721/EuJRS20124535
- Czajkowska, J., Bugdol, M., & Pietka, E. (2012), “Kernelized fuzzy C- means method and Gaussian mixture model in unsupervised cascade clustering”, Information Technologies in Biomedicine, 7339, 58 –66.
- Deshpande, D.S., Rajurkar, A.M., & Manthalkar, R.M. (2013), “Medical image analysis an attempt for mammogram classification using texture based association rule mining”, IEEE National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics. pp. 1–5.
- Do, M. N., & Vetterli, M. (2005), “The contourlet transform: an efficient directional multiresolution image representation”, IEEE Transactions Image on Processing, 14(12), 2091– 2106
- Dos.Santos,J.A.,Penatti,O.A.B.,DaTorres,R.S.,Go sselin,P,PhilippFoliguet,S.,&Falco,A.(2012), “Improving texture description in remote sensing image multi-scale classification tasks by using visual words”, IEEE International Conference on Pattern Recognition. pp. 3090–3093.
- Heinrich, A., Gen, D., Znamenskiy, D., Vink, J. P., & de Haan, G. (2014), “Robust and sensitive video motion detection for sleep analysis”, IEEE Journal on Biomedical and Health Informatics, 18(3), 790– 798.
- Halkidi, M., Batistakis, Y., & Vazirgiannis, M. (2002a), “Cluster validity methods: Part-I”, ACM SIGMOD Record, 31(2), 40–45.
- Halkidi, M., Batistakis, Y., & Vazirgiannis, M. (2002b), “Clustering validity checking methods: part II”, ACM SIGMOD Record, 31(3), 19–27.
- Lillesand, M.T., Ralph Kiefer, W., & Jonathan Chipman, W., (2004), “Remote Sensing and Image Interpretation”, 5th edition, Wiley International edition.
- Mioulet, L., Breckon, T.P., Mouton, A., Haichao Liang, & Morie, T. (2013), “Gabor features for real-time road environment classification”, IEEE International Conference on Industrial Technology. pp. 1117– 1121.
- Meher, S. K., Uma Shankar, B., & Ghosh, A. (2007), “Wavelet-feature- based classifiers for multispectral remote-sensing images”, IEEE Transactions on Geoscience and Remote Sensing, 45(6), 1881– 1886
- Performance Analysis of Audio and Video Synchronization using Spreaded Code Delay Measurement Technique
Abstract Views :186 |
PDF Views:0
Authors
A. Thenmozhi
1,
P. Kannan
1
Affiliations
1 Department of ECE, Anna University, Chennai, IN
1 Department of ECE, Anna University, Chennai, IN
Source
International Journal of Advanced Networking and Applications, Vol 10, No 1 (2018), Pagination: 3728-3734Abstract
The audio and video synchronization plays an important role in speech recognition and multimedia communication. The audio-video sync is a quite significant problem in live video conferencing. It is due to use of various hardware components which introduces variable delay and software environments. The objective of the synchronization is used to preserve the temporal alignment between the audio and video signals. This paper proposes the audio-video synchronization using spreading codes delay measurement technique. The performance of the proposed method made on home database and achieves 99% synchronization efficiency. The audio-visual signature technique provides a significant reduction in audio-video sync problems and the performance analysis of audio and video synchronization in an effective way. This paper also implements an audio- video synchronizer and analyses its performance in an efficient manner by synchronization efficiency, audio-video time drift and audio-video delay parameters. The simulation result is carried out using mat lab simulation tools and simulink. It is automatically estimating and correcting the timing relationship between the audio and video signals and maintaining the Quality of Service.Keywords
Audio Spreading Codes, Hamming Distance Correlation, Spectrograph, Synchronization, Video Spreading Codes.Full Text
References
- Alka Jindal, Sucharu Aggarwal, “Comprehensive overview of various lip synchronization techniques” IEEE International transaction on Biometrics and Security technologies, 2008.
- Anitha Sheela.k, Balakrishna Gudla, Srinivasa Rao Chalamala, Yegnanarayana.B, “Improved lip contour extraction for visual speech recognition” IEEE International transaction on Consumer Electronics,pp.459-462, 2015.
- N. J. Bryan, G. J. Mysore and P. Smaragdis, “Clustering and synchronizing multicamera video via landmark cross-correlation,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), March 2012, pp. 2389–2392.
- Claus Bauer, Kent Terry, Regunathan Radhakrishnan, “Audio and video signature for synchronization” IEEE International conference on Multimedia and Exposition Community (ICME), pp.1549-1552, 2008.
- N. Dave, N. M. Patel. "Phoneme and Viseme based Approach for Lip Synchronization.", International Journal of Signal Processing, Image Processing and Pattern Recognition, pp. 385-394, 2014.
- Dragan Sekulovski, Hans Weda, Mauro Barbieri and Prarthana Shrestha, “Synchronization of Multiple Camera Videos Using Audio-Visual Features,” in IEEE Transactions On Multimedia, Vol. 12, No. 1, January 2010.
- Fumei Liu, Wenliang, Zeliang Zhang, “Review of the visual feature extraction research” IEEE 5th International Conference on software Engineering and Service Science, pp.449-452, 2014.
- Josef Chalaupka, Nguyen Thein Chuong, “Visual feature extraction for isolated word visual only speech recognition of Vietnamese” IEEE 36th International conference on Telecommunication and signal processing (TSP), pp.459-463, 2013.
- K. Kumar, V. Libal, E. Marcheret, J. Navratil, G.Potamianos and G. Ramaswamy, “AudioVisual speech synchronization detection using a bimodal linear prediction model”. in Computer Vision and Pattern Recognition Workshops, 2009, p. 54.
- Laszlo Boszormenyi, Mario Guggenberger, Mathias Lux, “Audio Align-synchronization of A/V streams based on audio data” IEEE International journal on Multimedia, pp.382-383, 2012.
- Y. Liu, Y. Sato, “Recovering audio-to-video synchronization by audiovisual correlation analysis”. in Pattern Recognition, 2008, p. 2.
- C. Lu and M. Mandal, “An efficient technique for motion-based view-variant video sequences synchronization,” in IEEE International Conference on Multimedia and Expo, July 2011, pp. 1–6.
- Luca Lombardi, Waqqas ur Rehman Butt, “A survey of automatic lip reading approaches” IEEE 8th International Conference Digital Information Management (ICDIM), pp.299-302, 2013.
- Namrata Dave, “A lip localization based visual feature extraction methods” An International journal on Electrical and computer Engineering, vol.4, no.4, December 2015.
- P. Shrstha, M. Barbieri, and H. Weda, “Synchronization of multi-camera video recordings based on audio,” in Proceedings of the 15th international conference on Multimedia 2007, pp.545–548.