Spread Spectrum Watermarking: Principles and Applications in Fading Channel

ISBN 978-953-51-0618-

Entropy based fuzzy classification of images on quality assessment

AbstractReferenced image quality assessment methods require huge memory and time involvement, therefore not suitable to use in real time environment. On the other hand development of an automated system to assessing quality of images without reference to the original image is difficult due to uncertainty in relations between features and quality of images. The paper aims at developing a fuzzy based no-reference image quality assessment system by utilizing human perception and entropy of images. The proposed approach selects important features to reduce complexity of the system and based on entropy of feature vector the images are partitioned into different clusters. To assign soft class labels to different images, continuous weights are estimated using entropy of mean opinion score (MOS) unlike the previous works where crisp weights were used. Finally, fuzzy relational classifier (FRC) has been built using MOS based weight matrix and fuzzy partition matrix to establish correlation between features and class labels. Quality of the distorted/decompressed test images are predicted using the proposed fuzzy system, showing satisfactory results with the existing no-reference techniques

Optimizing Large Search Space using DE Based Q-learning Algorithm

Finding global optimum solution in minimum time from large search space is challenging due to involvement of large no. of variables and their varied degree of participation in problem solving process. Complexity of a problem increases with the dimensionality, which must be learnt efficiently to improve performance of the method. Q-learning, a reinforcement learning algorithm is used widely to learn the environment dynamically. However, the conventional Q-learning is not fast and becomes inefficient while solving large scale problem. In the proposed approach by hybridizing Differential Evolution (DE) algorithm and Q-learning (QL) method (QL-DE) optimal partitioning of the search space is obtained involving multiple agents with an objective to achieve maximum classification accuracy. Performance of the proposed algorithm has been compared with state of the art optimization algorithms

N (2013) Genetic algorithm and fuzzyrough based dimensionality reduction applied on real valued dataset

Abstract: Real-world datasets are often vague and redundant, creating problem to take decision accurately. Very recently, Rough-set theory has been used successfully for dimensionality reduction but is applicable only on discrete dataset. Discretisation of data leads to information loss and may add inconsistency in the datasets. The paper aims at applying fuzzy-rough concept to overcome the above limitations. However, handling of non discretized values increases computational complexity of the system. Therefore, to build an efficient classifier Genetic Algorithm (GA) has been applied to obtain optimal subset of attributes, sufficient to classify the objects. The proposed algorithm reduces dimensionality to a great extent without degrading the accuracy of classification and avoid of being trapped at local minima. Results are compared with the existing algorithms, demonstrate compatible outcome

PREDICTION OF PARTICLE SIZE DISTRIBUTION OF A BALL MILL USING IMPROVISED NEURAL NETWORK TECHNIQUE AND TIME SERIES

In the mining industry, it is important to minimize the wastage of raw materials while achieving the desired particle size distribution by grinding the original input mix. To date, the procedure is performed manually, and there is no such control mechanism for grinding that reduces wastage to achieve the desired output, resulting in the loss of material. This study aims to develop an autonomous system for predicting the desired states of breakage by analyzing the acoustic signatures of the materials being crushed. The signal envelope is detected from the time-series acoustic data, which changed gradually during grinding. We designed an autoregression model using the signal envelopes of different grinding stages to predict the desired particle size. In another scenario, the acoustic signatures are approximated to a Gaussian distribution, and the kernel density estimation function is applied to obtain the best-fitted observed data points with the help of local points. An improvised neural network technique is used to classify the unknown patterns of crushing at different breakage states, which validates the experimental results. The network is trained with the input patterns corresponding to the observed data points and the output of the autoregression model. The prediction accuracy of the proposed approach is approximately 97%

Optimized Spread Spectrum Watermarking for Fading-like Collusion Attack

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