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Chincholkar, Y. D.
- Steganography for Two and Three Lsbs Using Extended Substitution Algorithm
Abstract Views :173 |
PDF Views:0
Authors
Affiliations
1 Department of Electronics and Telecommunications Engineering, Dnyanganga College of Engineering and Research, IN
2 Department of Electronics and Telecommunications Engineering, Sinhgad College of Engineering, IN
3 Department of Electronics and Telecommunications Engineering, RMD Sinhgad School of Engineering, IN
1 Department of Electronics and Telecommunications Engineering, Dnyanganga College of Engineering and Research, IN
2 Department of Electronics and Telecommunications Engineering, Sinhgad College of Engineering, IN
3 Department of Electronics and Telecommunications Engineering, RMD Sinhgad School of Engineering, IN
Source
ICTACT Journal on Communication Technology, Vol 4, No 1 (2013), Pagination: 685-690Abstract
The Security of data on internet has become a prior thing. Though any message is encrypted using a stronger cryptography algorithm, it cannot avoid the suspicion of intruder. This paper proposes an approach in such way that, data is encrypted using Extended Substitution Algorithm and then this cipher text is concealed at two or three LSB positions of the carrier image. This algorithm covers almost all type of symbols and alphabets. The encrypted text is concealed variably into the LSBs. Therefore, it is a stronger approach. The visible characteristics of the carrier image before and after concealment remained almost the same. The algorithm has been implemented using Matlab.Keywords
Steganography, Cryptography, Encryption, Decryption, Extended Square Substitution Algorithm.
- Mutual Coupling Reduction between Microstrip Antennas Using Electromagnetic Bandgap Structure
Abstract Views :200 |
PDF Views:0
Authors
G. N. Gaikwad
1,
V. V. Dixit
1,
D. G. Ganage
1,
Y. D. Chincholkar
1,
U. P. Alwandi
2,
S. R. Pawar
2,
J. P. Kolhe
3
Affiliations
1 Department of Electronics and Telecommunication Engineering, Sinhgad College of Engineering, Maharashtra, IN
2 Department of Electronics and Telecommunication Engineering, Sou. Venutai Chavan Polytechnic, Maharashtra, IN
3 Department of Aerospace Engineering, Defence Institute of Advanced Technology, Maharashtra, IN
1 Department of Electronics and Telecommunication Engineering, Sinhgad College of Engineering, Maharashtra, IN
2 Department of Electronics and Telecommunication Engineering, Sou. Venutai Chavan Polytechnic, Maharashtra, IN
3 Department of Aerospace Engineering, Defence Institute of Advanced Technology, Maharashtra, IN
Source
ICTACT Journal on Communication Technology, Vol 2, No 1 (2011), Pagination: 241-245Abstract
When the number of antenna elements is placed in forming the arrays, mutual coupling between the antenna elements is a critical issue. This is particularly concern in phase array antennas. Mutual coupling is a potential source of performance degradation in the form of deviation of the radiation pattern from the desired one, gain reduction due to excitation of surface wave, increased side lobe levels etc. EBG (Electromagnetic Band Gap) structure (also called as Photonic Bandgap Structure PBG) not only enhances the performance of the patch antennas but also provides greater amount of isolation when placed between the microstrip arrays. This greatly reduces the mutual coupling between the antenna elements. The radiation efficiency, gain, antenna efficiency, VSWR, frequency, directivity etc greatly improves over the conventional patch antennas using EBG. The EBG structure and normal patch antenna is simulated using IE3D antenna simulation software.Keywords
Antenna, Electromagnetic Band Gap (EBG), Mutual Coupling, Surface Waves, Antenna Efficiency.- Effective Robust Patchwork Method to the Vulnerable Attack for Digital Audio Watermarking
Abstract Views :205 |
PDF Views:4
Authors
Affiliations
1 Department of Electronics and Telecommunications Engineering, Savitribai Phule Pune University, IN
1 Department of Electronics and Telecommunications Engineering, Savitribai Phule Pune University, IN
Source
ICTACT Journal on Image and Video Processing, Vol 8, No 4 (2018), Pagination: 1753-1758Abstract
This paper presents patchwork based digital audio watermarking. The advanced growth in transmission of digital data has resulted in a corresponding elevation in the need for copyright protection of signal. Cryptography and steganography are used for the content protection but do not completely solve the copyright issue. Watermarking is a method to protect and identify the digital data while maintaining the quality of the host media, it permits various types of watermarks to be hidden in audio signal e.g. image, audio and video. This paper limits on image embedding technique using patchwork-based method. In patchwork based method average of all segments of approximate coefficients is calculated for embedding watermark into sound signal. The experimental results shows that proposed method achieves imperceptibility for audio signal as watermarked audio signal is inaudible after embedding watermark and robustness of watermark against different signal processing attacks with higher PSNR. The resulting audio is robust to attacks and exhibits good quality in term of peak signal to noise ratio. The simulation results show the effectiveness of the proposed system.Keywords
Audio Watermarking, Stationary Wavelet Transform, Patchwork.References
- Y. Xiang, I. Natgunanathan, S. Guo, W. Zhou and S. Nahavandi, “Patchwork-Based Audio Watermarking Method Robust to Desynchronization Attacks”, IEEE/ACM Transactions on Audio, Speech, and Language Processing, Vol. 22, No. 9, pp. 1413-1423, 2014.
- Pranab Kumar Dhar and Tetsuya Shimamura. “Blind SVDbased Audio Watermarking using Entropy and Log-Polar Transformation”, Journal of Information Security and Applications, Vol. 20, pp. 74-83, 2015.
- H. Huang, C. Yang and W. Hsu, “A Video Watermarking Technique based on Pseudo-3-D DCT and Quantization Index Modulation”, IEEE Transactions on Information Forensics and Security, Vol. 5, No. 4, pp. 625-637, 2010.
- F. Guerrini, M. Okuda, N. Adami and R. Leonardi, “High Dynamic Range Image Watermarking Robust against ToneMapping Operators”, IEEE Transactions on Information Forensics and Security, Vol. 6, No. 2, pp. 283-295, 2011.
- K. Yeo and H.J. Kim, “Modified Patchwork Algorithm: A Novel Audio Watermarking Scheme”, IEEE Transactions on Speech and Audio Processing, Vol. 11, No. 4, pp. 381-386, 2003.
- W.N. Lie and L.C. Chang, “Robust and High-Quality TimeDomain Audio Watermarking based on Low-Frequency Amplitude Modification”, IEEE Transactions on Multimedia, Vol. 8, No. 1, pp. 46-59, 2006.
- Y.D. Chincholkar, S.R. Ganorkar and A.S. Sawai , “Implementation Of Audio Watermarking Technique For Copyright Protection using SWT Algorithm”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 4, No. 3, pp. 42-48, 2016.
- X. Wang, W. Qi and P. Niu, “A New Adaptive Digital Audio Watermarking based on Support Vector Regression”, IEEE/ACM Transactions on Audio Speech and Language Processing, Vol. 15, No. 8, pp. 2270-2277, 2007.
- S. Valizadeh and Z.J. Wang, “Correlation-and-Bit-Aware Spread Spectrum Embedding for Data Hiding”, IEEE Transactions on Information Forensics Security, Vol. 6, No. 2, pp. 267-282, 2011.
- S. Valizadeh and Z. J.Wang, “An Improved Multiplicative Spread Spectrum Embedding Scheme for Data Hiding”, IEEE Transactions on Information Forensics Security, Vol. 7, No. 4, pp. 1127-1143, 2012.
- X. Wang, W. Qi, and P. Niu, “A New Adaptive Digital Audio Watermarking based on Support Vector Regression”, IEEE/ACM Transactions on Audio Speech and Language Processing, Vol. 15, No. 8, pp. 2270-2277, 2007.
- D. Lakshmi, R. Ganesh, R. Marni, R. Prakash and P. Arulmozhivarman, “SVM based Effective Watermarking Scheme for Embedding Binary Logo and Audio Signals in Images”, Proceedings of IEEE Region 10 Conference, pp. 1-5, 2008.
- M. Arnold, “Audio Watermarking: Features, Applications and Algorithm”, Proceedings of IEEE International Conference on Multimedia Expo, pp. 1013-1016, 2000.
- Traffic Sign Board Detection for Advanced Driver Assistance Systems and Autonomous Vehicles
Abstract Views :232 |
PDF Views:0
Authors
Source
Automation and Autonomous Systems, Vol 10, No 6 (2018), Pagination: 119-122Abstract
The basic idea for the ADAS system to analyse live road situations with a camera which will be placed on the vehicle and a processing unit on board that will assist the driver while they are driving in different traffic conditions on the road to avoid accidents. As autonomous vehicles, such as Google’s ‘self-driving car has become more prominent recently because of the ability to detect and recognise informational road. A majority of existing approaches to traffic sign recognition separate the task into two phases designed to capitalize on these advantages The first phase, known as the “segmentation phase,” determines which regions of an image are likely to yield traffic signs, and the second phase is known as the “classification phase,” determines what kind of sign (if any) is contained in this region. Here, we describe a new approach to the “segmentation” phase. This paper shows a programmed street sign detection and acknowledgment framework that depends on a computational model of human visual acknowledgment handling. The tangible analyser removes the spatial and worldly data of enthusiasm from video arrangements. The removed limit data at that point fills in as the contribution to region Analyzer, which at that point looks for specific shapes in the picture. Later these recognized items are encouraged into a neural system. Potential highlights of street signs are then removed from the question regions comparing to the concentrations, and the neural system perceived activity signs and recognized sign board is shown to the driver.
Keywords
ADAS (Advanced Driver Assistance Systems), CNN (Convolutional Neural Network), Real-Time Image Processing, Autonomous Vehicle- Traffic Sign Board Detection and Recognition for Autonomous Vehicles and Driver Assistance Systems
Abstract Views :203 |
PDF Views:0
Authors
Affiliations
1 Department of Electronics and Telecommunication Engineering, Sinhgad College of Engineering, IN
1 Department of Electronics and Telecommunication Engineering, Sinhgad College of Engineering, IN
Source
ICTACT Journal on Image and Video Processing, Vol 9, No 3 (2019), Pagination: 1954-1959Abstract
In the recent year's many approaches have been made that uses image processing algorithms to detect traffic sign boards. Edge detection is used to avoid segmentation problems of the existing method. Color based segmentation faces the challenge of adaptive thresholding which fails in real time scenarios. This proposed algorithm is yet another approach to detect traffic sign boards from video sequences. The first step of this work is the pre-processing of the video frame which is achieved by the gray scale conversion and edge detection and the second step is the extraction of the objects. Hough Transform algorithm is then applied to measure properties of image regions for further analysis. The different feature points which include perimeter, area, filled area, solidity and centroid are extracted for the detection of the traffic sign board. Feature generation and classification are done on the recognition side to get the class of the detected object. The input for the project is video sequences taken from a camera placed on the vehicle.Keywords
Hough Transform, Machine Learning Algorithm, Traffic Detection, Feature Classification.References
- Zhihui Zheng, Bo Wang and Zhifeng Gao, “Robust Traffic Sign Recognition and Tracking for Advanced Driver Assistance Systems”, Proceedings of 15th International IEEE Conference on Intelligent Transportation System, pp. 704-709, 2012.
- Seokwoo Jung, Unghui Lee, Jiwon Jung and David Hyunchul, “Real-Time Traffic Sign Recognition System with Deep Convolutional Neural Network”, Proceedings of 13th International Conference on Robotics and Ambient Intelligence, pp. 31-34, 2016.
- Rafael C. Gonzalez and Richard E. Woods, “Digital Image Processing”, 2nd Edition, Pearson Education, 2004.
- F. Moutarde, A. Bargeton, A. Herbin and L. Chanussot, “Modular Traffic Signs Recognition Applied to On-Vehicle-Time Visual Detection of American and European Speedlimit Signs”, Proceedings of IEEE International Symposium on Intelligent Vehicles, pp. 1122-1126, 2007.
- Angela Tam, Hua Shen, Liu and Xiaoou Tang, “Quadrilateral Signboard Detection and Text Extraction”, Proceedings of the International Conference on Imaging Science, Systems and Technology, pp. 708-713, 2003.
- N. Barnes and G. Loy, “Fast Shape-Based Road Sign Detection for a Driver Assistance System. Intelligent Robots and Systems”, Proceedings of the International Conference on Intelligent Robots and Systems, pp. 70-75, 2004.
- N. Barnes and A. Zelinsky, “Real-Time Radial Symmetry for Speed Sign Detection”, Proceedings of International Symposium on Intelligent Vehicles, pp. 566-571, 2004.
- M.K. Hu, “Visual Pattern Recognition by Moment Invariants”, IRE Transactions on Information Theory, Vol. 8, No. 2, pp. 179-187, 1962.
- F. Zernike, “Diffraction Theory of the Cutting Process and its Improved Shape, the Phase Contrast Method”, Physica, Vol. 1, No. 7, pp. 689-704, 1934.
- Juan J. Rodriguez, Cesar Garcia-Osorio and Jesus Maudes, “License Plate Number Recognition-New Heuristics and A Comparative Study of Classifiers”, Proceedings of International Conference on Informatics in Control, Automation and Robotics, pp. 268-273, 2008.
- Hsien-Chu Wu, Chwei-Shyong Tsai and Ching-Hao Lai, “A License Plate Recognition System in E-Government”, Information and Security, Vol. 15, No. 2, pp. 199-210, 2004.
- F. Martin, M. Garcia and J.L. Alba, “New Methods for Automatic Reading of Vehicle License Plates”, Proceedings of International Conference on Signal Processing, Pattern Recognition, and Applications, pp. 331-338, 2002.
- K.K. Kim, K.I. Kim, J.B. Kim and H.J. Kim, “Learning-Based Approach, for LicensePlate Recognition”, Proceedings of IEEE Signal Processing Society Workshop on Neural Networks for Signal Processing, pp. 614-623, 2000.
- P. Comelli, P. Ferragina, M.N. Granieri and F. Stabile, “Optical Recognition of Motor Vehicle License Plates”, IEEE Transactions on Vehicular Technology, Vol. 44, No. 4, pp. 790-799, 1995.
- Yo Ping Huang, Shi-Yong Lai and Wei-Po Chuang, “A Template-Based Model for License Plate Recognition”, Proceedings of International Conference on Networking, Sensing and Control, pp. 737-742, 2004.
- Tran Duc Duan, Tran Le Hong Du, Tran Vinh Phuoc and Nguyen Viet Hoang, “Building an Automatic Vehicle License-Plate Recognition System”, Proceedings of International Conference on Computer Science, pp. 59-63, 2005.
- GiYeong Bae, JeongMok Ha, Jea Young Jeon, Sung Yong Jo and Hong Jeong, “LED Traffic Sign Detection using Rectangular Hough Transform”, Proceedings of International Conference on Information Science and Applications, pp. 1-4, 2014.
- Seokwoo Jung, Unghui Lee, Jiwan Jung and David Hyunchul Shim, “Real Time Traffic Sign Board Recognition with Deep Convolutional Neural Network”, Proceedings of International Conference on Ubiquitous Robots and Ambient Intelligence, pp. 23-28, 2016.
- Zhihui Zheng, Hanxizi Zhang, Bo Wang and Zhifeng Gao, “Robust Traffic Sign Recognition and Tracking for Advanced Driver Assistance Systems”, Proceedings of 15th International Conference on Intelligent Transportation Systems, pp. 704-709, 2012.
- R. Thendral, A, Suhasini and N. Senthil, “A Comparative Study of Color and Edge based Segmentation”, Proceedings of International Conference on Communication and Signal Processing, pp. 463-466, 2014.