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Articles by Y. Venkataramani
Total Records ( 5 ) for Y. Venkataramani
  M. Padmaa , Y. Venkataramani and Rengarajan Amirtharajan
  The study of communication security includes not just encryption but also traffic security, whose essence lies in "Information Hiding". The security of an image can be enhanced by cleverly embedding data without affecting its quality. This can be done by using information hiding techniques like steganography and cryptography. Combining steganography with cryptography becomes an essential facet for secure communication. In present study, enhanced image quality and security is obtained by consorting pixel indicator technique with PVD technique. Here, the raw data is first encrypted to get two different forms of message T1 and T2 using two distinct keys K1, K2 which is done by using encryption algorithms. As two encrypted messages can be embedded in this process, we have to first extract and then decrypt the message to retrieve the original data. The enhanced level of security is defined by the fact that even if one retrieves the message from the image it’s still incomprehensible to get the original message without the two keys K1 and K2.
  M. Padmaa and Y. Venkataramani
  Signals, images, emails, voice and videos, everything comes under screening before or after it is communicated. Protecting the information is highly essential especially in electronic communication which has become a stipulation in the routine life of zillions. Since safeguarding has many literal connotations, this study ntrates on one of them, privacy; technically secrecy or security. From the family of Information security, the proposed method can pick out cryptography and steganography to make this algorithm more secure and effective as well. As far as Visual Cryptography is concerned, the primary terminologies here are share, user and transparency. Its blend with steganography is worth mentionable here as it forms a new platform in information security and secret sharing. For embedding, Pixel Indicator (PI), Pixel Value Differencing (PVD) and OPAP are used. With reference to Pixel Indicator, two methods are discussed here. The effectiveness of the proposed method is assessed by calculating MSE and PSNR and the outcomes are tabulated and compared with existing methods.
  M. Padmaa and Y. Venkataramani
  Steganography presents us with a secure means of data transfer which protects confidential information from unauthorized change. Putting it simple, it is a boon to prevent the unauthorized access or misuse of secret information. There are a lot of techniques (both spatial and transform domain) in Steganography. The question of “Which technique is the best” can only be answered hypothetically. Every steganographic technique is unique in its own way and produces desired result, that is, protection of information. The technique proposed in this study sheds light on capacity of embedding and high quality visual while being immune to steg analysis attack. Complexity is driven by means of stego key and LSB substitution.
  V. Mohan and Y. Venkataramani
  Medical imaging plays a vital role in the field of diagnosis and surgical planning. Compression of medical images differs from standard image coding as it needs to preserve the clinically critical information with reduction in storage space required. Recent research has demonstrated the merit of encoding the Clinical ROI separately and encoding the insignificant part of the image using standard SPIHT technique. One unaddressed combination is the segmentation of CROI using soft computing approaches such as spatial FCM. This study proposes a technique for extracting the clinical ROI using spatial FCM and encoding the segmented portion using a suitable transform with directionality and multi resolution capability. The goal of the proposed technique is to preserve the clinical useful information with smooth contours to obtain a better compression ratio. In the proposed study, modified set partitioning in hierarchical tree (modified SPIHT) is used to code the wavelet based contourlet transformed coefficients of the Clinical Region of Interest (CROI) and remaining portion is encoded using DWT and SPIHT. The proposed study produces a better compression ratio for the MRI brain images with increased PSNR. Also it provides efficient representation of smooth edges in Digital Imaging and Communications in Medicine (DICOM) images.
  P. Shanmugapriya and Y. Venkataramani
  In order to improve the verification rate of automatic speaker verification system, a novel training algorithm for Gaussian Mixture Model is proposed in this study. A novel feature extraction method for automatic speaker verification system is also presented. This system includes extraction of discrete wavelet transform based Mel frequency cepstral coefficients from speech and Fuzzy Learning Vector Quantization based gaussian mixture model training. This feature extraction approach utilizes the dynamic spectral features which are useful for recognizing the speaker. The proposed training method for speaker model not only reduces the number of features vectors used to train the model but also increases the verification rate than the conventional GMM-expectation maximization algorithm. The proposed method of speaker verification is evaluated using TIMIT database. Experiments are also conducted with other vector quantization algorithms: (1) Learning vector quantization, (2) K-means, (3) Fuzzy C-means and (4) Linde-buzo-grey algorithm as training algorithms for GMM. Experimental results demonstrate that the performance of the proposed system is better when compared to conventional systems in terms of verification rate.
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