Duche R.,LTCOE |
International Journal of Engineering and Technology Innovation | Year: 2016
In WSNs, the large numbers of portable sensor nodes are deployed randomly and can fail due to battery problem, environmental conditions or are unattended. Faulty sensor node detection techniques are mainly affected due to energy consumption of sensor nodes in WSNs. Therefore, the primary goal of this investigation is to design energy efficient fault tolerant sensor node failure detection. A faulty sensor node is detected by measuring the Round Trip Delay (RTD) times of Round Trip Paths (RTPs) in WSNs. Fault tolerance is achieved by assigning unique source node or Cluster Head (CH) for each RTP in WSNs. Energy consumed by individual sensor node is minimized due to optimal involvement of sensor nodes in the detection process. The proposed method is implemented and tested on WSNs with six sensor nodes. © TAETI.
Sabnis S.K.,Rajiv Gandhi Institute of Technology |
Procedia Computer Science | Year: 2016
Steganalysis of high capacity Wavelet based fusion image steganography with encryption, using Image quality metrics (as a set of features) is proposed. As the first order image statistics using the proposed algorithm are inherently preserved, which is desirable feature of the scheme, improving the security of algorithm against the targeted attacks.In addition comparing the present steganography scheme with two different encryption techniques, on the undetectibility ground, the generalized objective metric like SVD is used as a steganalysis tool. DFrFT encryption is found statistically and visually undetectable achieving the desired robustness though PSNR values are better in DNA encryption. © 2016 The Authors.
Deshmukh S.,GHRCEM |
International Conference on Emerging Trends in Engineering and Technology, ICETET | Year: 2012
Singer identification is most important application of Music information retrieval. The process starts with identifying first the audio descriptors then using these feature vectors as input to further classification using Gaussian Mixture Model or Hidden Markov Model as classifiers to identify the singer. The process becomes chaotic if all audio descriptors are used for finding the feature vector, instead if the audio descriptors are selected with respect to the application then the process becomes comparatively simple. In this paper we propose a Hybrid method of selecting correct audio descriptors for the identification of singer of North Indian Classical Music. First only strong (primary) audio descriptors are released on the system in forward pass and the classification impact is to be recorded. Then only selecting the top few audio descriptors having largest impact on the singer identification process are selected and rest are eliminated in the backward pass. Then selecting and releasing all the less significant audio descriptors from the groups that had maximum impact on singer identification process increases the success of correctly identifying the singer. The method reduces substantially the large number of audio descriptors to few, important audio descriptors. The selected audio descriptors are then fed as input to further classifiers. © 2012 IEEE.
2014 International Conference on Control, Instrumentation, Communication and Computational Technologies, ICCICCT 2014 | Year: 2014
Steganography is the art of hiding data in a particular form of media and making it accessible for the recipient. This process of encrypting data can be embedded in media like image and audio. This paper represents a snake and ladder based algorithm for encrypting a streamline of bits in a greyscale image. © 2014 IEEE.
Mangala T.R.,Institute of Technology |
European Journal of Scientific Research | Year: 2010
Automatic road extraction enables the creation, maintenance, and revision of transportation networks from satellite images. Though numerous methods are available in the literature, works that deal with road extraction from rural areas' imagery are very few. This is mainly due to the difficulty present in extracting roads from rural areas, as they do not have a proper layout. In this paper, an effective classification system is proposed to automatically extract roads from the satellite images of rural areas. The proposed classification system has four stages of processing, namely, image segmentation, morphological operation, dominant objects extraction and neural network-based classification. In the segmentation process, the images are segmented using a pre-developed region-scalable fitting model. Then, the dominant objects are extracted after performing a two-stage morphological operation. Finally, we use a well-trained neural network to extract roads from the given satellite image. The proposed system is implemented and its performance is evaluated using standard quality measures. © EuroJournals Publishing, Inc. 2010.